TY - JOUR
T1 - Validation and Recalibration of Seattle Heart Failure Model in Japanese Acute Heart Failure Patients
AU - Shiraishi, Yasuyuki
AU - Kohsaka, Shun
AU - Nagai, Toshiyuki
AU - Goda, Ayumi
AU - Mizuno, Atsushi
AU - Nagatomo, Yuji
AU - Sujino, Yasumori
AU - Fukuoka, Ryoma
AU - Sawano, Mitsuaki
AU - Kohno, Takashi
AU - Fukuda, Keiichi
AU - Anzai, Toshihisa
AU - Shadman, Ramin
AU - Dardas, Todd
AU - Levy, Wayne C.
AU - Yoshikawa, Tsutomu
PY - 2019/7
Y1 - 2019/7
N2 - Background: Precise risk stratification in heart failure (HF) patients enables clinicians to tailor the intensity of their management. The Seattle Heart Failure Model (SHFM), which uses conventional clinical variables for its prediction, is widely used. We aimed to externally validate SHFM in Japanese HF patients with a recent episode of acute decompensation requiring hospital admission. Methods and Results: SHFM was applied to 2470 HF patients registered in the West Tokyo Heart Failure and National Cerebral And Cardiovascular Center Acute Decompensated Heart Failure databases from 2006 to 2016. Discrimination and calibration were assessed with the use of the c-statistic and calibration plots, respectively, in HF patients with reduced ejection fraction (HFrEF; <40%) and preserved ejection fraction (HFpEF; ≥40%). In a perfectly calibrated model, the slope and intercept would be 1.0 and 0.0, respectively. The method of intercept recalibration was used to update the model. The registered patients (mean age 74 ± 13 y) were predominantly men (62%). Overall, 572 patients (23.2%) died during a mean follow-up of 2.1 years. Among HFrEF patients, SHFM showed good discrimination (c-statistic = 0.75) but miscalibration, tending to overestimate 1-year survival (slope = 0.78; intercept = −0.22). Among HFpEF patients, SHFM showed modest discrimination (c-statistic = 0.69) and calibration, tending to underestimate 1-year survival (slope = 1.18; intercept = 0.16). Intercept recalibration (replacing the baseline survival function) successfully updated the model for HFrEF (slope = 1.03; intercept = −0.04) but not for HFpEF patients. Conclusions: In Japanese acute HF patients, SHFM showed adequate performance after recalibration among HFrEF patients. Using prediction models to tailor the care for HF patients may improve the allocation of medical resources.
AB - Background: Precise risk stratification in heart failure (HF) patients enables clinicians to tailor the intensity of their management. The Seattle Heart Failure Model (SHFM), which uses conventional clinical variables for its prediction, is widely used. We aimed to externally validate SHFM in Japanese HF patients with a recent episode of acute decompensation requiring hospital admission. Methods and Results: SHFM was applied to 2470 HF patients registered in the West Tokyo Heart Failure and National Cerebral And Cardiovascular Center Acute Decompensated Heart Failure databases from 2006 to 2016. Discrimination and calibration were assessed with the use of the c-statistic and calibration plots, respectively, in HF patients with reduced ejection fraction (HFrEF; <40%) and preserved ejection fraction (HFpEF; ≥40%). In a perfectly calibrated model, the slope and intercept would be 1.0 and 0.0, respectively. The method of intercept recalibration was used to update the model. The registered patients (mean age 74 ± 13 y) were predominantly men (62%). Overall, 572 patients (23.2%) died during a mean follow-up of 2.1 years. Among HFrEF patients, SHFM showed good discrimination (c-statistic = 0.75) but miscalibration, tending to overestimate 1-year survival (slope = 0.78; intercept = −0.22). Among HFpEF patients, SHFM showed modest discrimination (c-statistic = 0.69) and calibration, tending to underestimate 1-year survival (slope = 1.18; intercept = 0.16). Intercept recalibration (replacing the baseline survival function) successfully updated the model for HFrEF (slope = 1.03; intercept = −0.04) but not for HFpEF patients. Conclusions: In Japanese acute HF patients, SHFM showed adequate performance after recalibration among HFrEF patients. Using prediction models to tailor the care for HF patients may improve the allocation of medical resources.
KW - Seattle Heart Failure Model
KW - heart failure
KW - recalibration
KW - risk model
KW - validation
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U2 - 10.1016/j.cardfail.2018.07.463
DO - 10.1016/j.cardfail.2018.07.463
M3 - Article
C2 - 30099192
AN - SCOPUS:85069731760
VL - 25
SP - 561
EP - 567
JO - Journal of Cardiac Failure
JF - Journal of Cardiac Failure
SN - 1071-9164
IS - 7
ER -