CT quantification of COVID-19 pneumonia extent to predict individualized outcome.

IF 1.5 4区 医学 Q2 MEDICINE, GENERAL & INTERNAL
Zuzana Berecova, Dominik Juskanic, Martin Hazlinger, Marek Uhnak, Pavol Janega, Maros Rudnay, Robert Hatala
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引用次数: 0

Abstract

Objectives:  This study aimed to predict individual COVID-19 patient prognosis at hospital admission using artificial intelligence (AI)-based quantification of computed tomography (CT) pulmonary involvement.

Background: Assessing patient prognosis in COVID-19 pneumonia is crucial for patient management and hospital and ICU organization.

Methods: We retrospectively analyzed 559 patients with PCR-verified COVID-19 pneumonia referred to the hospital for a severe disease course. We correlated the CT extent of pulmonary involvement with patient outcome. We also attempted to define cut-off values of pulmonary involvement for predicting different outcomes.

Results:  CT-based disease extent quantification is an independent predictor of patient morbidity and mortality, with the prognosis being impacted also by age and cardiovascular comorbidities. With the use of explored cut-off values, we divided patients into three groups based on their extent of disease: (1) less than 28 % (sensitivity 65.4 %; specificity 89.1 %), (2) ranging from 28 % (31 %) to 47 % (sensitivity 87.1 %; specificity 62.7 %), and (3) above 47 % (sensitivity 87.1 %; specificity, 62.7 %), representing low risk, risk for oxygen therapy and invasive pulmonary ventilation, and risk of death, respectively.

Conclusion: CT quantification of pulmonary involvement using AI-based software helps predict COVID-19 patient outcomes (Tab. 4, Fig. 4, Ref. 38).

通过 CT 量化 COVID-19 肺炎程度,预测个体化预后。
研究目的 本研究旨在利用基于人工智能(AI)的计算机断层扫描(CT)肺部受累量化技术,预测COVID-19患者入院时的个体预后:背景:评估COVID-19肺炎患者的预后对患者管理、医院和重症监护室的组织至关重要:我们回顾性分析了559例经PCR验证为COVID-19肺炎的重症转诊患者。我们将 CT 肺部受累程度与患者预后相关联。我们还试图确定肺部受累的临界值,以预测不同的结果: 结果:基于 CT 的疾病范围量化是预测患者发病率和死亡率的独立指标,年龄和心血管合并症也会影响预后。利用探索出的临界值,我们根据疾病范围将患者分为三组:(1) 小于 28 %(灵敏度 65.4 %;特异度 89.1 %),(2) 28 %(31 %)至 47 %(灵敏度 87.1 %;特异度 62.7 %),(3) 超过 47 %(灵敏度 87.1 %;特异度 62.7 %),分别代表低风险、氧治疗和有创肺通气风险以及死亡风险:结论:使用基于人工智能的软件对肺部受累进行 CT 定量有助于预测 COVID-19 患者的预后(表 4,图 4,参考文献 38)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.60
自引率
0.00%
发文量
185
审稿时长
3-8 weeks
期刊介绍: The international biomedical journal - Bratislava Medical Journal – Bratislavske lekarske listy (Bratisl Lek Listy/Bratisl Med J) publishes peer-reviewed articles on all aspects of biomedical sciences, including experimental investigations with clear clinical relevance, original clinical studies and review articles.
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