Evidence map›Paper›PMID 41761097›Full record

ArticleBMC anesthesiology2026

Predictive value of the clinical frailty scale for intensive care unit mortality in elderly and very elderly patients: a prospective cohort study.

Fatih Seğmen, Cihangir Doğu, Hakan Sarıcı, Çilem Bayındır Di̇cle, Temel Kayan, Onur Küçük, Semih Aydemi̇r

Abstract read
In one paragraph

Article in BMC anesthesiology, 2026. 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

7 authors.

Fatih SeğmenDepartment of Intensive Care Unit, Ankara Bilkent City Hospital, University of Health Sciences, Ankara, Turkey.ORCID http://orcid.org/0000-0002-9255-9084
Cihangir DoğuDepartment of Intensive Care Unit, Ankara Bilkent City Hospital, University of Health Sciences, Ankara, Turkey.ORCID http://orcid.org/0000-0003-2581-541X
Hakan SarıcıDepartment of Intensive Care Unit, Ankara Bilkent City Hospital, University of Health Sciences, Ankara, Turkey.ORCID http://orcid.org/0000-0002-1468-5288
Çilem Bayındır Di̇cleDepartment of Intensive Care Unit, Ankara Bilkent City Hospital, University of Health Sciences, Ankara, Turkey.ORCID http://orcid.org/0000-0003-3554-9450
Temel KayanDepartment of Intensive Care Unit, Ankara Bilkent City Hospital, University of Health Sciences, Ankara, Turkey.ORCID http://orcid.org/0000-0002-1193-1391
Onur KüçükDepartment of Anesthesiology and Reanimation, School of Medicine, Dokuz Eylul University, İzmir, Turkey. dr.okucuk@gmail.com.ORCID http://orcid.org/0000-0001-5534-7579
Semih Aydemi̇rDepartment of Anesthesiology and Reanimation, Yenimahalle Training and Research Hospital, University of Yıldırım Beyazit, Ankara, Turkey.ORCID http://orcid.org/0000-0002-1087-3070

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

aimThe Clinical Frailty Scale (CFS) is a clinician-rated tool used to assess frailty. Previous studies have shown that it predicts mortality and morbidity in older adults aged ≥ 65 years. However, its predictive value in the very elderly population (≥ 80 years) remains unclear. This study aimed to evaluate the ability of CFS to predict intensive care unit (ICU) mortality among elderly patients and to determine whether this relationship differs in the very elderly subgroup. MATERIALS AND

methodsThis prospective cohort study was conducted after ethical approval. All patients aged ≥ 65 years admitted to the ICU and staying > 48 h during the study period were included when frailty assessment was feasible. CFS scores at ICU admission were recorded, and patients were categorized as survivors or non-survivors. Subgroup analyses were performed for elderly patients aged 65–79 years (Group E) and very elderly patients aged ≥ 80 years (Group VE). Multiple logistic regression analysis was applied to identify independent predictors of ICU mortality, and receiver operating characteristic (ROC) analysis was used to assess model performance. Relative risk (RR) for mortality according to age subgroups was calculated for CFS categories.

resultsA total of 173 patients were included (Group E: 96 [55.5%]). The mean CFS score was 6.2 ± 1.8 (median: Group E = 6, Group VE = 7; p = 0.066), and the mean Charlson Comorbidity Index was 8.3 ± 2.5 (p = 0.406 between groups). The mean ICU stay was 21 ± 24 days. Overall, 102 patients (59%) were discharged, while 71 (41%) died. CFS, renal replacement therapy (RRT), APACHE II, lymphocyte count, procalcitonin, and albumin levels were independent predictors of ICU mortality. CFS and RRT had the greatest contributions to the model (χ² = 16.32 and 20.36, respectively). The model demonstrated 77.5% sensitivity, 85.3% specificity, and an area under the curve (AUC) of 0.90. When considered alone, CFS yielded an AUC of 0.74 with an optimal cutoff of ≥ 7 (sensitivity 77.4%, specificity 56.8%). ICU mortality risk was 2.5-fold higher for all patients with CFS ≥ 7, 2.3-fold in Group E, and 2.8-fold in Group VE (p < 0.05).

conclusionCFS independently predicts ICU mortality among elderly patients. A CFS ≥ 7 and the need for RRT identify a high-risk subgroup, emphasizing the importance of routine frailty assessment and early individualized management in this population.

Indexed as

Frail ElderlyFrailtyGeriatric AssessmentHospital MortalityIntensive Care UnitsAgedAged, 80 and overCohort StudiesFemaleHumansMalePredictive Value of TestsProspective StudiesROC CurveClinical frailty scaleElderlyGeriatricsIntensive careMortalityVery elderly

Identifiers

PMID41761097
PMCPMC13049732

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.