King Abdullah International Medical Research Center (KAIMRC), King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), King Abdulaziz Medical City (KAMC), Ministry of National Guard - Health Affairs, Riyadh 11426, Saudi Arabia.
King Faisal Specialist Hospital and Research Centre, Jeddah, Saudi Arabia.
Int J Infect Dis. 2018 May;70:51-56. doi: 10.1016/j.ijid.2018.03.005. Epub 2018 Mar 14.
The rapid and accurate identification of individuals who are at high risk of Middle East respiratory syndrome coronavirus (MERS-CoV) infection remains a major challenge for the medical and scientific communities. The aim of this study was to develop and validate a risk prediction model for the screening of suspected cases of MERS-CoV infection in patients who have developed pneumonia.
A two-center, retrospective case-control study was performed. A total of 360 patients with confirmed pneumonia who were evaluated for MERS-CoV infection by real-time reverse transcription polymerase chain reaction (rRT-PCR) between September 1, 2012 and June 1, 2016 at King Abdulaziz Medical City in Riyadh and King Fahad General Hospital in Jeddah, were included. According to the rRT-PCR results, 135 patients were positive for MERS-CoV and 225 were negative. Demographic characteristics, clinical presentations, and radiological and laboratory findings were collected for each subject.
A risk prediction model to identify pneumonia patients at increased risk of MERS-CoV was developed. The model included male sex, contact with a sick patient or camel, diabetes, severe illness, low white blood cell (WBC) count, low alanine aminotransferase (ALT), and high aspartate aminotransferase (AST). The model performed well in predicting MERS-CoV infection (area under the receiver operating characteristics curves (AUC) 0.8162), on internal validation (AUC 0.8037), and on a goodness-of-fit test (p=0.592). The risk prediction model, which produced an optimal probability cut-off of 0.33, had a sensitivity of 0.716 and specificity of 0.783.
This study provides a simple, practical, and valid algorithm to identify pneumonia patients at increased risk of MERS-CoV infection. This risk prediction model could be useful for the early identification of patients at the highest risk of MERS-CoV infection. Further validation of the prediction model on a large prospective cohort of representative patients with pneumonia is necessary.
快速准确地识别中东呼吸综合征冠状病毒（MERS-CoV）感染高危个体仍然是医学和科学界面临的重大挑战。本研究旨在开发和验证一种预测模型，以筛选发生肺炎的疑似 MERS-CoV 感染患者。
这是一项在利雅得阿卜杜勒阿齐兹国王医疗城和吉达法赫德国王综合医院进行的、为期 2 年的回顾性病例对照研究。共纳入 2012 年 9 月 1 日至 2016 年 6 月 1 日期间，因疑似 MERS-CoV 感染而接受实时逆转录聚合酶链反应（rRT-PCR）检测的 360 例确诊肺炎患者。根据 rRT-PCR 结果，135 例患者 MERS-CoV 阳性，225 例患者 MERS-CoV 阴性。收集每位患者的人口统计学特征、临床表现、影像学和实验室检查结果。
我们建立了一个预测模型，用于识别发生肺炎的患者中 MERS-CoV 感染风险增加的患者。该模型包括男性、接触过患病患者或骆驼、糖尿病、严重疾病、白细胞计数低、丙氨酸转氨酶（ALT）低和天冬氨酸转氨酶（AST）高。该模型在预测 MERS-CoV 感染方面表现良好（接受者操作特征曲线下面积（AUC）0.8162），内部验证（AUC 0.8037）和拟合优度检验（p=0.592）。该风险预测模型的最佳概率截断值为 0.33，其敏感性为 0.716，特异性为 0.783。
本研究提供了一种简单、实用和有效的算法，用于识别发生肺炎的患者中 MERS-CoV 感染风险增加的患者。该风险预测模型可用于早期识别 MERS-CoV 感染风险最高的患者。有必要在具有代表性的肺炎大样本前瞻性队列中进一步验证该预测模型。
