Abstract
Background:
Sleep-disordered breathing (SDB) is common among stroke survivors and is associated with worse functional, cognitive, and neurologic outcomes after stroke. Little is known about the association between changes in SDB and changes in these outcomes over time.
Methods:
Ischemic stroke (IS) patients identified through the Brain Attack Surveillance in Corpus Christi project were offered SDB testing with a portable respiratory monitor (ApneaLink Plus) shortly after stroke, and at 3-, 6-, and 12-months post-stroke. SDB was quantified using the respiratory event index (REI; apneas plus hypopneas per hour of recording). At 3-, 6-, and 12-months post-stroke, functional outcomes, cognitive outcomes, and neurologic outcomes were measured. Linear mixed models were fitted to obtain random slopes reflecting individual changes in REI and each of outcome over time, adjusted for multiple covariates. Associations between the resulting individual slopes for REI and each outcome were then evaluated using linear regression models.
Results:
Of 482 IS patients with at least one REI measurement, in fully adjusted models, faster reduction in REI was not associated with faster improvement in functional (β = −0.06; 95% CI: −0.15, 0.03, p=0.16), cognitive (β = −0.03; 95% CI: −0.12, 0.06, p=0.51), or neurologic outcomes (β = −0.04; 95% CI: −0.13, 0.05, p=0.41).
Conclusions:
In this observational study of stroke survivors, there was no clear association between the rate of improvement in SDB and improvement in functional, cognitive, or neurologic outcomes. It remains to be seen whether treatment of SDB might lead to improved outcomes among stroke survivors.
Introduction
Sleep-disordered breathing (SDB) is an increasingly prevalent condition in the general population,[1] and is associated with increased risk of stroke, cardiovascular disease, and mortality.[2–4] Among stroke survivors, SDB prevalence is particularly high, with greater than 70% being affected to some degree.[5] The presence of SDB following a stroke is associated with worse outcomes including increased risk of recurrent stroke and greater post-stroke mortality.[6–9]
SDB may also impact post-stroke recovery. Prior studies have observed that stroke survivors with SDB are less likely to achieve favorable functional, cognitive, and neurologic outcomes.[8, 10–12] Yet, much remains unknown about the relationship between SDB and post-stroke outcomes, including whether changes in SDB severity over time are associated with changes in the trajectory of post-stroke recovery.
Our aim was to evaluate whether improvements in SDB severity were associated with improvements in functional, cognitive, and neurologic outcomes in the first year after stroke. Such a finding may suggest a potential role for treatment of post-stroke SDB as a means to optimize post-stroke recovery.
Methods
Study setting
This study was associated with the Brain Attack Surveillance in Corpus Christi (BASIC) project. Detailed methods have been published previously.[13] Briefly, BASIC is a population-based study that uses both active and passive surveillance to identify all ischemic and hemorrhagic stroke cases occurring among residents of Nueces County, Texas, age ≥45. The community is essentially biethnic, composed predominantly of Mexican American (MA) and non-Hispanic White (NHW) residents. All cases are validated by fellowship-trained vascular neurologists based on review of source documentation, including medical charts and imaging reports. For the purposes of this study, only patients with ischemic stroke occurring between May 2016 and December 2019 were included. Patients who had a confirmed ischemic stroke and were not pregnant and did not use oxygen, mechanical ventilation, or positive pressure ventilation were offered SDB assessment. Detailed methods of the SDB study have been published previously.[14, 15] Written informed consent was obtained from the patient or surrogate and the study was approved by the Institutional Review Boards of the University of Michigan and the Corpus Christi hospital systems.
Sleep-disordered breathing
The primary exposure was SDB severity. To be performed, baseline SDB assessments had to occur within 30 days of the index stroke if identified by active surveillance, and within 45 if identified by passive surveillance. SDB was then serially assessed at 3-, 6-, and 12-months after the index stroke. SDB assessments were completed using the ApneaLink Plus (ResMed, San Diego, California, United States), a portable device that assesses oxygen saturation, nasal pressure, respiratory effort, and pulse, and has excellent concordance with polysomnography in quantifying key SDB measures including apneas and hypopneas.[16–18] Apneas and hypopneas were identified using ApneaLink Plus default settings, in concordance with validation studies.[16] An apnea was defined as a reduction in airflow by ≥80% for ≥10 seconds and a hypopnea was defined as a reduction in airflow by ≥30% for ≥10 seconds associated with an oxygen desaturation ≥4%. The automated scoring of respiratory events was edited by an experienced, registered polysomnographic technologist. SDB severity was quantified by the respiratory event index (REI), which reflects the sum of apneas plus hypopneas per hour of recording (ie, events per hour). Approximately 3 months after each SDB assessment, a letter summarizing the ApneaLink results was provided to each participant. In addition, a detailed report and cover letter was sent to each participant’s physician if the participant elected to provide their name and contact information.
Post-stroke outcomes
The primary outcomes included functional, cognitive, and neurologic status, assessed via in-person interviews with the patient (or their proxy if the patient is unable to communicate) at 3-, 6-, and 12-months after stroke. Functional outcomes were assessed using a validated scale[19] by asking participants to rate their degree of difficulty in performing 7 activities of daily living (ADL) and 15 instrumental activities of daily living (IADL) tasks on a Likert scale ranging from 1 (no difficulty) to 4 (can only do with help), as we have reported previously.[10, 20, 21] Responses were averaged to create a single functional outcome score ranging from 1 to 4, with higher scores representing worse function. Cognitive outcomes were measured using the modified Mini-Mental State Examination (3MSE).[22, 23] Scores range from 0 to 100 with higher scores representing better cognitive function. Neurologic outcomes were measured by the National Institutes of Health Stroke Scale (NIHSS),[24, 25] assessed by certified study coordinators, with higher scores representing greater neurologic deficits. The NIHSS provides a reproducible quantification of neurologic deficits, can be administered by non-neurologists who have undergone training,[24] and its use as a longitudinal measure of neurologic outcomes is well established.[26–28]
Covariates
Potential confounders included sociodemographic and pre-stroke characteristics ascertained from baseline interviews, as well as clinical factors obtained from review of medical charts. Sociodemographic factors included age, sex, race/ethnicity, education, and marital status. Pre-stroke cognitive impairment was measured using the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE; categorized as normal cognitive function, cognitive impairment without dementia, or dementia).[29] Pre-stroke functional disability was measured using the modified Rankin Scale (mRS; categorized as no disability, slight/moderate disability, or severe disability). Clinical factors included body mass index (BMI; measured at baseline, 3-, 6-, and 12-months), smoking status, depression, stroke severity (measured using initial NIHSS and thrombolytic treatment), and vascular risk factors including hypertension, hyperlipidemia, diabetes, coronary artery disease, atrial fibrillation, history of prior stroke/TIA, and recurrent stroke during the study period. Pre-stroke sleep apnea risk was evaluated using the Berlin questionnaire, asked in reference to the pre-stroke period, using standard scoring.[30]
Statistical Methods
A total of 938 patients were eligible to participate in the SDB study, of whom a total of 482 MA or NHW patients consented and completed sleep apnea testing at one or more time points. Missing data were assumed to be missing at random. Multiple imputation by chained equations (R package mice, version 3.14.0) was used to impute missing values in all variables. To determine the number of imputed datasets we followed an established approach,[31] where the number of datasets is max(5, 100f), with f being the proportion of subjects with any variables missing. Given that we have 50% of patients with missing variables, we initially generated 50 imputed datasets. To improve model stability, we ultimately generated 100 imputed datasets, and all subsequent analyses were conducted with each of the imputed datasets and their results were combined using Rubin’s rule.[32] To assess sensitivity to imputation, we also performed a complete case analysis.
The primary analysis was conducted in two stages. In the first stage, linear mixed models were separately fitted to obtain individual random slopes reflecting change over time for the primary exposure (REI) and each of the primary outcomes (ADL/IADL, 3MSE, and NIHSS). In the second stage, the resulting slopes were entered into linear regression models to determine whether change over time in REI was related to change over time in each of the outcomes. Our initial step in the first stage was to determine the functional form of the time variable. To do this, we fit unadjusted and fully adjusted linear mixed models using REI, ADL/IADL, 3MSE, and NIHSS as outcomes with a linear time variable. We then fit an otherwise identical set of models that also included a quadratic form of the time variable. We applied the multivariate Wald test to compare the fit of the models with or without quadratic time, the results of which indicated that the quadratic form should be included in main effects for the REI and 3MSE models, but not the NIHSS or ADL/IADL models. We then fit the appropriate models separately using linear mixed models, which allowed us to obtain individual slopes reflecting change over time for REI, ADL/IADL, 3MSE, and NIHSS to use in the second stage of the analysis. In each case, fully adjusted models included the following set of prespecified covariates: age, sex, ethnicity, pre-stroke functional status, pre-stroke cognitive status, education, marital status, stroke history, tPA use, recurrent stroke, and depression. In addition, we included time-varying BMI and pre-stroke sleep apnea risk (high vs. low).
In the second stage of the analysis, the individual random slopes for REI and for each outcome from the above unadjusted and fully adjusted linear mixed models were entered into linear regression models to determine whether the slopes reflecting change in REI over time were associated with the slopes reflecting changes in each outcome over time. REI and each of the three outcomes were scaled (centered, divided by standard deviation) so that the resulting coefficients reflected the impact of a one standard deviation change in REI in terms of standard deviation change of each outcome.
Results
A total of 482 unique MA or NHW stroke participants completed at least one SDB screening and were included in the analysis. Baseline characteristics of the participants are included in Table 1. Participants had a mean age of 64.6 years (SD 11), 45% were female, and 70% were MA. Baseline SDB assessments were conducted a median of 5 days (IQR: 2, 11) after the initial stroke presentation. Sample sizes at each time point were 411 at baseline, 278 at 3-months, 253 at 6-months, and 193 at 12-months. Reasons for loss to follow up at 12-months included 34 patients (8.3%) who could not be located, 66 (16.1%) who declined to continue participating, 24 (5.8%) who were deceased, and 80 (19.5%) who were ineligible (most ineligible cases were due to either out-of-area moves or the end of data collection in mid-2020).
Table 1.
Baseline characteristics
| Variable | n = 482 |
|---|---|
|
| |
| Age, mean (SD) | 64.6 (11.0) |
| Female, n (%) | 218 (45.2) |
| Race/Ethnicity, n (%) | |
| Non-Hispanic White | 145 (30.1) |
| Mexican American | 337 (70.0) |
| Comorbidities, n (%) | |
| Prior stroke/TIA | 165 (34.3) |
| Hypertension | 429 (89.0) |
| High cholesterol | 264 (54.9) |
| Diabetes | 288 (59.9) |
| Atrial fibrillation | 35 (7.3) |
| Coronary artery disease | 135 (28.1) |
| Heart failure | 40 (8.3) |
| Current/former smoker | 216 (45.4) |
| Body mass index, mean (SD) | 30.4 (6.6) |
| History of depression | 190 (43.7) |
| tPA use, n (%) | 81 (16.8) |
| Baseline REI, mean (SD) | 22.7 (17.1) |
| Initial NIHSS, mean (SD) | 4.2 (4.7) |
| Prestroke functional status, n (%) | |
| mRS 0–1 | 214 (44.9) |
| mRS 2 | 147 (30.1) |
| mRS 3–5 | 116 (24.3) |
| Prestroke cognition (IQCODE), n (%) | |
| Normal (≤3) | 309 (84.2) |
| Moderately impaired (>3–4) | 42 (11.4) |
| Cognitively impaired/dementia (>4) | 16 (4.4) |
| Recurrent stroke during study period, n (%) | 19 (6.0) |
Abbreviations: REI, respiratory event index; NIHSS, National Institutes of Health Stroke Scale; mRS, modified Rankin scale, IQCODE, Informant Questionnaire on Cognitive Decline in the Elderly
SDB was common, with a mean baseline REI of 22.7 (SD 17.1). Only 24 participants (6%) had no SDB (REI <5) at baseline, whereas 138 (34%) had mild SDB (REI 5 to <15), 134 (33%) had moderate SDB (REI 15 to <30), and 115 (28%) had severe SDB (REI ≥30). The proportions in each category remained relatively stable over time (see Table 2), though within individuals REI did vary over time (see Figure 1). Despite the high prevalence of moderate and severe SDB within the study sample at all timepoints (as shown in Table 2), SDB treatment was uncommon, with only a total of 10 (3.6%) participants reporting routine use of PAP therapy by 3-months, 11 (4.3%) by 6-months, and 15 (7.8%) by 12-months. BMI remained relatively stable over time, with a median of 29.6 (IQR: 26, 34) at baseline, 29.2 (25.6, 33.7) at 3-months, 29.0 (25.7, 33.5) at 6-months, and 29.4 (25.8, 35.0) at 12-months.
Table 2.
Proportion of patients in each category of sleep-disordered breathing severity at each timepoint
| No SDB (REI <5) | Mild SDB (REI 5 to <15) | Moderate SDB (REI 15 to <30) | Severe SDB (REI ≥30) | Sleep apnea test recording time in minutes, mean (SD) | |
|---|---|---|---|---|---|
|
| |||||
| Baseline (n=411) | 24 (5.8%) | 138 (33.6%) | 134 (32.6%) | 115 (28.0%) | 515.7 (150.3) |
| 3-months (n=278) | 21 (7.6%) | 100 (36.0%) | 92 (33.1%) | 65 (23.4%) | 558.4 (149.7) |
| 6-months (n=253) | 21 (8.3%) | 90 (35.6%) | 81 (32.0%) | 61 (24.1%) | 556.3 (163.6) |
| 12-months (n=193) | 11 (5.7%) | 65 (33.7%) | 72 (37.3%) | 45 (23.3%) | 555.2 (156.0) |
Figure 1.
Histogram depicting change in respiratory event index (REI) from baseline (shortly after stroke) to 12-months for each study participant, showing that the distribution of change in REI was centered around 0, that most patients had relatively small changes in REI over time, and that some had substantial change of >25 units in either direction.
Model results are shown in Table 3. In unadjusted models, the rate of change in REI was not associated with rate of change in functional outcome (β = −0.06; 95% CI: −0.15, 0.03, p=0.16), cognitive outcome (β = −0.03; 95% CI: −0.12, 0.06, p=0.51), or neurologic outcome (β = −0.04; 95% CI: −0.13, 0.05, p=0.41). Similarly, in fully adjusted models, the rate of change in REI was not associated with the rate of change in functional outcome (β = −0.06; 95% CI: −0.14, 0.03, p=0.22), cognitive outcome (β = −0.02; 95% CI: −0.11, −0.07, p=0.64), or neurologic outcome (β = −0.02; 95% CI: −0.11, 0.07, p=0.65). Model results from the fully adjusted analysis of complete cases only (n=236) are shown in Table S1, and reveal no substantive change compared to the results of analyses with multiple imputation for missing datapoints.
Table 3.
Association between change in REI and change in each of the three outcomes over time in both unadjusted and fully adjusted* models
| Coefficient** (95%CI) | p-value | |
|---|---|---|
|
| ||
| Unadjusted models | ||
| Association between ΔREI and ΔADL/IADL | −0.06 (−0.15, 0.03) | 0.16 |
| Association between ΔREI and Δ3MSE | −0.03 (−0.12, 0.06) | 0.51 |
| Association between ΔREI and ΔNIHSS | −0.04 (−0.13, 0.05) | 0.41 |
| Fully adjusted models | ||
| Association between ΔREI and ΔADL/IADL | −0.06 (−0.14, 0.03) | 0.22 |
| Association between ΔREI and Δ3MSE | −0.02 (−0.11, 0.07) | 0.64 |
| Association between ΔREI and ΔNIHSS | −0.02 (−0.11, 0.07) | 0.65 |
Abbreviations: REI, respiratory event index; ADL/IADL, activities of daily living/instrumental activities of daily living scale (functional outcome), NIHSS, National Institutes of Health Stroke Scale (neurologic outcome); 3MSE, modified Mini-Mental State Examination (cognitive outcome)
Adjusted for the following covariates: age, sex, ethnicity, pre-stroke functional status, pre-stroke cognitive status, education, marital status, stroke history, tPA use, recurrent stroke, depression, time-varying BMI, and pre-stroke Berlin sleep apnea risk (high vs low)
Random slopes were scaled (centered, divided by standard deviation) such that the coefficients reflect the impact of a one standard deviation change in REI with respect to a standard deviation change in each outcome.
Discussion
In this study of a large, multicenter, population-based sample of ischemic stroke patients who underwent serial objective evaluation of SDB severity and outcomes in the first year after stroke, we found that SDB was common, but the rate of change in SDB severity was not associated with the rate of change in functional, cognitive, or neurologic outcomes. Prior studies have shown that SDB severity measured shortly after stroke is associated with worse functional and cognitive outcomes at 90 days poststroke.[10, 11] Our results extend this work by including serial evaluation of both SDB and outcomes over the first year after stroke, which allowed us to assess the degree to which changes in SDB severity are related to changes in outcomes. Our prior work showed that mean REI across time was relatively stable,[15] though there were small increases in obstructive apneas in the overall study population over time (0.22 units per month), and in central apneas only among non-Hispanic White participants (0.14 units per month). The presence of individual variation left open the question of whether changes in SDB severity would be related to changes in outcomes within an individual. The results of the current study indicate that individual changes in SDB severity were not clearly associated with changes in post-stroke outcomes.
Similar to our previous report just among those with an REI ≥10 on the baseline sleep apnea test,[33] only a small proportion of the stroke patients in our current study were on PAP therapy during the first year after stroke, and those who reported regularly using PAP therapy were excluded from SDB testing in our study. As such, changes in SDB severity observed throughout the course of the study reflect the natural history of post-stroke SDB, and the observed changes in functional, cognitive, and neurologic outcomes reflect the natural history of each outcome, consistent with what has been reported previously, with improvements observed in each outcome occurring primarily within the first 6 months and relative stability thereafter.[27] It remains to be seen whether treatment of SDB leads to improved post-stroke outcomes. A definitive clinical trial to evaluate the impact of SDB treatment on poststroke recovery is currently underway (NCT03812653).
Though SDB is linked to stroke outcomes,[10, 11] the mechanisms remain unproven, are likely multifactorial, and may vary at different time points post-stroke. For example, in the immediate aftermath of stroke, periods of hypoxia and blood pressure fluctuation may lead to cell death in penumbral regions where perfusion and oxygen supply are tenuous.[34] In the subacute period, sleep fragmentation may impair neuroplasticity and limit recovery,[35] and daytime sleepiness and fatigue may interfere with full engagement in post-stroke rehabilitation activities.[36] Over the longer term, associations between SDB and potentially adverse changes in brain structure and neurodegeneration may result in suboptimal outcomes.[37, 38] Overall, there is much left to learn in this area and multiple additional factors may be at play. It is also worth noting that sleep apnea and stroke share several risk factors including age, obesity, smoking, and alcohol intake.[39, 40] Although our analyses controlled for potentially influential third variables where possible, others may have affected the course of SDB and recovery simultaneously.
Strengths of this study include recruitment from a large, ethnically diverse, population-based study, with physician confirmation of stroke cases, objective quantification of SDB severity, and in-person assessment of outcomes by trained study personnel. Limitations include a relatively low degree of variability in mean SDB over time, which may have limited our ability to detect associated changes in outcomes. Similarly, most patients had normal pre-stroke cognitive status, which may have limited our ability to detect changes in cognition in association with SDB. We used a home sleep apnea test rather than full polysomnography, though the ApneaLink Plus device has been well validated with excellent concordance with polysomnography,[16–18] and the same test was used at each timepoint to provide consistent measures of change over time. We did have missing data, and we used multiple imputation to minimize bias. We included multiple covariates in our analysis, which may lead to over-adjustment, though results were similar in unadjusted analyses. We were unable to determine whether changes in REI may have been associated with individual components of our outcome (i.e., individual NIHSS, 3MSE, and ADL/IADL scale items). We did not determine ischemic stroke subtype in this study, though we have previously reported that SDB prevalence and severity do not differ across ischemic stroke subtypes.[41] We also did not record infarct size or location, though multiple prior studies have failed to find an association between infarct size or location with SDB prevalence or severity,[42, 43] with the exception of brainstem location.[14] We did not collect data on utilization of post-stroke rehabilitation such as physical therapy, which may have impacted recovery. We did not explore associations between medication use and post-stroke recovery.
In conclusion, we found that reductions in SDB severity over time among stroke survivors were not clearly associated with corresponding improvements in functional, cognitive, or neurologic outcomes during the first year after stroke. Our study evaluated the natural history of post-stroke SDB, with only a small proportion of patients receiving PAP therapy. Whether treatment of SDB leads to improved post-stroke outcomes remains to be determined.
Supplementary Material
Highlights:
Longitudinal cohort study following ischemic stroke survivors for one year
Serial testing of sleep apnea (SA) and functional, cognitive, and neurologic outcomes
Sleep apnea was common, treatment was uncommon (<10%)
No clear association between rate of change in SA and rate of change in outcomes
Further studies needed to determine if SA treatment may improve stroke recovery
Acknowledgements
This study was performed in the Corpus Christi Medical Center and CHRISTUS Spohn hospitals, CHRISTUS Health system, in Corpus Christi, TX.
Sources of funding:
This research is supported by the National Institutes of Health and National Institute of Neurological Disorders and Stroke (grant R01HL126700 and R01NS038916).
Footnotes
Declaration of interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Disclosures:
The authors report no conflicts of interest.
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References
- 1.Peppard PE, Young T, Barnet JH, et al. , Increased prevalence of sleep-disordered breathing in adults. Am J Epidemiol, 2013. 177(9): p. 1006–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Xie C, Zhu R, Tian Y, et al. , Association of obstructive sleep apnoea with the risk of vascular outcomes and all-cause mortality: a meta-analysis. BMJ Open, 2017. 7(12): p. e013983. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Wang X, Ouyang Y, Wang Z, et al. , Obstructive sleep apnea and risk of cardiovascular disease and all-cause mortality: a meta-analysis of prospective cohort studies. Int J Cardiol, 2013. 169(3): p. 207–14. [DOI] [PubMed] [Google Scholar]
- 4.Li M, Hou WS, Zhang XW, et al. , Obstructive sleep apnea and risk of stroke: a meta-analysis of prospective studies. Int J Cardiol, 2014. 172(2): p. 466–9. [DOI] [PubMed] [Google Scholar]
- 5.Seiler A, Camilo M, Korostovtseva L, et al. , Prevalence of sleep-disordered breathing after stroke and TIA: A meta-analysis. Neurology, 2019. 92(7): p. e648–e654. [DOI] [PubMed] [Google Scholar]
- 6.Brown DL, Shafie-Khorassani F, Kim S, et al. , Sleep-Disordered Breathing Is Associated With Recurrent Ischemic Stroke. Stroke, 2019. 50(3): p. 571–576. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Parra O, Arboix A, Montserrat JM, et al. , Sleep-related breathing disorders: impact on mortality of cerebrovascular disease. Eur Respir J, 2004. 24(2): p. 267–72. [DOI] [PubMed] [Google Scholar]
- 8.Turkington PM, Allgar V, Bamford J, et al. , Effect of upper airway obstruction in acute stroke on functional outcome at 6 months. Thorax, 2004. 59(5): p. 367–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Sahlin C, Sandberg O, Gustafson Y, et al. , Obstructive sleep apnea is a risk factor for death in patients with stroke: a 10-year follow-up. Arch Intern Med, 2008. 168(3): p. 297–301. [DOI] [PubMed] [Google Scholar]
- 10.Lisabeth LD, Sanchez BN, Lim D, et al. , Sleep-disordered breathing and poststroke outcomes. Ann Neurol, 2019. 86(2): p. 241–250. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Ott SR, Fanfulla F, Miano S, et al. , SAS Care 1: sleep-disordered breathing in acute stroke an transient ischaemic attack - prevalence, evolution and association with functional outcome at 3 months, a prospective observational polysomnography study. ERJ Open Res, 2020. 6(2). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Hermann DM and Bassetti CL, Role of sleep-disordered breathing and sleep-wake disturbances for stroke and stroke recovery. Neurology, 2016. 87(13): p. 1407–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Morgenstern LB, Smith MA, Lisabeth LD, et al. , Excess stroke in Mexican Americans compared with non-Hispanic Whites: the Brain Attack Surveillance in Corpus Christi Project. Am J Epidemiol, 2004. 160(4): p. 376–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Brown DL, McDermott M, Mowla A, et al. , Brainstem infarction and sleep-disordered breathing in the BASIC sleep apnea study. Sleep Med, 2014. 15(8): p. 887–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Lisabeth LD, Zhang G, Chervin RD, et al. , Longitudinal Assessment of Sleep Apnea in the Year After Stroke in a Population-Based Study. Stroke, 2023. 54(9): p. 2356–2365. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Ng SS, Chan TO, To KW, et al. , Validation of a portable recording device (ApneaLink) for identifying patients with suspected obstructive sleep apnoea syndrome. Intern Med J, 2009. 39(11): p. 757–62. [DOI] [PubMed] [Google Scholar]
- 17.Erman MK, Stewart D, Einhorn D, et al. , Validation of the ApneaLink™ for the Screening of Sleep Apnea: a Novel and Simple Single-Channel Recording Device. J Clin Sleep Med, 2007. 3(4): p. 387–392. [PMC free article] [PubMed] [Google Scholar]
- 18.Chen H, Lowe AA, Bai Y, et al. , Evaluation of a portable recording device (ApneaLink) for case selection of obstructive sleep apnea. Sleep Breath, 2009. 13(3): p. 213–9. [DOI] [PubMed] [Google Scholar]
- 19.Spector WD and Fleishman JA, Combining activities of daily living with instrumental activities of daily living to measure functional disability. J Gerontol B Psychol Sci Soc Sci, 1998. 53(1): p. S46–57. [DOI] [PubMed] [Google Scholar]
- 20.Brown DL, Burns JW, Kwicklis M, et al. , Novel metrics of sleep-disordered breathing are associated with outcome after ischemic stroke. Sleep Med, 2024. 113: p. 116–130. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Vargas A, Zhang G, Shi X, et al. , Stroke Outcomes Among English- and Spanish-Speaking Mexican American Patients. Neurology, 2023. 101(9): p. 407–411. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Teng EL and HC C, The Modified Mini-Mental State (3MS) examination. J Clin Psychiatry, 1987. 48(8): p. 314–8. [PubMed] [Google Scholar]
- 23.Tombaugh TN, McDowell I, Kristjansson B, et al. , Mini-Mental State Examination (MMSE) and the Modified MMSE (3MS): A psychometric comparison and normative data. Psychological Assessment, 1996. 8(1): p. 48–59. [Google Scholar]
- 24.Goldstein LB and Samsa GP, Reliability of the National Institutes of Health Stroke Scale. Extension to non-neurologists in the context of a clinical trial. Stroke, 1997. 28(2): p. 307–10. [DOI] [PubMed] [Google Scholar]
- 25.Brott T, Adams HP, Olinger CP, et al. , Measurements of acute cerebral infarction: a clinical examination scale. Stroke, 1989. 20(7): p. 864–870. [DOI] [PubMed] [Google Scholar]
- 26.Goldstein LB, Lennihan L, Rabadi MJ, et al. , Effect of Dextroamphetamine on Poststroke Motor Recovery. JAMA Neurology, 2018. 75(12). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Lisabeth LD, Brown DL, Dong L, et al. , Outcomes in the Year After First-Ever Ischemic Stroke in a Bi-Ethnic Population. Ann Neurol, 2023. 93(2): p. 348–356. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Lyden P, Using the National Institutes of Health Stroke Scale. Stroke, 2017. 48(2): p. 513–519. [DOI] [PubMed] [Google Scholar]
- 29.Jorm AF and Jacomb PA, The Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE): socio-demographic correlates, reliability, validity and some norms. Psychol Med, 1989. 19(4): p. 1015–22. [DOI] [PubMed] [Google Scholar]
- 30.Netzer NC, Stoohs RA, Netzer CM, et al. , Using the Berlin Questionnaire to identify patients at risk for the sleep apnea syndrome. Ann Intern Med, 1999. 131(7): p. 485–91. [DOI] [PubMed] [Google Scholar]
- 31.Harrell FE, Regression Modeling Strategies. 2 ed. Springer Series in Statistics. 2015: Springer. [Google Scholar]
- 32.Rubin DB, Multiple imputation for nonresponse in surveys. 1987, New York: John Wiley & Sons. [Google Scholar]
- 33.Schutz SG, Lisabeth LD, Kwicklis M, et al. , Positive airway pressure treatment for sleep-disordered breathing is rare during the first year after stroke: The BASIC project. Sleep Med, 2023. 107: p. 26–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Turkington PM and Elliott MW, Sleep disordered breathing following stroke. Monaldi Arch Chest Dis, 2004. 61(3): p. 157–61. [DOI] [PubMed] [Google Scholar]
- 35.Duss SB, Seiler A, Schmidt MH, et al. , The role of sleep in recovery following ischemic stroke: A review of human and animal data. Neurobiol Sleep Circadian Rhythms, 2017. 2: p. 94–105. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Leotard A, Levy J, Perennou D, et al. , Sleep might have a pivotal role in rehabilitation medicine: A road map for care improvement and clinical research. Ann Phys Rehabil Med, 2021. 64(4): p. 101392. [DOI] [PubMed] [Google Scholar]
- 37.Baril AA, Martineau-Dussault ME, Sanchez E, et al. , Obstructive Sleep Apnea and the Brain: a Focus on Gray and White Matter Structure. Curr Neurol Neurosci Rep, 2021. 21(3): p. 11. [DOI] [PubMed] [Google Scholar]
- 38.Bubu OM, Andrade AG, Umasabor-Bubu OQ, et al. , Obstructive sleep apnea, cognition and Alzheimer’s disease: A systematic review integrating three decades of multidisciplinary research. Sleep Med Rev, 2020. 50: p. 101250. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Yayan J and Rasche K, A Systematic Review of Risk factors for Sleep Apnea. Preventive Medicine Reports, 2024. 42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.O’Donnell MJ, Xavier D, Liu L, et al. , Risk factors for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the INTERSTROKE study): a case-control study. Lancet, 2010. 376(9735): p. 112–23. [DOI] [PubMed] [Google Scholar]
- 41.Brown DL, Mowla A, McDermott M, et al. , Ischemic stroke subtype and presence of sleep-disordered breathing: the BASIC sleep apnea study. J Stroke Cerebrovasc Dis, 2015. 24(2): p. 388–93. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Fisse AL, Kemmling A, Teuber A, et al. , The Association of Lesion Location and Sleep Related Breathing Disorder in Patients with Acute Ischemic Stroke. PLoS One, 2017. 12(1): p. e0171243. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Stahl SM, Yaggi HK, Taylor S, et al. , Infarct location and sleep apnea: evaluating the potential association in acute ischemic stroke. Sleep Medicine, 2015. 16(10): p. 1198–1203. [DOI] [PMC free article] [PubMed] [Google Scholar]
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