MP08-14 TRENDS AND PREDICTORS OF 30 DAY READMISSIONS FOLLOWING PERCUTANEOUS NEPHROLITHOTOMY IN KIDNEY STONES FORMERS AND IMPLICATIONS FOR READMISSIONS-BASED QUALITY METRICS
Bibliographic record
Abstract
You have accessJournal of UrologyStone Disease: Epidemiology & Evaluation I (MP08)1 Apr 2019MP08-14 TRENDS AND PREDICTORS OF 30 DAY READMISSIONS FOLLOWING PERCUTANEOUS NEPHROLITHOTOMY IN KIDNEY STONES FORMERS AND IMPLICATIONS FOR READMISSIONS-BASED QUALITY METRICS David-Dan Nguyen*, Sabrina A. Harmouch, Alexander Putnam Cole, Ashwin Ramaswamy, Stuart R. Lipsitz, Quoc-Dien Trinh, and Naeem Bhojani David-Dan Nguyen*David-Dan Nguyen* More articles by this author , Sabrina A. HarmouchSabrina A. Harmouch More articles by this author , Alexander Putnam ColeAlexander Putnam Cole More articles by this author , Ashwin RamaswamyAshwin Ramaswamy More articles by this author , Stuart R. LipsitzStuart R. Lipsitz More articles by this author , Quoc-Dien TrinhQuoc-Dien Trinh More articles by this author , and Naeem BhojaniNaeem Bhojani More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555118.62412.55AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: The use of hospital-readmission rates as a hospital quality metric has been debated as hospitals’ post-surgical readmission rates may be more due to patient factors (case mix) compared to hospital factors. It is not known whether a similar trend is present in advanced endoscopic procedures. We therefore sought to evaluate the contribution of individual hospitals on the patient-level probability of readmission after a typical high-risk endoscopic procedure, percutaneous nephrolithotomy (PCNL). METHODS: Using the Nationwide Readmission Database, we identified non-elective 30-day readmissions following PCNL in U.S. hospitals in 2014. Using a multilevel mixed effects model, we estimated the influence of hospital and clinical variables on patients′ odds of readmission. A hospital-level random effects term was used to estimate the contribution of unmeasured hospital characteristics on their patients′ probability of readmission. In order to assess the relative contribution of each group on the predicted probability readmissions, a pseudo R-squared was calculated for predictor variables. RESULTS: For a weighted sample of 6,974 patients who received PCNL at 485 hospitals, the 30-day readmission rate was 8.5% (95% CI 7.4 - 9.7). In our adjusted model, hospital characteristics such as surgical volume were not associated with increased likelihood of readmission. Individual hospitals contributed marginally to their patients′ probability of readmission. Patient-level characteristics explained far more of the variability in readmissions than hospital characteristics (R squared 0.53963 vs 0.00305). CONCLUSIONS: Compared to patient-level characteristics, hospital characteristics contributed minimally to a model predicting patient-level probability of readmission. These findings underscore the potential limitations of 30-day post-discharge readmissions to evaluate hospital quality of care. Source of Funding: Brigham Research Institute, Bruce A. Beal and Robert L. Beal Surgical Fellowship, Conquer Cancer Foundation, Defense Health Agency, Intuitive Surgical, Prostate Cancer Foundation, Vattikuti Urology Institute. Boston, MA; Montreal, Canada© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e103-e104 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information David-Dan Nguyen* More articles by this author Sabrina A. Harmouch More articles by this author Alexander Putnam Cole More articles by this author Ashwin Ramaswamy More articles by this author Stuart R. Lipsitz More articles by this author Quoc-Dien Trinh More articles by this author Naeem Bhojani 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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".