MP64-07 SIMPLIFIED FRAILTY INDEX PREDICTS ADVERSE OUTCOMES IN RADICAL CYSTECTOMY: AN ANALYSIS OF THE ACS- NSQIP DATABASE
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Résumé
You have accessJournal of UrologyBladder Cancer: Natural History and Pathophysiology1 Apr 2015MP64-07 SIMPLIFIED FRAILTY INDEX PREDICTS ADVERSE OUTCOMES IN RADICAL CYSTECTOMY: AN ANALYSIS OF THE ACS- NSQIP DATABASE Danny Lascano, Jamie S Pak, Michael J Lipsky, Julia B Finkelstein, Mitchell C Benson, G. Joel DeCastro, and James M McKiernan Danny LascanoDanny Lascano More articles by this author , Jamie S PakJamie S Pak More articles by this author , Michael J LipskyMichael J Lipsky More articles by this author , Julia B FinkelsteinJulia B Finkelstein More articles by this author , Mitchell C BensonMitchell C Benson More articles by this author , G. Joel DeCastroG. Joel DeCastro More articles by this author , and James M McKiernanJames M McKiernan More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2015.02.2318AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Frailty is a very difficult attribute to measure when assessing a surgical candidate. It is an established predictor for adverse health outcomes and very important to consider in an elderly population. The objective of this study was to analyze the data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) and apply a simplified frailty index to assess whether we can better predict adverse events in post radical cystectomy patients. METHODS We accessed the ACS-NSQIP Participant Utilization File from 2005-2012 for inpatient radical cystectomy patients. Using the Canadian Study of Health and Aging Frailty Index (FI), eleven variables were matched to NSQIP to create a modified frailty index (FI) including: diabetes mellitus, functional status, CHF, MI, prior cardiac surgery, hypertension, peripheral vascular disease, impaired sensorium, TIA or CVA with neurological sequela. Four variables specific to cancer that were also included were: chemotherapy or radiation, weight loss, renal failure, and metastasis. Outcomes assessed included 30-day mortality, surgical site infection (SSI), MI, DVT/PE, Clavien IV complications, possible never events (UTI, surgical site infections, DVT/PE), length of stay (LOS), and all combined adverse events. Chi-square was used for comparing categorical variables, Student-T test for continuous variables, and logistic regression for comparing different clinical tests. RESULTS A total of 2065 patients were identified in the ACS-NSQIP. An increased ratio of the FI was associated with increased adverse outcomes of any type, Clavien IV complications, and number of SSI (p= 0.015, 0.029, 0.022, respectively). LOS was increased in those with a FI greater than 0 (10.3 vs 9.2, p= 0.012). The FI was not significant for mortality, PE and DVT. On multivariate analysis, FI predicted MI better than existing methodologies including the work relative value unit, Charlson Comorbidity Index Score, American Society of Anesthesiologist (ASA) score, and functional status (OR 1.809, p= 0.044). CONCLUSIONS Using a large national database, a modified frailty index was shown to correlate with post-cystectomy 30-day morbidity and length of stay but not mortality. This simple tool may be useful for surgical planning and risk assessment for the high-risk elderly population prone to bladder cancer. © 2015 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 193Issue 4SApril 2015Page: e800 Advertisement Copyright & Permissions© 2015 by American Urological Association Education and Research, Inc.Metrics Author Information Danny Lascano More articles by this author Jamie S Pak More articles by this author Michael J Lipsky More articles by this author Julia B Finkelstein More articles by this author Mitchell C Benson More articles by this author G. Joel DeCastro More articles by this author James M McKiernan More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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 | 0,002 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| 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 | 0,001 |
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 ».