Feasibility and Assessment of a Machine Learning-Based Predictive Model of Outcome After Lumbar Decompression Surgery
Résumé
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.
Mots clés
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
Domaines
Médecine humaine et pathologie
Fichier principal
andre-et-al-2020-feasibility-and-assessment-of-a-machine-learning-based-predictive-model-of-outcome-after-lumbar.pdf (971.21 Ko)
Télécharger le fichier
| Origine | Publication financée par une institution |
|---|---|
| Licence |
