A logistic regression model for predicting health-related quality of life in kidney transplant recipients.

BACKGROUND: To develop a logistic regression model capable of predicting health-related quality of life (HRQOL) among kidney transplant recipients and determine its accuracy. METHODS: Three groups of patients were selected: 70 healthy controls, 136 kidney transplant patients as a derivation set, and another 110 kidney transplant patients as a validation set. SF-36 score was used for HRQOL measurement. A cutoff point to define poor versus good HRQOL was calculated using the SF-36 scores of healthy controls. A logistic regression model was used to derive predictive parameters from the derivation set. The derived model was then tested among the validation set. HRQOL predictions made by the model for the patients in the validation set and the SF-36 scores were compared. We calculated sensitivity, specificity, positive and negative predictive values, and model accuracy. RESULTS: SF-36 scores below 58.8 were defined as an indication of poor HRQOL. The regression model suggested that poor HRQOL was positively associated with lower education (below high school diploma), being single or widowed, and diabetes/hypertension as etiology. It was negatively associated with younger age (<45 years) at the time of transplantation. Optimal sensitivity and specificity were achieved at a cutoff value of 0.74 for the estimated probability of poor HRQOL. Sensitivity, specificity, positive and negative predictive values, and accuracy of the model were 73%, 70%, 80%, 60%, and 72%, respectively. CONCLUSION: The suggested model can be used to predict poor posttransplant HRQOL among renal graft recipients using simple variables with acceptable accuracy. This modal can be of use in decision making in the recipients for whom achieving good HRQOL is the main aim of transplantation, to select high-risk patients and to start interventional programs to prevent a poor HRQOL.

Are all domains of quality of life poor among elderly kidney recipients?

BACKGROUND: This study sought to answer whether all domains of HRQoL are low among elderly kidney recipients. METHODS: A cross-sectional study of 162 renal transplanted subjects included group I (age<40 years, n=85), group II (age between 40 and 55 years, n=55), and group III (age >55 years, n=22). We compared the total score of the Short Form health survey (SF-36) and its eight subscales, including physical functioning (PF), social functioning (SF), role limitations due to physical health problems (RPh), role limitations due to emotional problems (REm), mental health (MH), vitality (VT), bodily pain (BP), and general health perceptions (GH) between the study groups. RESULTS: As compared to groups II and I, group III, showed significantly lower scores of REm (49.12 +/- 23.22, 63.03 +/- 26.33, 64.36 +/- 26.54, P=.08), PF (48.94 +/- 27.41, 72.69 +/- 25.54, 72.14 +/- 22.79, P=.001) and SF-36 total score (46.79 +/- 10.52, 54.77 +/- 10.66, 54.09 +/- 9.35, P=.01). There were no significant differences among SF, RPh, MH, VT, and BP. Group III reported better GH than groups II and I (52.36 +/- 9.18, 48.71 +/- 12.01, 43.50 +/- 14.81, P=.020). CONCLUSIONS: Increasing age did not result in poor health-related quality of life in all domains. The general health perception was better in the elderly, which might be due to their better coping ability.

Marital quality in kidney transplant recipients: easy to predict, hard to neglect.

BACKGROUND: Given the significant role of post-renal transplant familial support in the patient's adherence to treatment, a study into the contributors to marital quality in this population seems necessary. This study sought to identify the predictors of poor post-renal transplant marital quality. METHODS: This cross-sectional study was conducted in 2006 on 125 married kidney transplant recipients. Marital quality was evaluated using the Revised Marital Adjustment Scale (RMAS). A score below the fourth-quartile MAS score of a group of age- and sex- matched healthy controls was interpreted as poor marital relationship. Multiple logistic regression analysis was utilized to evaluate the predictors of poor marital relationship. RESULTS: The mean time interval between transplantation and assessment of marital quality was 43 +/- 15 months. Poor post-renal transplant marital quality can be predicted by the kidney transplant recipient's sex (M/F) (odds ratio [OR]; 0.31; 95% confidence interval [CI], 0.11 to 0.90; P=.031), age at transplantation (OR, 0.93; 95% CI, 0.89 to 0.98; P=.005), educational level (OR, 0.67; 95% CI, 0.44 to 1.03; P=.067), and monthly family income (OR, 2.20; 95% CI, 1.09 to 4.44; P=.028). CONCLUSION: Presenting a simple prediction model for poor post-renal transplant marital relationship, this study will make it possible to detect patients at a higher risk of poor marital quality and thus avoid treatment noncompliance. At the time of transplantation, using simple demographic variables and providing couple-based health education programs as a part of a familial approach to renal transplantation may improve the outcome of such high-risk patients.