Predicting Clinical Deterioration and Mortality at Differing Stages During Hospitalization: A Systematic Review of Risk Prediction Models in Children in Low- and Middle-Income Countries.

OBJECTIVE: To determine which risk prediction model best predicts clinical deterioration in children at different stages of hospital admission in low- and middle-income countries. METHODS: For this systematic review, Embase and MEDLINE databases were searched, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. The key search terms were "development or validation study with risk-prediction model" AND "deterioration or mortality" AND "age 0-18 years" AND "hospital-setting: emergency department (ED), pediatric ward (PW), or pediatric intensive care unit (PICU)" AND "low- and middle-income countries." The Prediction Model Risk of Bias Assessment Tool was used by two independent authors. Forest plots were used to plot area under the curve according to hospital setting. Risk prediction models used in two or more studies were included in a meta-analysis. RESULTS: We screened 9486 articles and selected 78 publications, including 67 unique predictive models comprising 1.5 million children. The best performing models individually were signs of inflammation in children that can kill (SICK) (ED), pediatric early warning signs resource limited settings (PEWS-RL) (PW), and Pediatric Index of Mortality (PIM) 3 as well as pediatric sequential organ failure assessment (pSOFA) (PICU). Best performing models after meta-analysis were SICK (ED), pSOFA and Pediatric Early Death Index for Africa (PEDIA)-immediate score (PW), and pediatric logistic organ dysfunction (PELOD) (PICU). There was a high risk of bias in all studies. CONCLUSIONS: We identified risk prediction models that best estimate deterioration, although these risk prediction models are not routinely used in low- and middle-income countries. Future studies should focus on large scale external validation with strict methodological criteria of multiple risk prediction models as well as study the barriers in the way of implementation. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews: Prospero ID: CRD42021210489.

The clinical use of longitudinal bio-electrical impedance vector analysis in assessing stabilization of children with severe acute malnutrition.

BACKGROUND & AIMS: Severe Acute Malnutrition (SAM) in children is determined using anthropometry. However, bio-electrical impedance (BI) analysis could improve the estimation of altered body composition linked to edema and/or loss of lean body mass in children with SAM. We aimed to assess: 1) the changes in BI parameters during clinical stabilization and 2) whether BI parameters add prognostic value for clinical outcome beyond the use of anthropometry. METHODS: This prospective observational study enrolled children, aged 6-60 months, that were admitted at Queen Elizabeth Central Hospital in Blantyre, Malawi, for complicated SAM (i.e., having either severe wasting or edematous SAM with a complicating illness). Height, weight, mid-upper arm circumference (MUAC), and BI were measured on admission and after clinical stabilization. BI measures were derived from height-adjusted indices of resistance (R/H), reactance (Xc/H), and phase angle (PA) and considered to reflect body fluids and soft tissue in BI vector analysis (BIVA). RESULTS: We studied 183 children with SAM (55% edematous; age 23.0 ± 12.0 months; 54% male) and 42 community participants (age 20.1 ± 12.3 months; male 62%). Compared to community participants, the BIVA of children with edematous SAM were short with low PA and positioned low on the hydration axis which reflects severe fluid retention. In contrast, children with severe wasting had elongated vectors with a PA that was higher than children with edematous SAM but lower than community participants. Their BIVA position fell within the top right quadrant linked to leanness and dehydration. BIVA from severely wasted and edematous SAM patients differed between groups and from community children both at admission and after stabilization (p < 0.001). Vector position shifted during treatment only in children with edematous SAM (p < 0.001) and showed a upward translation suggestive of fluid loss. While PA was lower in children with SAM, PA did not contribute more than anthropometry alone towards explaining mortality, length of stay, or time-to-discharge or time-to-mortality. The variability and heterogeneity in BI measures was high and their overall added predictive value for prognosis of individual children was low. CONCLUSIONS: BIVA did not add prognostic value over using anthropometry alone to predict clinical outcome. Several implementation challenges need to be optimized. Thus, in low-resource settings, the routine use of BI in the management of pediatric malnutrition is questionable without improved implementation.