External Validation of Prediction Models for Unilateral Primary Aldosteronism
Notice bibliographique
Résumé
Abstract Primary aldosteronism (PA) is the most common cause of remediable hypertension. Treatment is informed by establishing whether disease is unilateral (localized to one adrenal gland) or bilateral. Adrenalectomy is the guideline-recommended treatment of choice for unilateral PA. However, the currently recommended subtyping test, adrenal vein sampling (AVS), is often limited in accessibility. Thus, prediction models have been developed to diagnose unilateral PA and therefore bypass AVS. However, their generalizability remains unknown. In this retrospective study, we aimed to externally validate the performance of prediction models for unilateral PA in a large population of PA patients at a Canadian referral center who underwent AVS during 2006–2018. The presence of unilateral disease was indicated by a lateralization index of >3 on AVS. We identified 6 clinical prediction models from the literature. The discrimination and calibration of each model were systematically evaluated. For the original models, the derivation cohorts were based out of Japan, France, Italy, and England, with mean age between 46–54 years and 43–56% being male. The derivation cohorts were generally small, with 4 of the 6 studies reporting less than 50 people with unilateral PA. Common variables reported to be predictive of unilateral PA included male sex, hypokalemia, elevated aldosterone-renin ratio, and the presence of a unilateral adrenal nodule on imaging. The validation cohort included 342 PA patients who underwent successful AVS (average age, 52.1 years; 58.8% male). Among them, 186 (54.4%) demonstrated unilateral disease, and the remaining 156 (45.6%) were considered to have bilateral disease. The baseline characteristics of the validation cohort were broadly similar to those of the derivation cohorts, except for potential differences in ethnicity. When applying the models to the validation cohort, subjects were excluded if any candidate variables were missing. All 6 models demonstrated poor discrimination in the validation set (C-statistics; range, 0.59–0.72), representing a marked decrease compared to the derivation sets where they were reported (range, 0.80–0.87). Assessment of calibration by comparing observed and predicted probabilities of the unilateral subtype revealed significant miscalibration. Calibration-in-the-large for every model was >0 (range, 0.36–2.23), signifying systematic underprediction of unilateral PA. Calibration slopes were all <1 (range, 0.35–0.85), indicating poor performance at the extremes of risk. These results suggest that the original models were optimistic due to overfitting in the derivation cohorts and therefore lack generalizability. This is primarily because these models were developed in small data sets. In conclusion, clinical assessment with prediction models for unilateral PA cannot be readily used to bypass AVS in the general PA population.
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 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 | 0,059 | 0,123 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».