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Infections in Children Admitted with Complicated Severe Acute Malnutrition in Niger
Authors:Anne-Laure Page  Nathalie de Rekeneire  Sani Sayadi  Said Aberrane  Ann-Carole Janssens  Claire Rieux  Ali Djibo  Jean-Claude Manuguerra  Hubert Ducou-le-Pointe  Rebecca F. Grais  Myrto Schaefer  Philippe J. Guerin  Emmanuel Baron
Abstract:

Background

Although malnutrition affects thousands of children throughout the Sahel each year and predisposes them to infections, there is little data on the etiology of infections in these populations. We present a clinical and biological characterization of infections in hospitalized children with complicated severe acute malnutrition (SAM) in Maradi, Niger.

Methods

Children with complicated SAM hospitalized in the intensive care unit of a therapeutic feeding center, with no antibiotics in the previous 7 days, were included. A clinical examination, blood, urine and stool cultures, and chest radiography were performed systematically on admission.

Results

Among the 311 children included in the study, gastroenteritis was the most frequent clinical diagnosis on admission, followed by respiratory tract infections and malaria. Blood or urine culture was positive in 17% and 16% of cases, respectively, and 36% had abnormal chest radiography. Enterobacteria were sensitive to most antibiotics, except amoxicillin and cotrimoxazole. Twenty-nine (9%) children died, most frequently from sepsis. Clinical signs were poor indicators of infection and initial diagnoses correlated poorly with biologically or radiography-confirmed diagnoses.

Conclusions

These data confirm the high level of infections and poor correlation with clinical signs in children with complicated SAM, and provide antibiotic resistance profiles from an area with limited microbiological data. These results contribute unique data to the ongoing debate on the use and choice of broad-spectrum antibiotics as first-line treatment in children with complicated SAM and reinforce the call for an update of international guidelines on management of complicated SAM based on more recent data.
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