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使用简化临床数据算法识别以脂蛋白代谢失调为特征的脓毒症亚表型

IDENTIFYING A SEPSIS SUBPHENOTYPE CHARACTERIZED BY DYSREGULATED LIPOPROTEIN METABOLISM USING A SIMPLIFIED CLINICAL DATA ALGORITHM.

作者信息

Labilloy Guillaume, Tanaka Sébastien, Black Lauren Page, Augustin Beulah, Hopson Charlotte, Bethencourt Joanne, Wu Dongyuan, Sulaiman Dawoud, Bertrand Andrew, Salomão Reinaldo, Graim Kiley, Datta Susmita, Reddy Srinivasa, Guirgis Faheem W, Hofmaenner Daniel A

机构信息

UF Health Jacksonville, Center for Data Solutions, Jacksonville, Florida.

Department of Emergency Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois.

出版信息

Shock. 2025 Aug 1;64(2):218-225. doi: 10.1097/SHK.0000000000002605. Epub 2025 Apr 23.

Abstract

Background: Cholesterol metabolism is dysregulated in sepsis contributing to patient heterogeneity. Subphenotypes displaying lower lipoprotein levels and higher mortality (previously subphenotyped hypolipoprotein phenotype [HYPO]) or higher lipoprotein levels and lower mortality (previously subphenotyped normolipoprotein phenotype [NORMO]) were described. We developed a simplified clinical algorithm for bedside subphenotype recognition. Methods: We analyzed data from four prospective studies (internal dataset), focusing on HYPO and NORMO subphenotypes. A 1,000-tree random forest classifier and logistic regression models were built, using clinical features to predict subphenotypes. Performance was evaluated by comparing predictions to actual subphenotypes derived from a machine learning model. The model was applied to an external dataset of 281 patients from three French studies. Results: The internal cohort consisted of 386 patients (median age, 63 years; 46% female). Four clinical features (hepatic SOFA, cardiovascular SOFA, low [low-density lipoprotein cholesterol {LDL-C}] and high-density lipoprotein cholesterol [high-density lipoprotein cholesterol {HDL-C}]) predicted HYPO versus NORMO subphenotypes with an area under the receiver operating characteristic curve of 0.86, a sensitivity of 0.771, and a specificity of 0.779. In the internal dataset, 28-day mortality for HYPO versus NORMO patients was 26% versus 15%, and in the external cohort, 30% versus 10%. HYPO internal versus external dataset LDL-C levels were similar ( P = 0.99), but HDL-C ( P = 0.02) levels were different. Median NORMO internal versus external dataset LDL-C ( P = 0.99) and HDL-C ( P = 0.12) levels were similar. HYPO patients had lower LDL-C, HDL-C and total cholesterol than NORMO patients in both internal and external datasets. Conclusions: Our simplified clinical data algorithm may allow for bedside recognition of septic patients displaying lipid dysregulation subphenotypes. External validation is needed to verify these results.

摘要

背景

脓毒症时胆固醇代谢失调,导致患者存在异质性。已描述了两种亚表型,一种显示较低的脂蛋白水平和较高的死亡率(先前被亚表型化为低脂蛋白表型[HYPO]),另一种显示较高的脂蛋白水平和较低的死亡率(先前被亚表型化为正常脂蛋白表型[NORMO])。我们开发了一种用于床旁亚表型识别的简化临床算法。方法:我们分析了四项前瞻性研究(内部数据集)的数据,重点关注HYPO和NORMO亚表型。使用临床特征构建了一个1000棵树的随机森林分类器和逻辑回归模型,以预测亚表型。通过将预测结果与源自机器学习模型的实际亚表型进行比较来评估性能。该模型应用于来自三项法国研究的281例患者的外部数据集。结果:内部队列由386例患者组成(中位年龄63岁;46%为女性)。四个临床特征(肝脏序贯器官衰竭评估[SOFA]、心血管SOFA、低[低密度脂蛋白胆固醇{LDL-C}]和高密度脂蛋白胆固醇[高密度脂蛋白胆固醇{HDL-C}])预测HYPO与NORMO亚表型,受试者工作特征曲线下面积为0.86,灵敏度为0.771,特异性为0.779。在内部数据集中,HYPO患者与NORMO患者的28天死亡率分别为26%和15%,在外部队列中分别为30%和10%。HYPO内部与外部数据集的LDL-C水平相似(P = 0.99),但HDL-C水平不同(P = 0.02)。NORMO内部与外部数据集的LDL-C(P = 0.99)和HDL-C(P = 0.12)中位水平相似。在内部和外部数据集中,HYPO患者的LDL-C、HDL-C和总胆固醇均低于NORMO患者。结论:我们的简化临床数据算法可能有助于在床旁识别表现出脂质代谢失调亚表型的脓毒症患者。需要进行外部验证以证实这些结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93c4/12278747/4a75fe8e7749/shock-64-218-g001.jpg

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