Clinical features of COVID-19 mortality: development and validation of a clinical prediction model.
Clinical features of COVID-19 mortality: development and validation of a clinical prediction model.
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DOI：
10.1016/s2589-7500(20)30217-x
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Pandey G
中科院分区：
文献类型：
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作者：
Yadaw AS;Li YC;Bose S;Iyengar R;Bunyavanich S;Pandey G
2019冠状病毒病大流行已影响到全世界数百万人，并造成数十万人死亡。预测出现一系列并发症的COVID-19患者的死亡率非常困难，这阻碍了疾病的预测和管理。我们的目的是使用无偏计算方法建立准确的COVID-19死亡率预测模型，并确定最能预测这一结果的临床特征。在这项预测模型开发和验证研究中，我们将机器学习技术应用于在美国纽约州纽约市西奈山卫生系统接受治疗的大量COVID-19患者的临床数据，以预测死亡率。我们分析了西奈山数据仓库数据库中捕获的患者级数据，这些数据是针对在2020年3月9日至4月6日期间在卫生系统就诊的确诊COVID-19患者。为了进行初步分析，我们使用了3月9日至4月5日的患者数据，并随机（80:20）将患者分配到开发数据集或测试数据集1（回顾性）。2020年4月6日遭遇病例的患者数据用于测试数据集2（前瞻性）。我们设计了基于临床特征和卫生系统就诊期间患者特征的预测模型，利用发展数据集预测死亡率。我们根据测试数据集中受试者工作特征曲线（AUC）得分下的面积评估了所得模型。利用开发数据集（n=3841）和系统的机器学习框架，我们开发了一个COVID-19死亡率预测模型，当应用于回顾性（n=961）和前瞻性（n=249）患者的测试数据集时，该模型显示出较高的准确性（AUC= 0.91）。该模型基于三个临床特征：患者的年龄、就诊过程中的最低血氧饱和度以及就诊类型（住院患者与门诊患者和远程医疗就诊）。基于三个特征的准确、简洁的COVID-19死亡率预测模型可用于临床指导该病患者的管理和预后。需要在其他人群中对该预测模型进行外部验证。国立卫生研究院。
The COVID-19 pandemic has affected millions of individuals and caused hundreds of thousands of deaths worldwide. Predicting mortality among patients with COVID-19 who present with a spectrum of complications is very difficult, hindering the prognostication and management of the disease. We aimed to develop an accurate prediction model of COVID-19 mortality using unbiased computational methods, and identify the clinical features most predictive of this outcome. In this prediction model development and validation study, we applied machine learning techniques to clinical data from a large cohort of patients with COVID-19 treated at the Mount Sinai Health System in New York City, NY, USA, to predict mortality. We analysed patient-level data captured in the Mount Sinai Data Warehouse database for individuals with a confirmed diagnosis of COVID-19 who had a health system encounter between March 9 and April 6, 2020. For initial analyses, we used patient data from March 9 to April 5, and randomly assigned (80:20) the patients to the development dataset or test dataset 1 (retrospective). Patient data for those with encounters on April 6, 2020, were used in test dataset 2 (prospective). We designed prediction models based on clinical features and patient characteristics during health system encounters to predict mortality using the development dataset. We assessed the resultant models in terms of the area under the receiver operating characteristic curve (AUC) score in the test datasets. Using the development dataset (n=3841) and a systematic machine learning framework, we developed a COVID-19 mortality prediction model that showed high accuracy (AUC=0·91) when applied to test datasets of retrospective (n=961) and prospective (n=249) patients. This model was based on three clinical features: patient's age, minimum oxygen saturation over the course of their medical encounter, and type of patient encounter (inpatient vs outpatient and telehealth visits). An accurate and parsimonious COVID-19 mortality prediction model based on three features might have utility in clinical settings to guide the management and prognostication of patients affected by this disease. External validation of this prediction model in other populations is needed. National Institutes of Health.