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

IF 1.5 4区医学 Q2 MEDICINE, GENERAL & INTERNAL

Bratislava Medical Journal-Bratislavske Lekarske Listy Pub Date : 2024-01-01 DOI:10.4149/BLL_2024_25

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).

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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

Bratislava Medical Journal-Bratislavske Lekarske Listy 医学-医学：内科

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.