You have accessJournal of UrologyKidney Cancer: Epidemiology & Evaluation/Staging II1 Apr 2018MP36-18 PREDICTORS OF READMISSIONS WITHIN 90 DAYS FOLLOWING RADICAL NEPHRECTOMY: A POPULATION-BASED ANALYSIS FROM 1995 TO 2015 Anthony Yang, Mark Finkelstein, Khawaja Bilal, Natan Davoudzadeh, and Michael Palese Anthony YangAnthony Yang More articles by this author , Mark FinkelsteinMark Finkelstein More articles by this author , Khawaja BilalKhawaja Bilal More articles by this author , Natan DavoudzadehNatan Davoudzadeh More articles by this author , and Michael PaleseMichael Palese More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.1152AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES We examined the trends and risk factors for all-cause unplanned readmissions following radical nephrectomy (RN) up to 90 days. METHODS Patients who underwent RN between 1995 and 2015 were identified from the Statewide Planning and Research Cooperative System (SPARCS) database, a comprehensive all-payer reporting system containing patient level data on all hospital discharges in New York State. Donor nephrectomy patients were excluded. The chi-square test was used to compare variables. Multivariable logistic regression was used to determine independent predictors of readmission at 90 days. RESULTS 47,153 patients underwent RN in 1995-2015. The readmission rate at 90 days was 14.9%. The top diagnoses associated with readmissions at 90 days were postoperative infection (4.6%), and acute kidney failure (3.2%). Multivariable analysis demonstrated that risk factors for 90 day readmission included sex (OR female vs. male: 1.09, [1.03,1.16], p=0.002), ethnicity (OR Hispanic vs. non-Hispanic: 1.2, [1.09,1.39], p=0.001), race (OR black vs. white: 1.28, [1.17,1.4], p<0.001), income (OR bottom quartile vs. top quartile: 1.14, [1.05,1.24], p=0.003), having more comorbidities (p<0.001), insurance (OR Medicaid vs. private insurance: 1.25, [1.12,1.39], p<0.001) and large hospital bed size (OR beds greater than 600 vs. less than 200: 1.19, [1.05,1.35], p=0.006). Protective factors against 90 day readmission were age (OR 45-65 year old vs. less than 45 years old: 0.8, [0.73,0.87], p<0.001), surgeon volume (OR top quartile vs. bottom quartile: 0.73, [0.67,0.81], p<0.001) and procedure type (OR laparoscopic vs. open: 0.88, [0.8,0.96], p=0.006) (OR robotic vs. open: 0.79, [0.69,0.91], p=0.001). CONCLUSIONS Readmission at 90 days following RN is significant. Multiple patient and provider characteristics, including demographics, volume and procedure type, can predict readmission up to 90 days. This data can be used to identify patients at high risk of readmissions as well as aid in quality improvement efforts. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e465-e466 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Anthony Yang More articles by this author Mark Finkelstein More articles by this author Khawaja Bilal More articles by this author Natan Davoudzadeh More articles by this author Michael Palese More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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