Evidence map›Paper›PMID 38932769›Full record

SynthesisFrontiers in public health2024

A novel multistep approach to standardize the reported risk factors for in-hospital falls: a proof-of-concept study.

Fabio La Porta, Giorgia Valpiani, Giada Lullini, Antonella Negro, Leonardo Pellicciari, Erika Bassi, Serena Caselli, Valentina Pecoraro, Erika Govoni

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Fabio La PortaIRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.
Giorgia ValpianiResearch and Innovation Unit, Biostatistics and Clinical Trial Area, University Hospital of Ferrara, Ferrara, Italy.
Giada LulliniIRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.
Antonella NegroInnovation in Healthcare and Social Services, Emilia-Romagna Region, Bologna, Emilia-Romagna, Italy.
Leonardo PellicciariIRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.
Erika BassiDepartment of Translational Medicine, University of Eastern Piedmont, Novara, Italy.
Serena CaselliUnità Operativa Complessa di Medicina Riabilitativa, Azienda Ospedaliero-Universitaria di Modena, Modena, Italy.
Valentina PecoraroDepartment of Laboratory Medicine and Pathology, AUSL Modena, Modena, Italy.
Erika GovoniInnovation in Healthcare and Social Services, Emilia-Romagna Region, Bologna, Emilia-Romagna, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Uncertainty and inconsistency in terminology regarding the risk factors (RFs) for in-hospital falls are present in the literature. Objective: (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. Methods: 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. Results: 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. Conclusion: 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.

Indexed as

Accidental FallsProof of Concept StudyHospitalizationHumansRisk Factorsaccidental fallshospitalsmeta-analysisrisk factorssystematic review

Identifiers

PMID38932769
PMCPMC11199548

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.