Paul T. Ogink, Aditya V. Karhade, Quirina C. B. S. Thio, Stuart H. Hershman, Thomas D. Cha, Christopher M. Bono, Joseph H. Schwab

August 2019, Volume 28, Issue 8, pp 1775 - 1782 Original Article Read Full Article 10.1007/s00586-019-05936-z

First Online: 27 March 2019

Development of a machine learning algorithm predicting discharge placement after surgery for spondylolisthesis


We aimed to develop a machine learning algorithm that can accurately predict discharge placement in patients undergoing elective surgery for degenerative spondylolisthesis.


The National Surgical Quality Improvement Program (NSQIP) database was used to select patients that underwent surgical treatment for degenerative spondylolisthesis between 2009 and 2016. Our primary outcome measure was non-home discharge which was defined as any discharge not to home for which we grouped together all non-home discharge destinations including rehabilitation facility, skilled nursing facility, and unskilled nursing facility. We used Akaike information criterion to select the most appropriate model based on the outcomes of the stepwise backward logistic regression. Four machine learning algorithms were developed to predict discharge placement and were assessed by discrimination, calibration, and overall performance.


Nine thousand three hundred and thirty-eight patients were included. Median age was 63 (interquartile range [IQR] 54–71), and 63% (n = 5,887) were female. The non-home discharge rate was 18.6%. Our models included age, sex, diabetes, elective surgery, BMI, procedure, number of levels, ASA class, preoperative white blood cell count, and preoperative creatinine. The Bayes point machine was considered the best model based on discrimination (AUC = 0.753), calibration (slope = 1.111; intercept = − 0.002), and overall model performance (Brier score = 0.132).


This study has shown that it is possible to create a predictive machine learning algorithm with both good accuracy and calibration to predict discharge placement. Using our methodology, this type of model can be developed for many other conditions and (elective) treatments.

Graphical abstract

These slides can be retrieved under Electronic Supplementary Material.[Figure not available: see fulltext.]

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