基于血管生成素样蛋白 4 表达水平的列线图预测社区获得性肺炎严重程度。
A nomogram based on the expression level of angiopoietin-like 4 to predict the severity of community-acquired pneumonia.
机构信息
Pulmonary and Critical Care Medicine, Shunde Hospital, Southern Medical University, No.1, Jiazi Road, Lunjiao Street, Shunde District, Foshan, 528300, China.
Pulmonary and Critical Care Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
出版信息
BMC Infect Dis. 2023 Oct 11;23(1):677. doi: 10.1186/s12879-023-08648-4.
BACKGROUND
The morbidity and mortality of community-acquired pneumonia (CAP) remain high among infectious diseases. It was reported that angiopoietin-like 4 (ANGPTL4) could be a diagnostic biomarker and a therapeutic target for pneumonia. This study aimed to develop a more objective, specific, accurate, and individualized scoring system to predict the severity of CAP.
METHODS
Totally, 31 non-severe community-acquired pneumonia (nsCAP) patients and 14 severe community-acquired pneumonia (sCAP) patients were enrolled in this study. The CURB-65 and pneumonia severity index (PSI) scores were calculated from the clinical data. Serum ANGPTL4 level was measured by enzyme-linked immunosorbent assay (ELISA). After screening factors by univariate analysis and receiver operating characteristic (ROC) curve analysis, multivariate logistic regression analysis of ANGPTL4 expression level and other risk factors was performed, and a nomogram was developed to predict the severity of CAP. This nomogram was further internally validated by bootstrap resampling with 1000 replications through the area under the ROC curve (AUC), the calibration curve, and the decision curve analysis (DCA). Finally, the prediction performance of the new nomogram model, CURB-65 score, and PSI score was compared by AUC, net reclassification index (NRI), and integrated discrimination improvement (IDI).
RESULTS
A nomogram for predicting the severity of CAP was developed using three factors (C-reactive protein (CRP), procalcitonin (PCT), and ANGPTL4). According to the internal validation, the nomogram showed a great discrimination capability with an AUC of 0.910. The Hosmer-Lemeshow test and the approximately fitting calibration curve suggested a satisfactory accuracy of prediction. The results of DCA exhibited a great net benefit. The AUC values of CURB-65 score, PSI score, and the new prediction model were 0.857, 0.912, and 0.940, respectively. NRI comparing the new model with CURB-65 score was found to be statistically significant (NRI = 0.834, P < 0.05).
CONCLUSION
A robust model for predicting the severity of CAP was developed based on the serum ANGPTL4 level. This may provide new insights into accurate assessment of the severity of CAP and its targeted therapy, particularly in the early-stage of the disease.
背景
社区获得性肺炎（CAP）的发病率和死亡率在传染病中仍然很高。有报道称，血管生成素样 4（ANGPTL4）可以作为肺炎的诊断生物标志物和治疗靶点。本研究旨在开发一种更客观、更特异、更准确和个体化的评分系统来预测 CAP 的严重程度。
方法
本研究共纳入 31 例非重症社区获得性肺炎（nsCAP）患者和 14 例重症社区获得性肺炎（sCAP）患者。根据临床资料计算 CURB-65 和肺炎严重指数（PSI）评分。采用酶联免疫吸附试验（ELISA）检测血清 ANGPTL4 水平。通过单因素分析和受试者工作特征（ROC）曲线分析筛选因素后，对 ANGPTL4 表达水平及其他危险因素进行多因素 logistic 回归分析，并建立预测 CAP 严重程度的列线图。通过 1000 次重复的 ROC 曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）对该列线图进行内部验证。最后，通过 AUC、净重新分类指数（NRI）和综合判别改善指数（IDI）比较新列线图模型、CURB-65 评分和 PSI 评分的预测性能。
结果
使用三个因素（C 反应蛋白（CRP）、降钙素原（PCT）和 ANGPTL4）建立了预测 CAP 严重程度的列线图。内部验证显示，该列线图具有较高的区分能力，AUC 为 0.910。Hosmer-Lemeshow 检验和近似拟合校准曲线提示预测准确性较高。DCA 结果显示具有较大的净获益。CURB-65 评分、PSI 评分和新预测模型的 AUC 值分别为 0.857、0.912 和 0.940。新模型与 CURB-65 评分比较的 NRI 有统计学意义（NRI=0.834，P<0.05）。
结论
基于血清 ANGPTL4 水平建立了预测 CAP 严重程度的稳健模型。这可能为 CAP 严重程度的准确评估及其靶向治疗提供新的思路，特别是在疾病的早期阶段。
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