Objective: Nonhome discharge and unplanned readmissions represent important cost drivers following spinal fusion. The authors sought to utilize different machine learning algorithms to predict discharge to rehabilitation and unplanned readmissions in patients receiving spinal fusion.
Methods: The authors queried the 2012-2013 American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) for patients undergoing cervical or lumbar spinal fusion. Outcomes assessed included discharge to nonhome facility and unplanned readmissions within 30 days after surgery. A total of 7 machine learning algorithms were evaluated. Predictive hierarchical clustering of procedure codes was used to increase model performance. Model performance was evaluated using overall accuracy and area under the receiver operating characteristic curve (AUC), as well as sensitivity, specificity, and positive and negative predictive values. These performance metrics were computed for both the imputed and unimputed (missing values dropped) datasets.
Results: A total of 59,145 spinal fusion cases were analyzed. The incidence rates of discharge to nonhome facility and 30-day unplanned readmission were 12.6% and 4.5%, respectively. All classification algorithms showed excellent discrimination (AUC > 0.80, range 0.85-0.87) for predicting nonhome discharge. The generalized linear model showed comparable performance to other machine learning algorithms. By comparison, all models showed poorer predictive performance for unplanned readmission, with AUC ranging between 0.63 and 0.66. Better predictive performance was noted with models using imputed data.
Conclusions: In an analysis of patients undergoing spinal fusion, multiple machine learning algorithms were found to reliably predict nonhome discharge with modest performance noted for unplanned readmissions. These results provide early evidence regarding the feasibility of modern machine learning classifiers in predicting these outcomes and serve as possible clinical decision support tools to facilitate shared decision making.
Keywords: ACC = accuracy; ACS-NSQIP = American College of Surgeons National Surgical Quality Improvement Program; ALP = alkaline phosphatase; ANN = artificial neural network; ASA = American Society of Anesthesiologists; AUC = area under the receiver operating characteristic curve; BUN = blood urea nitrogen; Bayes theorem; CHF = congestive heart failure; COPD = chronic obstructive pulmonary disease; CPT = Current Procedural Terminology; GBM = gradient boosting machine; GLM = generalized linear model; GLMnet = elastic-net GLM; HTN = hypertension; INR = international normalized ratio; LASSO = least absolute shrinkage and selection operator; NPV = negative predictive value; NSQIP; NSQIP = National Surgical Quality Improvement Program; ODI = Oswestry Disability Index; PHC = predictive hierarchical clustering; PPV = positive predictive value; PTT = partial thromboplastin time; RF = random forest; ROC = receiver operating characteristic; SGOT = serum glutamic oxaloacetic transaminase; WBC = white blood cell count; cervical; discharge; elastic net; generalized linear model; gradient boosting machines; logistic regression; lumbar; machine learning; neural networks; outcomes; pLDA = penalized linear discriminant analysis; penalized discriminant analysis; predictive modeling; random forest; rehabilitation; skilled nursing facility; spinal fusion; spine surgery; unplanned readmission.