Journal article
Refined Lab-score, a Risk Score Predicting Serious Bacterial Infection in Febrile Children Less Than 3 Years of Age.
- Leroy S From the Department of Mobile Pediatric Intensive Care Unit, Avicenne Hospital, APHP, Paris, France.
- Bressan S Department of Pediatrics, University of Padova, Italy.
- Lacroix L Division of Pediatric Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland.
- Andreola B Department of Pediatrics, University of Padova, Italy.
- Zamora S Division of Pediatric Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland.
- Bailey B Division of Emergency Medicine, Department of Pediatrics, CHU Sainte-Justine, Université de Montréal, Montréal, Quebec, Canada.
- Da Dalt L Department of Pediatrics, University of Padova, Italy.
- Manzano S Division of Pediatric Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland.
- Gervaix A Division of Pediatric Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland.
- Galetto-Lacour A Division of Pediatric Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland.
- 2018-01-27
Published in:
- The Pediatric infectious disease journal. - 2018
Adolescent
Algorithms
Bacterial Infections
Biomarkers
C-Reactive Protein
Child
Child, Preschool
Cohort Studies
Emergency Service, Hospital
Female
Fever
Humans
Male
Predictive Value of Tests
Procalcitonin
ROC Curve
Reproducibility of Results
Risk
Sensitivity and Specificity
English
BACKGROUND
The identification of serious bacterial infection (SBI) in children with fever without source remains a challenge. A risk score called Lab-score, based on C-reactive protein, procalcitonin and urinary dipstick results was derived to predict SBI. However, all biomarkers were initially dichotomized, leading to weak statistical reliability and lack of transportability across diverse settings. We aimed to refine and validate this risk-score algorithm.
METHODS
The Lab-score was refined using a secondary analysis of a multicenter cohort study of children with fever without source via multilevel regression modeling. The external validation was conducted on data from a Canadian cohort study.
RESULTS
Eight hundred seventy-seven children (24% SBI) were included for the derivation study, and 347 (16% SBI) for validation. Only C-reactive protein, procalcitonin, age and urinary dipstick remained independently associated with SBI. The model achieved an area under the receiver operating characteristic (ROC) curve of 0.94 (95% confidence interval [CI]: 0.93-0.96), which was significantly higher than any other isolated biomarker (P < 0.0001), and the original Lab-score (P < 0.0001). According to a decision curve analysis, the model yielded a better strategy than those based on independently considered biomarkers, or on the original Lab-score. The threshold analysis led to a cutoff that yielded 96% (95% CI: 92-98) sensitivity and 73% (95% CI: 70-77) specificity. The external validation found similar predictive abilities: 0.96 area under the ROC curve (95% CI: 0.93-0.99), 95% sensitivity (95% CI: 85-99) and 87% specificity (95% CI: 83-91).
CONCLUSION
The refined Lab-score demonstrated higher prediction ability for SBI than the original Lab-score, with promising wider applicability across settings. These results require validation in additional populations.
The identification of serious bacterial infection (SBI) in children with fever without source remains a challenge. A risk score called Lab-score, based on C-reactive protein, procalcitonin and urinary dipstick results was derived to predict SBI. However, all biomarkers were initially dichotomized, leading to weak statistical reliability and lack of transportability across diverse settings. We aimed to refine and validate this risk-score algorithm.
METHODS
The Lab-score was refined using a secondary analysis of a multicenter cohort study of children with fever without source via multilevel regression modeling. The external validation was conducted on data from a Canadian cohort study.
RESULTS
Eight hundred seventy-seven children (24% SBI) were included for the derivation study, and 347 (16% SBI) for validation. Only C-reactive protein, procalcitonin, age and urinary dipstick remained independently associated with SBI. The model achieved an area under the receiver operating characteristic (ROC) curve of 0.94 (95% confidence interval [CI]: 0.93-0.96), which was significantly higher than any other isolated biomarker (P < 0.0001), and the original Lab-score (P < 0.0001). According to a decision curve analysis, the model yielded a better strategy than those based on independently considered biomarkers, or on the original Lab-score. The threshold analysis led to a cutoff that yielded 96% (95% CI: 92-98) sensitivity and 73% (95% CI: 70-77) specificity. The external validation found similar predictive abilities: 0.96 area under the ROC curve (95% CI: 0.93-0.99), 95% sensitivity (95% CI: 85-99) and 87% specificity (95% CI: 83-91).
CONCLUSION
The refined Lab-score demonstrated higher prediction ability for SBI than the original Lab-score, with promising wider applicability across settings. These results require validation in additional populations.
- Language
-
- English
- Open access status
- closed
- Identifiers
-
- DOI 10.1097/INF.0000000000001915
- PMID 29373477
- Persistent URL
- https://sonar.ch/global/documents/46515
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