Selected article for: "admission threshold and logistic regression"

Author: Choudhuri, Jui; Carter, Jamal; Nelson, Randin; Skalina, Karin; Osterbur-Badhey, Marika; Johnston, Andrew; Goldstein, Doctor; Paroder, Monika; Szymanski, James
Title: SARS-CoV-2 PCR cycle threshold at hospital admission associated with patient mortality
  • Cord-id: qyb2z95v
  • Document date: 2020_12_31
  • ID: qyb2z95v
    Snippet: BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cycle threshold (Ct) has been suggested as an approximate measure of initial viral burden. The utility of cycle threshold, at admission, as a predictor of disease severity has not been thoroughly investigated. METHODS AND FINDINGS: We conducted a retrospective study of SARS-CoV-2 positive, hospitalized patients from 3/26/2020 to 8/5/2020 who had SARS-CoV-2 Ct data within 48 hours of admission (n = 1044). Only patients with
    Document: BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cycle threshold (Ct) has been suggested as an approximate measure of initial viral burden. The utility of cycle threshold, at admission, as a predictor of disease severity has not been thoroughly investigated. METHODS AND FINDINGS: We conducted a retrospective study of SARS-CoV-2 positive, hospitalized patients from 3/26/2020 to 8/5/2020 who had SARS-CoV-2 Ct data within 48 hours of admission (n = 1044). Only patients with complete survival data, discharged (n = 774) or died in hospital (n = 270), were included in our analysis. Laboratory, demographic, and clinical data were extracted from electronic medical records. Multivariable logistic regression was applied to examine the relationship of patient mortality with Ct values while adjusting for established risk factors. Ct was analyzed as continuous variable and subdivided into quartiles to better illustrate its relationship with outcome. Cumulative incidence curves were created to assess whether there was a survival difference in the setting of the competing risks of death versus patient discharge. Mean Ct at admission was higher for survivors (28.6, SD = 5.8) compared to non-survivors (24.8, SD = 6.0, P<0.001). In-hospital mortality significantly differed (p<0.05) by Ct quartile. After adjusting for age, gender, BMI, hypertension and diabetes, increased cycle threshold was associated with decreased odds of in-hospital mortality (0.91, CI 0.89–0.94, p<0.001). Compared to the 4(th) Quartile, patients with Ct values in the 1st Quartile (Ct <22.9) and 2nd Quartile (Ct 23.0–27.3) had an adjusted odds ratio of in-hospital mortality of 3.8 and 2.6 respectively (p<0.001). The discriminative ability of Ct to predict inpatient mortality was found to be limited, possessing an area under the curve (AUC) of 0.68 (CI 0.63–0.71). CONCLUSION: SARS-CoV-2 Ct was found to be an independent predictor of patient mortality. However, further study is needed on how to best clinically utilize such information given the result variation due to specimen quality, phase of disease, and the limited discriminative ability of the test.

    Search related documents:
    Co phrase search for related documents
    • acute kidney injury and additional benefit: 1
    • acute kidney injury and admission ct: 1, 2, 3, 4, 5, 6
    • acute kidney injury and logistic model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20
    • acute kidney injury and logistic regression: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • acute kidney injury and logistic regression model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18
    • acute kidney injury and low ct mortality: 1
    • additional benefit and logistic regression: 1, 2, 3, 4, 5, 6, 7
    • admission ct and logistic model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
    • admission ct and logistic regression: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • admission ct and logistic regression model: 1, 2, 3, 4, 5, 6, 7, 8
    • admission ct and low ct mortality: 1
    • admission cycle threshold and logistic model: 1, 2
    • admission cycle threshold and logistic regression: 1, 2, 3
    • admission cycle threshold and logistic regression model: 1, 2