Emergency department (ED) triage scale determines the priority of patient care and foretells the prognosis. However, the information retrieved from the initial assessment is limited, hindering the risk identification accuracy of triage. Therefore, we sought to develop a 'dynamic' triage system as secondary screening, using artificial intelligence (AI) techniques to integrate information from initial assessment data and subsequent examinations. This retrospective cohort study included 134,112 ED visits with at least one electrocardiography (ECG) and chest X-ray (CXR) in a medical center from 2012 to 2022. Additionally, an independent community hospital provided 45,614 ED visits as an external validation set. We trained an eXtreme gradient boosting (XGB) model using initial assessment data to predict all-cause mortality in 7 days. Two deep learning models (DLMs) using ECG and CXR were trained to stratify mortality risks. The dynamic triage levels were based on output from the XGB-triage and DLMs from ECG and CXR. During the internal and external validation, the area under the receiver operating characteristic curve (AUC) of the XGB-triage model was >0.866; furthermore, the AUCs of DLMs using ECG and CXR were >0.862 and >0.886, respectively. The dynamic triage scale provided a higher C-index (0.914-0.920 vs. 0.827-0.843) than the original one and demonstrated better predictive ability for 5-year mortality, 30-day ED revisit, and 30-day discharge. The AI-based risk scale provides a more accurate and dynamic stratification of mortality risk in ED patients, particularly in identifying patients who tend to be overlooked due to atypical symptoms.
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http://dx.doi.org/10.1007/s10916-023-01980-x | DOI Listing |
Diagnostics (Basel)
November 2024
Gloucestershire Hospitals Foundation Trust, Cheltenham General Hospital, Sandford Road, Cheltenham GL53 7AN, UK.
Permanent pacemaker implantation is a low-risk procedure. However, complications may occur at a rate of around 4-8%. We present a case where initial implantation resulted in complications that could have been avoided by meticulous assessment of lead position in different projections and early post-procedure X-ray that would have delineated other serious complications.
View Article and Find Full Text PDFBMJ Case Rep
September 2024
Pulmonary Medicine, KS Hegde Medical Academy, Deralakkate, Mangaluru, Karnataka, India
A man in his early 50s with previously treated pulmonary tuberculosis (TB) presented with a 3-month history of cough, expectoration and progressive breathlessness, accompanied by significant weight loss. Examination revealed tachycardia, tachypnoea, hypoxaemia and unilateral diminished breath sounds. Investigations showed anaemia, leucocytosis and a homogeneous opacity on the left side of the CXR.
View Article and Find Full Text PDFApplications of Artificial Intelligence (AI) deep-learning models to screening for clinical conditions continue to evolve. Instances provided in this treatise include using a simple one-view PA chest radiograph to screen for Type 2 Diabetes Mellitus (T2DM), congestive heart failure, valvular heart disease, and to assess mortality in asymptomatic persons with respiratory diseases. This technology incorporates hundreds of thousands of CXRs into a convoluted neural network and is generally named AI CXR.
View Article and Find Full Text PDFJ Imaging Inform Med
August 2024
Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan R.O.C..
The diagnosis and treatment of pulmonary hypertension have changed dramatically through the re-defined diagnostic criteria and advanced drug development in the past decade. The application of Artificial Intelligence for the detection of elevated pulmonary arterial pressure (ePAP) was reported recently. Artificial Intelligence (AI) has demonstrated the capability to identify ePAP and its association with hospitalization due to heart failure when analyzing chest X-rays (CXR).
View Article and Find Full Text PDFCureus
April 2024
Microbiology, College of Medicine, University of Bisha, Bisha, SAU.
Background Preoperative investigations are important to assess the clinical condition of patients who undergo elective surgical procedures. However, there is still debate about the usefulness of performing preoperative investigations. We aimed to determine the prevalence of routine preoperative investigation abnormalities among elective general surgery patients.
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