Post kidney transplant quality of life prediction models

Quality of life (QoL) is generally found to improve for renal transplant recipients, although some patients continue to experience health-related problems. It was within this context that we undertook our investigation which focused on identifying the factors predictive of QoL following kidney transplantation.

The sample included 91 non-diabetic patients of which 69 provided 6-month data and 68 provided 12-month data. Three QoL questionnaires were administered to capture as many QoL dimensions as possible. Repeated measure analyses of variance with multiple post hoc comparisons of LS means was conducted to determine how QoL outcomes differed over time. Correlational analyses were performed on the 12-month dataset to determine which variables to include in the modeling process. Multiple stepwise regression with forward and backward entry were used in the prediction modeling.

Essentially all patients experienced a significant improvement in QoL and the improvement occurred early and appeared to be sustained. Five separate prediction models were constructed, each including number of hospital days in first 6 months, employment, and social support.

The similarity of the five models is of note. It is not necessarily these specific variables per se that predict QoL outcomes, but rather what they conceptually represent. These findings provide direction for interventions designed to enhance post-transplant QoL.