Predicting In‐hospital Mortality in Patients Admitted from the Emergency Department for Pulmonary Embolism: Incidence and Prognostic Value of Deep Vein Thrombosis. A Retrospective Study
Clinical Respiratory Journal(2024)SCI 4区
Univ Ferrara | St Anna Univ Hosp
Abstract
Background Pulmonary embolism (PE) is one of the most common causes of death from cardiovascular disease. Although deep vein thrombosis (DVT) is the leading cause of PE, its prognostic role is unclear. This study investigated the incidence and prognostic value of DVT in predicting in-hospital mortality (IHM) in patients admitted from the emergency department (ED) for PE.Methods This retrospective cohort study was conducted in the ED of a third-level university hospital. Patients over 18 years admitted for PE between 1 January 2018 and 31 December 2022 were included.Results Five hundred and thirty patients (mean age 73.13 years, 6% IHM) were included. 69.1% of cases had DVT (36.4% unilateral femoral vein, 3.6% bilateral, 39.1% unilateral popliteal vein, 2.8% bilateral, 45.7% distal vein thrombosis and 7.4% iliocaval involvement). Patients who died in hospital had a higher Pulmonary Embolism Severity Index (PESI) (138.6 vs. 99.65, p < 0.001), European Society of Cardiology risk class (15.6% vs. 1%, intermediate-high in 50% vs. 6.4%, p < 0.001) and more DVT involving the iliac-caval vein axis (18.8% vs. 6.6%, p = 0.011). PESI class >II, right ventricular dysfunction, increased blood markers of myocardial damage and involvement of the iliocaval venous axis were independent predictors of IHM on multivariate analysis.Conclusions Although further studies are needed to confirm the prognostic role of DVT at PE, involvement of the iliocaval venous axis should considered to be a sign of a higher risk of IHM and may be a key factor in prognostic stratification.
MoreTranslated text
Key words
clinical prediction rule,deep vein thrombosis,emergency care,in-hospital mortality,prognosis,pulmonary embolism,ultrasound
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
1972
被引用160 | 浏览
Growth Differentiation Factor-15 for Prognostic Assessment of Patients with Acute Pulmonary Embolism
2008
被引用168 | 浏览
2010
被引用145 | 浏览
1971
被引用518 | 浏览
1999
被引用182 | 浏览
2010
被引用128 | 浏览
2011
被引用84 | 浏览
2011
被引用90 | 浏览
2011
被引用112 | 浏览
2016
被引用11 | 浏览
2019
被引用2644 | 浏览
2020
被引用12 | 浏览
2020
被引用55 | 浏览
2020
被引用13 | 浏览
2021
被引用3 | 浏览
2011
被引用91 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper