PDF(Pubmed)
Abstract：
BACKGROUND: This study aims to implement a validated prediction model and application medium for postoperative pneumonia (POP) in elderly patients with hip fractures in order to facilitate individualized intervention by clinicians.
METHODS: Employing clinical data from elderly patients with hip fractures, we derived and externally validated machine learning models for predicting POP. Model derivation utilized a registry from Nanjing First Hospital, and external validation was performed using data from patients at the Fourth Affiliated Hospital of Nanjing Medical University. The derivation cohort was divided into the training set and the testing set. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used for feature screening. We compared the performance of models to select the optimized model and introduced SHapley Additive exPlanations (SHAP) to interpret the model.
RESULTS: The derivation and validation cohorts comprised 498 and 124 patients, with 14.3% and 10.5% POP rates, respectively. Among these models, Categorical boosting (Catboost) demonstrated superior discrimination ability. AUROC was 0.895 (95%CI: 0.841-0.949) and 0.835 (95%CI: 0.740-0.930) on the training and testing sets, respectively. At external validation, the AUROC amounted to 0.894 (95% CI: 0.821-0.966). The SHAP method showed that CRP, the modified five-item frailty index (mFI-5), and ASA body status were among the top three important predicators of POP.
CONCLUSIONS: Our model\'s good early prediction ability, combined with the implementation of a network risk calculator based on the Catboost model, was anticipated to effectively distinguish high-risk POP groups, facilitating timely intervention.
METHODS: Employing clinical data from elderly patients with hip fractures, we derived and externally validated machine learning models for predicting POP. Model derivation utilized a registry from Nanjing First Hospital, and external validation was performed using data from patients at the Fourth Affiliated Hospital of Nanjing Medical University. The derivation cohort was divided into the training set and the testing set. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used for feature screening. We compared the performance of models to select the optimized model and introduced SHapley Additive exPlanations (SHAP) to interpret the model.
RESULTS: The derivation and validation cohorts comprised 498 and 124 patients, with 14.3% and 10.5% POP rates, respectively. Among these models, Categorical boosting (Catboost) demonstrated superior discrimination ability. AUROC was 0.895 (95%CI: 0.841-0.949) and 0.835 (95%CI: 0.740-0.930) on the training and testing sets, respectively. At external validation, the AUROC amounted to 0.894 (95% CI: 0.821-0.966). The SHAP method showed that CRP, the modified five-item frailty index (mFI-5), and ASA body status were among the top three important predicators of POP.
CONCLUSIONS: Our model\'s good early prediction ability, combined with the implementation of a network risk calculator based on the Catboost model, was anticipated to effectively distinguish high-risk POP groups, facilitating timely intervention.
摘要：
背景：本研究旨在对老年髋部骨折患者的术后肺炎（POP）实施有效的预测模型和应用介质，以促进临床医生的个性化干预。
方法：利用老年髋部骨折患者的临床资料，我们推导并外部验证了用于预测POP的机器学习模型。模型推导利用南京市第一医院的注册表，使用南京医科大学第四附属医院患者的数据进行外部验证.推导队列分为训练集和测试集。使用最小绝对收缩和选择算子（LASSO）和多变量逻辑回归进行特征筛选。我们比较了模型的性能以选择优化的模型，并引入了SHapley加法扩张（SHAP）来解释模型。
结果：推导和验证队列包括498名和124名患者，有14.3%和10.5%的流行率，分别。在这些模型中，分类提升(Catboost)表现出优越的辨别能力。训练集和测试集的AUROC分别为0.895（95CI：0.841-0.949）和0.835（95CI：0.740-0.930），分别。在外部验证时，AUROC为0.894（95%CI：0.821-0.966）。SHAP方法显示CRP,修改后的五项脆弱指数(mFI-5)，ASA的身体状态是POP的三大重要预测因素。
结论：我们的模型具有良好的早期预测能力，结合基于Catboost模型的网络风险计算器的实现，预计将有效区分高危人群，促进及时干预。
方法：利用老年髋部骨折患者的临床资料，我们推导并外部验证了用于预测POP的机器学习模型。模型推导利用南京市第一医院的注册表，使用南京医科大学第四附属医院患者的数据进行外部验证.推导队列分为训练集和测试集。使用最小绝对收缩和选择算子（LASSO）和多变量逻辑回归进行特征筛选。我们比较了模型的性能以选择优化的模型，并引入了SHapley加法扩张（SHAP）来解释模型。
结果：推导和验证队列包括498名和124名患者，有14.3%和10.5%的流行率，分别。在这些模型中，分类提升(Catboost)表现出优越的辨别能力。训练集和测试集的AUROC分别为0.895（95CI：0.841-0.949）和0.835（95CI：0.740-0.930），分别。在外部验证时，AUROC为0.894（95%CI：0.821-0.966）。SHAP方法显示CRP,修改后的五项脆弱指数(mFI-5)，ASA的身体状态是POP的三大重要预测因素。
结论：我们的模型具有良好的早期预测能力，结合基于Catboost模型的网络风险计算器的实现，预计将有效区分高危人群，促进及时干预。
