Clin Lab. 2022 Nov 1;68(11). doi: 10.7754/Clin.Lab.2022.211255.
As a serious and common out-of-hospital infectious disease, community-acquired pneumonia (CAP) ranks among the leading causes of death in both developing and developed countries. In recent years, the increasing incidence of CAP has led to an increase in the number of hospitalizations. Although CURB-65 (or CRB-65) and pneumonia severity Index (PSI) scoring systems are widely used in CAP prognostic scoring systems, each score had some limitations in predicting whether patients with CAP would require prolonged hospitalization. The aim of this study was to analyze serum inflammatory biomarkers combined with age to establish a novel predictive model for predicting prolonged hospitalization in patients with CAP.
In a retrospective study, serum inflammatory biomarkers were collected from all enrolled CAP patients, including white blood cell count (WBC), high-sensitivity C-reactive protein (hs-CRP), erythrocyte sedimentation rate (ESR), D-dimer, procalcitonin (PCT), fibrinogen (FIB), and ICU treatment. Length of hospital stay and age were also recorded. The 75th percentile of length of stay in the enrolled population was defined as long hospitalization over time, and the primary predictor of outcome was prolonged hospitalization. Univariate analysis and binary logistic regression analysis were used to explore the independent risk factors which could be components of a new predicting model for prolonged hospitalization in CAP patients. ROC curves were used to evaluate the sensitivity and specificity of the new model, which consisted of the combination of all independent risk factors in predicting the main outcomes.
The results showed that among 364 patients with CAP, 85 had extended hospitalization (85/364). Further analysis showed that age, white blood cell, fibrinogen, and high-sensitivity C-reactive protein were independent risk factors for extended hospitalization in patients with CAP. Finally, the AUC of the ROC curve of the new prediction model (the joint model consists of age, WBC, FIB, and hs-CRP) was 0.93 (95% CI 0.90 - 0.96), and the sensitivity and specificity were 87.1% and 87.8%, respectively.
Serum inflammatory biomarkers combined age have high specificity and sensitivity in predicting prolonged hospitalization in adult CAP patients.
社区获得性肺炎（CAP）是一种严重且常见的院外感染性疾病，在发展中国家和发达国家均为主要致死病因之一。近年来，CAP 的发病率不断上升，导致住院人数增加。尽管 CURB-65（或 CRB-65）和肺炎严重指数（PSI）评分系统广泛应用于 CAP 预后评分系统，但每种评分系统在预测 CAP 患者是否需要延长住院时间方面都存在一定的局限性。本研究旨在分析血清炎症标志物并结合年龄，建立一种新的预测模型，以预测 CAP 患者的延长住院时间。
本回顾性研究收集了所有入组 CAP 患者的血清炎症标志物，包括白细胞计数（WBC）、高敏 C 反应蛋白（hs-CRP）、红细胞沉降率（ESR）、D-二聚体、降钙素原（PCT）、纤维蛋白原（FIB）和 ICU 治疗。同时记录住院时间和年龄。将入组人群的住院时间第 75 百分位数定义为长时间住院，主要结局预测指标为延长住院时间。采用单因素分析和二元逻辑回归分析探讨延长住院时间的独立危险因素，这些因素可能是 CAP 患者延长住院时间的新预测模型的组成部分。ROC 曲线用于评估新模型预测主要结局的灵敏度和特异性，该模型由所有独立危险因素组合而成。
在 364 例 CAP 患者中，85 例患者延长了住院时间（85/364）。进一步分析显示，年龄、白细胞、纤维蛋白原和高敏 C 反应蛋白是 CAP 患者延长住院时间的独立危险因素。最终，新预测模型（由年龄、WBC、FIB 和 hs-CRP 组成的联合模型）的 ROC 曲线下面积为 0.93（95%CI 0.90-0.96），灵敏度和特异性分别为 87.1%和 87.8%。
血清炎症标志物联合年龄对预测成人 CAP 患者延长住院时间具有较高的特异性和灵敏度。
