Early risk assessment for COVID-19 patients from emergency department data using machine learning.
Early risk assessment for COVID-19 patients from emergency department data using machine learning.
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DOI：
10.1038/s41598-021-83784-y
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发表时间：
2021-02-18
影响因子：
4.6
通讯作者：
Khan RT
中科院分区：
文献类型：
--
作者：
Heldt FS;Vizcaychipi MP;Peacock S;Cinelli M;McLachlan L;Andreotti F;Jovanović S;Dürichen R;Lipunova N;Fletcher RA;Hancock A;McCarthy A;Pointon RA;Brown A;Eaton J;Liddi R;Mackillop L;Tarassenko L;Khan RT
自2019年底出现以来，严重急性呼吸综合征冠状病毒2 （SARS-CoV-2）已引起大流行，全球报告病例超过5500万例，估计死亡人数超过130万人。虽然已经报告了COVID-19的流行病学和临床特征，但对患者从轻度疾病向严重疾病转变的风险因素仍然知之甚少。在这项回顾性研究中，我们分析了2020年1月1日至5月26日期间在英国伦敦一家NHS信托医院住院的879名确诊的SARS-CoV-2阳性患者的数据，其中大多数病例发生在3月和4月。我们从电子医疗记录（EHR）中提取匿名人口统计数据、生理临床变量和实验室结果，并应用多元逻辑回归、随机森林和极端梯度增强树。为了评估早期风险评估的潜力，我们使用了患者在急诊科（ED）初次就诊期间的可用数据来预测在剩余住院期间的三个临床终点之一的恶化：入住重症监护室、需要有创机械通气和住院死亡率。基于训练的模型，我们提取了确定这些患者轨迹的最有信息的临床特征。考虑到我们的纳入标准，我们确定879例患者中有129例（15%）需要重症监护，878例患者中有62例（7%）需要机械通气，619例患者中有193例（31%）住院死亡。我们的模型成功地从早期临床数据中学习，并以较高的准确率预测临床终点，最佳模型在受试者工作特征下面积（AUC-ROC）得分为0.76 ~ 0.87 （F1得分为0.42 ~ 0.60）。患者年龄越小，接受重症监护和通气的风险越高，但死亡风险越低。患者供氧的临床指标和选定的实验室结果，如血乳酸和肌酐水平，最能预测COVID-19患者的发展轨迹。在COVID-19患者中，机器学习可以使用患者首次在急诊科就诊时收集的电子病历数据，帮助早期识别预后不良的患者。患者年龄和急诊科住院期间的氧合状态测量是患者预后不良的主要指标。
Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic with more than 55 million reported cases and 1.3 million estimated deaths worldwide. While epidemiological and clinical characteristics of COVID-19 have been reported, risk factors underlying the transition from mild to severe disease among patients remain poorly understood. In this retrospective study, we analysed data of 879 confirmed SARS-CoV-2 positive patients admitted to a two-site NHS Trust hospital in London, England, between January 1st and May 26th, 2020, with a majority of cases occurring in March and April. We extracted anonymised demographic data, physiological clinical variables and laboratory results from electronic healthcare records (EHR) and applied multivariate logistic regression, random forest and extreme gradient boosted trees. To evaluate the potential for early risk assessment, we used data available during patients’ initial presentation at the emergency department (ED) to predict deterioration to one of three clinical endpoints in the remainder of the hospital stay: admission to intensive care, need for invasive mechanical ventilation and in-hospital mortality. Based on the trained models, we extracted the most informative clinical features in determining these patient trajectories. Considering our inclusion criteria, we have identified 129 of 879 (15%) patients that required intensive care, 62 of 878 (7%) patients needing mechanical ventilation, and 193 of 619 (31%) cases of in-hospital mortality. Our models learned successfully from early clinical data and predicted clinical endpoints with high accuracy, the best model achieving area under the receiver operating characteristic (AUC-ROC) scores of 0.76 to 0.87 (F1 scores of 0.42–0.60). Younger patient age was associated with an increased risk of receiving intensive care and ventilation, but lower risk of mortality. Clinical indicators of a patient’s oxygen supply and selected laboratory results, such as blood lactate and creatinine levels, were most predictive of COVID-19 patient trajectories. Among COVID-19 patients machine learning can aid in the early identification of those with a poor prognosis, using EHR data collected during a patient’s first presentation at ED. Patient age and measures of oxygenation status during ED stay are primary indicators of poor patient outcomes.
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影响因子：
64.8
作者：
Harris CR;Millman KJ;van der Walt SJ;Gommers R;Virtanen P;Cournapeau D;Wieser E;Taylor J;Berg S;Smith NJ;Kern R;Picus M;Hoyer S;van Kerkwijk MH;Brett M;Haldane A;Del Río JF;Wiebe M;Peterson P;Gérard-Marchant P;Sheppard K;Reddy T;Weckesser W;Abbasi H;Gohlke C;Oliphant TE
通讯作者：
Oliphant TE
影响因子：
7.5
作者：
Klok, F. A.;Kruip, M. J. H. A.;Endeman, H.
通讯作者：
Endeman, H.
中国武汉市 138 例 2019 新型冠状病毒感染肺炎住院患者的临床特征。
影响因子：
120.7
作者：
Wang, Dawei;Hu, Bo;Peng, Zhiyong
通讯作者：
Peng, Zhiyong
影响因子：
105.7
作者：
Docherty, Annemarie B.;Harrison, Ewen M.;Semple, Malcolm G.
通讯作者：
Semple, Malcolm G.
DOI：
10.1111/j.1467-9868.2005.00503.x
发表时间：
2005-01-01
影响因子：
5.8
作者：
Zou, H;Hastie, T
通讯作者：
Hastie, T