Sleep apnea prediction in acute ischemic stroke (SLAPS score): a derivation study

Šiarnik, Pavel; Jurík, Matúš; Klobučníková, Katarína; Kollár, Branislav; Pirošová, Margita; Malik, M.; Turčãni, Peter; Sýkora, Marek

doi:10.1016/j.sleep.2020.11.022

Cited by 10 publications

(16 citation statements)

References 33 publications

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“…The characteristics of the 2837participants in these 11 studies are summarized in Table 1 . The average age of the participants was 60.7 years [ 21 – 31 ]. The majority of participants (71.3%) were in the acute stage of stroke (less than 7 days), while 22.8% were in both the acute and subacute stages (less than 6 months).…”

Section: Resultsmentioning

confidence: 99%

“…The majority of participants (71.3%) were in the acute stage of stroke (less than 7 days), while 22.8% were in both the acute and subacute stages (less than 6 months). Almost 85% participants were from hospitals [ 21 – 23 , 25 – 28 , 30 , 31 ], including neurology, stroke unit, or emergency units, while 15% were from stroke clinics [ 24 , 29 ]. The studies were conducted in seven different countries: the USA, Canada, China, Brazil, Slovakia, India, and Italy.…”

Section: Resultsmentioning

confidence: 99%

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

Yang,

Lu,

Yang

2024

Syst Rev

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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

Yang,

Lu,

Yang

2024

Syst Rev

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“…Since performing a sleep study in all stroke patients is compelling, current research is focusing on identifying predictors of sleep apnea in this population applying promising artificial intelligent techniques [64•, 65, 66], new scores [67], or alternative recording techniques [68].…”

Section: Diagnosis Of Sleep Apnea In Stroke Patientsmentioning

confidence: 99%

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

Brunetti

Rollo

Broccolini

et al. 2022

Curr Neurol Neurosci Rep

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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. Summary 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.

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“…In this process, studies unrelated to our topic and inappropriate types of studies were excluded. After reviewing the full text and according to the inclusion and exclusion criteria, 13 studies were considered eligible and included in the metaanalysis with 1 study excluded for the reason of insufficient data [8][9][10][11][12][13][14][15][16][17][18][19][20]. The process of the study selection is shown in Figure 1.…”

Section: Study Selectionmentioning

confidence: 99%

Investigating the Association between Wake-Up Stroke and Obstructive Sleep Apnea: A Meta-Analysis

2021

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Background: Management of wake-up stroke (WUS) is always a challenge as no clear time of onset could be ascertained, and how to choose an appropriate therapy remains unclear. Sleep-disordered breathing (SDB) has been regarded as a potential risk factor to WUS, yet no consensus was achieved. Motivated by the need for a deeper understanding of WUS and its association with sleep apnea, meta-analyses summarizing the available evidence of respiratory events and indices were conducted, and sensitivity analysis was also used for heterogeneity. Methods: Electronic databases were systematically searched, and cross-checking was done for relevant studies. Collected data included demographic characteristics, and sleep apnea parameters were extracted with stroke patients divided into WUS and NWUS groups. Clinical data of stroke patients accompanied with sleep apnea syndrome (OSA, SAS, and severe SAS) were also extracted for meta-analysis. Results: A total of 13 studies were included in the analysis. The meta-analysis results showed that OSA, SAS, and severe SAS were significantly higher in WUS patients. A significantly higher AHI (WMD 7.74, 95% CI: 1.38–14.11; p = 0.017) and ODI (WMD of 3.85, 95% CI: 0.261–7.438; p = 0.035) than NWUS patients was also observed in the analysis of respiratory indices. Conclusion: WUS patients have severer SDB problems compared to NWUS patients suggesting that respiratory events during sleep might be underlying the induction of WUS. Besides, the induction of WUS was significantly associated with men rather than women. Therefore, early diagnosis and management of potential WUS patients should benefit from the detection of SDB status and respiratory effects.

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Sleep apnea prediction in acute ischemic stroke (SLAPS score): a derivation study

Cited by 10 publications

References 33 publications

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

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

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

Investigating the Association between Wake-Up Stroke and Obstructive Sleep Apnea: A Meta-Analysis

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