The impact of presurgical comorbidities on discharge disposition and length of hospitalization following craniotomy for brain tumor.

Background: Identifying risk factors for negative postoperative outcomes is an important part of providing quality care. Here, we build machine learning (ML) ensembles to model the independent impact of presurgical comorbidities on discharge disposition and length of stay (LOS) following brain tumor resection from the HCUP National Inpatient Sample (NIS).

Methods: We performed a retrospective cohort study of 41,222 patients who underwent craniotomy for brain tumors during 2002-2011 and were registered in the NIS. Twenty-six ML algorithms were trained on prehospitalization variables to predict nonhome discharge and extended LOS (>7 days), and the most predictive algorithms combined to create ensemble models. Models were validated to demonstrate generalizability. Analysis was done to identify which and how specific comorbidities influence ensemble predictions.

Results: Receiver operating curve analysis showed area under the curve of 0.796 and 0.824 for the disposition and LOS ensembles, respectively. The disposition ensemble was most strongly influenced by preoperative paralysis and fluid/electrolyte abnormalities, which independently increased the risk of nonhome discharge in craniotomy patients by 35.4% and 13.9%, respectively. The LOS ensemble was most strongly influenced by the presence of preoperative paralysis, fluid/electrolyte abnormalities, and other nonparalysis neurological deficits, which independently increased the risk of extended LOS in craniotomy patients by 20.4%, 22.5%, and 38.3%, respectively.

Conclusions: In this study, we used ML ensembles to identify preoperative comorbidities that increased the risk of nonhome discharge and extended LOS following craniotomy for brain tumor. Recognizing these risk factors for poor postsurgical outcomes can improve patient counseling and offer opportunities for quality improvement.