A model to predict risk of blood transfusion after gynecologic surgery

• Published in: American journal of obstetrics and gynecology 2017-05, Vol.216 (5), p.506.e1-506.e14
• Main Authors: Stanhiser, Jamie, MD, Chagin, Kevin, MS, Jelovsek, J. Eric, MD, MMEd
• Format: Article
• Language: English
• Subjects: blood management; blood transfusion; Blood Transfusion - statistics & numerical data; blood transfusion risk; Body Mass Index; Cohort Studies; Decision Support Techniques; Female; gynecologic surgery; Gynecologic Surgical Procedures; Hemoglobins - analysis; Humans; Hypertension - complications; Logistic Models; Obstetrics and Gynecology; Ovarian Neoplasms - complications; Parity; perioperative; predict; predictive model; preoperative testing; Retrospective Studies; Risk Assessment; Risk Factors
• ISSN: 0002-9378; 1097-6868
• DOI: 10.1016/j.ajog.2017.01.004

Background A model that predicts a patient’s risk of receiving a blood transfusion may facilitate selective preoperative testing and more efficient perioperative blood management utilization. Objective We sought to construct and validate a model that predicts a patient’s risk of receiving a blood transfusion after gynecologic surgery. Study Design In all, 18,319 women who underwent gynecologic surgery at 10 institutions in a single health system by 116 surgeons from January 2010 through June 2014 were analyzed. The data set was split into a model training cohort of 12,219 surgeries performed from January 2010 through December 2012 and a separate validation cohort of 6100 surgeries performed from January 2013 through June 2014. In all, 47 candidate risk factors for transfusion were collected. Multiple logistic models were fit onto the training cohort to predict transfusion within 30 days of surgery. Variables were removed using stepwise backward reduction to find the best parsimonious model. Model discrimination was measured using the concordance index. The model was internally validated using 1000 bootstrapped samples and temporally validated by testing the model’s performance in the validation cohort. Calibration and decision curves were plotted to inform clinicians about the accuracy of predicted probabilities and whether the model adds clinical benefit when making decisions. Results The transfusion rate in the training cohort was 2% (95% confidence interval, 1.72–2.22). The model had excellent discrimination and calibration during internal validation (bias-corrected concordance index, 0.906; 95% confidence interval, 0.890–0.928) and maintained accuracy during temporal validation using the separate validation cohort (concordance index, 0.915; 95% confidence interval, 0.872–0.954). Calibration curves demonstrated the model was accurate up to 40% then it began to overpredict risk. The model provides superior net benefit when clinical decision thresholds are between 0-50% predicted risk. Conclusion This model accurately predicts a patient’s risk of transfusion after gynecologic surgery facilitating selective preoperative testing and more efficient perioperative blood management utilization.