Prediction modelling in the early detection of neonatal sepsis

Abstract

Background 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. Methods 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. Results 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. Conclusion 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.

Utilizing big data from electronic health records in pediatric clinical care

“… 15 With increasing accessibility to large healthcare datasets and computational power, more data-driven approaches for prediction of sepsis have emerged by learning from an institution’s sepsis data to refine a prediction tool with weighted predictors that may not have been included or existing predictors that had different weighting in the initial rule-based model. Most of these rely on a combination of expert-informed selection of predictors and application of a statistical model or learning algorithm, 14 , 16 such as the PELOD-2 score that has been applied to the task of pediatric sepsis detection. 17 , 18 More recent models have relied more on knowledge gleaned from data instead of experts by leveraging advanced AI and ML algorithms.…”

“…In pediatric sepsis, several high-performing predictive tools have been developed for a wide range of sepsis definitions and patient populations. [14][15][16] A continuous, automated EHR-based sepsis screening algorithm to identify severe sepsis among children in the inpatient and emergency department settings is an example of rule-based prediction designed by experts to provide decision support for early detection of sepsis. 15 With increasing accessibility to large healthcare datasets and computational power, more data-driven approaches for prediction of sepsis have emerged by learning from an institution's sepsis data to refine a prediction tool with weighted predictors that may not have been included or existing predictors that had different weighting in the initial rule-based model.…”

“…(2) non-knowledge based (or data-driven) systems that employ statistical and artificial intelligence (with or without machine learning techniques) to identify and learn from patterns in data 57,58 Improved patient outcomes Organizational maturity in analytics over time In both adult and pediatric sepsis, predictive performance has been the primary focus of most evaluation studies. 14,16,25,26 High performance in sepsis prediction is critical in order to prevent missed cases while minimizing over-diagnosis that may lead to unintended consequences (e.g., alarm fatigue or inappropriate or overuse of antibiotics); 25,27 however, little attention has been paid to other factors that may influence adoption and acceptance into clinical practice. There is evidence that implementation of pediatric sepsis prediction tools may be associated with decreases in acute kidney injuries, hospital and ICU lengths of stay, mortality rates, and durations of organ dysfunction.…”

Automated digital technologies for supporting sepsis prediction in children: a scoping review protocol

“…With the call from the WHO to improve sepsis identification and the potential for data-driven and knowledge-based technologies,3 16 digital prediction technologies are becoming more advanced using mathematical, statistical and machine learning techniques to support sepsis prediction using clinical information, symptoms, biomarkers and other signs at the bedside 17–20. While recent reviews have explored the literature on the effectiveness of digital technologies for adult and neonate sepsis prediction,17 18 21–25 there is currently no review on the design and implementation of these predictive technologies for children. Considering the pathophysiology and aetiology for paediatric sepsis are different from that seen in adults and neonates,26 combined with the lack of widely accessible digital technologies for children compared with adults,27 it is critically important to review the literature on this age cohort.…”

Care of the Newly Born Needs to Begin Prior to Birth and to Continue then After

“…9 Early diagnosis is a key determinant of outcomes in most neonatal conditions. 10 The timing of disease onset may be particularly important because of the possibility of interruption in the structural and functional changes that are going on during that period of development. 3 Hence, serial imaging and/or laboratory tests can be used not only for monitoring fetuses/infants with known disease conditions but also for the evaluation of normal or hitherto asymptomatic fetuses/infants who have known familial or genetic risk factors.…”