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Background <p>In-hospital cardiac arrest (IHCA) is an infrequent but lethal complication in patients with ST-elevation myocardial infarction (STEMI). Data on the characteristics and predictors of IHCA in STEMI patients after successful primary percutaneous coronary intervention (PPCI) are limited. This study aimed to identify the risk factors of IHCA and construct a predictive nomogram.</p> Method <p>A total of 5121 STEMI patients treated with successful PPCI from January 2018 to July 2023 at Beijing Anzhen Hospital were retrospectively enrolled in our study. Subjects were randomly divided into a development group and a validation group in a 7:3 ratio. Optimal predictive variables were selected using the least absolute shrinkage and selection operator (Lasso) and logistic regression models. A nomogram based on these predictors was then created to estimate IHCA probability.</p> Results <p>Among them, 94 patients (1.8% [95% CI: 1.4%-2.1%]) experienced IHCA after PPCI, and the in-hospital mortality rate was 35.1% in these patients. Lasso regression was implemented to identify predictors of IHCA, including age, Killip III-IV, systolic blood pressure, and moderate to severe calcification, with non-zero coefficients. A nomogram model for predicting the risk of IHCA after PPCI in STEMI patients was constructed with the above independent predictors, with a C-index of 0.823 (95%CI 0.764–0.881). The model’s calibration curve closely aligned with the ideal reference, indicating strong agreement between predicted and actual outcomes, and the internal validation showed a C-index of 0.828 (95%CI 0.751–0.905). Clinical decision analysis further confirmed that the nomogram model demonstrated good clinical effectiveness.</p> Conclusion <p>Our study identified several independent risk factors for IHCA in STEMI patients after successful PPCI based on a Lasso-logistic regression model, offering individualized and reliable cardiac arrest risk predictions during hospitalization.</p> Trial registration <p>Not applicable.</p> Graphical Abstract <p></p>

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Development, validation and visualization of a nomogram for the prediction of in-hospital cardiac arrest in patients after successful primary PCI for STEMI

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月25日

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  • Wei Luo,
  • Zixu Zhao,
  • Minghui Zhang,
  • Can Zhou,
  • Bingbing Ke,
  • Chen Li,
  • Zhili Jiang,
  • Fei Liu,
  • Lin Wang,
  • Wangyang Yang,
  • Yunxiao Yang,
  • Keyang Zheng,
  • Yu Liu,
  • Chengqian Yin,
  • Xinyong Zhang,
  • Hai Gao

摘要

Background

In-hospital cardiac arrest (IHCA) is an infrequent but lethal complication in patients with ST-elevation myocardial infarction (STEMI). Data on the characteristics and predictors of IHCA in STEMI patients after successful primary percutaneous coronary intervention (PPCI) are limited. This study aimed to identify the risk factors of IHCA and construct a predictive nomogram.

Method

A total of 5121 STEMI patients treated with successful PPCI from January 2018 to July 2023 at Beijing Anzhen Hospital were retrospectively enrolled in our study. Subjects were randomly divided into a development group and a validation group in a 7:3 ratio. Optimal predictive variables were selected using the least absolute shrinkage and selection operator (Lasso) and logistic regression models. A nomogram based on these predictors was then created to estimate IHCA probability.

Results

Among them, 94 patients (1.8% [95% CI: 1.4%-2.1%]) experienced IHCA after PPCI, and the in-hospital mortality rate was 35.1% in these patients. Lasso regression was implemented to identify predictors of IHCA, including age, Killip III-IV, systolic blood pressure, and moderate to severe calcification, with non-zero coefficients. A nomogram model for predicting the risk of IHCA after PPCI in STEMI patients was constructed with the above independent predictors, with a C-index of 0.823 (95%CI 0.764–0.881). The model’s calibration curve closely aligned with the ideal reference, indicating strong agreement between predicted and actual outcomes, and the internal validation showed a C-index of 0.828 (95%CI 0.751–0.905). Clinical decision analysis further confirmed that the nomogram model demonstrated good clinical effectiveness.

Conclusion

Our study identified several independent risk factors for IHCA in STEMI patients after successful PPCI based on a Lasso-logistic regression model, offering individualized and reliable cardiac arrest risk predictions during hospitalization.

Trial registration

Not applicable.

Graphical Abstract