2019 Volume 1 Issue 1 Pages 29-34
Background: Accurate prognosis for heart failure (HF) survival is important for quality of life, treatment decisions, and early evaluation of new therapies and devices. Here, we developed a multivariate risk model for predicting survival in Japanese patients with HF, using parameters that are readily observable in a clinical setting.
Methods and Results: We analyzed data for 1,214 adults with HF (EF <35%). Of 424 available clinical baseline factors in the derivation dataset, 17 candidate predictors were identified on Cox proportional hazards regression. These predictors were assessed for clinical relevance and tested in candidate models using cross-validated 5-year C-statistics. This process yielded a set of 14 covariates with good accuracy for predicting actual 5-year survival: age; LVEF; albumin; BMI; Hb; sodium; history of renal dysfunction, diabetes, or chronic dialysis; times HF recurred or required readmission to the hospital; use of cardiac drip, thiazide diuretic, or per oral inotropic agent; and loop diuretic dosage. These 14 variables were used to establish the Japan Heart Failure Model (JHFM) for predicting survival in patients with HF. When applied to an independent validation dataset, the results from the JHFM were closer to actual survival than those of the Seattle Heart Failure Model.
Conclusions: JHFM predictions for 5-year survival had good accuracy for Japanese patients with HF. The JHFM uses parameters that can be measured at any hospital.
