Predictive Modeling for Blood Transfusion After Adult Spinal Deformity Surgery: A Tree-Based Machine Learning Approach

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Abstract

Study Design.

Retrospective cohort study.

Objective.

Blood transfusion is frequently necessary after adult spinal deformity (ASD) surgery. We sought to develop predictive models for blood transfusion after ASD surgery, utilizing both classification tree and random forest machine-learning approaches.

Summary of Background Data.

Past models for transfusion risk among spine surgery patients are disadvantaged through use of single-institutional data, potentially limiting generalizability.

Methods.

This investigation was conducted utilizing the American College of Surgeons National Surgical Quality Improvement Program dataset years 2012 to 2015. Patients undergoing surgery for ASD were identified using primary-listed current procedural terminology codes. In total, 1029 patients were analyzed. The primary outcome measure was intra-/postoperative blood transfusion. Patients were divided into training (n = 824) and validation (n = 205) datasets. Single classification tree and random forest models were developed. Both models were tested on the validation dataset using area under the receiver operating characteristic curve (AUC), which was compared between models.

Results.

Overall, 46.5% (n = 479) of patients received a transfusion intraoperatively or within 72 hours postoperatively. The final classification tree model used operative duration, hematocrit, and weight, exhibiting AUC = 0.79 (95% confidence interval 0.73–0.85) on the validation set. The most influential variables in the random forest model were operative duration, surgical invasiveness, hematocrit, weight, and age. The random forest model exhibited AUC = 0.85 (95% confidence interval 0.80–0.90). The difference between the classification tree and random forest AUCs was nonsignificant at the validation cohort size of 205 patients (P = 0.1551).

Conclusion.

This investigation produced tree-based machine-learning models of blood transfusion risk after ASD surgery. The random forest model offered very good predictive capability as measured by AUC. Our single classification tree model offered superior ease of implementation, but a lower AUC as compared to the random forest approach, although this difference was not statistically significant at the size of our validation cohort. Clinicians may choose to implement either of these models to predict blood transfusion among their patients. Furthermore, policy makers may use these models on a population-based level to assess predicted transfusion rates after ASD surgery.

Conclusion.

Level of Evidence: 3

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