Loreto Melina, Lisboa Thiago, Moreira Viviane P
Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil.
Programa de Pós-Graduação Ciencias Pneumologicas - UFRGS, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil; Universidade LaSalle, Canoas, RS, Brazil.
Comput Biol Med. 2020 Mar;118:103636. doi: 10.1016/j.compbiomed.2020.103636. Epub 2020 Feb 1.
Determining which patients are ready for discharge from an Intensive Care Unit (ICU) presents a huge challenge, as ICU readmissions are associated with several negative outcomes such as increased mortality, length of stay, and cost compared to those patients who are not readmitted during their hospital stay. For these reasons, enhancing risk stratification in order to identify patients at high risk of clinical deterioration might benefit and improve the outcomes of critically ill hospitalized patients. Existing work on predicting ICU readmissions relies on information available at the time of discharge, however, in order to be more useful and to prevent complications, predictions need to be made earlier.
In this work, we investigate the hypothesis that the basal characteristics and information collected at the time of the patient's admission can enable accurate predictions of ICU readmission.
We analyzed an anonymized dataset of 11,805 adult patients from three ICUs in a Brazilian university hospital. After excluding 1879 patients who died during their first ICU admission, our final dataset contained 9,926 patients. Of these, 658 patients (6.6%) had been readmitted to the ICU. The original dataset had 185 attributes, including demographics, length of stay prior to ICU admission, comorbidities, severity indexes, interventions, organ support care during ICU stay and laboratory results. The problem of predicting ICU readmissions was modeled as a binary classification task. We tested eight classification algorithms (including Bayesian algorithms, decision trees, rule-based, and ensemble methods) over different sets of attributes and evaluated their results based on six metrics.
Predictions made solely based on the attributes collected at the admission are highly accurate. Their quality in terms of prediction is no different from predictions made using the complete set of attributes for our dataset and for a subset of attributes selected by a feature selection method. Furthermore, our AUROC score of 0.91 (95% CI [0.89,0.92]) is higher than existing results published in the literature for other datasets.
The results confirm our hypothesis. Our findings suggest that early markers can be used to anticipate patients at high risk of clinical deterioration after ICU discharge.
确定哪些患者准备好从重症监护病房（ICU）出院是一项巨大的挑战，因为与未在住院期间再次入住ICU的患者相比，ICU再入院与一些负面结果相关，如死亡率增加、住院时间延长和成本增加。出于这些原因，加强风险分层以识别临床恶化高风险患者可能会使危重症住院患者受益并改善其预后。然而，现有的预测ICU再入院的工作依赖于出院时可用的信息，为了更有用并预防并发症，需要更早地进行预测。
在这项工作中，我们研究了这样一个假设，即患者入院时收集的基础特征和信息能够准确预测ICU再入院。
我们分析了巴西一家大学医院三个ICU的11805名成年患者的匿名数据集。在排除1879名在首次ICU住院期间死亡的患者后，我们的最终数据集包含9926名患者。其中，658名患者（6.6%）再次入住了ICU。原始数据集有185个属性，包括人口统计学、ICU入院前的住院时间、合并症、严重程度指数、干预措施、ICU住院期间的器官支持护理以及实验室结果。预测ICU再入院的问题被建模为一个二元分类任务。我们在不同的属性集上测试了八种分类算法（包括贝叶斯算法、决策树、基于规则的算法和集成方法），并基于六个指标评估了它们的结果。
仅基于入院时收集的属性进行的预测非常准确。就预测质量而言，它们与使用我们数据集的完整属性集以及通过特征选择方法选择的属性子集进行的预测没有差异。此外，我们的曲线下面积（AUROC）得分为0.91（95%置信区间[0.89,0.92]），高于文献中针对其他数据集发表的现有结果。
结果证实了我们的假设。我们的研究结果表明，早期标志物可用于预测ICU出院后临床恶化高风险患者。
