Xiang Long, Wang Hansong, Fan Shujun, Zhang Wenlan, Lu Hua, Dong Bin, Liu Shijian, Chen Yiwei, Wang Ying, Zhao Liebin, Fu Lijun
Department of Pediatrics Intensive Care Unit, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Pediatric Artificial Intelligence Clinical Application and Research Center, Shanghai, China.
Front Oncol. 2021 Jun 15;11:678743. doi: 10.3389/fonc.2021.678743. eCollection 2021.
The purpose of this article was to establish and validate clinically applicable septic shock early warning model (SSEW model) that can identify septic shock in hospitalized children with onco-hematological malignancies accompanied with fever or neutropenia.
Data from EMRs were collected from hospitalized pediatric patients with hematological and oncological disease at Shanghai Children's Medical Center. Medical records of patients (>30 days and <19 years old) with fever (≥38°C) or absolute neutrophil count (ANC) below 1.0 × 10/L hospitalized with hematological or oncological disease between January 1, 2017 and August 1, 2019 were considered. Patients in whom septic shock was diagnosed during the observation period formed the septic shock group, whereas non-septic-shock group was the control group. In the septic shock group, the time points at 4, 8, 12, and 24 hours prior to septic shock were taken as observation points, and corresponding observation points were obtained in the control group after matching. We employed machine learning artificial intelligence (AI) to filter features and used XGBoost algorithm to build SSEW model. Area under the ROC curve (AU-ROC) was used to compare the effectiveness among the SSEW Model, logistic regression model, and pediatric sequential organ failure score (pSOFA) for early warning of septic shock.
A total of 64 observation periods in the septic shock group and 2191 in the control group were included. AU-ROC of the SSEW model had higher predictive value for septic shock compared with the pSOFA score (0.93 vs. 0.76, Z = -2.73, P = 0.006). Further analysis showed that the AU-ROC of the SSEW model was superior to the pSOFA score at the observation points 4, 8, 12, and 24 h before septic shock. At the 24 h observation point, the SSEW model incorporated 14 module root features and 23 derived features.
The SSEW model for hematological or oncological pediatric patients could help clinicians to predict the risk of septic shock in patients with fever or neutropenia 24 h in advance. Further prospective studies on clinical application scenarios are needed to determine the clinical utility of this AI model.
本文旨在建立并验证一种临床适用的脓毒性休克早期预警模型（SSEW模型），该模型可识别患有血液肿瘤性恶性疾病且伴有发热或中性粒细胞减少的住院儿童中的脓毒性休克。
收集上海儿童医学中心住院的血液肿瘤疾病患儿电子病历中的数据。纳入2017年1月1日至2019年8月1日期间因血液或肿瘤疾病住院、发热（≥38°C）或绝对中性粒细胞计数（ANC）低于1.0×10⁹/L的患者（年龄>30天且<19岁）。在观察期内被诊断为脓毒性休克的患者组成脓毒性休克组，非脓毒性休克组作为对照组。在脓毒性休克组中，将脓毒性休克发生前4、8、12和24小时的时间点作为观察点，并在匹配后在对照组中获取相应的观察点。我们采用机器学习人工智能（AI）筛选特征，并使用XGBoost算法构建SSEW模型。采用ROC曲线下面积（AU-ROC）比较SSEW模型、逻辑回归模型和儿童序贯器官衰竭评分（pSOFA）对脓毒性休克早期预警的有效性。
脓毒性休克组共纳入64个观察期，对照组纳入2191个观察期。与pSOFA评分相比，SSEW模型对脓毒性休克的AU-ROC具有更高的预测价值（0.93对0.76，Z = -2.73，P = 0.006）。进一步分析表明，在脓毒性休克发生前4、8、12和24小时的观察点，SSEW模型的AU-ROC优于pSOFA评分。在24小时观察点，SSEW模型纳入了14个模块根特征和23个衍生特征。
用于血液或肿瘤儿科患者的SSEW模型可帮助临床医生提前24小时预测发热或中性粒细胞减少患者发生脓毒性休克的风险。需要对临床应用场景进行进一步的前瞻性研究，以确定该AI模型的临床实用性。
