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Background <p>Patients with heart failure with mildly reduced ejection fraction (HFmrEF) and hypertension represent a high-risk population with limited prognostic tools. The triglyceride-glucose-body mass index (TyG-BMI), a novel metric of insulin resistance and metabolic dysregulation, may enhance risk stratification but has not been evaluated in this specific cohort.</p> Methods <p>We retrospectively analyzed 2,550 hospitalized hypertensive patients with HFmrEF from a single tertiary center (May 2012 to October 2023). Participants were randomly assigned to training (<i>n</i> = 1,785) and validation (<i>n</i> = 765) cohorts. Using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression, we developed a nomogram to predict in-hospital mortality. Restricted cubic spline (RCS) analysis was used to characterize the dose-response relation between the TyG-BMI index and mortality risk. Model performance was assessed by area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA).</p> Results <p>In-hospital mortality occurred in 157 patients (6.16%). Multivariate analysis identified six independent risk factors (age ≥ 75 years [OR 2.79, 95% CI 1.55–5.03], TyG-BMI (per 100-unit increase) [OR 4.47, 95% CI 2.99–6.68], C-reactive protein ≥ 10&#xa0;mg/L [OR 3.83, 95% CI 1.33–11.03], NT-proBNP elevation [OR 3.86, 95% CI 1.68–8.85], diabetes [OR 2.77, 95% CI 1.64–4.70], and cerebral infarction [OR 4.79, 95% CI 2.81–8.16]) and two factors that were independently associated with a lower risk of in-hospital mortality (ACEI/ARB/ARNI use [OR 0.33, 95% CI 0.20–0.55] and SGLT2i use [OR 0.39, 95% CI 0.18–0.85]). RCS disclosed a non-linear relation between TyG-BMI and in-hospital mortality (P for non-linearity &lt; 0.001), with a steep increase in risk when TyG-BMI was at or above 300. The nomogram demonstrated excellent discrimination in both training (AUC 0.842, 95% CI 0.802–0.882) and validation (AUC 0.844, 95% CI 0.794–0.894) cohorts with good calibration (training <i>P</i> = 0.17; validation <i>P</i> = 0.67) and significant clinical net benefit on DCA.</p> Conclusions <p>TyG-BMI displayed a threshold-dependent, non-linear association with in-hospital mortality in hypertensive patients with HFmrEF. A prediction model incorporating TyG-BMI and related variables demonstrated strong discriminative performance, showing potential as a practical tool for early identification of high-risk individuals and supporting risk stratification and clinical vigilance during hospitalization.</p>

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A novel risk prediction model incorporating triglyceride-glucose-body mass index for in-hospital mortality in hypertensive patients with HFmrEF

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  • 发表日期：

    2026年2月11日

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  • Qilong Guo,
  • Meng Wei,
  • Shuai Shang,
  • Huasheng Lv,
  • TuEr-Hong Zukela,
  • Yajie Xu,
  • XingLi Gu,
  • Yanmei Lu,
  • Baopeng Tang

摘要

Background

Patients with heart failure with mildly reduced ejection fraction (HFmrEF) and hypertension represent a high-risk population with limited prognostic tools. The triglyceride-glucose-body mass index (TyG-BMI), a novel metric of insulin resistance and metabolic dysregulation, may enhance risk stratification but has not been evaluated in this specific cohort.

Methods

We retrospectively analyzed 2,550 hospitalized hypertensive patients with HFmrEF from a single tertiary center (May 2012 to October 2023). Participants were randomly assigned to training (n = 1,785) and validation (n = 765) cohorts. Using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression, we developed a nomogram to predict in-hospital mortality. Restricted cubic spline (RCS) analysis was used to characterize the dose-response relation between the TyG-BMI index and mortality risk. Model performance was assessed by area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA).

Results

In-hospital mortality occurred in 157 patients (6.16%). Multivariate analysis identified six independent risk factors (age ≥ 75 years [OR 2.79, 95% CI 1.55–5.03], TyG-BMI (per 100-unit increase) [OR 4.47, 95% CI 2.99–6.68], C-reactive protein ≥ 10 mg/L [OR 3.83, 95% CI 1.33–11.03], NT-proBNP elevation [OR 3.86, 95% CI 1.68–8.85], diabetes [OR 2.77, 95% CI 1.64–4.70], and cerebral infarction [OR 4.79, 95% CI 2.81–8.16]) and two factors that were independently associated with a lower risk of in-hospital mortality (ACEI/ARB/ARNI use [OR 0.33, 95% CI 0.20–0.55] and SGLT2i use [OR 0.39, 95% CI 0.18–0.85]). RCS disclosed a non-linear relation between TyG-BMI and in-hospital mortality (P for non-linearity < 0.001), with a steep increase in risk when TyG-BMI was at or above 300. The nomogram demonstrated excellent discrimination in both training (AUC 0.842, 95% CI 0.802–0.882) and validation (AUC 0.844, 95% CI 0.794–0.894) cohorts with good calibration (training P = 0.17; validation P = 0.67) and significant clinical net benefit on DCA.

Conclusions

TyG-BMI displayed a threshold-dependent, non-linear association with in-hospital mortality in hypertensive patients with HFmrEF. A prediction model incorporating TyG-BMI and related variables demonstrated strong discriminative performance, showing potential as a practical tool for early identification of high-risk individuals and supporting risk stratification and clinical vigilance during hospitalization.