Research
1 Depatment of Medicine, University of Toronto, Toronto M5S1A8, Canada
2 Sandra A. Rotman Laboratories, Sandra Rotman Centre for Global Health, University Health Network-Toronto General Hospital, University of Toronto, Toronto M5G1L7, Canada
3 Division of Pediatric Infectious Diseases, University of Alberta, Edmonton T6G1C9, Canada
4 Department of Pediatrics, Jinja Regional Referral Hospital, Jinja, Uganda
5 Department of Paediatrics and Child Health, Mulago Hospital and Makerere University, Kampala, Uganda
6 Division of Global Pediatrics, Department of Pediatrics, University of Minnesota, Minneapolis 55414, Minnesota, USA
7 Department of Medicine, University of Washington, Seattle 98195, WA, USA
8 Tropical Disease Unit, Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, Canada
9 Sandra Rotman Centre, Suite 10–351, Toronto Medical Discovery Tower, MaRS Centre, Toronto M5G1L7, 101 College Street, Canada
Critical Care 2015, 19:47Â
doi:10.1186/s13054-015-0773-4
Published: 23 February 2015
Abstract (provisional)
Introduction The development of simple clinical tools to identify children at risk
of death would enable rapid and rational implementation of lifesaving measures to
reduce childhood mortality globally. Methods We evaluated the ability of three clinical
scoring systems to predict in-hospital mortality in a prospective observational study
of Ugandan children with fever. We computed the Lambaréné Organ Dysfunction Score
(LODS), Signs of Inflammation in Children that Kill (SICK), and the Pediatric Early
Death Index for Africa (PEDIA). Model discrimination was evaluated by comparing areas
under receiver operating characteristic curves (AUCs) and calibration was assessed
using the Hosmer-Lemeshow goodness-of-fit test. Sub-analyses were performed in malaria
versus non-malaria febrile illness (NMFI), and in early (?48Â hours) versus late (gt;48Â hours)
deaths. Results In total, 2089 children with known outcomes were included in the study
(99 deaths, 4.7% mortality). All three scoring systems yielded good discrimination
(AUCs, 95% confidence interval (CI): LODS, 0.90, 0.88 to 0.91; SICK, 0.85, 0.83 to
0.86; PEDIA, 0.90, 0.88 to 0.91). Using the Youden index to identify the best cut-offs,
LODS had the highest positive likelihood ratio (+LR, 95% CI: LODS, 6.5, 5.6 to 7.6;
SICK, 4.4, 3.9 to 5.0; PEDIA, 4.4, 3.9 to 5.0), whereas PEDIA had the lowest negative
likelihood ratio (?LR, 95% CI: LODS, 0.21, 0.1 to 0.3; SICK, 0.22, 0.1 to 0.3; PEDIA,
0.16, 0.1 to 0.3), LODS and PEDIA were well calibrated (P?=?0.79 and P?=?0.21 respectively),
and had higher AUCs than SICK in discriminating between survivors and non-survivors
in malaria (AUCs, 95% CI: LODS, 0.92, 0.90 to 0.93; SICK, 0.86, 0.84 to 0.87; PEDIA,
0.92, 0.90 to 0.93), but comparable AUCs in NMFI (AUCs, 95% CI: LODS, 0.86, 0.83 to
0.89; SICK, 0.82, 0.79 to 0.86; PEDIA, 0.87, 0.83 to 0.893). The majority of deaths
in the study occurred early (n?=?85, 85.9%) where LODS and PEDIA had good discrimination.
Conclusions All three scoring systems predicted outcome, but LODS holds the most promise
as a clinical prognostic score based on its simplicity to compute, requirement for
no equipment, and good discrimination.