Nephrologist’s Perceptions of Risk of Severe Chronic Kidney Disease and Outpatient Follow-up After Hospitalization With AKI: Multinational Randomized Survey Study
Notice bibliographique
Résumé
Background: Patients hospitalized with acute kidney injury (AKI) have variable risks for chronic kidney disease (CKD); however, there is limited knowledge about how this risk influences outpatient follow-up with nephrologists. Objective: This survey study examined the likelihood that nephrologists would recommend outpatient follow-up of patients with varying risk profiles for CKD after hospitalization with AKI and the effect of reporting the predicted risk of severe CKD on their decision-making. Design: A randomized survey study examining the impact of providing predicted risks of severe CKD on nephrologists' follow-up recommendations for patients with AKI. Setting: The study included nephrologists from the United States, the United Kingdom, and Canada between September and December 2023. Patients: Participants reviewed clinical vignettes of patients with AKI and varying risks of severe CKD (G4 or G5), using an externally validated prediction model. Measurements: The primary outcome was the likelihood of recommending nephrologist specialist follow-up for each case, scored on a 7-point Likert scale (1 = "definitely not" and 7 = "definitely would"). Methods: Participants were randomized to receive a version of the survey either with or without the predicted risk of severe CKD included for each vignette. Responses were compared across categories of predicted risk (<10%, 10%-49%, and ≥50%) using generalized estimating equations. Results: Of the 203 nephrologists who participated, 73 (36%) were from the United Kingdom, 71 (35%) from Canada, and 45 (22%) from the United States. Mean (95% confidence interval [CI]) Likert scores increased from 4.01 (3.68, 4.34) for patients with a <10% predicted risk to 6.06 (5.76, 6.37) for those with a ≥ 50% predicted risk of severe CKD. Nephrologists were significantly less likely to recommend outpatient nephrology follow-up for patients with a <10% predicted risk of severe CKD when the risk was reported (mean difference = -0.71 [95% CI = -1.19, -0.23]), and significantly more likely to recommend follow-up for patients with a ≥50% predicted risk when the risk of severe CKD was reported (mean difference = 0.49 [95% CI = 0.04, 0.93]). Limitations: This study focuses on nephrologists from high-income countries and relies on hypothetical scenarios rather than real-world practices. Survey respondents may not be representative of all nephrologists, although consistent findings across diverse subgroups strengthen findings. Conclusions: When the predicted risk of severe CKD is reported, nephrologists are less likely to recommend follow-up for lower risk patients with AKI and more likely to recommend follow-up for higher risk patients, leading to better alignment of recommendations for outpatient follow-up with patient risk of severe CKD.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,019 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».