PDF(Pubmed)
Abstract：
UNASSIGNED: The predictive effect of systemic inflammatory factors on postoperative pulmonary complications in elderly patients remains unclear. In addition, machine learning models are rarely used in prediction models for elderly patients.
UNASSIGNED: We retrospectively evaluated elderly patients who underwent general anesthesia during a 6-year period. Eligible patients were randomly assigned in a 7:3 ratio to the development group and validation group. The Least logistic absolute shrinkage and selection operator (LASSO) regression model and multiple logistic regression analysis were used to select the optimal feature. The discrimination, calibration and net reclassification improvement (NRI) of the final model were compared with \"the Assess Respiratory Risk in Surgical Patients in Catalonia\" (ARISCAT) model.
UNASSIGNED: Of the 9775 patients analyzed, 8.31% developed PPCs. The final model included age, preoperative SpO2, ANS (the Albumin/NLR Score), operation time, and red blood cells (RBC) transfusion. The concordance index (C-index) values of the model for the development cohort and the validation cohort were 0.740 and 0.748, respectively. The P values of the Hosmer-Lemeshow test in two cohorts were insignificant. Our model outperformed ARISCAT model, with C-index (0.740 VS 0.717, P = 0.003) and NRI (0.117, P < 0.001).
UNASSIGNED: Based on LASSO machine learning algorithm, we constructed a prediction model superior to ARISCAT model in predicting the risk of PPCs. Clinicians could utilize these predictors to optimize prospective and preventive interventions in this patient population.
UNASSIGNED: We retrospectively evaluated elderly patients who underwent general anesthesia during a 6-year period. Eligible patients were randomly assigned in a 7:3 ratio to the development group and validation group. The Least logistic absolute shrinkage and selection operator (LASSO) regression model and multiple logistic regression analysis were used to select the optimal feature. The discrimination, calibration and net reclassification improvement (NRI) of the final model were compared with \"the Assess Respiratory Risk in Surgical Patients in Catalonia\" (ARISCAT) model.
UNASSIGNED: Of the 9775 patients analyzed, 8.31% developed PPCs. The final model included age, preoperative SpO2, ANS (the Albumin/NLR Score), operation time, and red blood cells (RBC) transfusion. The concordance index (C-index) values of the model for the development cohort and the validation cohort were 0.740 and 0.748, respectively. The P values of the Hosmer-Lemeshow test in two cohorts were insignificant. Our model outperformed ARISCAT model, with C-index (0.740 VS 0.717, P = 0.003) and NRI (0.117, P < 0.001).
UNASSIGNED: Based on LASSO machine learning algorithm, we constructed a prediction model superior to ARISCAT model in predicting the risk of PPCs. Clinicians could utilize these predictors to optimize prospective and preventive interventions in this patient population.
摘要：
■全身炎症因子对老年患者术后肺部并发症的预测作用尚不清楚。此外,机器学习模型很少用于老年患者的预测模型。
■我们回顾性评估了在6年内接受全身麻醉的老年患者。符合条件的患者以7:3的比例随机分配到开发组和验证组。采用最小逻辑绝对收缩和选择算子(LASSO)回归模型和多元逻辑回归分析选择最优特征。歧视,将最终模型的校准和净重新分类改善（NRI）与“评估加泰罗尼亚手术患者的呼吸风险”（ARISCAT）模型进行了比较。
■在分析的9775名患者中，8.31%开发PPC。最终的模型包括年龄，术前SpO2，ANS（白蛋白/NLR评分），操作时间,和红细胞（RBC）输血。开发队列和验证队列模型的一致性指数(C指数)值分别为0.740和0.748。两个队列中Hosmer-Lemeshow检验的P值不显著。我们的模型优于ARISCAT模型，C指数（0.740VS0.717，P=0.003）和NRI（0.117，P<0.001）。
■基于LASSO机器学习算法，在预测PPC风险方面，我们构建了优于ARISCAT模型的预测模型。临床医生可以利用这些预测因子来优化该患者人群的前瞻性和预防性干预措施。
■我们回顾性评估了在6年内接受全身麻醉的老年患者。符合条件的患者以7:3的比例随机分配到开发组和验证组。采用最小逻辑绝对收缩和选择算子(LASSO)回归模型和多元逻辑回归分析选择最优特征。歧视,将最终模型的校准和净重新分类改善（NRI）与“评估加泰罗尼亚手术患者的呼吸风险”（ARISCAT）模型进行了比较。
■在分析的9775名患者中，8.31%开发PPC。最终的模型包括年龄，术前SpO2，ANS（白蛋白/NLR评分），操作时间,和红细胞（RBC）输血。开发队列和验证队列模型的一致性指数(C指数)值分别为0.740和0.748。两个队列中Hosmer-Lemeshow检验的P值不显著。我们的模型优于ARISCAT模型，C指数（0.740VS0.717，P=0.003）和NRI（0.117，P<0.001）。
■基于LASSO机器学习算法，在预测PPC风险方面，我们构建了优于ARISCAT模型的预测模型。临床医生可以利用这些预测因子来优化该患者人群的前瞻性和预防性干预措施。
