Back

Long-term sequelae of Pneumococcal Sepsis in children: a systematic review

Ogunbameru, A. A.; Miranda, R. N.; Bielecki, J.; Sander, B.

2021-03-17 infectious diseases
10.1101/2021.03.15.21253639 medRxiv
Show abstract

BackgroundLong-term sequelae associated with pneumococcal sepsis (PS) in pediatric patients in existing literature is currently unclear. AimTo review the evidence on sequelae and prognostic factors associated with PS among pediatric patients. MethodWe conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines. We screened six databases from their inception to January 15, 2021. Study population were neonates, infants, children and adolescents less than 18 years old with suspected or confirmed PS disease. Outcomes included sequelae types, prognostic factors, pooled death estimate and length of hospital stay (LOS) for survivors and deceased patients. Quality of studies was assessed using Joanna Briggs Institute appraisal checklists. ResultsWe screened 981 abstracts, and 24 full-text articles for final review. Septic shock was the most prevalent physical sequelae reported (13%, n=1492 patients). No functional, cognitive or neurological sequelae were reported in included studies. Meta-analysis of pooled mortality estimate was 14.6% (95%CI: 9.9 -19.4%). Prognostic factors associated with increased risk of PS sequelae and death included pediatric risk of mortality score [≥] 10 and co-infection with meningitis. LOS for survivors and non-survivors ranged between 5-30 days and 1-30 days. Nine included studies met at least 50% of the quality assessment criteria. ConclusionPhysical sequelae and death are the PS sequelae types currently identified in existing literature. Lack of information about other possible sequelae types suggests the long-term consequences of PS disease maybe underreported, especially in resource-limited settings. Future studies should consider exploring reasons for the existing of this knowledge gap.

Matching journals

The top 13 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.