Predicting patients with false negative SARS-CoV-2 testing at hospital admission: A retrospective multi-center study

Abstract

False negative SARS-CoV-2 tests can lead to spread of infection in the inpatient setting to other patients and healthcare workers. However, the population of patients with COVID who are admitted with false negative testing is unstudied.

Objective

To characterize and develop a model to predict true SARS-CoV-2 infection among patients who initially test negative for COVID by PCR.

Design

Retrospective cohort study.

Setting

Five hospitals within the Yale New Haven Health System between 3/10/2020 and 9/1/2020.

Participants

Adult patients who received diagnostic testing for SARS-CoV-2 virus within the first 96 hours of hospitalization.

Exposure

We developed a logistic regression model from readily available electronic health record data to predict SARS-CoV-2 positivity in patients who were positive for COVID and those who were negative and never retested.

Main outcomes and measures

This model was applied to patients testing negative for SARS-CoV-2 who were retested within the first 96 hours of hospitalization. We evaluated the ability of the model to discriminate between patients who would subsequently retest negative and those who would subsequently retest positive.

Results

We included 31,459 hospitalized adult patients; 2,666 of these patients tested positive for COVID and 3,511 initially tested negative for COVID and were retested. Of the patients who were retested, 61 (1.7%) had a subsequent positive COVID test. The model showed that higher age, vital sign abnormalities, and lower white blood cell count served as strong predictors for COVID positivity in these patients. The model had moderate performance to predict which patients would retest positive with a test set area under the receiver-operator characteristic (ROC) of 0.76 (95% CI 0.70–0.83). Using a cutpoint for our risk prediction model at the 90 ^th percentile for probability, we were able to capture 35/61 (57%) of the patients who would retest positive. This cutpoint amounts to a number-needed-to-retest range between 15 and 77 patients.

Conclusion and relevance

We show that a pragmatic model can predict which patients should be retested for COVID. Further research is required to determine if this risk model can be applied prospectively in hospitalized patients to prevent the spread of SARS-CoV-2 infections.

SciScore for 10.1101/2020.11.30.20241414: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Institutional Review Board Statement	Consent: This study operated under a waiver of informed consent and was approved by the Yale Human Investigation Committee (HIC #
Randomization	not detected.
Blinding	not detected.
Power Analysis	not detected.
Sex as a biological variable	not detected.

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Our study should be viewed in light of several weaknesses. First, our risk model demonstrated moderate performance, thus we do acknowledge that many patients would need to be retested to find a single COVID positive patient. Second, our model was built from and applied to patients who had vital signs, a basic metabolic panel, and a complete blood count measured on admission; thus the model would not be generalizable to patients who may not have vital signs or laboratory values obtained (e.g psychiatric patients or routine obstetric patients). Third, our study is retrospective in nature and we are unable to conclude the efficacy of the implementation of this model for retesting. Another limitation is that our model was evaluated on patients who were tested twice for COVID; there were many patients who were COVID negative on presentation and never retested, therefore we are unable to provide a clear number-needed-to-test as some of these patients may have been false negatives. To our knowledge, this is the first study to investigate the population of false negative patients with COVID in the hospitalized setting. We suggest that by building and embedding a model using variables commonly available in the EHR, hospitals could flag patients for targeted retesting, potentially reducing nosocomial spread of COVID-19.. Testing between 15 and 77 patients to find a single COVID negative patient who is truly positive should be considered in light of several logistic concerns. On one han...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
No protocol registration statement was detected.

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