Research and Innovation Department, Portsmouth Hospitals University National Health Service (NHS) Trust, Portsmouth, United Kingdom.
School of Pharmacy & Biomedical Science, University of Portsmouth, Portsmouth, United Kingdom.
Front Immunol. 2023 Sep 5;14:1192765. doi: 10.3389/fimmu.2023.1192765. eCollection 2023.
Clinical triage in coronavirus disease 2019 (COVID-19) places a heavy burden on senior clinicians during a pandemic situation. However, risk stratification based on serum biomarker bioprofiling could be implemented by a larger, nonspecialist workforce.
Measures of Complement Activation and inflammation in patientS with CoronAvirus DisEase 2019 (CASCADE) patients ( = 72), (clinicaltrials.gov: NCT04453527), classified as mild, moderate, or severe (by support needed to maintain SpO > 93%), and healthy controls (HC, = 20), were bioprofiled using 76 immunological biomarkers and compared using ANOVA. Spearman correlation analysis on biomarker pairs was visualised heatmaps. Linear Discriminant Analysis (LDA) models were generated to identify patients likely to deteriorate. An X-Gradient-boost (XGB) model trained on CASCADE data to triage patients as mild, moderate, and severe was retrospectively employed to classify COROnavirus Nomacopan Emergency Treatment for covid 19 infected patients with early signs of respiratory distress (CORONET) patients ( = 7) treated with nomacopan.
The LDA models distinctly discriminated between deteriorators, nondeteriorators, and HC, with IL-27, IP-10, MDC, ferritin, C5, and sC5b-9 among the key predictor variables during deterioration. C3a and C5 were elevated in all severity classes vs. HC ( < 0.05). sC5b-9 was elevated in the "moderate" and "severe" categories vs. HC ( < 0.001). Heatmap analysis shows a pairwise increase of negatively correlated pairs with IL-27. The XGB model indicated sC5b-9, IL-8, MCP1, and prothrombin F1 and F2 were key discriminators in nomacopan-treated patients (CORONET study).
Distinct immunological fingerprints from serum biomarkers exist within different severity classes of COVID-19, and harnessing them using machine learning enabled the development of clinically useful triage and prognostic tools. Complement-mediated lung injury plays a key role in COVID-19 pneumonia, and preliminary results hint at the usefulness of a C5 inhibitor in COVID-19 recovery.
在大流行期间，2019 年冠状病毒病（COVID-19）的临床分诊给资深临床医生带来了沉重的负担。然而，通过更大的、非专业人员的劳动力，基于血清生物标志物的风险分层是可以实现的。
使用 76 种免疫生物标志物对 Measures of Complement Activation and inflammation in patientS with CoronAvirus DisEase 2019 (CASCADE) 患者（n = 72）（临床试验.gov：NCT04453527）进行生物特征分析，这些患者被分为轻度、中度或重度（通过维持 SpO > 93%所需的支持来分类）和健康对照（n = 20），并使用 ANOVA 进行比较。对生物标志物对进行 Spearman 相关性分析，并以热图可视化。生成线性判别分析（LDA）模型以识别可能恶化的患者。使用基于 CASCADE 数据训练的 X-Gradient-boost（XGB）模型对 7 名接受 nomacopan 治疗的有早期呼吸窘迫迹象的冠状病毒 Nomacopan 紧急治疗新冠感染患者（CORONET）患者进行分诊，将患者分为轻度、中度和重度。
LDA 模型明确区分了恶化者、非恶化者和 HC，IL-27、IP-10、MDC、铁蛋白、C5 和 sC5b-9 是恶化过程中的关键预测变量。与 HC 相比，所有严重程度类别中的 C3a 和 C5 均升高（<0.05）。sC5b-9 在“中度”和“重度”类别中均高于 HC（<0.001）。热图分析显示，与 IL-27 呈负相关的成对物呈增加趋势。XGB 模型表明，sC5b-9、IL-8、MCP1 和凝血酶原 F1 和 F2 是 nomacopan 治疗患者（CORONET 研究）的关键鉴别指标。
COVID-19 不同严重程度类别中存在不同的血清生物标志物的免疫学特征，利用机器学习对其进行分析可以开发出具有临床应用价值的分诊和预后工具。补体介导的肺损伤在 COVID-19 肺炎中起着关键作用，初步结果表明 C5 抑制剂在 COVID-19 康复中的有用性。
