MP08-14 TRENDS AND PREDICTORS OF 30 DAY READMISSIONS FOLLOWING PERCUTANEOUS NEPHROLITHOTOMY IN KIDNEY STONES FORMERS AND IMPLICATIONS FOR READMISSIONS-BASED QUALITY METRICS
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Résumé
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 ...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».