Validation and Recalibration of Seattle Heart Failure Model in Japanese Acute Heart Failure Patients

Yasuyuki Shiraishi, Shun Kohsaka, Toshiyuki Nagai, Ayumi Goda, Atsushi Mizuno, Yuji Nagatomo, Yasumori Sujino, Ryoma Fukuoka, Mitsuaki Sawano, Takashi Kohno, Keiichi Fukuda, Toshihisa Anzai, Ramin Shadman, Todd Dardas, Wayne C. Levy, Tsutomu Yoshikawa

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)561-567
Number of pages7
JournalJournal of Cardiac Failure
Volume25
Issue number7
DOIs
Publication statusPublished - 2019 Jul 1

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Heart Failure
Calibration
Survival
Resource Allocation
Tokyo
Databases

Keywords

  • heart failure
  • recalibration
  • risk model
  • Seattle Heart Failure Model
  • validation

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

Cite this

Validation and Recalibration of Seattle Heart Failure Model in Japanese Acute Heart Failure Patients. / Shiraishi, Yasuyuki; Kohsaka, Shun; Nagai, Toshiyuki; Goda, Ayumi; Mizuno, Atsushi; Nagatomo, Yuji; Sujino, Yasumori; Fukuoka, Ryoma; Sawano, Mitsuaki; Kohno, Takashi; Fukuda, Keiichi; Anzai, Toshihisa; Shadman, Ramin; Dardas, Todd; Levy, Wayne C.; Yoshikawa, Tsutomu.

In: Journal of Cardiac Failure, Vol. 25, No. 7, 01.07.2019, p. 561-567.

Research output: Contribution to journalArticle

Shiraishi, Y, Kohsaka, S, Nagai, T, Goda, A, Mizuno, A, Nagatomo, Y, Sujino, Y, Fukuoka, R, Sawano, M, Kohno, T, Fukuda, K, Anzai, T, Shadman, R, Dardas, T, Levy, WC & Yoshikawa, T 2019, 'Validation and Recalibration of Seattle Heart Failure Model in Japanese Acute Heart Failure Patients', Journal of Cardiac Failure, vol. 25, no. 7, pp. 561-567. https://doi.org/10.1016/j.cardfail.2018.07.463
Shiraishi, Yasuyuki ; Kohsaka, Shun ; Nagai, Toshiyuki ; Goda, Ayumi ; Mizuno, Atsushi ; Nagatomo, Yuji ; Sujino, Yasumori ; Fukuoka, Ryoma ; Sawano, Mitsuaki ; Kohno, Takashi ; Fukuda, Keiichi ; Anzai, Toshihisa ; Shadman, Ramin ; Dardas, Todd ; Levy, Wayne C. ; Yoshikawa, Tsutomu. / Validation and Recalibration of Seattle Heart Failure Model in Japanese Acute Heart Failure Patients. In: Journal of Cardiac Failure. 2019 ; Vol. 25, No. 7. pp. 561-567.
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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

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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.

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