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.

Fig 1. Study cohort.

Fig 2. Pairwise comparisons of in hospital mortality and patient characteristics at presentation.

Kendall correlation matrix plot shows correlation coefficients on one side (top and right) and bivariate scatter plots, along with linear regression lines of best fit on the other side (bottom and left). The top and right panels also include asterisks, which show significance of each pairwise comparison: *p < 0.05, **p<0.01, ***p < 0.001. The dividing diagonal boxes demonstrate the distribution of each variable studied.

Fig 3. Stacked bar graph of mortality by PCR cycle quartile and age group.

*Fisher’s exact test used.

Fig 4. Cycle number and mortality by age and gender.

Fig 5. Cumulative incidence function of COVID-19 mortality vs discharge by initial PCR cycle threshold quartile.

Fig 6. Scatterplot of averaged value of repeated samples versus absolute cycle number difference between replicates.

The solid line indicates the mean absolute replicate cycle difference, and the dashed line marks 2 standard deviations from the mean. The top and right portions show a histogram of the axis variable.

Fig 7

(Panel A): Receiver Operating characteristic (ROC) curve of Inpatient Mortality using a logistic model only containing the Cycle Threshold value; (Panel B): Density plot of optimal Cycle Threshold cutoffs using bootstrapping (1500 replications) and maximizing Youden’s index; (Panel C): Sensitivity and Specificity Plot of Inpatient Mortality by PCR Threshold value.