Author + information
- Suveen Angraal, MDa,b,∗,
- Bobak J. Mortazavi, PhDc,∗,
- Aakriti Gupta, MDd,
- Rohan Khera, MDe,
- Tariq Ahmad, MD, MPHf,
- Nihar R. Desai, MD MPHa,f,
- Daniel L. Jacoby, MDf,
- Frederick A. Masoudi, MD, MSPHg,
- John A. Spertus, MD, MPHh and
- Harlan M. Krumholz, MD, SMa,f,i,∗ (, )@hmkyale
- aCenter for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut
- bDepartment of Internal Medicine, University of Missouri Kansas City, Kansas City, School of Medicine, Missouri
- cDepartment of Computer Science and Engineering, Texas A & M, College Station, Texas
- dDivision of Cardiology, Columbia University Medical Center, New York, New York
- eDivision of Cardiology, University of Texas Southwestern Medical Center, Dallas, Texas
- fSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
- gDivision of Cardiology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado
- hHealth Outcomes Research, Saint Luke’s Mid America Heart Institute/University of Missouri-Kansas City, Kansas City, Missouri
- iDepartment of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut
- ↵∗Address for correspondence:
Dr. Harlan M. Krumholz, 1 Church Street, Suite 200, New Haven, Connecticut 06510.
Objectives This study sought to develop models for predicting mortality and heart failure (HF) hospitalization for outpatients with HF with preserved ejection fraction (HFpEF) in the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist) trial.
Background Although risk assessment models are available for patients with HF with reduced ejection fraction, few have assessed the risks of death and hospitalization in patients with HFpEF.
Methods The following 5 methods: logistic regression with a forward selection of variables; logistic regression with a lasso regularization for variable selection; random forest (RF); gradient descent boosting; and support vector machine, were used to train models for assessing risks of mortality and HF hospitalization through 3 years of follow-up and were validated using 5-fold cross-validation. Model discrimination and calibration were estimated using receiver-operating characteristic curves and Brier scores, respectively. The top prediction variables were assessed by using the best performing models, using the incremental improvement of each variable in 5-fold cross-validation.
Results The RF was the best performing model with a mean C-statistic of 0.72 (95% confidence interval [CI]: 0.69 to 0.75) for predicting mortality (Brier score: 0.17), and 0.76 (95% CI: 0.71 to 0.81) for HF hospitalization (Brier score: 0.19). Blood urea nitrogen levels, body mass index, and Kansas City Cardiomyopathy Questionnaire (KCCQ) subscale scores were strongly associated with mortality, whereas hemoglobin level, blood urea nitrogen, time since previous HF hospitalization, and KCCQ scores were the most significant predictors of HF hospitalization.
Conclusions These models predict the risks of mortality and HF hospitalization in patients with HFpEF and emphasize the importance of health status data in determining prognosis. (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist [TOPCAT]; NCT00094302)
↵∗ Drs. Angraal and Mortazavi contributed equally to this work.
Dr. Gupta is supported by U.S. National Institutes of Health/National Heart, Lung, and Blood Institute grant T32 HL007854. Dr. Khera is supported by NIH grants 5T32HL125247-02 and UL1TR001105. Dr. Ahmad is supported by Agency for Healthcare Research and Quality grant K12HS023000. No funding source had any role in the study design, collection, analysis, and interpretation of data, writing of the report, or decision to submit the article for publication. Dr. Gupta is cofounder of Heartbeat Health, Inc. Dr. Desai has received research support from and is consultant for Amgen, Boehringer Ingelheim, and Relypsa. Dr. Spertus is a consultant for United Healthcare, Novartis, Bayer, AstraZeneca, Janssen, V-wave, and Corvia; holds copyrights for the Kansas City Cardiomyopathy Questionnaire, Seattle Angina Questionnaire, and Peripheral Artery Questionnaire; and holds equity in Health Outcomes Sciences. Dr. Krumholz was a recipient of a research grant, through Yale, from Medtronic and the U.S. Food and Drug Administration to develop methods for post-market surveillance of medical devices; was a recipient of a research grant with Medtronic and Johnson & Johnson, through Yale, to develop methods of clinical trial data sharing; was a recipient of a research agreement, through Yale, from the Shenzhen Center for Health Information for work to advance intelligent disease prevention and health promotion; collaborates with the National Center for Cardiovascular Diseases in Beijing; and received payment from the Arnold & Porter Law Firm for work related to the Sanofi clopidogrel litigation and from the Ben C. Martin Law Firm for work related to the Cook IVC filter litigation. Dr. Krumholz chairs a Cardiac Scientific Advisory Board for UnitedHealth; is a participant/participant representative of the IBM Watson Health Life Sciences Board; is a member of the Advisory Board for Element Science, the Advisory Board for Facebook, and the Physician Advisory Board for Aetna; and is the founder of HugoHealth, a personal health information platform. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Received June 4, 2019.
- Accepted June 21, 2019.
- 2019 The Authors