用于细菌性败血症和 COVID-19 的预后和免疫疗法指导的临床表型。

Q4 Medicine Critical care explorations Pub Date : 2024-09-10 eCollection Date: 2024-09-01 DOI:10.1097/CCE.0000000000001153
Eleni Karakike, Simeon Metallidis, Garyfallia Poulakou, Maria Kosmidou, Nikolaos K Gatselis, Vasileios Petrakis, Nikoletta Rovina, Eleni Gkeka, Styliani Sympardi, Ilias Papanikolaou, Ioannis Koutsodimitropoulos, Vasiliki Tzavara, Georgios Adamis, Konstantinos Tsiakos, Vasilios Koulouras, Eleni Mouloudi, Eleni Antoniadou, Gykeria Vlachogianni, Souzana Anisoglou, Nikolaos Markou, Antonia Koutsoukou, Periklis Panagopoulos, Haralampos Milionis, George N Dalekos, Miltiades Kyprianou, Evangelos J Giamarellos-Bourboulis
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引用次数: 0

摘要

目的:有研究表明,脓毒症可通过使用 29 种入院参数的算法分为四种临床表型。我们在细菌性败血症和严重 COVID-19 患者中应用了简化的表型算法,并评估了衍生表型的特征和预后:设计:对前瞻性临床研究数据的回顾性分析:环境:希腊重症监护室和内科:我们分析了 1498 例患者,其中 620 例为细菌性败血症患者,878 例为严重 COVID-19 患者。我们采用六参数算法(肌酐、乳酸、天门冬氨酸转氨酶、胆红素、C 反应蛋白和国际标准化比率)对细菌性败血症患者进行分类,并引入了之前定义的表型。随后对两项开放标签免疫疗法试验中的重症 COVID-19 患者进行了分类。对anakinra治疗效果的异质性进行了评估。主要结果为28天死亡率:该算法验证了细菌性败血症队列和纳入该队列的各项研究中存在的四种表型。表型α代表死亡风险低的年轻患者,β与高并发症相关,而δ则代表最高死亡率。即使在调整了夏尔森合并症指数后,表型分配仍与预后独立相关。严重COVID-19的表型分布和结果也遵循类似的模式:结论:一种简化的算法成功识别了之前得出的细菌性败血症表型,这些表型可预测预后。这种分类方法可能适用于严重 COVID-19 患者,并对预后有影响。
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Clinical Phenotyping for Prognosis and Immunotherapy Guidance in Bacterial Sepsis and COVID-19.

Objectives: It is suggested that sepsis may be classified into four clinical phenotypes, using an algorithm employing 29 admission parameters. We applied a simplified phenotyping algorithm among patients with bacterial sepsis and severe COVID-19 and assessed characteristics and outcomes of the derived phenotypes.

Design: Retrospective analysis of data from prospective clinical studies.

Setting: Greek ICUs and Internal Medicine departments.

Patients and interventions: We analyzed 1498 patients, 620 with bacterial sepsis and 878 with severe COVID-19. We implemented a six-parameter algorithm (creatinine, lactate, aspartate transaminase, bilirubin, C-reactive protein, and international normalized ratio) to classify patients with bacterial sepsis intro previously defined phenotypes. Patients with severe COVID-19, included in two open-label immunotherapy trials were subsequently classified. Heterogeneity of treatment effect of anakinra was assessed. The primary outcome was 28-day mortality.

Measurements and main results: The algorithm validated the presence of the four phenotypes across the cohort of bacterial sepsis and the individual studies included in this cohort. Phenotype α represented younger patients with low risk of death, β was associated with high comorbidity burden, and δ with the highest mortality. Phenotype assignment was independently associated with outcome, even after adjustment for Charlson Comorbidity Index. Phenotype distribution and outcomes in severe COVID-19 followed a similar pattern.

Conclusions: A simplified algorithm successfully identified previously derived phenotypes of bacterial sepsis, which were predictive of outcome. This classification may apply to patients with severe COVID-19 with prognostic implications.

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CiteScore
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