Evidence map›Paper›PMID 31930024›Full record

ArticleAnnals of translational medicine2019

Pretreatment neutrophil-to-lymphocyte ratio plus albumin-to-gamma-glutamyl transferase ratio predict the diagnosis of grade III glioma.

Zhen-Qiang He, Hao Duan, Fu-Hua Lin, Ji Zhang, Yin-Sheng Chen, Guan-Hua Zhang, Cheng-Cheng Guo, Chao Ke, Xiang-Heng Zhang, Zheng-He Chen and 4 more

Abstract read
In one paragraph

Article in Annals of translational medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

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

  1. Pooled it
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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

14 authors.

Zhen-Qiang HeDepartment of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China.
Hao DuanDepartment of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China.
Fu-Hua LinDepartment of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China.
Ji ZhangDepartment of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China.
Yin-Sheng ChenDepartment of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China.
Guan-Hua ZhangDepartment of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China.
Cheng-Cheng GuoDepartment of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China.
Chao KeDepartment of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China.
Xiang-Heng ZhangDepartment of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China.
Zheng-He ChenDepartment of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China.
Jian WangDepartment of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China.
Zhong-Ping ChenDepartment of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China.
Xiao-Bing JiangDepartment of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China.
Yong-Gao MouDepartment of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe present study explored the predictive value of systemic inflammatory indexes in diagnosing grade III gliomas of oligodendroglial origin.

methodsA retrospective study of 154 patients with grade III gliomas was conducted. Systemic inflammatory indexes, including neutrophil-to-lymphocyte ratio (NLR), albumin-to-gamma-glutamyl transferase ratio (AGR), platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, prognostic nutritional index, and fibrinogen-to-albumin ratio, were reviewed. The resulting predictive model was externally validated using a demographic-matched cohort of 49 grade III glioma patients.

resultsIn the training set, gliomas of oligodendroglial origin tended to have a lower NLR (P=0.018) and a higher AGR (P=0.036) than those with tumors of astrocytic origin. Moreover, both NLR and AGR had predictive value for oligodendroglial tumors, when compared with astrocytic tumors. The best diagnostic value was obtained using NLR + AGR (AUC =64.9%, 95% CI: 55.5-74.3%, P=0.005). In the validation set, NLR + AGR satisfactorily predicted the presence of oligodendroglial tumors (AUC =66.5%, 95% CI: 50.6-82.4%, P<0.05) and co-deletion of 1p/19q (AUC =73.7%, 95% CI: 59.2-88.1%, P=0.005). Multivariate analysis further demonstrated NLR + AGR as an independent predictor for overall survival.

conclusionsPretreatment NLR and AGR aid in prognosis and diagnosing grade III oligodendroglial gliomas.

Indexed as

albumin-to-gamma-glutamyl transferase ratio (AGR)Gliomaneutrophil-lymphocyte ratio (NLR)oligodendroglialsystemic inflammatory index

Identifiers

PMID31930024
PMCPMC6944567

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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.