Wiens Matthew O, Nguyen Vuong, Bone Jeffrey N, Kumbakumba Elias, Businge Stephen, Tagoola Abner, Sherine Sheila Oyella, Byaruhanga Emmanuel, Ssemwanga Edward, Barigye Celestine, Nsungwa Jesca, Olaro Charles, Ansermino J Mark, Kissoon Niranjan, Singer Joel, Larson Charles P, Lavoie Pascal M, Dunsmuir Dustin, Moschovis Peter P, Novakowski Stefanie, Komugisha Clare, Tayebwa Mellon, Mwesigwa Douglas, Knappett Martina, West Nicholas, Mugisha Nathan Kenya, Kabakyenga Jerome
Institute for Global Health at BC Children's and Women's Hospital, Vancouver, Canada.
Department of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, Canada.
PLOS Glob Public Health. 2024 Apr 29;4(4):e0003050. doi: 10.1371/journal.pgph.0003050. eCollection 2024.
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.
在许多低收入国家，超过5% 的住院儿童在出院后死亡。由于缺乏可用于识别出院后死亡风险儿童的工具，改善治疗结果的进展受到了限制。我们旨在开发算法，以预测疑似脓毒症患儿出院后的死亡率。2012年至2021年期间，在乌干达的六家医院对两个年龄组（0至6个月和6至60个月）的儿童进行了四项前瞻性队列研究。根据入院时收集的候选预测指标（每个指标最多包含八个变量），推导出出院后六个月死亡率的预测模型，并使用10倍交叉验证进行内部验证。共纳入8810名儿童：470名（5.3%）在医院死亡；0至6个月龄组和6至60个月龄组分别有257名（7.7%）和233名（4.8%）儿童在出院后死亡。主要模型在0至6个月龄儿童中的受试者工作特征曲线下面积（AUROC）为0.77（95%CI 0.74 - 0.80），在6至60个月龄儿童中为0.75（95%CI 0.72 - 0.79）；10次交叉验证折中的平均AUROC分别为0.75和0.73。各风险分层的校准良好：布里尔评分分别为0.07和0.04。最重要的变量包括人体测量指标和血氧饱和度。其他变量包括：0至6个月龄儿童的疾病持续时间、黄疸与年龄的相互作用以及囟门隆起；6至60个月龄儿童的既往住院史、昏迷评分、体温、年龄与呼吸频率的相互作用以及艾滋病毒感染状况。疑似脓毒症患儿入院时的简单预测模型可以识别出院后有死亡风险的儿童。建议在不同背景下进行进一步的外部验证。这些模型可以数字化整合到现有流程中，以改善儿童从医院过渡到社区期间的出院前后护理。
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