FP447INCIDENCE OF FRACTURE IN KIDNEY TRANSPLANTATION ACCORDING TO THE FOLLOW UP TIME: A POPULATION-BASED HEALTHCARE ADMINISTRATIVE STUDY
Notice bibliographique
Résumé
INTRODUCTION: The risk of fracture in end-stage kidney disease is at least 4-fold that in general population (GP) and remains higher even after kidney transplantation (KT). However, recent literature reports conflicting results regarding the risk of fracture in KT compared to the GP. We aimed to develop an algorithm for identification of KT patients from healthcare administrative database of Quebec (Canada), and measure the risk of fracture in KT compared to the GP. METHODS: Multiple algorithms combining billing codes related to KT were applied in the Quebec physician claims database to identify all new adult KT patients from 1996 to 2016. Algorithms were validated against the Quebec hospital discharge database. We estimated sensitivity (Sen), specificity (Spe), positive (PPV) and negative predictive value (NPV) to assess the accuracy of each algorithm. KT patients and age and sex matched group (10/case) from the Quebec GP were followed up from their index date to occurrence of first fracture, death, loss to follow-up or 31st of March 2016. Incidence of overall and hip fracture were compared in KT vs the GP using a cox regression model. We estimated Hazard ratios (HR) of fracture and 95% CI according to the follow up time, adjusted for antecedent of fracture, age, sex, diabetes, COPD and social and material deprivation index. Analyses were also stratified according to the date (period) of KT (1996-2001, 2002-2008, 2009-2016). RESULTS: Twelve algorithms were derived. Spe and NPV were 100% for all algorithms. Algorithm 1 (at least one physician claim with a billing code for KT) with excellent Sen (93.05%) and PPV (94.38%) was adopted. incidence rate were 11.42 and 5.44 for 1000 person-years for overall fracture, and 1.29 and 0.48 for hip fracture from 4630 KT and 46300 controls, respectively. Adjusted incidences of overall and hip fracture were significantly higher in KT comparatively to GP for all follow up time. HR of overall fracture was 2.01 (CI: 1.70 to 2.37) at one and 2.04 (CI: 1.47 to 2.82) at seventeen years of follow up. HR for hip fracture progressively increased with follow-up time, estimated at 2.31 (CI: 1.33 to 4.00), 2.53 (CI: 1.75 to 3.64), 2.84 (CI: 1.92 to 4.19), 3.18 (CI: 1.65 to 6.14), and 3.33 (CI: 1.52 to 7.30), at one, five, ten, fifteen, and seventeen years, respectively. The KT period also modified the association between KT and overall or hip fracture risk (Figure I). CONCLUSIONS: Our results demonstrate that algorithms using physician-claims database are accurate and reliable for identifying new cases of KT. Overall and hip fracture risk in KT is higher compared to the GP, while this association changes according to the period KT was carried out.
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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».