Achieving the achievable in muscle-invasive bladder cancer
Notice bibliographique
Résumé
Patient outcomes reported from clinical trials and case series from centres of excellence define the benchmark for what is achievable among patients with muscle-invasive bladder cancer (MIBC). However, because patients, medical care and health systems can be very different in routine clinical practice there is often a gap between efficacy (i.e., results observed in trials) and effectiveness (i.e., results observed in the general population).1,2 Population-based studies are important to identify gaps in care and areas for improvement so that clinicians and patients might move towards “achieving the achievable.”3 Multiple population-based studies and a meta-analysis have consistently demonstrated an inverse relationship between hospital cystectomy volume and postoperative mortality.4–8 However critical questions remain unanswered including: what factors are responsible for the observed volume effect?; how much of the observed effect relates to hospital volume versus individual surgeon volume?; and how should volume be defined, classified and analyzed? Furthermore, beyond operative mortality and complications there is considerably less literature describing the relationship between cystectomy volume and long-term survival.9 In the paper by Bianchi and colleagues published in this issue of CUAJ, the authors have evaluated the impact of hospital academic affiliation on short term radical cystectomy outcomes.10 Using records from the Health Care Utilization Project Nationwide Inpatient Sample the authors explore postoperative complications and mortality across hospitals in the United States. The unadjusted results suggest greater complication rates, length of stay (LOS), and post-operative mortality in patients who have surgery at non-academic hospitals. However, in the multivariate analysis the there is no difference in LOS and post-operative mortality and a statistically significant but clinically modest increase in complications. A more fundamental question is how to disentangle the relationship between hospital volume, academic status, and outcome? While most previous studies have analyzed volume as either a continuous variable or a categorical variable using tertiles/quartiles, Bianchi and colleagues dichotomize annual hospital caseload as greater than 15 or less than 15 cystectomies per year.10 The cut-point is very high relative to other studies where “high volume” hospitals are usually defined as those that perform >5 to 10 cystectomies per year.4–8 In dichotomizing this outcome and using such a high threshold, Bianchi and colleagues are left with only 12% (n = 1515) of their study population in the high volume group and all of these cases had surgery at academic hospitals. Accordingly it is very likely that any potential volume effect has been lost in the statistics. The authors suggest that patients treated at academic hospitals are slightly younger, have less comorbidity, and are more likely to have private health insurance.10 Despite adjusted analyses there remains the potential for unmeasured confounding. Higher volume hospitals might have higher volume surgeons with better surgical technique, improved perioperative care and more multidisciplinary co-management. It is less straightforward to conceptualize or measure how academic status in itself might be associated with outcome independent of hospital volume. This highlights the importance in any volume-outcomes research to sequentially control for covariates that might partially explain any observed association between volume and outcome. This is critical because it can provide insight into the reasons why higher volume hospitals (or academic hospitals) have better outcomes and thereby creates a model to improve outcomes at low- and medium-volume centres. The alternative is to consolidate all care at high-volume hospitals which may not be feasible, practical, or desirable and needs to be balanced against the very real risk of reduced access to care. This issue has been nicely explored by Elting and colleagues in their study of all cystectomy cases in Texas during 1999–2001.5 Although unadjusted postoperative mortality was lower in high-volume hospitals they discovered that much of the association was explained by differences in the nurse-to-patient ratio such that good outcomes could also be achieved in lower volume hospitals with higher staffing ratios. Management of MIBC is complex and best managed by a multidisciplinary team. In addition to maximizing the effectiveness of cystectomy in routine clinical practice, efforts are required to improve uptake of perioperative chemotherapy and ensure that patients who are not candidates for cystectomy are considered for radical radiotherapy which also offers the chance of long term survival. It is imperative to understand how quality and processes of care can be maximized to close the efficacy-effectiveness gap and improve patient outcomes.
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 | 0,147 | 0,434 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,010 | 0,010 |
| Science ouverte | 0,003 | 0,010 |
| Intégrité de la recherche | 0,004 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».