Frailty impact on postoperative complications and early mortality rates in patients undergoing radical cystectomy for bladder cancer: a systematic review
Notice bibliographique
Résumé
Objective: To assess the prevalence of frailty, a status of vulnerability to stressors leading to adverse health events, in bladder cancer patients undergoing radical cystectomy (RC), and test the impact of frailty measurements on postoperative adverse outcomes. Methods: A systematic review of English-language articles published up to April 2020 was performed. Electronic databases were searched to quantify the frailty prevalence in RC patients and assess the predictive ability of frailty indexes on RC-related outcomes as postoperative complications, early mortality, hospitalization length (LOS), costs, discharge dispositions, readmission rate. Results: Eleven studies were selected. Patients’ frailty was identified by Johns Hopkins indicator (JHI) in two studies, 11-item modified Frailty Index (mFI) in four, 5-item simplified FI (sFI) in three, 15-point mFI in one, Fried Frailty Criteria in one. Considering all the frailty measurements applied, 8% and 31% of patients were frail or pre-frail, respectively. Frail (43%) and pre-frail patients (35%) were more at risk of major complications compared to non-frail (27%) using sFI; with JHI the percentages of frail and non-frail were 53% versus 19%. According to JHI and mFI frailty was related to longer LOS and higher costs. JHI identified that 3% of frail patients experience in-hospital mortality versus 1.5% of non-frail. Finally, using sFI, frail (28%), and pre-frail (19%) were more likely to be discharged non-home compared to non-frail patients (8%) and had a higher risk of 30-day mortality (4% and 2% versus 1%). Conclusions: Almost half of RC patients were frail or pre-frail, conditions significantly related to an increased risk of postoperative adverse events with higher rates of major complications and early mortality. The most-used frailty index was mFI, while JHI and sFI resulted the most reliable to predict early postoperative RC-related adverse outcomes and should be routinely included in clinical practice after better standardization throughout prospective comparative studies. Abbreviations: ACG: Adjusted Clinical Groups; ACS: American College Surgeons; AUC: area under the curve; BCa: bladder cancer; CCI: Charlson Comorbidity Index; CSHA-FI: Canadian Study of Health and Aging Frailty Index; CCS: Clavien-Dindo Classification Score; ERAS: Enhanced Recovery After Surgery; FFC: Fried Frailty Criteria; (e)(m)(s)FI: (extended) (modified) (simplified) Frailty Index; ICU: intensive care unit; IQR: interquartile range; (p)LOS: (prolonged) length of hospital stay; NSQIP: National Surgical Quality Improvement Program; OR: odds ratio; (O)PN: (open) partial nephrectomy; PRISMA: Preferred Reporting Items for Systematic reviews and Meta-Analyses; (O)(RA)RC: (open)(robot-assisted) radical cystectomy; (O)RN: (open) radical nephrectomy; ROC: receiver operating characteristic; RNU: radical nephroureterectomy; (R)RP: (retropubic) radical prostatectomy; RR: relative risk; THCs: total hospital charges; nephrectomy; UD: urinary diversion
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 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,009 |
| Bibliométrie | 0,007 | 0,007 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 ».