Evidence map›Paper›PMID 35833264›Full record

ArticleJournal of cachexia, sarcopenia and muscle2022

The advanced lung cancer inflammation index is the optimal inflammatory biomarker of overall survival in patients with lung cancer.

Mengmeng Song, Qi Zhang, Chunhua Song, Tong Liu, Xi Zhang, Guotian Ruan, Meng Tang, Hailun Xie, Heyang Zhang, Yizhong Ge and 11 more

Open access · goldAbstract read
In one paragraph

Article in Journal of cachexia, sarcopenia and muscle, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 146 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
146citing papers in PubMed, 2 pooled it
14.5field-weighted citation impact, top 1% of its field
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

146 citing papers in PubMed, 2 syntheses or guidelines pooled it, 154 citations in OpenAlex.

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86 more citing papers are in PubMed but not listed here.

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

21 authors at 6 institutions in 1 country.

Mengmeng SongDepartment of Gastrointestinal Surgery/Clinical Nutrition, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, China.
Qi ZhangDepartment of Gastrointestinal Surgery/Clinical Nutrition, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, China.
Chunhua SongDepartment of Epidemiology and Statistics, Henan Key Laboratory of Tumor Epidemiology College of Public Health, Zhengzhou University, Zhengzhou, China.
Tong LiuDepartment of Gastrointestinal Surgery/Clinical Nutrition, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, China.
Xi ZhangDepartment of Gastrointestinal Surgery/Clinical Nutrition, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, China.
Guotian RuanDepartment of Gastrointestinal Surgery/Clinical Nutrition, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, China.
Meng TangDepartment of Gastrointestinal Surgery/Clinical Nutrition, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, China.
Hailun XieDepartment of Gastrointestinal Surgery/Clinical Nutrition, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, China.
Heyang ZhangDepartment of Gastrointestinal Surgery/Clinical Nutrition, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, China.
Yizhong GeDepartment of Gastrointestinal Surgery/Clinical Nutrition, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, China.
Xiangrui LiDepartment of Gastrointestinal Surgery/Clinical Nutrition, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, China.
Kangping ZhangDepartment of Gastrointestinal Surgery/Clinical Nutrition, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, China.
Ming YangDepartment of Gastrointestinal Surgery/Clinical Nutrition, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, China.
Qinqin LiDepartment of Gastrointestinal Surgery/Clinical Nutrition, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, China.
Xiaoyue LiuDepartment of Gastrointestinal Surgery/Clinical Nutrition, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, China.
Shiqi LinDepartment of Gastrointestinal Surgery/Clinical Nutrition, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, China.
Yu XuThe First Affiliated Hospital of Kunming Medical University, Kunming, China.
Hongxia XuDepartment of Nutrition, Daping Hospital & Research Institute of Surgery, Third Military Medical University, Chongqing, China.
Kunhua WangYunnan University, Kunming, China.
Wei LiCancer Center of the First Hospital of Jilin University, Changchun, China.
Hanping ShiDepartment of Gastrointestinal Surgery/Clinical Nutrition, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, China.ORCID 0000-0003-4514-8693
Capital Medical University · CNArmy Medical University · CNJilin University · CNKunming Medical University · CNYunnan University · CNZhengzhou University · CN

Funding

Beijing Municipal Science and Technology Commission SCW2018-06General Surgery Clinical Medical Center of Yunnan Province ZX20190303National Key Research and Development Program 2017YFC1309200
6 · The paper itself

Abstract

backgroundsMalnutrition and systemic inflammatory responses are associated with poor overall survival (OS) in lung cancer patients, but it remains unclear which biomarkers are better for predicting their prognosis. This study tried to determine the best one among the existing common nutrition/inflammation-based indicators of OS for patients with lung cancer. MATERIALS AND

methodsThere were 16 nutrition or systemic inflammation-based indicators included in this study. The cut-off points for the indicators were calculated using maximally selected rank statistics. The OS was evaluated using the Kaplan-Meier estimator, and univariate and multivariate Cox proportional hazard models were used to determine the relationship between the indicators and OS. A time-dependent receiver operating characteristic curves (time-ROC) and C-index were calculated to assess the predictive ability of the different indicators.

resultsThere were 1772 patients with lung cancer included in this study. In univariate analysis, all 16 indicators were significantly associated with OS of the patients (all P < 0.001). Except for platelet-to-lymphocyte ratio, all other indicators were independent predictors of OS in multivariate analysis (all P < 0.05). Low advanced lung cancer inflammation index (ALI) was associated with higher mortality risk of lung cancer [hazard ratio, 1.30; 95% confidence interval (CI), 1.13-1.49]. The results of the time-AUC and C-index analyses indicated that the ALI (C-index: 0.611) had the best predictive ability on the OS in patients with lung cancer. In different sub-groups, the ALI was the best indicator for predicting the OS of lung cancer patients regardless of sex (C-index, 0.609 for men and 0.613 for women) or smoking status (C-index, 0.629 for non-smoker and 0.601 for smoker) and in patients aged <65 years (C-index, 0.613). However, the modified Glasgow prognostic score was superior to the other indicators in non-small cell lung cancer patients (C-index, 0.639) or patients aged ≥65 years (C-index, 0.610), and the glucose-to-lymphocyte ratio performed better prognostic ability in patients with small cell lung cancer (C-index, 0.601).

conclusionsThe prognostic ability of the ALI is superior to the other inflammation/nutrition-based indicators for all patients with lung cancer.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsBiomarkersFemaleGlucoseHumansInflammationMaleNeutrophilsBiomarkersGlucoseInflammation indicatorsLung cancerPrognosis

Identifiers

PMID35833264
PMCPMC9530543
OpenAlexW4285389869

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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 Socratic graph.