Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education (MAHE), Manipal, 576104, Karnataka, India.
Department of Paediatrics, Kasturba Medical College, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India, 576104.
World J Pediatr. 2022 Mar;18(3):160-175. doi: 10.1007/s12519-021-00505-1. Epub 2022 Jan 5.
Prediction modelling can greatly assist the health-care professionals in the management of diseases, thus sparking interest in neonatal sepsis diagnosis. The main objective of the study was to provide a complete picture of performance of prediction models for early detection of neonatal sepsis.
PubMed, Scopus, CINAHL databases were searched and articles which used various prediction modelling measures for the early detection of neonatal sepsis were comprehended. Data extraction was carried out based on Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. Extricate data consisted of objective, study design, patient characteristics, type of statistical model, predictors, outcome, sample size and location. Prediction model Risk of Bias Assessment Tool was applied to gauge the risk of bias of the articles.
An aggregate of ten studies were included in the review among which eight studies had applied logistic regression to build a prediction model, while the remaining two had applied artificial intelligence. Potential predictors like neonatal fever, birth weight, foetal morbidity and gender, cervicovaginitis and maternal age were identified for the early detection of neonatal sepsis. Moreover, birth weight, endotracheal intubation, thyroid hypofunction and umbilical venous catheter were promising factors for predicting late-onset sepsis; while gestational age, intrapartum temperature and antibiotics treatment were utilised as budding prognosticators for early-onset sepsis detection.
Prediction modelling approaches were able to recognise promising maternal, neonatal and laboratory predictors in the rapid detection of early and late neonatal sepsis and thus, can be considered as a novel way for clinician decision-making towards the disease diagnosis if not used alone, in the years to come.
预测模型可以极大地帮助医疗保健专业人员管理疾病，因此激发了人们对新生儿败血症诊断的兴趣。本研究的主要目的是全面描述用于早期检测新生儿败血症的预测模型的性能。
检索了 PubMed、Scopus 和 CINAHL 数据库，并理解了使用各种预测建模措施来早期检测新生儿败血症的文章。根据预测模型研究系统评价的批判性评价和数据提取清单进行了数据提取。提取的数据包括目标、研究设计、患者特征、统计模型类型、预测因子、结局、样本量和地点。应用预测模型偏倚风险评估工具来评估文章的偏倚风险。
综述共纳入了十项研究，其中八项研究应用逻辑回归构建了预测模型，而另外两项研究则应用了人工智能。为了早期检测新生儿败血症，确定了一些潜在的预测因子，如新生儿发热、出生体重、胎儿发病率和性别、宫颈阴道炎和产妇年龄等。此外，出生体重、气管插管、甲状腺功能减退和脐静脉导管是预测晚发性败血症的有前途的因素；而胎龄、产时体温和抗生素治疗则被用作早期败血症检测的有前途的预后因素。
预测模型方法能够快速识别有希望的母体、新生儿和实验室预测因子，用于早期和晚期新生儿败血症的检测，因此，如果不单独使用，在未来几年，它可以被认为是临床医生决策的一种新方法。
