Personalized predictive models for symptomatic COVID-19 patients using basic preconditions: Hospitalizations, mortality, and the need for an ICU or ventilator.
Personalized predictive models for symptomatic COVID-19 patients using basic preconditions: Hospitalizations, mortality, and the need for an ICU or ventilator.
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DOI：
10.1101/2020.05.03.20089813
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发表时间：
2020-10-01
影响因子：
4.9
通讯作者：
Paschalidis, Ioannis Ch
中科院分区：
文献类型：
--
作者：
Wollenstein-Betech, Salomon;Cassandras, Christos G;Paschalidis, Ioannis Ch
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，如何最好地分配非常有限的医疗资源的决定已成为当务之急。具体而言，正在考虑的是决定对谁进行检测，让谁住院，在重症监护病房（ICU）治疗，以及用呼吸机支持谁。考虑到今天收集、共享、分析和处理数据的能力，基于人口统计数据和有关先前条件的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)根据个人的风险概况建议个人如何更好地保护自己，(3)根据风险区分社交距离指南，以及(4)在疫苗可用后优先接种疫苗。目的：建立预测以下事件的个性化模型：(1)住院，(2)死亡，(3)需要ICU，(4)需要呼吸机。为了预测住院情况，假设一个人可以获得病人的基本先决条件，这些条件可以很容易地收集，而不需要在医院。对于其余的模型，开发的不同版本包括患者的不同特征集，其中一些包括疾病进展的信息（例如，肺炎的诊断）。材料和方法：数据来自每日更新的公开存储库，其中包含来自墨西哥约91,000名患者的信息。每位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。应用了几种分类方法，包括鲁棒版本的逻辑回归、支持向量机、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）在准确性和检出率方面的表现与更复杂的模型一样好，并且具有阐明预测所基于的变量的额外好处。预测住院率、死亡率、ICU需求和呼吸机需求的分类准确率分别达到61%、76%、83%和84%。分析揭示了做出预测的最重要的先决条件。对于导出的四种模型，这些是：(1)住院：年龄，性别，慢性肾功能不全，糖尿病，免疫抑制；(2)死亡率：年龄、SARS-CoV-2检测状态、免疫抑制和妊娠；(3) ICU需求：肺炎（如有）、心血管疾病、哮喘和SARS-CoV-2检测状态；(4)呼吸机需求：ICU和肺炎（如有）、年龄、性别、心血管疾病、肥胖、妊娠、SARS-CoV-2检测结果。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和为弱势群体设计有针对性的政策的决定已成为首要问题。具体而言，正在考虑的是决定对谁进行检测，让谁住院，在重症监护病房（ICU）治疗，以及用呼吸机支持谁。考虑到今天收集、共享、分析和处理数据的能力，基于人口统计数据和有关先前条件的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)根据个人的风险概况建议个人如何更好地保护自己，(3)根据风险区分社交距离指南，以及(4)在疫苗可用后优先接种疫苗。目的：建立预测以下事件的个性化模型：(1)住院，(2)死亡，(3)需要ICU，(4)需要呼吸机。为了预测住院情况，假设人们可以获得患者的基本先决条件，这些条件可以很容易地收集，而无需在医院，从而为公民和政策制定者在大流行期间评估个人风险提供服务。对于其余的模型，开发的不同版本包括患者的不同特征集，其中一些包括疾病进展的信息（例如，肺炎的诊断）。材料和方法：使用来自每日更新的公共存储库的国家数据，其中包含来自墨西哥约91,000名患者的信息。每位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，包括鲁棒版本的逻辑回归、支持向量机、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）在准确性和检出率方面的表现与更复杂的模型一样好，并且具有阐明预测所基于的变量的额外好处。预测住院率、死亡率、ICU需求和呼吸机需求的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先决条件。对于导出的四种模型，这些是：(1)住院：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需求：肺炎（如有）、年龄、肥胖、糖尿病和高血压；(4)是否需要呼吸机：ICU和肺炎（如果有的话）、年龄、肥胖和高血压。
BACKGROUND: The rapid global spread of the virus SARS-CoV-2 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 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. 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: 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, 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 61%, 76%, 83%, and 84% 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, gender, chronic renal insufficiency, diabetes, immunosuppression; (2) for mortality: age, SARS-CoV-2 test status, immunosuppression and pregnancy; (3) for ICU need: development of pneumonia (if available), cardiovascular disease, asthma, and SARS-CoV-2 test status; and (4) for ventilator need: ICU and pneumonia (if available), age, gender, cardiovascular disease, obesity, pregnancy, and SARS-CoV-2 test result.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.