Rishi J Desai1, Kueiyu Joshua Lin1,2, Elisabetta Patorno1, Julie Barberio1, Moa Lee1, Raisa Levin1, Thomas Evers3, Shirley V Wang1, Sebastian Schneeweiss1. 1. Department of Medicine, Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA (R.J.D., K.J.L., E.P., J.B., M.L., R.L., S.V.W., S.S.). 2. Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston (K.J.L.). 3. Market Access, Bayer AG, Wuppertal, Germany (T.E.).
Abstract
BACKGROUND: Ejection fraction (EF) class is an important predictor of treatment response in heart failure (HF); however, administrative claims databases lack information on EF, limiting their usefulness in clinical and health services research of HF. METHODS AND RESULTS: We linked Medicare claims data to electronic medical records containing EF measurements for a cohort of 11 073 patients with HF from 2 academic medical centers. A a claims-based model predicting EF class was constructed using data from center 1 ("training sample") and validated using data from center 2 ("testing sample). Linear and logistic regression models with least absolute square shrinkage operator and Bayesian information criteria were developed to select the relevant predictor variables out of the total 57 candidate variables in the training sample. Higher accuracy was noted in the testing sample with models classifying patients into 2 EF classes (reduced EF <0.45) versus preserved EF (≥0.45) when compared with classifying patients into 3 EF classes (reduced, <0.40, moderately reduced, 0.40-0.49, or preserved, ≥0.50). In the testing sample, the most efficient model had 35 predictors and resulted in 83% of patients being correctly classified (95% CI, 82%-84%). The model had positive predictive value of 0.73 (95% CI, 0.68-0.78) and 0.84 (95% CI, 0.83-0.86) and sensitivity of 0.29 (95% CI, 0.25-0.32) and 0.97 (95% CI, 0.97-0.98) for reduced and preserved EF, respectively. In addition to HF-specific diagnosis codes, other factors including age, sex, medication use, and comorbidities, such as myocardial infarction and valve disorders, were important discriminators between EF classes. CONCLUSIONS: The claims-based model developed in this study may be used to identify patient subgroups with specific EF class in studies evaluating the health outcomes, utilization patterns, and cost, of HF patients in routine care when EF measurements are not available.
BACKGROUND: Ejection fraction (EF) class is an important predictor of treatment response in heart failure (HF); however, administrative claims databases lack information on EF, limiting their usefulness in clinical and health services research of HF. METHODS AND RESULTS: We linked Medicare claims data to electronic medical records containing EF measurements for a cohort of 11 073 patients with HF from 2 academic medical centers. A a claims-based model predicting EF class was constructed using data from center 1 ("training sample") and validated using data from center 2 ("testing sample). Linear and logistic regression models with least absolute square shrinkage operator and Bayesian information criteria were developed to select the relevant predictor variables out of the total 57 candidate variables in the training sample. Higher accuracy was noted in the testing sample with models classifying patients into 2 EF classes (reduced EF <0.45) versus preserved EF (≥0.45) when compared with classifying patients into 3 EF classes (reduced, <0.40, moderately reduced, 0.40-0.49, or preserved, ≥0.50). In the testing sample, the most efficient model had 35 predictors and resulted in 83% of patients being correctly classified (95% CI, 82%-84%). The model had positive predictive value of 0.73 (95% CI, 0.68-0.78) and 0.84 (95% CI, 0.83-0.86) and sensitivity of 0.29 (95% CI, 0.25-0.32) and 0.97 (95% CI, 0.97-0.98) for reduced and preserved EF, respectively. In addition to HF-specific diagnosis codes, other factors including age, sex, medication use, and comorbidities, such as myocardial infarction and valve disorders, were important discriminators between EF classes. CONCLUSIONS: The claims-based model developed in this study may be used to identify patient subgroups with specific EF class in studies evaluating the health outcomes, utilization patterns, and cost, of HF patients in routine care when EF measurements are not available.
Entities:
Keywords:
Medicare; electronic health records; heart failure
Authors: Carolyn S P Lam; Robert Wood; Muthiah Vaduganathan; Hector Bueno; Alex Chin; Gabriela Luporini Saraiva; Elisabeth Sörstadius; Theo Tritton; Joseph Thomas; Lei Qin Journal: Clin Cardiol Date: 2021-03-11 Impact factor: 2.882
Authors: Phyo T Htoo; John Buse; Matthew Cavender; Tiansheng Wang; Virginia Pate; Jess Edwards; Til Stürmer Journal: J Am Heart Assoc Date: 2022-02-08 Impact factor: 6.106
Authors: Alicia Uijl; Lars H Lund; Ilonca Vaartjes; Jasper J Brugts; Gerard C Linssen; Folkert W Asselbergs; Arno W Hoes; Ulf Dahlström; Stefan Koudstaal; Gianluigi Savarese Journal: ESC Heart Fail Date: 2020-06-17
Authors: Sarah S Cohen; Véronique L Roger; Susan A Weston; Ruoxiang Jiang; Naimisha Movva; Akeem A Yusuf; Alanna M Chamberlain Journal: Pharmacol Res Perspect Date: 2020-12