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Journal Articles Global Spine Journal Year : 2020

Feasibility and Assessment of a Machine Learning-Based Predictive Model of Outcome After Lumbar Decompression Surgery

Bruno Peyrou
  • Function : Author
Jean-Jacques Vignaux

Abstract

Study design: Retrospective study at a unique center. Objective: The aim of this study is twofold, to develop a virtual patients model for lumbar decompression surgery and to evaluate the precision of an artificial neural network (ANN) model designed to accurately predict the clinical outcomes of lumbar decompression surgery. Methods: We performed a retrospective study of complete Electronic Health Records (EHR) to identify potential unfavorable criteria for spine surgery (predictors). A cohort of synthetics EHR was created to classify patients by surgical success (green zone) or partial failure (orange zone) using an Artificial Neural Network which screens all the available predictors. Results: In the actual cohort, we included 60 patients, with complete EHR allowing efficient analysis, 26 patients were in the orange zone (43.4%) and 34 were in the green zone (56.6%). The average positive criteria amount for actual patients was 8.62 for the green zone (SD+/- 3.09) and 10.92 for the orange zone (SD 3.38). The classifier (a neural network) was trained using 10,000 virtual patients and 2000 virtual patients were used for test purposes. The 12,000 virtual patients were generated from the 60 EHR, of which half were in the green zone and half in the orange zone. The model showed an accuracy of 72% and a ROC score of 0.78. The sensitivity was 0.885 and the specificity 0.59. Conclusion: Our method can be used to predict a favorable patient to have lumbar decompression surgery. However, there is still a need to further develop its ability to analyze patients in the “failure of treatment” zone to offer precise management of patient health before spinal surgery.

Keywords

NRS-BP NRS for back pain NRS-LP NRS for leg pain ODI Oswestry Disability Index PHC predictive hierarchical clustering PPV positive predictive value PROMs patient-reported outcome measures RF random forest ROC machine learning lumbar decompression surgery retrospective study synthetic electronic medical record ROC curve ACC accuracy ACS-NSQIP American College of Surgeons National Surgical Quality Improvement Program ANN artificial neural networks AUC area under the receiver operating characteristic curve COPD chronic obstructive pulmonary disease DNN deep neural networks HER electronic health records GBM gradient boosting machine GLM generalized linear model GLMnet elastic-net GLM LSS lumbar spinal stenosis MCID minimum clinically important difference ML machine learning NPV negative predictive value NRS numeric rating scale NRS-BP NRS for back pain NRS-LP NRS for leg pain ODI Oswestry Disability Index PHC predictive hierarchical clustering PPV positive predictive value PROMs patient-reported outcome measures RF random forest ROC receiver operating characteristic accuracy ACS-NSQIP American College of Surgeons National Surgical Quality Improvement Program ANN artificial neural networks AUC area under the receiver operating characteristic curve COPD chronic obstructive pulmonary disease DNN deep neural networks HER electronic health records GBM gradient boosting machine GLM generalized linear model GLMnet elastic-net GLM LSS lumbar spinal stenosis MCID minimum clinically important difference ML NPV negative predictive value NRS numeric rating scale
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Dates and versions

hal-04514767 , version 1 (21-03-2024)

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Attribution - NonCommercial - NoDerivatives

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Arthur André, Bruno Peyrou, Alexandre Carpentier, Jean-Jacques Vignaux. Feasibility and Assessment of a Machine Learning-Based Predictive Model of Outcome After Lumbar Decompression Surgery. Global Spine Journal, 2020, 12 (5), pp.894-908. ⟨10.1177/2192568220969373⟩. ⟨hal-04514767⟩
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