Residual Renal Function and Obstructive Sleep Apnea in Peritoneal Dialysis: A Pilot Study

Sleep apnoea syndrome prevalence in chronic kidney disease and end-stage kidney disease patients: a systematic review and meta-analysis

Background

Several studies have examined the frequency of sleep apnoea (SA) in patients with chronic kidney disease (CKD), reporting different prevalence rates. Our systematic review and meta-analysis aimed to define the clinical penetrance of SA in CKD and end-stage kidney disease (ESKD) patients.

Methods

Ovid-MEDLINE and PubMed databases were explored up to 5 June 2023 to identify studies providing SA prevalence in CKD and ESKD patients assessed by different diagnostic methods, either sleep questionnaires or respiration monitoring equipment [such as polysomnography (PSG), type III portable monitors or other diagnostic tools]. Single-study data were pooled using the random-effects model. The Chi2 and Cochrane-I2 tests were used to assess the presence of heterogeneity, which was explored performing sensitivity and/or subgroup analyses.

Results

A cumulative analysis from 32 single-study data revealed a prevalence of SA of 57% [95% confidence interval (CI) 42%-71%] in the CKD population, whereas a prevalence of 49% (95% CI 47%-52%) was found pooling data from 91 studies in ESKD individuals. The prevalence of SA using instrumental sleep monitoring devices, including classical PSG and type III portable sleep monitors, was 62% (95% CI 52%-72%) and 56% (95% CI 42%-69%) in CKD and ESKD populations, respectively. Sleep questionnaires revealed a prevalence of 33% (95% CI 16%-49%) and 39% (95% CI 30%-49%).

Conclusions

SA is commonly seen in both non-dialysis CKD and ESKD patients. Sleep-related questionnaires underestimated the presence of SA in this population. This emphasizes the need to use objective diagnostic tools to identify such a syndrome in kidney disease.

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

Objectives

Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features.

Methods

We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening.

Results

The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk.

Conclusions

The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.

Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features

Obstructive sleep apnea (OSA) is a global health concern and is typically diagnosed using in-laboratory polysomnography (PSG). However, PSG is highly time-consuming and labor-intensive. We, therefore, developed machine learning models based on easily accessed anthropometric features to screen for the risk of moderate to severe and severe OSA. We enrolled 3503 patients from Taiwan and determined their PSG parameters and anthropometric features. Subsequently, we compared the mean values among patients with different OSA severity and considered correlations among all participants. We developed models based on the following machine learning approaches: logistic regression, k-nearest neighbors, naïve Bayes, random forest (RF), support vector machine, and XGBoost. Collected data were first independently split into two data sets (training and validation: 80%; testing: 20%). Thereafter, we adopted the model with the highest accuracy in the training and validation stage to predict the testing set. We explored the importance of each feature in the OSA risk screening by calculating the Shapley values of each input variable. The RF model achieved the highest accuracy for moderate to severe (84.74%) and severe (72.61%) OSA. The level of visceral fat was found to be a predominant feature in the risk screening models of OSA with the aforementioned levels of severity. Our machine learning models can be employed to screen for OSA risk in the populations in Taiwan and in those with similar craniofacial structures.

Automated Peritoneal Dialysis: Patient Perspectives and Outcomes

A steadily increasing number of end stage kidney disease (ESKD) patients are maintained on automated peritoneal dialysis (APD) worldwide, in long-standing as well as in more recently established peritoneal dialysis (PD) programs. A better understanding of the technique, paralleled by progress in involved technology, sustained this growth to the point that APD has become the prevalent mode of PD delivery in most high-income countries. While APD is now regarded to be at least as efficient as continuous ambulatory peritoneal dialysis (CAPD) with regard to major biomedical outcomes, its impact on patient-reported outcomes has been less investigated. This paper reviews the main outcomes of APD from a clinical point of view and from the person on dialysis perspective.

Is kidney a new organ target in patients with obstructive sleep apnea? Research priorities in a rapidly evolving field

The bidirectional relationship between sleep disordered breathing and chronic kidney disease (CKD) has recently gained a lot of interest. Several lines of evidence suggest the high prevalence of coexistent obstructive sleep apnea (OSA) in patients with CKD and end-stage renal disease (ESRD). In addition, OSA seems to result in loss of kidney function in some patients, especially in those with cardio-metabolic comorbidities. Treatment of CKD/ESRD and OSA can alter the natural history of each other; still better phenotyping with selection of appropriate treatment approaches is urgently needed. The aim of this narrative review is to provide an update of recent studies on epidemiological associations, pathophysiological interactions, and management of patients with OSA and CKD or ESRD.

Sleep apnea is associated with residual kidney function and mortality in patients with peritoneal dialysis: Prospective cohort study

INTRODUCTION

Fluid overload and sleep apnea (SA) are known risk factors for mortality in dialysis patients. Although incidence and severity of SA were shown higher in peritoneal dialysis (PD) patients than in hemodialysis patients, data regarding the association of SA with body fluid status and mortality are limited. Therefore, the association of SA with body fluid status and mortality were investigated in a prospective cohort with patients undergoing PD.

METHODS

The present study included 103 prevalent PD patients who were followed up for median 70 months. At baseline, the subjects underwent in-home polysomnography, bioelectrical impedance analysis, and urea kinetics. Excessive daytime sleepiness and sleep quality were assessed using sleep questionnaires. SA was defined as apnea/hypopnea index higher than 15 events per hour.

RESULTS

Sleep apnea was diagnosed in 57 (55.3%) patients (SA group); the subjects had significantly higher extracellular water (10.3 ± 1.4 vs. 9.2 ± 1.8, p = 0.001) and lower residual kidney function (RKF) (3.3 ± 3.3 vs. 5.9 ± 7.2, p = 0.02) compared with subjects in the non-SA group. SA was significantly associated with RKF [odds ratio, 0.84; 95% confidence interval (CI), 0.73-0.97] in multivariable logistic regression analysis. In multivariable Cox regression models, SA was a significant predictor of mortality in PD patients (adjusted hazard ratio, 5.74; 95% CI, 1.09-30.31) after adjusting for well-known risk factors.

CONCLUSIONS

Sleep apnea was very common in PD patients and significantly associated with lower RKF. SA was also a novel risk predictor of mortality in PD patients.

Functional outcomes of sleep predict cardiovascular intermediary outcomes and all-cause mortality in incident hemodialysis patients

STUDY OBJECTIVES

Patients with end-stage kidney disease (ESKD) commonly experience sleep disturbances. Sleep disturbance has been inconsistently associated with mortality risk in hemodialysis patients, but the burden of symptoms from sleep disturbances has emerged as a marker that may shed light on these discrepancies and guide treatment decisions. This study examines whether functional outcomes of sleep are associated with increased risk of intermediary CV outcomes or mortality among adults initiating hemodialysis.

METHODS

In 228 participants enrolled in the Predictors of Arrhythmic and Cardiovascular risk in ESRD (PACE) study, the Functional Outcomes of Sleep Questionnaire-10 (FOSQ-10), which assesses functional outcomes of daytime sleepiness, was administered within 6 months of enrollment. Intermediary CV outcomes included QTc [ms], heart rate variance [ms²], left ventricular mass index [g/m², LVMI], and left ventricular hypertrophy [LVH]. The association of FOSQ-10 score with all-cause mortality was examined using proportional hazards regression. Results: Mean age was 55 years, median BMI was 28 kg/m² (IQR 24,33), with 70% African Americans. Median FOSQ-10 score was 19.7 (IQR: 17.1,20.0). A 10% lower FOSQ-10 score was associated with increased mortality risk (HR 1.09, 95%CI 1.01-1.18). Lower FOSQ-10 scores were associated with longer QTc duration and lower heart rate variance, but not LVMI or LVH.

CONCLUSIONS

In adults initiating dialysis, sleep-related functional impairment is common and is associated with intermediary cardiovascular disease measures and increased mortality risk. Future studies should assess the impact of screening for sleep disturbances in ESKD patients to identify individuals at increased risk for cardiovascular complications and death.

APD or CAPD: one glove does not fit all

The use of Automated Peritoneal Dialysis (APD) in its various forms has increased over the past few years mainly in developed countries. This could be attributed to improved cycler design, apparent lifestyle benefits and the ability to achieve adequacy and ultrafiltration targets. However, the dilemma of choosing the superior modality between APD and Continuous Ambulatory Peritoneal Dialysis (CAPD) has not yet been resolved. When it comes to fast transporters and assisted PD, APD is certainly considered the most suitable Peritoneal Dialysis (PD) modality. Improved patients’ compliance, lower intraperitoneal pressure and possibly lower incidence of peritonitis have been also associated with APD. However, concerns regarding increased cost, a more rapid decline in residual renal function, inadequate sodium removal and disturbed sleep are APD’s setbacks. Besides APD superiority over CAPD in fast transporters, the other medical advantages of APD still remain controversial. In any case, APD should be readily available for all patients starting PD and the most important indication for its implementation remains patient’s choice.

Risk, Severity, and Predictors of Obstructive Sleep Apnea in Hemodialysis and Peritoneal Dialysis Patients

Our study aimed to determine the incidence and severity of obstructive sleep apnea (OSA) in patients with end-stage renal disease (ESRD) and also whether different dialysis modalities confer different risk and treatment response for OSA. We used Taiwan's National Health Insurance Research Database for analysis and identified 29,561 incident dialysis patients as the study cohort between 2000 and 2011. Each dialysis patient was matched with four non-dialysis control cases by age, sex, and index date. Cox regression hazard models were used to identify the risk of OSA. The incidence rate of OSA was higher in the peritoneal dialysis (PD) cohort than the hemodialysis (HD) and control cohort (18.9, 7.03 vs. 5.5 per 10,000 person-years, respectively). The risk of OSA was significantly higher in the PD (crude subhazard ratio (cSHR) 3.50 [95% CI 2.71⁻4.50], p < 0.001) and HD cohort (cSHR 1.31 [95% CI 1.00⁻1.72], p < 0.05) compared with the control cohort. Independent risk factors for OSA in this population were age, sex, having coronary artery disease (CAD), hyperlipidemia, chronic obstructive pulmonary disease (COPD), and hypertension. Major OSA (MOSA) occurred in 68.6% in PD and 50.0% in HD patients with OSA. In the PD subgroup, the incidence of mortality was significantly higher in OSA patients without continuous positive airway pressure (CPAP) treatment compared with OSA patients undergoing CPAP treatment. The results of this study indicate that ESRD patients were at higher risk for OSA, especially PD patients, compared with control. The severity of OSA was higher in PD patients than HD patients. Treatment of MOSA with CPAP was associated with reduced mortality in PD patients.