A58 POPULATION CHARACTERISTICS AND OUTCOMES OF CARE IN PATIENTS WITH DECOMPENSATED CIRRHOSIS ADMITTED TO MEDICAL SERVICES AT THE OTTAWA HOSPITAL
Notice bibliographique
Résumé
Patients with decompensated cirrhosis have increased healthcare needs and consequentially greater healthcare utilization. Whether specific types of decompensating events including ascites, spontaneous bacterial peritonitis (SBP), hepatic encephalopathy (HE), variceal bleeding (VB), and hepatorenal syndrome (HRS) are associated with greater risk of hospital readmission is unclear. We aim to describe an inpatient cohort admitted with decompensated cirrhosis at a single tertiary care center and evaluate important clinical endpoints including length of stay (LOS), inpatient mortality and 30-day readmission rates (R-30) by presence and type of liver decompensation events. Patients with decompensated cirrhosis admitted to a medical service at TOH between July 2014–2016 were identified using ICD codes. Cirrhosis and decompensating events were confirmed by chart review. Of the 302 patient admissions reviewed to date, 190 were first presentation to TOH with decompensated cirrhosis. Among those, 40.5% consumed alcohol in the prior week, and 62.6% of patients had one or more pre-existing complications of cirrhosis (ascites 53.7%, HE 25.8%, SBP 6.8%, VB 12.6%, HRS 1.1%). The average MELD-Na on admission was 19.2 (SD 6.8) for all unique patients. Admissions were complicated by ascites in 70.5%, HE in 25.5%, VB in 20.5%, SBP in 9.5%, and HRS in 5.8%. Patients with ascites, HE and HRS had significantly longer LOS compared to those without: ascites median LOS 6.9 days (IQR 4.0–17.5, p = 0.025), HE 8.7 (4.5–22.0, p = 0.046), HRS 20.7 (11.7–26.9, p = 0.0022). VB trended towards prolonging LOS but not significantly (median 5.29, IQR 3.28–6.89, p=0.051). SBP did not significantly impact LOS (median 12.1, IQR 4.9–24.0, p = 0.13). R-30 was significantly greater in those with HE (37.5%, p=0.008) compared to those without, while other in-hospital decompensating events did not significantly impact R-30 (ascites 23.9%, p=0.72; SBP 27.8%, p=0.63; HRS 9.1%, p=0.26; VB 18.0%, p=0.65). Pre-existing HE was also a significant predictor of R-30 (34.7%, p=0.026). Overall inpatient mortality rate was 19.0%. Inpatient mortality was greatest in those with an in-hospital diagnosis of HRS (72.7%, p<0.0001), followed by SBP (55.6%, p<0.0001) and ascites (19.0%, p=0.002). Mortality rates were not significantly different in patients with HE and VB compared to those without (18.8%, p=0.89; and 20.5%, p=0.94, respectively). In our cohort, patients admitted with decompensated cirrhosis have high in-hospital mortality, prolonged LOS and increased risk of 30-day readmission. Decompensating events, notably HE, negatively impact hospital outcomes such as LOS and R-30, while HRS, SBP and ascites are associated with increased mortality. Future work will target strategies to improve the care for these patients. None
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».