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A Controlled Mouse Model for Neonatal Polymicrobial Sepsis
Published on: January 27, 2019
Predictors of neonatal sepsis in developing countries
Martin W Weber1, John B Carlin, Salvacion Gatchalian
1Medical Research Council Laboratories, Fajara, The Gambia. weberm@who.int
Insights
Identifying severe neonatal infections is crucial. A combination of 14 clinical signs can help, but specificity is limited, leading to unnecessary referrals for infants needing treatment.
Area of Science:
- Pediatrics
- Infectious Diseases
- Public Health
Background:
- Neonatal infections represent a significant global cause of mortality.
- Developing simple, effective identification methods for infants requiring treatment is a public health priority.
Purpose of the Study:
- To investigate clinical signs and historical factors predicting severe neonatal disease.
- To evaluate the performance of simple diagnostic rules for identifying infants at risk.
Main Methods:
- A standardized approach was used to investigate 3303 infants under two months of age across four countries.
- Logistic regression analyzed historical factors and clinical signs for predicting sepsis, meningitis, hypoxemia, death, and severe disease.
- The efficacy of simple combination rules for diagnosis was explored.
Main Results:
- Fourteen independent predictors of severe neonatal disease were identified, including reduced feeding, lack of movement, fever, drowsiness, agitation, respiratory distress, and cyanosis.
- A rule using any of these 14 signs achieved 87% sensitivity but only 54% specificity for severe disease.
- Reducing the list to 9 signs improved specificity with minimal loss of sensitivity, while fever plus another sign showed very low sensitivity.
Conclusions:
- Physical signs can identify young infants at risk of severe disease, but limited specificity leads to many unnecessary referrals.
- Further research is needed to validate and refine prediction models, especially for the first week of life.
- There may be inherent limitations to the achievable accuracy in predicting severe neonatal disease.
Background:
Neonatal infections are a major cause of death worldwide. Simple procedures for identifying infants with infection that need referral for treatment are therefore of major public health importance.
Methods:
We investigated 3303 infants <2 months of age presenting with illness to health facilities in Ethiopia, The Gambia, Papua New Guinea and The Philippines, using a standardized approach. Historical factors and clinical signs predicting sepsis, meningitis, hypoxemia, deaths and an ordinal scale indicating severe disease were investigated by logistic regression, and the performance of simple combination rules was explored.
Results:
In multivariable analysis, reduced feeding ability, no spontaneous movement, temperature >38 degrees C, being drowsy/unconscious, a history of a feeding problem, history of change in activity, being agitated, the presence of lower chest wall indrawing, respiratory rate >60 breaths/min, grunting, cyanosis, a history of convulsions, a bulging fontanel and slow digital capillary refill were independent predictors of severe disease. The presence of any 1 of these 14 signs had a sensitivity for severe disease (defined as sepsis, meningitis, hypoxemia, or radiologically proven pneumonia) of 87% and a specificity of 54%. More stringent combinations, such as demanding 2 signs from the list, resulted in a considerable loss of sensitivity. By contrast only slight loss of sensitivity and considerable gain of specificity resulted from reducing the list to 9 signs. Requiring the presence of fever and any other sign produced a diagnostic rule with extremely low sensitivity (25%).
Conclusions:
Physical signs can be used to identify young infants at risk of severe disease, however with limited specificity, resulting in large numbers of unnecessary referrals. Further studies are required to validate and refine the prediction of severe disease, especially in the first week of life, but there appear to be limits on the accuracy of prediction that is achievable.
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