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
Bibliographic record
Abstract
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 ...
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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".