1.
Systematic review highlights high risk of bias of clinical prediction models for blood transfusion in patients undergoing elective surgery
Dhiman P, Ma J, Gibbs VN, Rampotas A, Kamal H, Arshad SS, Kirtley S, Doree C, Murphy MF, Collins GS, et al
Journal of clinical epidemiology. 2023
-
-
-
Free full text
-
Editor's Choice
Abstract
BACKGROUND Blood transfusion can be a lifesaving intervention after perioperative blood loss. Many prediction models have been developed to identify patients most likely to require blood transfusion during elective surgery, but it is unclear whether any are suitable for clinical practice. STUDY DESIGN AND SETTING We conducted a systematic review, searching MEDLINE, Embase, PubMed, The Cochrane Library, Transfusion Evidence Library, Scopus, and Web of Science databases for studies reporting the development or validation of a blood transfusion prediction model in elective surgery patients between 01/01/2000 to 30/06/2021. We extracted study characteristics, discrimination performance (c-statistics) of final models and data which we used to perform risk of bias assessment using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). RESULTS We reviewed 66 studies (72 developed and 48 externally validated models). Pooled c-statistics of externally validated models ranged from 0.67 to 0.78. Most developed and validated models were at high risk of bias due to handling of predictors, validation methods, and too small sample sizes. CONCLUSION Most blood transfusion prediction models are at high risk of bias and suffer from poor reporting and methodological quality, which must be addressed before they can be safely used in clinical practice.
PICO Summary
Population
Patients undergoing elective surgery (66 studies).
Intervention
Blood transfusion prediction models used perioperatively.
Comparison
Outcome
This systematic review appraised 120 prediction models developed or validated for predicting blood transfusion in elective surgery (72 developed and 48 externally validated models). Pooled c-statistics of externally validated models ranged from 0.67 to 0.78. Most developed and validated models were at high risk of bias due to handling of predictors, validation methods, and too small sample sizes.