Farhat Imrana, Rosolowski Maciej, Ahrens Katharina, Lienau Jasmin, Ahnert Peter, Pletz Mathias, Rohde Gernot, Rupp Jan, Scholz Markus, Witzenrath Martin
University of Leipzig, Institute for Medical Informatics, Statistics and Epidemiology, Leipzig, Germany.
Charité - Universitaetsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases, Respiratory Medicine and Critical Care, Berlin, Germany.
ERJ Open Res. 2024 Dec 2;10(6). doi: 10.1183/23120541.00420-2024. eCollection 2024 Nov.
Community-acquired pneumonia (CAP) remains a leading cause of infectious disease mortality globally, necessitating intensive care unit (ICU) admission for ∼10% of hospitalised patients. Accurate prediction of disease severity facilitates timely therapeutic interventions.
Our study aimed to enhance the predictive capacity of the clinical CRB-65 score by evaluating eight candidate biomarkers: troponin T high-sensitive (TnT-hs), procalcitonin (PCT), N-terminal pro-brain natriuretic peptide, angiopoietin-2, copeptin, endothelin-1, lipocalin-2 and mid-regional pro-adrenomedullin. We utilised a machine-learning approach on 800 samples from the German CAPNETZ network (competence network for CAP) to refine risk prediction models combining these biomarkers with the CRB-65 score regarding our defined end-point: death or ICU admission during the current CAP episode within 28 days after study inclusion.
Elevated levels of biomarkers were associated with the end-point. TnT-hs exhibited the highest predictive performance among individual features (area under the receiver operating characteristic curve, AUC=0.74), followed closely by PCT (AUC=0.73). Combining biomarkers with the CRB-65 score significantly improved prediction accuracy. The combined model of CRB-65, TnT-hs and PCT demonstrated the best balance between high predictive value and parsimony, with an AUC of 0.77 (95% CI: 0.72-0.82), while CRB-65 alone achieved an AUC of 0.67 (95% CI: 0.64-0.73).
Our findings suggest that augmenting the CRB-65 score with TnT-hs and PCT enhances the prediction of death or ICU admission in hospitalised CAP patients. Validation of this improved risk score in additional CAP cohorts and prospective clinical studies is warranted to assess its broad clinical utility.
社区获得性肺炎（CAP）仍是全球传染病死亡的主要原因，约10%的住院患者需要入住重症监护病房（ICU）。准确预测疾病严重程度有助于及时进行治疗干预。
我们的研究旨在通过评估八种候选生物标志物来提高临床CRB-65评分的预测能力：高敏肌钙蛋白T（TnT-hs）、降钙素原（PCT）、N末端脑钠肽前体、血管生成素-2、 copeptin、内皮素-1、脂质运载蛋白-2和中段肾上腺髓质素原。我们对来自德国CAPNETZ网络（CAP能力网络）的800个样本采用机器学习方法，以完善风险预测模型，将这些生物标志物与CRB-65评分相结合，以确定我们定义的终点：在纳入研究后28天内当前CAP发作期间死亡或入住ICU。
生物标志物水平升高与终点相关。TnT-hs在个体特征中表现出最高的预测性能（受试者工作特征曲线下面积，AUC=0.74），紧随其后的是PCT（AUC=0.73）。将生物标志物与CRB-65评分相结合显著提高了预测准确性。CRB-65、TnT-hs和PCT的联合模型在高预测价值和简约性之间表现出最佳平衡，AUC为0.77（95%CI：0.72-0.82），而单独的CRB-65的AUC为0.67（95%CI：0.64-0.73）。
我们的研究结果表明，用TnT-hs和PCT增加CRB-65评分可提高住院CAP患者死亡或入住ICU的预测能力。有必要在其他CAP队列和前瞻性临床研究中验证这种改进的风险评分，以评估其广泛的临床应用价值。
