Evidence map›Paper›PMID 40322241›Full record

ArticleIndian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine2025

Impact of the Clinical Frailty Score on Outcomes of Critically Ill Patients in a Tertiary Care ICU.

Sulekha Saxena, Priyamvada Gupta, Puneet Panwar, Ashish Jain, Srishti S Jain, Rohit Jain, Divyansh Gupta, Munesh Meena, Hemraj Acharya, Ravi Jain

Abstract read
In one paragraph

Article in Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. [Frailty in intensive care medicine].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2026
    Pooled it
  2. Article
  3. Impact of Frailty (Clinical Frailty Scale) on Weaning from Mechanical Ventilation: A Systematic Review and Meta-analysis.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2026
    Article
  4. Impact of Personalized Parenteral Nutrition on Inflammatory Markers and Clinical Outcomes in Critically Ill Patients: A Systematic Review and Meta-analysis.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2025
    Article
  5. Utility of Clinical Frailty Scale in Intensive Care Unit.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2025
    Article
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

10 authors.

Sulekha SaxenaDepartment of Critical Care Medicine, Mahatma Gandhi Medical College and Hospital, Jaipur, Rajasthan, India.ORCID https://orcid.org/0000-0001-6894-6482
Priyamvada GuptaDepartment of Anesthesiology Critical Care and Pain Medicine, Mahatma Gandhi Medical College and Hospital, Jaipur, Rajasthan, India.ORCID https://orcid.org/0000-0002-6437-1447
Puneet PanwarDepartment of Critical Care Medicine, Mahatma Gandhi Medical College and Hospital, Jaipur, Rajasthan, India.ORCID https://orcid.org/0000-0003-1436-1416
Ashish JainDepartment of Critical Care Medicine, Mahatma Gandhi Medical College and Hospital, Jaipur, Rajasthan, India.ORCID https://orcid.org/0000-0001-9310-3911
Srishti S JainDepartment of Critical Care Medicine, Mahatma Gandhi Medical College and Hospital, Jaipur, Rajasthan, India.ORCID https://orcid.org/0000-0001-8355-1497
Rohit JainDepartment of Critical Care Medicine, Mahatma Gandhi Medical College and Hospital, Jaipur, Rajasthan, India.ORCID https://orcid.org/0000-0002-3776-4093
Divyansh GuptaDepartment of Critical Care Medicine, Mahatma Gandhi Medical College and Hospital, Jaipur, Rajasthan, India.ORCID https://orcid.org/0000-0003-2067-0311
Munesh MeenaDepartment of Anesthesiology Critical Care and Pain Medicine, Mahatma Gandhi Medical College and Hospital, Jaipur, Rajasthan, India.ORCID https://orcid.org/0000-0002-7431-6142
Hemraj AcharyaDepartment of Critical Care Medicine, Mahatma Gandhi Medical College and Hospital, Jaipur, Rajasthan, India.ORCID https://orcid.org/0009-0007-2095-9322
Ravi JainDepartment of Critical Care Medicine, Mahatma Gandhi Medical College and Hospital, Jaipur, Rajasthan, India.ORCID https://orcid.org/0000-0001-9260-479X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Advanced age is a known marker of vulnerability, but frailty is an independent predictor of poor outcomes in critically ill patients. The clinical frailty score (CFS) facilitates rapid assessment, aiding prognostication, care improvement, and resource allocation, particularly in resource-limited intensive care units (ICUs). Materials and methods: A prospective observational cohort study was conducted from April to September 2023 at a tertiary care ICU. The study included 166 patients aged ≥50 years with ICU stays longer than 48 hours, excluding those with contraindications for care escalation. Data were collected on demographics, Clinical parameters, and scoring systems including acute physiological and chronic health evaluation II (APACHE-II), sequential organ failure assessment (SOFA), Charlson comorbidity index (CCI), and CFS. Predictive analyses were performed using receiver operating curve (ROC) curves, cut-offs, and logistic regression. Results: The median age of patients was 65 years, with an APACHE-II score of 18 and a CFS of 4. In-hospital mortality was 46.4%. The CFS outperformed other scoring systems in predicting both in-hospital mortality [Area under the receiver operating characteristic curve (AUC-ROC) 0.73] and net negative outcomes (AUC ROC 0.75). Frailty (CFS ≥6) was present in 39.75% of patients, with each unit increase in CFS associated with a 41.8% higher odds of mortality and a 50.7% higher odds of net negative outcomes. The optimal CFS cut-offs were 4 for 80% sensitivity and 6 for 80% specificity. Conclusion: The CFS is a practical and reliable tool for predicting ICU outcomes, outperforming traditional scoring systems. It supports improved decision-making and resource allocation. Further multicenter studies are necessary to validate its broader use in critical care practice. How to cite this article: Saxena S, Gupta P, Panwar P, Jain A, Jain SS, Jain R,

Indexed as

Clinical frailty scoreCritical illnessFrailty assessment in hospital mortality resource allocationPatient outcome assessment

Identifiers

PMID40322241
PMCPMC12045041

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.