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Record W4416093040 · doi:10.1186/s12877-025-06517-0

Risk factors of pressure injury in elderly inpatients: a systematic review and meta-analysis

2025· review· en· W4416093040 on OpenAlexaboutno aff
Qingyi Wu, Huan Wen, Mei Sun

Bibliographic record

VenueBMC Geriatrics · 2025
Typereview
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsnot available
Fundersnot available
KeywordsCINAHLPressure injuryRehabilitationMEDLINERisk assessmentDiabetes mellitusRisk factor

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this systematic review is to systematically identify and synthesize the risk factors contributing to pressure injury in elderly inpatients. Methods: PubMed, Embase, Web of Science, Cochrane Library, and CINAHL were systematically searched from database inception to March 1, 2025. Two researchers independently screened the retrieved studies and extracted the data. The risk of bias was assessed using the AHRQ criteria or the Newcastle–Ottawa Scale (NOS). Meta-analysis was performed using R version 4.5.0. Results: A total of 3,629 studies were retrieved, and 23 studies met the inclusion criteria. Altogether, 70,340 participants were included in the analysis, of whom 5,792 developed PI during hospitalization, corresponding to an overall prevalence of 8.2%. Meta-analysis showed that age (OR = 1.06, 95%CI: 1.03 to 1.09), immobility (OR = 4.54, 95%CI: 3.09 to 6.67), incontinence (OR = 4.54, 95%CI: 2.33 to 42.75), nutritional risk (OR = 3.00, 95%CI: 1.78 to 5.05), diabetes (OR = 1.60, 95%CI: 1.21 to 2.11), durations of prior ICU stay (OR = 1.93, 95%CI: 1.46 to 2.55), time from admission to surgery (OR = 2.07, 95%CI: 1.60 to 2.67) and length of stay (OR = 1.05, 95%CI: 1.01 to 1.01) were associated with pressure injury in elderly inpatients. Conclusions: Pressure injury remains a serious and preventable complication among elderly inpatients. This study provided a comprehensive evaluation of the risk factors of pressure injury development among elderly inpatients, identifying eight major contributors. These findings may promote valuable evidence to inform clinical assessment, risk stratification, and the development of targeted prevention strategies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.033
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0200.030
Bibliometrics0.0090.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.105
GPT teacher head0.437
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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