Suveen Angraal1, Bobak J Mortazavi2, Aakriti Gupta3, Rohan Khera4, Tariq Ahmad5, Nihar R Desai6, Daniel L Jacoby5, Frederick A Masoudi7, John A Spertus8, Harlan M Krumholz9. 1. Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut; Department of Internal Medicine, University of Missouri Kansas City, School of Medicine, Kansas City, Missouri. 2. Department of Computer Science and Engineering, Texas A & M, College Station, Texas. 3. Division of Cardiology, Columbia University Medical Center, New York, New York. 4. Division of Cardiology, University of Texas Southwestern Medical Center, Dallas, Texas. 5. Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut. 6. Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut. 7. Division of Cardiology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado. 8. Health Outcomes Research, Saint Luke's Mid America Heart Institute/University of Missouri-Kansas City, Kansas City, Missouri. 9. Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut; Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut. Electronic address: harlan.krumholz@yale.edu.
Abstract
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).
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 ureanitrogen levels, body mass index, and Kansas City Cardiomyopathy Questionnaire (KCCQ) subscale scores were strongly associated with mortality, whereas hemoglobin level, blood ureanitrogen, 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).
Authors: Geoffrey H Tison; Robert Avram; Gregory Nah; Liviu Klein; Barbara V Howard; Matthew A Allison; Ramon Casanova; Rachael H Blair; Khadijah Breathett; Randi E Foraker; Jeffrey E Olgin; Nisha I Parikh Journal: Can J Cardiol Date: 2021-08-13 Impact factor: 5.223