Evidence map›Paper›PMID 39162396›Full record

ArticleCancer medicine2024

Development and validation of a nomogram model for predicting venous thromboembolism risk in lung cancer patients treated with immune checkpoint inhibitors: A cohort study in China.

Guanzhong Liang, Zuhai Hu, Qianjie Xu, Guixue Wang, Ying Wang, Xiaosheng Li, Wei Zhang, Haike Lei

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

  1. Pooled it
  2. Article
  3. Review
  4. 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

8 authors.

Guanzhong LiangChongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, China.
Zuhai HuDepartment of Health Statistics, School of Public Health, Chongqing Medical University, Chongqing, China.
Qianjie XuDepartment of Health Statistics, School of Public Health, Chongqing Medical University, Chongqing, China.
Guixue WangMOE Key Lab for Biorheological Science and Technology, State and Local Joint Engineering Laboratory for Vascular Implants, College of Bioengineering Chongqing University, Chongqing, China.ORCID 0000-0002-9192-8757
Ying WangChongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, China.
Xiaosheng LiChongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, China.
Wei ZhangChongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, China.
Haike LeiChongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, China.ORCID 0000-0003-0284-2052

Funding

Chongqing Municipal Science and Technology Bureau CSTB2022TIAD-GPX0066Natural Science Foundation of Chongqing Municipality CSTB2024NSCQ-MSX0229
6 · The paper itself

Abstract

objectiveVenous thromboembolism (VTE) poses a significant threat to lung cancer patients, particularly those receiving treatment with immune checkpoint inhibitors (ICIs). We aimed to develop and validate a nomogram model for predicting the occurrence of VTE in lung cancer patients undergoing ICI therapy.

methodsThe data for this retrospective cohort study was collected from cancer patients admitted to Chongqing University Cancer Hospital for ICI treatment between 2019 and 2022. The research data is divided into training and validation sets using a 7:3 ratio. Univariate and multivariate analyses were employed to identify risk factors for VTE. Based on these analyses, along with clinical expertise, a nomogram model was crafted. The model's predictive accuracy was assessed through receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis, clinical impact curve, and other relevant metrics.

resultsThe initial univariate analysis pinpointed 13 potential risk factors for VTE. The subsequent stepwise multivariate regression analysis identified age, Karnofsky performance status, chemotherapy, targeted, platelet count, lactate dehydrogenase, monoamine oxidase, D-dimer, fibrinogen, and white blood cell count as significant predictors of VTE. These 10 variables were the foundation for a predictive model, illustrated by a clear and intuitive nomogram. The model's discriminative ability was demonstrated by the ROC curve, which showed an area under the curve of 0.815 (95% CI 0.772-0.858) for the training set, and 0.753 (95% CI 0.672-0.835) for the validation set. The model's accuracy was further supported by Brier scores of 0.068 and 0.080 for the training and validation sets, respectively, indicating a strong correlation with actual outcomes.

conclusionWe have successfully established and validated a nomogram model for predicting VTE risk in lung cancer patients treated with ICIs.

Indexed as

Immune Checkpoint InhibitorsLung NeoplasmsNomogramsVenous ThromboembolismAdultAgedChinaFemaleFibrin Fibrinogen Degradation ProductsHumansMaleMiddle AgedRetrospective StudiesRisk AssessmentRisk FactorsROC CurveFibrin Fibrinogen Degradation ProductsImmune Checkpoint Inhibitorsimmune checkpoint inhibitorslung cancernomogramvenous thromboembolism

Identifiers

PMID39162396
PMCPMC11334300

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.