<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Wiens MO</submitter><funding>Grand Challenges Canada</funding><funding>Thrasher Research Fund</funding><funding>BC Children’s Hospital Foundation</funding><funding>Mining4Life</funding><pagination>e0003050</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11057737</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>4(4)</volume><pubmed_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.</pubmed_abstract><journal>PLOS global public health</journal><pubmed_title>Prediction models for post-discharge mortality among under-five children with suspected sepsis in Uganda: A multicohort analysis.</pubmed_title><pmcid>PMC11057737</pmcid><funding_grant_id>TTS-1809-1939</funding_grant_id><funding_grant_id>13878</funding_grant_id><pubmed_authors>Olaro C</pubmed_authors><pubmed_authors>Bone JN</pubmed_authors><pubmed_authors>Novakowski S</pubmed_authors><pubmed_authors>West N</pubmed_authors><pubmed_authors>Knappett M</pubmed_authors><pubmed_authors>Ansermino JM</pubmed_authors><pubmed_authors>Barigye C</pubmed_authors><pubmed_authors>Larson CP</pubmed_authors><pubmed_authors>Byaruhanga E</pubmed_authors><pubmed_authors>Nsungwa J</pubmed_authors><pubmed_authors>Tagoola A</pubmed_authors><pubmed_authors>Lavoie PM</pubmed_authors><pubmed_authors>Mugisha NK</pubmed_authors><pubmed_authors>Kabakyenga J</pubmed_authors><pubmed_authors>Wiens MO</pubmed_authors><pubmed_authors>Sherine SO</pubmed_authors><pubmed_authors>Nguyen V</pubmed_authors><pubmed_authors>Moschovis PP</pubmed_authors><pubmed_authors>Tayebwa M</pubmed_authors><pubmed_authors>Kumbakumba E</pubmed_authors><pubmed_authors>Businge S</pubmed_authors><pubmed_authors>Dunsmuir D</pubmed_authors><pubmed_authors>Ssemwanga E</pubmed_authors><pubmed_authors>Komugisha C</pubmed_authors><pubmed_authors>Singer J</pubmed_authors><pubmed_authors>Kissoon N</pubmed_authors><pubmed_authors>Mwesigwa D</pubmed_authors></additional><is_claimable>false</is_claimable><name>Prediction models for post-discharge mortality among under-five children with suspected sepsis in Uganda: A multicohort analysis.</name><description>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.</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024</publication><modification>2026-07-01T03:24:52.109Z</modification><creation>2026-07-01T03:12:35.574Z</creation></dates><accession>S-EPMC11057737</accession><cross_references><pubmed>38683787</pubmed><doi>10.1371/journal.pgph.0003050</doi></cross_references></HashMap>