Sepsis among Neonates in a Ghanaian Tertiary Military Hospital: Culture Results and Turnaround Times.
Francis Kwame Morgan TettehRaymond FatchuKingsley AckahTrudy Janice PhilipsHemant Deepak ShewadeAma Pokuaa FennyCollins TimireJeffrey Karl EdwardsEmmanuel Abbeyquaye ParbiePublished in: International journal of environmental research and public health (2022)
In this study, we described the bacterial profile, antibiotic resistance pattern, and laboratory result turnaround time (TAT) in neonates with suspected sepsis from a tertiary-level, military hospital in Accra, Ghana (2017-2020). This was a cross-sectional study using secondary data from electronic medical records. Of 471 neonates clinically diagnosed with suspected sepsis in whom blood samples were collected, the median TAT from culture request to report was three days for neonates who were culture-positive and five days for neonates who were culture-negative. There were 241 (51%) neonates discharged before the receipt of culture reports, and of them, 37 (15%) were culture-positive. Of 471 neonates, twenty-nine percent ( n = 139) were bacteriologically confirmed, of whom 61% ( n = 85) had late-onset sepsis. Gram-positive bacterial infection (89%, n = 124) was the most common cause of culture-positive neonatal sepsis. The most frequent Gram-positive pathogen was coagulase-negative Staphylococcus (55%, n = 68) followed by Staphylococcus aureus (36%, n = 45), of which one in two were multidrug resistant. The reasons for large numbers being discharged before the receipt of culture reports need to be further explored. There is a need for improved infection prevention and control, along with ongoing local antimicrobial resistance surveillance and antibiotic stewardship to guide future empirical treatment.
Keyphrases
- low birth weight
- staphylococcus aureus
- acute kidney injury
- intensive care unit
- late onset
- septic shock
- multidrug resistant
- antimicrobial resistance
- preterm infants
- public health
- pulmonary embolism
- early onset
- emergency department
- drug resistant
- artificial intelligence
- machine learning
- escherichia coli
- big data
- pseudomonas aeruginosa
- klebsiella pneumoniae
- acinetobacter baumannii
- replacement therapy
- data analysis