Publication Details

Title :

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

Journal:

International Journal of Infectious Diseases

Impact Factor:

2.532

Authors:

Ahmed AE1, Al-Jahdali H2, Alshukairi AN3, Alaqeel M4, Siddiq SS5, Alsaab H6, Sakr EA7, Alyahya HA8, Alandonisi MM9, Subedar AT10, Aloudah NM11, Baharoon S12, Alsalamah MA13, Al Johani S14, Alghamdi MG15.

Affiliations:

1 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. Electronic address: ahmeda5@vcu.edu.

2 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. Electronic address: jahdalih@gamil.com.

3 King Faisal Specialist Hospital and Research Centre, Jeddah, Saudi Arabia. Electronic address: Abeer.Alshukairi@gmail.com.

4 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. Electronic address: modyalaqeel@gmail.com.

5 King Fahad General Hospital, Jeddah, Saudi Arabia. Electronic address: salmasiddiqua123@gmail.com.

6 King Fahad General Hospital, Jeddah, Saudi Arabia. Electronic address: haaalsaab@moh.gov.sa.

7 King Fahad General Hospital, Jeddah, Saudi Arabia. Electronic address: ezzsakr@hotmail.com.

8 King Fahad General Hospital, Jeddah, Saudi Arabia. Electronic address: hamed88807@hotmail.com.

9 King Fahad General Hospital, Jeddah, Saudi Arabia. Electronic address: munzir.andonisi@gmail.com.

10 King Fahad General Hospital, Jeddah, Saudi Arabia. Electronic address: alaa.subedar@gmail.com.

11 King Saud University, Riyadh, Saudi Arabia. Electronic address: naloudah@ksu.edu.sa.

12 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. Electronic address: baharoon@hotmail.com.

13 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. Electronic address: malsalamah@gmail.com.

14 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. Electronic address: Johanis@ngha.med.sa.

15 King Fahad General Hospital, Jeddah, Saudi Arabia. Electronic address: dr_id2012@yahoo.com.

Year of Publication:

2018

DOI:

10.1016/j.ijid.2018.03.005

Abstract:

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.