MP64-07 SIMPLIFIED FRAILTY INDEX PREDICTS ADVERSE OUTCOMES IN RADICAL CYSTECTOMY: AN ANALYSIS OF THE ACS- NSQIP DATABASE
Bibliographic record
Abstract
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 ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".