| Autor: |
Rayan Alsuwaigh, Christine Ang, Jessica Quah, Charlene Jin Yee Liew, Lin Zou, Xuan Han Koh, Venkataraman Narayan, Tian Yi Lu, Clarence Ngoh, Zhiyu Wang, Juan Zhen Koh, Zhiyan Fu, Han Leong Goh |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
BMJ Open Respiratory Research, Vol 8, Iss 1 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2052-4439 |
| DOI: |
10.1136/bmjresp-2021-001045 |
| Popis: |
Background Chest radiograph (CXR) is a basic diagnostic test in community-acquired pneumonia (CAP) with prognostic value. We developed a CXR-based artificial intelligence (AI) model (CAP AI predictive Engine: CAPE) and prospectively evaluated its discrimination for 30-day mortality.Methods Deep-learning model using convolutional neural network (CNN) was trained with a retrospective cohort of 2235 CXRs from 1966 unique adult patients admitted for CAP from 1 January 2019 to 31 December 2019. A single-centre prospective cohort between 11 May 2020 and 15 June 2020 was analysed for model performance. CAPE mortality risk score based on CNN analysis of the first CXR performed for CAP was used to determine the area under the receiver operating characteristic curve (AUC) for 30-day mortality.Results 315 inpatient episodes for CAP occurred, with 30-day mortality of 19.4% (n=61/315). Non-survivors were older than survivors (mean (SD)age, 80.4 (10.3) vs 69.2 (18.7)); more likely to have dementia (n=27/61 vs n=58/254) and malignancies (n=16/61 vs n=18/254); demonstrate higher serum C reactive protein (mean (SD), 109 mg/L (98.6) vs 59.3 mg/L (69.7)) and serum procalcitonin (mean (SD), 11.3 (27.8) μg/L vs 1.4 (5.9) μg/L). The AUC for CAPE mortality risk score for 30-day mortality was 0.79 (95% CI 0.73 to 0.85, p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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