Prediction of sleep-disordered breathing after stroke.
Sleep apnea
Sleep-disordered breathing
Stroke
Journal
Sleep medicine
ISSN: 1878-5506
Titre abrégé: Sleep Med
Pays: Netherlands
ID NLM: 100898759
Informations de publication
Date de publication:
11 2020
11 2020
Historique:
received:
08
03
2020
revised:
16
04
2020
accepted:
06
05
2020
pubmed:
25
8
2020
medline:
22
6
2021
entrez:
25
8
2020
Statut:
ppublish
Résumé
Sleep-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. Within 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. Within 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. In 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.
Sections du résumé
OBJECTIVE/BACKGROUND
Sleep-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/METHODS
Within 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.
RESULTS
Within 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.
CONCLUSIONS
In 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.
Identifiants
pubmed: 32835899
pii: S1389-9457(20)30206-9
doi: 10.1016/j.sleep.2020.05.004
pmc: PMC7666648
mid: NIHMS1623668
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1-6Subventions
Organisme : NICHD NIH HHS
ID : R01 HD082129
Pays : United States
Organisme : NIA NIH HHS
ID : R21 AG060277
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS070941
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL126700
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS038916
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL098065
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS091112
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL105999
Pays : United States
Organisme : NINDS NIH HHS
ID : U01 NS099043
Pays : United States
Organisme : NHLBI NIH HHS
ID : U01 HL125295
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL123379
Pays : United States
Organisme : NIDDK NIH HHS
ID : U01 DK062456
Pays : United States
Organisme : NHLBI NIH HHS
ID : T32 HL110952
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS107463
Pays : United States
Organisme : NHLBI NIH HHS
ID : R43 HL117421
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK070869
Pays : United States
Informations de copyright
Copyright © 2020 Elsevier B.V. All rights reserved.
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