The Revised Identification of Seniors At Risk screening tool predicts readmission in older hospitalized patients: a cohort study
Notice bibliographique
Résumé
BACKGROUND: The Identification of Seniors at Risk (ISAR) screening tool is a widely-used risk stratification tool for older adults in the emergency department (ED). Few studies have investigated the use of ISAR to predict outcomes of hospitalized patients. To improve usability a revised version of ISAR (ISAR-R), was developed in a quality improvement project. The ISAR-R is also widely used, although never formally validated. To address these two gaps in knowledge, we aimed to assess the ability of the ISAR-R to predict readmission in a cohort of older adults who were hospitalized (admitted from the ED) and discharged home. METHODS: This was a secondary analysis of data collected in a pre-post evaluation of a patient discharge education tool. Participants were patients aged 65 and older, admitted to hospital via the ED of two general community hospitals, and discharged home from the medical and geriatric units of these hospitals. Patients (or family caregivers for patients with mental or physical impairment) were recruited during their admission. The ISAR-R was administered as part of a short in-hospital interview. Providers were blinded to ISAR-R scores. Among patients discharged home, 90-day readmissions were extracted from hospital administrative data. The primary metrics of interest were sensitivity and negative predictive value. The Area Under the Curve (AUC) was also computed as an overall measure of performance. RESULTS: Of 711 attempted recruitments, 496 accepted, and ISAR-R was completed for 485. Of these 386 patients were discharged home with a complete ISAR-R, the 90-day readmission rate was 24.9%; the AUC was 0.63 (95% CI 0.57,0.69). Sensitivity and negative predictive value at the recommended cut-point of 2 + were 81% and 87%, respectively. Specificity was low (40%). CONCLUSIONS: The ISAR-R tool is a potentially useful risk stratification tool to predict patients at increased risk of readmission. Its high values of sensitivity and negative predictive value at a cut-point of 2 + make it suitable for rapid screening of patients to identify those suitable for assessment by a clinical geriatric team, who can identify those with geriatric problems requiring further treatment, education, and follow-up to reduce the risk of readmission.
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 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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».