Tabaie Azade, Orenstein Evan W, Nemati Shamim, Basu Rajit K, Clifford Gari D, Kamaleswaran Rishikesan
Department of Biomedical Informatics, Emory School of Medicine, Atlanta, GA, United States.
Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, United States.
Front Pediatr. 2021 Sep 15;9:726870. doi: 10.3389/fped.2021.726870. eCollection 2021.
Predict the onset of presumed serious infection, defined as a positive blood culture drawn and new antibiotic course of at least 4 days (PSI), among pediatric patients with Central Venous Lines (CVLs). Retrospective cohort study. Single academic children's hospital. All hospital encounters from January 2013 to December 2018, excluding the ones without a CVL or with a length-of-stay shorter than 24 h. Clinical features including demographics, laboratory results, vital signs, characteristics of the CVLs and medications used were extracted retrospectively from electronic medical records. Data were aggregated across all hospitals within a single pediatric health system and used to train a deep learning model to predict the occurrence of PSI during the next 48 h of hospitalization. The proposed model prediction was compared to prediction of PSI by a marker of illness severity (PELOD-2). The baseline prevalence of line infections was 0.34% over all segmented 48-h time windows. Events were identified among cases using onset time. All data from admission till the onset was used for cases and among controls we used all data from admission till discharge. The benchmarks were aggregated over all 48 h time windows [N=748,380 associated with 27,137 patient encounters]. The model achieved an area under the receiver operating characteristic curve of 0.993 (95% CI = [0.990, 0.996]), the enriched positive predictive value (PPV) was 23 times greater than the base prevalence. Conversely, prediction by PELOD-2 achieved a lower PPV of 1.5% [0.9%, 2.1%] which was 5 times the baseline prevalence. A deep learning model that employs common clinical features in the electronic health record can help predict the onset of CLABSI in hospitalized children with central venous line 48 hours prior to the time of specimen collection.
预测中心静脉导管（CVL）患儿中假定的严重感染的发病情况，定义为采集到阳性血培养且开始至少4天的新抗生素疗程（PSI）。回顾性队列研究。单一学术儿童医院。纳入2013年1月至2018年12月期间所有住院病例，但不包括没有CVL或住院时间短于24小时的病例。从电子病历中回顾性提取临床特征，包括人口统计学、实验室检查结果、生命体征、CVL特征和使用的药物。数据汇总自单一儿科医疗系统内的所有医院，并用于训练深度学习模型，以预测住院后48小时内PSI的发生情况。将所提出的模型预测结果与通过疾病严重程度标志物（PELOD-2）预测PSI的结果进行比较。在所有分段的48小时时间窗口中，导管感染的基线患病率为0.34%。使用发病时间在病例中识别事件。病例使用从入院到发病的所有数据，对照组使用从入院到出院的所有数据。基准数据汇总在所有48小时时间窗口内[与27,137例患者住院相关的N = 748,380]。该模型的受试者操作特征曲线下面积为0.993（95%CI = [0.990, 0.996]），富集阳性预测值（PPV）比基线患病率高23倍。相反，PELOD-2的预测PPV较低，为1.5%[0.9%，2.1%]，是基线患病率的5倍。一种利用电子健康记录中的常见临床特征的深度学习模型可以帮助预测住院儿童中心静脉导管相关血流感染（CLABSI）在标本采集前48小时的发病情况。
