Early recognition of neonatal sepsis using a bioinformatic vital sign monitoring tool

Continuous prediction and clinical alarm management of late-onset sepsis in preterm infants using vital signs from a patient monitor

BACKGROUND AND OBJECTIVE

Continuous prediction of late-onset sepsis (LOS) could be helpful for improving clinical outcomes in neonatal intensive care units (NICU). This study aimed to develop an artificial intelligence (AI) model for assisting the bedside clinicians in successfully identifying infants at risk for LOS using non-invasive vital signs monitoring.

METHODS

In a retrospective study from the NICU of the Máxima Medical Center in Veldhoven, the Netherlands, a total of 492 preterm infants less than 32 weeks gestation were included between July 2016 and December 2018. Data on heart rate (HR), respiratory rate (RR), and oxygen saturation (SpO2) at 1 Hz were extracted from the patient monitor. We developed multiple AI models using 102 extracted features or raw time series to provide hourly LOS risk prediction. Shapley values were used to explain the model. For the best performing model, the effect of different vital signs and also the input type of signals on model performance was tested. To further assess the performance of applying the best performing model in a real-world clinical setting, we performed a simulation using four different alarm policies on continuous real-time predictions starting from three days after birth.

RESULTS

A total of 51 LOS patients and 68 controls were finally included according to the patient inclusion and exclusion criteria. When tested by seven-fold cross-validations, the mean (standard deviation) area under the receiver operating characteristic curve (AUC) six hours before CRASH was 0.875 (0.072) for the best performing model, compared to the other six models with AUC ranging from 0.782 (0.089) to 0.846 (0.083). The best performing model performed only slightly worse than the model learning from raw physiological waveforms (0.886 [0.068]), successfully detecting 96.1 % of LOS patients before CRASH. When setting the expected alarm window to 24 h and using a multi-threshold alarm policy, the sensitivity metric was 71.6 %, while the positive predictive value was 9.9 %, resulting in an average of 1.15 alarms per day per patient.

CONCLUSIONS

The proposed AI model, which learns from routinely collected vital signs, has the potential to assist clinicians in the early detection of LOS. Combined with interpretability and clinical alarm management, this model could be better translated into medical practice for future clinical implementation.

Bioinformatics in Neonatal/Pediatric Medicine-A Literature Review

Bioinformatics is a scientific field that uses computer technology to gather, store, analyze, and share biological data and information. DNA sequences of genes or entire genomes, protein amino acid sequences, nucleic acid, and protein-nucleic acid complex structures are examples of traditional bioinformatics data. Moreover, proteomics, the distribution of proteins in cells, interactomics, the patterns of interactions between proteins and nucleic acids, and metabolomics, the types and patterns of small-molecule transformations by the biochemical pathways in cells, are further data streams. Currently, the objectives of bioinformatics are integrative, focusing on how various data combinations might be utilized to comprehend organisms and diseases. Bioinformatic techniques have become popular as novel instruments for examining the fundamental mechanisms behind neonatal diseases. In the first few weeks of newborn life, these methods can be utilized in conjunction with clinical data to identify the most vulnerable neonates and to gain a better understanding of certain mortalities, including respiratory distress, bronchopulmonary dysplasia, sepsis, or inborn errors of metabolism. In the current study, we performed a literature review to summarize the current application of bioinformatics in neonatal medicine. Our aim was to provide evidence that could supply novel insights into the underlying mechanism of neonatal pathophysiology and could be used as an early diagnostic tool in neonatal care.

A sepsis trigger tool reduces time to antibiotic administration in the NICU

OBJECTIVE

Prolonged time to antibiotic administration is associated with increased morbidity and mortality. Interventions to decrease the time to antibiotic administration may improve mortality and morbidity.

STUDY DESIGN

We identified possible change concepts for reducing time to antibiotic usage in the NICU. For the initial intervention, we developed a sepsis screening tool based on NICU-specific parameters. The main goal of the project was to reduce time to antibiotic administration by 10%.

RESULTS

The project was conducted from April 2017 until April 2019. There were no missed cases of sepsis in the project period. Time to antibiotic administration for patients who were started on antibiotics decreased during the project, with the mean shifting from 126 to 102 min, a reduction of 19%.

CONCLUSIONS

We successfully reduced time to antibiotic delivery in our NICU using a trigger tool to identifying potential cases of sepsis in the NICU environment. The trigger tool requires broader validation.

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

INTRODUCTION

While there have been several literature reviews on the performance of digital sepsis prediction technologies and clinical decision-support algorithms for adults, there remains a knowledge gap in examining the development of automated technologies for sepsis prediction in children. This scoping review will critically analyse the current evidence on the design and performance of automated digital technologies to predict paediatric sepsis, to advance their development and integration within clinical settings.

METHODS AND ANALYSIS

This scoping review will follow Arksey and O'Malley's framework, conducted between February and December 2022. We will further develop the protocol using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. We plan to search the following databases: Association of Computing Machinery (ACM) Digital Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, Google Scholar, Institute of Electric and Electronic Engineers (IEEE), PubMed, Scopus and Web of Science. Studies will be included on children >90 days postnatal to <21 years old, predicted to have or be at risk of developing sepsis by a digitalised model or algorithm designed for a clinical setting. Two independent reviewers will complete the abstract and full-text screening and the data extraction. Thematic analysis will be used to develop overarching concepts and present the narrative findings with quantitative results and descriptive statistics displayed in data tables.

ETHICS AND DISSEMINATION

Ethics approval for this scoping review study of the available literature is not required. We anticipate that the scoping review will identify the current evidence and design characteristics of digital prediction technologies for the timely and accurate prediction of paediatric sepsis and factors influencing clinical integration. We plan to disseminate the preliminary findings from this review at national and international research conferences in global and digital health, gathering critical feedback from multidisciplinary stakeholders. SCOPING REVIEW REGISTRATION: https://osf.io/veqha/?view_only=f560d4892d7c459ea4cff6dcdfacb086.

Serum Cystatin C Level Monitoring for Intervention Opportunity of CBP in Children with Severe Sepsis

Objective

The aim of this study is to investigate the instruction value of the serum cystatin C (Cys C) level monitoring for intervention opportunity of continuous blood purification technology (CBP) in children with severe sepsis.

Methods

67 children with severe sepsis in the pediatric intensive care unit (PICU) with CBP treatment were retrospectively selected from May 2016 to April 2020. According to the time intervals between the time point of serum Cys C level began to increase (>15 mg/L) and the time point of CBP began, all children were divided into group A (<24 h, 29 cases), group B (24–48 h, 22 cases), and group C (>48 h, 16 cases). The children's general characteristics, vital signs, biochemical parameters, acute physiology and chronic health evaluation (APACHE II), and sequential organ failure assessment (SOFA) scores were evaluated. The influence factors of prognosis of children with severe sepsis were analyzed by multivariate regression analysis.

Results

The intervals between the time point of PICU hospitalization and the time point of CBP began and the times of CBP in group A were significantly more than those in group B and C (P < 0.05). There was no statistically significant duration of CBP among three groups (P > 0.05). After follow-up of 28 d, there was no significant difference on the occurrence of coagulation disorders and hypovolemic shock induced by CBP among three groups (P > 0.05). However, the mortality of children in group A was lower than that in group C (P < 0.05). Children in group A had lower APACHE II scores, SOFA scores, serum K+, blood urea nitrogen (BUN), serum creatine (SCr), partial pressure of carbon dioxide (PCO₂), and higher partial pressure of oxygen (PO₂) than those of children in group C after CBP. (P < 0.05). SOFA scores ≥5 after CBP treatment and the time intervals between the time point of serum Cys C level began to increase (>15 mg/L) and the time point of CBP began ≥24 h were the independent influence factors on the prognosis by multivariate regression analysis.

Conclusion

There are significant evidences that continuous blood purification technology within 24 h of serum Cys C level may better control the condition of children with severe sepsis.

Paediatric and neonatal sepsis and inflammation

Sepsis has a huge impact on global mortality and has been declared as a priority by the World Health organisation the WHO.1 Children have a high incidence of sepsis especially in the neonatal with an estimated 3 million babies affected worldwide and mortality ranges from 11 to 19%.2 In addition, long-term neurodevelopmental outcomes are affected but this is largely unquantified. However, challenges remain in the early recognition, diagnosis and standardised management of sepsis. This series on Sepsis and inflammation in children reviews the conundrums of diagnostic criteria, biomarkers, management and future strategies to improve outcomes.