Evidence map›Paper›PMID 37794471›Full record

ArticleThrombosis journal2023

Development and validation of a new risk assessment model for immunomodulatory drug-associated venous thrombosis among Chinese patients with multiple myeloma.

Xiaozhe Li, Xiuli Sun, Baijun Fang, Yun Leng, Fangfang Sun, Yaomei Wang, Qing Wang, Jie Jin, Min Yang, Bing Xu and 16 more

Abstract read
In one paragraph

Article in Thrombosis journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 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, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
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  7. Article
  8. Incidence of thromboembolism and associated factors in multiple myeloma patients treated with immunomodulatory drugs: a retrospective analysis in Belo Horizonte, Brazil.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2023
    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

26 authors.

Xiaozhe Li *Department of Haematology, First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Er Road, Guangzhou, 510000, Guangdong, China.
Xiuli Sun *Department of Haematology, First Affiliated Hospital of Dalian Medical University, Dalian, China.
Baijun Fang *Department of Haematology, Henan Cancer Hospital, Zhengzhou, China.
Yun Leng *Department of Haematology, Beijing Chaoyang Hospital, Beijing, China.
Fangfang SunDepartment of Haematology, First Affiliated Hospital of Dalian Medical University, Dalian, China.
Yaomei WangDepartment of Haematology, Henan Cancer Hospital, Zhengzhou, China.
Qing WangDepartment of Haematology, Guizhou Provincial People's Hospital, Guiyang, China.
Jie JinDepartment of Haematology, First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Min YangDepartment of Haematology, First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Bing XuDepartment of Haematology, First Affiliated Hospital of Xiamen University, Xiamen, China.
Zhihong FangDepartment of Haematology, First Affiliated Hospital of Xiamen University, Xiamen, China.
Lijuan ChenDepartment of Haematology, Jiangsu Provincial People's Hospital, Nanjing, China.
Zhi ChenDepartment of Haematology, Jiangsu Provincial People's Hospital, Nanjing, China.
Qimei YangDepartment of Haematology, Shantou Central Hospital, Shantou, China.
Kejie ZhangDepartment of Haematology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China.
Yinhai YeDepartment of Haematology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China.
Hui GengDepartment of Haematology, Affiliated Hospital of Qinghai University, Xining, China.
Zhiqiang SunDepartment of Haematology, Shenzhen Hospital of Southern Medical University, Shenzhen, China.
Dan HaoDepartment of Haematology, Shenzhen Hospital of Southern Medical University, Shenzhen, China.
Hongming HuangDepartment of Haematology, Nantong University Hospital, Nantong, China.
Xiaotao WangDepartment of Haematology, Second Affiliated Hospital of Guilin Medical College, Guilin, China.
Hongmei JingDepartment of Haematology, Peking University Third Hospital, Beijing, China.
Lan MaDepartment of Haematology, Peking University Third Hospital, Beijing, China.
Xueyi PanDepartment of Haematology, First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China.
Wenming ChenDepartment of Haematology, Beijing Chaoyang Hospital, Beijing, China. 13910107759@163.com.
Juan LiDepartment of Haematology, First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Er Road, Guangzhou, 510000, Guangdong, China. ljuan@mail.sysu.edu.cn.

Funding

5010 Clinical Research Project of Sun Yat-sen University 2017005Guangdong Natural Science Foundation 2021A1515011715Guangzhou Municipal Science and Technology Project 202201010981National Natural Science Foundation of China 82070220National Natural Science Foundation of China 82200221
6 · The paper itself

Abstract

backgroundIndividuals with multiple myeloma (MM) receiving immunomodulatory drugs (IMiDs) are at risk of developing venous thromboembolism (VTE), a serious complication. There is no established clinical model for predicting VTE in the Chinese population. We develop a new risk assessment model (RAM) for IMiD-associated VTE in Chinese MM patients.

methodsWe retrospectively selected 1334 consecutive MM patients receiving IMiDs from 16 medical centers in China and classified them randomly into the derivation and validation cohorts. A multivariate Cox regression model was used for analysis.

resultsThe overall incidence of IMiD-related VTE in Chinese MM patients was 6.1%. Independent predictive factors of VTE (diabetes, ECOG performance status, erythropoietin-stimulating agent use, dexamethasone use, and VTE history or family history of thrombosis) were identified and merged to develop the RAM. The model identified approximately 30% of the patients in each cohort at high risk for VTE. The hazard ratios (HRs) were 6.08 (P < 0.001) and 6.23 (P < 0.001) for the high-risk subcohort and the low-risk subcohort, respectively, within both the derivation and validation cohorts. The RAM achieved satisfactory discrimination with a C statistic of 0.64. The stratification approach of the IMWG guidelines yielded respective HRs of 1.77 (P = 0.053) and 1.81 (P = 0.063). The stratification approach of the SAVED score resulted in HRs of 3.23 (P = 0.248) and 1.65 (P = 0.622), respectively. The IMWG guideline and the SAVED score-based method yielded C statistics of 0.58 and 0.51, respectively.

conclusionsThe new RAM outperformed the IMWG guidelines and the SAVED score and could potentially guide the VTE prophylaxis strategy for Chinese MM patients.

Indexed as

Immunomodulatory drugsMultiple myelomaRisk assessment modelThromboprophylaxisVenous thromboembolism

Identifiers

PMID37794471
PMCPMC10552366

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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.