Background

The rapid global spread of the SARS-CoV-2 virus has provoked a spike in demand for hospital care. Hospital systems across the world have been over-extended, including in Northern Italy, Ecuador, and New York City, and many other systems face similar challenges. As a result, decisions on how to best allocate very limited medical resources and design targeted policies for vulnerable subgroups have come to the forefront. Specifically, under consideration are decisions on who to test, who to admit into hospitals, who to treat in an Intensive Care Unit (ICU), and who to support with a ventilator. Given today’s ability to gather, share, analyze and process data, personalized predictive models based on demographics and information regarding prior conditions can be used to (1) help decision-makers allocate limited resources, when needed, (2) advise individuals how to better protect themselves given their risk profile, (3) differentiate social distancing guidelines based on risk, and (4) prioritize vaccinations once a vaccine becomes available.

Objective

To develop personalized models that predict the following events: (1) hospitalization, (2) mortality, (3) need for ICU, and (4) need for a ventilator. To predict hospitalization, it is assumed that one has access to a patient’s basic preconditions, which can be easily gathered without the need to be at a hospital and hence serve citizens and policy makers to assess individual risk during a pandemic. For the remaining models, different versions developed include different sets of a patient’s features, with some including information on how the disease is progressing (e.g., diagnosis of pneumonia).

Materials and Methods

National data from a publicly available repository, updated daily, containing information from approximately 91,000 patients in Mexico were used. The data for each patient include demographics, prior medical conditions, SARS-CoV-2 test results, hospitalization, mortality and whether a patient has developed pneumonia or not. Several classification methods were applied and compared, including robust versions of logistic regression, and support vector machines, as well as random forests and gradient boosted decision trees.

Results

Interpretable methods (logistic regression and support vector machines) perform just as well as more complex models in terms of accuracy and detection rates, with the additional benefit of elucidating variables on which the predictions are based. Classification accuracies reached 72%, 79%, 89%, and 90% for predicting hospitalization, mortality, need for ICU and need for a ventilator, respectively. The analysis reveals the most important preconditions for making the predictions. For the four models derived, these are: (1) for hospitalization:age, pregnancy, diabetes, gender, chronic renal insufficiency, and immunosuppression; (2) for mortality: age, immunosuppression, chronic renal insufficiency, obesity and diabetes; (3) for ICU need: development of pneumonia (if available), age, obesity, diabetes and hypertension; and (4) for ventilator need: ICU and pneumonia (if available), age, obesity, and hypertension.

中文翻译：

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使用基本前提条件为有症状的 COVID-19 患者提供个性化预测模型：住院治疗、死亡率以及对 ICU 或呼吸机的需求。

 背景

SARS-CoV-2 病毒在全球的迅速传播引发了对医院护理的需求激增。世界各地的医院系统都已经过度扩张，包括意大利北部、厄瓜多尔和纽约市，许多其他系统也面临着类似的挑战。因此，关于如何最好地分配非常有限的医疗资源并为弱势群体设计有针对性的政策的决策已成为当务之急。具体来说，正在考虑决定对谁进行检测、让谁入院、让谁在重症监护病房 (ICU) 接受治疗以及为谁提供呼吸机支持。鉴于当今收集、共享、分析和处理数据的能力，基于人口统计数据和先验条件信息的个性化预测模型可用于 (1) 帮助决策者在需要时分配有限的资源，(2) 建议个人如何更好地根据风险状况保护自己，(3) 根据风险区分社交距离准则，(4) 一旦有疫苗可用，就优先接种疫苗。

 客观的

开发预测以下事件的个性化模型：(1) 住院治疗，(2) 死亡率，(3) 需要 ICU，以及 (4) 需要呼吸机。为了预测住院情况，假设人们可以了解患者的基本先决条件，这些先决条件可以轻松收集，而无需去医院，从而为公民和政策制定者评估大流行期间的个人风险提供服务。对于其余模型，开发的不同版本包括不同组的患者特征，其中一些包括有关疾病如何进展的信息（例如，肺炎的诊断）。

 材料和方法

使用的国家数据来自公开可用的存储库，每天更新，其中包含来自墨西哥约 91,000 名患者的信息。每个患者的数据包括人口统计数据、既往医疗状况、SARS-CoV-2 检测结果、住院情况、死亡率以及患者是否患有肺炎。应用并比较了几种分类方法，包括逻辑回归的稳健版本、支持向量机、以及随机森林和梯度增强决策树。

 结果

可解释的方法（逻辑回归和支持向量机）在准确性和检测率方面与更复杂的模型表现一样好，并且具有阐明预测所依据的变量的额外好处。在预测住院、死亡率、ICU 需求和呼吸机需求方面，分类准确率分别达到 72%、79%、89% 和 90%。分析揭示了做出预测的最重要的前提条件。对于派生的四个模型，它们是：（1）住院治疗：年龄、妊娠、糖尿病、性别、慢性肾功能不全和免疫抑制； (2) 死亡率：年龄、免疫抑制、慢性肾功能不全、肥胖和糖尿病； (3) ICU 需要：肺炎的发生情况（如果有）、年龄、肥胖、糖尿病和高血压； (4) 呼吸机需求：ICU 和肺炎（如果有）、年龄、肥胖和高血压。