Aida Lazkani1, Tiba Delespierre2, Bernard Bauduceau3, Linda Benattar-Zibi4, Philippe Bertin5, Gilles Berrut6, Emmanuelle Corruble7, Nicolas Danchin8, Geneviève Derumeaux9, Jean Doucet10, Bruno Falissard11, Francoise Forette12, Olivier Hanon13, Florence Pasquier14, Michel Pinget15, Rissane Ourabah16, Celine Piedvache2, Laurent Becquemont2. 1. Pharmacology Department, Paris-Sud Faculty of Medicine, AP-HP, Bicêtre Hospital, Paris-Sud University, 63, rue Gabriel Péri, 94276, Le Kremlin-Bicêtre, France. didafarma@yahoo.com. 2. Pharmacology Department, Paris-Sud Faculty of Medicine, AP-HP, Bicêtre Hospital, Paris-Sud University, 63, rue Gabriel Péri, 94276, Le Kremlin-Bicêtre, France. 3. Endocrinology Department, Bégin Hospital, Saint-Mandé, France. 4. ORPEA/CLINEA, Puteaux, France. 5. Rheumatology Department, Limoges University Hospital, Limoges, France. 6. Clinical Gerontology, Nantes University Hospital, Nantes, France. 7. Paris-Sud Faculty of Medicine, Psychiatry Department, Bicêtre University Hospital, AP-HP, INSERM U 669, Paris-Sud University, Le Kremlin-Bicêtre, France. 8. HEGP, Coronary Diseases, Paris, France. 9. Cardiovascular Functional Exploration, Louis Pradel Hospital, Hospices Civils de Lyon, Bron, France. 10. Internal Medicine, Geriatrics and Therapeutics, Saint Julien University Hospital, Rouen University, Rouen, France. 11. Paris-Sud Faculty of Medicine, Biostatistics Department, AP-HP, Paul Brousse Hospital, INSERM U 669, Paris-Sud University, Le Kremlin-Bicêtre, France. 12. National Gerontology Foundation, Paris Descartes University, Paris, France. 13. AP-HP, Broca Hospital, Geriatrics Department, Paris Descartes University, EA 4468, Paris, France. 14. Lille University Hospital, Lille Nord de France University, UDSL, EA 1046, Lille, France. 15. Endocrinology, Diabetes and Nutrition-Related Diseases (NUDE Unit), Strasbourg University Hospital, and the European Centre for the Study of Diabetes (CeeD), Strasbourg University, Strasbourg, France. 16. General Practice Department, Paris-Sud Faculty of Medicine, Paris-Sud University, Le Kremlin-Bicêtre, France.
Abstract
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. CONCLUSION: Fall history, dependency in daily activities, age ≥75 and living alone are independent fall predictors in CP, AF and T2DM patients.
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. CONCLUSION: Fall history, dependency in daily activities, age ≥75 and living alone are independent fall predictors in CP, AF and T2DM patients.