Innovative model for older inpatients falls predictors assessment: a prognostic cohort study
Background. In-hospital falls are avoidable accidents, but continue to be a high prevalent and incident patient safety issue. The 60% of falls are caused by more than one factor. An early identification and assessment of inpatients' high risk of falling, at the beginning of hospitalization, is...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Milano University Press
2025-07-01
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| Series: | Dissertation Nursing |
| Subjects: | |
| Online Access: | https://riviste.unimi.it/index.php/dissertationnursing/article/view/28125 |
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| Summary: | Background. In-hospital falls are avoidable accidents, but continue to be a high prevalent and incident patient safety issue. The 60% of falls are caused by more than one factor. An early identification and assessment of inpatients' high risk of falling, at the beginning of hospitalization, is unclear.
Objective. The study evaluates prognostic factors that predict inpatients’ falls in a consecutive cohort of subjects.
Methods. A prospective multicentric prognostic cohort study was conducted between April 2015 and December 2016. The study involved 12 wards of Northern Italy. A total of 11,768 hospitalized patients, potentially at risk of falling, were included. The variables evaluated included gender, age, difficulty getting out of bed, history of falling, vertigo or dizziness, physical impairments, going to the bathroom more than two times for a nurse shift, patients with cardiovascular or neurological drug treatment and risk of falling. The Conley Scale was also assessed.
Results. Multivariate regression analysis showed that female gender, difficulty getting out of bed, history of falling, dizziness or vertigo in the last three months, physical impairments, as balance and impaired gait, judgement/lack of safety awareness, cardiovascular or neurological drug treatment were associated risk factors of falling.
Conclusions. This study identifies key predictors of in-hospital falls and proposes an innovative model for the early assessment of older in-patients’ fall risk. These findings can guide the development of risk assessment models and inform future research on leveraging electronic medical records to enhance fall prevention strategies.
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| ISSN: | 2785-7263 |