Patterns of repeated diagnostic testing for COVID‐19 in relation to patient characteristics and outcomes
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Abstract
Background
Whilst the COVID‐19 diagnostic test has a high false‐negative rate, not everyone initially negative is re‐tested. Michigan Medicine, a primary regional centre, provided an ideal setting for studying testing patterns during the first wave of the pandemic.
Objectives
To identify the characteristics of patients who underwent repeated testing for COVID‐19 and determine if repeated testing was associated with downstream outcomes amongst positive cases.
Methods
Characteristics, test results, and health outcomes for patients presenting for a COVID‐19 diagnostic test were collected. We examined whether patient characteristics differed with repeated testing and estimated a false‐negative rate for the test. We then studied repeated testing patterns in patients with severe COVID‐19‐related outcomes.
Results
Patient age, sex, body mass index, neighbourhood poverty levels, pre‐existing type 2 diabetes, circulatory, kidney, and liver diseases, and cough, fever/chills, and pain symptoms 14 days prior to a first test were associated with repeated testing. Amongst patients with a positive result, age (OR: 1.17; 95% CI: (1.05, 1.34)) and pre‐existing kidney diseases (OR: 2.26; 95% CI: (1.41, 3.68)) remained significant. Hospitalization (OR: 7.88; 95% CI: (5.15, 12.26)) and ICU‐level care (OR: 6.93; 95% CI: (4.44, 10.92)) were associated with repeated testing. The estimated false‐negative rate was 23.8% (95% CI: (19.5%, 28.5%)).
Conclusions
Whilst most patients were tested once and received a negative result, a meaningful subset underwent multiple rounds of testing. These results shed light on testing patterns and have important implications for understanding the variation of repeated testing results within and between patients.
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SciScore for 10.1101/2020.07.26.20162453: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IACUC: Study Sample: This cross-sectional study was approved by the committee for research ethics and compliance at Michigan Medicine and followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.
IRB: Study protocols were reviewed and approved by the University of Michigan Medical School Institutional Review Board (IRB ID HUM00180294).Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources All analysis was carried out in R, version 4.0.0 (R Project for Statistical Computing).14 R Project for …SciScore for 10.1101/2020.07.26.20162453: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IACUC: Study Sample: This cross-sectional study was approved by the committee for research ethics and compliance at Michigan Medicine and followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.
IRB: Study protocols were reviewed and approved by the University of Michigan Medical School Institutional Review Board (IRB ID HUM00180294).Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources All analysis was carried out in R, version 4.0.0 (R Project for Statistical Computing).14 R Project for Statisticalsuggested: (R Project for Statistical Computing, RRID:SCR_001905)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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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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SciScore for 10.1101/2020.07.26.20162453: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Methods Study Sample This cross-sectional study was approved by the committee for research ethics and compliance at Michigan Medicine and followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable These characteristics included age (years), body mass index (BMI; kg/m2 ), sex (male, female, or other/unknown), race/ethnicity (non-Hispanic white, non-Hispanic black, or other/unknown), smoking status, and seven pre-existing comorbidities extracted from the electronic medical record: respiratory diseases Table 2: …
SciScore for 10.1101/2020.07.26.20162453: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Methods Study Sample This cross-sectional study was approved by the committee for research ethics and compliance at Michigan Medicine and followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable These characteristics included age (years), body mass index (BMI; kg/m2 ), sex (male, female, or other/unknown), race/ethnicity (non-Hispanic white, non-Hispanic black, or other/unknown), smoking status, and seven pre-existing comorbidities extracted from the electronic medical record: respiratory diseases 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:
Limitations A potential limitation of our study is its generalizability to other regional testing centers, both in established “hot spots” and rural areas, due to the patient mix at Michigan Medicine. There are inherent limitations to using electronic health records for research purposes due to the incomplete information. For example, tests done at drive-thru testing stations or pharmacies are not captured. The definition of co-morbidities and patient characteristics using ICD code can be highly imperfect. Conclusions This study sought to quantify patterns of repeated testing for COVID-19 and its associated factors at Michigan Medicine. These results shed light on testing patterns and have important implications in understanding what is happening in real world with COVID testing in an academic medical center. It also gives a real world estimate of the false negative rate of the test.
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.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
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SciScore for 10.1101/2020.07.26.20162453: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Methods Study Sample This cross-sectional study was approved by the committee for research ethics and compliance at Michigan Medicine and followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable These characteristics included age (years), body mass index (BMI; kg/m2 ), sex (male, female, or other/unknown), race/ethnicity (non-Hispanic white, non-Hispanic black, or other/unknown), smoking status, and seven pre-existing comorbidities extracted from the electronic medical record: respiratory diseases Table 2: …
SciScore for 10.1101/2020.07.26.20162453: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Methods Study Sample This cross-sectional study was approved by the committee for research ethics and compliance at Michigan Medicine and followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable These characteristics included age (years), body mass index (BMI; kg/m2 ), sex (male, female, or other/unknown), race/ethnicity (non-Hispanic white, non-Hispanic black, or other/unknown), smoking status, and seven pre-existing comorbidities extracted from the electronic medical record: respiratory diseases 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:
Limitations A potential limitation of our study is its generalizability to other regional testing centers, both in established “hot spots” and rural areas, due to the patient mix at Michigan Medicine. There are inherent limitations to using electronic health records for research purposes due to the incomplete information. For example, tests done at drive-thru testing stations or pharmacies are not captured. The definition of co-morbidities and patient characteristics using ICD code can be highly imperfect. Conclusions This study sought to quantify patterns of repeated testing for COVID-19 and its associated factors at Michigan Medicine. These results shed light on testing patterns and have important implications in understanding what is happening in real world with COVID testing in an academic medical center. It also gives a real world estimate of the false negative rate of the test.
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.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
-
SciScore for 10.1101/2020.07.26.20162453: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Methods Study Sample This cross-sectional study was approved by the committee for research ethics and compliance at Michigan Medicine and followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable Females were found to have lower odds (OR: 0.86; 95% CI: (0.76, 0.96)) of receiving additional testing than males. Table 2: Resources
Software and Algorithms Sentences Resources All analysis was carried out in R, version 4.0.0 (R Project for Statistical Computing). R Project for …SciScore for 10.1101/2020.07.26.20162453: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Methods Study Sample This cross-sectional study was approved by the committee for research ethics and compliance at Michigan Medicine and followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable Females were found to have lower odds (OR: 0.86; 95% CI: (0.76, 0.96)) of receiving additional testing than males. Table 2: Resources
Software and Algorithms Sentences Resources All analysis was carried out in R, version 4.0.0 (R Project for Statistical Computing). R Project for Statisticalsuggested: (R Project for Statistical Computing, SCR_001905)Data from additional tools added to each annotation on a weekly basis.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
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