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  1. Article: Reduced Mortality With Ondansetron Use in SARS-CoV-2-Infected Inpatients.

    Bayat, Vafa / Ryono, Russell / Phelps, Steven / Geis, Eugene / Sedghi, Farshid / Etminani, Payam / Holodniy, Mark

    Open forum infectious diseases

    2021  Volume 8, Issue 7, Page(s) ofab336

    Abstract: Background: The coronavirus disease 2019 (COVID-19) pandemic has led to a surge in clinical trials evaluating investigational and approved drugs. Retrospective analysis of drugs taken by COVID-19 inpatients provides key information on drugs associated ... ...

    Abstract Background: The coronavirus disease 2019 (COVID-19) pandemic has led to a surge in clinical trials evaluating investigational and approved drugs. Retrospective analysis of drugs taken by COVID-19 inpatients provides key information on drugs associated with better or worse outcomes.
    Methods: We conducted a retrospective cohort study of 10 741 patients testing positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection within 3 days of admission to compare risk of 30-day all-cause mortality in patients receiving ondansetron using multivariate Cox proportional hazard models. All-cause mortality, length of hospital stay, adverse events such as ischemic cerebral infarction, and subsequent positive COVID-19 tests were measured.
    Results: Administration of ≥8 mg of ondansetron within 48 hours of admission was correlated with an adjusted hazard ratio for 30-day all-cause mortality of 0.55 (95% CI, 0.42-0.70;
    Conclusions: If confirmed by prospective clinical trials, our results suggest that ondansetron, a safe, widely available drug, could be used to decrease morbidity and mortality in at-risk populations.
    Language English
    Publishing date 2021-07-14
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2757767-3
    ISSN 2328-8957
    ISSN 2328-8957
    DOI 10.1093/ofid/ofab336
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: A Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Prediction Model From Standard Laboratory Tests.

    Bayat, Vafa / Phelps, Steven / Ryono, Russell / Lee, Chong / Parekh, Hemal / Mewton, Joel / Sedghi, Farshid / Etminani, Payam / Holodniy, Mark

    Clinical infectious diseases : an official publication of the Infectious Diseases Society of America

    2020  Volume 73, Issue 9, Page(s) e2901–e2907

    Abstract: Background: With the limited availability of testing for the presence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and concerns surrounding the accuracy of existing methods, other means of identifying patients are urgently ... ...

    Abstract Background: With the limited availability of testing for the presence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and concerns surrounding the accuracy of existing methods, other means of identifying patients are urgently needed. Previous studies showing a correlation between certain laboratory tests and diagnosis suggest an alternative method based on an ensemble of tests.
    Methods: We have trained a machine learning model to analyze the correlation between SARS-CoV-2 test results and 20 routine laboratory tests collected within a 2-day period around the SARS-CoV-2 test date. We used the model to compare SARS-CoV-2 positive and negative patients.
    Results: In a cohort of 75 991 veteran inpatients and outpatients who tested for SARS-CoV-2 in the months of March through July 2020, 7335 of whom were positive by reverse transcription polymerase chain reaction (RT-PCR) or antigen testing, and who had at least 15 of 20 lab results within the window period, our model predicted the results of the SARS-CoV-2 test with a specificity of 86.8%, a sensitivity of 82.4%, and an overall accuracy of 86.4% (with a 95% confidence interval of [86.0%, 86.9%]).
    Conclusions: Although molecular-based and antibody tests remain the reference standard method for confirming a SARS-CoV-2 diagnosis, their clinical sensitivity is not well known. The model described herein may provide a complementary method of determining SARS-CoV-2 infection status, based on a fully independent set of indicators, that can help confirm results from other tests as well as identify positive cases missed by molecular testing.
    MeSH term(s) COVID-19 ; COVID-19 Testing ; Clinical Laboratory Techniques ; Humans ; SARS-CoV-2 ; Sensitivity and Specificity
    Keywords covid19
    Language English
    Publishing date 2020-07-23
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 1099781-7
    ISSN 1537-6591 ; 1058-4838
    ISSN (online) 1537-6591
    ISSN 1058-4838
    DOI 10.1093/cid/ciaa1175
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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  3. Article: A SARS-CoV-2 Prediction Model from Standard Laboratory Tests

    Bayat, Vafa / Phelps, Steven / Ryono, Russell / Lee, Chong / Parekh, Hemal / Mewton, Joel / Sedghi, Farshid / Etminani, Payam / Holodniy, Mark

    Clin. infect. dis

    Abstract: BACKGROUND: With the limited availability of testing for the presence of the SARS-CoV-2 virus and concerns surrounding the accuracy of existing methods, other means of identifying patients are urgently needed. Previous studies showing a correlation ... ...

    Abstract BACKGROUND: With the limited availability of testing for the presence of the SARS-CoV-2 virus and concerns surrounding the accuracy of existing methods, other means of identifying patients are urgently needed. Previous studies showing a correlation between certain laboratory tests and diagnosis suggest an alternative method based on an ensemble of tests. METHODS: We have trained a machine learning model to analyze the correlation between SARS-CoV-2 test results and 20 routine laboratory tests collected within a 2-day period around the SARS-CoV-2 test date. We used the model to compare SARS-CoV-2 positive and negative patients. RESULTS: In a cohort of 75,991 veteran inpatients and outpatients who tested for SARS-CoV-2 in the months of March through July, 2020, 7,335 of whom were positive by RT-PCR or antigen testing, and who had at least 15 of 20 lab results within the window period, our model predicted the results of the SARS-CoV-2 test with a specificity of 86.8%, a sensitivity of 82.4%, and an overall accuracy of 86.4% (with a 95% confidence interval of [86.0%, 86.9%]). CONCLUSIONS: While molecular-based and antibody tests remain the reference standard method for confirming a SARS-CoV-2 diagnosis, their clinical sensitivity is not well known. The model described herein may provide a complementary method of determining SARS-CoV-2 infection status, based on a fully independent set of indicators, that can help confirm results from other tests as well as identify positive cases missed by molecular testing.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #712974
    Database COVID19

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  4. Article ; Online: A COVID-19 Prediction Model from Standard Laboratory Tests and Vital Signs

    Bayat, Vafa / Phelps, Steven / Ryono, Russell / Lee, Chong / Parekh, Hemal / Mewton, Joel / Sedghi, Farshid / Etminani, Payam / Holodniy, Mark

    SSRN Electronic Journal ; ISSN 1556-5068

    2020  

    Keywords covid19
    Language English
    Publisher Elsevier BV
    Publishing country us
    Document type Article ; Online
    DOI 10.2139/ssrn.3594614
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: A Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Prediction Model From Standard Laboratory Tests

    Bayat, Vafa / Phelps, Steven / Ryono, Russell / Lee, Chong / Parekh, Hemal / Mewton, Joel / Sedghi, Farshid / Etminani, Payam / Holodniy, Mark

    Clinical Infectious Diseases ; ISSN 1058-4838 1537-6591

    2020  

    Abstract: Abstract Background With the limited availability of testing for the presence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and concerns surrounding the accuracy of existing methods, other means of identifying patients are ... ...

    Abstract Abstract Background With the limited availability of testing for the presence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and concerns surrounding the accuracy of existing methods, other means of identifying patients are urgently needed. Previous studies showing a correlation between certain laboratory tests and diagnosis suggest an alternative method based on an ensemble of tests. Methods We have trained a machine learning model to analyze the correlation between SARS-CoV-2 test results and 20 routine laboratory tests collected within a 2-day period around the SARS-CoV-2 test date. We used the model to compare SARS-CoV-2 positive and negative patients. Results In a cohort of 75 991 veteran inpatients and outpatients who tested for SARS-CoV-2 in the months of March through July 2020, 7335 of whom were positive by reverse transcription polymerase chain reaction (RT-PCR) or antigen testing, and who had at least 15 of 20 lab results within the window period, our model predicted the results of the SARS-CoV-2 test with a specificity of 86.8%, a sensitivity of 82.4%, and an overall accuracy of 86.4% (with a 95% confidence interval of [86.0%, 86.9%]). Conclusions Although molecular-based and antibody tests remain the reference standard method for confirming a SARS-CoV-2 diagnosis, their clinical sensitivity is not well known. The model described herein may provide a complementary method of determining SARS-CoV-2 infection status, based on a fully independent set of indicators, that can help confirm results from other tests as well as identify positive cases missed by molecular testing.
    Keywords Microbiology (medical) ; Infectious Diseases ; covid19
    Language English
    Publisher Oxford University Press (OUP)
    Publishing country uk
    Document type Article ; Online
    DOI 10.1093/cid/ciaa1175
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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