Evidence map›Paper›PMID 40234099›Full record

ArticleJournal of cachexia, sarcopenia and muscle2025

Comprehensive Evaluation of Frailty and Sarcopenia Markers to Predict Survival in Glioblastoma Patients.

Chao Yang, Chao Ma, Cheng-Shi Xu, Si-Rui Li, Chen Li, Ze-Fen Wang, Zhi-Qiang Li

Abstract read
In one paragraph

Article in Journal of cachexia, sarcopenia and muscle, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. 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

7 authors.

Chao YangDepartment of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China.ORCID 0000-0003-4181-5356
Chao MaDepartment of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China.
Cheng-Shi XuDepartment of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China.
Si-Rui LiDepartment of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Chen LiDepartment of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China.
Ze-Fen WangDepartment of Physiology, Wuhan University School of Basic Medical Sciences, Wuhan, China.
Zhi-Qiang LiDepartment of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China.

Funding

Hubei Provincial Natural Science Foundation of China 2021CFB099Medicine Research Fund from Hubei Province YYXKNL2023011
6 · The paper itself

Abstract

backgroundGlioblastoma (GBM) is the most common primary malignant brain tumour in adults. Patients with GBM are particularly susceptible to moderate-to-high frail. Frailty status has been associated with the outcome of many types of cancer, including GBM, although there is still little consensus regarding the specific criteria for assessing frailty status. This study aimed to determine the predictive significance of the modified frailty score (mFS) in GBM patients using haematological and sarcopenia indicators.

methodsBetween January 2016 and September 2022, we enrolled 309 adult GBM patients. Data on demographics, haematological examination, and temporal muscle thickness (TMT) were collected and assessed. The prognostic relevance of the frailty parameters was established using Kaplan-Meier and Cox proportional model. The scoring systems were created by integrating these indicators. Variables with independent prognostic values were used to construct the nomograms. Nomogram accuracy was evaluated using the calibration curve, Harrell's concordance index (C-index), and time-dependent receiver operating characteristic curves. Clinical practicality was assessed using decision curve analysis.

resultsThe baseline characteristics of the 309 participants revealed a median age of 59 years (interquartile range 52-66) with a predominance of male patients (58.58%). TMT (hazard ratio [HR] = 3.787, 95% confidence interval [CI] 2.576-5.566, p < 0.001), prognostic nutritional index (HR = 1.722, 95% CI 1.098-2.703, p = 0.018), and mean corpuscular volume (HR = 1.958, 95% CI 1.111-3.451, p = 0.020) were identified as independent prognostic markers. The constructed mFS, obtained by integrating these three indices, exhibited independent prognostic significance (HR = 2.461, 95% CI 1.751-3.457, p < 0.001). The patients in the low-risk group had a median overall survival (OS) of 13.9 months, while the patients in the high risk had a median OS of 5.8 months. Importantly, the mFS demonstrated significant independent prognostic value in the subgroup aged > 65 (HR = 1.822, 95% CI 1.011-3.284, p = 0.046). The nomogram, which included the mFS, demonstrated high accuracy, with a c-index of 0.781. The nomogram bootstrapped calibration plot also performed well compared to the ideal model. Nomograms showed promising discriminative potential, with time-dependent areas under the curves of 0.945, 0.835, and 0.820 for 0.5-, 1-, and 2-year overall survival prediction, respectively.

conclusionsPreoperative mFS is a comprehensive frailty marker for predicting survival outcomes in patients with GBM. A dynamic nomogram incorporating the mFS may facilitate preoperative survival evaluation. Early and appropriate multimodal interventions, including nutritional support, rehabilitation, and psychological care, may help in the neurosurgical care of patients with GBM or other brain tumours.

Indexed as

Brain NeoplasmsFrailtyGlioblastomaSarcopeniaAgedBiomarkersFemaleHumansKaplan-Meier EstimateMaleMiddle AgedNomogramsPrognosisBiomarkersglioblastomamean corpuscular volumeprognosisprognostic nutritional indexsarcopeniatemporal muscle thickness

Identifiers

PMID40234099
PMCPMC11999731

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.