Diabetes Mellitus and Total Ankle Arthroplasty Complications

Helbing, Jordan; Farley, Benjamin; Gu, Alex; Zhao, Amy Y.; Siram, Gautam; Stein, Benjamin; Chodos, Marc D.

doi:10.1177/10711007241226929

Cited by 2 publications

References 19 publications

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Cerebrospinal Fluid Leakage Combined with Blood Biomarkers Predicts Poor Wound Healing After Posterior Lumbar Spinal Fusion: A Machine Learning Analysis

Pang,

Ou,

Liang

et al. 2024

Preprint

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Objective The objective of this study aimed to investigate the risk factors for poor wound healing (PWH) after posterior lumbar spinal fusion. Thus, our primary aim is to using machine learning identify these risk factors and construct a clinical risk prediction model. Methods We retrospectively reviewed 2516 patients who underwent posterior lumbar spinal fusion at Guangxi Medical University's Second Affiliated Hospital between August 2021 and August 2023. The data was divided into test and validation groups in a 7:3 ratio. In the test group, logistic regression analysis, support vector machine (SVM), random forest (RF), decision tree (DT), XGboost, Naïve Bayes (NB), k-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) were used to identify specific variables. The top six models from the eight machine learning models with the highest AUC values were selected and used to construct a dynamic nomograms model. Model performance was evaluated using ROC and calibration curves. The model's internal performance was then verified in the validation group using ROC and calibration curves. Results Data from 2516 patients were collected, with 411 eligible cases selected. By combining logistic regression analysis with six machine learning algorithms, this study identified six predictors associated with PWH: SLSI, albumin, postoperative glucose, CSFL, NEU, and CRP. These predictors were used to develop a prediction model, visually represented through a nomogram. The AUC value in the test group was 0.981, and the C-index of the model was 0.986 (95% CI 0.966–0.995), indicating excellent predictive capability. Calibration curve analysis showed good consistency between nomogram-predicted values and actual measurements. Conclusion SLSI, albumin, postoperative glucose, CSFL, NEU and CRP were identified as significant risk factors for PWH after posterior lumbar spinal fusion. The developed prediction model exhibited excellent predictive accuracy and usefulness.

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