Objectives: Attribute matching matches an explicit clinical profile of a patient to a reference database to estimate the numeric value for the pretest probability of an acute disease. The authors tested the accuracy of this method for forecasting a very low probability of venous thromboembolism (VTE) in symptomatic emergency department (ED) patients.
Methods: The authors performed a secondary analysis of five data sets from 15 hospitals in three countries. All patients had data collected at the time of clinical evaluation for suspected pulmonary embolism (PE). The criterion standard to exclude VTE required no evidence of PE or deep venous thrombosis (DVT) within 45 days of enrollment. To estimate pretest probabilities, a computer program selected, from a large reference database of patients previously evaluated for PE, patients who matched 10 predictor variables recorded for each current test patient. The authors compared the outcome frequency of having VTE [VTE(+)] in patients with a pretest probability estimate of <2.5% by attribute matching, compared with a value of 0 from the Wells score.
Results: The five data sets included 10,734 patients, and 747 (7.0%, 95% confidence interval [CI] = 6.5% to 7.5%) were VTE(+) within 45 days. The pretest probability estimate for PE was <2.5% in 2,975 of 10,734 (27.7%) patients, and within this subset, the observed frequency of VTE(+) was 48 of 2,975 (1.6%, 95% CI = 1.2% to 2.1%). The lowest possible Wells score (0) was observed in 3,412 (31.7%) patients, and within this subset, the observed frequency of VTE(+) was 79 of 3,412 (2.3%, 95% CI = 1.8% to 2.9%) patients.
Conclusions: Attribute matching categorizes over one-quarter of patients tested for PE as having a pretest probability of <2.5%, and the observed rate of VTE within 45 days in this subset was <2.5%.
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http://dx.doi.org/10.1111/j.1553-2712.2009.00648.x | DOI Listing |
BMC Cardiovasc Disord
January 2025
Department of Internal Medicine, Collage of Medicine and Health Science, Debre Markos University, Debre Markos, Ethiopia.
Background: In developing countries evidences regarding pulmonary hypertension (PH) in rheumatic heart disease (RHD) patients are lacking, despite being responsible for significant morbidity and mortality. As a result, identifying the factors that influence PH is crucial to improve the quality of care.
Objective: To determine prevalence of pulmonary hypertension and its associated factors among rheumatic heart disease patients at the public hospitals of Bahir Dar city, Ethiopia.
Neurology
February 2025
Department of Neurology, John Hunter Hospital, Newcastle, Australia.
Postgrad Med
January 2025
Department of Internal Medicine, Lankenau Medical Center, Wynnewood, PA, USA.
Venous thromboembolism (VTE), consisting of both deep vein thrombosis (DVT) and pulmonary embolism (PE), is an extremely common condition both in the United States and worldwide. Not only is the diagnosis associated with significant morbidity and mortality for patients but it also imposes a deleterious financial burden on the US healthcare system. Diagnosis may be challenging due to variability in clinical presentation and requires a sequential workup including assessment of clinical pretest probability for VTE, D-dimer testing, and imaging.
View Article and Find Full Text PDFJAMA Netw Open
January 2025
City of Hope National Medical Center, Duarte, California.
Importance: Enhanced breast cancer screening with magnetic resonance imaging (MRI) is recommended to women with elevated risk of breast cancer, yet uptake of screening remains unclear after genetic testing.
Objective: To evaluate uptake of MRI after genetic results disclosure and counseling.
Design, Setting, And Participants: This multicenter cohort study was conducted at the University of Southern California Norris Cancer Hospital, the Los Angeles General Medical Center, and the Stanford University Cancer Institute.
JAMIA Open
February 2025
Department of Medicine, University of Wisconsin-Madison, Madison, WI 53792, United States.
Objective: To evaluate large language models (LLMs) for pre-test diagnostic probability estimation and compare their uncertainty estimation performance with a traditional machine learning classifier.
Materials And Methods: We assessed 2 instruction-tuned LLMs, Mistral-7B-Instruct and Llama3-70B-chat-hf, on predicting binary outcomes for Sepsis, Arrhythmia, and Congestive Heart Failure (CHF) using electronic health record (EHR) data from 660 patients. Three uncertainty estimation methods-Verbalized Confidence, Token Logits, and LLM Embedding+XGB-were compared against an eXtreme Gradient Boosting (XGB) classifier trained on raw EHR data.
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