Screening for COVID-19: Patient factors predicting positive PCR test

Challener, Douglas W.; Challener, Gregory J; Gow-Lee, Vanessa J; Fida, Madiha; Shah, Aditya; O’Horo, John C.

doi:10.1017/ice.2020.249

Cited by 11 publications

(29 citation statements)

References 6 publications

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“…Anosmia or ageusia as seen in other emerging studies was a strong predictor of a positive COVID-19 test [8]. Whilst contact and/or international travel was a predictor of COVID-19 infection in our model, as seen in US model from Challenger et al [9], it may be less relevant in outbreak settings and during periods of travel bans, however these criteria alone are not required for a patient to be at high risk of COVID-19. Our model has some limitations, including the single centre prospective data source, jurisdictional guided testing criteria, testing of symptomatic only patients and absence of external validation.…”

Section: Discussionmentioning

confidence: 47%

“…Our model has some limitations, including the single centre prospective data source, jurisdictional guided testing criteria, testing of symptomatic only patients and absence of external validation. However, only one small retrospective US cohort (n = 49 COVID-19 positive /n = 98 COVID-19 negative) [9] and two non-peer reviewed publications from China have examined the role of clinical decision rules from large datasets-Meng et al (n = 620 outpatients; 48.7% positive) [10] and Song et al [11] (n = 304 inpatients; 24.0% positive), both limited by requirement for clinical and laboratory parameters. COVID-MATCH65 uses readily available clinical information without laboratory test results, with a score of � 1.5 associated with high sensitivity (92.6 [95% CI 85.9, 96.7]) and NPV (99.5 [95% CI 98.9, 99.8]), enabling application in the outpatient and potentially early inpatient setting.…”

Section: Discussionmentioning

confidence: 99%

“…Whilst contact and/or international travel was a predictor of COVID-19 infection in our model, as seen in US model from Challenger et al . [ 9 ], it may be less relevant in outbreak settings and during periods of travel bans, however these criteria alone are not required for a patient to be at high risk of COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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COVID-MATCH65—A prospectively derived clinical decision rule for severe acute respiratory syndrome coronavirus 2

et al. 2020

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Objectives We report on the key clinical predictors of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and present a clinical decision rule that can risk stratify patients for COVID-19. Design, participants and setting A prospective cohort of patients assessed for COVID-19 at a screening clinic in Melbourne, Australia. The primary outcome was a positive COVID-19 test from nasopharyngeal swab. A backwards stepwise logistic regression was used to derive a model of clinical variables predictive of a positive COVID-19 test. Internal validation of the final model was performed using bootstrapped samples and the model scoring derived from the coefficients, with modelling performed for increasing prevalence. Results Of 4226 patients with suspected COVID-19 who were assessed, 2976 patients underwent SARS-CoV-2 testing (n = 108 SARS-CoV-2 positive) and were used to determine factors associated with a positive COVID-19 test. The 7 features associated with a positive COVID-19 test on multivariable analysis were: COVID-19 patient exposure or international travel, Myalgia/malaise, Anosmia or ageusia, Temperature, Coryza/sore throat, Hypoxia–oxygen saturation < 97%, 65 years or older—summarized in the mnemonic COVID-MATCH65. Internal validation showed an AUC of 0.836. A cut-off of ≥ 1.5 points was associated with a 92.6% sensitivity and 99.5% negative predictive value (NPV) for COVID-19. Conclusions From the largest prospective outpatient cohort of suspected COVID-19 we define the clinical factors predictive of a positive SARS-CoV-2 test. The subsequent clinical decision rule, COVID-MATCH65, has a high sensitivity and NPV for SARS-CoV-2 and can be employed in the pandemic, adjusted for disease prevalence, to aid COVID-19 risk-assessment and vital testing resource allocation.

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Section: Discussionmentioning

confidence: 47%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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COVID-MATCH65—A prospectively derived clinical decision rule for severe acute respiratory syndrome coronavirus 2

et al. 2020

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“…The symptoms which are more prevalent in COVID-19 negative ILI patients are cough and sore throat, though only sore-throat is statistically more likely to be associated with influenza [63]. The associations of these symptoms with COVID-19 have been corroborated in other studies [10; 43]. Symptom based COVID-19 detection is somewhat able to distinguish COVID-19 positive patients from other respiratory illnesses using a simple logistic regression model [6].…”

Section: Discussionmentioning

confidence: 80%

Dear Watch, Should I Get a COVID-19 Test? Designing deployable machine learning for wearables

Nestor

Hunter

Kainkaryam

et al. 2021

Preprint

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Commercial wearable devices are surfacing as an appealing mechanism to detect COVID-19 and potentially other public health threats, due to their widespread use. To assess the validity of wearable devices as population health screening tools, it is essential to evaluate predictive methodologies based on wearable devices by mimicking their real-world deployment. Several points must be addressed to transition from statistically significant differences between infected and uninfected cohorts to COVID-19 inferences on individuals. We demonstrate the strengths and shortcomings of existing approaches on a cohort of 32,198 individuals who experience influenza like illness (ILI), 204 of which report testing positive for COVID-19. We show that, despite commonly made design mistakes resulting in overestimation of performance, when properly designed wearables can be effectively used as a part of the detection pipeline. For example, knowing the week of year, combined with naive randomised test set generation leads to substantial overestimation of COVID-19 classification performance at 0.73 AUROC. However, an average AUROC of only 0.55 ± 0.02 would be attainable in a simulation of real-world deployment, due to the shifting prevalence of COVID-19 and non-COVID-19 ILI to trigger further testing. In this work we show how to train a machine learning model to differentiate ILI days from healthy days, followed by a survey to differentiate COVID-19 from influenza and unspecified ILI based on symptoms. In a forthcoming week, models can expect a sensitivity of 0.50 (0-0.74, 95% CI), while utilising the wearable device to reduce the burden of surveys by 35%. The corresponding false positive rate is 0.22 (0.02-0.47, 95% CI). In the future, serious consideration must be given to the design, evaluation, and reporting of wearable device interventions if they are to be relied upon as part of frequent COVID-19 or other public health threat testing infrastructures.

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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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Screening for COVID-19: Patient factors predicting positive PCR test

Cited by 11 publications

References 6 publications

COVID-MATCH65—A prospectively derived clinical decision rule for severe acute respiratory syndrome coronavirus 2

COVID-MATCH65—A prospectively derived clinical decision rule for severe acute respiratory syndrome coronavirus 2

Dear Watch, Should I Get a COVID-19 Test? Designing deployable machine learning for wearables

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

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