Deeptee Jain1, Paramjit Singh2, Mayur Kardile2, Sigurd H Berven2. 1. Department of Orthopaedic Surgery, University of California, San Francisco, 500 Parnassus Avenue, MU West 321, Box 0728, San Francisco, CA, 94143, USA. deeptee.jain@gmail.com. 2. Department of Orthopaedic Surgery, University of California, San Francisco, 500 Parnassus Avenue, MU West 321, Box 0728, San Francisco, CA, 94143, USA.
Abstract
PURPOSE: To develop a model to predict 30-day readmission rates in elective 1-2 level posterior lumbar spine fusion (PSF) patients. METHODS: In this retrospective case control study, patients were identified in the State Inpatient Database using ICD-9 codes. Data were queried for 30-day readmission, as well as demographic and surgical data. Patients were randomly assigned to either the derivation or the validation cohort. Stepwise multivariate analysis was conducted on the derivation cohort to predict 30-day readmission. Readmission after posterior spinal fusion (RAPSF) score was created by including variables with odds ratio (OR) > 1.1 and p < 0.01; value assigned to each variable was based on the OR and calibrated to 100. Linear regression was performed between readmission rate and RAPSF score to test correlation in both cohorts. RESULTS: There were 92,262 and 90,257 patients in the derivation and validation cohorts. Thirty-day readmission rates were 10.9% and 11.1%, respectively. Variables in RAPSF included: age, female gender, race, insurance, anterior approach, cerebrovascular disease, chronic pulmonary disease, congestive heart failure, diabetes, hemiplegia/paraplegia, rheumatic disease, drug abuse, electrolyte disorder, osteoporosis, depression, obesity, and morbid obesity. Linear regression between readmission rate and RAPSF fits the derivation cohort and validation cohort with an adjusted r2 of 0.92 and 0.94, respectively, and a coefficient of 0.011 (p < 0.001) in both cohorts. CONCLUSION: The RAPSF can accurately predict readmission rates in PSF patients and may be used to guide an evidence-based approach to preoperative optimization and risk adjustment within alternative payment models for elective spine surgery. LEVEL OF EVIDENCE: 3. These slides can be retrieved under Electronic Supplementary Material.
PURPOSE: To develop a model to predict 30-day readmission rates in elective 1-2 level posterior lumbar spine fusion (PSF) patients. METHODS: In this retrospective case control study, patients were identified in the State Inpatient Database using ICD-9 codes. Data were queried for 30-day readmission, as well as demographic and surgical data. Patients were randomly assigned to either the derivation or the validation cohort. Stepwise multivariate analysis was conducted on the derivation cohort to predict 30-day readmission. Readmission after posterior spinal fusion (RAPSF) score was created by including variables with odds ratio (OR) > 1.1 and p < 0.01; value assigned to each variable was based on the OR and calibrated to 100. Linear regression was performed between readmission rate and RAPSF score to test correlation in both cohorts. RESULTS: There were 92,262 and 90,257 patients in the derivation and validation cohorts. Thirty-day readmission rates were 10.9% and 11.1%, respectively. Variables in RAPSF included: age, female gender, race, insurance, anterior approach, cerebrovascular disease, chronic pulmonary disease, congestive heart failure, diabetes, hemiplegia/paraplegia, rheumatic disease, drug abuse, electrolyte disorder, osteoporosis, depression, obesity, and morbid obesity. Linear regression between readmission rate and RAPSF fits the derivation cohort and validation cohort with an adjusted r2 of 0.92 and 0.94, respectively, and a coefficient of 0.011 (p < 0.001) in both cohorts. CONCLUSION: The RAPSF can accurately predict readmission rates in PSF patients and may be used to guide an evidence-based approach to preoperative optimization and risk adjustment within alternative payment models for elective spine surgery. LEVEL OF EVIDENCE: 3. These slides can be retrieved under Electronic Supplementary Material.
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