心力衰竭再入院风险的肺部超声生物标志物预后价值：一项试点数据驱动分析
Prognostic Value of Lung Ultrasound Biomarkers for Readmission Risk in Congestive Heart Failure: A Pilot Data-Driven Analysis
AI总结：
本研究通过数据驱动方法利用住院期间获得的B型肺部超声（LUS）数据，预测30天内心力衰竭再入院风险，发现依赖性下肺区域、时间差特征以及多视图特征拼接在预测中表现最佳，展示了超声生物标志物在非侵入性心力衰竭风险分层中的实用性。
摘要：
住院后30天内再入院是心力衰竭（CHF）导致发病率、死亡率和可避免医疗支出的主要驱动因素。当前的临床风险分层工具主要依赖于非成像数据，且预测性能有限。床旁肺部超声（LUS）提供了一个敏感的、非侵入性的窗口，以观察肺部充血，这特征于CHF失代偿，但其用于再入院预测的预后作用仍待探索。我们提出了一个试点可行性研究，这是首个系统使用住院期间获得的B型LUS进行机器学习预测30天内CHF再入院的系统研究。从预训练的Temporal Shift Module（TSM）ResNet-18编码器中提取定量时空嵌入，并分别评估可解释的生物标志物特征。通过结构化消融研究肺部视图、时间表示、多视图融合和跨肺增强，我们识别出驱动再入院风险的关键成像因素。我们的发现表明（1）依赖性下肺区域（左3、右3）携带最强的预后信号，与它们对静水性充血的更大易感性一致；（2）连续检查之间的时间差特征显著优于单时间点表示，突显了捕捉疾病轨迹的重要性；（3）多视图特征拼接产生了最佳整体性能，我们的最佳MLP模型实现了F1得分为0.80（95% CI: 0.62-0.96）。生物标志物分析进一步表明，胸膜线异常，包括断裂和凹陷，的信息量与传统A线和B线标志物相当。这些结果支持POCUS衍生的生物标志物作为实用、可解释的非侵入性CHF风险分层工具。
英文摘要：
Hospital readmission within 30 days of discharge is a leading driver of morbidity, mortality, and avoidable healthcare expenditure in congestive heart failure (CHF). Current clinical risk stratification tools rely primarily on non-imaging data and exhibit limited predictive performance. Point-of-care lung ultrasound (LUS) offers a sensitive, noninvasive window into the pulmonary congestion that characterizes CHF decompensation, yet its prognostic utility for readmission prediction remains largely unexplored. We present a pilot feasibility study, the first systematic machine learning study using B-mode LUS acquired during hospitalization to predict 30-day CHF readmission. Quantitative spatiotemporal embeddings are extracted from a pretrained Temporal Shift Module (TSM) ResNet-18 encoder, and interpretable biomarker features are separately evaluated. Through structured ablations over lung view, temporal representation, multi-view fusion, and cross-lung augmentation, we identify the key imaging factors driving readmission risk. Our findings reveal that (1) dependent lower-lung regions (Left-3, Right-3) carry the strongest prognostic signal, consistent with their greater susceptibility to hydrostatic congestion; (2) temporal difference features between sequential examinations substantially outperform single-timepoint representations, highlighting the importance of capturing disease trajectory; and (3) multi-view feature concatenation yields the best overall performance, with our top MLP model achieving an F1 score of 0.80 (95% CI: 0.62-0.96). Biomarker analysis further reveals that pleural-line abnormalities, including breaks and indentations, are as informative as the canonical A-line and B-line markers. These results support POCUS-derived biomarkers as practical, interpretable tools for noninvasive CHF risk stratification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
