A Nomogram for Predicting Delayed Viral Shedding in Non-Severe SARS-CoV-2 Omicron Infection

Abstract

Purpose: The Omicron variant of SARS-CoV-2 has emerged as a significant global concern, characterized by its rapid transmission and resistance to existing treatments and vaccines. However, the specific hematological and biochemical factors that may impact the clearance of Omicron variant infection remain unclear. The present study aimed to identify easily accessible laboratory markers that are associated with prolonged virus shedding in non-severe patients with COVID-19 caused by the Omicron variant.

Patients and methods: A retrospective cohort study was conducted on 882 non-severe COVID-19 patients who were diagnosed with the Omicron variant in Shanghai between March and June 2022. The least absolute shrinkage and selection operator regression model was used for feature selection and dimensional reduction, and multivariate logistic regression analysis was performed to construct a nomogram for predicting the risk of prolonged SARS-CoV-2 RNA positivity lasting for more than 7 days. The receiver operating characteristic (ROC) curve and calibration curves were used to assess predictive discrimination and accuracy, with bootstrap validation.

Results: Patients were randomly divided into derivation (70%, n = 618) and validation (30%, n = 264) cohorts. Optimal independent markers for prolonged viral shedding time (VST) over 7 days were identified as Age, C-reactive protein (CRP), platelet count, leukocyte count, lymphocyte count, and eosinophil count. These factors were subsequently incorporated into the nomogram utilizing bootstrap validation. The area under the curve (AUC) in the derivation (0.761) and validation (0.756) cohorts indicated good discriminative ability. The calibration curve showed good agreement between the nomogram-predicted and actual patients with VST over 7 days.

Conclusion: Our study confirmed six factors associated with delayed VST in non-severe SARS-CoV-2 Omicron infection and constructed a Nomogram which may assist non-severely affected patients to better estimate the appropriate length of self-isolation and optimize their self-management strategies.

Keywords: COVID-19; Omicron; SARS-CoV-2; nomogram; viral shedding time.

Figure 1

Flow diagram of patient selection. Among the 1235 patients, 52 were classified as presenting with severe COVID-19 upon admission or throughout the progression of their illness. Furthermore, 141 were minors under the age of 18 years, and 160 individuals possessed insufficient medical records. Finally, a cohort of 882 patients was incorporated into this study and stratified into the training cohort (n=618) and validation cohort (n=264) randomly.

Figure 2

The selection of optimal variables for the model was carried out using LASSO regression. (A) LASSO model coefficient trendlines of the 14 variables for risk of SARS-Cov2 RNA positivity lasting longer than 7 days. (B) Tuning parameter (Lambda, λ) selection cross-validation error curve. Vertical lines were drawn at the optimal values given minimum criteria and 1-SE criteria, resulting in λ = 0.04373 and the optimization of six non-zero coefficients.

Figure 3

RCS mode evaluates the non-linearity relationship of age (A), CRP levels (B) and counts of platelets (C), leukocytes (D), lymphocytes (E), eosinophils (F) and basophils (G) to risk of SARS-Cov2 RNA positivity lasting longer than 7 days.

Figure 4

Nomogram to predict the probabilities of viral shedding at 7 days. For each clinicopathological factor, find the corresponding point by drawing a vertical line from that variable to the points axis. The sum of each variable score corresponds to probability of viral shedding at 7 days.

Figure 5

AUC curve and calibration plot of the nomogram for the risk of SARS-Cov2 RNA positivity lasting longer than 7 days. (A) The area under ROC curve (0.761, 95% CI: 0.724–0.799) was utilized to assess the accuracy of nomogram. (B) Calibration plot for judging the prediction of nomogram. The red line denotes bias, while the green diagonal line at a 45-degree angle represents perfect prediction. The nomogram is thought to be more reliable the closer these two lines are to one another. (C) The area under ROC curve (0.756, 95% CI: 0.697–0.816) of validation cohort. (D) Calibration plot for nomogram in validation cohort.