Edris Kakemam, Ahmed Hassan Albelbeisi, Mahtab Rouzbahani, Mehdi Gharakhani, Hamideh Zahedi, Roohangiz Taheri
Nurses' perceptions of patient safety competency: A cross-sectional study of relationships with occurrence and reporting of adverse events
PloS one
expanding integration artificial intelligence ai machine learning ml structural heart domain numerous ml models emerged prediction adverse outcomes following transcatheter aortic valve implantation tavi aim identify describe critically appraise ml prediction models adverse outcomes tavi key objectives consisted summarizing model performance evaluating adherence reporting guidelines transparency searched pubmed scopus embase august 2023 selected published machine learning models predicting tavi outcomes two reviewers independently screened articles extracted data assessed study quality according prisma guidelines preferred reporting items systematic reviews metaanalyses outcomes included summary cstatistics model risk bias assessed prediction model risk bias assessment tool probast cstatistics pooled using randomeffects model twentyone studies 118153 patients employing various ml algorithms 76 models included systematic review predictive ability models varied 118 inadequate cstatistic 060 263 adequate cstatistic 060070 316 acceptable cstatistic 070080 303 demonstrated excellent cstatistic 080 performance metaanalyses revealed excellent predictive performance early mortality cstatistic 081 95 ci 065091 acceptable performance 1year mortality cstatistic 076 95 ci 067084 acceptable performance predicting permanent pacemaker implantation cstatistic 075 95 ci 051090 ml models tavi outcomes exhibit adequate excellent performance suggesting potential clinical utility identified concerns methodology transparency emphasizing need improved scientific reporting standards