Garrett K Harada1, Bryce A Basques2, Dino Samartzis2, Edward J Goldberg2, Matthew W Colman2, Howard S An2. 1. Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA; International Spine Research and Innovation Initiative (ISRII), Rush University Medical Center, Chicago, IL, USA. Electronic address: an.research@rushortho.com. 2. Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA; International Spine Research and Innovation Initiative (ISRII), Rush University Medical Center, Chicago, IL, USA.
Abstract
BACKGROUND CONTEXT: Discharge to acute/intermediate care facilities is a common occurrence after posterior lumbar fusion and can be associated with increased costs and complications after these procedures. This is particularly relevant with the growing popularity of bundled payment plans, creating a need to identify patients at greatest risk. PURPOSE: To develop and validate a risk-stratification tool to identify patients at greatest risk for facility discharge after open posterior lumbar fusion. STUDY DESIGN: Retrospective cohort study. PATIENT SAMPLE: Patients were queried using separate databases from the institution of study and the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) for all patients undergoing open lumbar fusion between 2011 and 2018. OUTCOME MEASURES: Discharge to intermediate care and/or rehabilitation facilities. METHODS: Using an 80:20 training and testing NSQIP data split, collected preoperative demographic and operative variables were used in a multivariate logistic regression to identify potential risk factors for postoperative facility discharge, retaining those with a p value <.05. A nomogram was generated to develop a scoring system from this model, with probability cutoffs determined for facility discharge. This model was subsequently validated within the NSQIP database, in addition to external validation at the institution of study. Overall model performance and calibration was assessed using the Brier score and calibration plots, respectively. RESULTS: A total of 11,486 patients (10,453 NSQIP, 1,033 local cohort) were deemed eligible for study, of which 16.1% were discharged to facilities (16.7% NSQIP, 9.6% local cohort). Utilizing training data, age (p<.001), body mass index (p<.001), female sex (p<.001), diabetes (p=.043), peripheral vascular disease (p=.001), cancer (p=.010), revision surgery (p<.001), number of levels fused (p<.001), and spondylolisthesis (p=.049) were identified as significant risk factors for facility discharge. The area under the receiver operating characteristic curve (AUC) indicated a strong predictive model (AUC=0.750), with similar predictive ability in the testing (AUC=0.757) and local data sets (AUC=0.773). Using this tool, patients identified as low- and high-risk had a 7.94% and 33.28% incidence of facility discharge in the testing data set, while rates of 4.44% and 16.33% were observed at the institution of study. CONCLUSIONS: Using preoperative variables as predictors, this scoring system demonstrated high efficiency in risk-stratifying patients with an approximate four to fivefold difference in rates of facility discharge after posterior lumbar fusion. This tool may help inform medical decision-making and guide reimbursement under bundled-care repayment plans.
BACKGROUND CONTEXT: Discharge to acute/intermediate care facilities is a common occurrence after posterior lumbar fusion and can be associated with increased costs and complications after these procedures. This is particularly relevant with the growing popularity of bundled payment plans, creating a need to identify patients at greatest risk. PURPOSE: To develop and validate a risk-stratification tool to identify patients at greatest risk for facility discharge after open posterior lumbar fusion. STUDY DESIGN: Retrospective cohort study. PATIENT SAMPLE: Patients were queried using separate databases from the institution of study and the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) for all patients undergoing open lumbar fusion between 2011 and 2018. OUTCOME MEASURES: Discharge to intermediate care and/or rehabilitation facilities. METHODS: Using an 80:20 training and testing NSQIP data split, collected preoperative demographic and operative variables were used in a multivariate logistic regression to identify potential risk factors for postoperative facility discharge, retaining those with a p value <.05. A nomogram was generated to develop a scoring system from this model, with probability cutoffs determined for facility discharge. This model was subsequently validated within the NSQIP database, in addition to external validation at the institution of study. Overall model performance and calibration was assessed using the Brier score and calibration plots, respectively. RESULTS: A total of 11,486 patients (10,453 NSQIP, 1,033 local cohort) were deemed eligible for study, of which 16.1% were discharged to facilities (16.7% NSQIP, 9.6% local cohort). Utilizing training data, age (p<.001), body mass index (p<.001), female sex (p<.001), diabetes (p=.043), peripheral vascular disease (p=.001), cancer (p=.010), revision surgery (p<.001), number of levels fused (p<.001), and spondylolisthesis (p=.049) were identified as significant risk factors for facility discharge. The area under the receiver operating characteristic curve (AUC) indicated a strong predictive model (AUC=0.750), with similar predictive ability in the testing (AUC=0.757) and local data sets (AUC=0.773). Using this tool, patients identified as low- and high-risk had a 7.94% and 33.28% incidence of facility discharge in the testing data set, while rates of 4.44% and 16.33% were observed at the institution of study. CONCLUSIONS: Using preoperative variables as predictors, this scoring system demonstrated high efficiency in risk-stratifying patients with an approximate four to fivefold difference in rates of facility discharge after posterior lumbar fusion. This tool may help inform medical decision-making and guide reimbursement under bundled-care repayment plans.
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: Daniel Lubelski; Andrew Hersh; Tej D Azad; Jeff Ehresman; Zachary Pennington; Kurt Lehner; Daniel M Sciubba Journal: Global Spine J Date: 2021-04