A166 RISK STRATIFICATION OF EARLY RE-HOSPITALIZATION IN PERSONS WITH INFLAMMATORY BOWEL DISEASES USING MULTIVARIABLE MODELS
Notice bibliographique
Résumé
Abstract Background Hospitalization for persons with inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), is a significant contributor to morbidity and health care costs in Canada. Recognition of individuals at high risk of re-hospitalization could help inform targeted outpatient interventions that mitigate this risk. Purpose The aim of our study is to derive prediction models of risk of early (90-day) re-hospitalization among persons with IBD. Method We conducted a retrospective cohort study of all adult persons with IBD admitted to The Ottawa Hospital, Canada, for an acute IBD-related indication between April 2009 - March 2016. Demographic, clinical, and health services variables were obtained through chart review. Persons were linked to population-based health administrative datasets to identify historical and future IBD-related hospitalizations across the greater Ottawa region. Multivariable logistic regression models of 90-day re-hospitalization in persons with CD and UC were derived, and candidate predictors that demonstrated an independent association with the outcome at a p-value of 0.1 were retained. Bootstrap internal validation (200 iterations) was performed on the final models. Model performance and calibration were evaluated using the optimism-corrected c-statistic value and Hosmer-Lemeshow goodness of fit test, respectively. Adjusted odds ratios are reported with 95% confidence intervals (CI). Optimal probability cut points for re-hospitalization were selected to optimize sensitivity, specificity, and the J (Youden’s) index. Result(s) There were 524 CD and 248 UC hospitalizations during the study period. Of these, 57 (10.9%) CD and 27 (10.9%) UC hospitalizations were associated with re-hospitalization within 90 days of discharge. Forty-two candidate predictors were tested among CD hospitalizations, and 35 were tested among UC hospitalizations. Four variables were retained in each of the final models. Model performance and calibration for each variable are described in Table 1. The optimal range of probability cut points allowed for a sensitivity/positive predictive value (PPV)/false positive rate (FPR) of 0.72/0.23/0.29 (maximum J-index of 0.43) in the model for CD, and 0.78/0.33/0.19 (maximum J-index of 0.59) in the model for UC, respectively. Image Conclusion(s) Demographic, clinical, and health services variables at the time of discharge have the potential to help identify persons with IBD at risk of early re-hospitalization, thereby permitting targeted outpatient intervention. Application of the models to our reference cohorts would earmark 1/3 or less of patients for early post-discharge intervention, with the potential to benefit more than 70% of patients destined for early re-hospitalization. Although the PPVs of our models were low, the models incorrectly predicted early re-hospitalization in less than 30% of patients. We are in process of externally validating these models in other jurisdictions across Ontario to test their generalizability. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared
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 | 0,004 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 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 ».