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A Multivariable Prediction Model for Mortality in Individuals Admitted for Heart Failure
Garrett S Bowen1,2, Michelle S Diop1,2, Lan Jiang2
1Primary Care and Population Medicine Program, Warren Alpert Medical School, Brown University, Providence, Rhode Island.
Journal of the American Geriatrics Society
|March 7, 2018
Summary
A new heart failure (HF) prediction rule identifies 13 risk factors to stratify patient mortality risk upon admission. This tool aids clinicians in targeting high-risk individuals for timely interventions and goal-setting discussions.
Area of Science:
- Cardiology
- Clinical Epidemiology
- Health Informatics
Background:
- Heart failure (HF) exacerbation is a leading cause of hospitalization among veterans.
- Accurate risk stratification upon admission is crucial for optimizing patient care and resource allocation.
- Existing prediction models may not fully leverage electronic medical record (EMR) data for real-time clinical decision support.
Purpose of the Study:
- To develop and validate a clinical prediction rule for 30-day mortality in patients admitted for heart failure.
- To assess the rule's discriminatory performance for 1- and 2-year mortality.
- To identify key predictors of mortality using readily available admission data and prior healthcare utilization.
Main Methods:
- An observational cohort study design was employed, utilizing data from 124 Veterans Affairs inpatient medical centers.
- A derivation cohort (n=36,021) and a validation cohort (n=30,364) of veterans admitted for HF exacerbation were randomly selected.
- Candidate variables were extracted from EMRs, and discriminatory function was measured using the area under the receiver operating characteristic curve (C-statistic).
Main Results:
- Thirteen significant risk factors were identified, including age, ejection fraction, vital signs, laboratory values, and healthcare utilization history.
- The prediction rule effectively stratified patients into four mortality risk groups (low, intermediate, high, very high) with C-statistics of 0.72 in the derivation cohort and 0.70 in the validation cohort.
- Performance was consistent across different age strata, confirming the model's generalizability.
Conclusions:
- A simple, validated prediction rule using EMR data can accurately risk-stratify patients admitted for heart failure.
- This tool enables clinicians to identify high-risk individuals for targeted interventions, discussions about goals of care, and treatment planning.
- The rule enhances clinical decision-making by providing objective mortality risk assessment at the point of care.
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