Quantitative Computed Tomography Lung COVID Scores with Laboratory Markers: Utilization to Predict Rapid Progression and Monitor Longitudinal Changes in Patients with Coronavirus 2019 (COVID-19) Pneumonia.
Da Hyun KangGrace Hyun J KimSa-Beom ParkSong I LeeJeong Suk KohMatthew S BrownFereidoun AbtinMichael F McNitt-GrayJonathan G GoldinJeong Seok LeePublished in: Biomedicines (2024)
Coronavirus disease 2019 (COVID-19), is an ongoing issue in certain populations, presenting rapidly worsening pneumonia and persistent symptoms. This study aimed to test the predictability of rapid progression using radiographic scores and laboratory markers and present longitudinal changes. This retrospective study included 218 COVID-19 pneumonia patients admitted at the Chungnam National University Hospital. Rapid progression was defined as respiratory failure requiring mechanical ventilation within one week of hospitalization. Quantitative COVID (QCOVID) scores were derived from high-resolution computed tomography (CT) analyses: (1) ground glass opacity (QGGO), (2) mixed diseases (QMD), and (3) consolidation (QCON), and the sum, quantitative total lung diseases (QTLD). Laboratory data, including inflammatory markers, were obtained from electronic medical records. Rapid progression was observed in 9.6% of patients. All QCOVID scores predicted rapid progression, with QMD showing the best predictability (AUC = 0.813). In multivariate analyses, the QMD score and interleukin(IL)-6 level were important predictors for rapid progression (AUC = 0.864). With >2 months follow-up CT, remained lung lesions were observed in 21 subjects, even after several weeks of negative reverse transcription polymerase chain reaction test. AI-driven quantitative CT scores in conjugation with laboratory markers can be useful in predicting the rapid progression and monitoring of COVID-19.
Keyphrases
- coronavirus disease
- sars cov
- computed tomography
- high resolution
- respiratory failure
- mechanical ventilation
- respiratory syndrome coronavirus
- loop mediated isothermal amplification
- dual energy
- image quality
- positron emission tomography
- magnetic resonance imaging
- contrast enhanced
- intensive care unit
- extracorporeal membrane oxygenation
- ejection fraction
- machine learning
- artificial intelligence
- end stage renal disease
- prognostic factors
- mass spectrometry
- physical activity
- cross sectional
- depressive symptoms
- peritoneal dialysis
- big data
- sleep quality
- quantum dots
- patient reported
- tandem mass spectrometry