Garrett S Bowen1,2, Michelle S Diop1,2, Lan Jiang2, Wen-Chih Wu2,3,4, James L Rudolph2,3,4. 1. Primary Care and Population Medicine Program, Warren Alpert Medical School, Brown University, Providence, Rhode Island. 2. Center of Innovation in Long-term Services and Supports, Providence Veterans Affairs Medical Center, Providence, Rhode Island. 3. Department of Medicine, Warren Alpert Medical School, Brown University, Providence, Rhode Island. 4. Center for Gerontology, School of Public Health, Brown University, Providence, Rhode Island.
Abstract
OBJECTIVES: To derive and validate a 30-day mortality clinical prediction rule for heart failure based on admission data and prior healthcare usage. A secondary objective was to determine the discriminatory function for mortality at 1 and 2 years. DESIGN: Observational cohort. SETTING: Veterans Affairs inpatient medical centers (n=124). PARTICIPANTS: The derivation (2010-12; n=36,021) and validation (2013-15; n=30,364) cohorts included randomly selected veterans admitted for HF exacerbation (mean age 71±11; 98% male). MEASUREMENTS: The primary outcome was 30-day mortality. Secondary outcomes were 1- and 2-year mortality. Candidate variables were drawn from electronic medical records. Discriminatory function was measured as the area under the receiver operating characteristic curve. RESULTS: Thirteen risk factors were identified: age, ejection fraction, mean arterial pressure, pulse, brain natriuretic peptide, blood urea nitrogen, sodium, potassium, more than 7 inpatient days in the past year, metastatic disease, and prior palliative care. The model stratified participants into low- (1%), intermediate- (2%), high- (5%), and very high- (15%) mortality risk groups (C-statistic=0.72, 95% confidence interval (CI)=0.71-0.74). These findings were confirmed in the validation cohort (C-statistic=0.70, 95% CI=0.68-0.71). Subgroup analysis of age strata confirmed model discrimination. CONCLUSION: This simple prediction rule allows clinicians to risk-stratify individuals on admission for HF using characteristics captured in electronic medical record systems. The identification of high-risk groups allows individuals to be targeted for discussion of goals and treatment. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.
OBJECTIVES: To derive and validate a 30-day mortality clinical prediction rule for heart failure based on admission data and prior healthcare usage. A secondary objective was to determine the discriminatory function for mortality at 1 and 2 years. DESIGN: Observational cohort. SETTING: Veterans Affairs inpatient medical centers (n=124). PARTICIPANTS: The derivation (2010-12; n=36,021) and validation (2013-15; n=30,364) cohorts included randomly selected veterans admitted for HF exacerbation (mean age 71±11; 98% male). MEASUREMENTS: The primary outcome was 30-day mortality. Secondary outcomes were 1- and 2-year mortality. Candidate variables were drawn from electronic medical records. Discriminatory function was measured as the area under the receiver operating characteristic curve. RESULTS: Thirteen risk factors were identified: age, ejection fraction, mean arterial pressure, pulse, brain natriuretic peptide, blood ureanitrogen, sodium, potassium, more than 7 inpatient days in the past year, metastatic disease, and prior palliative care. The model stratified participants into low- (1%), intermediate- (2%), high- (5%), and very high- (15%) mortality risk groups (C-statistic=0.72, 95% confidence interval (CI)=0.71-0.74). These findings were confirmed in the validation cohort (C-statistic=0.70, 95% CI=0.68-0.71). Subgroup analysis of age strata confirmed model discrimination. CONCLUSION: This simple prediction rule allows clinicians to risk-stratify individuals on admission for HF using characteristics captured in electronic medical record systems. The identification of high-risk groups allows individuals to be targeted for discussion of goals and treatment. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.
Authors: Kevin F O'Sullivan; Mohammad Amin Kashef; Alexander B Knee; Alexander S Roseman; Penelope S Pekow; Mihaela S Stefan; Meng-Shiou Shieh; Quinn R Pack; Peter K Lindenauer; Tara Lagu Journal: J Hosp Med Date: 2019-07-24 Impact factor: 2.960
Authors: Michelle S Diop; Garrett S Bowen; Lan Jiang; Wen-Chih Wu; Portia Y Cornell; Pedro Gozalo; James L Rudolph Journal: J Am Heart Assoc Date: 2020-05-27 Impact factor: 5.501