IMR Press / RCM / Volume 24 / Issue 10 / DOI: 10.31083/j.rcm2410281
Open Access Original Research
Algorithms of Electrocardiographic Changes for Quantitative and Localization Analysis of Thrombus Burden in Patients with Acute Pulmonary Thromboembolism
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1 Department of Cardiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, 200072 Shanghai, China
2 Department of Pulmonary Circulation, Shanghai Pulmonary Hospital, Tongji University School of Medicine, 201209 Shanghai, China
3 Department of Cardiology, Qidong People's Hospital Affiliated to Nantong University, 226200 Nantong, Jiangsu, China
4 Department of Hematology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, 200072 Shanghai, China
*Correspondence: xuyawei@tongji.edu.cn (Yawei Xu); xdc77@tongji.edu.cn (Dachun Xu)
Rev. Cardiovasc. Med. 2023, 24(10), 281; https://doi.org/10.31083/j.rcm2410281
Submitted: 13 November 2022 | Revised: 23 March 2023 | Accepted: 28 March 2023 | Published: 7 October 2023
Copyright: © 2023 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.
Abstract

Background: Various electrocardiographic (ECG) abnormalities are associated with the severity of pulmonary thromboembolism (PTE). The utility of evaluating the clot burden of PTE based on ECG findings alone has yet to be thoroughly investigated in Chinese patients. The aim of this study was therefore to use ECG signs to establish novel models for quantitative and localization analysis of clot burden in patients with acute PTE. Methods: Acute PTE patients from three centers were enrolled between 2015 and 2019 in a retrospective cohort study (NCT03802929). We analyzed the 12-lead ECGs at admission and studied computed tomography pulmonary angiography (CTPA) features to obtain the Qanadli score of clot burden and location of thrombus. Novel risk prediction models were developed and validated using derivation and external validation cohorts, respectively. Results: A total of 341 acute PTE patients were screened, of whom 246 (72.1%) were from Shanghai Tenth People’s Hospital, 71 (20.8%) were from Shanghai Pulmonary Hospital and 24 (7.0%) were from Qidong People’s Hospital. In the derivation cohort, predictors included in the final models were congestive heart failure, chronic obstructive pulmonary disease, hypertension, coronary heart disease, atrial fibrillation and ECG abnormalities. The CHARIS (COPD/CHF/CHD, HTN, Atrial arrhythmias/AF, RBBB/RAD, Inverted T wave and S1Q3T3/ Sinus tachycardia) I model was established for quantitatively assessing Qanadli score. It had moderate discrimination in both the derivation cohort (concordance index (c-index) of 0.720, 95% CI 0.655–0.780) and the validation cohort (c-index of 0.663, 95% CI 0.559–0.757). The CHARIS II model was used to predict the probability of trunk obstruction. It showed similar discrimination in the derivation cohort (c-index of 0.753, 95% CI 0.691–0.811) and in the validation cohort (c-index of 0.741, 95% CI 0.641–0.827). Calibration curves and Hosmer-Lemeshow test confirmed the accuracy of the risk prediction equations in the external validation dataset. Decision curve analysis showed the CHARIS I and CHARIS II algorithms had positive net benefits in both the derivation and validation cohorts. Conclusions: From quantitative and localization perspectives, the CHARIS algorithms can identify acute PTE patients with heavy thrombus burdens prior to imaging diagnosis. Clinical Trial Registration: NCT03802929, https://www.clinicaltrials.gov/study/NCT03802929.

Keywords
acute pulmonary thromboembolism
electrocardiographic changes
thrombus burden
risk model
Funding
SHDC2020CR3030B/Clinical Research Plan of Shanghai Hospital Development Center
Figures
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