The International journal of health planning and management · 2022
Predictors of hospital readmission within 30 days after surgery for thoracolumbar fractures: A mixed approach.
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Nunes Altacílio Aparecido, Pinheiro Rômulo Pedroza, Costa Herton Rodrigo Tavares, Defino Helton Luiz Aparecido
Аннотация
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.
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.