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Machine Learning Prediction for Spinal Deformity Surgery Blood Transfusion.

Meijia Luo¹, Xiaotian Lei², Zhendong Ding³

¹School of Nursing, Hunan Normal University, Changsha, China; The First Hospital of Hunan University of Chinese Medicine, Changsha, China.

World Neurosurgery

|September 18, 2025

Summary

Predicting intraoperative blood transfusion (IBT) risk in spinal deformity surgery (SDS) is crucial. Machine learning models accurately identified high-risk patients, optimizing blood use and transfusion strategies.

Keywords:

Chinese spinal degenerative disease cohort Intraoperative blood transfusion Machine learning Multicenter Spinal deformity surgery

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Area of Science:

Orthopedic Surgery
Medical Informatics
Transfusion Medicine

Background:

Spinal deformity surgery (SDS) carries significant risks associated with intraoperative blood loss and transfusion.
Blood transfusions can lead to adverse events, including transfusion reactions, infection transmission, and immunosuppression.
Accurate prediction of intraoperative blood transfusion (IBT) needs is vital for patient safety and resource management.

Purpose of the Study:

To develop and evaluate machine learning (ML) models for predicting IBT risk in patients undergoing SDS.
To identify key factors influencing IBT requirements in spinal deformity surgery.
To create a tool for clinicians to assess IBT risk.

Main Methods:

A retrospective study involving 162 patients undergoing SDS across 11 centers in China.
Utilized 39 candidate factors and employed Lasso regression for feature selection.
Evaluated ten ML algorithms, including Random Forest (RF), with performance assessed via ROC, precision-recall, and calibration curves.
Applied SHapley Additive exPlanations for model interpretability and developed a web calculator.

Main Results:

The Random Forest (RF) model demonstrated superior predictive performance (AUC of ROC: 0.8716).
Seven key predictors for IBT were identified: age, BMI, preoperative hematocrit, fibrinogen, prefunction, bone graft, and fusion levels.
Level 4 vertebral fusion surgery was associated with the highest IBT risk (OR = 20.78).

Conclusions:

Machine learning algorithms can effectively predict IBT risk in spinal deformity surgery.
These predictive models aid in optimizing blood resource allocation and transfusion strategies.
The developed web calculator provides a practical tool for clinical risk assessment.