Hu Aixiang, Ma Dayan, Lei Yanni, Li Fangqiang, Wang Xi, Zhang Yuewei
Infection Control Department, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Information Management and Data Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Front Public Health. 2025 Jun 25;13:1623968. doi: 10.3389/fpubh.2025.1623968. eCollection 2025.
Machine learning models have emerged as pivotal tools for enhancing the predictive accuracy of multidrug-resistant bacterial pneumonia (MDR-BP) risk in critically ill patients following neurosurgery procedures. By enabling early risk stratification, these models facilitate timely diagnosis and proactive therapeutic interventions. However, existing prediction frameworks exhibit limitations in elucidating the relative importance of risk factors, thereby impeding precise clinical decision-making and individualized patient management.
To evaluate the performance of six ensemble classification algorithms and three single classification algorithms in predicting MDR-BP risk factors among neurosurgical postoperative critically ill patients, identify the optimal predictive model, and determine key influential factors.
We conducted a retrospective study involving 750 neurosurgical patients admitted to a neurosurgery center at a tertiary hospital in Beijing between January 2020 and December 2023. Following rigorous data preprocessing, univariate analysis was performed to screen statistically significant variables. The Synthetic Minority Over-sampling Technique (SMOTE) was applied to address class imbalance. Predictive models for MDR-BP risk factors were constructed, and their performance was validated using 10-fold cross-validation to assess mean accuracy, recall, and specificity. The SHapley Additive exPlanations (SHAP) framework was employed to quantify feature importance.
The Random Forest model demonstrated superior performance, achieving the highest mean accuracy (0.775) and AUC value (0.860) compared to other models. SHAP interpretation revealed three critical predictors of MDR-BP: intensive care unit length of stay (ICU-LOS), antibiotic treatment duration, and serum albumin level.
The Random Forest algorithm demonstrates superior predictive accuracy for MDR-BP risk in critically ill post-neurosurgical patients. ICU-LOS, antibiotic treatment duration, and serum albumin level are significant predictors of MDR-BP.
机器学习模型已成为提高神经外科手术后重症患者多重耐药菌肺炎（MDR - BP）风险预测准确性的关键工具。通过实现早期风险分层，这些模型有助于及时诊断和积极的治疗干预。然而，现有的预测框架在阐明风险因素的相对重要性方面存在局限性，从而阻碍了精确的临床决策和个体化的患者管理。
评估六种集成分类算法和三种单一分类算法在预测神经外科术后重症患者MDR - BP风险因素方面的性能，确定最佳预测模型，并确定关键影响因素。
我们进行了一项回顾性研究，纳入了2020年1月至2023年12月在北京一家三级医院神经外科中心收治的750例神经外科患者。经过严格的数据预处理后，进行单变量分析以筛选具有统计学意义的变量。应用合成少数过采样技术（SMOTE）来解决类别不平衡问题。构建了MDR - BP风险因素的预测模型，并使用10折交叉验证对其性能进行验证，以评估平均准确率、召回率和特异性。采用SHapley加法解释（SHAP）框架来量化特征重要性。
随机森林模型表现出卓越的性能，与其他模型相比，实现了最高的平均准确率（0.775）和AUC值（0.860）。SHAP解释揭示了MDR - BP的三个关键预测因素：重症监护病房住院时间（ICU - LOS）、抗生素治疗持续时间和血清白蛋白水平。
随机森林算法在神经外科术后重症患者MDR - BP风险预测方面具有卓越的准确性。ICU - LOS、抗生素治疗持续时间和血清白蛋白水平是MDR - BP的重要预测因素。
