Paul T Ogink1, Aditya V Karhade2, Quirina C B S Thio2, William B Gormley3, Fetullah C Oner4, Jorrit J Verlaan4, Joseph H Schwab2. 1. UMC Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands. ptogink@gmail.com. 2. Massachusetts General Hospital - Harvard Medical School, Boston, MA, USA. 3. Brigham and Women's Hospital - Harvard Medical School, Boston, MA, USA. 4. UMC Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
Abstract
PURPOSE: An excessive amount of total hospitalization is caused by delays due to patients waiting to be placed in a rehabilitation facility or skilled nursing facility (RF/SNF). An accurate preoperative prediction of who would need a RF/SNF place after surgery could reduce costs and allow more efficient organizational planning. We aimed to develop a machine learning algorithm that predicts non-home discharge after elective surgery for lumbar spinal stenosis. METHODS: We used the American College of Surgeons National Surgical Quality Improvement Program to select patient that underwent elective surgery for lumbar spinal stenosis between 2009 and 2016. The primary outcome measure for the algorithm was non-home discharge. Four machine learning algorithms were developed to predict non-home discharge. Performance of the algorithms was measured with discrimination, calibration, and an overall performance score. RESULTS: We included 28,600 patients with a median age of 67 (interquartile range 58-74). The non-home discharge rate was 18.2%. Our final model consisted of the following variables: age, sex, body mass index, diabetes, functional status, ASA class, level, fusion, preoperative hematocrit, and preoperative serum creatinine. The neural network was the best model based on discrimination (c-statistic = 0.751), calibration (slope = 0.933; intercept = 0.037), and overall performance (Brier score = 0.131). CONCLUSIONS: A machine learning algorithm is able to predict discharge placement after surgery for lumbar spinal stenosis with both good discrimination and calibration. Implementing this type of algorithm in clinical practice could avert risks associated with delayed discharge and lower costs. These slides can be retrieved under Electronic Supplementary Material.
PURPOSE: An excessive amount of total hospitalization is caused by delays due to patients waiting to be placed in a rehabilitation facility or skilled nursing facility (RF/SNF). An accurate preoperative prediction of who would need a RF/SNF place after surgery could reduce costs and allow more efficient organizational planning. We aimed to develop a machine learning algorithm that predicts non-home discharge after elective surgery for lumbar spinal stenosis. METHODS: We used the American College of Surgeons National Surgical Quality Improvement Program to select patient that underwent elective surgery for lumbar spinal stenosis between 2009 and 2016. The primary outcome measure for the algorithm was non-home discharge. Four machine learning algorithms were developed to predict non-home discharge. Performance of the algorithms was measured with discrimination, calibration, and an overall performance score. RESULTS: We included 28,600 patients with a median age of 67 (interquartile range 58-74). The non-home discharge rate was 18.2%. Our final model consisted of the following variables: age, sex, body mass index, diabetes, functional status, ASA class, level, fusion, preoperative hematocrit, and preoperative serum creatinine. The neural network was the best model based on discrimination (c-statistic = 0.751), calibration (slope = 0.933; intercept = 0.037), and overall performance (Brier score = 0.131). CONCLUSIONS: A machine learning algorithm is able to predict discharge placement after surgery for lumbar spinal stenosis with both good discrimination and calibration. Implementing this type of algorithm in clinical practice could avert risks associated with delayed discharge and lower costs. These slides can be retrieved under Electronic Supplementary Material.
Authors: James N Weinstein; Tor D Tosteson; Jon D Lurie; Anna Tosteson; Emily Blood; Harry Herkowitz; Frank Cammisa; Todd Albert; Scott D Boden; Alan Hilibrand; Harley Goldberg; Sigurd Berven; Howard An Journal: Spine (Phila Pa 1976) Date: 2010-06-15 Impact factor: 3.468
Authors: James N Weinstein; Tor D Tosteson; Jon D Lurie; Anna N A Tosteson; Emily Blood; Brett Hanscom; Harry Herkowitz; Frank Cammisa; Todd Albert; Scott D Boden; Alan Hilibrand; Harley Goldberg; Sigurd Berven; Howard An Journal: N Engl J Med Date: 2008-02-21 Impact factor: 91.245
Authors: Ewout W Steyerberg; Andrew J Vickers; Nancy R Cook; Thomas Gerds; Mithat Gonen; Nancy Obuchowski; Michael J Pencina; Michael W Kattan Journal: Epidemiology Date: 2010-01 Impact factor: 4.822
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: Akash A Shah; Sai K Devana; Changhee Lee; Amador Bugarin; Elizabeth L Lord; Arya N Shamie; Don Y Park; Mihaela van der Schaar; Nelson F SooHoo Journal: World Neurosurg Date: 2021-05-28 Impact factor: 2.210
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