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Background <p>Postoperative pulmonary infections (POPIs) occur in approximately 13–38% of patients who undergo surgery for esophageal cancer, negatively impacting patient outcomes and prolonging hospital stays. This study aims to develop a novel clinical prediction model to identify patients at risk for POPIs early, thereby enabling timely intervention by clinicians.</p> Methods <p>This study included 910 patients from two hospitals. Of these, 795 patients from one hospital were randomly assigned to the training cohort (<i>n</i> = 556) and the validation cohort (<i>n</i> = 239) at a 7:3 ratio. The external test cohort consisted of 115 patients from the second hospital. A nomogram was developed via logistic regression to predict the incidence of POPIs. The model’s discrimination, precision and clinical benefit were evaluated by constructing a receiver operating characteristic (ROC) curve, calculating the area under the ROC curve (AUC), performing a calibration plot, conducting decision curve analysis (DCA) and clinical impact curves (CIC).</p> Results <p>Multivariate logistic regression revealed that age, anemia, neoadjuvant therapy, T stage, thoracic adhesions and duration of surgery were independent risk factors for POPIs. The AUC for the training cohort was 0.8095 (95% CI: 0.7664–0.8527), that for the validation cohort was 0.8039 (95% CI: 0.7436–0.8643), and that for the external test cohort was 0.7174 (95% CI: 0.6145–0.8204). Calibration plots demonstrated good agreement between the predicted and observed probabilities, while DCA and CIC demonstrated good clinical applicability of the model in three cohorts.</p> Conclusion <p>The nomogram, which incorporates six key factors, effectively predicts the risk of POPIs and can serve as a valuable tool for clinicians in identifying high-risk patients.</p>

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A nomogram for postoperative pulmonary infections in esophageal cancer patients: a two-center retrospective clinical study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月18日

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  • Shuang Li,
  • Chen Fang,
  • Zheng Tao,
  • Jingfeng Zhu,
  • Haitao Ma

摘要

Background

Postoperative pulmonary infections (POPIs) occur in approximately 13–38% of patients who undergo surgery for esophageal cancer, negatively impacting patient outcomes and prolonging hospital stays. This study aims to develop a novel clinical prediction model to identify patients at risk for POPIs early, thereby enabling timely intervention by clinicians.

Methods

This study included 910 patients from two hospitals. Of these, 795 patients from one hospital were randomly assigned to the training cohort (n = 556) and the validation cohort (n = 239) at a 7:3 ratio. The external test cohort consisted of 115 patients from the second hospital. A nomogram was developed via logistic regression to predict the incidence of POPIs. The model’s discrimination, precision and clinical benefit were evaluated by constructing a receiver operating characteristic (ROC) curve, calculating the area under the ROC curve (AUC), performing a calibration plot, conducting decision curve analysis (DCA) and clinical impact curves (CIC).

Results

Multivariate logistic regression revealed that age, anemia, neoadjuvant therapy, T stage, thoracic adhesions and duration of surgery were independent risk factors for POPIs. The AUC for the training cohort was 0.8095 (95% CI: 0.7664–0.8527), that for the validation cohort was 0.8039 (95% CI: 0.7436–0.8643), and that for the external test cohort was 0.7174 (95% CI: 0.6145–0.8204). Calibration plots demonstrated good agreement between the predicted and observed probabilities, while DCA and CIC demonstrated good clinical applicability of the model in three cohorts.

Conclusion

The nomogram, which incorporates six key factors, effectively predicts the risk of POPIs and can serve as a valuable tool for clinicians in identifying high-risk patients.