Risk factors and machine learning model for predicting hospitalization outcomes in geriatric patients with dementia.
Risk factors and machine learning model for predicting hospitalization outcomes in geriatric patients with dementia.
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DOI：
10.1002/trc2.12351
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发表时间：
2022
影响因子：
4.8
通讯作者：
Wong, Stephen T C
中科院分区：
文献类型：
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作者：
Wang, Xin;Ezeana, Chika F;Wang, Lin;Puppala, Mamta;Huang, Yan-Siang;He, Yunjie;Yu, Xiaohui;Yin, Zheng;Zhao, Hong;Lai, Eugene C;Wong, Stephen T C
老年痴呆症患者比其他老年患者需要更高的医疗费用和更长的住院时间。我们的目的是确定住院结果的风险因素，这些因素可以早期缓解，以改善结果并影响整体生活质量。我们确定了危险因素，即人口统计学、医院并发症、入院前和入院后的危险因素，包括病史和合并症，这些因素影响住院时间和出院处置决定的住院结果。从我们机构的数据仓库中检索了15,678次就诊（8407例患者）的150多个临床和人口统计学因素。通过方差分析（ANOVA）和Glmnet的特征选择工程，进一步将其缩小到20个因素。我们开发了一个可解释的机器学习模型来预测老年痴呆患者的住院结果。该模型基于叠加集成学习，准确率为95.6%，曲线下面积（AUC）为0.757。它在阿尔茨海默病痴呆（ADD）（4993）、血管性痴呆（VD）（4173）、帕金森病伴痴呆（PDD）（3735）和其他未明确的痴呆（OUD）（2777）患者的风险评估中优于流行的风险评估方法。确定的住院结局风险因素主要来自病史，包括脑病、入院时的医疗问题数量、压疮、尿路感染、跌倒、入院来源、年龄、种族、贫血等，在多重痴呆组中有几个重叠。我们的模型确定了几个可以修改或干预的预测因素，从而可以努力防止复发或减轻其不利影响。了解可改变的风险因素将有助于指导对住院时间超过7天、不良出院处置或两者兼有的不良住院结果高风险患者的早期干预。干预措施包括针对脑病、跌倒和感染等不存在或不常规的可改变风险因素启动特定方案，以改善老年痴呆患者的住院结果。共有15678例老年痴呆症患者，最后有20个风险因素。建立了多种痴呆类型住院治疗结果的预测模型。确定了每种类型的风险因素，包括可采取干预措施的风险因素。最主要的因素是脑病、压疮、尿路感染（UTI）、跌倒和入院来源。我们的集成预测模型以95.6%的准确率优于其他模型。
Geriatric patients with dementia incur higher healthcare costs and longer hospital stays than other geriatric patients. We aimed to identify risk factors for hospitalization outcomes that could be mitigated early to improve outcomes and impact overall quality of life. We identified risk factors, that is, demographics, hospital complications, pre‐admission, and post‐admission risk factors including medical history and comorbidities, affecting hospitalization outcomes determined by hospital stays and discharge dispositions. Over 150 clinical and demographic factors of 15,678 encounters (8407 patients) were retrieved from our institution's data warehouse. We further narrowed them down to twenty factors through feature selection engineering by using analysis of variance (ANOVA) and Glmnet. We developed an explainable machine‐learning model to predict hospitalization outcomes among geriatric patients with dementia. Our model is based on stacking ensemble learning and achieved accuracy of 95.6% and area under the curve (AUC) of 0.757. It outperformed prevalent methods of risk assessment for encounters of patients with Alzheimer's disease dementia (ADD) (4993), vascular dementia (VD) (4173), Parkinson's disease with dementia (PDD) (3735), and other unspecified dementias (OUD) (2777). Top identified hospitalization outcome risk factors, mostly from medical history, include encephalopathy, number of medical problems at admission, pressure ulcers, urinary tract infections, falls, admission source, age, race, anemia, etc., with several overlaps in multi‐dementia groups. Our model identified several predictive factors that can be modified or intervened so that efforts can be made to prevent recurrence or mitigate their adverse effects. Knowledge of the modifiable risk factors would help guide early interventions for patients at high risk for poor hospitalization outcome as defined by hospital stays longer than seven days, undesirable discharge disposition, or both. The interventions include starting specific protocols on modifiable risk factors like encephalopathy, falls, and infections, where non‐existent or not routine, to improve hospitalization outcomes of geriatric patients with dementia. A total 15,678 encounters of Geriatrics with dementia with a final 20 risk factors. Developed a predictive model for hospitalization outcomes for multi‐dementia types. Risk factors for each type were identified including those amenable to interventions. Top factors are encephalopathy, pressure ulcers, urinary tract infection (UTI), falls, and admission source. With accuracy of 95.6%, our ensemble predictive model outperforms other models.