Whitney E Muhlestein1, Dallin S Akagi2, Silky Chotai3, Lola B Chambless3. 1. Department of Neurological Surgery, Vanderbilt University, Vanderbilt University Medical Center, Nashville, Tennessee, USA. Electronic address: whitney.muhlestein@gmail.com. 2. DataRobot Inc., Boston, Massachusetts, USA. 3. Department of Neurological Surgery, Vanderbilt University, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Abstract
BACKGROUND: Racial disparities exist in health care, frequently resulting in unfavorable outcomes for minority patients. Here, we use guided machine learning (ML) ensembles to model the impact of race on discharge disposition and length of stay (LOS) after brain tumor surgery from the Healthcare Cost and Utilization Project National Inpatient Sample. METHODS: We performed a retrospective cohort study of 41,222 patients who underwent craniotomies for brain tumors from 2002 to 2011 and were registered in the National Inpatient Sample. Twenty-six ML algorithms were trained on prehospitalization variables to predict non-home discharge and extended LOS (>7 days) after brain tumor resection, and the most predictive algorithms combined to create ensemble models. Partial dependence analysis was performed to measure the independent impact of race on the ensembles. RESULTS: The guided ML ensembles predicted non-home disposition (area under the curve, 0.796) and extended LOS (area under the curve, 0.824) with good discrimination. Partial dependence analysis showed that black race increases the risk of non-home discharge and extended LOS over white race by 6.9% and 6.5%, respectively. Other, nonblack race increases the risk of extended LOS over white race by 6.0%. The impact of race on these outcomes is not seen when analyzing the general inpatient or general operative population. CONCLUSIONS: Minority race independently increases the risk of extended LOS and black race increases the risk of non-home discharge in patients undergoing brain tumor resection, a finding not mimicked in the general inpatient or operative population. Recognition of the influence of race on discharge and LOS could generate interventions that may improve outcomes in this population.
BACKGROUND: Racial disparities exist in health care, frequently resulting in unfavorable outcomes for minority patients. Here, we use guided machine learning (ML) ensembles to model the impact of race on discharge disposition and length of stay (LOS) after brain tumor surgery from the Healthcare Cost and Utilization Project National Inpatient Sample. METHODS: We performed a retrospective cohort study of 41,222 patients who underwent craniotomies for brain tumors from 2002 to 2011 and were registered in the National Inpatient Sample. Twenty-six ML algorithms were trained on prehospitalization variables to predict non-home discharge and extended LOS (>7 days) after brain tumor resection, and the most predictive algorithms combined to create ensemble models. Partial dependence analysis was performed to measure the independent impact of race on the ensembles. RESULTS: The guided ML ensembles predicted non-home disposition (area under the curve, 0.796) and extended LOS (area under the curve, 0.824) with good discrimination. Partial dependence analysis showed that black race increases the risk of non-home discharge and extended LOS over white race by 6.9% and 6.5%, respectively. Other, nonblack race increases the risk of extended LOS over white race by 6.0%. The impact of race on these outcomes is not seen when analyzing the general inpatient or general operative population. CONCLUSIONS: Minority race independently increases the risk of extended LOS and black race increases the risk of non-home discharge in patients undergoing brain tumor resection, a finding not mimicked in the general inpatient or operative population. Recognition of the influence of race on discharge and LOS could generate interventions that may improve outcomes in this population.
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