Unattended sleep study in screening for sleep apnea in the acute phase of ischemic stroke

Screening for OSA in stroke patients: The role of a sleep educator

Obstructive Sleep Apnea (OSA) is prevalent in patients with stroke or Transient Ischemic Attack (TIA). OSA is also a risk factor for recurrent stroke and TIA. Screening for and addressing OSA in acute stroke settings is difficult because of variety of factors not the least of which is the added burden on the healthcare team. We describe the preliminary results of a pilot program instituted at our medical center and the positive impact it has on OSA screening in the acute stroke unit.

Lesions causing central sleep apnea localize to one common brain network

Objectives

To characterize the specific brain regions for central sleep apnea (CSA) and identify its functional connectivity network.

Methods

We performed a literature search and identified 27 brain injuries causing CSA. We used a recently validated methodology termed “lesion network mapping” to identify the functional brain network subtending the pathophysiology of CSA. Two separate statistical approaches, the two-sample t-test and the Liebermeister test, were used to evaluate the specificity of this network for CSA through a comparison of our results with those of two other neurological syndromes. An additional independent cohort of six CSA cases was used to assess reproducibility.

Results

Our results showed that, despite lesions causing CSA being heterogeneous for brain localization, they share a common brain network defined by connectivity to the middle cingulate gyrus and bilateral cerebellar posterior lobes. This CSA-associated connectivity pattern was unique when compared with lesions causing the other two neurological syndromes. The CAS-specific regions were replicated by the additional independent cohort of six CSA cases. Finally, we found that all lesions causing CSA aligned well with the network defined by connectivity to the cingulate gyrus and bilateral cerebellar posterior lobes.

Conclusion

Our results suggest that brain injuries responsible for CSA are part of a common brain network defined by connectivity to the middle cingulate gyrus and bilateral cerebellar posterior lobes, lending insight into the neuroanatomical substrate of CSA.

Diagnostic value of smartphone in obstructive sleep apnea syndrome: A systematic review and meta-analysis

Objectives

To assess the diagnostic utility of smartphone-based measurement in detecting moderate to severe obstructive sleep apnea syndrome (OSAS).

Methods

Six databases were thoroughly reviewed. Random-effect models were used to estimate the summary sensitivity, specificity, negative predictive value, positive predictive value, diagnostic odds ratio, summary receiver operating characteristic curve and measured the areas under the curve. To assess the accuracy and precision, pooled mean difference and standard deviation of apnea hypopnea index (AHI) between smartphone and polysomnography (95% limits of agreement) across studies were calculated using the random-effects model. Study methodological quality was evaluated using the QUADAS-2 tool.

Results

Eleven studies were analyzed. The smartphone diagnostic odds ratio for moderate-to-severe OSAS (apnea/hypopnea index > 15) was 57.3873 (95% confidence interval [CI]: [34.7462; 94.7815]). The area under the summary receiver operating characteristic curve was 0.917. The sensitivity, specificity, negative predictive value, and positive predictive value were 0.9064 [0.8789; 0.9282], 0.8801 [0.8227; 0.9207], 0.9049 [0.8556; 0.9386], and 0.8844 [0.8234; 0.9263], respectively. We performed subgroup analysis based on the various OSAS detection methods (motion, sound, oximetry, and combinations thereof). Although the diagnostic odds ratios, specificities, and negative predictive values varied significantly (all p < 0.05), all methods afforded good sensitivity (> 80%). The sensitivities and positive predictive values were similar for the various methods (both p > 0.05). The mean difference with standard deviation in the AHI between smartphone and polysomnography was -0.6845 ± 1.611 events/h [-3.8426; 2.4735].

Conclusions

Smartphone could be used to screen the moderate-to-severe OSAS. The mean difference between smartphones and polysomnography AHI measurements was small, though limits of agreement was wide. Therefore, clinicians should be cautious when making clinical decisions based on these devices.

Diagnosis of Obstructive Sleep Apnea in Patients with Associated Comorbidity

Obstructive sleep apnea (OSA) is a heterogeneous disease with many physiological implications. OSA is associated with a great diversity of diseases, with which it shares common and very often bidirectional pathophysiological mechanisms, leading to significantly negative implications on morbidity and mortality. In these patients, underdiagnosis of OSA is high. Concerning cardiorespiratory comorbidities, several studies have assessed the usefulness of simplified screening tests for OSA in patients with hypertension, COPD, heart failure, atrial fibrillation, stroke, morbid obesity, and in hospitalized elders.The key question is whether there is any benefit in the screening for the existence of OSA in patients with comorbidities. In this regard, there are few studies evaluating the performance of the various diagnostic procedures in patients at high risk for OSA. The purpose of this chapter is to review the existing literature about diagnosis in those diseases with a high risk for OSA, with special reference to artificial intelligence-related methods.

Sleep and Stroke: Opening Our Eyes to Current Knowledge of a Key Relationship

PURPOSE OF REVIEW

To elucidate the interconnection between sleep and stroke.

RECENT FINDINGS

Growing data support a bidirectional relationship between stroke and sleep. In particular, there is strong evidence that sleep-disordered breathing plays a pivotal role as risk factor and concur to worsening functional outcome. Conversely, for others sleep disorders (e.g., insomnia, restless legs syndrome, periodic limb movements of sleep, REM sleep behavior disorder), the evidence is weak. Moreover, sleep disturbances are highly prevalent also in chronic stroke and concur to worsening quality of life of patients. Promising novel technologies will probably allow, in a near future, to guarantee a screening of commonest sleep disturbances in a larger proportion of patients with stroke. Sleep assessment and management should enter in the routinary evaluation of stroke patients, of both acute and chronic phase. Future research should focus on the efficacy of specific sleep intervention as a therapeutic option for stroke patients.

Prevalence and Determinants of Sleep Apnea in Patients with Stroke: A Meta-Analysis

OBJECTIVES

Recent meta-analyses have noted that ∼70% of transient ischemic attack (TIA)/stroke patients have sleep apnea. However, the heterogeneity between studies was high and did not appear to be accounted by the phase of stroke. We conducted an updated meta-analysis and aimed to determine whether the prevalence of sleep apnea amongst stroke patients differs by the subtype, etiology, severity and location of stroke and hence could account for some of the unexplained heterogeneity observed in previous studies.

MATERIALS AND METHODS

We searched Medline, Embase, CINAHL and Cochrane Library (from their commencements to July 2020) for studies which reported the prevalence of sleep apnea by using polysomnography in TIA/stroke patients. We used random-effects model to calculate the pooled prevalence of sleep apnea and explored whether the prevalence differed by stroke characteristics.

RESULTS

Seventy-five studies describing 8670 stroke patients were included in this meta-analysis. The overall prevalence of sleep apnea was numerically higher in patients with hemorrhagic vs. ischemic stroke [82.7% (64.4-92.7%) vs. 67.5% (63.2-71.5%), p=0.098], supratentorial vs. infratentorial stroke [64.4% (56.7-71.4%) vs. 56.5% (42.2-60.0%), p=0.171], and cardioembolic [74.3% (59.6-85.0%)] vs. other ischemic stroke subtypes [large artery atherosclerosis: 68.3% (52.5-80.7%), small vessel occlusion: 56.1% (38.2-72.6%), others/undetermined: 47.9% (31.6-64.6%), p=0.089]. The heterogeneity in sleep apnea prevalence was partially accounted by the subtype (1.9%), phase (5.0%) and location of stroke (14.0%) among reported studies.

CONCLUSIONS

The prevalence of sleep apnea in the stroke population appears to differ by the subtype, location, etiology and phase of stroke.

Neural network analysis of nocturnal SpO2 signal enables easy screening of sleep apnea in patients with acute cerebrovascular disease

Current diagnostics of sleep apnea relies on the time-consuming manual analysis of complex sleep registrations, which is impractical for routine screening in hospitalized patients with a high probability for sleep apnea, e.g. those experiencing acute stroke or transient ischemic attacks (TIA). To overcome this shortcoming, we aimed to develop a convolutional neural network (CNN) capable of estimating the severity of sleep apnea in acute stroke and TIA patients based solely on the nocturnal oxygen saturation (SpO₂) signal. The CNN was trained with SpO₂ signals derived from 1379 home sleep apnea tests (HSAT) of suspected sleep apnea patients and tested with SpO₂ signals of 77 acute ischemic stroke or TIA patients. The CNN's performance was tested by comparing the estimated respiratory event index (REI) and oxygen desaturation index (ODI) with manually obtained values. Median estimation errors for REI and ODI in patients with stroke or TIA were 1.45 events/hour and 0.61 events/hour, respectively. Furthermore, based on estimated REI and ODI, 77.9% and 88.3% of these patients were classified into the correct sleep apnea severity categories. The sensitivity and specificity to identify sleep apnea (REI > 5 events/hour) were 91.8% and 78.6%, respectively. Moderate-to-severe sleep apnea was detected (REI > 15 events/hour) with sensitivity of 92.3% and specificity of 96.1%. The CNN analysis of the SpO₂ signal has great potential as a simple screening tool for sleep apnea. This novel automatic method accurately detects sleep apnea in acute cerebrovascular disease patients and facilitates their referral for a differential diagnostic HSAT or polysomnography evaluation.