Evidence map›Paper›PMID 37804372›Full record

ArticleCardiovascular toxicology2023

Development and Validation of a Nomogram Model for the Risk of Cardiac Death in Patients Treated with Chemotherapy for Esophageal Cancer.

Xinfang Lv, Xue Wu, Kai Liu, Xinke Zhao, Chenliang Pan, Jing Zhao, Juan Chang, Huan Guo, Xiang Gao, Xiaodong Zhi and 5 more

Abstract read
PubMed Publisher
In one paragraph

Article in Cardiovascular toxicology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 37% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

15 authors at 4 institutions in 1 country.

Xinfang Lv *Department of Geriatrics, Affiliated Hospital of Gansu University of Traditional Chinese Medicine, Lanzhou City, Gansu Province, China.
Xue Wu *Department of Cardiology, The Second Hospital of Lanzhou University, Lanzhou City, Gansu Province, China.
Kai LiuSchool of Integrative Medicine, Gansu University of Chinese Medicine, Lanzhou City, Gansu Province, China.
Xinke ZhaoSchool of Integrative Medicine, Gansu University of Chinese Medicine, Lanzhou City, Gansu Province, China.
Chenliang PanCardiovascular Disease Center, The First Hospital of Lanzhou University, Lanzhou City, Gansu Province, China.
Jing ZhaoCardiovascular Disease Center, The First Hospital of Lanzhou University, Lanzhou City, Gansu Province, China.
Juan ChangDepartment of Traditional Medicine, Gansu Provincial Hospital, Lanzhou City, Gansu Province, China.
Huan GuoCenter for Translational Medicine, Gansu Provincial Academic Institute for Medical Research, Lanzhou City, Gansu Province, China.
Xiang GaoSchool of Integrative Medicine, Gansu University of Chinese Medicine, Lanzhou City, Gansu Province, China.
Xiaodong ZhiSchool of Integrative Medicine, Gansu University of Chinese Medicine, Lanzhou City, Gansu Province, China.
Chunzhen RenSchool of Integrative Medicine, Gansu University of Chinese Medicine, Lanzhou City, Gansu Province, China.
Qilin ChenSchool of Integrative Medicine, Gansu University of Chinese Medicine, Lanzhou City, Gansu Province, China.
Hugang JiangSchool of Integrative Medicine, Gansu University of Chinese Medicine, Lanzhou City, Gansu Province, China.
Chunling WangSchool of Integrative Medicine, Gansu University of Chinese Medicine, Lanzhou City, Gansu Province, China.
Yingdong LiSchool of Integrative Medicine, Gansu University of Chinese Medicine, Lanzhou City, Gansu Province, China. lydj412@163.com.
Gansu University of Traditional Chinese Medicine · CNLanzhou University · CNGansu Provincial Cancer Hospital Gansu Provincial Academic Instiute for Medical Research · CNGansu Provincial Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The primary cause of mortality in esophageal cancer survivors is cardiac death. Early identification of cardiac mortality risk during chemotherapy for esophageal cancer is crucial for improving the prognosis. We developed and validated a nomogram model to identify patients with high cardiac mortality risk after chemotherapy for esophageal cancer for early screening and clinical decision-making. We randomly allocated 37,994 patients with chemotherapy-treated esophageal cancer into two groups using a 7:3 split ratio: model training (n = 26,598) and validation (n = 11,396). 5- and 10-year survival rates were used as endpoints for model training and validation. Decision curve analysis and the consistency index (C-index) were used to evaluate the model's net clinical advantage. Model performance was evaluated using receiver operating characteristic curves and computing the area under the curve (AUC). Kaplan-Meier survival analysis based on the prognostic index was performed. Patient risk was stratified according to the death probability. Age, surgery, sex, and year were most closely related to cardiac death and used to plot the nomograms. The C-index for the training and validation datasets were 0.669 and 0.698, respectively, indicating the nomogram's net clinical advantage in predicting cardiac death risk at 5 and 10 years. The 5- and 10-year AUCs were 0.753 and 0.772 for the training dataset and 0.778 and 0.789 for the validation dataset, respectively. The accuracy of the model in predicting cardiac death risk was moderate. This nomogram can identify patients at risk of cardiac death after chemotherapy for esophageal cancer at an early stage.

Indexed as

Cancer SurvivorsEsophageal NeoplasmsClinical Decision-MakingDeathHumansNomogramsPrognosisCardiac deathCardio-oncologyChemotherapyEsophageal cancerNomogram

Identifiers

PMID37804372
OpenAlexW4387423198

What OpenQuestion holds

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Registered trials

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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.