Background
30-day readmissions (30DRA) are a highly scrutinized measure of healthcare quality and relatively frequent among kidney transplants (KTX). Development of predictive risk models are critical to reducing 30DRA and improving outcomes. Current approaches rely on fixed variables derived from administrative data. These models may not capture clinical evolution that is critical to predicting outcomes.
Methods
We directed a retrospective analysis towards: 1) developing parsimonious risk models for 30DRA and 2) comparing efficiency of models based on the use of immutable versus dynamic data. Baseline and in-hospital clinical and outcomes data were collected from adult KTX recipients between 2005 – 12. Risk models were developed using backward logistic regression and compared for predictive efficacy using ROC Curves.
Results
Of 1,147 KTX patients, 123 had 30DRA. Risk factors for 30DRA included recipient comorbidities, transplant factors, and index hospitalization patient level clinical data. The initial fixed variable model included 9 risk factors and was modestly predictive (AUC 0.64, 95% CI 0.58–0.69). The model was parsimoniously reduced to 6 risks, which remained modestly predictive (AUC 0.63, 95% CI 0.58–0.69). The initial predictive model using 13 fixed and dynamic variables was significantly predictive (AUC 0.73, 95% CI 0.67–0.80), with parsimonious reduction to 9 variables maintaining predictive efficacy (AUC 0.73, 95% CI 0.67–0.79). The final model using dynamically evolving clinical data outperformed the model using static variables (p=0.009). Internal validation demonstrated the final model was stable with minimal bias.
Conclusion
We demonstrate that modeling dynamic clinical data outperformed models utilizing immutable data in predicting 30DRA.