Recognizable Clinical Subtypes of Obstructive Sleep Apnea After Ischemic Stroke: A Cluster Analysis

Clinical phenotypes of patients with acute stroke: a secondary analysis

INTRODUCTION

Stroke is a leading cause of mortality worldwide and a major cause of disability having a high burden on patients, society, and caregiving systems. This study was conducted to investigate the presence of clusters of in-hospital patients with acute stroke based on demographic and clinical data. Cluster analysis reveals patterns in patient characteristics without requiring knowledge of a predefined patient category or assumptions about likely groupings within the data.

METHODS

We performed a secondary analysis of open-access anonymized data from patients with acute stroke admitted to a hospital between December 2019 to June 2021. In total, 216 patients (78; 36.1% men) were included in the analytical dataset with a mean (SD) age of 60.3 (14.4). Many demographic and clinical features were included in the analysis and the Barthel Index on discharge was used for comparing the functional recovery of the identified clusters.

RESULTS

Hierarchical clustering based on the principal components identified two clusters of 109 and 107 patients. The clusters were different in the Barthel Index scores on discharge with the mean (SD) of 39.3 (29.3) versus 62.6 (29.4); t (213.87) = -5.818, P <0.001, Cohen's d (95%CI) = -0.80 (-1.07, -0.52). A logistic model showed that age, systolic blood pressure, pulse rate, D-dimer blood level, low-density lipoprotein, hemoglobin, creatinine concentration, the National Institute of Health Stroke Scale value, and the Barthel Index scores on admission were significant predictors of cluster profiles (all P ≤0.029).

CONCLUSION

There are two clusters in hospitalized patients with acute stroke with significantly different functional recovery. This allows prognostic grouping of hospitalized acute stroke patients for prioritization of care or resource allocation. The clusters can be recognized using easily measured demographic and clinical features.

Using a k-means clustering to identify novel phenotypes of acute ischemic stroke and development of its Clinlabomics models

Objective

Acute ischemic stroke (AIS) is a heterogeneous condition. To stratify the heterogeneity, identify novel phenotypes, and develop Clinlabomics models of phenotypes that can conduct more personalized treatments for AIS.

Methods

In a retrospective analysis, consecutive AIS and non-AIS inpatients were enrolled. An unsupervised k-means clustering algorithm was used to classify AIS patients into distinct novel phenotypes. Besides, the intergroup comparisons across the phenotypes were performed in clinical and laboratory data. Next, the least absolute shrinkage and selection operator (LASSO) algorithm was used to select essential variables. In addition, Clinlabomics predictive models of phenotypes were established by a support vector machines (SVM) classifier. We used the area under curve (AUC), accuracy, sensitivity, and specificity to evaluate the performance of the models.

Results

Of the three derived phenotypes in 909 AIS patients [median age 64 (IQR: 17) years, 69% male], in phenotype 1 (N = 401), patients were relatively young and obese and had significantly elevated levels of lipids. Phenotype 2 (N = 463) was associated with abnormal ion levels. Phenotype 3 (N = 45) was characterized by the highest level of inflammation, accompanied by mild multiple-organ dysfunction. The external validation cohort prospectively collected 507 AIS patients [median age 60 (IQR: 18) years, 70% male]. Phenotype characteristics were similar in the validation cohort. After LASSO analysis, Clinlabomics models of phenotype 1 and 2 were constructed by the SVM algorithm, yielding high AUC (0.977, 95% CI: 0.961-0.993 and 0.984, 95% CI: 0.971-0.997), accuracy (0.936, 95% CI: 0.922-0.956 and 0.952, 95% CI: 0.938-0.972), sensitivity (0.984, 95% CI: 0.968-0.998 and 0.958, 95% CI: 0.939-0.984), and specificity (0.892, 95% CI: 0.874-0.926 and 0.945, 95% CI: 0.923-0.969).

Conclusion

In this study, three novel phenotypes that reflected the abnormal variables of AIS patients were identified, and the Clinlabomics models of phenotypes were established, which are conducive to individualized treatments.

Heterogeneity of obstructive sleep apnea phenotypes after ischemic stroke: Outcome variation by cluster analysis

INTRODUCTION

Obstructive sleep apnea (OSA) is common but under-recognized after stroke. The aim of this study was to determine whether post-stroke phenotypic OSA subtypes are associated with stroke outcome in a population-based observational cohort.

METHODS

Ischemic stroke patients (n = 804) diagnosed with OSA (respiratory event index ≥10) soon after ischemic stroke were identified from the Brain Attack Surveillance in Corpus Christi (BASIC) project. Functional, cognitive, and quality of life outcomes were assessed at 90 days post-stroke and long-term stroke recurrence was ascertained. Latent profile analysis was performed based on demographic and clinical features, pre-stroke sleep characteristics, OSA severity, and vascular risk factors. Regression models were used to assess the association between phenotypic clusters and outcomes.

RESULTS

Four distinct phenotypic clusters provided the best fit. Cluster 1 was characterized by more severe stroke; cluster 2 by severe OSA and higher prevalence of medical comorbidities; cluster 3 by mild stroke and mild OSA; and cluster 4 by moderate OSA and mild stroke. Compared to cluster 3 and after adjustment for baseline stroke severity, cluster 1 and cluster 2 had worse 90-day functional outcome and cluster 1 also had worse quality of life. No difference in cognitive outcome or stroke recurrence rate was noted by cluster.

CONCLUSION

Post-stroke OSA is a heterogeneous disorder with different clinical phenotypes associated with stroke outcomes, including both daily function and quality of life. The unique presentations of OSA after stroke may have important implications for stroke prognosis and personalized treatment strategies.

Clinical prediction models for the early diagnosis of obstructive sleep apnea in stroke patients: a systematic review

BACKGROUND

Obstructive sleep apnea (OSA) is a common sleep disorder characterized by repetitive cessation or reduction in airflow during sleep. Stroke patients have a higher risk of OSA, which can worsen their cognitive and functional disabilities, prolong their hospitalization, and increase their mortality rates.

METHODS

We conducted a comprehensive literature search in the databases of PubMed, CINAHL, Embase, PsycINFO, Cochrane Library, and CNKI, using a combination of keywords and MeSH words in both English and Chinese. Studies published up to March 1, 2022, which reported the development and/or validation of clinical prediction models for OSA diagnosis in stroke patients.

RESULTS

We identified 11 studies that met our inclusion criteria. Most of the studies used logistic regression models and machine learning approaches to predict the incidence of OSA in stroke patients. The most frequently selected predictors included body mass index, sex, neck circumference, snoring, and blood pressure. However, the predictive performance of these models ranged from poor to moderate, with the area under the receiver operating characteristic curve varying from 0.55 to 0.82. All the studies have a high overall risk of bias, mainly due to the small sample size and lack of external validation.

CONCLUSION

Although clinical prediction models have shown the potential for diagnosing OSA in stroke patients, their limited accuracy and high risk of bias restrict their implications. Future studies should focus on developing advanced algorithms that incorporate more predictors from larger and representative samples and externally validating their performance to enhance their clinical applicability and accuracy.

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.

Sleep apnoea and ischaemic stroke: current knowledge and future directions

Sleep apnoea, one of the most common chronic diseases, is a risk factor for ischaemic stroke, stroke recurrence, and poor functional recovery after stroke. More than half of stroke survivors present with sleep apnoea during the acute phase after stroke, with obstructive sleep apnoea being the most common subtype. Following a stroke, sleep apnoea frequency and severity might decrease over time, but moderate to severe sleep apnoea is nevertheless present in up to a third of patients in the chronic phase after an ischaemic stroke. Over the past few decades evidence suggests that treatment for sleep apnoea is feasible during the acute phase of stroke and might favourably affect recovery and long-term outcomes. Nevertheless, sleep apnoea still remains underdiagnosed and untreated in many cases, due to challenges in the detection and prediction of post-stroke sleep apnoea, uncertainty as to the optimal timing for its diagnosis, and a scarcity of clear treatment guidelines (ie, uncertainty on when to treat and the optimal treatment strategy). Moreover, the pathophysiology of sleep apnoea associated with stroke, the proportion of stroke survivors with obstructive and central sleep apnoea, and the temporal evolution of sleep apnoea subtypes following stroke remain to be clarified. To address these shortcomings, the management of sleep apnoea associated with stroke should be integrated into a multidisciplinary diagnostic, treatment, and follow-up strategy.