MP31-10 SYSTEMATIC REVIEW AND META-ANALYSIS OF FRAILTY INDICES IN UROLOGIC SURGERY: RISK PREDICTION OF POSTOPERATIVE COMPLICATIONS
Bibliographic record
Abstract
You have accessJournal of UrologyCME1 May 2022MP31-10 SYSTEMATIC REVIEW AND META-ANALYSIS OF FRAILTY INDICES IN UROLOGIC SURGERY: RISK PREDICTION OF POSTOPERATIVE COMPLICATIONS Jane Kurtzman, Preston Kerr, Rashed Kosber, and Steven Brandes Jane KurtzmanJane Kurtzman More articles by this author , Preston KerrPreston Kerr More articles by this author , Rashed KosberRashed Kosber More articles by this author , and Steven BrandesSteven Brandes More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002580.10AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Contemporary surgical planning rarely involves formally assessing patient frailty, especially among urology (GU) patients. Our aim was to systematically review the literature to assess the ability of frailty indices to predict the risk of postoperative complications after GU surgery. METHODS: We systematically reviewed EMBASE, PubMed and SCOPUS according to PRISMA criteria in June 2021. Studies that utilized a validated frailty index (FI) to assess risk of major postoperative complication (Clavien-Dindo ≥3), following GU surgery were eligible for inclusion. Charlson Comorbity Index and Eastern Cooperative Oncology Group were not considered FIs. Administrative studies, those without odds ratios (OR) and/or raw data were excluded. The pooled effect size was calculated as OR and corresponding 95% CI through a random effect model using inverse variance weighing. RESULTS: Of 1,265 unique articles initially identified, 9 studies - from 6 different countries, published from 2019-2021, were eligible for inclusion (4 prospective; 5 retrospective). 8/9 studies assessed only GU oncologic surgery. The Modified Frailty Index (mFI) was the most commonly used index (n=3), followed by the Fried Phenotype Criteria (n=2), the Canadian Study of Health and Aging (CSHA) Index (n=2) and the Rockwood Frailty Index (n=2). Data from a total of 2,153 patients was included. Based on pooled OR from both univariable and multivariable analyses, frailty was associated with a significantly higher odds of postoperative complication at 30 days (OR 2.7, 95% CI: 1.8-4.0, p <0.001 and OR 2.1, 95% CI: 1.5-3.0, p <0.001, Fig 1A-B), but may not be at 90 days (OR 2.0, p=0.11 and OR 1.6, p=0.35, Fig 1C-D). Stratified by FI, higher Rockwood and CSHA scores were associated with an increased odds of 30 d complication (OR 1.8, p <0.001 and OR 2.6, p=0.002), but mFI ≥2 was not (OR 5.0, p=0.11). CONCLUSIONS: Frailty indices are a relatively new addition to the urologist’s toolkit. Preoperative assessment of frailty can help predict risk of early (30 d) major postoperative complications but may be less useful for later complications, though significant additional studies are needed. Further work is also needed to evaluate the impact of frailty on non-oncologic urology patients who undergo major GU surgery. Source of Funding: None © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e525 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Jane Kurtzman More articles by this author Preston Kerr More articles by this author Rashed Kosber More articles by this author Steven Brandes More articles by this author Expand All 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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".