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  1. Article: Prognosis of COVID-19: Red Cell Distribution Width, Platelet Distribution Width, and C-Reactive Protein.

    Bommenahalli Gowda, Shivakumar / Gosavi, Siddharth / Ananda Rao, Amogh / Shastry, Shashank / Raj, Sharanya C / Menon, Sanjana / Suresh, Ashutosh / Sharma, Anirudha

    Cureus

    2021  Volume 13, Issue 2, Page(s) e13078

    Abstract: ... platelet sequestration.  Conclusion Red cell distribution width, platelet distribution width, and C ... in red cell distribution width (RDW) and hematocrit. Platelet production increases alongside its destruction, inviting newly ... formed immature platelets into the circulation. Thus, the platelet distribution width (PDW) and mean ...

    Abstract Introduction Cytokine storm is central in the pathobiology of Coronavirus disease 2019 (COVID-19). The pro-inflammatory state and hypoxia disrupt erythropoiesis leading to alterations in red cell distribution width (RDW) and hematocrit. Platelet production increases alongside its destruction, inviting newly formed immature platelets into the circulation. Thus, the platelet distribution width (PDW) and mean platelet volume (MPV) are also affected. The study's objective is to analyze these indices and C-reactive protein (CRP) to elucidate prognostic insights in COVID-19 patients at the time of admission. Methodology This study was a retrospective cross-sectional study conducted at Chigateri General Hospital, attached to JJM Medical College, Davangere, over two months, July and August of 2020. Patients falling under categories B and C according to the interim guidelines issued by the Ministry of Health and Family Welfare, Government of India were enrolled in this study. Patients requiring mechanical ventilation and those with a prior diagnosis of malignancy were excepted from the study. Results The study population comprised a total of hundred patients. Seventy-five patients survived the disease and were discharged; twenty-five patients succumbed to the viral illness. The mean age of survivors (43.0 +/- 13.6 years) was significantly lesser than that of non-survivors (59.1 +/- 11.5 years) (p <0.001). RDW was significantly different among survivors (p=0.002); PDW and CRP were lower among the deceased (p=0.05 and p=0.10, respectively). Cut off values for RDW as 15%, CRP as 67 mg/l, and PDW as 17% were significantly associated with mortality. Hematocrit and MPV were not significantly associated with mortality. RDW has a sensitivity of 92% and a negative predictive value of 95% in predicting mortality. Discussion RDW showed a significant association with increased mortality. Impaired cell-mediated immunity at the onset of infection is responsible for rapid progression to moderate or even severe COVID disease. Since the investigations in our study were ordered at the time of admission, it may lead us to believe that higher RDW is associated with a better patient outcome. Lower C-reactive protein levels are associated with higher mortality. CRP is a non-specific marker for inflammation. Raised CRP is customarily an indicator of acute inflammation. Notwithstanding, the raised CRP may be an indicator of baseline immune response in early COVID infection. High PDW shows a significant association with increased mortality. The pathobiology of change in platelet indices in COVID-19 patients is presumably multifactorial: infection of the bone marrow; autoimmune platelet destruction; platelet sequestration.  Conclusion Red cell distribution width, platelet distribution width, and C-reactive protein are useful early predictive markers of mortality in COVID-19. Although serial investigations would provide a better picture, these indices at admission can gauge the clinical outcome early in the disease. As there is still a lot to be understood about the natural history of COVID-19, our study aims to propose relatively inexpensive indices of mortality that can aid efficient management.
    Language English
    Publishing date 2021-02-02
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2747273-5
    ISSN 2168-8184
    ISSN 2168-8184
    DOI 10.7759/cureus.13078
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Predicting COVID-19 prognosis in hospitalized patients based on early status

    David Natanov / Byron Avihai / Erin McDonnell / Eileen Lee / Brennan Cook / Nicole Altomare / Tomohiro Ko / Angelo Chaia / Carolayn Munoz / Samantha Ouellette / Suraj Nyalakonda / Vanessa Cederbaum / Payal D. Parikh / Martin J. Blaser

    mBio, Vol 14, Iss

    2023  Volume 5

    Abstract: ... reactive protein (PLABAC) and platelet count, red blood cell distribution width, age, blood urea nitrogen ... we generated two models, platelet count, lactate, age, blood urea nitrogen, aspartate aminotransferase, and C ... COVID-19 patients from the pre-vaccination period and 1547 from the vaccination period, yielding ROC ...

    Abstract ABSTRACT Predicting which patients are at greatest risk of severe disease from COVID-19 has the potential to improve patient outcomes and improve resource allocation. We developed machine learning models for predicting COVID-19 prognosis from a retrospective chart review of 969 hospitalized COVID-19 patients at Robert Wood Johnson University Hospital during the first pandemic wave in the United States, focusing on 77 variables from patients’ first day of hospital admission. Our best 77-variable model was better able to predict mortality (receiver operating characteristic area under the curve [ROC AUC] = 0.808) than CURB-65, a commonly used clinical prediction rule for pneumonia severity (ROC AUC = 0.722). After identifying highly predictive variables in our full models using Shapley additive explanations values, we generated two models, platelet count, lactate, age, blood urea nitrogen, aspartate aminotransferase, and C-reactive protein (PLABAC) and platelet count, red blood cell distribution width, age, blood urea nitrogen, lactate, and eosinophil count (PRABLE), that use age and five common laboratory tests to predict mortality (PLABAC: ROC AUC = 0.796, PRABLE: ROC AUC = 0.793), which also outperformed CURB-65. We externally validated PLABAC using data from the National COVID Cohort Collaborative Data Enclave from 7901 hospitalized COVID-19 patients from the pre-vaccination period and 1547 from the vaccination period, yielding ROC AUCs of 0.755 and 0.766, respectively. This study demonstrates that our models can accurately predict COVID-19 outcomes from a small number of variables obtained early in a patient’s hospital stay in patients from institutions around the United States after the initial pandemic wave. These models can serve as a clinical prediction aid and accurately capture a patient’s prognosis using a small number of routinely obtained laboratory values. IMPORTANCE COVID-19 remains the fourth leading cause of death in the United States. Predicting COVID-19 patient prognosis is essential to help ...
    Keywords SARS-CoV-2 infection ; viral infections ; machine learning ; hospital medicine ; biomarkers ; pneumonia ; Microbiology ; QR1-502
    Subject code 310
    Language English
    Publishing date 2023-10-01T00:00:00Z
    Publisher American Society for Microbiology
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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