Department of Physical Medicine and Rehabilitation, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Republic of Korea.
Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
Sci Rep. 2024 Jul 23;14(1):16912. doi: 10.1038/s41598-024-67625-2.
Severe pneumonia results in high morbidity and mortality despite advanced treatments. This study investigates thoracic muscle mass from chest CT scans as a biomarker for predicting clinical outcomes in ICU patients with severe pneumonia. Analyzing electronic medical records and chest CT scans of 778 ICU patients with severe community-acquired pneumonia from January 2016 to December 2021, AI-enhanced 3D segmentation was used to assess thoracic muscle mass. Patients were categorized into clusters based on muscle mass profiles derived from CT scans, and their effects on clinical outcomes such as extubation success and in-hospital mortality were assessed. The study identified three clusters, showing that higher muscle mass (Cluster 1) correlated with lower in-hospital mortality (8% vs. 29% in Cluster 3) and improved clinical outcomes like extubation success. The model integrating muscle mass metrics outperformed conventional scores, with an AUC of 0.844 for predicting extubation success and 0.696 for predicting mortality. These findings highlight the strong predictive capacity of muscle mass evaluation over indices such as APACHE II and SOFA. Using AI to analyze thoracic muscle mass via chest CT provides a promising prognostic approach in severe pneumonia, advocating for its integration into clinical practice for better outcome predictions and personalized patient management.
尽管有先进的治疗方法，严重肺炎仍会导致高发病率和死亡率。本研究通过胸部 CT 扫描调查胸肌量作为 ICU 重症肺炎患者预测临床结局的生物标志物。分析了 2016 年 1 月至 2021 年 12 月期间 778 例 ICU 重症社区获得性肺炎患者的电子病历和胸部 CT 扫描，采用人工智能增强 3D 分割来评估胸肌量。根据 CT 扫描得出的肌肉量图谱，患者分为不同的簇，评估其对临床结局（如拔管成功率和住院死亡率）的影响。研究确定了三个簇，表明更高的肌肉量（簇 1）与较低的住院死亡率（簇 3 中为 29%，簇 1 中为 8%）和更好的临床结局（如拔管成功率）相关。整合肌肉量指标的模型优于常规评分，对拔管成功率的预测 AUC 为 0.844，对死亡率的预测 AUC 为 0.696。这些发现强调了肌肉量评估比 APACHE II 和 SOFA 等指数具有更强的预测能力。使用人工智能通过胸部 CT 分析胸肌量为严重肺炎提供了一种有前途的预后方法，提倡将其纳入临床实践，以更好地预测结果和进行个性化患者管理。
