SP210EXTERNAL VALIDATION OF A RISK SCORE TO PREDICT AKI IN EITHER THE COMMUNITY OR HOSPITAL SETTING
Notice bibliographique
Résumé
INTRODUCTION: Acute Kidney Injury (AKI) affects approximately 15% of all hospitalised patients in developed countries with a significant proportion originating in the community. Even small changes in kidney function are associated with adverse outcomes, including increased mortality even in patients with Stage 1 AKI compared to those without AKI. It has been suggested that up to 30% of AKI episodes may be preventable. Recognition of individuals at risk of AKI is therefore a critical first step in implementing strategies to prevent AKI. The aim of this study was to develop and externally validate a practical score to predict the risk of any AKI (either in hospital or the community) for use in the general population using routinely collected data. METHODS: Routinely collected linked data sets from Tayside, Scotland, were used to develop the risk score, and data sets from Kent in the United Kingdom and Alberta in Canada were used to externally validate it. AKI was defined using the Kidney Disease Kidney Improving Global Outcomes serum creatinine based criteria based on the standardised United Kingdom National Health Service algorithm. Multivariable logistic regression analysis was performed, with occurrence of AKI within one year as the dependent variable. Model performance was determined by assessing discrimination (c-statistic) and calibration. RESULTS: The risk score was developed in 273,450 patients from the Tayside region of Scotland and externally validated in two other populations; a cohort of 218,091 patients from Kent, United Kingdom and a cohort of 1,173,607 patients from Alberta, Canada. Four independent predictors for AKI were included in the risk score; older age, lower baseline eGFR, diabetes and heart failure. A risk score including these four variables had good predictive performance, with a c-statistic of 0.80 (95% CI 0.80-0.81) in the development cohort, 0.71 (95% CI 0.70-0.72) in the Kent, UK external validation cohort and 0.76 (95% CI 0.75- 0.76) in the Canadian validation cohort. Better discrimination was observed for predicting more severe (KDIGO Stage 2 or 3) AKI with a c-statistic of 0.81 (95% CI 0.80 -0.82) in the development cohort, 0.74 (95% CI 0.73-0.75) in the Kent, UK external validation cohort and 0.78 (95%CI 0.77- 0.78) in the Canadian validation cohort. CONCLUSIONS: Identification of patients at high risk for AKI is key to early identification and prevention of AKI. We have devised and validated both within and out with the UK a simple risk score from routinely collected data which can aid both primary and secondary care physicians in identifying these patients.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 | 0,000 |
| 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 ».