Effect of Serum Albumin Changes on Mortality in Patients with Peritoneal Dialysis: A Joint Modeling Approach and Personalized Dynamic Risk Predictions

Peritoneal dialysis (PD) is a frequently used and growing therapy for end-stage renal diseases (ESRD). Survival analysis of PD patients is an ongoing research topic in the field of nephrology. Several biochemical parameters (e.g., serum albumin, creatinine, and blood urea nitrogen) are measured repeatedly in the follow-up period; however, baseline or averaged values are primarily associated with mortality. Although this strategy is not incorrect, it leads to information loss, resulting in erroneous conclusions and biased estimates. This retrospective study used the trajectory of common renal function indexes in PD patients and mainly investigated the association between serum albumin change and mortality. Furthermore, we considered patient-specific variability in serum albumin change and obtained personalized dynamic risk predictions for selected patients at different follow-up thresholds to investigate the effect of serum albumin trajectories on patient-specific mortality. We included 417 patients from the Erciyes University Nephrology Department whose data were retrospectively collected using medical records. A joint modeling approach for longitudinal and survival data was used to investigate the relationship between serum albumin trajectory and mortality of PD patients. Results showed that averaged serum albumin levels were not associated with mortality. However, serum albumin change was significantly and inversely associated with mortality (HR: 2.43, 95% CI: 1.48 to 4.16). Risk of death was positively associated with peritonitis rate, hemodialysis history, and the total number of comorbid and renal diseases with hazard ratios 1.74, 3.21, and 1.41. There was also significant variability between patients. The personalized risk predictions showed that overall survival estimates were not representative for all patients. Using the patient-specific trajectories provided better survival predictions within the follow-up period as more data become available in serum albumin levels. In conclusion, using the trajectory of risk predictors via an appropriate statistical method provided better predictive accuracy and prevented biased findings. We also showed that personalized risk predictions were much informative than overall estimations in the presence of significant patient variability. Furthermore, personalized estimations may play an essential role in monitoring and managing patients during the follow-up period

The effect of footbath applied to patients receiving hemodialysis treatment on comfort, fatigue, and dialysis symptoms: A randomized controlled study

Introduction: This study aimed to evaluate the effect of warm water footbaths on comfort, fatigue, and dialysis symptoms in patients undergoing hemodialysis. Methods: Data were collected from a total of 58 patients, 31 in the intervention group and 27 in the placebo group. The data in the study are collected using the intervention and control group informed volunteer Form, Patient Demonstration Form, foot Bath Application Monitoring Chart, fatigue VAS Scale Form, Dialysis Symptom Index, and Hemodialysis Comfort Scale (HCS). Results: In the second follow-up in the intervention group, HCS was determined to significantly increase all sub-size and total score averages by the first trace (p < 0.05). VAS fatigue point averages were significantly lower (p < 0.05) in the intervention group. Conclusion: It was determined that the footbath applied to patients who received hemodialysis treatment increased comfort and reduced fatigue and dialysis symptoms

The joint modeling approach with a simulation study for evaluating the association between the trajectory of serum albumin levels and mortality in peritoneal dialysis patients

We aimed to study the association between mortality and trajectory of serum albumin levels (g/dL) in peritoneal dialysis patients via a joint modeling approach. Joint modeling is a statistical method used to evaluate the relationship between longitudinal and time-to event processes by fitting both sub-models simultaneously. A comprehensive simulation study was conducted to evaluate model performances and generalize the findings to more general scenarios. Model performances and prediction accuracies were evaluated using the time-dependent ROC area under the curve (AUC) and Brier score (BS). According to the real-life dataset results, the trajectory of serum albumin levels was inversely associated with mortality increasing the risk of death 2.21 times (p=0.003). The simulation results showed that the model performances increased with sample size. However, the model complexity had increased as more repeated measurements were taken from patients and resulted in lower prediction accuracy unless the sample size was increased. In conclusion, using the trajectory of risk predictors rather than baseline (or averaged) values provided better predictive accuracy and prevented biased results. Finally, the study design (e.g., number of samples and repeated measurements) should be carefully defined since it played an important role in model performances

Important determinants of quality of life in a peritoneal dialysis population in Turkey

Background: Patients' health-related quality of life (HRQoL) is an important indicator for predicting the effectiveness of treatment, morbidity, and mortality. The aim of this study was to determine the level of HRQoL and the most important factors affecting HRQoL in patients receiving peritoneal dialysis (PD). Methods: This cross-sectional study was performed with 156 patients, 30 of whom (19.2%) had automated PD (APD), were over 18 years of age, and were followed up at the Erciyes University Continuous Ambulatory Peritoneal Dialysis (CAPD) Unit during the previous year. HRQoL, depression, and fatigue were measured by means of the Short Form-36 (SF-36), Beck Depression Inventory (BDI), and Fatigue Severity Scale (FSS), respectively. Results: The mean mental component summary (MCS) score was 42.1 +/- 11.9 and physical component summary (PCS) score was 39.1 +/- 11.2, which was lower than MCS. Depression was the strongest predictor for both diminished mental (beta = -24.4, p < 0.001) and physical (beta = -16.5, p < 0.001) HRQoL. Fatigue was the next strongest predictor for diminished physical HRQoL only (beta = -7.74, p < 0.001). Depression and fatigue accounted for 37% of physical HRQoL impairment. Depression as a sole factor was responsible for 31% of mental HRQoL impairment. Age, hospitalization, total cholesterol, serum albumin levels, and Kt/V urea had affected the SF-36 in some domains score but not in all. Conclusion: HRQoL in our PD patients can be evaluated at a slightly poor level compared to the results of previous studies. Impaired HRQoL is more closely associated with depression and fatigue. Depression was the strongest predictor of both mental and physical HRQoL. Fatigue was the next strongest predictor for physical HRQoL only