Details

Autor(en) / Beteiligte

• Taber, David J
• Palanisamy, Arun P
• Srinivas, Titte R
• Gebregziabher, Mulugeta
• Odeghe, John
• Chavin, Kenneth D
• Egede, Leonard E
• Baliga, Prabhakar K

Titel

Inclusion of dynamic clinical data improves the predictive performance of a 30-day readmission risk model in kidney transplantation

Ist Teil von

• Transplantation, 2015-02, Vol.99 (2), p.324-330

Ort / Verlag

United States

Erscheinungsjahr

2015

Quelle

MEDLINE

Beschreibungen/Notizen

• Thirty-day readmissions (30DRA) are a highly scrutinized measure of healthcare quality and relatively frequent among kidney transplants (KTX). Development of predictive risk models is 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. We directed a retrospective analysis toward: (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 and 2012. Risk models were developed using backward logistic regression and compared for predictive efficacy using receiver operating characteristic curves. Of 1147 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 (area under the curve, 0.64; 95% confidence interval [95% CI], 0.58-0.69). The model was parsimoniously reduced to 6 risks, which remained modestly predictive (area under the curve, 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 that the final model was stable with minimal bias. We demonstrate that modeling dynamic clinical data outperformed models using immutable data in predicting 30DRA.