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Nomogram Model for Prediction of SARS-CoV-2 Breakthrough Infection in Fujian: A Case-Control Real-World Study.

ABSTRACT: SARS-CoV-2 breakthrough infections have been reported because of the reduced efficacy of vaccines against the emerging variants globally. However, an accurate model to predict SARS-CoV-2 breakthrough infection is still lacking. In this retrospective study, 6,189 vaccinated individuals, consisting of SARS-CoV-2 test-positive cases (n = 219) and test-negative controls (n = 5970) during the outbreak of the Delta variant in September 2021 in Xiamen and Putian cities, Fujian province of China, were included. The vaccinated individuals were randomly split into a training (70%) cohort and a validation (30%) cohort. In the training cohort, a visualized nomogram was built based on the stepwise multivariate logistic regression. The area under the curve (AUC) of the nomogram in the training and validation cohorts was 0.819 (95% CI, 0.780-0.858) and 0.838 (95% CI, 0.778-0.897). The calibration curves for the probability of SARS-CoV-2 breakthrough infection showed optimal agreement between prediction by nomogram and actual observation. Decision curves indicated that nomogram conferred high clinical net benefit. In conclusion, a nomogram model for predicting SARS-CoV-2 breakthrough infection based on the real-world setting was successfully constructed, which will be helpful in the management of SARS-CoV-2 breakthrough infection.

SUBMITTER: Chen T

PROVIDER: S-EPMC9259977 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Real-world data are needed to establish SARS-CoV-2 rapid antigen testing (RAT) as an effective and reliable approach for SARS-CoV-2 screening. This study included 1,952,931 individuals who provided upper respiratory specimens during SARS-CoV-2 screening at CityMD urgent care locations in the New York metropolitan area from October 2020 to March 2021. Positive and negative results, as determined by the BD Veritor™ System for Rapid Detection of SARS-CoV-2 antigen (Veritor), were obtained for all individuals, with reflex reverse transcriptase-polymerase chain reaction (RT-PCR) testing performed on a case-by-case basis, per standard of care. Using verification bias adjustment, two alternative model assumptions were utilized for RAT results with missing reflex RT-PCR results. The worst antigen diagnostic performance estimates asserted that missing RT-PCR results would show a distribution similar to those RT-PCR results actually obtained, based on symptom category. The best antigen diagnostic performance estimates asserted that individuals without RT-PCR results had a clinical presentation consistent with RAT results, and, therefore, missing RT-PCR results would agree with RAT results. For patients with symptoms or high-risk exposure, 25.3% (n = 86,811/343,253) of RAT results were positive; vs. 3.4% (n = 53,046/1,559,733) positive for asymptomatic individuals without high-risk exposure. Reflex RT-PCR results were obtained from 46.3% (n = 158,836/343,253) and 13.8% (n = 215,708/1,559,733) of symptomatic and asymptomatic individuals, respectively. RT-PCR confirmed 94.4% (4,265/4,518) of positive and 90.6% (139,759/154,318) of negative RAT results in symptomatic individuals; and confirmed 83.4% (6,693/8,024) of positive and 95.3% (197,955/207,684) of negative RAT results in asymptomatic individuals. Applied assumptions for missing reflex RT-PCR results led to worst performance sensitivity estimates of 77.2 and 38.5% in the symptomatic and asymptomatic populations, respectively; assumptions for best performance estimates led to sensitivity values of 85.6 and 84.2%, respectively. Specificity values, regardless of assumptions or symptom category, ranged from 97.9-99.9%. At 10% SARS-CoV-2 prevalence, RAT positive predictive value was 86.9 and 99.0% for worst and best performance estimates across the total population, respectively; negative predictive values were >95% regardless of the applied assumption. Veritor test performance was consistent with that listed in the manufacturer instructions for use for symptomatic individuals. Real-world evidence should be gathered on RATs to support their efficacy as SARS-CoV-2 persists.

Project description:ObjectivesTo develop and validate a radiomics model for predicting 2019 novel coronavirus (COVID-19) pneumonia.MethodsFor this retrospective study, a radiomics model was developed on the basis of a training set consisting of 136 patients with COVID-19 pneumonia and 103 patients with other types of viral pneumonia. Radiomics features were extracted from the lung parenchyma window. A radiomics signature was built on the basis of reproducible features, using the least absolute shrinkage and selection operator method (LASSO). Multivariable logistic regression model was adopted to establish a radiomics nomogram. Nomogram performance was determined by its discrimination, calibration, and clinical usefulness. The model was validated in 90 consecutive patients, of which 56 patients had COVID-19 pneumonia and 34 patients had other types of viral pneumonia.ResultsThe radiomics signature, consisting of 3 selected features, was significantly associated with COVID-19 pneumonia (p?<?0.05) in both training and validation sets. The multivariable logistic regression model included the radiomics signature and distribution; maximum lesion, hilar, and mediastinal lymph node enlargement; and pleural effusion. The individualized prediction nomogram showed good discrimination in the training sample (area under the receiver operating characteristic curve [AUC], 0.959; 95% confidence interval [CI], 0.933-0.985) and in the validation sample (AUC, 0.955; 95% CI, 0.899-0.995) and good calibration. The mixed model achieved better predictive efficacy than the clinical model. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful.ConclusionsThe radiomics model derived has good performance for predicting COVID-19 pneumonia and may help in clinical decision-making.Key points• A radiomics model showed good performance for prediction 2019 novel coronavirus pneumonia and favorable discrimination for other types of pneumonia on CT images. • A central or peripheral distribution, a maximum lesion range?>?10 cm, the involvement of all five lobes, hilar and mediastinal lymph node enlargement, and no pleural effusion is associated with an increased risk of 2019 novel coronavirus pneumonia. • A radiomics model was superior to a clinical model in predicting 2019 novel coronavirus pneumonia.

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Nomogram Model for Prediction of SARS-CoV-2 Breakthrough Infection in Fujian: A Case-Control Real-World Study.

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