Wake-up strokes are linked to obstructive sleep apnea and worse early functional outcome

Presence of Self-Reported Sleep Alterations After Stroke and Their Relationship With Disability: A Longitudinal Study

BACKGROUND

Sleep disorders have a prevalence of 30% to 70% in post-stroke individuals. The presence of sleep disorders and poor sleep quality after stroke can affect important functions and lead to worse outcomes. However, most studies are restricted to the acute post-stroke stage only.

OBJECTIVE

To investigate the frequency of self-reported sleep alterations in a sample of chronic stroke individuals and to identify which self-reported sleep alterations were associated with disability.

METHODS

Prospective exploratory study. Self-reported sleep alterations were measured by the Pittsburgh Sleep Quality Index, Insomnia Severity Index, Epworth Sleepiness Scale, and STOP-Bang Questionnaire. The dependent variable was measured 3 years after the first contact by the Modified Rankin Scale (mRS). Step-wise multiple linear regression analysis was employed to identify which sleep alterations were associated with disability.

RESULTS

Sixty-five individuals with stroke participated. About 67.7% of participants had poor sleep quality, 52.4% reported insomnia symptoms, 33.9% reported excessive daytime sleepiness, and 80.0% were classified as intermediate or high risk for obstructive sleep apnea. Only risk for obstructive sleep apnea was significantly associated with disability and explained 5% of the variance in the mRS scores.

CONCLUSION

Self-reported sleep alterations had a considerable frequency in a sample of chronic stroke individuals. The risk of obstructive sleep apnea was associated with disability in the chronic stage of stroke. Sleep alterations must be considered and evaluated in the rehabilitation process even after a long period since the stroke onset.

Sleep apnea and ischemic stroke- a perspective for translational preclinical modelling

Obstructive sleep apnea (OSA) is associated with ischemic stroke. There is, however, a lack of knowledge on the exact cause-effect relationship, and preclinical models of OSA for experimental ischemic stroke investigations are not well characterized. In this review, we discuss sleep apnea and its relationship with stroke risk factors. We consider how OSA may lead to ischemic stroke and how OSA-induced metabolic syndrome and hypothalamic-pituitary axis (HPA) dysfunction could serve as therapeutic targets to prevent ischemic stroke. Further, we examine the translational potential of established preclinical models of OSA. We conclude that metabolic syndrome and HPA dysfunction, which are often overlooked in the context of experimental stroke and OSA studies, are crucial for experimental consideration to improve the body of knowledge as well as the translational potential of investigative efforts.

Nomogram for predicting moderate-to-severe sleep-disordered breathing in patients with acute ischaemic stroke: a retrospective cohort study

OBJECTIVES

Moderate-to-severe sleep-disordered breathing (SDB) is prevalent in patients with acute ischaemic stroke (AIS) and is associated with an increased risk of unfavourable prognosis. We aimed to develop and validate a reliable scoring system for the early screening of moderate-to-severe SDB in patients with AIS, with the objective of improving the management of those patients at risk.

STUDY DESIGN

We developed and validated a nomogram model based on univariate and multivariate logistic analyses to identify moderate-to-severe SDB in AIS patients. Moderate-to-severe SDB was defined as an apnoea-hypopnoea index (AHI) ≥15. To evaluate the effectiveness of our nomogram, we conducted a comparison with the STOP-Bang questionnaire by analysing the area under the receiver operating characteristic curve.

SETTING

Large stroke centre in northern Shanghai serving over 4000 inpatients, 100 000 outpatients and emergency visits annually.

PARTICIPANTS

We consecutively enrolled 116 patients with AIS from the Shanghai Tenth People's Hospital.

RESULTS

Five variables were independently associated with moderate-to-severe SDB in AIS patients: National Institutes of Health Stroke Scale score (OR=1.20; 95% CI 0.98 to 1.47), neck circumference (OR=1.50; 95% CI 1.16 to 1.95), presence of wake-up stroke (OR=21.91; 95% CI 3.08 to 156.05), neuron-specific enolase level (OR=1.27; 95% CI 1.05 to 1.53) and presence of brainstem infarction (OR=4.21; 95% CI 1.23 to 14.40). We developed a nomogram model comprising these five variables. The C-index was 0.872, indicated an optimal agreement between the observed and predicted SDB patients.

CONCLUSIONS

Our nomogram offers a practical approach for early detection of moderate-to-severe SDB in AIS patients. This tool enables individualised assessment and management, potentially leading to favourable outcomes.

Diffusion-/perfusion-weighted imaging fusion to automatically identify stroke within 4.5 h

OBJECTIVES

We aimed to develop machine learning (ML) models based on diffusion- and perfusion-weighted imaging fusion (DP fusion) for identifying stroke within 4.5 h, to compare them with DWI- and/or PWI-based ML models, and to construct an automatic segmentation-classification model and compare with manual labeling methods.

METHODS

ML models were developed from multimodal MRI datasets of acute stroke patients within 24 h of clear symptom onset from two centers. The processes included manual segmentation, registration, DP fusion, feature extraction, and model establishment (logistic regression (LR) and support vector machine (SVM)). A segmentation-classification model (X-Net) was proposed for automatically identifying stroke within 4.5 h. The area under the receiver operating characteristic curve (AUC), sensitivity, Dice coefficients, decision curve analysis, and calibration curves were used to evaluate model performance.

RESULTS

A total of 418 patients (≤ 4.5 h: 214; > 4.5 h: 204) were evaluated. The DP fusion model achieved the highest AUC in identifying the onset time in the training (LR: 0.95; SVM: 0.92) and test sets (LR: 0.91; SVM: 0.90). The DP fusion-LR model displayed consistent positive and greater net benefits than other models across a broad range of risk thresholds. The calibration curve demonstrated the good calibration of the DP fusion-LR model (average absolute error: 0.049). The X-Net model obtained the highest Dice coefficients (DWI: 0.81; Tmax: 0.83) and achieved similar performance to manual labeling (AUC: 0.84).

CONCLUSIONS

The automatic segmentation-classification models based on DWI and PWI fusion images had high performance in identifying stroke within 4.5 h.

CLINICAL RELEVANCE STATEMENT

Perfusion-weighted imaging (PWI) fusion images had high performance in identifying stroke within 4.5 h. The automatic segmentation-classification models based on DWI and PWI fusion images could provide clinicians with decision-making guidance for acute stroke patients with unknown onset time.

KEY POINTS

• The diffusion/perfusion-weighted imaging fusion model had the best performance in identifying stroke within 4.5 h. • The X-Net model had the highest Dice and achieved performance close to manual labeling in segmenting lesions of acute stroke. • The automatic segmentation-classification model based on DP fusion images performed well in identifying stroke within 4.5 h.

No Difference in Sleep Desaturations Severity between Patients with Wake-Up and Non-Wake-Up Stroke: A PRESS Study Results

BACKGROUND

Wake-up stroke (WUS) is a certain type of ischemic stroke in which a patient wakes up with a new neurological deficit due to cerebral ischemia. Sleep-disordered breathing is an independent risk factor for stroke, but the role of nocturnal oxygen desaturation in the pathophysiology of WUS is still insufficiently explored. According to several studies, patients with WUS have a significantly more severe sleep apnea syndrome and lower mean blood oxygen saturation. This study aimed to assess the severity of nocturnal desaturations in acute WUS and non-WUS patients using nocturnal pulse oximetry.

MATERIAL AND METHODS

The cohort of 225 consecutive patients with neuroimaging-verified acute cerebral ischemia was prospectively enrolled. For further analyses, 213 subjects with known WUS/non-WUS status were selected (111 males and 102 females, average age 70.4 ±12.9, median baseline NIHSS = 5, median baseline mRS = 3). Patients were divided into the WUS group (n = 45) and the non-WUS group (n = 168). Overnight pulse oximetry was performed within 7 days of the stroke onset and data of both of the studied groups were compared.

RESULTS

We found oxygen desaturation index (ODI) in the WUS group was 14.5 vs. 16.6 (p = 0.728) in the non-WUS group, basal O2 saturation was 92.2% vs. 92.5% (p = 0.475), average low O2 saturation was 90.3% vs. 89.6% (p = 0.375), minimal O2 saturation was 79.5% vs. 80.6% (p = 0.563), and time with O2 saturation <90% (T90) was 4.4% vs. 4.7% (p = 0.729).

CONCLUSIONS

In the studied sample, monitored respiratory parameters including ODI, basal O2 saturation, average low O2 saturation, minimal O2 saturation, and T90 did not significantly differ between groups of WUS and non-WUS patients.

Wake-up ischemic stroke associated with short sleep duration and sleep behavior: A stratified analysis according to risk of obstructive sleep apnea

OBJECTIVE

Wake-up stroke (WUS) is an ischemic stroke occurring during nocturnal sleep with neurological deficits observed upon awakening. Our study aimed to investigate the association between WUS, sleep curtailment, and sleep behavior according to the obstructive sleep apnea (OSA) risk in patients with acute ischemic stroke.

METHODS

This single-centered, retrospective study included hospitalized subjects with acute ischemic stroke occurring within 30 days. A total of 250 participants were classified as WUS or not and enquired about their sleep habits concerning sleep time on weekdays and weekends, demographic factors, and assessed comorbid medical conditions. Weekend catch-up sleep (CUS) was defined as the extension of sleep duration during weekends. The average weekly sleep duration and chronotype were assessed. The association between WUS and sleep factors was analyzed.

RESULTS

WUS was observed in 70 patients (28.0%) with acute ischemic stroke. There were no significant differences in the demographic and stroke-related variables between the WUS and non-WUS (NWUS) groups. Upon stratified analysis based on risk of OSA, average weekly sleep duration (odds ratio, [OR] = 0.60, 95% confidence interval, [CI] = 0.41-0.89; p = 0.011), the presence of weekend CUS (OR = 0.07, 95% CI = 0.01-0.97; p = 0.047), and chronotype (OR = 0.62, 95% CI = 0.39-0.98; p = 0.041) were independently associated with WUS in low-risk group with OSA, but not in the high-risk group.

CONCLUSIONS

Short sleep duration and lack of compensation are significantly associated with WUS in low-risk OSA group. Insufficient sleep and sleep behaviors could play a different role in causing ischemic stroke during sleep when patients are stratified by their risk of sleep apnea.

Clinically Silent Small Vessel Disease of the Brain in Patients with Obstructive Sleep Apnea Hypopnea Syndrome

Obstructive sleep apnea hypopnea syndrome (OSAHS) is associated with increased risk of cerebrovascular disease. The aim of the present study was to investigate the association between the presence of the small vessel disease (SVD) of the brain in patients with OSAHS. The study included 24 patients with moderate to severe OSAHS and 34 healthy volunteers. All the subjects underwent magnetic resonance imaging (MRI) of the brain, in order to sought periventricular white matter (PVWM), deep white matter (DWM) and brainstem SVD. Among patients with OSAHS, 79.1% had SVD (grade 1-3, Fazekas score) in DWM and 91.7% in PVWM while 22.4% had brainstem-white matter hyperintensities (B-WMH). Patients with OSAHS had a much higher degree of SVD in the DWM and PVWM compared to the control group (p < 0.001). The multivariate analysis showed an independent significant association of OSAHS with SVD (DWM and PVWM) (p = 0.033, OR 95% CI: 8.66 (1.19-63.08) and: p = 0.002, OR 95% CI: 104.98 (5.15-2141)). The same analysis showed a moderate association of OSAHS with B-WMH (p = 0.050, OR 15.07 (0.97-234.65)). Our study demonstrated an independent significant association of OSAHS with SVD and a moderate association of OSAHS with B-WMH.