Paul T Ogink1, Aditya V Karhade2, Quirina C B S Thio2, Stuart H Hershman2, Thomas D Cha3, Christopher M Bono4, Joseph H Schwab2. 1. Orthopaedic Spine Service, Massachusetts General Hospital - Harvard Medical School, 3.946, Yawkey Building, 55 Fruit Street, Boston, MA, 02114, USA. ptogink@gmail.com. 2. Orthopaedic Spine Service, Massachusetts General Hospital - Harvard Medical School, 3.946, Yawkey Building, 55 Fruit Street, Boston, MA, 02114, USA. 3. Assistant Chief Orthopaedic Spine Center, Orthopaedic Spine Service, Massachusetts General Hospital - Harvard Medical School, Boston, USA. 4. Executive Vice-Chair Department of Orthopaedic Surgery, Massachusetts General Hospital - Harvard Medical School, Boston, USA.
Abstract
PURPOSE: We aimed to develop a machine learning algorithm that can accurately predict discharge placement in patients undergoing elective surgery for degenerative spondylolisthesis. METHODS: 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. RESULTS: 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). CONCLUSION: 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. These slides can be retrieved under Electronic Supplementary Material.
PURPOSE: We aimed to develop a machine learning algorithm that can accurately predict discharge placement in patients undergoing elective surgery for degenerative spondylolisthesis. METHODS: 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. RESULTS: 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). CONCLUSION: 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. These slides can be retrieved under Electronic Supplementary Material.
Authors: John O Hwabejire; Haytham M A Kaafarani; Ayesha M Imam; Carolina V Solis; Justin Verge; Nancy M Sullivan; Marc A DeMoya; Hasan B Alam; George C Velmahos Journal: JAMA Surg Date: 2013-10 Impact factor: 14.766
Authors: Steven M Steinberg; Michael R Popa; Judith A Michalek; Matthew J Bethel; E Christopher Ellison Journal: Surgery Date: 2008-10 Impact factor: 3.982
Authors: Alexander L Hornung; Christopher M Hornung; G Michael Mallow; J Nicolás Barajas; Augustus Rush; Arash J Sayari; Fabio Galbusera; Hans-Joachim Wilke; Matthew Colman; Frank M Phillips; Howard S An; Dino Samartzis Journal: Eur Spine J Date: 2022-03-27 Impact factor: 2.721
Authors: Katherine E Pierce; Bhaveen H Kapadia; Sara Naessig; Waleed Ahmad; Shaleen Vira; Carl Paulino; Michael Gerling; Peter G Passias Journal: Int J Spine Surg Date: 2021-12
Authors: Babak Saravi; Frank Hassel; Sara Ülkümen; Alisia Zink; Veronika Shavlokhova; Sebastien Couillard-Despres; Martin Boeker; Peter Obid; Gernot Michael Lang Journal: J Pers Med Date: 2022-03-22
Authors: Daniel Lubelski; Andrew Hersh; Tej D Azad; Jeff Ehresman; Zachary Pennington; Kurt Lehner; Daniel M Sciubba Journal: Global Spine J Date: 2021-04
Authors: Jacobien H F Oosterhoff; Aditya V Karhade; Tarandeep Oberai; Esteban Franco-Garcia; Job N Doornberg; Joseph H Schwab Journal: Geriatr Orthop Surg Rehabil Date: 2021-12-13
Authors: Cesar D Lopez; Venkat Boddapati; Joseph M Lombardi; Nathan J Lee; Justin Mathew; Nicholas C Danford; Rajiv R Iyer; Marc D Dyrszka; Zeeshan M Sardar; Lawrence G Lenke; Ronald A Lehman Journal: Global Spine J Date: 2022-02-28
Authors: Khodayar Goshtasbi; Tyler M Yasaka; Mehdi Zandi-Toghani; Hamid R Djalilian; William B Armstrong; Tjoson Tjoa; Yarah M Haidar; Mehdi Abouzari Journal: Head Neck Date: 2020-11-03 Impact factor: 3.147