Pulmonary Embolism
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2022 ◽  
Vol 26 (1) ◽  
pp. 77-78
Author(s):  
Abdulsamet Sandal ◽  
◽  
Elif Tuğçe Korkmaz ◽  
Funda Aksu ◽  
Deniz Köksal ◽  
...  

Diagnostics ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 193
Author(s):  
Konstantinos Bartziokas ◽  
Christos Kyriakopoulos ◽  
Dimitrios Potonos ◽  
Konstantinos Exarchos ◽  
Athena Gogali ◽  
...  

Background: Uric acid (UA) is the final product of purine metabolism and a marker of oxidative stress that may be involved in the pathophysiology of cardiovascular and thromboembolic disease. The aim of the current study is to investigate the potential value of UA to creatinine ratio (UA/Cr) as a diagnostic tool for the outcome of patients admitted with acute pulmonary embolism (PE) and the correlations with other parameters. Methods: We evaluated 116 patients who were admitted for PE in a respiratory medicine department. PE was confirmed with computed tomography pulmonary angiography. Outcomes evaluated were hospitalization duration, mortality or thrombolysis and a composite endpoint (defined as mortality or thrombolysis). Patients were assessed for PE severity with the PE Severity Index (PESI) and the European Society of Cardiology (ESC) 2019 risk stratification. Results: The median (interquartile range) UA/Cr level was 7.59 (6.3–9.3). UA/Cr was significantly associated with PESI (p < 0.001), simplified PESI (p = 0.019), and ESC 2019 risk stratification (p < 0.001). The area under the curve (AUC) for prediction of 30-day mortality by UA/Cr was 0.793 (95% CI: 0.667–0.918). UA/Cr levels ≥7.64 showed 87% specificity and 94% negative predictive value for mortality. In multivariable analysis UA/Cr was an independent predictor of mortality (HR (95% CI): 1.620 (1.245–2.108), p < 0.001) and composite outcome (HR (95% CI): 1.521 (1.211–1.908), p < 0.001). Patients with elevated UA/Cr levels (≥7.64) had longer hospitalization (median (IQR) 7 (5–11) vs. 6 (5–8) days, p = 0.006)), higher mortality (27.3% vs. 3.2%, p = 0.001) and worse composite endpoint (32.7% vs. 3.4%, p < 0.001). Conclusion: Serum UA/Cr ratio levels at the time of PE diagnosis are associated with disease severity and risk stratification, and may be a useful biomarker for the identification of patients at risk of adverse outcomes.


2022 ◽  
Vol 12 (1) ◽  
Author(s):  
You Li ◽  
Yuncong He ◽  
Yan Meng ◽  
Bowen Fu ◽  
Shuanglong Xue ◽  
...  

AbstractVenous thromboembolism (VTE), clinically presenting as deep vein thrombosis (DVT) or pulmonary embolism (PE). Not all DVT patients carry the same risk of developing acute pulmonary embolism (APE). To develop and validate a prediction model to estimate risk of APE in DVT patients combined with past medical history, clinical symptoms, physical signs, and the sign of the electrocardiogram. We analyzed data from a retrospective cohort of patients who were diagnosed as symptomatic VTE from 2013 to 2018 (n = 1582). Among them, 122 patients were excluded. All enrolled patients confirmed by pulmonary angiography or computed tomography pulmonary angiography (CTPA) and compression venous ultrasonography. Using the LASSO and logistics regression, we derived a predictive model with 16 candidate variables to predict the risk of APE and completed internal validation. Overall, 52.9% patients had DVT + APE (773 vs 1460), 47.1% patients only had DVT (687 vs 1460). The APE risk prediction model included one pre-existing disease or condition (respiratory failure), one risk factors (infection), three symptoms (dyspnea, hemoptysis and syncope), five signs (skin cold clammy, tachycardia, diminished respiration, pulmonary rales and accentuation/splitting of P2), and six ECG indicators (SIQIIITIII, right axis deviation, left axis deviation, S1S2S3, T wave inversion and Q/q wave), of which all were positively associated with APE. The ROC curves of the model showed AUC of 0.79 (95% CI, 0.77–0.82) and 0.80 (95% CI, 0.76–0.84) in the training set and testing set. The model showed good predictive accuracy (calibration slope, 0.83 and Brier score, 0.18). Based on a retrospective single-center population study, we developed a novel prediction model to identify patients with different risks for APE in DVT patients, which may be useful for quickly estimating the probability of APE before obtaining definitive test results and speeding up emergency management processes.


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