Altacílio Aparecido Nunes1, Rômulo Pedroza Pinheiro2, Herton Rodrigo Tavares Costa2, Helton Luiz Aparecido Defino3. 1. Department of Social Medicine, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil. 2. Department of Orthopedics and Anesthesiology, Hospital das Clínicas at Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil. 3. Department of Orthopedics and Anesthesiology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil.
Abstract
BACKGROUND: Readmission followed by surgery to treat spinal fractures has a substantial impact on patient care costs and reflects a hospital's quality standards. This article analyzes the factors associated with hospital readmission followed by surgery to treat spinal fractures. METHODS: This was a cross-sectional study with time-series analysis. For prediction analysis, we used Cox proportional hazards and machine-learning models, using data from the Healthcare Cost and Utilization Project, Inpatient Database from Florida (USA). RESULTS: The sample comprised 215,999 patients, 8.8% of whom were readmitted within 30 days. The factors associated with a risk of readmission were male sex (1.1 [95% confidence interval 1.06-1.13]) and >60 years of age (1.74 [95% CI: 1.69-1.8]). Surgeons with a higher annual patient volume presented a lower risk of readmission (0.61 [95% CI: 0.59-0.63]) and hospitals with an annual volume >393 presented a lower risk (0.92 [95% CI: 0.89-0.95]). CONCLUSION: Surgical procedures and other selected predictors and machine-learning models can be used to reduce 30-day readmissions after spinal surgery. Identification of patients at higher risk for readmission and complications is the first step to reducing unplanned readmissions.
BACKGROUND: Readmission followed by surgery to treat spinal fractures has a substantial impact on patient care costs and reflects a hospital's quality standards. This article analyzes the factors associated with hospital readmission followed by surgery to treat spinal fractures. METHODS: This was a cross-sectional study with time-series analysis. For prediction analysis, we used Cox proportional hazards and machine-learning models, using data from the Healthcare Cost and Utilization Project, Inpatient Database from Florida (USA). RESULTS: The sample comprised 215,999 patients, 8.8% of whom were readmitted within 30 days. The factors associated with a risk of readmission were male sex (1.1 [95% confidence interval 1.06-1.13]) and >60 years of age (1.74 [95% CI: 1.69-1.8]). Surgeons with a higher annual patient volume presented a lower risk of readmission (0.61 [95% CI: 0.59-0.63]) and hospitals with an annual volume >393 presented a lower risk (0.92 [95% CI: 0.89-0.95]). CONCLUSION: Surgical procedures and other selected predictors and machine-learning models can be used to reduce 30-day readmissions after spinal surgery. Identification of patients at higher risk for readmission and complications is the first step to reducing unplanned readmissions.
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