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

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.