Development and validation of a simple tool composed of items on dyspnea, respiration rates, and C-reactive protein for pneumonia prediction among acute febrile respiratory illness patients in primary care settings.
Development and validation of a simple tool composed of items on dyspnea, respiration rates, and C-reactive protein for pneumonia prediction among acute febrile respiratory illness patients in primary care settings.
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DOI：
10.1186/s12916-022-02552-5
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发表时间：
2022-10-18
期刊：
影响因子：
9.3
通讯作者：
Zhang, Min
中科院分区：
文献类型：
--
作者：
Ding, Fengming;Han, Lei;Yin, Dongning;Zhou, Yan;Ji, Yong;Zhang, Pengyu;Wu, Wensheng;Chen, Jijing;Wang, Zufang;Fan, Xinxin;Zhang, Guoqing;Zhang, Min
急性发热性呼吸道疾病（AFRI）患者易患肺炎，并且在世界范围内具有显著的发病率和死亡率。在初级保健机构，情况更糟。由于计算机断层扫描资源和医生经验的限制，AFRI患者在初级保健环境中可能无法得到适当的诊断，这将影响后续治疗。在这项研究中，我们的目的是开发和验证一个简单的预测模型，以帮助医生快速识别AFRI患者的肺炎风险在初级保健设置。共有1977名AFRI患者在中国上海的两个发热诊所入组，其中727名接受CT扫描的患者被纳入分析。急性肺泡或间质浸润CT图像上发现诊断为肺炎。比较肺炎和非肺炎患者的特征和血液参数。通过Logistic回归分析建立了肺炎的多因素预测模型。在外部多中心人群中前瞻性评估了其对肺炎的预测价值，该人群包括来自中国5个不同省份的1299名AFRI患者。在模型开发人群中，肺炎患者（n = 227）与非肺炎患者（n = 500）相比，发热持续时间更长;脓痰、呼吸困难和胸痛的频率更高;呼吸频率和C反应蛋白（CRP）水平更高。Logistic回归分析建立了以呼吸困难、呼吸频率> 20次/min、CRP > 20 mg/l（DRC）为指标的肺炎预测模型，其曲线下面积（AUC）为0.8506。在外部验证人群中，当选择至少一个阳性项目（1分）作为临界点时，DRC模型的预测准确性最高，灵敏度为87.0%，特异性为80.5%。DRC评分随着肺炎严重程度和肺叶受累而增加，并且对细菌性和病毒性肺炎均表现出良好的表现。对于病毒性肺炎，无论CRP浓度如何，呼吸困难加呼吸频率> 20/min具有良好的预测能力。DRC模型是一个简单的预测AFRI患者肺炎的工具，这将有助于医生在基层医疗机构合理利用医疗资源。
Acute febrile respiratory illness (AFRI) patients are susceptible to pneumonia and suffer from significant morbidity and mortality throughout the world. In primary care settings, the situation is worse. Limited by computerized tomography resources and physician experiences, AFRI patients in primary care settings may not be diagnosed appropriately, which would affect following treatment. In this study, we aimed to develop and validate a simple prediction model to help physicians quickly identify AFRI patients of pneumonia risk in primary care settings. A total of 1977 AFRI patients were enrolled at two fever clinics in Shanghai, China, and among them, 727 patients who underwent CT scans were included in the analysis. Acute alveolar or interstitial infiltrates found on CT images were diagnosed with pneumonia. Characteristics and blood parameters were compared between pneumonia and non-pneumonia patients. Then a multivariable model for pneumonia prediction was developed through logistic regression analysis. Its value for pneumonia prediction was prospectively assessed in an external multi-center population, which included 1299 AFRI patients in primary settings from 5 different provinces throughout China. In the model development population, pneumonia patients (n = 227) had a longer duration of fever; higher frequencies of purulent sputum, dyspnea, and thoracic pain; and higher levels of respiration rates and C-reactive protein (CRP) than non-pneumonia patients (n = 500). Logistic regression analysis worked out a model composed of items on dyspnea, respiration rates > 20/min, and CRP > 20 mg/l (DRC) for pneumonia prediction with an area under curve (AUC) of 0.8506. In the external validation population, the predictive accuracy of the DRC model was the highest when choosing at least one positive item (1 score) as a cut-off point with a sensitivity of 87.0% and specificity of 80.5%. DRC scores increased with pneumonia severity and lung lobe involvement and showed good performance for both bacterial and viral pneumonia. For viral pneumonia, dyspnea plus respiration rates > 20/min had good predictive capacity regardless of CRP concentration. DRC model is a simple tool that predicts pneumonia among AFRI patients, which would help physicians utilize medical resources rationally in primary care settings.
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