Predictive Models for Length of Stay and Discharge Disposition in Elective Spine Surgery: Development, Validation, and Comparison to the ACS NSQIP Risk Calculator

Abstract

Study Design. A retrospective study at a single academic institution.Objective. The purpose of this study is to utilize machine learning to predict hospital length of stay (LOS) and discharge disposition following adult elective spine surgery, and to compare performance metrics of machine learning models to the American College of Surgeon's National Surgical Quality Improvement Program's (ACS NSQIP) prediction calculator. Summary of Background Data. A total of 3678 adult patients undergoing elective spine surgery between 2014 and 2019, acquired from the electronic health record. Methods. Patients were divided into three stratified cohorts: cervical degenerative, lumbar degenerative, and adult spinal deformity groups. Predictive variables included demographics, body mass index, surgical region, surgical invasiveness, surgical approach, and comorbidities. Regression, classification trees, and least absolute shrinkage and selection operator (LASSO) were used to build predictive models. Validation of the models was conducted on 16% of patients (N = 587), using area under the receiver operator curve (AUROC), sensitivity, specificity, and correlation. Patient data were manually entered into the ACS NSQIP online risk calculator to compare performance. Outcome variables were discharge disposition (home vs. rehabilitation) and LOS (days). Results. Of 3678 patients analyzed, 51.4% were male (n = 1890) and 48.6% were female (n = 1788). The average LOS was 3.66 days. In all, 78% were discharged home and 22% discharged to rehabilitation. Compared with NSQIP (Pearson R 2 = 0.16), the predictions of poisson regression (R 2 = 0.29) and LASSO (R 2 = 0.29) models were significantly more correlated with observed LOS (P = 0.025 and 0.004, respectively). Of the models generated to predict discharge location, logistic regression yielded an AUROC of 0.79, which was statistically equivalent to the AUROC of 0.75 for NSQIP (P = 0.135). Conclusion. The predictive models developed in this study can enable accurate preoperative estimation of LOS and risk of rehabilitation discharge for adult patients undergoing elective spine surgery. The demonstrated models exhibited better performance than NSQIP for prediction of LOS and equivalent performance to NSQIP for prediction of discharge location.

Development and external validation of a nomogram for predicting postoperative adverse events in elderly patients undergoing lumbar fusion surgery: comparison of three predictive models

“…Other discriminators of the present classification tree included age, intraoperative blood loss, delayed ambulation, and weight, similar to our regression analysis and some prior studies [ 13 , 18 , 24 , 25 ]. In a retrospective multicenter database analysis, Arora et al [ 11 ] performed a decision tree analysis and found that advanced age, obesity, and greater surgical invasiveness were significant variables increasing the likelihood of readmission and prolonged LOS. To reduce the incidence of postoperative AEs, in older patients (aged 79 years or older) who undergo long-segment fusion, hemostatic agents and minimally invasive surgery should be used to reduce intraoperative bleeding.…”

“…Many independent variables are associated with postoperative AEs following lumbar fusion surgery. Variables associated with increased length of stay (LOS) include increased age, morbid obesity, diabetes, opioid use, greater number of comorbid conditions, unemployment, drain use, and blood transfusion [ 9 – 11 ]. Older and non-married patients, those with obesity, positive smoking history, longer procedure times, and emergent cases were significantly more likely to be readmitted for complications or physical rehabilitation [ 9 , 10 , 12 ].…”

Limitations in Evaluating Machine Learning Models for Imbalanced Binary Outcome Classification in Spine Surgery: A Systematic Review

“…[15]. Similarly, Arora et al developed a well-performing model that predicts patient discharge to rehabilitation, achieving high AUROC, sensitivity, and specificity with an adjusted threshold of 0.16 [32]. Both studies also demonstrated well-calibrated models through calibration plots.…”

Machine Learning in Spine Surgery: A Narrative Review

“…93 ML can utilize intraoperative surgical details and a patient's socioeconomic factors to predict inpatient LOS. [94][95][96][97][98] This was performed in a 63 533-patient cohort after anterior lumbar and posterior lumbar interbody fusion, transforaminal lumbar interbody fusion, and posterior spine fusion. 98 To predict the time of discharge, models have been developed for patients after spinal fusion [99][100][101] and surgery for degenerative spondylolisthesis, 102 disk disorders, 103 and spinal stenosis.…”