Nomogram model of predicting postoperative deep sternal wound infection based on the front page information of medical records of Peking Union Medical College Hospital
ZHANG Chaoji1 LIU Jianzhou1 MA Guotao1 LIU Xingrong1 LIU Aimin2 LI Xiaofeng1 LIANG Mei1 MIAO Qi1
1.Department of Cardiac Surgery, Peking Union Medical College Hospital Chinese Academy of Medical Sciences, Beijing 100730, China;
2.Department of Medical Record, Peking Union Medical College Hospital Chinese Academy of Medical Sciences, Beijing 100730, China
Abstract：Objective To analyze the information on the front page of the medical records of Peking Union Medical College Hospital (“our hospital” for short), while to construct and internally verify a concatenated model that predicts the risk of deep sternal wound infection (DSWI) after cardiac surgery. Methods A total of 2610 patients with cardiac surgery in our hospital from January 2007 to October 2017 were enrolled. The information on the front page of the medical records was collected, and the best risk factor subset was obtained by using LASSO regression to apply 10-fold cross-validation. The risk predictors were analyzed by multivariate Logistic regression model. A nomogram for predicting cardiac DSWI risk was built by R software. The internal validation was using by bootstrap method, while the predictive performance of the model was evaluated by area under the curve (AUC) of receiver operating characteristic, calibration curve and decision curve. Results Major vascular surgery (OR = 2.625), redo operation (OR = 4.554), diabetes mellitus (OR = 2.463) and allogeneic bone transplantation (OR = 3.498) were independent prognostic factors for DSWI risk (P ＜ 0.05). AUC for the nomogram model was 0.725, showed a moderate degree of discrimination; the predicted probability was highly consistent with the actual probability (P = 0.754); the internal validation consistency index was 0.720. The analysis of the decision curve indicated that the clinical applicability was high. Conclusion The nomogram model established in this study for predicting the risk of DSWI has good discrimination, accuracy and clinical practicability. While it has certain guiding significance for predicting high-risk population of DSWI and making clinical decision.
张超纪1 刘剑州1 马国涛1 刘兴荣1 刘爱民2 李晓凤1 梁湄1 苗齐1. 基于北京协和医院病例首页信息构建预测心脏术后深部胸骨伤口感染风险的列连图模型[J]. 中国医药导报, 2019, 16(33): 178-封三.
ZHANG Chaoji1 LIU Jianzhou1 MA Guotao1 LIU Xingrong1 LIU Aimin2 LI Xiaofeng1 LIANG Mei1 MIAO Qi1. Nomogram model of predicting postoperative deep sternal wound infection based on the front page information of medical records of Peking Union Medical College Hospital. 中国医药导报, 2019, 16(33): 178-封三.
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2019, Vol. 16 
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