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Objectives <p>To develop and validate a nomogram model for predicting short-term outcomes in patients with acute myocardial infarction (AMI) by integrating serum biomarkers (GDF-15, MEG3, and HSP-70) with traditional clinical indicators.</p> Methods <p>A total of 230 patients with AMI who were admitted to our hospital from January 2020 to June 2024 were included retrospectively. They were randomly divided into a training set (<i>n</i> = 161) and a validation set (<i>n</i> = 69) according to the ratio of 7:3. In the training set, independent prognostic factors were screened by univariate and multivariate logistic regression analysis, and a nomogram model was constructed. The model performance was evaluated using the consistency index (C-index), calibration curve, and receiver operating characteristic curve (ROC), and was verified in the validation set.</p> Results <p>There was no significant difference in baseline data between the training set and the validation set (<i>P</i> &gt; 0.05). Multivariate analysis showed that hypertension, GDF-15, MEG3, HSP-70, cTnI, and LVEF were the independent risk factors for occurrence of MACE (<i>P</i> &lt; 0.05). The area under the ROC curves of the nomogram model were 0.884 (95% CI 0.811–0.956) and 0.829 (95% CI 0.670–0.988) in the training and validation sets, respectively. The calibration curve fitted well.</p> Conclusions <p>The nomogram model with integrated multi-biomarkers demonstrates good predictive performance for short-term outcomes of high-risk AMI patients treated at a tertiary emergency center in our cohort, and may provide a potential tool for clinical risk stratification, pending external validation in broader AMI populations.</p>

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Development and validation of a nomogram model incorporating serum GDF-15, MEG3, and HSP-70 for predicting short-term outcomes in patients with acute myocardial infarction: a retrospective study

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  • 发表日期：

    2026年3月17日

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  • Jianwen Wen,
  • Jiebin Huang,
  • Quan Sun,
  • Wei Ding

摘要

Objectives

To develop and validate a nomogram model for predicting short-term outcomes in patients with acute myocardial infarction (AMI) by integrating serum biomarkers (GDF-15, MEG3, and HSP-70) with traditional clinical indicators.

Methods

A total of 230 patients with AMI who were admitted to our hospital from January 2020 to June 2024 were included retrospectively. They were randomly divided into a training set (n = 161) and a validation set (n = 69) according to the ratio of 7:3. In the training set, independent prognostic factors were screened by univariate and multivariate logistic regression analysis, and a nomogram model was constructed. The model performance was evaluated using the consistency index (C-index), calibration curve, and receiver operating characteristic curve (ROC), and was verified in the validation set.

Results

There was no significant difference in baseline data between the training set and the validation set (P > 0.05). Multivariate analysis showed that hypertension, GDF-15, MEG3, HSP-70, cTnI, and LVEF were the independent risk factors for occurrence of MACE (P < 0.05). The area under the ROC curves of the nomogram model were 0.884 (95% CI 0.811–0.956) and 0.829 (95% CI 0.670–0.988) in the training and validation sets, respectively. The calibration curve fitted well.

Conclusions

The nomogram model with integrated multi-biomarkers demonstrates good predictive performance for short-term outcomes of high-risk AMI patients treated at a tertiary emergency center in our cohort, and may provide a potential tool for clinical risk stratification, pending external validation in broader AMI populations.