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<p>To analyze the risk factors for hospital-acquired infections following percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI) and to develop a nomogram prediction model. Clinical data from 324 AMI patients who underwent PCI between July 2021 and June 2023 were retrospectively analyzed. Patients were categorized into an infection group (n = 39) and a non-infection group (n = 285) based on the occurrence of nosocomial infection postoperatively. Optimal cutoff values were determined using receiver operating characteristic (ROC) curve analysis. Independent risk factors for nosocomial infection after PCI were identified through multivariate logistic regression, and a nomogram model was constructed accordingly. The model underwent internal validation via calibration curves, and its predictive performance was assessed using decision curve analysis. No significant differences were observed between the two groups in terms of gender, drinking history, smoking history, hypertension, infarct location, or number of stents implanted (all <i>P</i> &gt; 0.05). However, the infection group had significantly higher age, higher prevalence of diabetes, greater proportion of New York Heart Association (NYHA) class III/IV, more frequent invasive procedures, and longer hospital stays (all <i>P</i> &lt; 0.05). ROC analysis identified optimal cutoff values of 60 years for age and 6 days for hospitalization time. Multivariate logistic regression confirmed that age &gt; 60 years, diabetes, NYHA class III/IV, invasive procedures, and hospital stay &gt; 6 days were independent risk factors for nosocomial infection after PCI. The nomogram model demonstrated excellent discrimination, with a C-index of 0.915 (95% CI 0.877–0.953). The calibration curve indicated good agreement between predicted and observed outcomes. The nomogram provided higher net clinical benefit beyond threshold probabilities of 0.24 compared to individual predictors. A nomogram incorporating age, diabetes, cardiac function classification, invasive procedures, and hospitalization time was developed to predict the risk of nosocomial infection in AMI patients after PCI. The model exhibits strong predictive performance and may assist clinicians in identifying high-risk patients for intensified monitoring and preventive strategies. However, as a prognostic tool, it does not directly mitigate infection risk and requires external validation before routine clinical implementation.</p>

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Analysis of risk factors and construction of nomogram model for nosocomial infection in patients with acute myocardial infarction after percutaneous coronary intervention

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  • 发表日期：

    2025年11月5日

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  • Shuo Huang,
  • Yahui Zhou,
  • Dandan Han,
  • Ran Zhou

摘要

To analyze the risk factors for hospital-acquired infections following percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI) and to develop a nomogram prediction model. Clinical data from 324 AMI patients who underwent PCI between July 2021 and June 2023 were retrospectively analyzed. Patients were categorized into an infection group (n = 39) and a non-infection group (n = 285) based on the occurrence of nosocomial infection postoperatively. Optimal cutoff values were determined using receiver operating characteristic (ROC) curve analysis. Independent risk factors for nosocomial infection after PCI were identified through multivariate logistic regression, and a nomogram model was constructed accordingly. The model underwent internal validation via calibration curves, and its predictive performance was assessed using decision curve analysis. No significant differences were observed between the two groups in terms of gender, drinking history, smoking history, hypertension, infarct location, or number of stents implanted (all P > 0.05). However, the infection group had significantly higher age, higher prevalence of diabetes, greater proportion of New York Heart Association (NYHA) class III/IV, more frequent invasive procedures, and longer hospital stays (all P < 0.05). ROC analysis identified optimal cutoff values of 60 years for age and 6 days for hospitalization time. Multivariate logistic regression confirmed that age > 60 years, diabetes, NYHA class III/IV, invasive procedures, and hospital stay > 6 days were independent risk factors for nosocomial infection after PCI. The nomogram model demonstrated excellent discrimination, with a C-index of 0.915 (95% CI 0.877–0.953). The calibration curve indicated good agreement between predicted and observed outcomes. The nomogram provided higher net clinical benefit beyond threshold probabilities of 0.24 compared to individual predictors. A nomogram incorporating age, diabetes, cardiac function classification, invasive procedures, and hospitalization time was developed to predict the risk of nosocomial infection in AMI patients after PCI. The model exhibits strong predictive performance and may assist clinicians in identifying high-risk patients for intensified monitoring and preventive strategies. However, as a prognostic tool, it does not directly mitigate infection risk and requires external validation before routine clinical implementation.