批判性评价
医学
压力伤
计算机科学
重症监护医学
病理
替代医学
作者
Yihong Xu,Han Zhao,Shuang Wu,Jianan Wang,Jinyan Zhou,Shanni Ding,Wen Li,Willis Wu,Zhichao Yang,Hongxia Xu,Hongying Pan
标识
DOI:10.1089/wound.2024.0238
摘要
From 837 studies, 25 were included, covering 32 prediction models. Most studies (88%) were single-center and conducted in China, Korea, the United States, or Singapore, spanning various surgical specialties. Among 26,142 participants, IAPI incidence ranged from 4.1% to 41.75%. Common predictors included surgery duration, age, and diabetes. Areas Under the Curve (AUC) values varied from 0.702 to 0.984, but calibration was underreported. All studies had high bias risk, with 22 models exhibiting applicability concerns. [Figure: see text] Future Directions:The development of IAPI models requires a clear definition of the timing and personnel responsible for assessing PIs, with a preference for prospective data collection and thorough internal and external validation. Adherence to the critical appraisal and data extraction for systematic reviews of prediction modeling studies checklist and PROBAST guidelines can improve reporting quality. Models should be user-friendly, clinically applicable, and rigorously validated. Precisely defining and rigorously selecting predictors is critical to reducing variability. Future research should adopt more stringent designs to develop high-quality models capable of effectively guiding clinical practice.PROSPERO registration number: CRD42024502726.
科研通智能强力驱动
Strongly Powered by AbleSci AI