Early identification of pneumonia patients at increased risk of Middle East respiratory syndrome coronavirus infection in Saudi Arabia

• Main Authors: Anwar E. Ahmed, Hamdan Al-Jahdali, Abeer N. Alshukairi, Mody Alaqeel, Salma S. Siddiq, Hanan Alsaab, Ezzeldin A. Sakr, Hamed A. Alyahya, Munzir M. Alandonisi, Alaa T. Subedar, Nouf M. Aloudah, Salim Baharoon, Majid A. Alsalamah, Sameera Al Johani, Mohammed G. Alghamdi
• Format: Article
• Language: English
• Published: Elsevier 2018-05-01
• Series: International Journal of Infectious Diseases
• Online Access: http://www.sciencedirect.com/science/article/pii/S120197121830064X

Background: 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. Methods: 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. Results: 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. Conclusions: 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. Keywords: Pneumonia, MERS-CoV case definitions, Early diagnosis, Saudi Arabia