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Enregistrement W3037320721 · doi:10.1002/alr.22650

Challenges in interpreting the diagnostic performance of symptoms to predict COVID‐19 status: The case of anosmia

2020· review· en· W3037320721 sur OpenAlexaff
Paolo Boscolo‐Rizzo, Daniele Borsetto, Claire Hopkins, Jerry Polesel

Notice bibliographique

RevueInternational Forum of Allergy & Rhinology · 2020
Typereview
Langueen
DomaineNeuroscience
ThématiqueOlfactory and Sensory Function Studies
Établissements canadiensSt. Thomas Hospital
Organismes subventionnairesnon disponible
Mots-clésAnosmiaMedicineCoronavirus disease 2019 (COVID-19)Context (archaeology)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)DiseaseTastePositive predicative valuePredictive valueSeverity of illnessPredictive value of testsInternal medicineInfectious disease (medical specialty)

Résumé

récupéré en direct d'OpenAlex

There is mounting evidence that a sudden onset of altered sense of smell and/or taste is closely related to coronavirus disease-2019 (COVID-19). The severe acute respiratory syndrome‒coronavirus-2 (SARS-CoV-2) was observed to impair the sense of smell and/or taste in about two thirds of mild to moderate cases of COVID-19.1, 2 Consequently, several studies have tried to estimate the sensitivity and specificity as well as the positive predictive value of self-reported new onset of smell and/or taste impairment for COVID-19 in populations of patients with flulike symptoms. When faced with this task in the context of COVID-19, 2 main problems are predictably encountered. The first is that the standard diagnostic tool for diagnosis of SARS-CoV-2 infection, namely SARS-CoV-2 real-time polymerase chain reaction (RT-PCR) on a nasopharyngeal sample, is insufficient to rule-out COVID-19 when negative. Although its specificity is excellent, nasopharyngeal swab shows suboptimal sensitivity for SARS-CoV-2 detection in the early phase of infection being inconsistent during serial testing.3 Moreover, patients developing COVID-19‒related symptoms may be referred to nasopharyngeal swab later during the course of the disease when viral load is no longer detectable.4, 5 Thus, the diagnostic performance of new onset of smell and/or taste impairment for COVID-19 may be even higher than estimated. The other problem concerns the pretest probability of disease. Predictive values refer to the ability of a test result or symptom presence to confirm the presence or absence of a disease, based on positive predictive value (PPV) or negative predictive value (NPV), respectively. Although sensitivity and specificity are properties of a test itself that will not be affected by the characteristics and prevalence of disease in the population, PPV and NPV are strongly influenced by the prevalence of the disease in the target population.6 Among patients with flulike symptoms, the prevalence of SARS-CoV-2 infection may vary substantially according to geographic context and disease phase. For example, the study by Tostmann et al, conducted in The Netherlands during the early phase of the COVID-19 pandemic, demonstrated a prevalence of 11% in SARS-CoV-2‒positive subjects among patients with a flulike illness,7 whereas Zayet et al reported a prevalence of 44% in their cohort of patients evaluated in a French hospital.4 We performed a review of the literature to identify studies that tested patients with flulike symptoms for SARS-CoV-2 infection by RT-PCR and that reported data on the prevalence of loss of smell and/or taste. We identified a total of 6 studies (Fig. 1A).2, 4, 7-10 Sensitivity and specificity were represented using forest plots, and pooled estimates were calculated using a random intercept logistic regression model. Publication bias was assessed by funnel plot. PPVs and NPVs were calculated as a function of prevalence of COVID-19, ranging from 0% to 100%, using pooled sensitivity and specificity. Forest plots of the sensitivity and specificity of new-onset chemosensory impairment for diagnosing COVID-19 are shown in Figure 1A. Although the pooled sensitivity was 61% (95% confidence interval [CI], 55-68%), pooled specificity reached 87% (95% CI, 80-92%), with publication bias being possible (Fig. 1B). Given this sensitivity and specificity, Figure 1C shows the variation of PPV and NPV, with a prevalence of SARS-CoV-2 infection in patients with flulike symptoms. For example, if the prevalence is 50%, PPV and NPV would be 82% and 69%, respectively; however, when prevalence is 10%, PPV would decline to 34% and NPV would increase to 95%. Thus, in a different phase of the COVID-19 pandemic and in a different geographic context with a different diffusion of SARS-CoV-2, the PPV of the new onset of smell and/or taste loss may vary dramatically. Moreover, the increased impact of other viruses causing flulike symptoms may superimpose to SARS-CoV-2 circulation in next fall/winter season, thus further decreasing the PPV of new onset of smell and/or taste loss for COVID-19. In conclusion, we believe that a new onset of smell and/or taste loss during the COVID-19 pandemic should be considered a manifestation of SARS-CoV-2 infection until proven otherwise, sufficient to justify testing, self-isolation, and the use of personal protective equipment by medical personnel interacting with these subjects. However, taking into account the aforementioned considerations, diagnostic indications of individual symptoms should be fully understood and considered with caution when predicting SARS-CoV-2 infection in patients with flulike symptoms.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,006
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,994
Score d'incertitude au seuil0,678

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,006
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,161
Tête enseignante GPT0,345
Écart entre enseignants0,184 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations9
Publié2020
Routes d'admission1
Résumé présentoui

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