Prospektive Risikostratifizierung von COVID-19-Patienten auf der Basis eines KI-basierten CT-Algorithmus
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Philipps-Universität Marburg
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Abstract
Objective
Individual risk evaluation of COVID-19 patients based on a chest CT scan performed at the time of hospital admission in order to predict the need for intensive care treatment in the further clinical curse.
Material and methods
The thoracic CT scans of 34 symptomatic SARS-CoV-2 positive patients (58.2 ± 11.8 years) were analyzed using an artificial intelligence algorithm (AI algorithm) [CT Pneumonia Analysis, Siemens Healthineers]. In addition to the fully automated quantification of pneumonic infiltrates (Opacity), the vital parameters SpO2 and respiratory rate were recorded, and their influence on a potential intensive care treatment was analyzed. The subsequent intensive care and normal inpatient treatment as well as a pulmonary involvement < 10 % (Opacitylow) and ≥ 10 % (Opacityhigh) were defined as subcollectives.
Results
Patients who received intensive care treatment three days after admission showed a higher pulmonary involvement in the initial CT scan with a Mdn = 34.57 % (IQR = 59.76 %), compared to patients who received normal care with a Mdn = 5.53 % (IQR = 4.79 %) (z = -3.599, p < 0.001, r = 0.617). For the 7th and 14th day post-CT, similar results were obtained, with 28.45 % compared to 5.62 % initial involvement (z = -3.289 p = 0.001, r = 0.564). Opacityhigh patients (7/13) required intensive care treatment more often than Opacitylow patients (0/21) (p < 0.001). The combination of vital parameters and CT scan results with thresholds of SpO2 ≤ 95 %, respiratory rate ≥ 20 breaths/min and increased pulmonary involvement (≥ 10 %) resulted in a combined relative risk for intensive care treatment of 9.75 95 % CI [2.43, 39.16] 7 and 14 days after initial CT examination.
Conclusion
The AI algorithm-based evaluation of chest CT scans allows an automated and observer-independent quantification of pneumonic infiltrates in COVID-19 patients. This method may – by itself as well as in combination with the vital parameters SpO2 and respiratory rate – enable an early and individual prognostic risk stratification, which can be used for an early treatment escalation or as a basis for managing intensive care capacities in the context of the COVID-19 pandemic.
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Dates
Created: 2022Issued: 2022-04-28Updated: 2022-04-28
Faculty
Medizin
Publisher
Philipps-Universität Marburg
Language
ger
Data types
DoctoralThesis
DDC-Numbers
610
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Rusu, Alexandru: Prospektive Risikostratifizierung von COVID-19-Patienten auf der Basis eines KI-basierten CT-Algorithmus. : Philipps-Universität Marburg 2022-04-28. DOI: https://doi.org/10.17192/z2022.0180.