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Predictive modeling of perioperative blood transfusion in lumbar posterior interbody fusion using machine learning – Lumbar Fusion

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This article discusses the development of a machine learning-based model to predict the risk of perioperative blood transfusion in lumbar posterior interbody fusion surgery. The researchers collected data from 1905 patients who underwent the surgery and randomly divided it into a training set and a validation set. They used the eXtreme Gradient Boosting (XGBoost) algorithm to create five machine learning models and selected the optimal model based on its performance on the validation set. The study found that factors such as intraoperative blood loss, intraoperative fluid infusion, and the number of fused segments were important predictors of blood transfusion risk. The XGBoost model outperformed the traditional logistic regression model in predicting transfusion risk. The findings suggest that the machine learning model can be a valuable tool in surgical decision-making to reduce the need for blood transfusions

Summarised by Mr Mo Akmal – Lead Spinal Surgeon
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Published article

Background: Accurate estimation of perioperative blood transfusion risk in lumbar posterior interbody fusion is essential to reduce the number, cost, and complications associated with blood transfusions. Machine learning algorithms have the potential to outperform traditional prediction methods in predicting perioperative blood transfusion. This study aimed to construct a machine learning-based perioperative transfusion risk prediction model for lumbar posterior interbody fusion in order to…

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Front Physiol. 2023 Dec 22;14:1306453. doi: 10.3389/fphys.2023.1306453. eCollection 2023.ABSTRACTBackground: Accurate estimation of perioperative blood transfusion risk in lumbar posterior interbody fusion is essential to reduce the number, cost, and complications associated with blood transfusions. Machine learning algorithms have the potential to outperform traditional prediction methods in predicting perioperative blood transfusion. This study aimed to construct,

Front Physiol. 2023 Dec 22;14:1306453. doi: 10.3389/fphys.2023.1306453. eCollection 2023.

ABSTRACT

Background: Accurate estimation of perioperative blood transfusion risk in lumbar posterior interbody fusion is essential to reduce the number, cost, and complications associated with blood transfusions. Machine learning algorithms have the potential to outperform traditional prediction methods in predicting perioperative blood transfusion. This study aimed to construct a machine learning-based perioperative transfusion risk prediction model for lumbar posterior interbody fusion in order to improve the efficacy of surgical decision-making. Methods: We retrospectively collected clinical data on 1905 patients who underwent lumbar posterior interbody fusion surgery at the Second Hospital of Shanxi Medical University between January 2021 and March 2023. All the data was randomly divided into a training set and a validation set, and the “feature_importances” method provided by eXtreme Gradient Boosting (XGBoost) algorithm was applied to select statistically significant features on the training set to establish five machine learning prediction models. The optimal model was identified by utilizing the area under the curve (AUC) and the probability calibration curve on the validation set. Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) were employed for interpretable analysis of the optimal model. Results: In the postoperative outcomes of patients, the number of hospital days in the transfusion group was longer than that in the non-transfusion group. Additionally, the transfusion group experienced higher total hospital costs, 90-day readmission rates, and complication rates within 90 days after surgery than the non-transfusion group. A total of 9 features were selected for the models. The XGBoost model performed best with an AUC value of 0.958. The SHAP values showed that intraoperative blood loss, intraoperative fluid infusion, and number of fused segments were the top 3 most important features affecting perioperative blood transfusion in lumbar posterior interbody fusion. The LIME algorithm was used to interpret the individualized prediction. : Surgery, ASA class, levels fused, total intraoperative blood loss, operative time, and preoperative Hb are viable predictors of perioperative blood transfusion in lumbar posterior interbody fusion. The XGBoost model has demonstrated superior predictive efficacy compared to the traditional logistic regression model, making it a more effective decision-making tool for perioperative blood transfusion.

PMID:38187137 | PMC:PMC10767743 | DOI:10.3389/fphys.2023.1306453

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Predictive modeling of perioperative blood transfusion in lumbar posterior interbody fusion using machine learning

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Front Physiol. 2023 Dec 22;14:1306453. doi: 10.3389/fphys.2023.1306453. eCollection 2023.ABSTRACTBackground: Accurate estimation of perioperative blood transfusion risk in lumbar posterior interbody fusion is essential to reduce the number, cost, and complications associated with blood transfusions. Machine learning algorithms have the potential to outperform traditional prediction methods in predicting perioperative blood transfusion. This study aimed to construct

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