Prediction models for post-discharge mortality among under-five children with suspected sepsis in Uganda: A multicohort analysis
- Description:
Background: 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.
Methods: 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.
Findings: 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.
Data Processing Methods: The post-processed data files were created using R version 4.2.2. (R Foundation for Statistical Computing, Vienna, Austria) and briefly involved renaming columns from the different datasets so that they are consistent, converting categories coded as “unknown”, “don’t know”, or “missing” to NA, creating new columns, calculating z-scored variables, and converting relevant columns to factors or dates.
Ethics Declaration: These studies were approved by the Mbarara University of Science and Technology (No. 15/10-16), the Uganda National Council for Science and Technology (HS 2207), and the University of British Columbia (H16-02679).- Wiens, Matthew O, , , , , , , , , , , , , , , , , Dunsmuir, Dustin, , , , , , , , et
- Contributor(s):
- SENKORO, VANESSA et Wiens, Matthew O
- Dépôt source:
- UBC Dataverse
- Series:
- Pediatric Sepsis Data CoLab // Clinical studies // Smart Discharges // Smart Discharges - Manuscripts
- Éditeur(s):
- Borealis
- Accès:
- Restricted
- License:
- CC BY-NC-SA 4.0
- URL:
- https://doi.org/10.5683/SP3/M3OPKQ
- Date de publication:
- 2024-07-16
- Sujets (en):
- Mots-clés (en):
- Identificateur:
- https://doi.org/10.5683/SP3/M3OPKQ
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