MP14-13 PRIORITIZATION OF RENAL CANCER QUALITY INDICATORS USING VARIANCE DECOMPOSITION AND MEDIATION ANALYSIS METHODS
Bibliographic record
Abstract
You have accessJournal of UrologyKidney Cancer: Epidemiology & Evaluation/Staging/Surveillance II (MP14)1 Apr 2019MP14-13 PRIORITIZATION OF RENAL CANCER QUALITY INDICATORS USING VARIANCE DECOMPOSITION AND MEDIATION ANALYSIS METHODS Keith Lawson*, Katherine Daignault, Madhur Nayan, Bo Chen, Lisa Martin, Maria Komisarenko, Olli Saarela, and Antonio Finelli Keith Lawson*Keith Lawson* More articles by this author , Katherine DaignaultKatherine Daignault More articles by this author , Madhur NayanMadhur Nayan More articles by this author , Bo ChenBo Chen More articles by this author , Lisa MartinLisa Martin More articles by this author , Maria KomisarenkoMaria Komisarenko More articles by this author , Olli SaarelaOlli Saarela More articles by this author , and Antonio FinelliAntonio Finelli More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555309.07968.b6AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: We have previously demonstrated widespread quality of care variations between hospitals for patients with renal cell carcinoma (RCC) utilizing a validated set of quality indicators (QI). [1] However, the translation of this knowledge is limited by an incomplete understanding of the sources of this variation. Here we aimed to determine the relative contribution of surgeon versus hospital-level quality of care variations in order to inform the development of appropriate benchmarking strategies. Further, we sought to quantify the impact that hypothetical interventions on process-type indicators can have on outcome-type indicators. METHODS: Patients undergoing nephrectomy for RCC were identified using linked population-level administrative databases in Ontario, Canada. Hospitals and individual surgeons were benchmarked according to previously validated RCC-specific QIs using indirect standardization1, adjusting performance for clinicopathological variables inherent to their patient populations. Inter-hospital and -surgeon level variation in each QI was assessed via mixed effect models and meta-regression. Causal mediation analysis was employed to identify hospitals that could benefit from targeted quality improvement initiatives. RESULTS: A total of 10,111 nephrectomy patients with complete clinical and pathological data were identified between 1995 to 2014. Care was provided by 393 surgeons across 138 hospitals. After adjustment for case-mix, a total of 3-25% of surgeons and 4-32% of hospitals were statistically significant outliers for a given QI, performing worse than the provincial average. Significant variation at both hospital and surgeon levels was observed for four of the five indicators (P<0.001). Surgeon-level variance was equal to or greater than hospital-level in four of five indicators. The causal mediation analysis identified hospitals where improvements in minimally invasive surgery rates could translate to shorter average length of stay. CONCLUSIONS: Both surgeon- and hospital-level effects contribute to the observed variance in quality of care received by patients undergoing surgery for RCC. Further, we demonstrate the feasibility of identifying hospitals for targeted quality improvement interventions through causal mediation analysis. Collectively, these data demonstrate the utility of QI variance decomposition and mediation analysis methods for informing the development of data-driven quality benchmarking strategies for RCC. 1. Lawson et. al. Eur Urol 2017. 72(3):379-386. Source of Funding: Princess Margaret Cancer Centre Foundation, Canadian Institutes of Health Research Toronto, Canada© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e191-e191 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Keith Lawson* More articles by this author Katherine Daignault More articles by this author Madhur Nayan More articles by this author Bo Chen More articles by this author Lisa Martin More articles by this author Maria Komisarenko More articles by this author Olli Saarela More articles by this author Antonio Finelli 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 | 0.052 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.055 | 0.003 |
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".