Proposed clinical indicators for efficient screening and testing for COVID-19 infection using Classification and Regression Trees (CART) analysis

Zimmerman, Richard K.; Nowalk, Mary Patricia; Bear, Todd; Taber, Rachel; Clarke, Karen; Sax, Theresa M.; Eng, Heather; Clarke, Lloyd; Balasubramani, Goundappa K.

doi:10.1080/21645515.2020.1822135

Cited by 27 publications

(21 citation statements)

References 15 publications

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“…While patients with poor underlying health may be at higher-risk of severe COVID-19, the negative association between multi-morbidities and test positivity is consistent with prior studies and may reflect reduced exposure due to behavioral changes and/or lower testing thresholds [15, 37, 38, 39, 40]. Exceptions were noted for patients with obesity, diabetes, and dementia, which are also associated with severe disease [41, 42, 43].…”

Section: Discussionsupporting

confidence: 78%

“…Variable test access, quality and reliability have been key considerations in our understanding of COVID-19 [3, 4, 5, 6, 7, 8]. While surveillance data and targeted studies provide important information related to patient characteristics and symptoms, a large-scale systematic assessment of factors associated with testing and positivity in clinical practice would provide important information pertaining to testing decisions and interpretation of viral and antibody tests [9, 10, 11, 12, 13, 14, 15, 16].…”

Section: Introductionmentioning

confidence: 99%

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Viral And Antibody Testing For Coronavirus Disease 2019 (Covid-19): Factors Associated With Positivity In Electronic Health Records From The United States

Lindsay¹,

Mh²,

Rizzo³

et al. 2021

Preprint

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Background. Insufficient information on SARS-CoV-2 testing results exists in clinical practice from the United States. Methods. We conducted an observational retrospective cohort study using Optum® de-identified COVID-19 electronic health records from the United States to characterize patients who received a SARS-CoV-2 viral or antibody test between February 20, 2020 and July 10, 2020. We assessed temporal trends in testing and positivity by demographic and clinical characteristics; evaluated concordance between viral and antibody tests; and identified factors associated with positivity via multivariable logistic regression. Results. Our study population included 891,754 patients. Overall positivity rate for SARS-CoV-2 was 9% and 12% for viral and antibody tests, respectively. Positivity rate was inversely associated with the number of individuals tested and decreased over time across regions and race/ethnicities. Among patients who received a viral test followed by an antibody test, concordance ranged from 90%-93% depending on the duration between the two tests which is notable given uncertainties related to specific viral and antibody test characteristics. The following factors increased the odds of viral and antibody positivity in multivariable models: male, Hispanic or non-Hispanic Black and Asian, uninsured or Medicaid insurance, Northeast residence, dementia, diabetes, and obesity. Charlson Comorbidity Index was negatively associated with test positivity. We identified symptoms that were positively associated with test positivity, as well as, commonly co-occurring symptoms / conditions. Pediatric patients had reduced odds of a positive viral test, but conversely had increased odds of a positive antibody test. Conclusions. This study identified sociodemographic and clinical factors associated with SARS-CoV-2 testing and positivity within routine clinical practice from the United States.

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Section: Discussionsupporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Viral And Antibody Testing For Coronavirus Disease 2019 (Covid-19): Factors Associated With Positivity In Electronic Health Records From The United States

Lindsay¹,

Mh²,

Rizzo³

et al. 2021

Preprint

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“… 15 , 22 Other clinical prediction rules that were restricted to use of data available at the bedside have proposed three to 11 criteria in the rule, and all were derived on retrospective samples, but none have yet been tested for inter‐rater reliability or diagnostic accuracy in an independently and retrospectively collected validation sample. 12 , 13 , 14 , 15 , 16 …”

Section: Discussionmentioning

confidence: 99%

“… 9 , 10 , 11 Additionally, only a handful of studies have accompanied these SARS‐CoV‐2 prediction rules with specific cutoffs that represent low‐probability scenarios for which practitioners can forego diagnostic testing. 12 , 13 , 14 , 15 , 16 Fewer studies have examined the diagnostic accuracy of physician gestalt for SARS‐CoV‐2 infection. 17 , 18 The studies that have been published on prediction have been criticized for high bias, and none have undergone full stages of validation.…”

Section: Introductionmentioning

confidence: 99%

“…However, far fewer studies have reported structured prediction rules designed to estimate the pretest probability of SARS‐CoV‐2 at the bedside without the use of laboratory or radiological imaging 9–11 . Additionally, only a handful of studies have accompanied these SARS‐CoV‐2 prediction rules with specific cutoffs that represent low‐probability scenarios for which practitioners can forego diagnostic testing 12–16 . Fewer studies have examined the diagnostic accuracy of physician gestalt for SARS‐CoV‐2 infection 17,18 .…”

Section: Introductionmentioning

confidence: 99%

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Inter‐rater reliability and prospective validation of a clinical prediction rule for SARS‐CoV‐2 infection

Nevel

Kline

2021

Academic Emergency Medicine

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Objectives Accurate estimation of the risk of SARS‐CoV‐2 infection based on bedside data alone has importance to emergency department (ED) operations and throughput. The 13‐item CORC (COVID [or coronavirus] Rule‐out Criteria) rule had good overall diagnostic accuracy in retrospective derivation and validation. The objective of this study was to prospectively test the inter‐rater reliability and diagnostic accuracy of the CORC score and rule (score ≤ 0 negative, > 0 positive) and compare the CORC rule performance with physician gestalt. Methods This noninterventional study was conducted at an urban academic ED from February 2021 to March 2021. Two practitioners were approached by research coordinators and asked to independently complete a form capturing the CORC criteria for their shared patient and their gestalt binary prediction of the SARS‐CoV‐2 test result and confidence (0%–100%). The criterion standard for SARS‐CoV‐2 was from reverse transcriptase polymerase chain reaction performed on a nasopharyngeal swab. The primary analysis was from weighted Cohen's kappa and likelihood ratios (LRs). Results For 928 patients, agreement between observers was good for the total CORC score, κ = 0.613 (95% confidence interval [CI] = 0.579–0.646), and for the CORC rule, κ = 0.644 (95% CI = 0.591–0.697). The agreement for clinician gestalt binary determination of SARs‐CoV‐2 status was κ = 0.534 (95% CI = 0.437–0.632) with median confidence of 76% (first–third quartile = 66–88.5). For 425 patients who had the criterion standard, a negative CORC rule (both observers scored CORC < 0), the sensitivity was 88%, and specificity was 51%, with a negative LR (LR−) of 0.24 (95% CI = 0.10–0.50). Among patients with a mean CORC score of >4, the prevalence of a positive SARS‐CoV‐2 test was 58% (95% CI = 28%–85%) and positive LR was 13.1 (95% CI = 4.5–37.2). Clinician gestalt demonstrated a sensitivity of 51% and specificity of 86% with a LR− of 0.57 (95% CI = 0.39–0.74). Conclusion In this prospective study, the CORC score and rule demonstrated good inter‐rater reliability and reproducible diagnostic accuracy for estimating the pretest probability of SARs‐CoV‐2 infection.

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Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19

Struyf

Deeks

Dinnes

et al. 2021

Cochrane Database of Systematic Reviews

179

106

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Cochrane Database of Systematic ReviewsMost studies were conducted in hospital settings, so the results may not be entirely representative of primary care settings. The results do not apply to children or older adults specifically, and do not clearly di erentiate between disease severities.The results suggest that a single symptom or sign included in this review cannot accurately diagnose COVID-19. However, the presence of loss of taste or smell may serve as a red flag for the presence of the disease. The presence of high temperature or cough may also be useful to identify people who might have COVID-19. These symptoms may be useful to prompt further testing when they are present.Further research is needed to investigate combinations of symptoms and signs; and testing unselected populations, in primary care settings and in children and older adults. How up to date is this review?For this update of the review, the authors searched for studies published from January to July 2020.Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19 (Review)

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Proposed clinical indicators for efficient screening and testing for COVID-19 infection using Classification and Regression Trees (CART) analysis

Abstract: Proposed clinical indicators for efficient screening and testing for COVID-19 infection using Classification and Regression Trees (CART) analysis, Human Vaccines & Immunotherapeutics,

Cited by 27 publications

References 15 publications

Viral And Antibody Testing For Coronavirus Disease 2019 (Covid-19): Factors Associated With Positivity In Electronic Health Records From The United States

Viral And Antibody Testing For Coronavirus Disease 2019 (Covid-19): Factors Associated With Positivity In Electronic Health Records From The United States

Inter‐rater reliability and prospective validation of a clinical prediction rule for SARS‐CoV‐2 infection

Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19

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