Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, 610041, China.
Department of Critical Care Medicine, Affiliated Hospital of Chengdu University, Chengdu, Sichuan Province, 610081, China.
BMC Pulm Med. 2024 Oct 3;24(1):483. doi: 10.1186/s12890-024-03299-w.
Lung phenotypes have been extensively utilized to assess lung injury and guide precise treatment. However, current phenotypic evaluation methods rely on CT scans and other techniques. Although lung ultrasound (LUS) is widely employed in critically ill patients, there is a lack of comprehensive and systematic identification of LUS phenotypes based on clinical data and assessment of their clinical value.
Our study was based on a retrospective database. A total of 821 patients were included from September 2019 to October 2020. 1902 LUS examinations were performed in this period. Using a dataset of 55 LUS examinations focused on lung injuries, a group of experts developed an algorithm for classifying LUS phenotypes based on clinical practice, expert experience, and lecture review. This algorithm underwent validation and refinement with an additional 140 LUS images, leading to five iterative revisions and the generation of 1902 distinct LUS phenotypes. Subsequently, a validated machine learning algorithm was applied to these phenotypes. To assess the algorithm's effectiveness, experts manually verified 30% of the phenotypes, confirming its efficacy. Using K-means cluster analysis and expert image selection from the 1902 LUS examinations, we established seven distinct LUS phenotypes. To further explore the diagnostic value of these phenotypes for clinical diagnosis, we investigated their auxiliary diagnostic capabilities.
A total of 1902 LUS phenotypes were tested by randomly selecting 30% to verify the phenotypic accuracy. With the 1902 LUS phenotypes, seven lung ultrasound phenotypes were established through statistical K-means cluster analysis and expert screening. The acute respiratory distress syndrome (ARDS) exhibited gravity-dependent phenotypes, while the cardiogenic pulmonary edema exhibited nongravity phenotypes. The baseline characteristics of the 821 patients included age (66.14 ± 11.76), sex (560/321), heart rate (96.99 ± 23.75), mean arterial pressure (86.5 ± 13.57), Acute Physiology and Chronic Health Evaluation II (APACHE II)score (20.49 ± 8.60), and duration of ICU stay (24.50 ± 26.22); among the 821 patients, 78.8% were cured. In severe pneumonia patients, the gravity-dependent phenotype accounted for 42% of the cases, whereas the nongravity-dependent phenotype constituted 58%. These findings highlight the value of applying different LUS phenotypes in various diagnoses.
Seven sets of LUS phenotypes were established through machine learning analysis of retrospective data; these phenotypes could represent the typical characteristics of patients with different types of critical illness.
肺表型已被广泛用于评估肺损伤并指导精准治疗。然而，目前的表型评估方法依赖于 CT 扫描和其他技术。尽管肺部超声（LUS）在危重症患者中得到广泛应用，但缺乏基于临床数据的全面、系统的 LUS 表型识别以及对其临床价值的评估。
本研究基于回顾性数据库。纳入 2019 年 9 月至 2020 年 10 月期间的 821 例患者。在此期间共进行了 1902 次 LUS 检查。使用 55 次专注于肺损伤的 LUS 检查数据集，一组专家根据临床实践、专家经验和讲座回顾制定了 LUS 表型分类算法。该算法经过另外 140 次 LUS 图像的验证和改进，经过五次迭代修订，生成了 1902 种不同的 LUS 表型。随后，应用经过验证的机器学习算法对这些表型进行分析。为了评估算法的有效性，专家手动验证了 30%的表型，确认了其有效性。使用 K-means 聚类分析和从 1902 次 LUS 检查中选择的专家图像，我们建立了七个不同的 LUS 表型。为了进一步探讨这些表型对临床诊断的辅助诊断价值，我们研究了它们的辅助诊断能力。
通过随机选择 30%的表型进行验证，对 1902 种 LUS 表型进行了测试。利用 1902 种 LUS 表型，通过统计 K-means 聚类分析和专家筛选，建立了七种肺部超声表型。急性呼吸窘迫综合征（ARDS）表现为重力依赖性表型，而心源性肺水肿表现为非重力依赖性表型。821 例患者的基线特征包括年龄（66.14±11.76）、性别（560/321）、心率（96.99±23.75）、平均动脉压（86.5±13.57）、急性生理学和慢性健康评估 II 评分（20.49±8.60）和 ICU 住院时间（24.50±26.22）；821 例患者中，78.8%治愈。在严重肺炎患者中，重力依赖性表型占 42%，非重力依赖性表型占 58%。这些发现突出了在不同诊断中应用不同 LUS 表型的价值。
通过对回顾性数据进行机器学习分析，建立了七组 LUS 表型；这些表型可以代表不同类型危重症患者的典型特征。
