关键词: 肝切除, 肝功能衰竭, 预测, 模型, 系统评价, 护理

Abstract:

Objective We systematically retrieved and evaluated risk prediction models for liver failure after hepatic resection，so as to provide a reference for constructing higher quality risk prediction models for liver failure after hepatic resection. Methods CNKI，Wanfang，sinomed，PubMed，web of science，Embase，Cochrane Library，CINAHL databases were searched for studies on the construction of risk prediction models for liver failure after hepatectomy. The literature was independently screened by 2 researchers and the information was extracted，and the risk of bias and applicability of the included literature was evaluated according to the Predictive Modelling Study Data Extraction Form and the Risk of Bias Assessment Tool. Results A total of 18 studies on the construction of risk prediction models for liver failure after hepatectomy were included，involving 21 models with an area under the subject working characteristic curve of 0.690~0.883. The independent predictors that were repeatedly reported by the models were total bilirubin，residual liver volume，platelet count，prothrombin time，albumin concentration，liver stiffness，and extent of liver resection. Some predictors are controversial，including male，laparoscopic surgery，liver-muscle ratio. The risk of bias in all studies is high，mainly due to insufficient sample size，poor treatment of continuous variables，lack of model performance assessment and over-fitting of models. Conclusion The existing models for predicting the risk of liver failure after hepatectomy are still in the development stage，and there are certain commonalities and controversies in the predictors，which can be further improved by conducting model validation studies or developing localized prediction models with good performance in the future.

Key words: Liver Resection, Liver Failure, Forecasting, Model, Systematic Review, Nursing Care