Towards Validating the Effectiveness of Obstructive Sleep Apnea Classification from Electronic Health Records Using Machine Learning

A machine learning approach for the diagnosis of obstructive sleep apnoea using oximetry, demographic and anthropometric data

INTRODUCTION

Obstructive sleep apnoea (OSA) is a serious but underdiagnosed condition. Demand for the gold standard diagnostic polysomnogram (PSG) far exceeds its availability. More efficient diagnostic methods are needed, even in tertiary settings. Machine learning (ML) models have strengths in disease prediction and early diagnosis. We explored the use of ML with oximetry, demographic and anthropometric data to diagnose OSA.

METHODS

A total of 2,996 patients were included for modelling and divided into test and training sets. Seven commonly used supervised learning algorithms were trained with the data. Sensitivity (recall), specificity, positive predictive value (PPV) (precision), negative predictive value, area under the receiver operating characteristic curve (AUC) and F1 measure were reported for each model.

RESULTS

In the best performing four-class model (neural network model predicting no, mild, moderate or severe OSA), a prediction of moderate and/or severe disease had a combined PPV of 94%; one out of 335 patients had no OSA and 19 had mild OSA. In the best performing two-class model (logistic regression model predicting no-mild vs. moderate-severe OSA), the PPV for moderate-severe OSA was 92%; two out of 350 patients had no OSA and 26 had mild OSA.

CONCLUSION

Our study showed that the prediction of moderate-severe OSA in a tertiary setting with an ML approach is a viable option to facilitate early identification of OSA. Prospective studies with home-based oximeters and analysis of other oximetry variables are the next steps towards formal implementation.

Advancements in Obstructive Sleep Apnea Diagnosis and Screening Through Artificial Intelligence: A Systematic Review

BACKGROUND

Obstructive sleep apnea (OSA) is a prevalent yet underdiagnosed condition associated with a major healthcare burden. Current diagnostic tools, such as full-night polysomnography (PSG), pose a limited accessibility to diagnosis due to their elevated costs. Recent advances in Artificial Intelligence (AI), including Machine Learning (ML) and deep learning (DL) algorithms, offer novel potential tools for an accurate OSA screening and diagnosis. This systematic review evaluates articles employing AI-powered models for OSA screening and diagnosis in the last decade.

METHODS

A comprehensive electronic search was performed on PubMed/MEDLINE, Google Scholar, and SCOPUS databases. The included studies were original articles written in English, reporting the use of ML algorithms to diagnose and predict OSA in suspected patients. The last search was performed in June 2024. This systematic review is registered in PROSPERO (Registration ID: CRD42024563059).

RESULTS

Sixty-five articles, involving data from 109,046 patients, met the inclusion criteria. Due to the heterogeneity of the algorithms, outcomes were analyzed into six sections (anthropometric indexes, imaging, electrocardiographic signals, respiratory signals, and oximetry and miscellaneous signals). AI algorithms demonstrated significant improvements in OSA detection, with accuracy, sensitivity, and specificity often exceeding traditional tools. In particular, anthropometric indexes were most widely used, especially in logistic regression-powered algorithms.

CONCLUSIONS

The application of AI algorithms to OSA diagnosis and screening has great potential to improve patient outcomes, increase early detection, and lessen the load on healthcare systems. However, rigorous validation and standardization efforts must be made to standardize datasets.

Sleep apnea test prediction based on Electronic Health Records

The identification of Obstructive Sleep Apnea (OSA) is done by a Polysomnography test which is often done in later ages. Being able to notify potential insured members at earlier ages is desirable. For that, we develop predictive models that rely on Electronic Health Records (EHR) and predict whether a person will go through a sleep apnea test after the age of 50. A major challenge is the variability in EHR records in various insured members over the years, which this study investigates as well in the context of controls matching, and prediction. Since there are many temporal variables, the RankLi method was introduced for temporal variable selection. This approach employs the t-test to calculate a divergence score for each temporal variable between the target classes. We also investigate here the need to consider the number of EHR records, as part of control matching, and whether modeling separately for subgroups according to the number of EHR records is more effective. For each prediction task, we trained 4 different classifiers including 1-CNN, LSTM, Random Forest, and Logistic Regression, on data until the age of 40 or 50, and on several numbers of temporal variables. Using the number of EHR records for control matching was found crucial, and using learning models for subsets of the population according to the number of EHR records they have was found more effective. The deep learning models, particularly the 1-CNN, achieved the highest balanced accuracy and AUC scores in both male and female groups. In the male group, the highest results were also observed at age 50 with 100 temporal variables, resulting in a balanced accuracy of 90% and an AUC of 93%.

An Artificial Intelligence-Based Non-Invasive Approach for Cardiovascular Disease Risk Stratification in Obstructive Sleep Apnea Patients: A Narrative Review

Background

Obstructive sleep apnea (OSA) is a severe condition associated with numerous cardiovascular complications, including heart failure. The complex biological and morphological relationship between OSA and atherosclerotic cardiovascular disease (ASCVD) poses challenges in predicting adverse cardiovascular outcomes. While artificial intelligence (AI) has shown potential for predicting cardiovascular disease (CVD) and stroke risks in other conditions, there is a lack of detailed, bias-free, and compressed AI models for ASCVD and stroke risk stratification in OSA patients. This study aimed to address this gap by proposing three hypotheses: (i) a strong relationship exists between OSA and ASCVD/stroke, (ii) deep learning (DL) can stratify ASCVD/stroke risk in OSA patients using surrogate carotid imaging, and (iii) including OSA risk as a covariate with cardiovascular risk factors can improve CVD risk stratification.

Methods

The study employed the Preferred Reporting Items for Systematic reviews and Meta-analyses (PRISMA) search strategy, yielding 191 studies that link OSA with coronary, carotid, and aortic atherosclerotic vascular diseases. This research investigated the link between OSA and CVD, explored DL solutions for OSA detection, and examined the role of DL in utilizing carotid surrogate biomarkers by saving costs. Lastly, we benchmark our strategy against previous studies.

Results

(i) This study found that CVD and OSA are indirectly or directly related. (ii) DL models demonstrated significant potential in improving OSA detection and proved effective in CVD risk stratification using carotid ultrasound as a biomarker. (iii) Additionally, DL was shown to be useful for CVD risk stratification in OSA patients; (iv) There are important AI attributes such as AI-bias, AI-explainability, AI-pruning, and AI-cloud, which play an important role in CVD risk for OSA patients.

Conclusions

DL provides a powerful tool for CVD risk stratification in OSA patients. These results can promote several recommendations for developing unique, bias-free, and explainable AI algorithms for predicting ASCVD and stroke risks in patients with OSA.

Energy-Efficient Reservoir Computing Based on Solution-Processed Electrolyte/Ferroelectric Memcapacitive Synapses for Biosignal Classification

The classification of critical physiological signals using neuromorphic devices is essential for early disease detection. Physical reservoir computing (RC), a lightweight temporal processing neural network, offers a promising solution for low-power, resource-constrained hardware. Although solution-processed memcapacitive reservoirs have the potential to improve power efficiency as a result of their ultralow static power consumption, further advancements in synaptic tunability and reservoir states are imperative to enhance the capabilities of RC systems. This work presents solution-processed electrolyte/ferroelectric memcapacitive synapses. Leveraging the synergistic coupling of electrical double-layer (EDL) effects and ferroelectric polarization, these synapses exhibit tunable long- and short-term plasticity, ultralow power consumption (∼27 fJ per spike), and rich reservoir state dynamics, making them well-suited for energy-efficient RC systems. The classifications of critical electrocardiogram (ECG) signals, including arrhythmia and obstructive sleep apnea (OSA), are performed using the synapse-based RC system, demonstrating excellent accuracies of 97.8 and 80.0% for arrhythmia and OSA classifications, respectively. These findings pave the way for developing lightweight, energy-efficient machine-learning platforms for biosignal classification in wearable devices.

Improving Clinical Documentation with Artificial Intelligence: A Systematic Review

Clinicians dedicate significant time to clinical documentation, incurring opportunity cost. Artificial Intelligence (AI) tools promise to improve documentation quality and efficiency. This systematic review overviews peer-reviewed AI tools to understand how AI may reduce opportunity cost. PubMed, Embase, Scopus, and Web of Science databases were queried for original, English language research studies published during or before July 2024 that report a new development, application, and validation of an AI tool for improving clinical documentation. 129 studies were extracted from 673 candidate studies. AI tools improve documentation by structuring data, annotating notes, evaluating quality, identifying trends, and detecting errors. Other AI-enabled tools assist clinicians in real-time during office visits, but moderate accuracy precludes broad implementation. While a highly accurate end-to-end AI documentation assistant is not currently reported in peer-reviewed literature, existing techniques such as structuring data offer targeted improvements to clinical documentation workflows.

Improving Clinical Documentation with Artificial Intelligence: A Systematic Review

Clinicians dedicate significant time to clinical documentation, incurring opportunity cost. Artificial Intelligence (AI) tools promise to improve documentation quality and efficiency. This systematic review overviews peer-reviewed AI tools to understand how AI may reduce opportunity cost. PubMed, Embase, Scopus, and Web of Science databases were queried for original, English language research studies published during or before July 2024 that report a new development, application, and validation of an AI tool for improving clinical documentation. 129 studies were extracted from 673 candidate studies. AI tools improve documentation by structuring data, annotating notes, evaluating quality, identifying trends, and detecting errors. Other AI-enabled tools assist clinicians in real-time during office visits, but moderate accuracy precludes broad implementation. While a highly accurate end-to-end AI documentation assistant is not currently reported in peer-reviewed literature, existing techniques such as structuring data offer targeted improvements to clinical documentation workflows.

Automatic detection of obstructive sleep apnea based on speech or snoring sounds: a narrative review

Background and Objective

Obstructive sleep apnea (OSA) is a common chronic disorder characterized by repeated breathing pauses during sleep caused by upper airway narrowing or collapse. The gold standard for OSA diagnosis is the polysomnography test, which is time consuming, expensive, and invasive. In recent years, more cost-effective approaches for OSA detection based in predictive value of speech and snoring has emerged. In this paper, we offer a comprehensive summary of current research progress on the applications of speech or snoring sounds for the automatic detection of OSA and discuss the key challenges that need to be overcome for future research into this novel approach.

Methods

PubMed, IEEE Xplore, and Web of Science databases were searched with related keywords. Literature published between 1989 and 2022 examining the potential of using speech or snoring sounds for automated OSA detection was reviewed.

Key Content and Findings

Speech and snoring sounds contain a large amount of information about OSA, and they have been extensively studied in the automatic screening of OSA. By importing features extracted from speech and snoring sounds into artificial intelligence models, clinicians can automatically screen for OSA. Features such as formant, linear prediction cepstral coefficients, mel-frequency cepstral coefficients, and artificial intelligence algorithms including support vector machines, Gaussian mixture model, and hidden Markov models have been extensively studied for the detection of OSA.

Conclusions

Due to the significant advantages of noninvasive, low-cost, and contactless data collection, an automatic approach based on speech or snoring sounds seems to be a promising tool for the detection of OSA.

Predicting obstructive sleep apnea hypopnea syndrome using three-dimensional optical devices: A systematic review

Purpose

As a global health concern, the diagnosis of obstructive sleep apnea hypopnea syndrome (OSAHS), characterized by partial reductions and complete pauses in ventilation, has garnered significant scientific and public attention. With the advancement of digital technology, the utilization of three-dimensional (3D) optical devices demonstrates unparalleled potential in diagnosing OSAHS. This study aimed to review the current literature to assess the accuracy of 3D optical devices in identifying the prevalence and severity of OSAHS.

Methods

A systematic literature search was conducted in the Web of Science, Scopus, PubMed/MEDLINE, and Cochrane Library databases for English studies published up to April 2024. Peer-reviewed researches assessing the diagnostic utility of 3D optical devices for OSAHS were included. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) guideline was employed to appraise the risk of bias.

Results

The search yielded 3216 results, with 10 articles meeting the inclusion criteria for this study. Selected studies utilized structured light scanners, stereophotogrammetry, and red, green, blue-depth (RGB-D) cameras. Stereophotogrammetry-based 3D optical devices exhibited promising potential in OSAHS prediction.

Conclusions

The utilization of 3D optical devices holds considerable promise for OSAHS diagnosis, offering potential improvements in accuracy, cost reduction, and time efficiency. However, further clinical data are essential to assist clinicians in the early detection of OSAHS using 3D optical devices.

Integrating tabular data through image conversion for enhanced diagnosis: A novel intelligent decision support system for stratifying obstructive sleep apnoea patients using convolutional neural networks

Objective

High-dimensional databases make it difficult to apply traditional learning algorithms to biomedical applications. Recent developments in computer technology have introduced deep learning (DL) as a potential solution to these difficulties. This study presents a novel intelligent decision support system based on a novel interpretation of data formalisation from tabular data in DL techniques. Once defined, it is used to diagnose the severity of obstructive sleep apnoea, distinguishing between moderate to severe and mild/no cases.

Methods

The study uses a complete database extract from electronic health records of 2472 patients, including anthropometric data, habits, medications, comorbidities, and patient-reported symptoms. The novelty of this methodology lies in the initial processing of the patients' data, which is formalised into images. These images are then used as input to train a convolutional neural network (CNN), which acts as the inference engine of the system.

Results

The initial tests of the system were performed on a set of 247 samples from the Pulmonary Department of the Álvaro Cunqueiro Hospital in Vigo (Galicia, Spain), with an AUC value of ≈ 0.8.

Conclusions

This study demonstrates the benefits of an intelligent decision support system based on a novel data formalisation approach that allows the use of advanced DL techniques starting from tabular data. In this way, the ability of CNNs to recognise complex patterns using visual elements such as gradients and contrasts can be exploited. This approach effectively addresses the challenges of analysing large amounts of tabular data and reduces common problems such as bias and variance, resulting in improved diagnostic accuracy.

Causal Analysis of Physiological Sleep Data Using Granger Causality and Score-Based Structure Learning

Understanding how the human body works during sleep and how this varies in the population is a task with significant implications for medicine. Polysomnographic studies, or sleep studies, are a common diagnostic method that produces a significant quantity of time-series sensor data. This study seeks to learn the causal structure from data from polysomnographic studies carried out on 600 adult volunteers in the United States. Two methods are used to learn the causal structure of these data: the well-established Granger causality and "DYNOTEARS", a modern approach that uses continuous optimisation to learn dynamic Bayesian networks (DBNs). The results from the two methods are then compared. Both methods produce graphs that have a number of similarities, including the mutual causation between electrooculogram (EOG) and electroencephelogram (EEG) signals and between sleeping position and SpO2 (blood oxygen level). However, DYNOTEARS, unlike Granger causality, frequently finds a causal link to sleeping position from the other variables. Following the creation of these causal graphs, the relationship between the discovered causal structure and the characteristics of the participants is explored. It is found that there is an association between the waist size of a participant and whether a causal link is found between the electrocardiogram (ECG) measurement and the EOG and EEG measurements. It is concluded that a person's body shape appears to impact the relationship between their heart and brain during sleep and that Granger causality and DYNOTEARS can produce differing results on real-world data.

Design of an Intelligent Decision Support System Applied to the Diagnosis of Obstructive Sleep Apnea

Obstructive sleep apnea (OSA), characterized by recurrent episodes of partial or total obstruction of the upper airway during sleep, is currently one of the respiratory pathologies with the highest incidence worldwide. This situation has led to an increase in the demand for medical appointments and specific diagnostic studies, resulting in long waiting lists, with all the health consequences that this entails for the affected patients. In this context, this paper proposes the design and development of a novel intelligent decision support system applied to the diagnosis of OSA, aiming to identify patients suspected of suffering from the pathology. For this purpose, two sets of heterogeneous information are considered. The first one includes objective data related to the patient's health profile, with information usually available in electronic health records (anthropometric information, habits, diagnosed conditions and prescribed treatments). The second type includes subjective data related to the specific OSA symptomatology reported by the patient in a specific interview. For the processing of this information, a machine-learning classification algorithm and a set of fuzzy expert systems arranged in cascade are used, obtaining, as a result, two indicators related to the risk of suffering from the disease. Subsequently, by interpreting both risk indicators, it will be possible to determine the severity of the patients' condition and to generate alerts. For the initial tests, a software artifact was built using a dataset with 4400 patients from the Álvaro Cunqueiro Hospital (Vigo, Galicia, Spain). The preliminary results obtained are promising and demonstrate the potential usefulness of this type of tool in the diagnosis of OSA.

Design and Conceptual Proposal of an Intelligent Clinical Decision Support System for the Diagnosis of Suspicious Obstructive Sleep Apnea Patients from Health Profile

Obstructive Sleep Apnea (OSA) is a chronic sleep-related pathology characterized by recurrent episodes of total or partial obstruction of the upper airways during sleep. It entails a high impact on the health and quality of life of patients, affecting more than one thousand million people worldwide, which has resulted in an important public health concern in recent years. The usual diagnosis involves performing a sleep test, cardiorespiratory polygraphy, or polysomnography, which allows characterizing the pathology and assessing its severity. However, this procedure cannot be used on a massive scale in general screening studies of the population because of its execution and implementation costs; therefore, causing an increase in waiting lists which would negatively affect the health of the affected patients. Additionally, the symptoms shown by these patients are often unspecific, as well as appealing to the general population (excessive somnolence, snoring, etc.), causing many potential cases to be referred for a sleep study when in reality are not suffering from OSA. This paper proposes a novel intelligent clinical decision support system to be applied to the diagnosis of OSA that can be used in early outpatient stages, quickly, easily, and safely, when a suspicious OSA patient attends the consultation. Starting from information related to the patient's health profile (anthropometric data, habits, comorbidities, or medications taken), the system is capable of determining different alert levels of suffering from sleep apnea associated with different apnea-hypopnea index (AHI) levels to be studied. To that end, a series of automatic learning algorithms are deployed that, working concurrently, together with a corrective approach based on the use of an Adaptive Neuro-Based Fuzzy Inference System (ANFIS) and a specific heuristic algorithm, allow the calculation of a series of labels associated with the different levels of AHI previously indicated. For the initial software implementation, a data set with 4600 patients from the Álvaro Cunqueiro Hospital in Vigo was used. The results obtained after performing the proof tests determined ROC curves with AUC values in the range 0.8-0.9, and Matthews correlation coefficient values close to 0.6, with high success rates. This points to its potential use as a support tool for the diagnostic process, not only from the point of view of improving the quality of the services provided, but also from the best use of hospital resources and the consequent savings in terms of costs and time.

Automated deep neural network analysis of lateral cephalogram data can aid in detecting obstructive sleep apnea

STUDY OBJECTIVES

Information on obstructive sleep apnea (OSA) is often latently detected in diagnostic tests conducted for other purposes, providing opportunities for maximizing value. This study aimed to develop a convolutional neural network (CNN) to identify the risk of OSA using lateral cephalograms.

METHODS

The lateral cephalograms of 5,648 individuals (mean age, 49.0 ± 15.8 years; men, 62.3%) with or without OSA were collected and divided into training, validation, and internal test datasets in a 5:2:3 ratio. A separate external test dataset (n = 378) was used. A densely connected CNN was trained to diagnose OSA using a cephalogram. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Gradient-weighted class activation mapping (Grad-CAM) was used to evaluate the region of focus, and the relationships between the model outputs, anthropometric characteristics, and OSA severity were evaluated.

RESULTS

The AUROC of the model for the presence of OSA was 0.82 (95% confidence interval, 0.80-0.84) and 0.73 (95% confidence interval, 0.65-0.81) in the internal and external test datasets, respectively. Grad-CAM demonstrated that the model focused on the area of the tongue base and oropharynx in the cephalogram. Sigmoid output values were positively correlated with OSA severity, body mass index, and neck and waist circumference.

CONCLUSIONS

Deep learning may help develop a model that classifies OSA using a cephalogram, which may be clinically useful in the appropriate context. The definition of ground truth was the main limitation of this study.

CITATION

Jeong H-G, Kim T, Hong JE, et al. Automated deep neural network analysis of lateral cephalogram data can aid in detecting obstructive sleep apnea. J Clin Sleep Med. 2023;19(2):327-337.

Novel method combining multiscale attention entropy of overnight blood oxygen level and machine learning for easy sleep apnea screening

Objective

Sleep apnea is a common sleep disorder affecting a significant portion of the population, but many apnea patients remain undiagnosed because existing clinical tests are invasive and expensive. This study aimed to develop a method for easy sleep apnea screening.

Methods

Three supervised machine learning algorithms, including logistic regression, support vector machine, and light gradient boosting machine, were applied to develop apnea screening models at two apnea-hypopnea index cutoff thresholds: ≥ 5 and ≥ 30 events/hours. The SpO2 recordings of the Sleep Heart Health Study database (N = 5786) were used for model training, validation, and test. Multiscale entropy analysis was performed to derive a set of multiscale attention entropy features from the SpO2 recordings. Demographic features including age, sex, body mass index, and blood pressure were also used. The dependency among the multiscale attention entropy features were handled with the independent component analysis.

Results

For cutoff ≥ 5/hours, logistic regression model achieved the highest Matthew's correlation coefficient (0.402) and area under the curve (0.747), and reasonably good sensitivity (75.38%), specificity (74.02%), and positive predictive value (92.94%). For cutoff ≥ 30/hours, support vector machine model achieved the highest Matthew's correlation coefficient (0.545) and area under the curve (0.823), and good sensitivity (82.00%), specificity (82.69%), and negative predictive value (95.53%).

Conclusions

Our models achieved better performance than existing methods and have the potential to be integrated with home-use pulse oximeters.

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.

A review of automated sleep disorder detection

Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating sleep disorder detection. This paper provides an expert review of this work. We investigated whether digital technology and Artificial Intelligence (AI) can provide automated diagnosis support for sleep disorders. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during the content discovery phase. We compared the performance of proposed sleep disorder detection methods, involving differ-ent datasets or signals. During the review, we found eight sleep disorders, of which sleep apnea and insomnia were the most studied. These disorders can be diagnosed using several kinds of biomedical signals, such as Electrocardiogram (ECG), Polysomnography (PSG), Electroencephalogram (EEG), Electromyogram (EMG), and snore sound. Subsequently, we established areas of commonality and distinctiveness. Common to all reviewed papers was that AI models were trained and tested with labelled physiological signals. Looking deeper, we discovered that 24 distinct algorithms were used for the detection task. The nature of these algorithms evolved, before 2017 only traditional Machine Learning (ML) was used. From 2018 onward, both ML and Deep Learning (DL) methods were used for sleep disorder detection. The strong emergence of DL algorithms has considerable implications for future detection systems because these algorithms demand significantly more data for training and testing when compared with ML. Based on our review results, we suggest that both type and amount of labelled data is crucial for the design of future sleep disorder detection systems because this will steer the choice of AI algorithm which establishes the desired decision support. As a guiding principle, more labelled data will help to represent the variations in symptoms. DL algorithms can extract information from these larger data quantities more effectively, therefore; we predict that the role of these algorithms will continue to expand.

Review of Application of Machine Learning as a Screening Tool for Diagnosis of Obstructive Sleep Apnea

Obstructive sleep apnea syndrome (OSAS) is a pervasive disorder with an incidence estimated at 5-14 percent among adults aged 30-70 years. It carries significant morbidity and mortality risk from cardiovascular disease, including ischemic heart disease, atrial fibrillation, and cerebrovascular disease, and risks related to excessive daytime sleepiness. The gold standard for diagnosis of OSAS is the polysomnography (PSG) test which requires overnight evaluation in a sleep laboratory and expensive infrastructure, which renders it unsuitable for mass screening and diagnosis. Alternatives such as home sleep testing need patients to wear diagnostic instruments overnight, but accuracy continues to be suboptimal while access continues to be a barrier for many. Hence, there is a continued significant underdiagnosis and under-recognition of sleep apnea in the community, with at least one study suggesting that 80-90% of middle-aged adults with moderate to severe sleep apnea remain undiagnosed. Recently, we have seen a surge in applications of artificial intelligence and neural networks in healthcare diagnostics. Several studies have attempted to examine its application in the diagnosis of OSAS. Signals included in data analytics include Electrocardiogram (ECG), photo-pletysmography (PPG), peripheral oxygen saturation (SpO2), and audio signals. A different approach is to study the application of machine learning to use demographic and standard clinical variables and physical findings to try and synthesize predictive models with high accuracy in assisting in the triage of high-risk patients for sleep testing. The current paper will review this latter approach and identify knowledge gaps that may serve as potential avenues for future research.

Comparison of Composite Lipid Indices in Patients with Obstructive Sleep Apnoea

PURPOSE

Obstructive sleep apnoea (OSA) is a recognised risk factor for cardiovascular disease. However, it is difficult to evaluate the risk of cardiovascular disease in patients with OSA due to multiple shared risk factors. Composite lipid indices, such as atherogenic index of plasma (AIP), visceral adiposity index (VAI) and lipid accumulation product (LAP) have been shown to predict cardiovascular disease better than their individual lipid components. This study aimed to evaluate these indices in patients with OSA.

PATIENTS AND METHODS

Six hundred sixty-seven (667) patients with OSA and 139 non-OSA control volunteers participated in the study. Fasting serum triglycerides, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol (HDL-C) levels were measured, and AIP, LAP and VAI were calculated following cardiorespiratory polygraphy. The relationship between lipid parameters, OSA and its comorbidities was evaluated using receiver operating curve (ROC) analysis.

RESULTS

We found a significant difference in all lipid parameters between OSA patients and controls. Comparing ROCs, LAP was significantly more strongly associated with OSA compared to all the other parameters. The optimal cut-off value for LAP to detect OSA was 76.4, with a sensitivity of 63% and a specificity of 76%. In addition, LAP was the best parameter to predict hypertension and diabetes in patients with OSA, and it was predictive for ischaemic heart disease together with HDL-C.

CONCLUSION

Our results support the use of LAP in clinical practice when evaluating cardiovascular risk in patients with OSA. However, the optimal cut-off value should be determined in large-scale follow-up studies.