publications

2022

1. Development and validation of a model to predict cardiovascular death, nonfatal myocardial infarction, or nonfatal stroke in patients with type 2 diabetes mellitus and established atherosclerotic cardiovascular disease

   S. R. Stevens, M. W. Segar, A. Pandey, and 6 more authors

   2022

   Stevens, Susanna R Segar, Matthew W Pandey, Ambarish Lokhnygina, Yuliya Green, Jennifer B McGuire, Darren K Standl, Eberhard Peterson, Eric D Holman, Rury R eng England 2022/08/28 Cardiovasc Diabetol. 2022 Aug 27;21(1):166. doi: 10.1186/s12933-022-01603-8.

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   BACKGROUND: Among individuals with atherosclerotic cardiovascular disease (ASCVD), type 2 diabetes mellitus (T2DM) is common and confers increased risk for morbidity and mortality. Differentiating risk is key to optimize efficiency of treatment selection. Our objective was to develop and validate a model to predict risk of major adverse cardiovascular events (MACE) comprising the first event of cardiovascular death, myocardial infarction (MI), or stroke for individuals with both T2DM and ASCVD. METHODS: Using data from the Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS), we used Cox proportional hazards models to predict MACE among participants with T2DM and ASCVD. All baseline covariates collected in the trial were considered for inclusion, although some were excluded immediately because of large missingness or collinearity. A full model was developed using stepwise selection in each of 25 imputed datasets, and comprised candidate variables selected in 20 of the 25 datasets. A parsimonious model with a maximum of 10 degrees of freedom was created using Cox models with least absolute shrinkage and selection operator (LASSO), where the adjusted R-square was used as criterion for selection. The model was then externally validated among a cohort of participants with similar criteria in the ACCORD (Action to Control Cardiovascular Risk in Diabetes) trial. Discrimination of both models was assessed using Harrell’s C-index and model calibration by the Greenwood-Nam-D’Agostino statistic based on 4-year event rates. RESULTS: Overall, 1491 (10.2%) of 14,671 participants in TECOS and 130 (9.3%) in the ACCORD validation cohort (n = 1404) had MACE over 3 years’ median follow-up. The final model included 9 characteristics (prior stroke, age, chronic kidney disease, prior MI, sex, heart failure, insulin use, atrial fibrillation, and microvascular complications). The model had moderate discrimination in both the internal and external validation samples (C-index = 0.65 and 0.61, respectively). The model was well calibrated across the risk spectrum-from a cumulative MACE rate of 6% at 4 years in the lowest risk quintile to 26% in the highest risk quintile. CONCLUSION: Among patients with T2DM and prevalent ASCVD, this 9-factor risk model can quantify the risk of future ASCVD complications and inform decision making for treatments and intensity.

2. Supranormal Left Ventricular Ejection Fraction, Stroke Volume, and Cardiovascular Risk: Findings From Population-Based Cohort Studies

   S. Shah, M. W. Segar, N. Kondamudi, and 11 more authors

   2022

   Shah, Sonia Segar, Matthew W Kondamudi, Nitin Ayers, Colby Chandra, Alvin Matulevicius, Susan Agusala, Kartik Peshock, Ron Abbara, Suhny Michos, Erin D Drazner, Mark H Lima, Joao A C Longstreth, W T Jr Pandey, Ambarish eng Research Support, Non-U.S. Gov’t Research Support, N.I.H., Extramural 2022/07/29 JACC Heart Fail. 2022 Aug;10(8):583-594. doi: 10.1016/j.jchf.2022.05.007.

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   BACKGROUND: Supranormal ejection fraction by echocardiography in clinically referred patient populations has been associated with an increased risk of cardiovascular disease (CVD). The prognostic implication of supranormal left ventricular ejection fraction (LVEF)-assessed by cardiac magnetic resonance (CMR)-in healthy, community-dwelling individuals is unknown. OBJECTIVES: The purpose of this study is to investigate the prognostic implication of supranormal LVEF as assessed by CMR and its inter-relationship with stroke volume among community-dwelling adults without CVD. METHODS: Participants from the MESA (Multi-Ethnic Study of Atherosclerosis) and DHS (Dallas Heart Study) cohorts free of CVD who underwent CMR with LVEF above the normal CMR cutoff (>/=57%) were included. The association between cohort-specific LVEF categories and risk of clinically adjudicated major adverse cardiovascular events (MACE) was assessed using adjusted Cox models. Subgroup analysis was also performed to evaluate the association of LVEF and risk of MACE among individuals stratified by left ventricular stroke volume index. RESULTS: The study included 4,703 participants from MESA and 2,287 from DHS with 727 and 151 MACE events, respectively. In adjusted Cox models, the risk of MACE was highest among individuals in LVEF Q4 (vs Q1) in both cohorts after accounting for potential confounders (MESA: HR = 1.27 [95% CI: 1.01-1.60], P = 0.04; DHS: HR = 1.72 [95% CI: 1.05-2.79], P = 0.03). A significant interaction was found between the continuous measures of LVEF and left ventricular stroke volume index (P interaction = 0.02) such that higher LVEF was significantly associated with an increased risk of MACE among individuals with low but not high stroke volume. CONCLUSIONS: Among community-dwelling adults without CVD, LVEF in the supranormal range is associated with a higher risk of adverse cardiovascular outcomes, particularly in those with lower stroke volume.

3. Validation of the WATCH-DM and TRS-HFDM Risk Scores to Predict the Risk of Incident Hospitalization for Heart Failure Among Adults With Type 2 Diabetes: A Multicohort Analysis

   M. W. Segar, K. V. Patel, A. S. Hellkamp, and 11 more authors

   2022

   Segar, Matthew W Patel, Kershaw V Hellkamp, Anne S Vaduganathan, Muthiah Lokhnygina, Yuliya Green, Jennifer B Wan, Siu-Hin Kolkailah, Ahmed A Holman, Rury R Peterson, Eric D Kannan, Vaishnavi Willett, Duwayne L McGuire, Darren K Pandey, Ambarish eng R03 AG067960/AG/NIA NIH HHS/ UL1 TR002541/TR/NCATS NIH HHS/ T32 HL125247/HL/NHLBI NIH HHS/ Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t England 2022/06/04 J Am Heart Assoc. 2022 Jun 7;11(11):e024094. doi: 10.1161/JAHA.121.024094. Epub 2022 Jun 3.

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   Background The WATCH-DM (weight [body mass index], age, hypertension, creatinine, high-density lipoprotein cholesterol, diabetes control [fasting plasma glucose], ECG QRS duration, myocardial infarction, and coronary artery bypass grafting) and TRS-HFDM (Thrombolysis in Myocardial Infarction [TIMI] risk score for heart failure in diabetes) risk scores were developed to predict risk of heart failure (HF) among individuals with type 2 diabetes. WATCH-DM was developed to predict incident HF, whereas TRS-HFDM predicts HF hospitalization among patients with and without a prior HF history. We evaluated the model performance of both scores to predict incident HF events among patients with type 2 diabetes and no history of HF hospitalization across different cohorts and clinical settings with varying baseline risk. Methods and Results Incident HF risk was estimated by the integer-based WATCH-DM and TRS-HFDM scores in participants with type 2 diabetes free of baseline HF from 2 randomized clinical trials (TECOS [Trial Evaluating Cardiovascular Outcomes With Sitagliptin], N=12 028; and Look AHEAD [Look Action for Health in Diabetes] trial, N=4867). The integer-based WATCH-DM score was also validated in electronic health record data from a single large health care system (N=7475). Model discrimination was assessed by the Harrell concordance index and calibration by the Greenwood-Nam-D’Agostino statistic. HF incidence rate was 7.5, 3.9, and 4.1 per 1000 person-years in the TECOS, Look AHEAD trial, and electronic health record cohorts, respectively. Integer-based WATCH-DM and TRS-HFDM scores had similar discrimination and calibration for predicting 5-year HF risk in the Look AHEAD trial cohort (concordance indexes=0.70; Greenwood-Nam-D’Agostino P>0.30 for both). Both scores had lower discrimination and underpredicted HF risk in the TECOS cohort (concordance indexes=0.65 and 0.66, respectively; Greenwood-Nam-D’Agostino P<0.001 for both). In the electronic health record cohort, the integer-based WATCH-DM score demonstrated a concordance index of 0.73 with adequate calibration (Greenwood-Nam-D’Agostino P=0.96). TRS-HFDM score could not be validated in the electronic health record because of unavailability of data on urine albumin/creatinine ratio in most patients in the contemporary clinical practice. Conclusions The WATCH-DM and TRS-HFDM risk scores can discriminate risk of HF among intermediate-risk populations with type 2 diabetes.

4. Sex-based differences in patients undergoing transseptal transcatheter mitral valve replacement: Closing the sex disparity gap

   M. W. Segar, and Z. Krajcer

   2022

   Segar, Matthew W Krajcer, Zvonimir eng Editorial Comment 2022/04/28 Catheter Cardiovasc Interv. 2022 Apr;99(5):1645-1646. doi: 10.1002/ccd.30196.
5. Mediators of ertugliflozin effects on heart failure and kidney outcomes among patients with type 2 diabetes mellitus

   M. W. Segar, A. A. Kolkailah, R. Frederich, and 12 more authors

   2022

   Segar, Matthew W Kolkailah, Ahmed A Frederich, Robert Pong, Annpey Cannon, Christopher P Cosentino, Francesco Dagogo-Jack, Samuel McGuire, Darren K Pratley, Richard E Liu, Chih-Chin Maldonado, Mario Liu, Jie Cater, Nilo B Pandey, Ambarish Cherney, David Z I eng R03 AG067960/AG/NIA NIH HHS/ T32 HL125247/HL/NHLBI NIH HHS/ Randomized Controlled Trial Research Support, Non-U.S. Gov’t Research Support, N.I.H., Extramural England 2022/05/24 Diabetes Obes Metab. 2022 Sep;24(9):1829-1839. doi: 10.1111/dom.14769. Epub 2022 Jun 15.

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   AIMS: Sodium-glucose cotransporter 2 (SGLT2) inhibitors have been shown to reduce the risk of hospitalization for heart failure (HHF) and composite kidney outcomes, but the mediators underlying these benefits are unknown. MATERIALS AND METHODS: Among participants from VERTIS CV, a trial of patients with type 2 diabetes mellitus and atherosclerotic cardiovascular disease randomized to ertugliflozin versus placebo, Cox proportional hazards regression models were used to evaluate the percentage mediation of ertugliflozin efficacy on the first HHF and kidney composite outcome in 26 potential mediators. Time-dependent approaches were used to evaluate associations between early (change from baseline to the first post-baseline measurement) and average (weighted average of change from baseline using all post-baseline measurements) changes in covariates with clinical outcomes. RESULTS: For the HHF analyses, early changes in four biomarkers (haemoglobin, haematocrit, serum albumin and urate) and average changes in seven biomarkers (early biomarkers + weight, chloride and serum protein) were identified as fulfilling the criteria as mediators of ertugliflozin effects on the risk of HHF. Similar results were observed for the composite kidney outcome, with early changes in four biomarkers (glycated haemoglobin, haemoglobin, haematocrit and urate), and average changes in five biomarkers [early biomarkers (not glycated haemoglobin) + weight, serum albumin] mediating the effects of ertugliflozin on the kidney outcome. CONCLUSIONS: In these analyses from the VERTIS CV trial, markers of volume status and haemoconcentration and/or haematopoiesis were the strongest mediators of the effect of ertugliflozin on reducing risk of HHF and composite kidney outcomes in the early and average change periods. CLINICALTRIALS: GOV IDENTIFIER: NCT01986881.

6. Incorporation of natriuretic peptides with clinical risk scores to predict heart failure among individuals with dysglycaemia

   M. W. Segar, M. S. Khan, K. V. Patel, and 11 more authors

   2022

   Segar, Matthew W Khan, Muhammad Shahzeb Patel, Kershaw V Vaduganathan, Muthiah Kannan, Vaishnavi Willett, Duwayne Peterson, Eric Tang, W H Wilson Butler, Javed Everett, Brendan M Fonarow, Gregg C Wang, Thomas J McGuire, Darren K Pandey, Ambarish eng England 2021/11/04 Eur J Heart Fail. 2022 Jan;24(1):169-180. doi: 10.1002/ejhf.2375. Epub 2021 Nov 23.

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   AIMS: To evaluate the performance of the WATCH-DM risk score, a clinical risk score for heart failure (HF), in patients with dysglycaemia and in combination with natriuretic peptides (NPs). METHODS AND RESULTS: Adults with diabetes/pre-diabetes free of HF at baseline from four cohort studies (ARIC, CHS, FHS, and MESA) were included. The machine learning- [WATCH-DM(ml)] and integer-based [WATCH-DM(i)] scores were used to estimate the 5-year risk of incident HF. Discrimination was assessed by Harrell’s concordance index (C-index) and calibration by the Greenwood-Nam-D’Agostino (GND) statistic. Improvement in model performance with the addition of NP levels was assessed by C-index and continuous net reclassification improvement (NRI). Of the 8938 participants included, 3554 (39.8%) had diabetes and 432 (4.8%) developed HF within 5 years. The WATCH-DM(ml) and WATCH-DM(i) scores demonstrated high discrimination for predicting HF risk among individuals with dysglycaemia (C-indices = 0.80 and 0.71, respectively), with no evidence of miscalibration (GND P >/=0.10). The C-index of elevated NP levels alone for predicting incident HF among individuals with dysglycaemia was significantly higher among participants with low/intermediate (<13) vs. high (>/=13) WATCH-DM(i) scores [0.71 (95% confidence interval 0.68-0.74) vs. 0.64 (95% confidence interval 0.61-0.66)]. When NP levels were combined with the WATCH-DM(i) score, HF risk discrimination improvement and NRI varied across the spectrum of risk with greater improvement observed at low/intermediate risk [WATCH-DM(i) <13] vs. high risk [WATCH-DM(i) >/=13] (C-index = 0.73 vs. 0.71; NRI = 0.45 vs. 0.17). CONCLUSION: The WATCH-DM risk score can accurately predict incident HF risk in community-based individuals with dysglycaemia. The addition of NP levels is associated with greater improvement in the HF risk prediction performance among individuals with low/intermediate risk than those with high risk.

7. Machine Learning-Based Models Incorporating Social Determinants of Health vs Traditional Models for Predicting In-Hospital Mortality in Patients With Heart Failure

   M. W. Segar, J. L. Hall, P. S. Jhund, and 10 more authors

   2022

   Segar, Matthew W Hall, Jennifer L Jhund, Pardeep S Powell-Wiley, Tiffany M Morris, Alanna A Kao, David Fonarow, Gregg C Hernandez, Rosalba Ibrahim, Nasrien E Rutan, Christine Navar, Ann Marie Stevens, Laura M Pandey, Ambarish eng R03 AG067960/AG/NIA NIH HHS/ Research Support, Non-U.S. Gov’t Research Support, N.I.H., Extramural 2022/07/07 JAMA Cardiol. 2022 Aug 1;7(8):844-854. doi: 10.1001/jamacardio.2022.1900.

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   Importance: Traditional models for predicting in-hospital mortality for patients with heart failure (HF) have used logistic regression and do not account for social determinants of health (SDOH). Objective: To develop and validate novel machine learning (ML) models for HF mortality that incorporate SDOH. Design, Setting, and Participants: This retrospective study used the data from the Get With The Guidelines-Heart Failure (GWTG-HF) registry to identify HF hospitalizations between January 1, 2010, and December 31, 2020. The study included patients with acute decompensated HF who were hospitalized at the GWTG-HF participating centers during the study period. Data analysis was performed January 6, 2021, to April 26, 2022. External validation was performed in the hospitalization cohort from the Atherosclerosis Risk in Communities (ARIC) study between 2005 and 2014. Main Outcomes and Measures: Random forest-based ML approaches were used to develop race-specific and race-agnostic models for predicting in-hospital mortality. Performance was assessed using C index (discrimination), regression slopes for observed vs predicted mortality rates (calibration), and decision curves for prognostic utility. Results: The training data set included 123634 hospitalized patients with HF who were enrolled in the GWTG-HF registry (mean [SD] age, 71 [13] years; 58356 [47.2%] female individuals; 65278 [52.8%] male individuals. Patients were analyzed in 2 categories: Black (23453 [19.0%]) and non-Black (2121 [2.1%] Asian; 91154 [91.0%] White, and 6906 [6.9%] other race and ethnicity). The ML models demonstrated excellent performance in the internal testing subset (n = 82420) (C statistic, 0.81 for Black patients and 0.82 for non-Black patients) and in the real-world-like cohort with less than 50% missingness on covariates (n = 553506; C statistic, 0.74 for Black patients and 0.75 for non-Black patients). In the external validation cohort (ARIC registry; n = 1205 Black patients and 2264 non-Black patients), ML models demonstrated high discrimination and adequate calibration (C statistic, 0.79 and 0.80, respectively). Furthermore, the performance of the ML models was superior to the traditional GWTG-HF risk score model (C index, 0.69 for both race groups) and other rederived logistic regression models using race as a covariate. The performance of the ML models was identical using the race-specific and race-agnostic approaches in the GWTG-HF and external validation cohorts. In the GWTG-HF cohort, the addition of zip code-level SDOH parameters to the ML model with clinical covariates only was associated with better discrimination, prognostic utility (assessed using decision curves), and model reclassification metrics in Black patients (net reclassification improvement, 0.22 [95% CI, 0.14-0.30]; P < .001) but not in non-Black patients. Conclusions and Relevance: ML models for HF mortality demonstrated superior performance to the traditional and rederived logistic regressions models using race as a covariate. The addition of SDOH parameters improved the prognostic utility of prediction models in Black patients but not non-Black patients in the GWTG-HF registry.

8. Diabetes Status Modifies the Association Between Different Measures of Obesity and Heart Failure Risk Among Older Adults: A Pooled Analysis of Community-Based NHLBI Cohorts

   K. V. Patel, M. W. Segar, C. J. Lavie, and 8 more authors

   2022

   Patel, Kershaw V Segar, Matthew W Lavie, Carl J Kondamudi, Nitin Neeland, Ian J Almandoz, Jaime P Martin, Corby K Carbone, Salvatore Butler, Javed Powell-Wiley, Tiffany M Pandey, Ambarish eng P30 DK072476/DK/NIDDK NIH HHS/ R03 AG067960/AG/NIA NIH HHS/ U54 GM104940/GM/NIGMS NIH HHS/ UL1 TR002649/TR/NCATS NIH HHS/ Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t 2021/12/04 Circulation. 2022 Jan 25;145(4):268-278. doi: 10.1161/CIRCULATIONAHA.121.055830. Epub 2021 Dec 3.

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   BACKGROUND: Obesity and diabetes are associated with a higher risk of heart failure (HF). The interrelationships between different measures of adiposity-overall obesity, central obesity, fat mass (FM)-and diabetes status for HF risk are not well-established. METHODS: Participant-level data from the ARIC study (Atherosclerosis Risk in Communities; visit 5) and the CHS (Cardiovascular Health Study; visit 1) cohorts were obtained from the National Heart, Lung, and Blood Institute Biologic Specimen and Data Repository Information Coordinating Center, harmonized, and pooled for the present analysis, excluding individuals with prevalent HF. FM was estimated in all participants using established anthropometric prediction equations additionally validated using the bioelectrical impedance-based FM in the ARIC subgroup. Incident HF events on follow-up were captured across both cohorts using similar adjudication methods. Multivariable-adjusted Fine-Gray models were created to evaluate the associations of body mass index (BMI), waist circumference (WC), and FM with risk of HF in the overall cohort as well as among those with versus without diabetes at baseline. The population attributable risk of overall obesity (BMI>/=30 kg/m(2)), abdominal obesity (WC>88 and 102 cm in women and men, respectively), and high FM (above sex-specific median) for incident HF was evaluated among participants with and without diabetes. RESULTS: The study included 10 387 participants (52.9% ARIC; 25.1% diabetes; median age, 74 years). The correlation between predicted and bioelectrical impedance-based FM was high (R(2)=0.90; n=5038). During a 5-year follow-up, 447 participants developed HF (4.3%). Higher levels of each adiposity measure were significantly associated with higher HF risk (hazard ratio [95% CI] per 1 SD higher BMI=1.15 [1.05, 1.27], WC=1.22 [1.10, 1.36]; FM=1.13 [1.02, 1.25]). A significant interaction was noted between diabetes status and measures of BMI (P interaction=0.04) and WC (P interaction=0.004) for the risk of HF. In stratified analysis, higher measures of each adiposity parameter were significantly associated with higher HF risk in individuals with diabetes (hazard ratio [95% CI] per 1 SD higher BMI=1.29 [1.14-1.47]; WC=1.48 [1.29-1.70]; FM=1.25 [1.09-1.43]) but not those without diabetes, including participants with prediabetes and euglycemia. The population attributable risk percentage of overall obesity, abdominal obesity, and high FM for incident HF was higher among participants with diabetes (12.8%, 29.9%, and 13.7%, respectively) versus those without diabetes (</=1% for each). CONCLUSIONS: Higher BMI, WC, and FM are strongly associated with greater risk of HF among older adults, particularly among those with prevalent diabetes.

9. Frailty Status Modifies the Efficacy of Exercise Training Among Patients With Chronic Heart Failure and Reduced Ejection Fraction: An Analysis From the HF-ACTION Trial

   A. Pandey, M. W. Segar, S. Singh, and 7 more authors

   2022

   Pandey, Ambarish Segar, Matthew W Singh, Sumitabh Reeves, Gordon R O’Connor, Christopher Pina, Ileana Whellan, David Kraus, William E Mentz, Robert J Kitzman, Dalane W eng R03 AG067960/AG/NIA NIH HHS/ R01 AG018915/AG/NIA NIH HHS/ R01 AG045551/AG/NIA NIH HHS/ P30 AG021332/AG/NIA NIH HHS/ U24 AG059624/AG/NIA NIH HHS/ Randomized Controlled Trial Research Support, Non-U.S. Gov’t Research Support, N.I.H., Extramural 2022/05/27 Circulation. 2022 Jul 12;146(2):80-90. doi: 10.1161/CIRCULATIONAHA.122.059983. Epub 2022 May 26.

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   BACKGROUND: Supervised aerobic exercise training (ET) is recommended for stable outpatients with heart failure (HF) with reduced ejection fraction (HFrEF). Frailty, a syndrome characterized by increased vulnerability and decreased physiologic reserve, is common in patients with HFrEF and associated with a higher risk of adverse outcomes. The effect modification of baseline frailty on the efficacy of aerobic ET in HFrEF is not known. METHODS: Stable outpatients with HFrEF randomized to aerobic ET versus usual care in the HF-ACTION (Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training) trial were included. Baseline frailty was estimated using the Rockwood frailty index (FI), a deficit accumulation-based model of frailty assessment; participants with FI scores >0.21 were identified as frail. Multivariable Cox proportional hazard models with multiplicative interaction terms (frailty x treatment arm) were constructed to evaluate whether frailty modified the treatment effect of aerobic ET on the primary composite end point (all-cause hospitalization or mortality), secondary end points (composite of cardiovascular death or cardiovascular hospitalization, and cardiovascular death or HF hospitalization), and Kansas City Cardiomyopathy Questionnaire score. Separate models were constructed for continuous (FI) and categorical (frail versus not frail) measures of frailty. RESULTS: Among 2130 study participants (age, 59+/-13 years; 28% women), 1266 (59%) were characterized as frail (FI>0.21). Baseline frailty burden significantly modified the treatment effect of aerobic ET (P interaction: FI x treatment arm=0.02; frail status [frail versus nonfrail] x treatment arm=0.04) with a lower risk of primary end point in frail (hazard ratio [HR], 0.83 [95% CI, 0.72-0.95]) but not nonfrail (HR, 1.04 [95% CI, 0.87-1.25]) participants. The favorable effect of aerobic ET among frail participants was driven by a significant reduction in the risk of all-cause hospitalization (HR, 0.84 [95% CI, 0.72-0.99]). The treatment effect of aerobic ET on all-cause mortality and other secondary endpoints was not different between frail and nonfrail patients (P interaction>0.1 for each). Aerobic ET was associated with a nominally greater improvement in Kansas City Cardiomyopathy Questionnaire scores at 3 months among frail versus nonfrail participants without a significant treatment interaction by frailty status (P interaction>0.2). CONCLUSIONS: Among patients with chronic stable HFrEF, baseline frailty modified the treatment effect of aerobic ET with a greater reduction in the risk of all-cause hospitalization but not mortality.

10. Frailty Status Modifies the Efficacy of Exercise Training Among Patients With Chronic Heart Failure and Reduced Ejection Fraction: An Analysis From the HF-ACTION Trial

    A. Pandey, M. W. Segar, S. Singh, and 7 more authors

    2022

    Pandey, Ambarish Segar, Matthew W Singh, Sumitabh Reeves, Gordon O’Connor, Christopher Pina, Ileana Whellan, David Kraus, William Mentz, Robert Kitzman, Dalane eng 2022/05/27 Circulation. 2022 May 26:101161CIRCULATIONAHA122059983. doi: 10.1161/CIRCULATIONAHA.122.059983.

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    BACKGROUND: Supervised aerobic exercise training (ET) is recommended for stable outpatients with heart failure (HF) with reduced ejection fraction (HFrEF). Frailty, a syndrome characterized by increased vulnerability and decreased physiologic reserve, is common in patients with HFrEF and associated with a higher risk of adverse outcomes. The effect modification of baseline frailty on the efficacy of aerobic ET in HFrEF is not known. METHODS: Stable outpatients with HFrEF randomized to aerobic ET versus usual care in the HF-ACTION (Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training) trial were included. Baseline frailty was estimated using the Rockwood frailty index (FI), a deficit accumulation-based model of frailty assessment; participants with FI scores >0.21 were identified as frail. Multivariable Cox proportional hazard models with multiplicative interaction terms (frailty treatment arm) were constructed to evaluate whether frailty modified the treatment effect of aerobic ET on the primary composite end point (all-cause hospitalization and mortality), secondary end points (composite of cardiovascular death or cardiovascular hospitalization, and cardiovascular death or HF hospitalization), and Kansas City Cardiomyopathy Questionnaire score. Separate models were constructed for continuous (FI) and categorical (frail versus not frail) measures of frailty. RESULTS: Among 2130 study participants (age, 59+/-13 years; 28% women), 1266 (59%) were characterized as frail (FI>0.21). Baseline frailty burden significantly modified the treatment effect of aerobic ET (P interaction: FI x treatment arm=0.02; frail status [frail versus nonfrail] x treatment arm=0.04) with a lower risk of primary end point in frail (hazard ratio [HR], 0.83 [95% CI, 0.72-0.95]) but not nonfrail (HR, 1.04 [95% CI, 0.87-1.25]) participants. The favorable effect of aerobic ET among frail participants was driven by a significant reduction in the risk of all-cause hospitalization (HR, 0.84 [95% CI, 0.72-0.99]). The treatment effect of aerobic ET on all-cause mortality and other secondary endpoints was not different between frail and nonfrail patients (P interaction>0.1 for each). Aerobic ET was associated with a nominally greater improvement in Kansas City Cardiomyopathy Questionnaire scores at 3 months among frail versus nonfrail participants without a significant treatment interaction by frailty status (P interaction>0.2). CONCLUSION: Among patients with chronic stable HFrEF, baseline frailty modified the treatment effect of aerobic ET with a greater reduction in the risk of all-cause hospitalization but not mortality.

11. Frailty, Guideline-Directed Medical Therapy, and Outcomes in HFrEF: From the GUIDE-IT Trial

    M. S. Khan, M. W. Segar, M. S. Usman, and 8 more authors

    2022

    Khan, Muhammad Shahzeb Segar, Matthew W Usman, Muhammad Shariq Singh, Sumitabh Greene, Stephen J Fonarow, Gregg C Anker, Stefan D Felker, G Michael Januzzi, James L Jr Butler, Javed Pandey, Ambarish eng Research Support, N.I.H., Extramural 2022/04/02 JACC Heart Fail. 2022 Apr;10(4):266-275. doi: 10.1016/j.jchf.2021.12.004. Epub 2022 Mar 9.

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    OBJECTIVES: In this study, we sought to evaluate the association of frailty with the use of optimal guideline-directed medical therapy (GDMT) and outcomes in heart failure with reduced ejection fraction (HFrEF). BACKGROUND: The burden of frailty in HFrEF is high, and the patterns of GDMT use according to frailty status have not been studied previously. METHODS: A post hoc analysis of patients with HFrEF enrolled in the GUIDE-IT (Guiding Evidence-Based Therapy Using Biomarker Intensified Treatment in Heart Failure) trial was conducted. Frailty was assessed with the use of a frailty index (FI) using a 38-variable deficit model, and participants were categorized into 3 groups: class 1: nonfrail, FI <0.21); class 2: intermediate frailty, FI 0.21-0.31), and class 3: high frailty, FI >0.31). Multivariate-adjusted Cox models were used to study the association of frailty status with clinical outcomes. Use of optimal GDMT over time (beta-blockers, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, and mineralocorticoid receptor antagonists) across frailty strata was assessed with the use of adjusted linear and logistic mixed-effect models. RESULTS: The study included 879 participants, of which 56.3% had high frailty burden (class 3 FI). A higher frailty burden was associated with a significantly higher risk of HF hospitalization or death in adjusted Cox models: high frailty vs nonfrail HR: 1.76, 95% CI: 1.20-2.58. On follow-up, participants with high frailty burden also had a significantly lower likelihood of achieving optimal GDMT: high frailty vs non-frail GDMT triple therapy use at study end: 17.7% vs 28.4%; P interaction, frailty class x time <0.001. CONCLUSIONS: Patients with HFrEF with a high burden of frailty have a significantly higher risk for adverse clinical outcomes and are less likely to be initiated and up-titrated on an optimal GDMT regimen.

12. Phenomapping a Novel Classification System for Patients With Destination Therapy Left Ventricular Assist Devices

    N. S. Hendren, M. W. Segar, L. Zhong, and 6 more authors

    2022

    Hendren, Nicholas S Segar, Matthew W Zhong, Lin Michelis, Katherine C Drazner, Mark H Young, James B Tang, W H Wilson Pandey, Ambarish Grodin, Justin L eng Research Support, Non-U.S. Gov’t 2021/11/25 Am J Cardiol. 2022 Feb 1;164:93-99. doi: 10.1016/j.amjcard.2021.10.028. Epub 2021 Nov 20.

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    Patients with continuous flow destination therapy (DT) left ventricular assist devices (LVAD) comprise a heterogeneous population. We hypothesized that phenotypic clustering of individuals with DT LVADs by their implantation characteristics will be associated with different long-term risk profiles. We analyzed 5,999 patients with continuous flow DT LVADs in Interagency Registry for Mechanically Assisted Circulatory Support using 18 continuous variable baseline characteristics. We Z-transformed the variables and applied a Gaussian finite mixture model to perform unsupervised clustering resulting in identification of 4 phenogroups. Survival analyses considered the competing risk for cumulative incidence of transplant or the composite end point of death or heart transplant where appropriate. Phenogroup 1 (n = 1,163, 19%) was older (71 years) and primarily white (81%). Phenogroups 2 (n = 648, 11%) and 3 (n = 3,671, 61%) were of intermediate age (70 and 62 years), weight (85 and 87 kg), and ventricular size. Phenogroup 4 (n = 517, 9%) was younger (40 years), heavier (108 kg), and more racially diverse. The cumulative incidence of death, heart transplant, bleeding, LVAD malfunction, and LVAD thrombosis differed among phenogroups. The highest incidence of death and the lowest rate of heart transplant was seen in phenogroup 1 (p <0.001). For adverse outcomes, phenogroup 4 had the lowest incidence of bleeding, whereas LVAD device thrombosis and malfunction were lowest in phenogroup 1 (p <0.001 for all). Finally, the incidence of stroke, infection, and renal dysfunction were not statistically different. In conclusion, the present unsupervised machine learning analysis identified 4 phenogroups with different rates of adverse outcomes and these findings underscore the influence of phenotypic heterogeneity on post-LVAD implantation outcomes.

13. Transthyretin V142I Genetic Variant and Cardiac Remodeling, Injury, and Heart Failure Risk in Black Adults

    A. C. Coniglio, M. W. Segar, R. S. Loungani, and 13 more authors

    2022

    Coniglio, Amanda C Segar, Matthew W Loungani, Rahul S Savla, Jainy J Grodin, Justin L Fox, Ervin R Garg, Sonia de Lemos, James A Berry, Jarett D Drazner, Mark H Shah, Sanjiv Hall, Michael E Shah, Amil Khan, Sadiya S Mentz, Robert J Pandey, Ambarish eng HHSN268201800013I/MD/NIMHD NIH HHS/ HHSN268201800014I/HB/NHLBI NIH HHS/ HHSN268201800015I/HB/NHLBI NIH HHS/ HHSN268201800010I/HB/NHLBI NIH HHS/ HHSN268201800011I/HB/NHLBI NIH HHS/ HHSN268201800012I/HB/NHLBI NIH HHS/ K08 DK099415/DK/NIDDK NIH HHS/ P20 GM104357/GM/NIGMS NIH HHS/ U54 GM115428/GM/NIGMS NIH HHS/ R03 AG067960/AG/NIA NIH HHS/ Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t 2022/02/05 JACC Heart Fail. 2022 Feb;10(2):129-138. doi: 10.1016/j.jchf.2021.09.006. Epub 2021 Dec 8.

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    OBJECTIVES: This study evaluated the association of transthyretin (TTR) gene variant, in which isoleucine substitutes for valine at position 122 (V142I), with cardiac structure, function, and heart failure (HF) risk among middle-aged Black adults. BACKGROUND: The valine-to-isoleucine substitution in the TTR protein is prevalent in Black individuals and causes cardiac amyloidosis. METHODS: Jackson Heart Study participants without HF at baseline who had available data on the TTR V142I variant were included. The association of the TTR V142I variant with baseline echocardiographic parameters and repeated measures of high-sensitivity cardiac troponin-I (hs-cTnI) was assessed using adjusted linear regression models and linear mixed models, respectively. Adjusted Cox models, restricted mean survival time analysis, and Anderson-Gill models were constructed to determine the association of TTR V142I variant with the risk of incident HF, survival free of HF, and total HF hospitalizations. RESULTS: A total of 119 of 2,960 participants (4%) were heterozygous carriers of the TTR V142I variant. The TTR V142I variant was not associated with measures of cardiac parameters at baseline but was associated with a greater increase in high-sensitivity troponin I (hs-TnI) levels over time. In adjusted Cox models, TTR V142I variant carriers had significantly higher risk of incident HF (HR: 1.80; 95% CI: 1.07-3.05; P = 0.03), lower survival free of HF (mean difference: 4.0 year; 95% CI: 0.6-6.2 years); P = 0.02), and higher risk of overall HF hospitalizations (HR: 2.12; 95% CI: 1.23-3.63; P = 0.007). CONCLUSIONS: The TTR V142I variant in middle-aged Black adults is not associated with adverse cardiac remodeling but was associated with a significantly higher burden of chronic myocardial injury, and greater risk of incident HF and overall HF hospitalizations.

2021

1. Medicaid expansion and provision of prescription contraception to Medicaid beneficiaries

   A. Sumarsono, M. W. Segar, L. Xie, and 4 more authors

   2021

   Sumarsono, Andrew Segar, Matthew W Xie, Luyu Atem, Folefac Messiah, Sarah E Francis, Jenny Kr Keshvani, Neil eng K23 HD097291/HD/NICHD NIH HHS/ 2020/11/20 Contraception. 2021 Mar;103(3):199-202. doi: 10.1016/j.contraception.2020.11.005. Epub 2020 Nov 17.

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   INTRODUCTION: Medicaid expansion increased access to care, but longitudinal patterns of contraception use after the Medicaid expansion have not been described. METHODS: We evaluated the effects of Medicaid expansion on the amount and type of contraceptive prescriptions using the Medicaid State Utilization Dataset. RESULTS: Overall long-acting reversible contraception (LARC) use increased in both expansion and non-expansion states. In a difference-in-differences analysis, states that expanded Medicaid had no appreciable increase in per-capita prescription rates of LARC (p = 0.26) or short-acting hormonal contraception (p = 0.09) when compared to nonexpansion states. DISCUSSION: The Medicaid expansion was not associated with a change in per-capita LARC or short-acting hormonal contraception use.

2. Association of Medicaid Expansion With Rates of Utilization of Cardiovascular Therapies Among Medicaid Beneficiaries Between 2011 and 2018

   A. Sumarsono, H. Lalani, M. W. Segar, and 7 more authors

   2021

   Sumarsono, Andrew Lalani, Hussain Segar, Matthew W Rao, Shreya Vaduganathan, Muthiah Wadhera, Rishi K Das, Sandeep R Navar, Ann Marie Fonarow, Gregg C Pandey, Ambarish eng R03 AG067960/AG/NIA NIH HHS/ UL1 TR002541/TR/NCATS NIH HHS/ T32 HL125247/HL/NHLBI NIH HHS/ Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t 2020/11/10 Circ Cardiovasc Qual Outcomes. 2021 Jan;14(1):e007492. doi: 10.1161/CIRCOUTCOMES.120.007492. Epub 2020 Nov 9.

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   BACKGROUND: The Affordable Care Act expanded Medicaid eligibility allowing low-income individuals greater access to health care. However, the uptake of state Medicaid expansion has been variable. It remains unclear how the Medicaid expansion was associated with the temporal trends in use of evidence-based cardiovascular drugs. METHODS: We used the publicly available Medicaid Drug Utilization and Current Population Survey to extract filled prescription rates per 1000 Medicaid beneficiaries of statins, antihypertensives, P2Y12 inhibitors, and direct oral anticoagulants. We defined expander states as those who expanded Medicaid on or before January 1, 2014, and nonexpander states as those who had not expanded by December 31, 2018. Difference-in-differences (DID) analyses were performed to compare the association of the Medicaid expansion with per-capita cardiovascular drug prescription rates in expander versus nonexpander states. RESULTS: Between 2011 and 2018, the total number of prescriptions among all Medicaid beneficiaries increased, with gains of 89.7% in statins (11.0 to 20.8 million), 76% in antihypertensives (35.3 to 62.2 million), and 37% in P2Y12 inhibitors (1.7 to 2.3 million). Medicaid expansion was associated with significantly greater increases in quarterly prescriptions (per 1000 Medicaid beneficiaries) of statins (DID estimate [95% CI]: 22.5 [16.5-28.6], P<0.001), antihypertensives (DID estimate [95% CI]: 63.2 [47.3-79.1], P<0.001), and P2Y12 inhibitors (DID estimate [95% CI]: 1.7 [1.2-2.2], P<0.001). Between 2013 and 2018, >75% of the expander states had increases in prescription rates of both statins and antihypertensives. In contrast, 44% of nonexpander states saw declines in statins and antihypertensives. The Medicaid expansion was not associated with higher direct oral anticoagulants prescription rates (DID estimate [95% CI] 0.9 [-0.3 to 2.1], P=0.142). CONCLUSIONS: The 2014 Medicaid expansion was associated with a significant increase in per-capita utilization of cardiovascular prescription drugs among Medicaid beneficiaries. These gains in utilization may contribute to long-term cardiovascular benefits to lower-income and previously underinsured populations.

3. Prefrailty, impairment in physical function, and risk of incident heart failure among older adults

   M. W. Segar, S. Singh, P. Goyal, and 5 more authors

   2021

   Segar, Matthew W Singh, Sumitabh Goyal, Parag Hummel, Scott L Maurer, Mathew S Forman, Daniel E Butler, Javed Pandey, Ambarish eng P30AG024827/NH/NIH HHS/ R01AG051376/NH/NIH HHS/ R01AG058883/NH/NIH HHS/ R01AG060499/NH/NIH HHS/ CARA-009-16F9050/NH/NIH HHS/ R01HL139813/NH/NIH HHS/ R01AG062582/NH/NIH HHS/ 20CDA35310455/AHA/American Heart Association-American Stroke Association/ K76AG064428/AG/NIA NIH HHS/ K24AG036778/AG/NIA NIH HHS/ 1R03AG067960-01/AG/NIA NIH HHS/ Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t 2021/05/30 J Am Geriatr Soc. 2021 Sep;69(9):2486-2497. doi: 10.1111/jgs.17218. Epub 2021 May 29.

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   OBJECTIVE: Evaluate the association between prefrailty and the risk of heart failure (HF) among older adults. DESIGN, SETTING, AND PARTICIPANTS: This prospective, community-based cohort study included participants from the Atherosclerotic Risk in Communities study who underwent detailed frailty assessment using Fried Criteria and physical function assessment using the Short Performance Physical Battery (SPPB) score. Individuals with prevalent HF and frailty were excluded. MAIN OUTCOMES AND MEASURES: Adjusted association between prefrailty (vs robust), physical function measures (SPPB score, grip strength, and gait speed), and incident HF (overall and HF subtypes, HF with reduced [HFrEF, EF < 50%] and preserved ejection fraction [HFpEF]) were assessed using Cox proportional hazards models. RESULTS: Among 5210 participants (mean age 75 years, 58% women), 2565 (49.2%) were identified as prefrail. In cross-sectional analysis, prefrail individuals had a higher burden of chronic myocardial injury (troponin, Std beta = 0.08 [0.05-0.10]) and neurohormonal stress (NT-ProBNP, Std beta = 0.03 [0.02-0.05]) after adjustment for potential confounders. Over a median follow-up of 4.6 years, there were 232 (4.5%) HF events (HFrEF: 102; HFpEF: 97). Prefrailty was associated with an increased risk of HF after adjusting for potential clinical confounders and cardiac biomarkers (aHR [95% CI] = 1.65 [1.24-2.20]). Among HF subtypes, prefrailty was associated with an increased risk of HFpEF but not HFrEF (aHR [95% CI] = 1.73 [1.11-2.70] and 1.38 [0.90-2.10], respectively). A lower SPPB score was also associated with an increased risk of overall HF and HFpEF, but not HFrEF. Among individual components, increased gait speed were associated with a lower risk of HFpEF, but not HFrEF. CONCLUSIONS AND RELEVANCE: Subtle abnormalities in physiological reserve (prefrailty) and impairment in physical function (SPPB) were both significantly associated with a higher risk of incident HF, particularly HFpEF. These findings highlight the potential role of routine assessment of geriatric syndromes for early identification of HF risk.

4. Association of Visit-to-Visit Variability in Kidney Function and Serum Electrolyte Indexes With Risk of Adverse Clinical Outcomes Among Patients With Heart Failure With Preserved Ejection Fraction

   M. W. Segar, R. B. Patel, K. V. Patel, and 7 more authors

   2021

   Segar, Matthew W Patel, Ravi B Patel, Kershaw V Fudim, Marat DeVore, Adam D Martens, Pieter Hedayati, S Susan Grodin, Justin L Tang, W H Wilson Pandey, Ambarish eng KL2 TR001424/TR/NCATS NIH HHS/ UL1 TR001422/TR/NCATS NIH HHS/ 2020/11/19 JAMA Cardiol. 2021 Jan 1;6(1):68-77. doi: 10.1001/jamacardio.2020.5592.

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   Importance: Although kidney dysfunction and abnormalities in serum electrolyte levels are associated with poor clinical outcomes in patients with heart failure with preserved ejection fraction (HFpEF), the association of visit-to-visit variability in such laboratory measures with long-term outcomes is unclear. Objective: To evaluate the associations of visit-to-visit variability in indexes of kidney function (creatinine and blood urea nitrogen [BUN] levels) and serum electrolyte (sodium, chloride, and potassium) with the risk of adverse clinical outcomes among patients with chronic, stable HFpEF. Design, Setting, and Participants: This cohort analysis used data from the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trial. All participants with 3 or more serial laboratory measurements who were event free within the first 4 months of enrollment were included. Data were analyzed from March 1, 2019, to January 31, 2020. Main Outcomes and Measures: Adjusted associations between indexes of variability in serum laboratory measurements during the first 4 months of follow-up and risk of the primary composite outcome (a composite of aborted cardiac arrest, hospitalization for heart failure, or cardiovascular death) and all-cause mortality were assessed using Cox proportional hazards regression models. Results: Of the 3445 patients enrolled in the TOPCAT trial (mean [SD] age, 68-69 [10] years; 49.7%-51.5% female), 2479 (BUN) to 3195 (potassium) were analyzed, depending on availability of serial measurements. Participants with higher laboratory variability in kidney function parameters were older, had more comorbidities, and had more severe symptoms of HFpEF. Higher visit-to-visit variability in BUN (hazard ratio [HR] per 1-SD higher average successive variability [ASV], 1.21; 95% CI, 1.10-1.33) and creatinine (HR per 1-SD higher ASV, 1.13; 95% CI, 1.04-1.22) were independently associated with a higher risk of the primary composite outcome as well as mortality independent of other baseline confounders, changes in kidney function, changes in medication dosages, and variability in other cardiometabolic parameters (systolic blood pressure and body mass index). The higher risk associated with greater variability in kidney function was consistent across subgroups of patients stratified by the presence of chronic kidney disease (CKD) at baseline (CKD: HR per 1-SD higher ASV, 1.39; 95% CI, 1.16-1.67 and no CKD: HR per 1-SD higher ASV, 1.13; 95% CI, 1.01-1.27), among placebo and spironolactone treatment arms separately (spironolactone arm: 1.30; 95% CI, 1.03-1.65 and placebo arm: HR per 1-SD higher ASV, 1.27; 95% CI, 1.04-1.56). Among serum electrolytes, variability in sodium and potassium measures were also significantly associated with a higher risk of primary composite events (sodium: HR per 1-SD higher ASV, 1.14; 95% CI, 1.01-1.30 and potassium: HR per 1-SD higher ASV, 1.21; 95% CI, 1.02-1.44). Conclusions and Relevance: In HFpEF, visit-to-visit variability in laboratory indexes of kidney function and serum electrolytes is common and independently associated with worse long-term clinical outcomes.

5. Development and validation of optimal phenomapping methods to estimate long-term atherosclerotic cardiovascular disease risk in patients with type 2 diabetes

   M. W. Segar, K. V. Patel, M. Vaduganathan, and 9 more authors

   2021

   Segar, Matthew W Patel, Kershaw V Vaduganathan, Muthiah Caughey, Melissa C Jaeger, Byron C Basit, Mujeeb Willett, Duwayne Butler, Javed Sengupta, Partho P Wang, Thomas J McGuire, Darren K Pandey, Ambarish eng R03 AG067960/AG/NIA NIH HHS/ UL1 TR002541/TR/NCATS NIH HHS/ T32 HL125247/HL/NHLBI NIH HHS/ Research Support, Non-U.S. Gov’t Research Support, N.I.H., Extramural Germany Diabetologia. 2021 Jul;64(7):1583-1594. doi: 10.1007/s00125-021-05426-2. Epub 2021 Mar 13.

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   AIMS/HYPOTHESIS: Type 2 diabetes is a heterogeneous disease process with variable trajectories of CVD risk. We aimed to evaluate four phenomapping strategies and their ability to stratify CVD risk in individuals with type 2 diabetes and to identify subgroups who may benefit from specific therapies. METHODS: Participants with type 2 diabetes and free of baseline CVD in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial were included in this study (N = 6466). Clustering using Gaussian mixture models, latent class analysis, finite mixture models (FMMs) and principal component analysis was compared. Clustering variables included demographics, medical and social history, laboratory values and diabetes complications. The interaction between the phenogroup and intensive glycaemic, combination lipid and intensive BP therapy for the risk of the primary outcome (composite of fatal myocardial infarction, non-fatal myocardial infarction or unstable angina) was evaluated using adjusted Cox models. The phenomapping strategies were independently assessed in an external validation cohort (Look Action for Health in Diabetes [Look AHEAD] trial: n = 4211; and Bypass Angioplasty Revascularisation Investigation 2 Diabetes [BARI 2D] trial: n = 1495). RESULTS: Over 9.1 years of follow-up, 789 (12.2%) participants had a primary outcome event. FMM phenomapping with three phenogroups was the best-performing clustering strategy in both the derivation and validation cohorts as determined by Bayesian information criterion, Dunn index and improvement in model discrimination. Phenogroup 1 (n = 663, 10.3%) had the highest burden of comorbidities and diabetes complications, phenogroup 2 (n = 2388, 36.9%) had an intermediate comorbidity burden and lowest diabetes complications, and phenogroup 3 (n = 3415, 52.8%) had the fewest comorbidities and intermediate burden of diabetes complications. Significant interactions were observed between phenogroups and treatment interventions including intensive glycaemic control (p-interaction = 0.042) and combination lipid therapy (p-interaction < 0.001) in the ACCORD, intensive lifestyle intervention (p-interaction = 0.002) in the Look AHEAD and early coronary revascularisation (p-interaction = 0.003) in the BARI 2D trial cohorts for the risk of the primary composite outcome. Favourable reduction in the risk of the primary composite outcome with these interventions was noted in low-risk participants of phenogroup 3 but not in other phenogroups. Compared with phenogroup 3, phenogroup 1 participants were more likely to have severe/symptomatic hypoglycaemic events and medication non-adherence on follow-up in the ACCORD and Look AHEAD trial cohorts. CONCLUSIONS/INTERPRETATION: Clustering using FMMs was the optimal phenomapping strategy to identify replicable subgroups of patients with type 2 diabetes with distinct clinical characteristics, CVD risk and response to therapies.

6. Omics, machine learning, and personalized medicine in heart failure with preserved ejection fraction: promising future or false hope?

   M. W. Segar, and A. Pandey

   2021

   Segar, Matthew W Pandey, Ambarish eng R03 AG067960/AG/NIA NIH HHS/ Editorial Research Support, N.I.H., Extramural Comment England 2021/05/23 Eur J Heart Fail. 2021 Jun;23(6):992-994. doi: 10.1002/ejhf.2246. Epub 2021 Jun 14.
7. Prevalence and Prognostic Implications of Diabetes With Cardiomyopathy in Community-Dwelling Adults

   M. W. Segar, M. S. Khan, K. V. Patel, and 7 more authors

   2021

   Segar, Matthew W Khan, Muhammad Shahzeb Patel, Kershaw V Butler, Javed Tang, W H Wilson Vaduganathan, Muthiah Lam, Carolyn S P Verma, Subodh McGuire, Darren K Pandey, Ambarish eng R03 AG067960/AG/NIA NIH HHS/ Meta-Analysis Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t 2021/10/16 J Am Coll Cardiol. 2021 Oct 19;78(16):1587-1598. doi: 10.1016/j.jacc.2021.08.020.

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   BACKGROUND: Diabetes is associated with abnormalities in cardiac remodeling and high risk of heart failure (HF). OBJECTIVES: The purpose of this study was to evaluate the prevalence and prognostic implications of diabetes with cardiomyopathy (DbCM) among community-dwelling individuals. METHODS: Adults without prevalent cardiovascular disease or HF were pooled from 3 cohort studies (ARIC [Atherosclerosis Risk In Communities], CHS [Cardiovascular Health Study], CRIC [Chronic Renal Insufficiency Cohort]). Among participants with diabetes, DbCM was defined using different definitions: 1) least restrictive: >/=1 echocardiographic abnormality (left atrial enlargement, left ventricle hypertrophy, diastolic dysfunction); 2) intermediate restrictive: >/=2 echocardiographic abnormalities; and 3) most restrictive: elevated N-terminal pro-B-type natriuretic peptide levels (>125 in normal/overweight or >100 pg/mL in obese) plus >/=2 echocardiographic abnormalities. Adjusted Fine-Gray models were used to evaluate the risk of HF. RESULTS: Among individuals with diabetes (2,900 of 10,208 included), the prevalence of DbCM ranged from 67.0% to 11.7% in the least and most restrictive criteria, respectively. Higher fasting glucose, body mass index, and age as well as worse kidney function were associated with higher risk of DbCM. The 5-year incidence of HF among participants with DbCM ranged from 8.4%-12.8% in the least and most restrictive definitions, respectively. Compared with euglycemia, DbCM was significantly associated with higher risk of incident HF with the highest risk observed for the most restrictive definition of DbCM (HR: 2.55 [95% CI: 1.69-3.86]; least restrictive criteria HR: 1.99 [95% CI: 1.50-2.65]). A similar pattern of results was observed across cohort studies, across sex and race subgroups, and among participants without hypertension or obesity. CONCLUSIONS: Regardless of the criteria used to define cardiomyopathy, DbCM identifies a high-risk subgroup for developing HF.

8. Development and Validation of Machine Learning-Based Race-Specific Models to Predict 10-Year Risk of Heart Failure: A Multicohort Analysis

   M. W. Segar, B. C. Jaeger, K. V. Patel, and 16 more authors

   2021

   Segar, Matthew W Jaeger, Byron C Patel, Kershaw V Nambi, Vijay Ndumele, Chiadi E Correa, Adolfo Butler, Javed Chandra, Alvin Ayers, Colby Rao, Shreya Lewis, Alana A Raffield, Laura M Rodriguez, Carlos J Michos, Erin D Ballantyne, Christie M Hall, Michael E Mentz, Robert J de Lemos, James A Pandey, Ambarish eng HHSN268201100005C/HL/NHLBI NIH HHS/ HHSN268201100006C/HL/NHLBI NIH HHS/ HHSN268201100007C/HL/NHLBI NIH HHS/ HHSN268201100008C/HL/NHLBI NIH HHS/ HHSN268201100009C/HL/NHLBI NIH HHS/ HHSN268201100010C/HL/NHLBI NIH HHS/ HHSN268201100011C/HL/NHLBI NIH HHS/ HHSN268201100012C/HL/NHLBI NIH HHS/ R01 HL134320/HL/NHLBI NIH HHS/ UL1 TR001105/TR/NCATS NIH HHS/ HHSN268201800013I/MD/NIMHD NIH HHS/ HHSN261201800014I/CA/NCI NIH HHS/ HHSN268201800015I/HB/NHLBI NIH HHS/ HHSN261201800010I/CA/NCI NIH HHS/ HHSN268201100011I/HL/NHLBI NIH HHS/ R01 HL117323/HL/NHLBI NIH HHS/ KL2 TR002490/TR/NCATS NIH HHS/ Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t 2021/04/14 Circulation. 2021 Jun 15;143(24):2370-2383. doi: 10.1161/CIRCULATIONAHA.120.053134. Epub 2021 Apr 13.

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   BACKGROUND: Heart failure (HF) risk and the underlying risk factors vary by race. Traditional models for HF risk prediction treat race as a covariate in risk prediction and do not account for significant parameters such as cardiac biomarkers. Machine learning (ML) may offer advantages over traditional modeling techniques to develop race-specific HF risk prediction models and to elucidate important contributors of HF development across races. METHODS: We performed a retrospective analysis of 4 large, community cohort studies (ARIC [Atherosclerosis Risk in Communities], DHS [Dallas Heart Study], JHS [Jackson Heart Study], and MESA [Multi-Ethnic Study of Atherosclerosis]) with adjudicated HF events. The study included participants who were >40 years of age and free of HF at baseline. Race-specific ML models for HF risk prediction were developed in the JHS cohort (for Black race-specific model) and White adults from ARIC (for White race-specific model). The models included 39 candidate variables across demographic, anthropometric, medical history, laboratory, and electrocardiographic domains. The ML models were externally validated and compared with prior established traditional and non-race-specific ML models in race-specific subgroups of the pooled MESA/DHS cohort and Black participants of ARIC. The Harrell C-index and Greenwood-Nam-D’Agostino chi(2) tests were used to assess discrimination and calibration, respectively. RESULTS: The ML models had excellent discrimination in the derivation cohorts for Black (n=4141 in JHS, C-index=0.88) and White (n=7858 in ARIC, C-index=0.89) participants. In the external validation cohorts, the race-specific ML model demonstrated adequate calibration and superior discrimination (Black individuals, C-index=0.80-0.83; White individuals, C-index=0.82) compared with established HF risk models or with non-race-specific ML models derived with race included as a covariate. Among the risk factors, natriuretic peptide levels were the most important predictor of HF risk across both races, followed by troponin levels in Black and ECG-based Cornell voltage in White individuals. Other key predictors of HF risk among Black individuals were glycemic parameters and socioeconomic factors. In contrast, prevalent cardiovascular disease and traditional cardiovascular risk factors were stronger predictors of HF risk in White adults. CONCLUSIONS: Race-specific and ML-based HF risk models that integrate clinical, laboratory, and biomarker data demonstrated superior performance compared with traditional HF risk and non-race-specific ML models. This approach identifies distinct race-specific contributors of HF.

9. Longitudinal Trajectories and Factors Associated With US County-Level Cardiovascular Mortality, 1980 to 2014

   S. Rao, A. Hughes, M. W. Segar, and 5 more authors

   2021

   Rao, Shreya Hughes, Amy Segar, Matthew W Wilson, Brianna Ayers, Colby Das, Sandeep Halm, Ethan A Pandey, Ambarish eng 2021/12/01 JAMA Netw Open. 2021 Nov 1;4(11):e2136022. doi: 10.1001/jamanetworkopen.2021.36022.

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   Importance: Cardiovascular (CV) mortality has declined for more than 3 decades in the US. However, differences in declines among residents at a US county level are not well characterized. Objective: To identify unique county-level trajectories of CV mortality in the US during a 35-year study period and explore county-level factors that are associated with CV mortality trajectories. Design, Setting, and Participants: This longitudinal cross-sectional analysis of CV mortality trends used data from 3133 US counties from 1980 to 2014. County-level demographic, socioeconomic, environmental, and health-related risk factors were compiled. Data were analyzed from December 2019 to September 2021. Exposures: County-level characteristics, collected from 5 county-level data sets. Main Outcomes and Measures: Cardiovascular mortality data were obtained for 3133 US counties from 1980 to 2014 using age-standardized county-level mortality rates from the Global Burden of Disease study. The longitudinal K-means approach was used to identify 3 distinct clusters based on underlying mortality trajectory. Multinomial logistic regression models were constructed to evaluate associations between county characteristics and cluster membership. Results: Among 3133 US counties (median, 49.5% [IQR, 48.9%-50.5%] men; 30.7% [IQR, 27.1%-34.4%] older than 55 years; 9.9% [IQR, 4.5%-22.7%] racial minority group [individuals self-identifying as Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian, Pacific Islander, other, or multiple races/ethnicities]), CV mortality declined by 45.5% overall and by 38.4% in high-mortality strata (694 counties), by 45.0% in intermediate-mortality strata (1382 counties), and by 48.3% in low-mortality strata (1057 counties). Counties with the highest mortality in 1980 continued to demonstrate the highest mortality in 2014. Trajectory groups were regionally distributed, with high-mortality trajectory counties focused in the South and in portions of Appalachia. Low- vs high-mortality groups varied significantly in demographic (racial minority group proportion, 7.6% [IQR, 4.1%-14.5%]) vs 23.9% [IQR, 6.5%-40.8%]) and socioeconomic characteristics such as high-school education (9.4% [IQR, 7.3%-12.6%] vs 20.1% [IQR, 16.1%-23.2%]), poverty rates (11.4% [IQR, 8.8%-14.6%] vs 20.6% [IQR, 17.1%-24.4%]), and violent crime rates (161.5 [IQR, 89.0-262.4] vs 272.8 [IQR, 155.3-431.3] per 100 000 population). In multinomial logistic regression, a model incorporating demographic, socioeconomic, environmental, and health characteristics accounted for 60% of the variance in the CV mortality trajectory (R2 = 0.60). Sociodemographic factors such as racial minority group proportion (odds ratio [OR], 1.70 [95% CI, 1.35-2.14]) and educational attainment (OR, 6.17 [95% CI, 4.55-8.36]) and health behaviors such as smoking (OR for high vs low, 2.04 [95% CI, 1.58-2.64]) and physical inactivity (OR, 3.74 [95% CI, 2.83-4.93]) were associated with the high-mortality trajectory. Conclusions and Relevance: Cardiovascular mortality declined in all subgroups during the 35-year study period; however, disparities remained unchanged during that time. Disparate trajectories were associated with social and behavioral risks. Health policy efforts across multiple domains, including structural and public health targets, may be needed to reduce existing county-level cardiovascular mortality disparities.

10. Deep-Learning Models for the Echocardiographic Assessment of Diastolic Dysfunction

    A. Pandey, N. Kagiyama, N. Yanamala, and 4 more authors

    2021

    Pandey, Ambarish Kagiyama, Nobuyuki Yanamala, Naveena Segar, Matthew W Cho, Jung S Tokodi, Marton Sengupta, Partho P eng JACC Cardiovasc Imaging. 2021 May 12. pii: S1936-878X(21)00355-7. doi: 10.1016/j.jcmg.2021.04.010.

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    OBJECTIVES: The authors explored a deep neural network (DeepNN) model that integrates multidimensional echocardiographic data to identify distinct patient subgroups with heart failure with preserved ejection fraction (HFpEF). BACKGROUND: The clinical algorithms for phenotyping the severity of diastolic dysfunction in HFpEF remain imprecise. METHODS: The authors developed a DeepNN model to predict high- and low-risk phenogroups in a derivation cohort (n = 1,242). Model performance was first validated in 2 external cohorts to identify elevated left ventricular filling pressure (n = 84) and assess its prognostic value (n = 219) in patients with varying degrees of systolic and diastolic dysfunction. In 3 National Heart, Lung, and Blood Institute-funded HFpEF trials, the clinical significance of the model was further validated by assessing the relationships of the phenogroups with adverse clinical outcomes (TOPCAT [Aldosterone Antagonist Therapy for Adults With Heart Failure and Preserved Systolic Function] trial, n = 518), cardiac biomarkers, and exercise parameters (NEAT-HFpEF [Nitrate’s Effect on Activity Tolerance in Heart Failure With Preserved Ejection Fraction] and RELAX-HF [Evaluating the Effectiveness of Sildenafil at Improving Health Outcomes and Exercise Ability in People With Diastolic Heart Failure] pooled cohort, n = 346). RESULTS: The DeepNN model showed higher area under the receiver-operating characteristic curve than 2016 American Society of Echocardiography guideline grades for predicting elevated left ventricular filling pressure (0.88 vs. 0.67; p = 0.01). The high-risk (vs. low-risk) phenogroup showed higher rates of heart failure hospitalization and/or death, even after adjusting for global left ventricular and atrial longitudinal strain (hazard ratio [HR]: 3.96; 95% confidence interval [CI]: 1.24 to 12.67; p = 0.021). Similarly, in the TOPCAT cohort, the high-risk (vs. low-risk) phenogroup showed higher rates of heart failure hospitalization or cardiac death (HR: 1.92; 95% CI: 1.16 to 3.22; p = 0.01) and higher event-free survival with spironolactone therapy (HR: 0.65; 95% CI: 0.46 to 0.90; p = 0.01). In the pooled RELAX-HF/NEAT-HFpEF cohort, the high-risk (vs. low-risk) phenogroup had a higher burden of chronic myocardial injury (p < 0.001), neurohormonal activation (p < 0.001), and lower exercise capacity (p = 0.001). CONCLUSIONS: This publicly available DeepNN classifier can characterize the severity of diastolic dysfunction and identify a specific subgroup of patients with HFpEF who have elevated left ventricular filling pressures, biomarkers of myocardial injury and stress, and adverse events and those who are more likely to respond to spironolactone.

11. Gender-Based Differences in Outcomes Among Resuscitated Patients With Out-of-Hospital Cardiac Arrest

    P. Mody, A. Pandey, A. S. Slutsky, and 14 more authors

    2021

    Mody, Purav Pandey, Ambarish Slutsky, Arthur S Segar, Matthew W Kiss, Alex Dorian, Paul Parsons, Janet Scales, Damon C Rac, Valeria E Cheskes, Sheldon Bierman, Arlene S Abramson, Beth L Gray, Sara Fowler, Rob A Dainty, Katie N Idris, Ahamed H Morrison, Laurie eng R03 AG067960/AG/NIA NIH HHS/ Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t 2020/12/16 Circulation. 2021 Feb 16;143(7):641-649. doi: 10.1161/CIRCULATIONAHA.120.050427. Epub 2020 Dec 15.

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    BACKGROUND: Studies examining gender-based differences in outcomes of patients experiencing out-of-hospital cardiac arrest have demonstrated that, despite a higher likelihood of return of spontaneous circulation, women do not have higher survival. METHODS: Patients successfully resuscitated from out-of-hospital cardiac arrest enrolled in the CCC trial (Trial of Continuous or Interrupted Chest Compressions during CPR) were included. Hierarchical multivariable logistic regression models were constructed to evaluate the association between gender and survival after adjustment for age, gender, cardiac arrest rhythm, witnessed status, bystander cardiopulmonary resuscitation, episode location, epinephrine dose, emergency medical services response time, and duration of resuscitation. Do not resuscitate (DNR) and withdrawal of life-sustaining therapy (WLST) order status were used to assess whether differences in postresuscitation outcomes were modified by baseline prognosis. The analysis was replicated among ALPS trial (Amiodarone, Lidocaine, or Placebo in Out-of-Hospital Cardiac Arrest) participants. RESULTS: Among 4875 successfully resuscitated patients, 1825 (37.4%) were women and 3050 (62.6%) were men. Women were older (67.5 versus 65.3 years), received less bystander cardiopulmonary resuscitation (49.1% versus 54.9%), and had a lower proportion of cardiac arrests that were witnessed (55.1% versus 64.5%) or had shockable rhythm (24.3% versus 44.6%, P<0.001 for all). A significantly higher proportion of women received DNR orders (35.7% versus 32.1%, P=0.009) and had WLST (32.8% versus 29.8%, P=0.03). Discharge survival was significantly lower in women (22.5% versus 36.3%, P<0.001; adjusted odds ratio, 0.78 [95% CI, 0.66-0.93]; P=0.005). The association between gender and survival to discharge was modified by DNR and WLST order status such that women had significantly reduced survival to discharge among patients who were not designated DNR (31.3% versus 49.9%, P=0.005; adjusted odds ratio, 0.74 [95% CI, 0.60-0.91]) or did not have WLST (32.3% versus 50.7%, P=0.002; adjusted odds ratio, 0.73 [95% CI, 0.60-0.89]). In contrast, no gender difference in survival was noted among patients receiving a DNR order (6.7% versus 7.4%, P=0.90) or had WLST (2.8% versus 2.4%, P=0.93). Consistent patterns of association between gender and postresuscitation outcomes were observed in the secondary cohort. CONCLUSIONS: Among patients resuscitated after experiencing out-of-hospital cardiac arrest, discharge survival was significantly lower in women than in men, especially among patients considered to have a favorable prognosis.

2020

1. Response to Comment on Segar et al. Machine Learning to Predict the Risk of Incident Heart Failure Hospitalization Among Patients With Diabetes: The WATCH-DM Risk Score. Diabetes Care 2019;42:2298-2306

   M. W. Segar, M. Vaduganathan, D. K. McGuire, and 2 more authors

   2020

   Segar, Matthew W Vaduganathan, Muthiah McGuire, Darren K Basit, Mujeeb Pandey, Ambarish eng UL1 TR002541/TR/NCATS NIH HHS/ Letter Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t Comment 2020/01/22 Diabetes Care. 2020 Feb;43(2):e26-e27. doi: 10.2337/dci19-0059.
2. County-level phenomapping to identify disparities in cardiovascular outcomes: An unsupervised clustering analysis: Short title: Unsupervised clustering of counties and risk of cardiovascular mortality

   M. W. Segar, S. Rao, A. M. Navar, and 7 more authors

   2020

   Segar, Matthew W Rao, Shreya Navar, Ann Marie Michos, Erin D Lewis, Alana Correa, Adolfo Sims, Mario Khera, Amit Hughes, Amy E Pandey, Ambarish eng Netherlands 2021/07/31 Am J Prev Cardiol. 2020 Nov 20;4:100118. doi: 10.1016/j.ajpc.2020.100118. eCollection 2020 Dec.

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   INTRODUCTION: Significant heterogeneity in cardiovascular disease (CVD) risk and healthcare resource allocation has been demonstrated in the United States, but optimal methods to capture heterogeneity in county-level characteristics that contribute to CVD mortality differences are unclear. We evaluated the feasibility of unsupervised machine learning (ML)-based phenomapping in identifying subgroups of county-level social and demographic risk factors with differential CVD outcomes. METHODS: We performed a cross-sectional study using county-level data from 2008 to 2018 from the Centers for Disease Control (CDC) WONDER platform and the 2020 Robert Wood Johnson County Health Rankings program. Unsupervised clustering was performed on 46 facets of population characteristics spanning the demographic, health behaviors, socioeconomic, and healthcare access domains. Spatial autocorrelation was assessed using the Moran’s I test, and temporal trends in age-adjusted CVD outcomes were evaluated using linear mixed effect models and least square means. RESULTS: Among 2676 counties, 4 county-level phenogroups were identified (Moran’s I p-value <0.001). Phenogroup 1 (N = 924; 24.5%) counties were largely white, suburban households with high income and access to healthcare. Phenogroup 2 counties (N = 451; 16.9%) included predominantly Hispanic residents and below-average prevalence of CVD risk factors. Phenogroup 3 (N = 951; 35.5%) counties included rural, white residents with the lowest levels of access to healthcare. Phenogroup 4 (350; 13.1%) comprised counties with predominantly Black residents, substantial cardiovascular comorbidities, and physical and socioeconomic burdens. Least square means in age-adjusted cardiovascular mortality over time increased in a stepwise fashion from 223 in phenogroup 1 to 317 per 100,000 residents in phenogroup 4. CONCLUSIONS: Unsupervised ML-based clustering on county-level population characteristics can identify unique phenogroups with differential risk of CVD mortality. Phenogroup identification may aid in developing a uniform set of preventive initiatives for clustered counties to address regional differences in CVD mortality.

3. Association of Long-term Change and Variability in Glycemia With Risk of Incident Heart Failure Among Patients With Type 2 Diabetes: A Secondary Analysis of the ACCORD Trial

   M. W. Segar, K. V. Patel, M. Vaduganathan, and 6 more authors

   2020

   Segar, Matthew W Patel, Kershaw V Vaduganathan, Muthiah Caughey, Melissa C Butler, Javed Fonarow, Gregg C Grodin, Justin L McGuire, Darren K Pandey, Ambarish eng T32 HL125247/HL/NHLBI NIH HHS/ UL1 TR002541/TR/NCATS NIH HHS/ Multicenter Study Randomized Controlled Trial Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t 2020/06/17 Diabetes Care. 2020 Aug;43(8):1920-1928. doi: 10.2337/dc19-2541. Epub 2020 Jun 15.

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   OBJECTIVE: To evaluate the associations between long-term change and variability in glycemia with risk of heart failure (HF) among patients with type 2 diabetes mellitus (T2DM). RESEARCH DESIGN AND METHODS: Among participants with T2DM enrolled in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, variability in HbA1c was assessed from stabilization of HbA1c following enrollment (8 months) to 3 years of follow-up as follows: average successive variability (ASV) (average absolute difference between successive values), coefficient of variation (SD/mean), and SD. Participants with HF at baseline or within 3 years of enrollment were excluded. Adjusted Cox models were used to evaluate the association of percent change (from baseline to 3 years of follow-up) and variability in HbA1c over the first 3 years of enrollment and subsequent risk of HF. RESULTS: The study included 8,576 patients. Over a median follow-up of 6.4 years from the end of variability measurements at year 3, 388 patients had an incident HF hospitalization. Substantial changes in HbA1c were significantly associated with higher risk of HF (hazard ratio [HR] for >/=10% decrease 1.32 [95% CI 1.08-1.75] and for >/=10% increase 1.55 [1.19-2.04]; reference <10% change in HbA1c). Greater long-term variability in HbA1c was significantly associated with higher risk of HF (HR per 1 SD of ASV 1.34 [95% CI 1.17-1.54]) independent of baseline risk factors and interval changes in cardiometabolic parameters. Consistent patterns of association were observed with use of alternative measures of glycemic variability. CONCLUSIONS: Substantial long-term changes and variability in HbA1c were independently associated with risk of HF among patients with T2DM.

4. Phenomapping of patients with heart failure with preserved ejection fraction using machine learning-based unsupervised cluster analysis

   M. W. Segar, K. V. Patel, C. Ayers, and 6 more authors

   2020

   Segar, Matthew W Patel, Kershaw V Ayers, Colby Basit, Mujeeb Tang, W H Wilson Willett, Duwayne Berry, Jarett Grodin, Justin L Pandey, Ambarish eng UL1 TR001105/TR/NCATS NIH HHS/ Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t England Eur J Heart Fail. 2020 Jan;22(1):148-158. doi: 10.1002/ejhf.1621. Epub 2019 Oct 21.

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   AIM: To identify distinct phenotypic subgroups in a highly-dimensional, mixed-data cohort of individuals with heart failure (HF) with preserved ejection fraction (HFpEF) using unsupervised clustering analysis. METHODS AND RESULTS: The study included all Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) participants from the Americas (n = 1767). In the subset of participants with available echocardiographic data (derivation cohort, n = 654), we characterized three mutually exclusive phenogroups of HFpEF participants using penalized finite mixture model-based clustering analysis on 61 mixed-data phenotypic variables. Phenogroup 1 had higher burden of co-morbidities, natriuretic peptides, and abnormalities in left ventricular structure and function; phenogroup 2 had lower prevalence of cardiovascular and non-cardiac co-morbidities but higher burden of diastolic dysfunction; and phenogroup 3 had lower natriuretic peptide levels, intermediate co-morbidity burden, and the most favourable diastolic function profile. In adjusted Cox models, participants in phenogroup 1 (vs. phenogroup 3) had significantly higher risk for all adverse clinical events including the primary composite endpoint, all-cause mortality, and HF hospitalization. Phenogroup 2 (vs. phenogroup 3) was significantly associated with higher risk of HF hospitalization but a lower risk of atherosclerotic event (myocardial infarction, stroke, or cardiovascular death), and comparable risk of mortality. Similar patterns of association were also observed in the non-echocardiographic TOPCAT cohort (internal validation cohort, n = 1113) and an external cohort of patients with HFpEF [Phosphodiesterase-5 Inhibition to Improve Clinical Status and Exercise Capacity in Heart Failure with Preserved Ejection Fraction (RELAX) trial cohort, n = 198], with the highest risk of adverse outcome noted in phenogroup 1 participants. CONCLUSIONS: Machine learning-based cluster analysis can identify phenogroups of patients with HFpEF with distinct clinical characteristics and long-term outcomes.

5. Association of Genetic West African Ancestry, Blood Pressure Response to Therapy, and Cardiovascular Risk Among Self-Reported Black Individuals in the Systolic Blood Pressure Reduction Intervention Trial (SPRINT)

   S. Rao, M. W. Segar, A. P. Bress, and 8 more authors

   2020

   Rao, Shreya Segar, Matthew W Bress, Adam P Arora, Pankaj Vongpatanasin, Wanpen Agusala, Vijay Essien, Utibe R Correa, Adolfo Morris, Alanna A de Lemos, James A Pandey, Ambarish eng R03 AG067960/AG/NIA NIH HHS/ 2020/11/14 JAMA Cardiol. 2020 Nov 13. pii: 2773093. doi: 10.1001/jamacardio.2020.6566.

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   IMPORTANCE: Self-identified Black race is associated with higher hypertension prevalence and worse blood pressure (BP) control compared with other race/ethnic groups. The contribution of genetic West African ancestry to these racial disparities appears not to have been completely determined. OBJECTIVE: To determine the association between the proportion of West African ancestry with the response to antihypertensive medication, BP control, kidney function, and risk of adverse cardiovascular (CV) events among self-identified Black individuals in the Systolic Blood Pressure Intervention Trial (SPRINT). DESIGN, SETTING, AND PARTICIPANTS: This post hoc analysis of the SPRINT trial incorporated data from a multicenter study of self-identified Black participants with available West African ancestry proportion, estimated using 106 biallelic autosomal ancestry informative genetic markers. Recruitment started on October 20, 2010, and ended on August 20, 2015. Data were analyzed from May 2020 to September 2020. MAIN OUTCOMES AND MEASURES: Trajectories of BP and kidney function parameters on follow-up of the trial were assessed across tertiles of the proportion of West African ancestry using linear mixed-effect modeling after adjustment for potential confounders. Multivariable adjusted Cox models evaluated the association of West African ancestry with the risk of composite CV events (nonfatal myocardial infarction, CV death, and heart failure event). RESULTS: Among 2466 participants in the current analysis (1122 women [45.5%]; median West African ancestry, 81% [interquartile range, 73%-87%]), there were 120 composite CV events (4.9%) over a mean (SD) of 3.2 (0.9) years of follow-up. At baseline, mean (SD) high-density lipoprotein cholesterol levels were higher (tertile 3: 56.5 [15.0] mg/dL vs tertile 1: 54.2 [14.9] mg/dL; P = .006), smoking prevalence (never smoking: tertile 3: 367 [47.9%] vs tertile 1: 372 [42.2%]; P = .009) and mean (SD) Framingham Risk scores (tertile 3: 16.7 [9.7] vs tertile 1: 18.1 [10.2]; P = .01) were lower, and baseline BP was not different across increasing tertiles of West African ancestry. On follow-up, there was no evidence of differences in longitudinal trajectories of BP, kidney function parameters, or left ventricular mass (Cornell voltage by electrocardiogram) across tertiles of West African ancestry in either intensive or standard treatment arms. In adjusted Cox models, higher West African ancestry was associated with a lower risk of a composite CV event after adjustment for potential confounders (adjusted hazard ratio per 5% higher West African ancestry, 0.92 [95% CI, 0.85-0.99]). CONCLUSIONS AND RELEVANCE: Among self-reported Black individuals enrolled in SPRINT, the trajectories of BP, kidney function, and left ventricular mass over time were not different across tertiles of the proportion of West African ancestry. A higher proportion of West African ancestry was associated with a modestly lower risk for CV events. These findings suggest that extrinsic and structural societal factors, more than genetic ancestry, may be the major drivers of the well-established racial disparity in cardiovascular health associated with hypertension.

2019

1. Machine Learning to Predict the Risk of Incident Heart Failure Hospitalization Among Patients With Diabetes: The WATCH-DM Risk Score

   M. W. Segar, M. Vaduganathan, K. V. Patel, and 10 more authors

   2019

   Segar, Matthew W Vaduganathan, Muthiah Patel, Kershaw V McGuire, Darren K Butler, Javed Fonarow, Gregg C Basit, Mujeeb Kannan, Vaishnavi Grodin, Justin L Everett, Brendan Willett, Duwayne Berry, Jarett Pandey, Ambarish eng Diabetes Care. 2019 Sep 13. pii: dc19-0587. doi: 10.2337/dc19-0587.

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   OBJECTIVE: To develop and validate a novel, machine learning-derived model to predict the risk of heart failure (HF) among patients with type 2 diabetes mellitus (T2DM). RESEARCH DESIGN AND METHODS: Using data from 8,756 patients free at baseline of HF, with <10% missing data, and enrolled in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, we used random survival forest (RSF) methods, a nonparametric decision tree machine learning approach, to identify predictors of incident HF. The RSF model was externally validated in a cohort of individuals with T2DM using the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT). RESULTS: Over a median follow-up of 4.9 years, 319 patients (3.6%) developed incident HF. The RSF models demonstrated better discrimination than the best performing Cox-based method (C-index 0.77 [95% CI 0.75-0.80] vs. 0.73 [0.70-0.76] respectively) and had acceptable calibration (Hosmer-Lemeshow statistic chi(2) = 9.63, P = 0.29) in the internal validation data set. From the identified predictors, an integer-based risk score for 5-year HF incidence was created: the WATCH-DM (Weight [BMI], Age, hyperTension, Creatinine, HDL-C, Diabetes control [fasting plasma glucose], QRS Duration, MI, and CABG) risk score. Each 1-unit increment in the risk score was associated with a 24% higher relative risk of HF within 5 years. The cumulative 5-year incidence of HF increased in a graded fashion from 1.1% in quintile 1 (WATCH-DM score </=7) to 17.4% in quintile 5 (WATCH-DM score >/=14). In the external validation cohort, the RSF-based risk prediction model and the WATCH-DM risk score performed well with good discrimination (C-index = 0.74 and 0.70, respectively), acceptable calibration (P >/=0.20 for both), and broad risk stratification (5-year HF risk range from 2.5 to 18.7% across quintiles 1-5). CONCLUSIONS: We developed and validated a novel, machine learning-derived risk score that integrates readily available clinical, laboratory, and electrocardiographic variables to predict the risk of HF among outpatients with T2DM.

2. Generalizability and Implications of the H2FPEF Score in a Cohort of Patients With Heart Failure With Preserved Ejection Fraction

   M. W. Segar, K. V. Patel, J. D. Berry, and 2 more authors

   2019

   Segar, Matthew W Patel, Kershaw V Berry, Jarett D Grodin, Justin L Pandey, Ambarish eng Letter Circulation. 2019 Apr 9;139(15):1851-1853. doi: 10.1161/CIRCULATIONAHA.118.039051.

2016

1. Rifampin Regulation of Drug Transporters Gene Expression and the Association of MicroRNAs in Human Hepatocytes

   E. A. Benson, M. T. Eadon, Z. Desta, and 6 more authors

   2016

   Benson, Eric A Eadon, Michael T Desta, Zeruesenay Liu, Yunlong Lin, Hai Burgess, Kimberly S Segar, Matthew W Gaedigk, Andrea Skaar, Todd C eng R01 GM088076/GM/NIGMS NIH HHS/ T32 GM008425/GM/NIGMS NIH HHS/ UL1 TR001108/TR/NCATS NIH HHS/ Switzerland 2016/05/21 Front Pharmacol. 2016 Apr 26;7:111. doi: 10.3389/fphar.2016.00111. eCollection 2016.

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   UNLABELLED: Membrane drug transporters contribute to the disposition of many drugs. In human liver, drug transport is controlled by two main superfamilies of transporters, the solute carrier transporters (SLC) and the ATP Binding Cassette transporters (ABC). Altered expression of these transporters due to drug-drug interactions can contribute to differences in drug exposure and possibly effect. In this study, we determined the effect of rifampin on gene expression of hundreds of membrane transporters along with all clinically relevant drug transporters. METHODS: In this study, primary human hepatocytes (n = 7 donors) were cultured and treated for 24 h with rifampin and vehicle control. RNA was isolated from the hepatocytes, mRNA expression was measured by RNA-seq, and miRNA expression was analyzed by Taqman OpenArray. The effect of rifampin on the expression of selected transporters was also tested in kidney cell lines. The impact of rifampin on the expression of 410 transporter genes from 19 different transporter gene families was compared with vehicle control. RESULTS: Expression patterns of 12 clinically relevant drug transporter genes were changed by rifampin (FDR < 0.05). For example, the expressions of ABCC2, ABCB1, and ABCC3 were increased 1.9-, 1.7-, and 1.2-fold, respectively. The effects of rifampin on four uptake drug transporters (SLCO1B3, SLC47A1, SLC29A1, SLC22A9) were negatively correlated with the rifampin effects on specific microRNA expression (SLCO1B3/miR-92a, SLC47A1/miR-95, SLC29A1/miR-30d#, and SLC22A9/miR-20; r < -0.79; p < 0.05). Seven hepatic drug transporter genes (SLC22A1, SLC22A5, SLC15A1, SLC29A1, SLCO4C1, ABCC2, and ABCC4), whose expression was altered by rifampin in hepatocytes, were also present in a renal proximal tubular cell line, but in renal cells rifampin did not alter their gene expression. PXR expression was very low in the kidney cells; this may explain why rifampin induces gene expression in a tissue-specific manner. CONCLUSION: Rifampin alters the expression of many of the clinically relevant hepatic drug transporters, which may provide a rational basis for understanding rifampin-induced drug-drug interactions reported in vivo. The relevance of its effect on many other transporters remains to be studied.

2015

1. MMBIRFinder: A Tool to Detect Microhomology-Mediated Break-Induced Replication

   M. W. Segar, C. J. Sakofsky, A. Malkova, and 1 more author

   2015

   Segar, Matthew W Sakofsky, Cynthia J Malkova, Anna Liu, Yunlong eng R01 GM084242/GM/NIGMS NIH HHS/ GM084242/GM/NIGMS NIH HHS/ Research Support, N.I.H., Extramural Research Support, U.S. Gov’t, Non-P.H.S. 2015/09/12 IEEE/ACM Trans Comput Biol Bioinform. 2015 Jul-Aug;12(4):799-806. doi: 10.1109/TCBB.2014.2359450.

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   The introduction of next-generation sequencing technologies has radically changed the way we view structural genetic events. Microhomology-mediated break-induced replication (MMBIR) is just one of the many mechanisms that can cause genomic destabilization that may lead to cancer. Although the mechanism for MMBIR remains unclear, it has been shown that MMBIR is typically associated with template-switching events. Currently, to our knowledge, there is no existing bioinformatics tool to detect these template-switching events. We have developed MMBIRFinder, a method that detects template-switching events associated with MMBIR from whole-genome sequenced data. MMBIRFinder uses a half-read alignment approach to identify potential regions of interest. Clustering of these potential regions helps narrow the search space to regions with strong evidence. Subsequent local alignments identify the template-switching events with single-nucleotide accuracy. Using simulated data, MMBIRFinder identified 83 percent of the MMBIR regions within a five nucleotide tolerance. Using real data, MMBIRFinder identified 16 MMBIR regions on a normal breast tissue data sample and 51 MMBIR regions on a triple-negative breast cancer tumor sample resulting in detection of 37 novel template-switching events. Finally, we identified template-switching events residing in the promoter region of seven genes that have been implicated in breast cancer.

2. Age-Related Changes in MicroRNA Expression and Pharmacogenes in Human Liver

   K. S. Burgess, S. Philips, E. A. Benson, and 6 more authors

   2015

   Burgess, K S Philips, S Benson, E A Desta, Z Gaedigk, A Gaedigk, R Segar, M W Liu, Y Skaar, T C eng HHSN267200700004C/LM/NLM NIH HHS/ R01 GM088076/GM/NIGMS NIH HHS/ 5R24HD0008836/HD/NICHD NIH HHS/ N01-DK-7-0004/HHSN267200700004C/DK/NIDDK NIH HHS/ F31 GM119401/GM/NIGMS NIH HHS/ Z01 HD008836/ImNIH/Intramural NIH HHS/ UL1 TR001108/TR/NCATS NIH HHS/ HHSN267200700004G/LM/NLM NIH HHS/ Research Support, N.I.H., Extramural 2015/05/15 Clin Pharmacol Ther. 2015 Aug;98(2):205-15. doi: 10.1002/cpt.145.

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   Developmental changes in the liver can significantly impact drug disposition. Due to the emergence of microRNAs (miRNAs) as important regulators of drug disposition gene expression, we studied age-dependent changes in miRNA expression. Expression of 533 miRNAs was measured in 90 human liver tissues (fetal, pediatric [1-17 years], and adult [28-80 years]; n = 30 each). In all, 114 miRNAs were upregulated and 72 were downregulated from fetal to pediatric, and 2 and 3, respectively, from pediatric to adult. Among the developmentally changing miRNAs, 99 miRNA-mRNA interactions were predicted or experimentally validated (e.g., hsa-miR-125b-5p-CYP1A1; hsa-miR-34a-5p-HNF4A). In human liver samples (n = 10 each), analyzed by RNA-sequencing, significant negative correlations were observed between the expression of >1,000 miRNAs and mRNAs of drug disposition and regulatory genes. Our data suggest a mechanism for the marked changes in hepatic gene expression between the fetal and pediatric developmental periods, and support a role for these age-dependent miRNAs in regulating drug disposition.

2014

1. Spectral probabilities of top-down tandem mass spectra

   X. Liu, M. W. Segar, S. C. Li, and 1 more author

   2014

   Liu, Xiaowen Segar, Matthew W Li, Shuai Cheng Kim, Sangtae eng Research Support, Non-U.S. Gov’t England 2014/02/26 BMC Genomics. 2014;15 Suppl 1:S9. doi: 10.1186/1471-2164-15-S1-S9. Epub 2014 Jan 24.

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   BACKGROUND: In mass spectrometry-based proteomics, the statistical significance of a peptide-spectrum or protein-spectrum match is an important indicator of the correctness of the peptide or protein identification. In bottom-up mass spectrometry, probabilistic models, such as the generating function method, have been successfully applied to compute the statistical significance of peptide-spectrum matches for short peptides containing no post-translational modifications. As top-down mass spectrometry, which often identifies intact proteins with post-translational modifications, becomes available in many laboratories, the estimation of statistical significance of top-down protein identification results has come into great demand. RESULTS: In this paper, we study an extended generating function method for accurately computing the statistical significance of protein-spectrum matches with post-translational modifications. Experiments show that the extended generating function method achieves high accuracy in computing spectral probabilities and false discovery rates. CONCLUSIONS: The extended generating function method is a non-trivial extension of the generating function method for bottom-up mass spectrometry. It can be used to choose the correct protein-spectrum match from several candidate protein-spectrum matches for a spectrum, as well as separate correct protein-spectrum matches from incorrect ones identified from a large number of tandem mass spectra.