Tehran University Medical Journal

Background: Blood pressure decreases during sleep and is markedly increased in the morning in healthy individuals. Lack of nocturnal blood pressure fall (non-dipping) has been associated with cardiovascular morbidity, mortality and other organ damage. However, their importance in chronic renal failure is unclear. This study aimed to investigate relationship between circadian rhythm of blood pressure and renal failure severity in patients with chronic kidney disease.
Methods: This cross-section study was done in April 2016. The study population was 95 patients, more than 30 year old with hypertension and chronic renal failure. Patients were selected from clinics of two private and university hospitals affiliated to Qom University of Medical Sciences Shahid Beheshti Hospital and Vali-e-Asr Hospital, Iran. Checklist containing data such as age, sex, duration of renal failure and cause of renal failure were filled. Serum creatinine and serum urea levels were measured and entered in the checklist. The circadian rhythm of blood pressure in all patients was assessed by Holter monitoring. patients who had less than 10% decrease in blood pressure overnight were considered non-dipper and those who had 10% or more decrease in blood pressure overnight were considered dipper.
Results: Average (SD) 24-hour ambulatory systolic and diastolic of blood pressure was 136.56 (16.66) and 84.84 (10.86) mmHg, respectively. 70 patients (73.7%) had non-dipper blood pressure pattern and 25 patients (26.3%) had dipper blood pressure pattern. There was no significant difference between two groups (dipper and non-dipper) based on distribution of gender (P=0.744), age (P=0.407), serum creatinine (P=0.569), serum urea (P=0.689) and renal failure duration (P=0.812). Mean of glomerular filtration rate in dipper group was 68.64±4.13 and in non-dipper group was 65.09±16.27 (P=0.337).
Conclusion: The results of this study did not show a significant relationship between circadian rhythm of blood pressure and renal failure severity. In addition, patients with chronic renal failure showed higher rates of non-dipping pattern of blood pressure.

Background: Peritoneal dialysis is one of the most commonly used treatment methods for the patients with end stage renal failure. In recent years, the mortality rate of patients under this treatment has decreased; however, long-term survival is still an important challenge for health systems. The present study aimed to predict the survival of continuous ambulatory peritoneal dialysis patients.
Methods: In this retrospective study, according to the difference of relative importance of demographic characteristics, laboratory data, dialysis adequacy parameters and nutritional status in various patients, the factors affecting the survival of peritoneal dialysis patients have been identified by random forest algorithm. Then, the clinical and laboratory data of patients undergoing continuous ambulatory peritoneal dialysis treatment were evaluated retrospectively from July 1996 to April 2014 in 18 peritoneal dialysis centers, using multi-class one against all support vector machine (OAA-SVM) and multi-space mapped binary tree support vector machine (MBT-SVM) algorithms.
Results: 3097 patients were studied with the mean age of 50.63±15.67 years and average follow-up time of 24.48±19.13 months. The results of the random forest algorithm have identified 35 factors as the most important predictors of peritoneal dialysis patient’s survival. Then, the prediction of peritoneal dialysis patients’ survival status was evaluated using one against all support vector machine and multi-space mapped binary tree support vector machine algorithms in 5 classes of patients including “still on peritoneal dialysis”, “transferred to hemodialysis”, “received a kidney transplant”, “died” and “improved kidney function”. The reliability of survival prediction algorithms were 51.99% and 89.57% respectively.
Conclusion: An accurate prediction model would be a potentially useful way to evaluate patients’ survival at peritoneal dialysis that increased clinical scrutiny and timely intervention could be brought to bear. So, in this research, the multi-space mapped binary tree support vector machine algorithm has a high precision in predicting the survival of continuous ambulatory peritoneal dialysis patients considering multiple evaluation indices and different class distribution functions.