%0 Journal Article
%T A novel multistep approach to standardize the reported risk factors for in-hospital falls: a proof-of-concept study.
%A La Porta F
%A Valpiani G
%A Lullini G
%A Negro A
%A Pellicciari L
%A Bassi E
%A Caselli S
%A Pecoraro V
%A Govoni E
%J Front Public Health
%V 12
%N 0
%D 2024
%M 38932769
%F 6.461
%R 10.3389/fpubh.2024.1390185
%X <B>UNASSIGNED: </B>Uncertainty and inconsistency in terminology regarding the risk factors (RFs) for in-hospital falls are present in the literature.<BR><B>UNASSIGNED: </B>(1) To perform a literature review to identify the fall RFs among hospitalized adults; (2) to link the found RFs to the corresponding categories of international health classifications to reduce the heterogeneity of their definitions; (3) to perform a meta-analysis on the risk categories to identify the significant RFs; (4) to refine the final list of significant categories to avoid redundancies.<BR><B>UNASSIGNED: </B>Four databases were investigated. We included observational studies assessing patients who had experienced in-hospital falls. Two independent reviewers performed the inclusion and extrapolation process and evaluated the methodological quality of the included studies. RFs were grouped into categories according to three health classifications (ICF, ICD-10, and ATC). Meta-analyses were performed to obtain an overall pooled odds ratio for each RF. Finally, protective RFs or redundant RFs across different classifications were excluded.<BR><B>UNASSIGNED: </B>Thirty-six articles were included in the meta-analysis. One thousand one hundred and eleven RFs were identified; 616 were linked to ICF classification, 450 to ICD-10, and 260 to ATC. The meta-analyses and subsequent refinement of the categories yielded 53 significant RFs. Overall, the initial number of RFs was reduced by about 21 times.<BR><B>UNASSIGNED: </B>We identified 53 significant RF categories for in-hospital falls. These results provide proof of concept of the feasibility and validity of the proposed methodology. The list of significant RFs can be used as a template to build more accurate measurement instruments to predict in-hospital falls.