Machine Learning Prediction of Length of Stay in Adult Spinal Deformity Patients Undergoing Posterior Spine Fusion Surgery

Abstract

(1) Background: Length of stay (LOS) is a commonly reported metric used to assess surgical success, patient outcomes, and economic impact. The focus of this study is to use a variety of machine learning algorithms to reliably predict whether a patient undergoing posterior spinal fusion surgery treatment for Adult Spine Deformity (ASD) will experience a prolonged LOS. (2) Methods: Patients undergoing treatment for ASD with posterior spinal fusion surgery were selected from the American College of Surgeon’s NSQIP dataset. Prolonged LOS was defined as a LOS greater than or equal to 9 days. Data was analyzed with the Logistic Regression, Decision Tree, Random Forest, XGBoost, and Gradient Boosting functions in Python with the Sci-Kit learn package. Prediction accuracy and area under the curve (AUC) were calculated. (3) Results: 1281 posterior patients were analyzed. The five algorithms had prediction accuracies between 68% and 83% for posterior cases (AUC: 0.566–0.821). Multivariable regression indicated that increased Work Relative Value Units (RVU), elevated American Society of Anesthesiologists (ASA) class, and longer operating times were linked to longer LOS. (4) Conclusions: Machine learning algorithms can predict if patients will experience an increased LOS following ASD surgery. Therefore, medical resources can be more appropriately allocated towards patients who are at risk of prolonged LOS.

Preadmission assessment of extended length of hospital stay with RFECV-ETC and hospital-specific data

“…The modelling attempt ( T#10 ), which considered the following input features PAG, PGD, ADC, ADT, PCC, PRG, DTC, SES and CCI produced the best accuracy of 89.3%. This prediction accuracy is comparably higher than some of the prediction models for ELOHS carried out previously as shown in the following references [ 16 , 20 , 21 ].…”

“…The prediction accuracy of the ELOHS model, which is 89.3% is comparatively higher than the accuracy obtained by other researchers [ 9 , 16 , 20 , 35 ] even though it may be difficult to justify some of their techniques for defining ELOHS. This is because some of the patients who may have been classified as likely to exceed their expected length of stay in the hospital because they spent 3, 4, 5, 9, or 11 days based on the proposition of the models may have not exceed their expected length of stay in the hospital following the assessment of their DRG per the technique described in this study.…”

“…Staziak et al [ 16 ] obtained an accuracy of 80—81% for ELOHS prediction of torso trauma patients using clinical and image data with Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms. Zhang et al [ 20 ] predicted the prospects of ELOHS for adult spinal deformity patients undergoing posterior spinal fusion surgery to an accuracy of 68–83% using LR, Decision Tree classifier (DTC), Random Forest (RF), XGBoost (XGB), and Gradient Boosting Machine (GBM) by considering ELOHS as those spending > 9 days in the hospital. Zolbanin et al [ 26 ] predicted the length of stay (LOS) for patients suffering from chronic obstructive pulmonary disease (COPD) and pneumonia with a deep neural network algorithm and obtained an accuracy of 86–91% for COPD and 74–85% for pneumonia.…”

“…The contribution of this study can be summarized as: Design and development of a predictive technique for understanding patients’ susceptibility to ELOHS preadmission, which allows for the implementation of best practices in patient care to forestall extended hospital stay. Using a definitive description of ELOHS to identify patients on admission who may be at risk of extended stay rather than adopting a specific LOS as the boundary between ELOHS and normal LOS as exemplified by numerous researchers [ 9 , 16 , 20 , 35 ]. The use of Recursive Feature Elimination with Cross-Validation and Extra Tree Classifier (RFECV-ETC), to help determine the optimal features that will contribute to ELOHS prediction without overfitting the model.…”

“…Using a definitive description of ELOHS to identify patients on admission who may be at risk of extended stay rather than adopting a specific LOS as the boundary between ELOHS and normal LOS as exemplified by numerous researchers [ 9 , 16 , 20 , 35 ].…”