POST-OPERATIVE LIVER DECOMPENSATION EVENTS FOLLOWING PARTIAL HEPATECTOMY AMONG PATIENTS WITH CIRRHOSIS AND HEPATOCELLULAR CARCINOMA
Notice bibliographique
Résumé
Background: Partial hepatectomy, or liver resection, is a potentially-curative therapy for patients diagnosed with hepatocellular carcinoma (HCC). Because the majority of patients with HCC have pre-existing cirrhosis, pre-operative decision-making must consider severity of liver dysfunction to mitigate adverse liver-related post-operative outcomes. Objectives: The goals of this thesis were: 1) to critically appraise currently available prognostic models for predicting the risk of post-operative liver decompensation events (POLDEs), and 2) to identify patient-level, pre-operative predictors of POLDEs among individuals with cirrhosis and HCC undergoing liver resection. Methods: A systematic review of the literature was performed to identify multivariable prognostic models predicting the risk of POLDEs following liver resection. Study details regarding patients, outcomes, predictors, methodology, statistical analyses were abstracted. Studies were qualitatively assessed for risk of bias. A population-based retrospective cohort study was also conducted, of patients with cirrhosis and incident HCC diagnosed between 2007-2017 in the province of Ontario. Cox proportional hazards regression was used to identify independent predictors of POLDE-free survival, and cause-specific hazards for POLDEs and death. Results: In total, 36 multivariable prognostic modelling studies were identified; 25 focused on model development, 3 performed development and external validation, and 8 validated pre- existing models. Commonly used predictors in these models were serum bilirubin, platelet count, and indocyanine green retention rate at 15 minutes (ICGR15). Due to statistical and methodologic concerns, all studies were assessed a high risk of bias. In the population study, the cohort comprised 611 patients with cirrhosis and incident HCC, who subsequently underwent liver resection. Of these, 160 (26.2%) experienced at least 1 POLDE within 2 years of resection and 189 (30.9%) died in the same timeframe. Independent predictors of inferior POLDE-free survival were presence of diabetes, major liver resection, and previous non-malignant decompensation. In contrast, hepatitis B cirrhosis etiology appeared to be protective. In cause-specific analysis, the same risk factors were associated with POLDEs, except for planned extent of liver resection. Age (per year) and history of previous non-malignant decompensation were cause-specific predictors of death. Conclusions: Currently available multivariable prognostic models for predicting the risk of post- operative liver decompensation events following liver resection have limited validity and applicability for routine clinical use. We have identified patient and disease-related factors associated with POLDE-free survival and POLDEs, which can be used for improved patient selection and to develop prognostic tools, with the aim of improving post-operative outcomes among patients with cirrhosis and HCC.
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 | 0,003 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,001 | 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 ».