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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 5 of 5 Research Studies DisplayedPeng L, Luo G, Walker A
Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals.
The goals of this study were to compare a single-site, COVID-19 computer diagnosis system that used the Federated Averaging (FedAvg) algorithm with 3-client Federated learning (FL) models, and to evaluate the performance of the four FL variations. Researchers leveraged a FL healthcare collaborative that included data from five US and European healthcare systems encompassing 42 hospitals. They concluded that FedAvg could significantly improve generalization of the model in comparison with other personalization FL algorithms--FedProx, FedBN, and FedAMP--but at the cost of poor internal validity.
AHRQ-funded; HS026379.
Citation: Peng L, Luo G, Walker A .
Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals.
J Am Med Inform Assoc 2022 Dec 13;30(1):54-63. doi: 10.1093/jamia/ocac188..
Keywords: COVID-19, Diagnostic Safety and Quality, Imaging, Hospitals
Sun J, Peng L, Li T
Performance of a chest radiograph AI diagnostic tool for COVID-19: a prospective observational study.
The purpose of this observational study was to evaluate the real-time performance of an interpretable artificial intelligence (AI) model to detect COVID-19 on chest radiographs. The researchers utilized 95,363 chest radiographs for model training, external validation, and real-time validation. There were 5,335 real-time predictions and a COVID-19 prevalence of 4.8%. The study found that participants positive for COVID-19 had higher COVID-19 diagnostic scores than participants negative for COVID-19. Real-time model performance remained the same during the 19 weeks of implementation. Model sensitivity was higher in men than in women, but model specificity was higher in women. Sensitivity was higher for Asian and Black participants compared with White participants. The COVID-19 AI diagnostic system had worse accuracy compared with radiologist predictions. The researchers concluded that AI tools underperform when compared with radiologist results.
AHRQ-funded; HS026379.
Citation: Sun J, Peng L, Li T .
Performance of a chest radiograph AI diagnostic tool for COVID-19: a prospective observational study.
Radiol Artif Intell 2022 Jul;4(4):e210217. doi: 10.1148/ryai.210217..
Keywords: COVID-19, Imaging, Diagnostic Safety and Quality, Health Information Technology (HIT)
Lowry KP, Bissell MCS, Miglioretti DL
Breast biopsy recommendations and breast cancers diagnosed during the COVID-19 pandemic.
The authors sought to examine breast biopsy recommendations and breast cancers diagnosed before and during the COVID-19 pandemic by mode of detection and women's characteristics. Using data from the Breast Cancer Surveillance Consortium, they found that there were substantially fewer breast biopsies with cancer diagnoses during the COVID-19 pandemic from March to September 2020 compared to the same period in 2019, with Asian and Hispanic women experiencing the largest declines followed by Black women.
AHRQ-funded; HS018366.
Citation: Lowry KP, Bissell MCS, Miglioretti DL .
Breast biopsy recommendations and breast cancers diagnosed during the COVID-19 pandemic.
Radiology 2022 May;303(2):287-94. doi: 10.1148/radiol.2021211808..
Keywords: COVID-19, Cancer: Breast Cancer, Cancer, Diagnostic Safety and Quality
Woloshin S, Dewitt B, Krishnamurti T
Assessing how consumers interpret and act on results from at-home COVID-19 self-test kits: a randomized clinical trial.
The US Food and Drug Administration (FDA) authorized SARS-CoV-2 rapid at-home self-test kits for individuals with and without symptoms. How appropriately users interpret and act on the results of at-home COVID-19 self-tests is unknown. The objective of this study was to assess how users of at-home COVID-19 self-test kits interpreted and acted on results when given instructions authorized by the FDA, instructions based on decision science principles, or no instructions.
AHRQ-funded; HS024075.
Citation: Woloshin S, Dewitt B, Krishnamurti T .
Assessing how consumers interpret and act on results from at-home COVID-19 self-test kits: a randomized clinical trial.
JAMA Intern Med 2022 Mar;182(3):332-41. doi: 10.1001/jamainternmed.2021.8075..
Keywords: COVID-19, Diagnostic Safety and Quality
Roberts JE, Campbell JI, Gauvreau K
Differentiating multisystem inflammatory syndrome in children: a single-centre retrospective cohort study.
This study’s objective was to identify clinical and laboratory findings that distinguished multisystem inflammatory syndrome in children (MIS-C) from febrile illnesses in which MIS-C was considered but ultimately excluded, and to examine the diseases that most often mimicked MIS-C in a tertiary medical center. All children who were hospitalized at the author’s center with fever were evaluated for MIS-C and were compared for clinical signs and symptoms, SARS-CoV-2 status, and laboratory studies between those with and without MIS-C. The authors identified 50 confirmed MIS-C cases (MIS-C(+)) and 68 children evaluated for, but ultimately not diagnosed with, MIS-C (MIS-C(-)). Symptoms of conjunctivitis, abdominal pain, fatigue, hypoxaemia, tachypnoea and hypotension at presentation were significantly more common among MIS-C(+) patients, with MIS-C(+) and MIS-C(-) patients having similar elevations in C-reactive protein (CRP), but were differentiated by thrombocytopenia, lymphopenia, and elevated ferritin, neutrophil/lymphocyte ratio, BNP and troponin. Children hospitalized with MIS-C were older, more likely to present with conjunctivitis, oral mucosa changes, abdominal pain and hypotension, and had higher neutrophil/lymphocyte ratios and lower platelet counts.
AHRQ-funded; HS000063.
Citation: Roberts JE, Campbell JI, Gauvreau K .
Differentiating multisystem inflammatory syndrome in children: a single-centre retrospective cohort study.
Arch Dis Child 2022 Mar; 107(3):e3. doi: 10.1136/archdischild-2021-322290..
Keywords: Children/Adolescents, COVID-19, Chronic Conditions, Diagnostic Safety and Quality