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Impact of renal dysfunction on the Seattle Heart Failure Model
Kairav P Vakil1, Todd Dardas2, Sunil Dhar1
1Division of Cardiology, University of Minnesota, Minneapolis, Minnesota.
Summary
Renal dysfunction predicts heart failure mortality, especially in lower-risk patients. Adding creatinine to the Seattle Heart Failure Model offers minimal improvement in predicting patient outcomes.
Area of Science:
- Cardiology
- Nephrology
- Medical Statistics
Background:
- Renal dysfunction (RD) is a significant predictor of mortality in heart failure (HF) patients.
- The impact of RD on the predictive accuracy of the Seattle Heart Failure Model (SHFM) remains unclear.
Purpose of the Study:
- To evaluate the independent predictive value of serum creatinine (SCr) and creatinine clearance (CrCl) for mortality in HF patients.
- To assess the incremental benefit of adding RD markers to the SHFM for risk prediction.
Main Methods:
- Analysis of SCr and CrCl in 7,146 patients from SHFM derivation and validation cohorts.
- Multivariable Cox proportional hazards models and ROC curve analysis were used to assess prediction.
- Patients were stratified by SHFM risk categories.
Main Results:
- Both SCr and CrCl independently predicted mortality in HF patients.
- RD markers provided additional prognostic information primarily in lower-risk individuals.
- Adding SCr or CrCl to the SHFM resulted in minimal improvement in 1- and 2-year event prediction discrimination.
Conclusions:
- Renal dysfunction independently predicts mortality in heart failure patients, particularly those at lower risk.
- Incorporating SCr or CrCl into the SHFM offers only a slight enhancement in overall predictive discrimination.
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