Effects of nationwide alerting for acute kidney injury on healthcare and patient outcomes: population based, regression discontinuity analysis
Notice bibliographique
Résumé
Abstract Objective To determine whether the implementation of electronic alerts for acute kidney injury improves care and outcomes in real-world clinical practice. Design Population-based, regression discontinuity analysis Setting Hospital and community-based systems across all 8 health boards in Wales, from 2016 to 2021 following implementation of AKI alerts. Participants 3.1 million adults aged ≥18 years resident and registered at GP practices in Wales between the years of 2016-2021, following implementation of AKI e-alerts across all 8 Wales health boards. Exposure Electronic alerts for acute kidney injury provided in either a passive (7 boards) or interruptive (1 board) manner to physicians through laboratory reporting systems. Main outcome measures Mortality, hospital admission or readmission, severity and recovery of AKI, coding of acute kidney injury, proteinuria and blood pressure measurements. Results Among 861,494 and 354,505 eligible patient encounters, 5.8% and 2.0% AKI alerts were generated from hospital and community settings, respectively; mean age 64 years, 54% female. In both hospital and community settings, respectively, AKI alerts led to no significant differences in mortality [complier average treatment effect +1.31% (95% CI −3.07, 4.74); +2.07% (95% CI −3.44, 6.65)] or admissions/readmissions [+0.13% (95% CI −3.82, 4.21); +4.07% (95% CI −1.84, 8.27)]. There was a modest increase in hospital coding of AKI with alerts [+5.88% (95% CI 2.22, 7.58)], but no difference in primary care coding of AKI after discharge [+0.72% (95% CI −0.67, 1.30)]. Alerts exerted small increases on subsequent checks for proteinuria and of blood pressure. Findings were consistent for passive and interruptive alerts. There were no meaningful differences by rurality, deprivation, sex, history of surgery, diabetes, or vascular disease. Conclusions The nationwide implementation of AKI alerts in Wales produced small effects on documentation of AKI and some processes of care but exerted no effects on survival or hospital admissions in acute care or community settings. The consistently poor outcomes, and the deficiencies in documentation and care after AKI highlight the ongoing need for an improved clinical response. Registration A prespecified analysis protocol is available at Open Science Framework (OSF) repository ( https://osf.io/f4wz9/?view_only=d20e49817bc74f1fa66afc7aa8dda0ab ) Summary What is known Previous randomised trials and real-world observational studies of electronic alerts for acute kidney injury (AKI) have produced mixed results. The applicability of previous trials for real-world clinical practice remains uncertain and contested. AKI e-alerts are still widely used. What we did We applied Regression Discontinuity Design (RDD), a method gaining traction in clinical research. RDD can work like an ideal target trial to enable causal inference in real-world settings. RDD is particularly effective when clinical actions are triggered by a specific measurement threshold. This approach allowed us we to evaluate a nationwide AKI e-alert initiative across Wales. What we found We found no evidence that AKI e-alerts improved or worsened outcomes across any clinical setting, patient subgroup, or alert delivery method. Nonetheless, the consistently poor outcomes, and the deficiencies in documentation and care after AKI highlight the ongoing need for an improved clinical response.
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 | 0,062 | 0,123 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,008 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».