Use of the SNOT-22 and UPSIT to Appropriately Select Pediatric Patients With Cystic Fibrosis Who Should Be Referred to an Otolaryngologist
Notice bibliographique
Résumé
IMPORTANCE: Sinonasal disease and, specifically, nasal polyps, occur frequently in children with cystic fibrosis (CF). As survival rates have improved, it has become imperative that otolaryngologists become involved in the care of patients with CF to provide appropriate medical and surgical interventions for sinonasal disease. Despite significant variability in the subjective reporting of clinical symptoms, previous work has suggested there may be a relationship between clinical indicators and sinonasal disease in this population. OBJECTIVE: To determine whether the 22-item Sino-Nasal Outcome Test (SNOT-22), the University of Pennsylvania Smell Identification Test (UPSIT), and other measures of sinonasal disease could be used to predict the presence of subclinical nasal polyps in children with CF. DESIGN, SETTING, AND PARTICIPANTS: This was a cross-sectional study performed from May 2012 through April 2013 at a cystic fibrosis clinic at BC Children's Hospital in Vancouver, British Columbia, Canada. There were 72 eligible children with CF for this study (with a confirmed diagnosis of CF based on genetic testing; their ages ranged from 6 to 18 years, and they were not actively being treated by an otolaryngologist). Thirty-seven of these patients (23 males, 14 females) consented to participate in this study. Twenty-three declined participation, and 12 could not be contacted. MAIN OUTCOMES AND MEASURES: Potential clinical predictors for the presence of subclinical nasal polyps were determined a priori. All 37 recruited participants completed a full study assessment. Nasal endoscopy (the gold standard) was performed to determine the presence of nasal polyps. Potential predictors that were assessed included age, sex, genotype, pancreatic function, SNOT-22 and UPSIT scores, oral culture swab result, and severity of forced expiratory volume in 1 second (FEV(1)). RESULTS: A SNOT-22 score of greater than 11 was the only statistically significant predictor of nasal polyps (P = .04). The positive predictive value was 68.1%, the negative predictive value was 66.7%, and the positive likelihood ratio was 1.82. CONCLUSIONS AND RELEVANCE: Given that the SNOT-22 is easy to administer and inexpensive, this sinus disease-specific questionnaire seems to be an appropriate tool for routine use by respirologists when assessing patients with CF to help predict subclinical nasal polyps.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».