Diabetes and cardiovascular diseases are associated with the worsening of intermittent hypoxaemia

A Review of Novel Oximetry Parameters for the Prediction of Cardiovascular Disease in Obstructive Sleep Apnoea

Obstructive sleep apnoea (OSA) is a sleep disorder with repetitive collapse of the upper airway during sleep, which leads to intermittent hypoxic events overnight, adverse neurocognitive, metabolic complications, and ultimately an increased risk of cardiovascular disease (CVD). The standard diagnostic parameter for OSA, apnoea-hypopnoea index (AHI), is inadequate to predict CVD morbidity and mortality, because it focuses only on the frequency of apnoea and hypopnoea events, and fails to reveal other physiological information for the prediction of CVD events. Novel parameters have been introduced to compensate for the deficiencies of AHI. However, the calculation methods and criteria for these parameters are unclear, hindering their use in cross-study analysis and studies. This review aims to discuss novel parameters for predicting CVD events from oximetry signals and to summarise the corresponding computational methods.

Recent Progress in Long-Term Sleep Monitoring Technology

Sleep is an essential physiological activity, accounting for about one-third of our lives, which significantly impacts our memory, mood, health, and children's growth. Especially after the COVID-19 epidemic, sleep health issues have attracted more attention. In recent years, with the development of wearable electronic devices, there have been more and more studies, products, or solutions related to sleep monitoring. Many mature technologies, such as polysomnography, have been applied to clinical practice. However, it is urgent to develop wearable or non-contacting electronic devices suitable for household continuous sleep monitoring. This paper first introduces the basic knowledge of sleep and the significance of sleep monitoring. Then, according to the types of physiological signals monitored, this paper describes the research progress of bioelectrical signals, biomechanical signals, and biochemical signals used for sleep monitoring. However, it is not ideal to monitor the sleep quality for the whole night based on only one signal. Therefore, this paper reviews the research on multi-signal monitoring and introduces systematic sleep monitoring schemes. Finally, a conclusion and discussion of sleep monitoring are presented to propose potential future directions and prospects for sleep monitoring.

Desaturation event scoring criteria affect the perceived severity of nocturnal hypoxic load

OBJECTIVES/BACKGROUND

Interest in using blood oxygen desaturations in the diagnostics of sleep apnea has risen in recent years. However, no standardized criteria for desaturation scoring exist which complicates the drawing of solid conclusions from literature.

PATIENTS/METHODS

We investigated how different desaturation scoring criteria affect the severity of nocturnal hypoxic load and the prediction of impaired daytime vigilance in 845 patients. Desaturations were scored based on three features: 1) minimum oxygen saturation drop during the event (2-20%, 1% interval), 2) minimum duration of the event (2-20s, 1s interval), and 3) maximum plateau duration within the event (5-60s, 5s interval), resulting in 4332 different scoring criteria. The hypoxic load was described with oxygen desaturation index (ODI), desaturation severity (DesSev), and desaturation duration (DesDur) parameters. Association between hypoxic load and impaired vigilance was investigated with covariate-adjusted area under curve (AUC) analyses by dividing patients into normal (≤5 lapses) and impaired (≥36 lapses) vigilance groups based on psychomotor vigilance task performance.

RESULTS

The severity of hypoxic load varied greatly between different scoring criteria. For example, median ODI ranged between 0.4 and 12.9 events/h, DesSev 0.01-0.23 %-point, and DesDur 0.3-9.6 %-point when the minimum transient drop criterion of 3% was used and other two features were altered. Overall, the minimum transient drop criterion had the largest effect on parameter values. All models with differently determined parameters predicted impaired vigilance moderately (AUC = 0.722-0.734).

CONCLUSIONS

Desaturation scoring criteria greatly affected the severity of hypoxic load. However, the difference in the prediction of impaired vigilance between different criteria was rather small.

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients.

ABOSA - Freely available automatic blood oxygen saturation signal analysis software: Structure and validation

BACKGROUND AND OBJECTIVE

Many sleep recording software used in clinical settings have some tools to automatically analyze the blood oxygen saturation (SpO₂) signal by detecting desaturations. However, these tools are often inadequate for scientific research as they do not provide SpO₂ signal-based parameters which are superior in the estimation of sleep apnea severity and related medical consequences. In addition, these software require expensive licenses and they lack batch analysis tools. Thus, we developed the first freely available automatic blood oxygen saturation analysis software (ABOSA) that provides sophisticated SpO₂ signal-based parameters and enables batch analysis of large datasets.

METHODS

ABOSA was programmed with MATLAB. ABOSA automatically detects desaturation and recovery events from the SpO₂ signals (EDF files) and calculates numerous parameters, such as oxygen desaturation index (ODI) and desaturation severity (DesSev). The accuracy of the ABOSA software was evaluated by comparing its desaturation scorings to manual scorings in Kuopio (n = 1981) and Loewenstein (n = 930) sleep apnea patient datasets. Validation was performed in a second-by-second manner by calculating Matthew's correlation coefficients (MCC) and median differences in parameter values. Finally, the performance of the ABOSA software was compared to two commercial software, Noxturnal and Profusion, in 100 patient subpopulations. As Noxturnal or Profusion does not calculate novel desaturation parameters, these were calculated with custom-made functions.

RESULTS

The agreements between ABOSA and manual scorings were great in both Kuopio (MCC = 0.801) and Loewenstein (MCC = 0.898) datasets. However, ABOSA slightly overestimated the desaturation parameter values. The median differences in ODIs were 0.8 (Kuopio) and 0.0 (Loewenstein) events/h. Similarly, the median differences in DesSevs were 0.02 (Kuopio) and 0.01 (Loewenstein) percentage points. In a second-by-second analysis, ABOSA performed very similarly to Noxturnal and Profusion software in both Kuopio (MCC_ABOSA = 0.807, MCC_Noxturnal = 0.807, MCC_Profusion = 0.811) and Loewenstein (MCC_ABOSA = 0.904, MCC_Noxturnal = 0.911, MCC_Profusion = 0.871) datasets. Based on Noxturnal and Profusion scorings, the desaturation parameter values were similarly overestimated compared to ABOSA.

CONCLUSIONS

ABOSA is an accurate and freely available software that calculates both traditional clinical parameters and novel parameters, provides a detailed characterization of desaturation and recovery events, and enables batch analysis of large datasets. These are features that no other software currently provides making ABOSA uniquely suitable for scientific research use.

Effect of nocturnal hypoxemia on glycemic control among diabetic Saudi patients presenting with obstructive sleep apnea

BACKGROUND

Obstructive sleep apnea (OSA) is a prevalent disease that is associated with an increased incidence of type II diabetes mellitus (DM) if left untreated. We aimed to determine the association between glycosylated hemoglobin (HbA1c) levels and both nocturnal hypoxemia and apnea-hypopnea index (AHI) among a Saudi patients with OSA.

METHODS

A cross-sectional study that enrolled 103 adult patients diagnosed with DM and confirmed to have OSA by full night attended polysomnography between 2018 and 2021. Those who presented with acute illness, chronic obstructive pulmonary disease (COPD)/restrictive lung diseases causing sleep-related hypoxemia, or no available HbA1c level within 6 months before polysomnography were excluded from the study. Univariate and multivariate linear regression analyses between HbA1c levels and parameters of interest were tested.

RESULTS

Sixty-seven (65%) of the studied population had uncontrolled DM (HbA1c ≥7%). In univariate regression analysis, there was a significant positive association between HbA1c, and sleep time spent with an oxygen saturation below 90% (T90), female gender, and body mass index (BMI) (p<0.05) but not AHI, or associated comorbidities (p>0.05). In the multivariate analysis, HbA1c was positively associated with increasing T90 (p<0.05), and ODI (p<0.05), but not with AHI (p>0.05).

CONCLUSION

Nocturnal hypoxemia could be an important factor affecting glycemic control in patients with OSA suffering from DM irrespective of the severity of both diseases.

Association and Risk Factors for Obstructive Sleep Apnea and Cardiovascular Diseases: A Systematic Review

Obstructive sleep apnea (OSA) is a serious, potentially life-threatening condition. Epidemiologic studies show that sleep apnea increases cardiovascular diseases risk factors including hypertension, obesity, and diabetes mellitus. OSA is also responsible for serious illnesses such as congestive heart failure, stroke, arrhythmias, and bronchial asthma. The aim of this systematic review is to evaluate evidence for the association between OSA and cardiovascular disease morbidities and identify risk factors for the conditions. In a review of 34 studies conducted in 28 countries with a sample of 37,599 people, several comorbidities were identified in patients with severe OSA—these were: heart disease, stroke, kidney disease, asthma, COPD, acute heart failure, chronic heart failure, hyperlipidemia, thyroid disease, cerebral infarct or embolism, myocardial infarction, and psychological comorbidities including stress and depression. Important risk factors contributing to OSA included: age > 35 years; BMI ≥ 25 kg/m²; alcoholism; higher Epworth sleepiness scale (ESS); mean apnea duration; oxygen desaturation index (ODI); and nocturnal oxygen desaturation (NOD). Severe OSA (AHI ≥ 30) was significantly associated with excessive daytime sleepiness and oxygen desaturation index. The risk of OSA and associated disease morbidities can be reduced by controlling overweight/obesity, alcoholism, smoking, hypertension, diabetes mellitus, and hyperlipidemia.