Daniel J. Rubin|Elizabeth A. Handorf|Sherita Hill Golden|Deborah B. Nelson|Marie E. McDonnell|Huaqing Zhao
Development and Validation of a Novel Tool to Predict Hospital Readmission Risk Among Patients with Diabetes
Temple University|Temple University Health System|Fox Chase Cancer Center|Johns Hopkins Medicine|Johns Hopkins University|Temple University|Harvard University|Brigham and Women's Hospital|Temple University|Clinical Research Institute
objective develop validate tool predict risk allcause readmission within 30 days 30d readmission among hospitalized patients diabetes methods cohort 44203 discharges retrospectively selected electronic records adult patients diabetes hospitalized urban academic medical center discharges 60 patients n 26402 randomly selected training sample develop index remaining 40 n 17801 selected validation sample multivariable logistic regression generalized estimating equations used develop diabetes early readmission risk indicator derri results ten statistically significant predictors identified employment status living within 5 miles hospital preadmission insulin use burden macrovascular diabetes complications admission serum hematocrit creatinine sodium hospital discharge within 90 days admission recent discharge status 1 year admission diagnosis anemia discrimination model acceptable c statistic 070 calibration good characteristics validation training samples similar performance derri validation sample essentially unchanged c statistic 069 mean predicted 30d readmission risks also similar training validation samples 393 387 highest quintiles conclusion derri found valid tool predict allcause 30d readmission risk individual patients diabetes identification highrisk patients may encourage use interventions targeting greatest risk potentially leading better outcomes lower healthcare costs abbreviations derri diabetes early readmission risk indicator icd9cm international classification diseases ninth revision clinical modification gee generalized estimating equations roc receiver operating characteristic