Development and Internal Validation of a Prediction Model for Early Hospital Readmissions in Kidney Transplant Recipients. (July 2018) - Rana, Jayoti; Gumabay, Franz Marie; Chan, Emilie; Huizenga, Robyn; Chen, Pei Xuan; Famure, Olusegun; Li, Yanhong; Singh, Sunita; Kim, Sang Joseph
Abstract : Background: Early hospital readmission or EHR (i.e., an unplanned rehospitalization event within 30-days of initial discharge) following kidney transplantation is associated with poor clinical outcomes and confers high healthcare costs. Development of an EHR risk prediction model will enable identification of higher risk patients and the opportunity to reduce EHR and improve clinical outcomes. However, there are few EHR risk prediction models for kidney transplant recipients (KTR), and none developed or validated in a Canadian centre. Methods: We conducted a single-centre, retrospective cohort study, including adult patients who received a kidney transplant between July 1, 2004 and December 31, 2014 and were followed for at least 30 days after discharge from the transplant admission. EHR risk prediction models were developed using stepwise backward logistic regression and compared for predictive efficacy using ROC curves. Bootstrapping was used to internally validate the final EHR risk prediction moDedel. Results: In our cohort of 1381 KTR, the majority were male (60%), white (64%), and on hemodialysis pre-transplant (65%). There were 267 patients who experienced at least one EHR post-transplant. Our full model contained 14 variables with a moderate discrimination (ROC=0.65). The most parsimonious model resulted in a similar discrimination, (ROC=0.64), and consisted of 12 variables, with no individual variable being highly predictive of EHR (Table 1). Internal validation of our parsimonious model resulted in slightly lower discrimination vs. the development model (ROC=0.61). Conclusions: Our prediction model was only modestly predictive of EHR in Canadian cohort of kidney transplant recipients. To improve model performance, additional predictors such as surgical complications and infections may need to be considered.