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The Prevalence of Frailty in Critically Ill Patients: A Systematic Review and Meta-analysis

2025· review· en· W4410269198 on OpenAlexaffabout
John Muscedere, N. Koert van der Linden, Martin Albert, Patrick Archambault, Sean M. Bagshaw, Ian Ball, D.J. Cook, Lisa M. Freeman, Hyung J. Cho, Carmel Montgomery, Patrick A. Norman, Akash Patel, Oleksa Rewa, Bram Rochwerg, H. Al Shibli, Hao Wang, Michelle Y. Wong

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsKingston Health Sciences CentreHamilton Health SciencesUniversity of TorontoHôpital du Sacré-Cœur de MontréalWestern UniversityUniversité de MontréalUniversité LavalUniversity of AlbertaMcMaster UniversityQueen's University
Fundersnot available
KeywordsMedicineCritically illMeta-analysisIntensive care medicineMEDLINECritical illnessInternal medicine

Abstract

fetched live from OpenAlex

Abstract Rationale: Frailty, a state of reduced physiological reserve, increases vulnerability to adverse outcomes in critically ill patients. Prior systematic reviews have identified a high prevalence of frailty in the critically ill. Given the growing number of studies reporting on frailty in critically ill patients, the objective of this systematic review and meta-analysis was to comprehensively examine the prevalence of frailty in critically ill populations. Methods: We conducted this review according to the Meta-analysis of Observational Studies in Epidemiology (MOOSE), and the PRISMA guidelines and pre-registered on Prospero (CRD42023473646). We searched MEDLINE, EMBASE, CINAHL, ClinicalTrials.gov, and the Cochrane Library up to October 23, 2023. We included observational studies and randomized controlled trials (RCTs) reporting the prevalence of frailty in adult critically ill populations from any type of ICU, as defined by validated frailty tools. Abstracts and reviews were excluded. Two independent reviewers screened citations and abstracted data in duplicate. We performed subgroup analysis based on the frailty instrument used and the type of critical care unit. We used R software for analysis, and pooled data using random-effects models. Results: We included 92 reports encompassing 90 separate studies and 102 frailty analyses of over 266,000 frail patients and 927,000 non-frail patients. The aggregated prevalence of frailty across all studies was 28% (95% confidence interval [CI]: 26%-31%) (Figure 1). Subgroup analyses by ICU type demonstrated prevalence of frailty to be 28% (95% CI: 25%-31%) in general medical surgical ICUs and 33% (95% CI: 24%-42%) in cardiac ICUs. Frailty prevalence by frailty instrument was 28% (95% CI: 25%-31%) when assessed with the Clinical Frailty Scale, 37% (95% CI: 27%-49%) when assessed with the Frailty Index, 32% (95% CI: 24%-42%) when assessed with the Frailty Phenotype, 22% (95% CI: 12%-37%) when assessed with the Modified Frailty Index-11 and 28% (95% CI: 13%-50%) when assessed with the Modified Frailty Index-5. Conclusions: The prevalence of frailty varies among critically ill patients with point estimates ranging from 22-37% depending on the frailty instrument used for ascertainment and the type of critical care unit. The high prevalence of frailty in critically ill populations likely reflects reduced physiological reserve underpinning frailty predisposing to critical illness. Future research should focus on standardizing assessments. Funding: The Canadian Frailty Network

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)0.026
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.026
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.061
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0210.033
Bibliometrics0.0130.012
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.002
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 head0.053
GPT teacher head0.391
Teacher spread0.338 · 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 designSystematic review
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

Citations0
Published2025
Admission routes2
Has abstractyes

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