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MeSH Disease

Human Phenotype

Transmission

Seroprevalence
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    Personalized Predictive Models for Symptomatic COVID-19 Patients Using Basic Preconditions: Hospitalizations, Mortality, and the Need for an ICU or Ventilator

    Authors: Salomon Wollenstein-Betech; Christos G. Cassandras; Ioannis Ch. Paschalidis

    doi:10.1101/2020.05.03.20089813 Date: 2020-05-08 Source: medRxiv

    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 MESHD is progressing (e.g., diagnosis of pneumonia MESHD pneumonia HP). 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 MESHD pneumonia HP 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 TRANS, gender TRANS, chronic renal insufficiency MESHD renal insufficiency HP, diabetes, immunosuppression; (2) for mortality: age TRANS, SARS-CoV-2 test status, immunosuppression and pregnancy; (3) for ICU need: development of pneumonia MESHD pneumonia HP (if available), cardiovascular disease MESHD, asthma MESHD asthma HP, and SARS-CoV-2 test status; and (4) for ventilator need: ICU and pneumonia MESHD pneumonia HP (if available), age TRANS, gender TRANS, cardiovascular disease MESHD, obesity MESHD obesity HP, pregnancy, and SARS-CoV-2 test result.

    Hypertension MESHD Hypertension HP in patients hospitalized with COVID-19 in Wuhan, China: A single-center retrospective observational study

    Authors: Zhenhua Zeng; Tong Sha; Yuan Zhang; Feng Wu; Hongbin Hu; Haijun Li; Jiafa Han; Wenhong Song; Qiaobing Huang; Zhongqing Chen

    doi:10.1101/2020.04.06.20054825 Date: 2020-04-11 Source: medRxiv

    Objectives: It is unclear whether patients with hypertension MESHD hypertension HP are more likely to be infected with SARS-COV-2 than the general population and whether there is a difference in the severity of COVID-19 pneumonia MESHD pneumonia HP in patients who have taken ACEI/ARB drugs to lower blood SERO pressure compared to those who have not. Methods: This observational study included data from all patients with clinically confirmed COVID-19 who were admitted to the Hankou Hospital, Wuhan, China between January 5 and March 8, 2020. Data were extracted from clinical and laboratory records. Follow-up was cutoff on March 8, 2020. Results: A total of 274 patients, 75 with hypertension MESHD hypertension HP and 199 without hypertension MESHD hypertension HP, were included in the analysis. Patients with hypertension MESHD hypertension HP were older and were more likely to have pre-existing comorbidities, including chronic renal insufficiency MESHD renal insufficiency HP, cardiovascular disease MESHD, diabetes mellitus MESHD diabetes mellitus HP, and cerebrovascular disease MESHD than patients without hypertension MESHD hypertension HP. Moreover, patients with hypertension MESHD hypertension HP tended to have higher positive COVID-19 PCR detection rates. Patients with hypertension MESHD hypertension HP who had previously taken ACEI/ARB drugs for antihypertensive treatment have an increased tendency to develop severe pneumonia MESHD pneumonia HP after infection MESHD with SARS-COV-2 (P = 0.064). Conclusions: COVID-19 patients with hypertension MESHD hypertension HP were significantly older and were more likely to have underlying comorbidities, including chronic renal insufficiency MESHD renal insufficiency HP, cardiovascular disease MESHD, diabetes mellitus MESHD diabetes mellitus HP, and cerebrovascular disease MESHD. Patients with hypertension MESHD hypertension HP who had taken ACEI/ARB drugs for antihypertensive treatment have an increased tendency to develop severe pneumonia MESHD pneumonia HP after infection MESHD with SARS-COV-2. In future studies, a larger sample size and multi-center clinical data will be needed to support our conclusions.

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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