The impact of presurgical comorbidities on discharge disposition and length of hospitalization following craniotomy for brain tumor.
The impact of presurgical comorbidities on discharge disposition and length of hospitalization following craniotomy for brain tumor.
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DOI：
10.4103/sni.sni_54_17
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发表时间：
2017
影响因子：
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通讯作者：
Chambless LB
中科院分区：
文献类型：
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作者：
Muhlestein WE;Akagi DS;Chotai S;Chambless LB
识别术后不良结局的风险因素是提供优质护理的重要组成部分。在这里，我们构建机器学习（ML）集成来模拟HCUP国家住院患者样本（NIS）中脑肿瘤切除术后术前合并症对出院处置和住院时间（LOS）的独立影响。我们对2002-2011年期间接受开颅术治疗脑肿瘤并在NIS中登记的41，222例患者进行了回顾性队列研究。在住院前变量上训练了26种ML算法，以预测非家庭出院和延长的LOS（>7天），并将最具预测性的算法组合起来创建集成模型。模型进行了验证，以证明普遍性。进行分析，以确定哪些特定的合并症以及如何影响合奏预测。受试者工作曲线分析显示，处置和LOS集合的曲线下面积分别为0.796和0.824。术前瘫痪和液体/电解质异常对处置总体的影响最大，分别使开颅手术患者的非家庭出院风险增加35.4%和13.9%。术前麻痹、液体/电解质异常和其他非麻痹性神经功能缺损对LOS总体影响最大，这些因素分别使开颅手术患者延长LOS的风险增加20.4%、22.5%和38.3%。在这项研究中，我们使用ML集合来识别术前合并症，这些合并症增加了脑肿瘤开颅术后非家庭出院和延长LOS的风险。认识到这些不良术后结局的风险因素可以改善患者咨询并提供质量改进的机会。
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). 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. 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. 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.