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A Controlled Mouse Model for Neonatal Polymicrobial Sepsis
Published on: January 27, 2019
Prediction models for post-discharge mortality among under-five children with suspected sepsis in Uganda: A
Matthew O Wiens1,2,3,4, Vuong Nguyen1, Jeffrey N Bone3
1Institute for Global Health at BC Children's and Women's Hospital, Vancouver, Canada.
Insights
Simple prediction models can now identify children at risk of post-discharge mortality after hospital admission for suspected sepsis. These algorithms, using key variables like anthropometry and oxygen saturation, aim to improve care transitions from hospital to community.
Area of Science:
- Pediatric critical care
- Global health
- Clinical prediction modeling
Background:
- High post-discharge mortality in hospitalized children in low-income countries necessitates tools for risk identification.
- Current lack of predictive tools hinders efforts to improve outcomes for children after hospital discharge.
Purpose of the Study:
- To develop and validate algorithms for predicting post-discharge mortality in children admitted with suspected sepsis.
- To identify key clinical variables for risk stratification in pediatric sepsis survivors.
Main Methods:
- Prospective cohort studies involving 8,810 children across six Ugandan hospitals (2012-2021).
- Development of prediction models for six-month post-discharge mortality using up to eight variables at admission.
- Internal validation through 10-fold cross-validation.
Main Results:
- Models demonstrated good predictive performance: AUROC of 0.77 (0-6 months) and 0.75 (6-60 months).
- Key predictors included anthropometry, oxygen saturation, illness duration, and specific clinical signs (e.g., bulging fontanelle, coma score).
- Good calibration observed across risk strata (Brier scores 0.07 and 0.04).
Conclusions:
- Simple, variable-limited prediction models at admission can identify children at high risk of post-discharge mortality.
- These models can be integrated into digital systems to enhance peri-discharge care.
- External validation in diverse settings is recommended to broaden applicability.
Abstract:
In many low-income countries, over five percent of hospitalized children die following hospital discharge. The lack of available tools to identify those at risk of post-discharge mortality has limited the ability to make progress towards improving outcomes. We aimed to develop algorithms designed to predict post-discharge mortality among children admitted with suspected sepsis. Four prospective cohort studies of children in two age groups (0-6 and 6-60 months) were conducted between 2012-2021 in six Ugandan hospitals. Prediction models were derived for six-months post-discharge mortality, based on candidate predictors collected at admission, each with a maximum of eight variables, and internally validated using 10-fold cross-validation. 8,810 children were enrolled: 470 (5.3%) died in hospital; 257 (7.7%) and 233 (4.8%) post-discharge deaths occurred in the 0-6-month and 6-60-month age groups, respectively. The primary models had an area under the receiver operating characteristic curve (AUROC) of 0.77 (95%CI 0.74-0.80) for 0-6-month-olds and 0.75 (95%CI 0.72-0.79) for 6-60-month-olds; mean AUROCs among the 10 cross-validation folds were 0.75 and 0.73, respectively. Calibration across risk strata was good: Brier scores were 0.07 and 0.04, respectively. The most important variables included anthropometry and oxygen saturation. Additional variables included: illness duration, jaundice-age interaction, and a bulging fontanelle among 0-6-month-olds; and prior admissions, coma score, temperature, age-respiratory rate interaction, and HIV status among 6-60-month-olds. Simple prediction models at admission with suspected sepsis can identify children at risk of post-discharge mortality. Further external validation is recommended for different contexts. Models can be digitally integrated into existing processes to improve peri-discharge care as children transition from the hospital to the community.
