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Risk-Prediction Tool Identifies In-Hospital Mortality Risk

The OPTIMIZE-HF risk tool may identify high-risk heart failure patients in need of more aggressive interventions

WEDNESDAY, July 23 (HealthDay News) -- Application of a risk-prediction algorithm may help identify congestive heart failure patients at high risk of in-hospital mortality, according to an article published in the July 29 issue of the Journal of the American College of Cardiology.

William T. Abraham, M.D., of Ohio State University in Columbus, Ohio, and colleagues examined the OPTIMIZE-HF (Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure) registry to identify predictors of mortality in hospitalized heart failure patients. The registry collected data on patients from 259 U.S. hospitals and enrolled 48,612 patients.

Overall mortality was 3.8 percent and logistic regression identified serum creatinine, admission systolic blood pressure and patient age as patient characteristics most strongly predictive of in-hospital mortality, the report indicates. For every 0.3 mg/dL increase in serum creatinine, in-hospital mortality increased by 18 percent, and for each increase in age of 10 years, a 34 percent higher risk for in-hospital mortality was seen, the researchers found. On the other hand, each 10-mm Hg increase in blood pressure up to 160 mm Hg was associated with a 17 percent reduction in in-hospital mortality.

"The OPTIMIZE-HF risk tool provides clinicians with a well-validated bedside tool for in-hospital mortality risk stratification," according to the authors. "Application of the risk-prediction score might help identify patients at high risk for in-hospital mortality who might benefit from aggressive monitoring and intervention."

The study was supported by GlaxoSmithKline. Several study authors report financial relationships with the pharmaceutical industry

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