MP63-01 MODIFIED FRAILTY INDEX PREDICTS MORTALITY AND ADVERSE OUTCOMES IN PATIENTS UNDERGOING RENAL SURGERY: ANALYSIS OF THE NATIONAL SURGICAL QUALITY IMPROVEMENT PROGRAM (NSQIP) DATABASE
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Résumé
You have accessJournal of UrologyKidney Cancer: Surgical Therapy IV1 Apr 2015MP63-01 MODIFIED FRAILTY INDEX PREDICTS MORTALITY AND ADVERSE OUTCOMES IN PATIENTS UNDERGOING RENAL SURGERY: ANALYSIS OF THE NATIONAL SURGICAL QUALITY IMPROVEMENT PROGRAM (NSQIP) DATABASE Jamie S. Pak, Danny Lascano, Julia B. Finkelstein, Mark V. Silva, G. Joel DeCastro, James M. McKiernan, and Mitchell C. Benson Jamie S. PakJamie S. Pak More articles by this author , Danny LascanoDanny Lascano More articles by this author , Julia B. FinkelsteinJulia B. Finkelstein More articles by this author , Mark V. SilvaMark V. Silva More articles by this author , G. Joel DeCastroG. Joel DeCastro More articles by this author , James M. McKiernanJames M. McKiernan More articles by this author , and Mitchell C. BensonMitchell C. Benson More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2015.02.2333AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Frailty, a concept of growing interest in light of the aging population, describes the gradual loss of physical and mental capacity. Practically, an objective measure of frailty can replace the often subjective assessment of a patient's ability to tolerate a surgical intervention. We propose that a modified version (mFI) of the Canadian Study of Health and Aging Frailty Index (CSHA-FI) can predict 30-day mortality and other adverse outcomes in patients undergoing renal surgery. METHODS We accessed the NSQIP database for all partial, simple, and radical nephrectomies as well as nephroureterectomies performed from 2005 to 2012. The mFI was calculated as the proportion of the following 11 CHSA-FI risk factors present in each patient: diabetes mellitus; dependent functional status; history of severe COPD or current pneumonia; CHF within 30 days before surgery; history of MI 6 months prior to surgery; previous PCI, cardiac surgery, or history of angina within 1 month before surgery; hypertension requiring medication; peripheral vascular disease or rest pain; impaired sensorium; history of TIA or CVA; history of CVA with neurologic deficit. Primary outcome was 30-day mortality. Chi-square analysis (± Fisher's exact test) and Kruskal-Wallis test were performed for statistical analysis. RESULTS A total of 8,542 patients were identified. There were 65 deaths, 52 MIs, 41 cardiac arrests requiring CPR, 100 DVT/PEs, 162 SSIs, 145 UTIs, 43 instances of septic shock, 76 instances of ventilator dependence >48 hours, 118 unplanned intubations, and 85 episodes of acute renal failure (ARF) requiring dialysis. Higher mFI was strongly associated with 30-day mortality, septic shock, ventilator dependence, unplanned intubation, Clavien IV complications, and any adverse outcome after renal surgery (all p<0.0005). mFI was also associated with MI, UTI, and ARF (p<0.05). Higher mFI correlated with increasing mean ranks in operative time (p=0.032) and in hospital length of stay (p<0.0005). Odds ratio of 30-day mortality in patients with mFI ≥0.27 was 6.47 (95% CI 1.96-21.30, p<0.0021). CONCLUSIONS Patients with mFI ≥0.27 were over 6 times more likely to die within 30 days after renal surgery. mFI was also associated with numerous other significant perioperative outcomes. These findings support the utility of this simple tool as a predictor of adverse outcomes in patients undergoing renal surgery and potentially urologic surgery in general. © 2015 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 193Issue 4SApril 2015Page: e789 Advertisement Copyright & Permissions© 2015 by American Urological Association Education and Research, Inc.MetricsAuthor Information Jamie S. Pak More articles by this author Danny Lascano More articles by this author Julia B. Finkelstein More articles by this author Mark V. Silva More articles by this author G. Joel DeCastro More articles by this author James M. McKiernan More articles by this author Mitchell C. Benson 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 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| 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 ».