Kamau Stephen, Kigo Joyce, Maina Michuki, Gachohi John
Health Services Unit, KEMRI-Wellcome Trust Research Programme Nairobi, Nairobi, Kenya.
School of Public Health, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya.
Wellcome Open Res. 2024 Dec 24;9:732. doi: 10.12688/wellcomeopenres.23471.1. eCollection 2024.
Early identification of children at risk of mortality during hospitalization is crucial in preventing mortality in low-and middle-income countries (LMICs). This study aimed to externally validate an admission risk score for predicting inpatient paediatric mortality in resource-limited settings.
This retrospective study utilized routine clinical data of children aged ≤12 years admitted to two Kenyan public hospitals between January 2017 and October 2023. The admission risk score includes 13 clinical predictors, each assigned a value. Aggregate values were used to predict inpatient pediatric mortality, with a higher score indicating a greater risk of death. Children with scores of 0, 1-4 and ≥5 were categorized as low, moderate and high-risk categories, respectively. Discrimination was assessed using area under the receiver operating characteristic curve (AUC). Sensitivity, specificity, and positive and negative predictive values were calculated at different cutoff points.
A total of 15,606 children were included in the study. Majority of the participants were male (8,847, 56.7%) and aged 12 - 59 months (7,222, 46.3%). Children classified as high-risk had a higher mortality rate (23.4%) than those classified as low risk (2%). The risk score demonstrated moderate discrimination, with an AUC of 0.73 (95% confidence interval (CI): 0.71 - 0.75). A cutoff of ≥3 achieved a balance between sensitivity and specificity, with values of 63.8% (95% CI: 60.7%-66.9%) and 72.2% (95% CI: 71.5% - 72.9%), respectively, compared to other cutoff points.
The risk score performed moderately in predicting inpatient paediatric mortality in two Kenyan public hospitals. The risk score can be used with other clinical assessments to rapidly identify high-risk children and guide targeted interventions to prevent mortality.
在低收入和中等收入国家（LMICs），早期识别住院期间有死亡风险的儿童对于预防死亡至关重要。本研究旨在对外验证一种用于预测资源有限环境下住院儿童死亡率的入院风险评分。
这项回顾性研究利用了2017年1月至2023年10月期间入住两家肯尼亚公立医院的12岁及以下儿童的常规临床数据。入院风险评分包括13个临床预测指标，每个指标都被赋予一个值。汇总值用于预测住院儿童死亡率，分数越高表明死亡风险越大。得分0、1 - 4和≥5的儿童分别被归类为低、中、高风险类别。使用受试者操作特征曲线下面积（AUC）评估区分度。在不同的截断点计算敏感性、特异性以及阳性和阴性预测值。
该研究共纳入15606名儿童。大多数参与者为男性（8847名，56.7%），年龄在12 - 59个月之间（7222名，46.3%）。被归类为高风险的儿童死亡率（23.4%）高于被归类为低风险的儿童（2%）。风险评分显示出中等区分度，AUC为0.73（95%置信区间（CI）：0.71 - 0.75）。与其他截断点相比，截断值≥3在敏感性和特异性之间取得了平衡，敏感性和特异性值分别为63.8%（95%CI：60.7% - 66.9%）和72.2%（95%CI：71.5% - 72.9%）。
该风险评分在预测两家肯尼亚公立医院住院儿童死亡率方面表现中等。该风险评分可与其他临床评估一起用于快速识别高风险儿童，并指导针对性干预措施以预防死亡。
