Early-Onset Neonatal Sepsis: A Retrospective Study among 1,119 Moroccan Newborns Admitted to the National Reference Center in Neonatology and Nutrition, Rabat, Morocco

Abstract

Background: Early-onset neonatal sepsis remains a common and serious problem for neonates. The clinical manifestations of neonatal sepsis are nonspecific and have varied clinical features. Therefore, their diagnosis is based on a combination of clinical presentation, the use of biological tests, and anamnestic arguments. The purpose of this study was to describe the infection risk factors and clinical, paraclinical and evolutionary characteristics of newborns admitted for suspicion of early-onset neonatal sepsis, and to highlights the importance of C-reactive protein to diagnose neonatal sepsis. Methods: This retrospective and analytical study was conducted from January 1 to December 31, 2016, at the National Reference Center for Neonatology and Nutrition at Children's Hospital, University Hospital Centre Ibn Sina of Rabat. Results: During that period, 1199 newborns met the inclusion criteria. At admission, 52% of the cases were under the age of one day. Symptomatic newborns represented 67.4% of the cases. The hospitalized cases with one or more infectious risk factors were represented 80.3% of cases. The C-reactive protein was positive (> 20 mg/L) in 698 (58%) newborns. Univariate analysis showed that C-reactive protein value was significantly associated with age groups (p<0.001), presence of at least one infectious risk factor (p<0.001), and the presence of respiratory (p<0.001), cutaneous (p<0.001), circulatory (p=0.02), and neurological (p=0.008) symptoms. The diagnosis of early-onset neonatal infection with a meningeal, pulmonary, or ocular location was retained in 5, 2, and 0.2% of the cases, respectively. The mortality rate was 7%. Conclusion: Screening, management, and reduction of early early-onset neonatal infection remain a crucial challenge, which requires coordination between pediatricians and obstetricians to obtain reliable data and identify newborns at risk.