Dataset Information

SARS-CoV-2 PCR cycle threshold at hospital admission associated with patient mortality.

ABSTRACT:

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 4th 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.

SUBMITTER: Choudhuri J

PROVIDER: S-EPMC7774957 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

SARS-CoV-2 PCR cycle threshold at hospital admission associated with patient mortality.

Choudhuri Jui J Carter Jamal J Nelson Randin R Skalina Karin K Osterbur-Badhey Marika M Johnston Andrew A Goldstein Doctor D Paroder Monika M Szymanski James J

PloS one 20201231 12

<h4>Background</h4>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.<h4>Methods and findings</h4>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 p ...[more]

PMID: 33382805

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Project description:ObjectivesTo exploit the features of digital PCR for implementing SARS-CoV-2 observational studies by reliably including the viral load factor expressed as copies/μL.MethodsA small cohort of 51 Covid-19 positive samples was assessed by both RT-qPCR and digital PCR assays. A linear regression model was built using a training subset, and its accuracy was assessed in the remaining evaluation subset. The model was then used to convert the stored cycle threshold values of a large dataset of 6208 diagnostic samples into copies/μL of SARS-CoV-2. The calculated viral load was used for a single cohort retrospective study. Finally, the cohort was randomly divided into a training set (n = 3095) and an evaluation set (n = 3113) to establish a logistic regression model for predicting case-fatality and to assess its accuracy.ResultsThe model for converting the Ct values into copies/μL was suitably accurate. The calculated viral load over time in the cohort of Covid-19 positive samples showed very low viral loads during the summer inter-epidemic waves in Italy. The calculated viral load along with gender and age allowed building a predictive model of case-fatality probability which showed high specificity (99.0%) and low sensitivity (21.7%) at the optimal threshold which varied by modifying the threshold (i.e. 75% sensitivity and 83.7% specificity). Alternative models including categorised cVL or raw cycle thresholds obtained by the same diagnostic method also gave the same performance.ConclusionThe modelling of the cycle threshold values using digital PCR had the potential of fostering studies addressing issues regarding Sars-CoV-2; furthermore, it may allow setting up predictive tools capable of early identifying those patients at high risk of case-fatality already at diagnosis, irrespective of the diagnostic RT-qPCR platform in use. Depending upon the epidemiological situation, public health authority policies/aims, the resources available and the thresholds used, adequate sensitivity could be achieved with acceptable low specificity.

Project description:Background and aimPatients with chronic kidney disease (CKD) are susceptible to SARS-CoV-2 infection and more prone to develop severe disease. It is important to know predictors of poor outcomes to optimize the strategies of care.Methods93 patients with CKD and 93 age-sex matched patients without CKD were included in the study. Data on demographic, clinical features, hematological indices and outcomes were noted and compared between the groups. Neutrophile to lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune inflammation index (SII) (platelet counts × neutrophil counts / lymphocyte counts) and lymphocyte-to-CRP ratio (LCR) were calculated on admission and the association of these markers with disease mortality in CKD patients was identified.ResultsCKD patients had higher risk of severe disease, and mortality compared to non-CKD patients (72% vs 50.5%, p=0.003, 36,6% vs 10.8%, p<0.001, respectively) and were more likely to have higher values of immuno-inflammatory indices (leucocyte count, neutrophil, NLR, SII and C-reactive protein etc.) and lower level of lymphocyte and LCR. Also, higher levels of NLR, SII, PLR and lower level of LCR were seen in CKD patients who died compared to those recovered. In a receiver operating characteristic curve analysis, NLR, SII, PLR and LCR area under the curve for in-hospital mortality of CKD patients were 0.830, 0.811, 0.664 and 0.712, respectively. Among all parameters, NLR and SII gave us the best ability to distinguish patients with higher risk of death. Based on the cut-off value of 1180.5, the sensitivity and specificity of the SII for predicting in-hospital mortality were found to be 67.5% and 79.6%, respectively. The corresponding sensitivity and specificity of the NLR were 85.2% and 66.1%, respectively, at the cut-off value of 5.1. Forward stepwise logistic regression analysis showed that NLR (≥5.1), SII (≥1180.5) and LCR (≤9) were predictors for in-hospital mortality.ConclusionWe report for the first time that SII is able to distinguish COVID-19 infected CKD patients of worse survival and it is as powerful as NLR in this regard. As SII is easily quantified from blood sample data, it may assist for early identification and timely management of CKD patients with worse survival.

Dataset Information

SARS-CoV-2 PCR cycle threshold at hospital admission associated with patient mortality.

Background

Methods and findings

Conclusion

Publications

SARS-CoV-2 PCR cycle threshold at hospital admission associated with patient mortality.

Similar Datasets

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