Aim: To date, no studies representing the Southeast Asian population have validated the Pulmonary Embolism Severity Index (PESI) and 2019 European Society of Cardiology (ESC) risk stratification. Therefore, this study aimed to validate the PESI score, simplified PESI (sPESI), PESI risk classification, and 2019 ESC risk stratification in Southeast Asian patients with acute pulmonary embolism (APE).
Methods: The present study is a 10-year cross-sectional study. Here, risk regressions were conducted to identify the PESI risk classification, sPESI, and 2019 ESC risk stratification as predictors for 30-day all-cause and PE-related mortalities. Receiver operating characteristic (ROC) curves were constructed to determine the diagnostic ability of the PESI score, sPESI score, PESI risk classification, and 2019 ESC risk stratification to predict 30-day mortality.
Results: A total of 696 patients (male, 286; female, 410; mean age, 57.7±15.7 years) were included in this study from 2011 to 2020. The risk of 30-day all-cause mortality progressively increased with the 2019 ESC risk stratification, being approximately 6-fold higher in the high-risk than in the low-risk class [risk ratio: 6.24 (95% confidence interval (CI), 3.12, 12.47), P<0.001]. The risk of 30-day all-cause mortality with the PESI risk classification also increased with the risk classes, being approximately 6-fold higher in class V than in class I [adjusted risk ratio: 5.91 (95% CI, 2.25, 15.51), P<0.001]. The highest area under the receiver operating characteristic curve (AuROC) of the predictive model was the PESI score [AuROC=0.733 (95% CI, 0.685, 0.782)].
Conclusion: Our study represents a good validation of the PESI and 2019 ESC risk stratification to predict 30-day mortality after APE diagnosis in the Southeast Asian population.
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http://dx.doi.org/10.5551/jat.64094 | DOI Listing |
J Med Internet Res
January 2025
Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, Changsha, China.
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Objective: This study aimed to develop and validate a machine learning-based model to predict MAKE30 in hospitalized older patients with AKI.
Rev Alerg Mex
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Jefe del servicio de Alergia, Hospital Central del Instituto de Previsión Social (IPS), Paraguay.
Objective: To develop a treatment algorithm for patients with penicillin allergy.
Methods: Retrospective study, carried out in adult patients with penicillin allergy, who were in group 3 or 4 of the established classification, and attended the outpatient clinic of the Department of Pulmonology and Allergy of the Central Hospital of the Social Security Institute, between January 2021 and December 2022. Each patient underwent an amoxicillin provocation test, after obtaining informed consent.
Liver Int
February 2025
General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China.
Background And Aims: Cirrhosis is characterised by hyperdynamic circulation, which contributes to cirrhotic cardiomyopathy (CCM). However, the expert consensus on CCM did not initially include cardiac structure because of scant evidence. Therefore, this study investigated the associations of cardiac chamber geometry with mortality and CCM.
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January 2025
Department of Radiology, University of Chicago, Chicago, IL, USA.
Purpose: Thyroid nodules are common, and ultrasound-based risk stratification using ACR's TIRADS classification is a key step in predicting nodule pathology. Determining thyroid nodule contours is necessary for the calculation of TIRADS scores and can also be used in the development of machine learning nodule diagnosis systems. This paper presents the development, validation, and multi-institutional independent testing of a machine learning system for the automatic segmentation of thyroid nodules on ultrasound.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Boston University Alzheimer's Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Background: Alzheimer's disease (AD) has both genetic and environmental risk factors. Gene-environment interaction may help explain some missing heritability. There is strong evidence for cigarette smoking as a risk factor for AD.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!