Concordance between survey and electronic health record data in the COVID-19 Citizen Science study: a retrospective cohort analysis (Preprint)
Notice bibliographique
Résumé
Background: Real-world data reported by patients and extracted from electronic health records (EHRs) are increasingly leveraged for research, policy, and clinical decision-making. However, it is not always obvious the extent to which these 2 data sources agree with each other. Objective: This study aimed to evaluate the concordance of variables reported by participants enrolled in an electronic cohort study and data available in their EHRs. Methods: Survey data from COVID-19 Citizen Science, an electronic cohort study, were linked to EHR data from 7 health systems, comprising 34,908 participants. Concordance was evaluated for demographics, chronic conditions, and COVID-19 characteristics. Overall agreement, sensitivity, specificity, positive predictive value, negative predictive value, and κ statistics with 95% CIs were calculated. Results: Of 34,017 participants with complete information, 62.3% (21,176/34,017) reported being female, and 62.4% (21,217/34,017) were female according to EHR data. The median age was 57 (IQR 42-68) years. Out of 34,017 participants, 81.6% (27,744/34,017) of participants reported being White, and 79.5% (27,054/34,017) were White according to EHR data. In addition, 9.2% (3,124/34,017) of participants reported being Hispanic, and 6.6% (2,249/34,017) were Hispanic according to EHR data. Statistically significant discordance between data sources was detected for all demographic characteristics (P<.05) except the female category (P=.57) and the American Indian and Alaska Native (P=.21) and "other" race categories (P=.33). Statistically significant discordance was detected for the 2 COVID-19 traits and all baseline medical conditions except diabetes (P=.17). The starkest absolute difference between data sources was for COVID-19 vaccination, which was 48.4% according to the EHR and 97.4% according to participant report. Overall agreement was high for all demographic characteristics, although chance-corrected agreement (κ) and sensitivity were lower for the "other" race category (κ=0.31, sensitivity =26.6%), Hispanic ethnicity (κ=0.82, sensitivity=74%), and current smoker status (κ=0.54, sensitivity=49.4%). Specificity and negative predictive value (NPV) were higher than corresponding specificity and positive predictive value (PPV) for all baseline medical conditions. Sleep apnea had the highest sensitivity of all medical conditions (83.5%), and anemia had the lowest (32.8%). Chance-corrected agreement (κ) was highly variable for baseline medical conditions, ranging from 0.26 for anemia to 0.71 for diabetes. Overall and chance-corrected agreement between data sources for COVID-19 traits such as infection (84.6%, κ=0.34) and vaccination (51.0%, κ=0.05) was relatively lower than all other evaluated traits. The sensitivity for COVID-19 infection was 32.2%, and the sensitivity for COVID-19 vaccination was 49.7%. Although PPV for COVID-19 vaccination was 99.9%, the NPV was 5%. Conclusions: Results suggest the need for improvements to point-of-care capture of patient demographic traits and COVID-19 infection and vaccination history, patient education about their medical conditions, and linkage to external data sources in EHR-only pragmatic research. Further, these results indicate that additional work is required to integrate and prioritize participant-reported data in pragmatic research.
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 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 | 0,073 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,015 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,005 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 ».