Accelerated Chest Pain Treatment With Artificial Intelligence-Informed, Risk-Driven Triage.
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ABSTRACT:
SUBMITTER: Hinson JS
PROVIDER: S-EPMC11264065 | biostudies-literature | 2024 Sep
REPOSITORIES: biostudies-literature
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Accelerated Chest Pain Treatment With Artificial Intelligence-Informed, Risk-Driven Triage.
Hinson Jeremiah S JS Taylor R Andrew RA Venkatesh Arjun A Steinhart Benjamin D BD Chmura Christopher C Sangal Rohit B RB Levin Scott R SR
JAMA internal medicine 20240901 9
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Project description:While COVID-19 diagnosis and prognosis artificial intelligence models exist, very few can be implemented for practical use given their high risk of bias. We aimed to develop a diagnosis model that addresses notable shortcomings of prior studies, integrating it into a fully automated triage pipeline that examines chest radiographs for the presence, severity, and progression of COVID-19 pneumonia. Scans were collected using the DICOM Image Analysis and Archive, a system that communicates with a hospital's image repository. The authors collected over 6,500 non-public chest X-rays comprising diverse COVID-19 severities, along with radiology reports and RT-PCR data. The authors provisioned one internally held-out and two external test sets to assess model generalizability and compare performance to traditional radiologist interpretation. The pipeline was evaluated on a prospective cohort of 80 radiographs, reporting a 95% diagnostic accuracy. The study mitigates bias in AI model development and demonstrates the value of an end-to-end COVID-19 triage platform.
| S-EPMC8760275 | biostudies-literature
Project description:BackgroundIn settings without access to rapid expert radiographic interpretation, artificial intelligence (AI)-based chest radiograph (CXR) analysis can triage persons presenting with possible tuberculosis (TB) symptoms, to identify those who require additional microbiological testing. However, there is limited evidence of the cost-effectiveness of this technology as a triage tool.MethodsA decision analysis model was developed to evaluate the cost-effectiveness of triage strategies with AI-based CXR analysis for patients presenting with symptoms suggestive of pulmonary TB in Karachi, Pakistan. These strategies were compared to the current standard of care using microbiological testing with smear microscopy or GeneXpert, without prior triage. Positive triage CXRs were considered to improve referral success for microbiologic testing, from 91% to 100% for eligible persons. Software diagnostic accuracy was based on a prospective field study in Karachi. Other inputs were obtained from the Pakistan TB Program. The analysis was conducted from the healthcare provider perspective, and costs were expressed in 2020 US dollars.ResultsCompared to upfront smear microscopy for all persons with presumptive TB, triage strategies with AI-based CXR analysis were projected to lower costs by 19%, from $23233 per 1000 persons, and avert 3%-4% disability-adjusted life-years (DALYs), from 372 DALYs. Compared to upfront GeneXpert, AI-based triage strategies lowered projected costs by 37%, from $34346 and averted 4% additional DALYs, from 369 DALYs. Reinforced follow-up for persons with positive triage CXRs but negative microbiologic tests was particularly cost-effective.ConclusionsIn lower-resource settings, the addition of AI-based CXR triage before microbiologic testing for persons with possible TB symptoms can reduce costs, avert additional DALYs, and improve TB detection.
| S-EPMC8671604 | biostudies-literature
Project description:BackgroundThe History Electrocardiogram Age Risk factor Troponin (HEART) Pathway and Emergency Department Assessment of Chest pain Score (EDACS) are validated accelerated diagnostic pathways designed to risk stratify patients presenting to the emergency department with chest pain. Data from large multisite prospective studies comparing these accelerated diagnostic pathways are limited.MethodsThe HEART Pathway Implementation is a prospective three-site cohort study, which accrued adults with symptoms concerning for acute coronary syndrome. Physicians completed electronic health record HEART Pathway and EDACS risk assessments on participants. Major adverse cardiac events (death, myocardial infarction and coronary revascularisation) at 30 days were determined using electronic health record, insurance claims and death index data. Test characteristics for detection of major adverse cardiac events were calculated for both accelerated diagnostic pathways and McNemar's tests were used for comparisons.Results5799 patients presenting to the emergency department were accrued, of which HEART Pathway and EDACS assessments were completed on 4399. Major adverse cardiac events at 30 days occurred in 449/4399 (10.2%). The HEART Pathway identified 38.4% (95% CI 37.0% to 39.9%) of patients as low-risk compared with 58.1% (95% CI 56.6% to 59.6%) identified as low-risk by EDACS (p<0.001). Major adverse cardiac events occurred in 0.4% (95% CI 0.2% to 0.9%) of patients classified as low-risk by the HEART Pathway compared with 1.0% (95% CI 0.7% to 1.5%) of patients identified as low-risk by EDACS (p<0.001). Thus, the HEART Pathway had a negative predictive value of 99.6% (95% CI 99.1% to 99.8%) for major adverse cardiac events compared with a negative predictive value of 99.0% (95% CI 98.5% to 99.3%) for EDACS.ConclusionsEDACS identifies a larger proportion of patients as low-risk than the HEART Pathway, but has a higher missed major adverse cardiac events rate at 30 days. Physicians will need to consider their risk tolerance when deciding whether to adopt the HEART Pathway or EDACS accelerated diagnostic pathway.Trial registration numberNCT02056964.
| S-EPMC7962144 | biostudies-literature
Project description:The clinical prioritisation criteria (CPC) are a clinical decision support tool that ensures patients referred for public specialist outpatient services to Queensland Health are assessed according to their clinical urgency. Medical referrals are manually triaged and prioritised into three categories by the associated health service before appointments are booked. We have developed a method using artificial intelligence to automate the process of categorizing medical referrals based on clinical prioritization criteria (CPC) guidelines. Using machine learning techniques, we have created a tool that can assist clinicians in sorting through the substantial number of referrals they receive each year, leading to more efficient use of clinical specialists' time and improved access to healthcare for patients. Our research included analyzing 17,378 ENT referrals from two hospitals in Queensland between 2019 and 2022. Our results show a level of agreement between referral categories and generated predictions of 53.8%.
| S-EPMC10642163 | biostudies-literature
Project description:The sensitivity of triage systems in identifying acute cardiovascular events in patients presented to the emergency department with chest pain is not optimal. Recently, a clinical score, the Emergency Department Assessment of Chest Pain Score (EDACS), has been proposed for a rapid assessment without additional instruments. To evaluate whether the integration of EDACS into triage evaluation of patients with chest pain can improve the triage's predictive validity for an acute cardiovascular event, a single-center prospective observational study was conducted. This study involved all patients who needed a triage admission for chest pain between January 1, 2020, and December 31, 2020. All enrolled patients first underwent a standard triage assessment and then the EDACS was calculated. The primary outcome of the study was the presence of an acute cardiovascular event. The discriminatory ability of EDACS in triage compared with standard triage assessment was evaluated by comparing the areas under the receiver operating characteristic curve, decision curve analysis, and net reclassification improvement. The study involved 1,596 patients, of that 7.3% presented the study outcome. The discriminatory ability of triage presented an area under the receiver operating characteristic curve of 0.688 that increased to 0.818 after the application of EDACS in the triage assessment. EDACS improved the baseline assessment of priority assigned in triage, with a net reclassification improvement of 33.6% (p <0.001), and the decision curve analyses demonstrated that EDACS in triage resulted in a clear net clinical benefit. In conclusion, the results of the study suggest that EDACS has a good discriminatory capacity for acute cardiovascular events and that its implementation in routine triage may improve triage performance in patients with chest pain.
| S-EPMC9336201 | biostudies-literature
Project description:Human-in-the-loop (HITL) AI may enable an ideal symbiosis of human experts and AI models, harnessing the advantages of both while at the same time overcoming their respective limitations. The purpose of this study was to investigate a novel collective intelligence technology designed to amplify the diagnostic accuracy of networked human groups by forming real-time systems modeled on biological swarms. Using small groups of radiologists, the swarm-based technology was applied to the diagnosis of pneumonia on chest radiographs and compared against human experts alone, as well as two state-of-the-art deep learning AI models. Our work demonstrates that both the swarm-based technology and deep-learning technology achieved superior diagnostic accuracy than the human experts alone. Our work further demonstrates that when used in combination, the swarm-based technology and deep-learning technology outperformed either method alone. The superior diagnostic accuracy of the combined HITL AI solution compared to radiologists and AI alone has broad implications for the surging clinical AI deployment and implementation strategies in future practice.
| S-EPMC6861262 | biostudies-literature
Project description:AimsWe aimed to develop models to detect aortic stenosis (AS) from chest radiographs-one of the most basic imaging tests-with artificial intelligence.Methods and resultsWe used 10 433 retrospectively collected digital chest radiographs from 5638 patients to train, validate, and test three deep learning models. Chest radiographs were collected from patients who had also undergone echocardiography at a single institution between July 2016 and May 2019. These were labelled from the corresponding echocardiography assessments as AS-positive or AS-negative. The radiographs were separated on a patient basis into training [8327 images from 4512 patients, mean age 65 ± (standard deviation) 15 years], validation (1041 images from 563 patients, mean age 65 ± 14 years), and test (1065 images from 563 patients, mean age 65 ± 14 years) datasets. The soft voting-based ensemble of the three developed models had the best overall performance for predicting AS with an area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of 0.83 (95% confidence interval 0.77-0.88), 0.78 (0.67-0.86), 0.71 (0.68-0.73), 0.71 (0.68-0.74), 0.18 (0.14-0.23), and 0.97 (0.96-0.98), respectively, in the validation dataset and 0.83 (0.78-0.88), 0.83 (0.74-0.90), 0.69 (0.66-0.72), 0.71 (0.68-0.73), 0.23 (0.19-0.28), and 0.97 (0.96-0.98), respectively, in the test dataset.ConclusionDeep learning models using chest radiographs have the potential to differentiate between radiographs of patients with and without AS.Lay summaryWe created artificial intelligence (AI) models using deep learning to identify aortic stenosis (AS) from chest radiographs. Three AI models were developed and evaluated with 10 433 retrospectively collected radiographs and labelled from echocardiography reports. The ensemble AI model could detect AS in a test dataset with an area under the receiver operating characteristic curve of 0.83 (95% confidence interval 0.78-0.88). Since chest radiography is a cost-effective and widely available imaging test, our model can provide an additive resource for the detection of AS.
| S-EPMC9707887 | biostudies-literature
Project description:BackgroundPatients from racial and ethnic minority groups presenting to the Emergency Department (ED) with chest pain experience lower odds of receiving stress testing compared with nonminorities. Studies have demonstrated that care pathways administered within the ED can reduce health disparities, but this has yet to be studied as a strategy to increase stress testing equity.MethodsA secondary analysis from 3 randomized clinical trials involving ED patients with acute chest pain was performed to determine whether a care pathway, ACES (Accelerated Chest pain Evaluation with Stress imaging), reduces the racial disparity in index visit cardiac testing between African American (AA) and White patients. Three hundred thirty-four participants with symptoms and findings indicating intermediate to high risk for acute coronary syndrome were enrolled in 3 clinical trials. Major exclusions were ST-segment elevation, initial troponin elevation, and hemodynamic instability. Participants were randomly assigned to receive usual inpatient care, or ACES. The ACES care pathway includes placement in observation for serial cardiac markers, with an expectation for stress imaging. The primary outcome was index visit objective cardiac testing, compared among AA and White participants.ResultsAA participants represented 111/329 (34%) of the study population, 80/220 (36%) of the ACES group and 31/109 (28%) of the usual care group. In usual care, objective testing occurred less frequently among AA (22/31, 71%) than among White (69/78, 88%, P = 0.027) participants, primarily driven by cardiac catheterization (3% vs. 24%; P = 0.012). In ACES, testing rates did not differ by race [AA 78/80 (98%) vs. White 138/140 (99%); P = 0.623]. At 90 days, death, MI, and revascularization did not differ in either group between AA and White participants.ConclusionsA care pathway with the expectation for stress imaging eliminates the racial disparity among AA and White participants with chest pain in the acquisition of index-visit cardiovascular testing.
| S-EPMC6375104 | biostudies-literature
Project description:BackgroundArtificial intelligence (AI) is capable of integrating a large amount of related information to predict therapeutic relationships such as disease treatment with known drugs, gene expression, and drug-target binding. AI has gained increasing attention as a promising tool for next-generation drug development.MethodsAn AI method was used for drug repurposing and target identification for cancer. Among 8 survived candidates after background checking, N-(1-propyl-1H-1,3-benzodiazol-2-yl)-3-(pyrrolidine-1-sulfonyl) benzamide (Z29077885) was newly selected as an new anti-cancer drug, and the anti-cancer efficacy of Z29077885 was confirmed using cell viability, western blot, cell cycle, apoptosis assay in MDA-MB 231 and A549 in vitro. Then, anti-tumor efficacy of Z29077885 was validated in an in vivo A549 xenograft in BALB/c nude mice.ResultsFirst, we discovered an antiviral agent, Z29077885, as a new anticancer drug candidate using the AI deep learning method. Next, we demonstrated that Z29077885 inhibits Serine/threonine kinase 33 (STK33) enzymatic function in vitro and showed the anticancer efficacy in various cancer cells. Then, we found enhanced apoptosis via S-phase cell cycle arrest as the mechanism underlying the anticancer efficacy of Z29077885 in both lung and breast cancer cells. Finally, we confirmed the anti-tumor efficacy of Z29077885 in an in vivo A549 xenograft.ConclusionsIn this study, we used an AI-driven screening strategy to find a novel anticancer medication targeting STK33 that triggers cancer cell apoptosis and cell cycle arrest at the s phase. It will pave a way to efficiently discover new anticancer drugs.
| S-EPMC10717841 | biostudies-literature
Project description:Antimalarial drugs are becoming less effective due to the emergence of drug resistance. Resistance has been reported for all available malaria drugs, including artemisinin, thus creating a perpetual need for alternative drug candidates. The traditional drug discovery approach of high throughput screening (HTS) of large compound libraries for identification of new drug leads is time-consuming and resource intensive. While virtual in silico screening is a solution to this problem, however, the generalization of the models is not ideal. Artificial intelligence (AI), utilizing either structure-based or ligand-based approaches, has demonstrated highly accurate performances in the field of chemical property prediction. Leveraging the existing data, AI would be a suitable alternative to blind-search HTS or fingerprint-based virtual screening. The AI model would learn patterns within the data and help to search for hit compounds efficiently. In this work, we introduce DeepMalaria, a deep-learning based process capable of predicting the anti-Plasmodium falciparum inhibitory properties of compounds using their SMILES. A graph-based model is trained on 13,446 publicly available antiplasmodial hit compounds from GlaxoSmithKline (GSK) dataset that are currently being used to find novel drug candidates for malaria. We validated this model by predicting hit compounds from a macrocyclic compound library and already approved drugs that are used for repurposing. We have chosen macrocyclic compounds as these ligand-binding structures are underexplored in malaria drug discovery. The in silico pipeline for this process also consists of additional validation of an in-house independent dataset consisting mostly of natural product compounds. Transfer learning from a large dataset was leveraged to improve the performance of the deep learning model. To validate the DeepMalaria generated hits, we used a commonly used SYBR Green I fluorescence assay based phenotypic screening. DeepMalaria was able to detect all the compounds with nanomolar activity and 87.5% of the compounds with greater than 50% inhibition. Further experiments to reveal the compounds' mechanism of action have shown that not only does one of the hit compounds, DC-9237, inhibits all asexual stages of Plasmodium falciparum, but is a fast-acting compound which makes it a strong candidate for further optimization.
| S-EPMC6974622 | biostudies-literature