A Multivariable Prediction Model for Mortality in Individuals Admitted for Heart Failure.

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.