Results
Study Population
Among 383 study participants, the mean age was 53.5 years (SD = 13.9), 39.7% were female, 38.9% were African American and 18.8% were frail at KT. Consistent with previous findings of frailty as an independent domain, no recipient factors were associated with frailty except sex (Table 1). Similar to our findings from the national registry, 31.3% of KT recipients at our center were readmitted within 30 days of initial discharge.
Frailty and Early Hospital Readmission
Frail KT recipients were much more likely to experience EHR (45.8% vs. 28.0%, p = 0.005), regardless of age (Figure 1. Interestingly, frail younger recipients had the highest EHR rate (46.2%). In an unadjusted regression model, frail KT recipients were 1.64-fold (95% CI: 1.20–2.23, p = 0.002) more likely to experience EHR than their non-frail counterparts. After adjusting for sex, age, race, BMI, recipient diabetes, recipient heart disease, time on dialysis, donor type, donor age, use of induction therapy and HLA mismatches, frailty independently predicted 61% higher risk of EHR (adjusted RR = 1.61, 95%CI: 1.18–2.19, p = 0.002) in KT recipients. Adjusting for DGF did not alter the association of frailty and EHR (adjusted RR = 1.59, 95% CI: 1.17–2.17, p = 0.003). Consistent with our previous findings in KT, the frailty effect did not differ between older and younger KT recipients (interaction p = 0.39) or African American and white KT recipients (interaction p = 0.81).
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Figure 1.
Early hospital readmission, by age and frailty.
Improved Prediction With Frailty
Importantly, frailty improved the ability to predict EHR, above and beyond our previously published registry-based model. The area under the ROC curve was statistically higher with the addition of frailty (0.70 vs. 0.63, p = 0.008) to the 11 factors in the registry-based model (Figure 2). Frailty also improved the classification of KT recipients (Table 2); 10% (13/120) of those with EHR were correctly reclassified as being at higher risk for EHR by frailty and 10% (25/263) of those without EHR were correctly reclassified as being at lower risk of EHR by frailty (p = 0.04).
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Figure 2.
Receiver operating characteristic curve for early hospital readmission prediction models with and without frailty. Note: AUC is the area under the receiver operating characteristic curve. The registry-based model with frailty had statistically significant improvement in EHR prediction (p = 0.08). The p value was obtained from a chi-squared test of the difference in the AUC for the registry-based models with and without frailty.