Incidence, complications and risk factors for severe falls in patients on maintenance haemodialysis.
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2012
- doi: 10.1093/ndt/gfr326
- issn: 0931-0509
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Frontier troubles Complications and sequelae Populations, Human Human populations Population growth Human population Upkeep Preventive maintenance Annals Bones--Fractures Demography DemographicsCiter ce document
A. Rossier et al., « Incidence, complications and risk factors for severe falls in patients on maintenance haemodialysis. », Serveur académique Lausannois, ID : 10.1093/ndt/gfr326
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BACKGROUND: Falls have been insufficiently studied in patients on maintenance haemodialysis (MHD). This study assessed the incidence and complications of severe falls and the ability of risk factors, including the Performance-Oriented Mobility Assessment (POMA) test, to predict them in this population. METHODS: All patients on MHD from our centre were asked to participate in this survey. POMA test and a record of risk factors for falls were obtained at baseline. Severe falls, as defined by an admission in an emergency ward, were documented prospectively. RESULTS: Eighty-four patients (median age 69.5 years, minimum 26 years, maximum 85 years) were enrolled. Predialytic POMA scores were low (median 20, minimum 5, maximum 26). After a mean follow-up of 20.6 months (142.2 patient-years), 31 severe falls were recorded in 24 patients (28.6%; incidence 0.22 per patient-year) and complicated by fractures in 54.8% of severe falls. In univariate analysis, age, a past history of falls, malnutrition, depression, but not POMA score, were associated with severe falls. A POMA score of >21 had a negative predictive value of 82%. CONCLUSIONS: Severe falls were common in MHD patients in this study and resulted in fractures in >50% of the cases. They were associated with ageing, a past history of falls, malnutrition and depression. Although there was a trend towards a lower POMA score in fallers as compared to non-fallers, the POMA score was not an independent predictor of severe falls in this study. These data may help to stratify the patient's risk of falling in order to target programmes to prevent falls in this population.