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

Ahmed, Anwar E.; Al-Jahdali, Hamdan; Alshukairi, Abeer N.; Alaqeel, Mody; Siddiq, Salma S.; Alsaab, Hanan; Sakr, Ezzeldin A.; Alyahya, Hamed A.; Alandonisi, Munzir M.; Subedar, Alaa T.; Aloudah, Nouf M.; Baharoon, Salim; Alsalamah, Majid A.; Al Johani, Sameera; Alghamdi, Mohammed G.

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.