Evidence map›Paper›PMID 33987256›Full record

ArticleAnnals of translational medicine2021

A predictive model for postthrombotic syndrome in proximal deep vein thrombosis patients.

Peng Qiu, Junchao Liu, Fuzhen Wan, Yuqian Chen, Kaichuang Ye, Jinbao Qin, Qun Huang, Xinwu Lu

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PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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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

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Predicting the Risk of Deep Venous Thrombosis in Elderly Patients: A Comparative Analysis of Seven Machine Learning Models.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
    Article
4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Peng QiuDepartment of Vascular Surgery, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Junchao LiuDepartment of Vascular Surgery, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Fuzhen WanMenzies Institute for Medical Research, University of Tasmania, Hobart, Australia.
Yuqian ChenSchool of Public Economics and Administration, Shanghai University of Finance and Economics, Shanghai, China.
Kaichuang YeDepartment of Vascular Surgery, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jinbao QinDepartment of Vascular Surgery, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Qun HuangDepartment of Vascular Surgery, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xinwu LuDepartment of Vascular Surgery, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPostthrombotic syndrome (PTS) is the most common long-term complication of deep vein thrombosis (DVT). Predictive models for PTS after hospitalized DVT patients, especially those with proximal DVT for whom preventative intervention decisions need to be made, are rare. We aimed to develop and externally validate a clinical predictive model for PTS in patients with proximal DVT.

methodsThis study was a retrospective, single-center, case-control study. The data used in our model were retrospectively collected from a prospective registry database in which 210 (derivation) and 90 (validation) consecutive patients were first diagnosed with proximal DVT. We developed a nomogram using the multivariate logistic regression model. External validation of our predictive model and previous predictive models in our validation set was assessed by discrimination, calibration, and clinical utility.

resultsOf the 30 candidate predictors, 5 were significantly associated with PTS in our final multivariable model, including the number of signs and symptoms (OR 1.33, 95% CI: 1.17 to 1.53, P<0.001), male sex (OR 1.79, 95% CI: 1.07 to 3.06, P=0.028), varicose vein history (OR 3.02, 95% CI: 1.04 to 7.60, P<0.001), BMI (OR 1.06, 95% CI: 1.00 to 1.12, P=0.052), and chronic DVT (OR 2.66, 95% CI: 1.49 to 4.79, P<0.001). The area under the curve was 0.724 in our predictive model, indicating suitable external performance.

conclusionsA simple-to-use nomogram effectively predicts the risk of PTS in patients with proximal DVT. This predictive model may be considered for use in clinical care.

Indexed as

clinical prediction ruledeep vein thrombosis (DVT)logistic modelsPostthrombotic syndrome (PTS)

Identifiers

PMID33987256
PMCPMC8105809

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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.