Predicting falls in elderly patients with ...
Document type :
Article dans une revue scientifique: Article original
PMID :
Permalink :
Title :
Predicting falls in elderly patients with chronic pain and other chronic conditions
Author(s) :
Lazkani, Aida [Auteur]
Delespierre, Tiba [Auteur]
Bauduceau, Bernard [Auteur]
Benattar-Zibi, Linda [Auteur]
Bertin, Philippe [Auteur]
Berrut, Gilles [Auteur]
Corruble, Emmanuelle [Auteur]
Danchin, Nicolas [Auteur]
Derumeaux, Genevieve [Auteur]
Doucet, Jean [Auteur]
Falissard, Bruno [Auteur]
Forette, Françoise [Auteur]
Hanon, Olivier [Auteur]
Pasquier, Florence [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U1171
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Pinget, Michel [Auteur]
Ourabah, Rissane [Auteur]
Piedvache, Céline [Auteur]
Becquemont, Laurent [Auteur]
Delespierre, Tiba [Auteur]
Bauduceau, Bernard [Auteur]
Benattar-Zibi, Linda [Auteur]
Bertin, Philippe [Auteur]
Berrut, Gilles [Auteur]
Corruble, Emmanuelle [Auteur]
Danchin, Nicolas [Auteur]
Derumeaux, Genevieve [Auteur]
Doucet, Jean [Auteur]
Falissard, Bruno [Auteur]
Forette, Françoise [Auteur]
Hanon, Olivier [Auteur]
Pasquier, Florence [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U1171
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Pinget, Michel [Auteur]
Ourabah, Rissane [Auteur]
Piedvache, Céline [Auteur]
Becquemont, Laurent [Auteur]
Journal title :
Aging Clinical and Experimental Research
Abbreviated title :
Aging Clin. Exp. Res.
Volume number :
27
Pages :
653-661
Publication date :
2015-10-01
ISSN :
1594-0667
English keyword(s) :
Elderly
Chronic pain
Fall
Risk factor
Chronic pain
Fall
Risk factor
HAL domain(s) :
Sciences du Vivant [q-bio]
English abstract : [en]
BACKGROUND: The aim was to identify fall predictors in elderly suffering from chronic pain (CP) and to test their applicability among patients with other chronic conditions.
METHODS: 1,379 non-institutionalized patients ...
Show more >BACKGROUND: The aim was to identify fall predictors in elderly suffering from chronic pain (CP) and to test their applicability among patients with other chronic conditions. METHODS: 1,379 non-institutionalized patients aged 65 years and older who were suffering from CP (S.AGE CP sub-cohort) were monitored every 6 months for 1 year. Socio-demographic, clinical and pain data and medication use were assessed at baseline for the association with falls in the following year. Falls were assessed retrospectively at each study visit. Logistic regression analyses were performed to identify fall predictors. The derived model was applied to two additional S.AGE sub-cohorts: atrial fibrillation (AF) (n = 1,072) and type-2 diabetes mellitus (T2DM) (n = 983). RESULTS: Four factors predicted falls in the CP sub-cohort: fall history (OR: 4.03, 95 % CI 2.79-5.82), dependency in daily activities (OR: 1.81, 95 % CI 1.27-2.59), age ≥75 (OR: 1.53, 95 % CI 1.04-2.25) and living alone (OR: 1.73, 95 % CI 1.24-2.41) (Area Under the Curve: AUC = 0.71, 95 % CI 0.67-0.75). These factors were relevant in AF (AUC = 0.71, 95 % CI 0.66-0.75) and T2DM (AUC = 0.67, 95 % CI 0.59-0.73) sub-cohorts. Fall predicted probability in CP, AF and T2DM sub-cohorts increased from 7, 7 and 6 % in patients with no risk factors to 59, 66 and 45 % respectively, in those with the four predictors. Fall history was the strongest predictor in the three sub-cohorts. CONCLUSIONS: Fall history, dependency in daily activities, age ≥75 and living alone are independent fall predictors in CP, AF and T2DM patients.Show less >
Show more >BACKGROUND: The aim was to identify fall predictors in elderly suffering from chronic pain (CP) and to test their applicability among patients with other chronic conditions. METHODS: 1,379 non-institutionalized patients aged 65 years and older who were suffering from CP (S.AGE CP sub-cohort) were monitored every 6 months for 1 year. Socio-demographic, clinical and pain data and medication use were assessed at baseline for the association with falls in the following year. Falls were assessed retrospectively at each study visit. Logistic regression analyses were performed to identify fall predictors. The derived model was applied to two additional S.AGE sub-cohorts: atrial fibrillation (AF) (n = 1,072) and type-2 diabetes mellitus (T2DM) (n = 983). RESULTS: Four factors predicted falls in the CP sub-cohort: fall history (OR: 4.03, 95 % CI 2.79-5.82), dependency in daily activities (OR: 1.81, 95 % CI 1.27-2.59), age ≥75 (OR: 1.53, 95 % CI 1.04-2.25) and living alone (OR: 1.73, 95 % CI 1.24-2.41) (Area Under the Curve: AUC = 0.71, 95 % CI 0.67-0.75). These factors were relevant in AF (AUC = 0.71, 95 % CI 0.66-0.75) and T2DM (AUC = 0.67, 95 % CI 0.59-0.73) sub-cohorts. Fall predicted probability in CP, AF and T2DM sub-cohorts increased from 7, 7 and 6 % in patients with no risk factors to 59, 66 and 45 % respectively, in those with the four predictors. Fall history was the strongest predictor in the three sub-cohorts. CONCLUSIONS: Fall history, dependency in daily activities, age ≥75 and living alone are independent fall predictors in CP, AF and T2DM patients.Show less >
Language :
Anglais
Audience :
Internationale
Popular science :
Non
Administrative institution(s) :
CHU Lille
CNRS
Inserm
Université de Lille
CNRS
Inserm
Université de Lille
Collections :
Research team(s) :
Troubles cognitifs dégénératifs et vasculaires
Submission date :
2019-11-27T14:29:12Z