Predictors of in-hospital length of stay among cardiac patients: A machine learning approach
Predictors of in-hospital length of stay among cardiac patients: A machine learning approach
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DOI：
10.1016/j.ijcard.2019.01.046
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发表时间：
2019-08-01
影响因子：
3.5
通讯作者：
Al-Mallah, Mouaz H.
中科院分区：
文献类型：
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作者：
Daghistani, Tahani A.;Elshawi, Radwa;Al-Mallah, Mouaz H.
目的：随着心血管疾病复杂性的增加和人口老龄化，住院时间（LOS）预计将增加。这将影响医疗保健系统，特别是在目前床位容量减少和成本增加的情况下。因此，准确预测LOS将对医疗保健指标产生积极影响。本研究的目的是开发一种基于机器学习的模型方法，用于预测在医院的LOS为心脏patients.Design：使用电子病历，我们回顾性地提取了所有记录的患者的访问下，承认成人心脏病服务。对入院诊断和主治医生进行审查，以验证选择标准。应用基于预测机器学习的模型方法，在入院时结合简单的基线健康数据来预测LOS。患者根据其LOS分为三组：短（b3天），中间（3-5天）和长（N5天）。信息增益算法用于选择最相关的属性。只有信息增益大于零的属性被用于建模。四种不同的机器学习技术进行了评估，并比较其诊断准确性measurements.Setting：本研究的数据集包括2008年至2016年在阿卜杜勒阿齐兹国王心脏中心（KACC）收治的成人患者。该中心位于沙特阿拉伯首都利雅得的阿卜杜勒阿齐兹国王医疗城综合体。参与者（数据集）：纳入了2008年至2016年期间12，769名独特患者（平均年龄为58.8 ± 16岁，其中68.2%为男性）的16，414次连续住院访视。该研究队列的心血管危险因素患病率较高（高血压56%，糖尿病56%，血脂异常52%，肥胖33%和吸烟24%）。最常见的入院诊断是急性冠脉综合征（36%）。结果：对住院LOS预测影响最大的变量是入院心率、入院收缩压和舒张压、年龄和保险状况（合格性）。使用机器学习模型;随机森林（RF）模型优于所有其他模型（灵敏度（0.80）、准确度（0.80）和AUROC（0.94））。结论：我们表明机器学习方法为心脏病患者提供了准确的LOS预测。可用于临床床位管理和资源配置。(c)2019爱思唯尔B. V.保留所有权利。
Objective: The In-hospital length of stay (LOS) is expected to increase as cardiovascular diseases complexity increases and the population ages. This will affect healthcare systems especially with the current situation of decreased bed capacity and increasing costs. Therefore, accurately predicting LOS would have a positive impact on healthcare metrics. The aim of this study is to develop a machine learning-based model approach for predicting in-hospital LOS for cardiac patients.Design: Using electronic medical records, we retrospectively extracted all records of patients' visits that were admitted under adult cardiology service. Admission diagnosis and primary treating physician were reviewed to verify selection criteria. A predictive machine learning-based model approach was applied to incorporate simple baseline health data at admission time to predict LOS. Patients were divided into three groups based on their LOS: short (b3 days), intermediate (3-5 days) and long (N5 days). Information gain algorithmwas utilized to select the most relevant attributes. Only attributes with information gain of more than zero were used in model building. Four different machine learning techniques were evaluated and their diagnostic accuracy measures were compared.Setting: The dataset of this study included adult patients who were admitted between 2008 and 2016 in King Abdulaziz Cardiac Center (KACC). The center is located in King Abdulaziz Medical City Complex in Riyadh, the capital of Saudi Arabia. Participants (dataset): A total of 16,414 consecutive inpatient visits for 12,769 unique patients (mean age of 58.8 +/- 16 years of which 68.2% were males) between 2008 and 2016 were included. The study cohort had a high prevalence of cardiovascular risk factors (hypertension 56%, diabetes 56%, dyslipidemia 52%, obesity 33% and smoking 24%). The most common admitting diagnosis was acute coronary syndrome (36%).Results: The variables with highest impact on the prediction of in-hospital LOS were on admission heart rate, on admission systolic and diastolic blood pressure, age and insurance status (eligibility). Using machine learning models; Random Forest (RF) model outperformed among all other models (sensitivity (0.80), accuracy (0.80), and AUROC (0.94)).Conclusion: We showed that machine learning methods provide accurate prediction of LOS for cardiac patients. This is can be used in clinical bed management and resources allocation. (c) 2019 Elsevier B.V. All rights reserved.