Scholarlink

Purpose <p>Differentiating heart failure-related death (HFD) from arrhythmic events (ArEs) is clinically important for patients with chronic heart failure (CHF), as they have distinct mechanisms and therapeutic strategies. We developed and validated a multivariable model to predict HFD, ArEs, and survival using clinical parameters and cardiac <sup>123</sup>I-<i>meta</i>-iodobenzylguanidine (<sup>123</sup>I-<i>m</i>IBG) images.</p> Methods <p>We retrospectively analyzed data derived from 997 patients with CHF (mean age 70 ± 13 years, left ventricular ejection fraction (LVEF) 32% ± 13%) over a mean follow-up of 41 ± 27 months. Outcomes were survival, HFD, or ArEs (including sudden cardiac death). Appropriate implantable cardioverter defibrillator therapy for lethal arrhythmias was included in ArEs. Late heart-to-mediastinum ratios (HMRs) were derived from <sup>123</sup>I-<i>m</i>IBG images. A multinomial nested logistic regression model using 2 years of outcomes was constructed (<i>N</i> = 854). Internal validation used a 2:1 development-validation split, repeated 3 times. Model performance was assessed by receiver operating characteristic (ROC) analysis, calibration of predicted vs. actual event rates, survival curves, and sex-specific predictive models.</p> Results <p>Selected variables were age, sex, New York Heart Association (NYHA) functional class, LVEF, hemoglobin, estimated glomerular filtration rate, hypertension, ventricular tachycardia history, and late <sup>123</sup>I-<i>m</i>IBG HMR. Areas under ROC curves for survival, HFD, and ArEs in the final 9-variable model were 0.800, 0.717, and 0.838, respectively. The sex-specific 7-variable models showed comparable AUCs of 0.834/0.827 (male/female) for HFD and 0.714/0.826 for ArEs. Risk groups based on median predicted probabilities of HFD and ArEs separated survival curves and corresponded well with actual outcomes.</p> Conclusions <p>A practical, interpretable model incorporating clinical and <sup>123</sup>I-<i>m</i>IBG imaging data enabled reliable and separate prediction of HFD and ArEs, supporting personalized risk stratification in CHF.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Stratifying risk of heart failure death and arrhythmic events: a ¹²³I-meta-iodobenzylguanidine-based multinomial logistic model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月29日

查看全文
  • Kenichi Nakajima,
  • Takahiro Doi,
  • Tomoaki Nakata,
  • Takuya Nakahashi,
  • Hayato Tada,
  • Hiroshi Wakabayashi,
  • Hein J. Verberne

摘要

Purpose

Differentiating heart failure-related death (HFD) from arrhythmic events (ArEs) is clinically important for patients with chronic heart failure (CHF), as they have distinct mechanisms and therapeutic strategies. We developed and validated a multivariable model to predict HFD, ArEs, and survival using clinical parameters and cardiac 123I-meta-iodobenzylguanidine (123I-mIBG) images.

Methods

We retrospectively analyzed data derived from 997 patients with CHF (mean age 70 ± 13 years, left ventricular ejection fraction (LVEF) 32% ± 13%) over a mean follow-up of 41 ± 27 months. Outcomes were survival, HFD, or ArEs (including sudden cardiac death). Appropriate implantable cardioverter defibrillator therapy for lethal arrhythmias was included in ArEs. Late heart-to-mediastinum ratios (HMRs) were derived from 123I-mIBG images. A multinomial nested logistic regression model using 2 years of outcomes was constructed (N = 854). Internal validation used a 2:1 development-validation split, repeated 3 times. Model performance was assessed by receiver operating characteristic (ROC) analysis, calibration of predicted vs. actual event rates, survival curves, and sex-specific predictive models.

Results

Selected variables were age, sex, New York Heart Association (NYHA) functional class, LVEF, hemoglobin, estimated glomerular filtration rate, hypertension, ventricular tachycardia history, and late 123I-mIBG HMR. Areas under ROC curves for survival, HFD, and ArEs in the final 9-variable model were 0.800, 0.717, and 0.838, respectively. The sex-specific 7-variable models showed comparable AUCs of 0.834/0.827 (male/female) for HFD and 0.714/0.826 for ArEs. Risk groups based on median predicted probabilities of HFD and ArEs separated survival curves and corresponded well with actual outcomes.

Conclusions

A practical, interpretable model incorporating clinical and 123I-mIBG imaging data enabled reliable and separate prediction of HFD and ArEs, supporting personalized risk stratification in CHF.