Innovative model for older inpatients falls predictors assessment: a prognostic cohort study: Innovative model for older inpatients falls predictors assessment

Lea Godino; Daniela Mosci; Elisa Ambrosi; Luisa Sist; Roberta Decaro; Paolo Chiari; Domenica Gazineo

doi:10.54103/dn/28125

Authors

Lea Godino Medical Genetics Unit, IRCCS AOUBO https://orcid.org/0000-0002-3763-9372
Daniela Mosci Hospital Hygiene and Prevention, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
Elisa Ambrosi Department of Diagnostic and Public Health University of Verona Italy, Italy
Luisa Sist Department of Medical and Surgical Sciences, Alma Mater Studiorum University of Bologna, Italy https://orcid.org/0000-0002-1283-7364
Roberta Decaro Postoperative and Abdominal Organ Transplant Intensive Care Unit, IRCCS Azienda Ospedaliero-Universitaria Di Bologna, Bologna, Italy
Paolo Chiari Department of Medical and Surgical Sciences, Alma Mater Studiorum University of Bologna, Italy
Domenica Gazineo Governo Clinico e Qualità, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy https://orcid.org/0000-0003-1866-3358

DOI:

https://doi.org/10.54103/dn/28125

Keywords:

Prognostic cohort study, older in-patients, risk of falling, model, Conley scale

Abstract

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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