Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA.
Department of Pediatrics and Child Health, University of Gondar, College of Medicine and Health Sciences, Gondar, Ethiopia.
BMC Pediatr. 2020 May 20;20(1):238. doi: 10.1186/s12887-020-02107-8.
Early warning scores for neonatal mortality have not been designed for low income countries. We developed and validated a score to predict mortality upon admission to a NICU in Ethiopia.
We conducted a retrospective case-control study at the University of Gondar Hospital, Gondar, Ethiopia. Neonates hospitalized in the NICU between January 1, 2016 to June 31, 2017. Cases were neonates who died and controls were neonates who survived.
Univariate logistic regression identified variables associated with mortality. The final model was developed with stepwise logistic regression. We created the Neonatal Mortality Score, which ranged from 0 to 52, from the model's coefficients. Bootstrap analysis internally validated the model. The discrimination and calibration were calculated. In the derivation dataset, there were 207 cases and 605 controls. Variables associated with mortality were admission level of consciousness, admission respiratory distress, gestational age, and birthweight. The AUC for neonatal mortality using these variables in aggregate was 0.88 (95% CI 0.85-0.91). The model achieved excellent discrimination (bias-corrected AUC) under internal validation. Using a cut-off of 12, the sensitivity and specificity of the Neonatal Mortality Score was 81 and 80%, respectively. The AUC for the Neonatal Mortality Score was 0.88 (95% CI 0.85-0.91), with similar bias-corrected AUC. In the validation dataset, there were 124 cases and 122 controls, the final model and the Neonatal Mortality Score had similar discrimination and calibration.
We developed, internally validated, and externally validated a score that predicts neonatal mortality upon NICU admission with excellent discrimination and calibration.
新生儿死亡率的早期预警评分并未针对低收入国家设计。我们开发并验证了一种评分系统，用于预测埃塞俄比亚新生儿重症监护病房（NICU）入院时的死亡率。
我们在埃塞俄比亚贡德尔大学医院进行了一项回顾性病例对照研究。研究对象为 2016 年 1 月 1 日至 2017 年 6 月 31 日期间在 NICU 住院的新生儿。病例组为死亡的新生儿，对照组为存活的新生儿。
单因素逻辑回归确定了与死亡率相关的变量。最终模型通过逐步逻辑回归建立。我们从模型系数中创建了新生儿死亡率评分，范围为 0 至 52。使用bootstrap 分析对模型进行内部验证。计算了区分度和校准度。在推导数据集，有 207 例病例和 605 例对照。与死亡率相关的变量是入院时的意识水平、入院时的呼吸窘迫、胎龄和出生体重。使用这些变量综合评估新生儿死亡率的 AUC 为 0.88（95%CI 0.85-0.91）。模型在内部验证中实现了良好的区分度（校正后 AUC）。使用 12 作为截断值，新生儿死亡率评分的敏感性和特异性分别为 81%和 80%。新生儿死亡率评分的 AUC 为 0.88（95%CI 0.85-0.91），校正后 AUC 相似。在验证数据集中，有 124 例病例和 122 例对照，最终模型和新生儿死亡率评分具有相似的区分度和校准度。
我们开发、内部验证和外部验证了一种评分系统，用于预测 NICU 入院时的新生儿死亡率，具有良好的区分度和校准度。
