RESUMO
BACKGROUND: The number of COVID-19 infected patients has been soaring in the Middle East countries. The disease poses a significant threat, decisions about prioritizing care should be made in accordance with the proven risk factors for complications. OBJECTIVE: The present study provides the first bespoke prediction model in the Middle East to identify COVID-19 patients, who are at higher risk for complications. METHOD: A case-control study design was adopted to compare the characteristics of successfully recovered patients with those who had complications. Complications were defined as admission to the intensive care unit, mechanical ventilation, sepsis or septic shock, pneumonia or respiratory failure, and death. The prediction model was created through multivariable logistic regression. Overall statistical significance tests for the model were carried out. RESULTS: All COVID-19 infected hospitalized patients (n = 133) in Amman - Jordan were included in the study. Successfully recovered were 125 patients. The median age (IRQ) was 26 (10-40). Almost 30% were >40 years. Patients with complications were eight patients, age 63 (51.5-71.5). The prediction model identified the following variables as risk factors: diabetes (OR = 59.7; 95% CI: 3.5-1011.5, p = 0.005), fever (OR = 24.8; 95% CI: 1.4-447.3, p = 0.029), SHORTNESS OF BREATH (OR = 15.9; 95% CI: 1.3-189.7, p = 0.029), body mass index (OR = 0.74; 95% CI: 0.61-0.88, p = 0.001), abnormal Neutrophils (OR = 16.8; 95% CI: 1.0-292.0, p = 0.053). Prediction model was statistically significant, χ2(5) = 86.1, p < 0.0005. CONCLUSIONS: Unlike reports from China, the most influential variables that led to disease progression in Jordanian patients were diabetes, fever, shortness of breath, body mass index, and abnormal neutrophils. Similar to reports from the USA, smoking was not a leading factor for complications. Comorbidities and patient health status, rather than age, were the primary risk factors for complications. Treatment with Hydroxychloroquine showed no protective effect.