SynthesisBMC surgery2025
Risk prediction models for complications after flap repair surgery: a systematic review and meta-analysis.
Synthesis in BMC surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
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Abstract
objectiveTo systematically evaluate the performance and applicability of risk prediction models for complications after flap repair and to provide guidance for building and refining models.
methodsPubMed, 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.
resultsA 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).
conclusionThe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.