ArticleSleep medicine2020
Prediction of sleep-disordered breathing after stroke.
Article in Sleep medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Clinical prediction models for the early diagnosis of obstructive sleep apnea in stroke patients: a systematic review.Systematic reviews · 2024Pooled it
- Wireless wearable sensors can facilitate rapid detection of sleep apnea in hospitalized stroke patients.Sleep · 2024Trial
- SleepStageNet: A Lightweight and Explainable Deep Learning Architecture for Multi-Channel Sleep Staging.Brain sciences · 2026Article
- Prediction for Respiratory Failure in Ischemic Stroke Patients Admitted to ICU: A Retrospective Analysis Based on MIMIC-IV Database.Brain and behavior · 2025Article
- Prevalence, incidence, and the time trends of sleep-disordered breathing among patients with stroke: a systematic review and meta-analysis.Frontiers in neurology · 2024Review
- Inclusion of hypocretin-1 improved performance of poor sleep quality prediction for elderly patients with acute ischemic stroke: a prospective cohort study.Frontiers in aging neuroscience · 2024Article
- Racial and ethnic differences in sleep-disordered breathing and sleep duration among stroke patients.Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association · 2022Article
- Sleep and Stroke: Opening Our Eyes to Current Knowledge of a Key Relationship.Current neurology and neuroscience reports · 2022Review
- Diagnosis of Obstructive Sleep Apnea in Patients with Associated Comorbidity.Advances in experimental medicine and biology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
OBJECTIVE/
backgroundSleep-disordered breathing (SDB) is highly prevalent after stroke and is associated with poor outcomes. Currently, after stroke, objective testing must be used to differentiate patients with and without SDB. Within a large, population-based study, we evaluated the usefulness of a flexible statistical model based on baseline characteristics to predict post-stroke SDB. PATIENTS/
methodsWithin a population-based study, participants (2010-2018) underwent SDB screening, shortly after ischemic stroke, with a home sleep apnea test. The respiratory event index (REI) was calculated as the number of apneas and hypopneas per hour of recording; values ≥10 defined SDB. The distributed random forest classifier (a machine learning technique) was applied to predict SDB with the following as predictors: demographics, stroke risk factors, stroke severity (NIHSS), neck and waist circumference, palate position, and pre-stroke symptoms of snoring, apneas, and sleepiness.
resultsWithin the total sample (n = 1330), median age was 65 years; 47% were women; 32% non-Hispanic white, 62% Mexican American, and 6% African American. SDB was found in 891 (67%). The area under the receiver operating characteristic curve, a measure of predictive ability, applied to the validation sample was 0.75 for the random forest model. Random forest correctly classified 72.5% of validation samples.
conclusionsIn this large, ethnically diverse, population-based sample of ischemic stroke patients, prediction models based on baseline characteristics and clinical measures showed fair rather than clinically reliable performance, even with use of advanced machine learning techniques. Results suggest that objective tests are still needed to differentiate ischemic stroke patients with and without SDB.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.