Machine Learning Applied to Registry Data: Development of a Patient-Specific Prediction Model for Blood Transfusion Requirements During Craniofacial Surgery Using the Pediatric Craniofacial Perioperative Registry Dataset

Abstract

Background: Craniosynostosis is the premature fusion of ≥1 cranial sutures and often requires surgical intervention. Surgery may involve extensive osteotomies, which can lead to substantial blood loss. Currently, there are no consensus recommendations for guiding blood conservation or transfusion in this patient population. The aim of this study is to develop a machine-learning model to predict blood product transfusion requirements for individual pediatric patients undergoing craniofacial surgery. METHODS: Using data from 2143 patients in the Pediatric Craniofacial Surgery Perioperative Registry, we assessed 6 machine-learning classification and regression models based on random forest, adaptive boosting (AdaBoost), neural network, gradient boosting machine (GBM), support vector machine, and elastic net methods with inputs from 22 demographic and preoperative features. We developed classification models to predict an individual’s overall need for transfusion and regression models to predict the number of blood product units to be ordered preoperatively. The study is reported according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist for prediction model development. RESULTS: The GBM performed best in both domains, with an area under receiver operating characteristic curve of 0.87 ± 0.03 (95% confidence interval) and F-score of 0.91 ± 0.04 for classification, and a mean squared error of 1.15 ± 0.12, R-squared (R 2) of 0.73 ± 0.02, and root mean squared error of 1.05 ± 0.06 for regression. GBM feature ranking determined that the following variables held the most information for prediction: platelet count, weight, preoperative hematocrit, surgical volume per institution, age, and preoperative hemoglobin. We then produced a calculator to show the number of units of blood that should be ordered preoperatively for an individual patient. CONCLUSIONS: Anesthesiologists and surgeons can use this continually evolving predictive model to improve clinical care of patients presenting for craniosynostosis surgery.

Use of artificial neural networks in the prognosis of musculoskeletal diseases—a scoping review

“…The included studies were from sixteen journals, with only 2 articles each were published in Clinical Orthopaedics and Related Research [ 24 , 25 ] and Medicina [ 26 , 27 ]. Thirty-three percent of these studies (7/18) were conducted in the United States [ 24 , 25 , 28 – 32 ], with 17% (3) in China [ 26 , 33 , 34 ]. Study designs included cohort study (83%; 15) and cross-sectional database study (17%; 3).…”

“…The sample size ranged from 58 to 10534 [ 26 , 36 ]. Two studies [ 28 , 38 ] did not provide data on the age and sex of participants.…”

Technology Innovations in Anesthesiology

“…[11][12][13][14] Models predicting which patients require therapy, and the effect of treatment, have more recently been reported as well as those predicting adverse events. 15,16 Perhaps the best example is intraoperative hypotension, a common adverse event with a clear relationship to multiple organ system injuries. 17 The Hypotension Prediction Index (HPI) was one of the first models created that targeted hypotension and the only one demonstrated to reduce hypotension exposure when incorporated into care delivery, compared to care without it.…”

Prediction of red blood cell transfusion after orthopedic surgery using an interpretable machine learning framework

“…However, this strategy often ignores other potential preoperative indicators and may lead to one-sided and inappropriate transfusion. Recent studies have identified other risk factors including BMI, age, blood pressure, or use of medications, and showed that Hb might not be the most critical risk factor in postoperative RBC transfusion (10,11). Therefore, more modifiable factors should be considered before the RBC transfusion decision is made.…”