Liang Yongzhou, Zhao Liqing, Huang Jihong, Wu Yurong
Department of Pediatric Cardiology, Xinhua Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China.
Transl Pediatr. 2023 Sep 18;12(9):1690-1706. doi: 10.21037/tp-23-150. Epub 2023 Sep 5.
Sepsis is the second-leading cause of death in neonates. We established a predictive nomogram to identify critically ill neonates early and reduce the time to treatment.
It is a retrospective case-control study based on the MIMIC-III database. The study population comprised 924 neonates diagnosed with sepsis.
Neonates with sepsis included in the MIMIC-III database were enrolled, including 880 surviving neonates and 44 neonates who died. In the derivation dataset, stepwise regression and the Lasso algorithm were employed to select predictive variables, and the neonatal sequential organ failure assessment score (nSOFA) was calculated simultaneously. Bootstrap resampling was utilized to perform internal validation. The results indicated that the Lasso algorithm displayed superior discrimination, sensitivity, and specificity relative to stepwise regression and nSOFA scores. After 500 bootstrap resampling tests, the area under the receiver operating characteristic curve (AUC) of the Lasso algorithm was 0.912 (95% CI: 0.870-0.977). The nomogram based on the Lasso algorithm outperformed stepwise regression and nSOFA scores in terms of calibration and the clinical net benefit. This nomogram can assist in prognosticating neonatal severe sepsis and aid in guiding clinical practice while concurrently improving patient outcomes.
The established nomogram revealed that jaundice, corticosteroid use, weight, serum calcium, inotropes and base excess are all important predictors of 28-day mortality in neonates with sepsis. This nomogram can facilitate the early identification of neonates with severe sepsis. However, it still requires further modification and external validation to make it widely available.
脓毒症是新生儿死亡的第二大原因。我们建立了一个预测列线图，以早期识别危重新生儿并缩短治疗时间。
这是一项基于MIMIC-III数据库的回顾性病例对照研究。研究人群包括924例诊断为脓毒症的新生儿。
纳入MIMIC-III数据库中的脓毒症新生儿，包括880例存活新生儿和44例死亡新生儿。在推导数据集中，采用逐步回归和Lasso算法选择预测变量，并同时计算新生儿序贯器官衰竭评估评分（nSOFA）。利用自助重采样进行内部验证。结果表明，相对于逐步回归和nSOFA评分，Lasso算法显示出更好的辨别力、敏感性和特异性。经过500次自助重采样测试后，Lasso算法的受试者工作特征曲线（AUC）下面积为0.912（95%CI：0.870-0.977）。基于Lasso算法的列线图在校准和临床净效益方面优于逐步回归和nSOFA评分。该列线图可有助于预测新生儿严重脓毒症的预后，并有助于指导临床实践，同时改善患者结局。
所建立的列线图显示，黄疸、使用皮质类固醇、体重、血清钙、血管活性药物和碱剩余都是脓毒症新生儿28天死亡率的重要预测因素。该列线图可促进对严重脓毒症新生儿的早期识别。然而，它仍需要进一步修改和外部验证，以使其广泛可用。
