Yang Jiebin, Qin Xinya, Hou Lili, Liu Yamei
Chengdu University of Traditional Chinese Medicine, Sichuan, 610075, China.
Department of Nursing, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
BMC Surg. 2025 Aug 27;25(1):398. doi: 10.1186/s12893-025-03072-8.
To systematically evaluate the performance and applicability of risk prediction models for complications after flap repair and to provide guidance for building and refining models.
PubMed, Embase, Web of Science, the Cochrane Library, CNKI, SinoMed, VIP and Wanfang were searched for studies on risk prediction models for flap complications. The search period is from inception to December 28, 2024. The PROBAST tool was used to evaluate the quality of the prediction model research, and Stata 18 software was employed to meta-analyze the predictors of the models.
A total of 16 studies were included, 28 risk prediction models were constructed, and the area under the receiver operating characteristic curve (AUC) ranged from 0.655 to 0.964, with 16 prediction models performing well (AUC > 0.7). Eleven articles underwent model calibration, 16 were validated internally, and 3 were validated externally. The results of the PROBAST review revealed that all 16 studies were at high risk of bias. The incidence rate of flap complications was 14.8% (95% CI, 10.7 - 19.0%). Body mass index (BMI), smoking history, long flap reconstruction time, diabetes mellitus, hypertension, and postoperative infection were independent risk factors for complications after flap repair (P < 0.05).
The risk prediction model for complications after flap repair has certain predictive value, but the overall risk of bias is high, and there is a lack of external validation; thus, it needs to be further enhanced and optimized to increase its prediction accuracy and clinical practicability.
系统评价皮瓣修复术后并发症风险预测模型的性能及适用性，为模型构建和完善提供指导。
检索PubMed、Embase、Web of Science、Cochrane图书馆、中国知网、中国生物医学文献数据库、维普数据库和万方数据库中关于皮瓣并发症风险预测模型的研究。检索时间从建库至2024年12月28日。采用PROBAST工具评估预测模型研究的质量，使用Stata 18软件对模型的预测因素进行Meta分析。
共纳入16项研究，构建了28个风险预测模型，受试者工作特征曲线下面积（AUC）范围为0.655至0.964，其中16个预测模型表现良好（AUC>0.7）。11篇文章进行了模型校准，16篇进行了内部验证，3篇进行了外部验证。PROBAST评价结果显示，所有16项研究均存在较高的偏倚风险。皮瓣并发症发生率为14.8%（95%CI，10.7 - 19.0%）。体重指数（BMI）、吸烟史、皮瓣重建时间长、糖尿病、高血压和术后感染是皮瓣修复术后并发症的独立危险因素（P<0.05）。
皮瓣修复术后并发症风险预测模型具有一定的预测价值，但总体偏倚风险较高，且缺乏外部验证；因此，需要进一步加强和优化，以提高其预测准确性和临床实用性。
