Development and validation of a diagnostic model for early differentiation of sepsis and non-infectious SIRS in critically ill children - a data-driven approach using machine-learning algorithms

The Role of Artificial Intelligence in Pediatric Intensive Care: A Systematic Review

Pediatric intensive care units (PICUs) could transform due to artificial intelligence (AI), which could improve patient outcomes, increase diagnostic accuracy, and streamline repetitive procedures. The goal of this systematic review was to outline how AI can be used to enhance any health outcomes in pediatric intensive care. We searched four databases (PubMed, Scopus, Web of Science, and IEEE Xplore) for relevant studies using a predefined systematic search. We found 267 studies in these four databases. The studies were first screened to remove the duplicates and then screened by titles to remove irrelevant studies. The studies were further screened based on inclusion and exclusion criteria, in which 32 studies were found suitable for inclusion in this study. The studies were assessed for risk of bias using the Prediction Model Risk Of Bias Assessment Tool (PROBAST) tool. After AI was implemented, almost 22% (n = 7) of studies showed an immediate effect and enhanced health outcomes. A small number of studies involved AI implementation in actual PICUs, while the majority focused on experimental testing. AI models outperformed conventional clinical modalities among the remaining 78% (n = 25) and might have indirectly impacted patient outcomes. Significant variation in metrics and standardization was found when health outcomes were quantitatively assessed using statistical measures, including specificity (38%; n = 12) and area under the receiver operating characteristic curve (AUROC) (56%; n = 18). There are not sufficient studies showing that AI has significantly enhanced pediatric critical care patients' health outcomes. To evaluate AI's impact, more prospective, experimental research is required, utilizing verified outcome measures, defined metrics, and established application frameworks.

Comparison of Random Forest and Stepwise Regression for Variable Selection Using Low Prevalence Predictors: A case Study in Paediatric Sepsis

INTRODUCTION

Variable selection is a common technique to identify the most predictive variables from a pool of candidate predictors. Low prevalence predictors (LPPs) are frequently found in clinical data, yet few studies have explored their impact on model performance during variable selection. This study compared the Random Forest (RF) algorithm and stepwise regression (SWR) for variable selection using data from a paediatric sepsis screening tool, where 18 out of 32 predictors had a prevalence < 10%.

METHODS

Variable selection using RF was compared to forward and backward SWR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), and the variables retained. Additionally, a simulation study assessed how increasing the prevalence of the predictors impacted the variable selection results.

RESULTS

The best fitting RF and SWR models retained were 22, and 17 predictors, respectively, with 14 and 10 predictors having a prevalence < 10%. Both the RF and SWR models had similar predictive performance (RF: AUC [95% Confidence Interval] 0.79 [0.77, 0.81], LR: 0.80 [0.78, 0.82]). The simulation study revealed differences for both RF and SWR models in variable importance rankings and predictor selection with increasing prevalence thresholds, particularly for moderately and strongly associated predictors.

DISCUSSION

The RF algorithm retained a number of very low prevalence predictors compared to SWR. However, the predictive performance of both models were comparable, demonstrating that when applied correctly and the number of candidate predictors is small, both methods are suitable for variable selection when using low prevalence predictors.

Profile of Serum Inflammatory Biomarkers in Children with Peritonitis and their Role in Predicting the Severity and Outcome

Aims and Objectives

The aim of this study was to determine whether the levels of serum inflammatory markers (C-reactive protein [CRP], interleukin-6 [IL-6], calprotectin, and N terminal pro-B-type natriuretic peptide [NT-proBNP]) predict the severity and outcome in children with peritonitis. The primary objective was to evaluate the profile of these serum inflammatory biomarkers in children with peritonitis. The secondary objectives were to correlate the level of these biomarkers with pediatric sequential organ failure assessment (pSOFA) severity score at admission and predict the outcome (mortality).

Methods

In this prospective observational study, the level of above serum inflammatory biomarkers in children with peritonitis was measured at the time of admission. The disease severity was assessed using pSOFA score and the association of these biomarkers with the outcomes was studied.

Results

A total of 80 children with peritonitis (M: F:: 9:7, mean age: 6.22 ± 3.7 years) were included. The median values of serum CRP, IL-6, calprotectin, and NT-proBNP were 196.88 mg/L (interquartile range [IQR]: 124.37, 285.6), 6.74 pg/ml (IQR: 1.87, 12.54), 46750 μg/L (IQR: 17937.5, 84075), and 365.2 ng/L (IQR: 170, 1034), respectively. Serum CRP and NT-proBNP correlated with pSOFA score. The pSOFA score >4 and serum NT-proBNP were significant in predicting mortality in children with peritonitis (p < 0.001).

Conclusion

In children with peritonitis, serum levels of inflammatory biomarkers, i.e. CRP, calprotectin, and NT-proBNP were found to be raised, whereas IL-6 was not raised. The pSOFA score >4 predicted mortality. The serum levels of NT-proBNP were significantly raised in nonsurvivors in children with peritonitis, therefore, can be used as a predictor of severity and mortality in children with peritonitis.

A scoping review on pediatric sepsis prediction technologies in healthcare

This scoping review evaluates recent advancements in data-driven technologies for predicting non-neonatal pediatric sepsis, including artificial intelligence, machine learning, and other methodologies. Of the 27 included studies, 23 (85%) were single-center investigations, and 16 (59%) used logistic regression. Notably, 20 (74%) studies used datasets with a low prevalence of sepsis-related outcomes, with area under the receiver operating characteristic scores ranging from 0.56 to 0.99. Prediction time points varied widely, and development characteristics, performance metrics, implementation outcomes, and considerations for human factors-especially workflow integration and clinical judgment-were inconsistently reported. The variations in endpoint definitions highlight the potential significance of the 2024 consensus criteria in future development. Future research should strengthen the involvement of clinical users to enhance the understanding and integration of human factors in designing and evaluating these technologies, ultimately aiming for safe and effective integration in pediatric healthcare.

An escape from ESKAPE pathogens: A comprehensive review on current and emerging therapeutics against antibiotic resistance

The rise of antimicrobial resistance has positioned ESKAPE pathogens as a serious global health threat, primarily due to the limitations and frequent failures of current treatment options. This growing risk has spurred the scientific community to seek innovative antibiotic therapies and improved oversight strategies. This review aims to provide a comprehensive overview of the origins and resistance mechanisms of ESKAPE pathogens, while also exploring next-generation treatment strategies for these infections. In addition, it will address both traditional and novel approaches to combating antibiotic resistance, offering insights into potential new therapeutic avenues. Emerging research underscores the urgency of developing new antimicrobial agents and strategies to overcome resistance, highlighting the need for novel drug classes and combination therapies. Advances in genomic technologies and a deeper understanding of microbial pathogenesis are crucial in identifying effective treatments. Integrating precision medicine and personalized approaches could enhance therapeutic efficacy. The review also emphasizes the importance of global collaboration in surveillance and stewardship, as well as policy reforms, enhanced diagnostic tools, and public awareness initiatives, to address resistance on a worldwide scale.

Artificial intelligence and prescription of antibiotic therapy: present and future

INTRODUCTION

In the past few years, the use of artificial intelligence in healthcare has grown exponentially. Prescription of antibiotics is not exempt from its rapid diffusion, and various machine learning (ML) techniques, from logistic regression to deep neural networks and large language models, have been explored in the literature to support decisions regarding antibiotic prescription.

AREAS COVERED

In this narrative review, we discuss promises and challenges of the application of ML-based clinical decision support systems (ML-CDSSs) for antibiotic prescription. A search was conducted in PubMed up to April 2024.

EXPERT OPINION

Prescribing antibiotics is a complex process involving various dynamic phases. In each of these phases, the support of ML-CDSSs has shown the potential, and also the actual ability in some studies, to favorably impacting relevant clinical outcomes. Nonetheless, before widely exploiting this massive potential, there are still crucial challenges ahead that are being intensively investigated, pertaining to the transparency of training data, the definition of the sufficient degree of prediction explanations when predictions are obtained through black box models, and the legal and ethical framework for decision responsibility whenever an antibiotic prescription is supported by ML-CDSSs.

Continuous monitoring of physiological data using the patient vital status fusion score in septic critical care patients

Accurate and standardized methods for assessing the vital status of patients are crucial for patient care and scientific research. This study introduces the Patient Vital Status (PVS), which quantifies and contextualizes a patient's physical status based on continuous variables such as vital signs and deviations from age-dependent normative values. The vital signs, heart rate, oxygen saturation, respiratory rate, mean arterial blood pressure, and temperature were selected as input to the PVS pipeline. The method was applied to 70 pediatric patients in the intensive care unit (ICU), and its efficacy was evaluated by matching high values with septic events at different time points in patient care. Septic events included systemic inflammatory response syndrome (SIRS) and suspected or proven sepsis. The comparison of maximum PVS values between the presence and absence of a septic event showed significant differences (SIRS/No SIRS: p < 0.0001, η2 = 0.54; Suspected Sepsis/No Suspected Sepsis: p = 0.00047, η2 = 0.43; Proven Sepsis/No Proven Sepsis: p = 0.0055, η2 = 0.34). A further comparison between the most severe PVS in septic patients with the PVS at ICU discharge showed even higher effect sizes (SIRS: p < 0.0001, η2 = 0.8; Suspected Sepsis: p < 0.0001, η2 = 0.8; Proven Sepsis: p = 0.002, η2 = 0.84). The PVS is emerging as a data-driven tool with the potential to assess a patient's vital status in the ICU objectively. Despite real-world data challenges and potential annotation biases, it shows promise for monitoring disease progression and treatment responses. Its adaptability to different disease markers and reliance on age-dependent reference values further broaden its application possibilities. Real-time implementation of PVS in personalized patient monitoring may be a promising way to improve critical care. However, PVS requires further research and external validation to realize its true potential.

Machine learning algorithms in sepsis

Sepsis remains a significant global health challenge due to its high mortality and morbidity, compounded by the difficulty of early detection given its variable clinical manifestations. The integration of machine learning (ML) into laboratory medicine for timely sepsis identification and outcome forecasting is an emerging field of interest. This comprehensive review assesses the current body of research on ML applications for sepsis within the realm of laboratory diagnostics, detailing both their strengths and shortcomings. An extensive literature search was performed by two independent investigators across PubMed and Scopus databases, employing the keywords "Sepsis," "Machine Learning," and "Laboratory" without publication date limitations, culminating in January 2023. Each selected study was meticulously evaluated for various aspects, including its design, intent (diagnostic or prognostic), clinical environment, demographics, sepsis criteria, data gathering period, and the scope and nature of features, in addition to the ML methodologies and their validation procedures. Out of 135 articles reviewed, 39 fulfilled the criteria for inclusion. Among these, the majority (30 studies) were focused on devising ML algorithms for diagnosis, fewer (8 studies) on prognosis, and one study addressed both aspects. The dissemination of these studies across an array of journals reflects the interdisciplinary engagement in the development of ML algorithms for sepsis. This analysis highlights the promising role of ML in the early diagnosis of sepsis while drawing attention to the need for uniformity in validating models and defining features, crucial steps for ensuring the reliability and practicality of ML in clinical setting.

Biomarkers for Serious Bacterial Infections in Febrile Children

Febrile infections in children are a common cause of presentation to the emergency department (ED). While viral infections are usually self-limiting, sometimes bacterial illnesses may lead to sepsis and severe complications. Inflammatory biomarkers such as C reactive protein (CRP) and procalcitonin are usually the first blood exams performed in the ED to differentiate bacterial and viral infections; nowadays, a better understanding of immunochemical pathways has led to the discovery of new and more specific biomarkers that could play a role in the emergency setting. The aim of this narrative review is to provide the most recent evidence on biomarkers and predictor models, combining them for serious bacterial infection (SBI) diagnosis in febrile children. Literature analysis shows that inflammatory response is a complex mechanism in which many biochemical and immunological factors contribute to the host response in SBI. CRP and procalcitonin still represent the most used biomarkers in the pediatric ED for the diagnosis of SBI. Their sensibility and sensitivity increase when combined, and for this reason, it is reasonable to take them both into consideration in the evaluation of febrile children. The potential of machine learning tools, which represent a real novelty in medical practice, in conjunction with routine clinical and biological information, may improve the accuracy of diagnosis and target therapeutic options in SBI. However, studies on this matter are not yet validated in younger populations, making their relevance in pediatric precision medicine still uncertain. More data from further research are needed to improve clinical practice and decision making using these new technologies.

Artificial intelligence as a smart approach to develop antimicrobial drug molecules: A paradigm to combat drug-resistant infections

Antimicrobial resistance (AMR) is a silent pandemic with the third highest global mortality. The antibiotic development pipeline is scarce even though AMR has escalated uncontrollably. Artificial intelligence (AI) is a revolutionary approach, accelerating drug discovery because of its fast pace, cost efficiency, lower labor requirements, and fewer chances of failure. AI has been used to discover several beta-lactamase inhibitors and antibiotic alternatives from antimicrobial peptides (AMPs), nonribosomal peptides, bacteriocins, and marine natural products. The significant recent increase in the use of AI platforms by pharmaceutical companies could result in the discovery of efficient antibiotic alternatives with lower chances of resistance generation.

Artificial intelligence-based clinical decision support in pediatrics

Machine learning models may be integrated into clinical decision support (CDS) systems to identify children at risk of specific diagnoses or clinical deterioration to provide evidence-based recommendations. This use of artificial intelligence models in clinical decision support (AI-CDS) may have several advantages over traditional "rule-based" CDS models in pediatric care through increased model accuracy, with fewer false alerts and missed patients. AI-CDS tools must be appropriately developed, provide insight into the rationale behind decisions, be seamlessly integrated into care pathways, be intuitive to use, answer clinically relevant questions, respect the content expertise of the healthcare provider, and be scientifically sound. While numerous machine learning models have been reported in pediatric care, their integration into AI-CDS remains incompletely realized to date. Important challenges in the application of AI models in pediatric care include the relatively lower rates of clinically significant outcomes compared to adults, and the lack of sufficiently large datasets available necessary for the development of machine learning models. In this review article, we summarize key concepts related to AI-CDS, its current application to pediatric care, and its potential benefits and risks. IMPACT: The performance of clinical decision support may be enhanced by the utilization of machine learning-based algorithms to improve the predictive performance of underlying models. Artificial intelligence-based clinical decision support (AI-CDS) uses models that are experientially improved through training and are particularly well suited toward high-dimensional data. The application of AI-CDS toward pediatric care remains limited currently but represents an important area of future research.

Machine Learning in Infectious Disease for Risk Factor Identification and Hypothesis Generation: Proof of Concept Using Invasive Candidiasis

Background

Machine learning (ML) models can handle large datasets without assuming underlying relationships and can be useful for evaluating disease characteristics; yet, they are more commonly used for predicting individual disease risk rather than identifying factors at the population level. We offer a proof of concept applying random forest (RF) algorithms to Candida-positive hospital encounters in an electronic health record database of patients in the U.S.

Methods

Candida-positive encounters were extracted from the Cerner HealthFacts database; invasive infections were laboratory positive sterile site Candida infections. Features included demographics, admission source, care setting, physician specialty, diagnostic and procedure codes, and medications received prior to the first positive Candida culture. We used RF to assess risk factors for three outcomes: any invasive candidiasis (IC) vs non-IC, within-species IC vs non-IC (e.g. invasive C. glabrata vs non-invasive C. glabrata), and between-species IC (e.g. invasive C. glabrata vs all other IC).

Results

14 of 169 (8%) variables were consistently identified as important features in the ML models. When evaluating within-species IC, for example invasive C. glabrata vs non-invasive C. glabrata, we identified known features like central venous catheters, ICU stay, and gastrointestinal operations. In contrast, important variables for invasive C. glabrata vs all other IC included renal disease and medications like diabetes therapeutics, cholesterol medications, and antiarrhythmics.

Conclusions

Known and novel risk factors for IC were identified using ML, demonstrating the hypotheses generating utility of this approach for infectious disease conditions about which less is known, specifically at the species-level or for rarer diseases.

The Risk of Coding Racism into Pediatric Sepsis Care: The Necessity of Antiracism in Machine Learning

Machine learning holds the possibility of improving racial health inequalities by compensating for human bias and structural racism. However, unanticipated racial biases may enter during model design, training, or implementation and perpetuate or worsen racial inequalities if ignored. Pre-existing racial health inequalities could be codified into medical care by machine learning without clinicians being aware. To illustrate the importance of a commitment to antiracism at all stages of machine learning, we examine machine learning in predicting severe sepsis in Black children, focusing on the impacts of structural racism that may be perpetuated by machine learning and difficult to discover. To move toward antiracist machine learning, we recommend partnering with ethicists and experts in model development, enrolling representative samples for training, including socioeconomic inputs with proximate causal associations to racial inequalities, reporting outcomes by race, and committing to equitable models that narrow inequality gaps or at least have equal benefit.

A Pilot Assessment on the Role of Procalcitonin Dynamic Monitoring in the Early Diagnosis of Infection Post Cardiac Surgery

Purpose

To evaluate the value of dynamic monitoring of procalcitonin (PCT) as a biomarker for the early diagnosis of postoperative infections in patients undergoing cardiac surgery.

Methods

In total, 252 patients who underwent cardiac surgery were retrospectively included. The postoperative patients’ PCT level, change value (△PCT), and clearance rate (△PCTc) were compared between the infected and noninfected groups in adult and pediatric patients on postoperative days (PODs) 1, 3, and 5. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the diagnostic value.

Results

Procalcitonin concentration decreased progressively in the noninfected group in adult and pediatric patients; PCT concentration continued to rise until it peaked on POD 3 in the infected group. In adult patients, the AUC of PCT for diagnosis of infection on PODs 1, 3, and 5 were 0.626, 0.817, and 0.806, with the optimal cut-off values of 7.35, 3.63, and 1.73 ng/ml, respectively. The diagnostic efficiency of △PCT₃ and △PCT_C3 was significantly better than △PCT₅ and △PCT_C5, respectively. In pediatric patients, the AUC of PCT for diagnosis of infection on PODs 1, 3, and 5 were 0.677, 0.747, and 0.756, respectively, and the optimal cut-off values were 27.62, 26.15, and 10.20 ng/ml.

Conclusion

This study showed that dynamic monitoring of PCT levels could be an effective clinical means to help to discover postoperative infection earlier. The PCT level and its change indicators on POD 3 in adult patients and the PCT level on POD 5 in children can indicate infection.

Clinical Informatics and Quality Improvement in the Pediatric Intensive Care Unit

Clinical informatics can support quality improvement and patient safety in the pediatric intensive care unit (PICU) in several ways including data extraction, analysis, and decision support enabled by electronic health records (EHRs), and databases and registries. Clinical decision support (CDS), embedded in EHRs, now an integral part of the workflow in the PICU, includes several tools and is increasingly leveraging artificial intelligence (AI). Understanding the opportunities and challenges can improve the engagement of clinicians with the design, validation, and implementation of CDS, improve satisfaction with CDS, and improve patient safety, care quality, and value.

Artificial Intelligence in NICU and PICU: A Need for Ecological Validity, Accountability, and Human Factors

Pediatric patients, particularly in neonatal and pediatric intensive care units (NICUs and PICUs), are typically at an increased risk of fatal decompensation. That being said, any delay in treatment or minor errors in medication dosage can overcomplicate patient health. Under such an environment, clinicians are expected to quickly and effectively comprehend large volumes of medical information to diagnose and develop a treatment plan for any baby. The integration of Artificial Intelligence (AI) into the clinical workflow can be a potential solution to safeguard pediatric patients and augment the quality of care. However, before making AI an integral part of pediatric care, it is essential to evaluate the technology from a human factors perspective, ensuring its readiness (technology readiness level) and ecological validity. Addressing AI accountability is also critical to safeguarding clinicians and improving AI acceptance in the clinical workflow. This article summarizes the application of AI in NICU/PICU and consecutively identifies the existing flaws in AI (from clinicians’ standpoint), and proposes related recommendations, which, if addressed, can improve AIs’ readiness for a real clinical environment.

Artificial Intelligence in Infection Management in the ICU

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2022. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2022. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901.

Future of machine learning in paediatrics

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn without being explicitly programmed, through a combination of statistics and computer science. It encompasses a variety of techniques used to analyse and interpret extremely large amounts of data, which can then be applied to create predictive models. Such applications of this technology are now ubiquitous in our day-to-day lives: predictive text, spam filtering, and recommendation systems in social media, streaming video and e-commerce to name a few examples. It is only more recently that ML has started to be implemented against the vast amount of data generated in healthcare. The emerging role of AI in refining healthcare delivery was recently highlighted in the 'National Health Service Long Term Plan 2019'. In paediatrics, workforce challenges, rising healthcare attendance and increased patient complexity and comorbidity mean that demands on paediatric services are also growing. As healthcare moves into this digital age, this review considers the potential impact ML can have across all aspects of paediatric care from improving workforce efficiency and aiding clinical decision-making to precision medicine and drug development.

Development of a Novel Assessment Tool and Code Sepsis Checklist for Neonatal Late-Onset Sepsis

BACKGROUND

Accurate diagnosis and timely management of neonatal late-onset sepsis (nLOS) have been less well-studied than those of early-onset sepsis. We noticed a delay in nLOS detection and management in our neonatal intensive care unit.

PURPOSE

To develop an assessment tool to aid in the recognition and reporting of nLOS and to standardize the management process once sepsis is recognized.

METHODS

The Plan-Do-Study-Act (PDSA) improvement model provided the framework for interventions for our antibiotic stewardship program, including the aims of this project. A literature review was performed to evaluate tools and other literature available to guide the evaluation and management of suspected sepsis. A quality improvement project was initiated to develop tools for the detection and management of nLOS.

RESULTS

An nLOS assessment tool to help identify neonates at risk for nLOS and a Code Sepsis checklist to standardize the process of evaluation and management of nLOS were developed. The guiding principles of this tool development were empowerment of nurses to initiate the assessment process, clarification of team roles, and removal of barriers to appropriate antibiotic administration.

IMPLICATIONS FOR PRACTICE

Useful and practical tools valued by nursing and the multidisciplinary team may facilitate timely identification and treatment of infants with nLOS.

IMPLICATIONS FOR RESEARCH

Future directions include validation of the nLOS assessment tool and the Code Sepsis checklist as well as ensuring the reliability of the tool to improve detection of nLOS and to reduce time to administer antibiotics in cases of nLOS.

Artificial Intelligence to Improve Health Outcomes in the NICU and PICU: A Systematic Review

CONTEXT

Artificial intelligence (AI) technologies are increasingly used in pediatrics and have the potential to help inpatient physicians provide high-quality care for critically ill children.

OBJECTIVE

We aimed to describe the use of AI to improve any health outcome(s) in neonatal and pediatric intensive care.

DATA SOURCE

PubMed, IEEE Xplore, Cochrane, and Web of Science databases.

STUDY SELECTION

We used peer-reviewed studies published between June 1, 2010, and May 31, 2020, in which researchers described (1) AI, (2) pediatrics, and (3) intensive care. Studies were included if researchers assessed AI use to improve at least 1 health outcome (eg, mortality).

DATA EXTRACTION

Data extraction was conducted independently by 2 researchers. Articles were categorized by direct or indirect impact of AI, defined by the European Institute of Innovation and Technology Health joint report.

RESULTS

Of the 287 publications screened, 32 met inclusion criteria. Approximately 22% (n = 7) of studies revealed a direct impact and improvement in health outcomes after AI implementation. Majority were in prototype testing, and few were deployed into an ICU setting. Among the remaining 78% (n = 25) AI models outperformed standard clinical modalities and may have indirectly influenced patient outcomes. Quantitative assessment of health outcomes using statistical measures, such as area under the receiver operating curve (56%; n = 18) and specificity (38%; n = 12), revealed marked heterogeneity in metrics and standardization.

CONCLUSIONS

Few studies have revealed that AI has directly improved health outcomes for pediatric critical care patients. Further prospective, experimental studies are needed to assess AI's impact by using established implementation frameworks, standardized metrics, and validated outcome measures.

Novel Biomarkers Differentiating Viral from Bacterial Infection in Febrile Children: Future Perspectives for Management in Clinical Praxis

Differentiating viral from bacterial infections in febrile children is challenging and often leads to an unnecessary use of antibiotics. There is a great need for more accurate diagnostic tools. New molecular methods have improved the particular diagnostics of viral respiratory tract infections, but defining etiology can still be challenging, as certain viruses are frequently detected in asymptomatic children. For the detection of bacterial infections, time consuming cultures with limited sensitivity are still the gold standard. As a response to infection, the immune system elicits a cascade of events, which aims to eliminate the invading pathogen. Recent studies have focused on these host–pathogen interactions to identify pathogen-specific biomarkers (gene expression profiles), or “pathogen signatures”, as potential future diagnostic tools. Other studies have assessed combinations of traditional bacterial and viral biomarkers (C-reactive protein, interleukins, myxovirus resistance protein A, procalcitonin, tumor necrosis factor-related apoptosis-inducing ligand) to establish etiology. In this review we discuss the performance of such novel diagnostics and their potential role in clinical praxis. In conclusion, there are several promising novel biomarkers in the pipeline, but well-designed randomized controlled trials are needed to evaluate the safety of using these novel biomarkers to guide clinical decisions.

Early Prediction of Sepsis Based on Machine Learning Algorithm

Sepsis is an organ failure disease caused by an infection resulting in extremely high mortality. Machine learning algorithms XGBoost and LightGBM are applied to construct two processing methods: mean processing method and feature generation method, aiming to predict early sepsis 6 hours in advance. The feature generation methods are constructed by combining different features, including statistical strength features, window features, and medical features. Miceforest multiple interpolation method is applied to tackle large missing data problems. Results show that the feature generation method outperforms the mean processing method. XGBoost and LightGBM algorithms are both excellent in prediction performance (AUC: 0.910∼0.979), among which LightGBM boasts a faster running speed and is stronger in generalization ability especially on multidimensional data, with AUC reaching 0.979 in the feature generation method. PTT, WBC, and platelets are the key risk factors to predict early sepsis.

Accurate diagnosis of sepsis using a neural network: Pilot study using routine clinical variables

BACKGROUND AND OBJECTIVES

Sepsis is a severe infection that increases mortality risk and is one if the main causes of death in intensive care units. Accurate detection is key to successful interventions, but diagnosis of sepsis is complicated because the initial signs and symptoms are not specific. Biomarkers that have been proposed have low specificity and sensitivity, are expensive, and not available in every hospital. In this study, we propose the use of artificial intelligence in the form of a neural network to diagnose sepsis using only common laboratory tests and vital signs that are routine and widely available.

METHODS

A retrospective, cross sectional cohort of 113 patients from an intensive care unit, each with 48 routinely evaluated vital signs and biochemical parameters was used to train, validate and test a neural network with 48 inputs, 10 neurons in a single hidden layer and one output. The sensitivity and specificity of the neural network as a point sampled diagnostic test was calculated.

RESULTS

All but one case were correctly diagnosed by the neural network, with 91% sensitivity and 100% specificity in the validation data set, and 100% sensitivity and specificity in the test data set.

CONCLUSIONS

The designed neural network system can identify patients with sepsis, with minimal resources using standard laboratory tests widely available in most health care facilities. This should reduce the burden on the medical staff of a difficult diagnosis and should improve outcomes for patients with sepsis.

Role of soluble triggering receptor expressed in myeloid cells-1 in distinguishing SIRS, sepsis, and septic shock in the pediatric intensive care unit

BACKGROUND

Research into new markers has been intensified for early diagnosis, prognosis, and differentiation of SIRS, sepsis, and septic shock in recent years. This study aimed to investigate the role of soluble triggering receptor expressed in myeloid cells-1 (sTREM-1) and interleukin (IL)-6 in distinguishing between systemic inflammatory response syndrome (SIRS), sepsis, and septic shock in pediatric intensive care unit (PICU) patients.

METHODS

Between June 2014 and July 2015, 90 consecutive patients who were treated in the PICU were included in this prospective observational study. Patients were divided into four groups: control (n = 23), SIRS (n = 22), sepsis (n = 23), and septic shock (n = 22). All patients were evaluated for white blood cell (WBC), serum C-reactive protein (CRP), procalcitonin (PCT), IL-6, and sTREM-1 levels at 0, 24, and 72 h of admission. The prognostic evaluations were made using the Pediatric Risk of Mortality III (PRISM III) and Pediatric Logistic Organ Dysfunction (PELOD) scores. Patients were evaluated in terms of age, gender, prognosis, pathogen growth in culture, PRISM III and PELOD score, WBC, CRP, PCT, IL-6, and sTREM-1 levels and a comparison was made between groups.

RESULTS

There was no significant difference between all groups in terms of the 0-, 24-, and 72-h sTREM-1 values (p = 0.761, p = 0.360, and p = 0.822, respectively). CRP and PCT values did not differ between the septic shock, sepsis, and SIRS groups at 0, 24, and 72 h. In the septic shock group, the 0-h IL-6 value was significantly higher than that of the SIRS group (p = 0.025). The 24-h IL-6 value in the septic shock group was significantly higher than the values of the sepsis and SIRS groups (p = 0.048 and p = 0.043, respectively). No significant difference was detected between the septic shock, sepsis, and SIRS groups in terms of IL-6 values at 72 h.

CONCLUSION

sTREM-1 is not useful for the diagnosis of infection and for distinguishing between sepsis, septic shock, and SIRS since it does not offer a clear diagnostic value for PICU patients, unlike other reliable markers such as WBC, CRP, and PCT. Elevated IL-6 levels may indicate septic shock in PICU patients. More research on sTREM-1 is needed in this setting.

Neonatal mortality prediction with routinely collected data: a machine learning approach

BACKGROUND

Recent decreases in neonatal mortality have been slower than expected for most countries. This study aims to predict the risk of neonatal mortality using only data routinely available from birth records in the largest city of the Americas.

METHODS

A probabilistic linkage of every birth record occurring in the municipality of São Paulo, Brazil, between 2012 e 2017 was performed with the death records from 2012 to 2018 (1,202,843 births and 447,687 deaths), and a total of 7282 neonatal deaths were identified (a neonatal mortality rate of 6.46 per 1000 live births). Births from 2012 and 2016 (N = 941,308; or 83.44% of the total) were used to train five different machine learning algorithms, while births occurring in 2017 (N = 186,854; or 16.56% of the total) were used to test their predictive performance on new unseen data.

RESULTS

The best performance was obtained by the extreme gradient boosting trees (XGBoost) algorithm, with a very high AUC of 0.97 and F1-score of 0.55. The 5% births with the highest predicted risk of neonatal death included more than 90% of the actual neonatal deaths. On the other hand, there were no deaths among the 5% births with the lowest predicted risk. There were no significant differences in predictive performance for vulnerable subgroups. The use of a smaller number of variables (WHO's five minimum perinatal indicators) decreased overall performance but the results still remained high (AUC of 0.91). With the addition of only three more variables, we achieved the same predictive performance (AUC of 0.97) as using all the 23 variables originally available from the Brazilian birth records.

CONCLUSION

Machine learning algorithms were able to identify with very high predictive performance the neonatal mortality risk of newborns using only routinely collected data.

Development and validation of an early warning tool for sepsis and decompensation in children during emergency department triage

This study was designed to develop and validate an early warning system for sepsis based on a predictive model of critical decompensation. Data from the electronic medical records for 537,837 visits to a pediatric Emergency Department (ED) from March 2013 to December 2019 were collected. A multiclass stochastic gradient boosting model was built to identify early warning signs associated with death, severe sepsis, non-severe sepsis, and bacteremia. Model features included triage vital signs, previous diagnoses, medications, and healthcare utilizations within 6 months of the index ED visit. There were 483 patients who had severe sepsis and/or died, 1102 had non-severe sepsis, 1103 had positive bacteremia tests, and the remaining had none of the events. The most important predictors were age, heart rate, length of stay of previous hospitalizations, temperature, systolic blood pressure, and prior sepsis. The one-versus-all area under the receiver operator characteristic curve (AUROC) were 0.979 (0.967, 0.991), 0.990 (0.985, 0.995), 0.976 (0.972, 0.981), and 0.968 (0.962, 0.974) for death, severe sepsis, non-severe sepsis, and bacteremia without sepsis respectively. The multi-class macro average AUROC and area under the precision recall curve were 0.977 and 0.316 respectively. The study findings were used to develop an automated early warning decision tool for sepsis. Implementation of this model in pediatric EDs will allow sepsis-related critical decompensation to be predicted accurately after a few seconds of triage.

Early Detection of Late Onset Sepsis in Premature Infants Using Visibility Graph Analysis of Heart Rate Variability

OBJECTIVE

This study was designed to test the diagnostic value of visibility graph features derived from the heart rate time series to predict late onset sepsis (LOS) in preterm infants using machine learning.

METHODS

The heart rate variability (HRV) data was acquired from 49 premature newborns hospitalized in neonatal intensive care units (NICU). The LOS group consisted of patients who received more than five days of antibiotics, at least 72 hours after birth. The control group consisted of infants who did not receive antibiotics. HRV features in the days prior to the start of antibiotics (LOS group) or in a randomly selected period (control group) were compared against a baseline value calculated during a calibration period. After automatic feature selection, four machine learning algorithms were trained. All the tests were done using two variants of the feature set: one only included traditional HRV features, and the other additionally included visibility graph features. Performance was studied using area under the receiver operating characteristics curve (AUROC).

RESULTS

The best performance for detecting LOS was obtained with logistic regression, using the feature set including visibility graph features, with AUROC of 87.7% during the six hours preceding the start of antibiotics, and with predictive potential (AUROC above 70%) as early as 42 h before start of antibiotics.

CONCLUSION

These results demonstrate the usefulness of introducing visibility graph indexes in HRV analysis for sepsis prediction in newborns.

SIGNIFICANCE

The method proposed the possibility of non-invasive, real-time monitoring of risk of LOS in a NICU setting.

Clinical evaluation of an interoperable clinical decision-support system for the detection of systemic inflammatory response syndrome in critically ill children

Background

Systemic inflammatory response syndrome (SIRS) is defined as a non-specific inflammatory process in the absence of infection. SIRS increases susceptibility for organ dysfunction, and frequently affects the clinical outcome of affected patients. We evaluated a knowledge-based, interoperable clinical decision-support system (CDSS) for SIRS detection on a pediatric intensive care unit (PICU).

Methods

The CDSS developed retrieves routine data, previously transformed into an interoperable format, by using model-based queries and guideline- and knowledge-based rules. We evaluated the CDSS in a prospective diagnostic study from 08/2018–03/2019. 168 patients from a pediatric intensive care unit of a tertiary university hospital, aged 0 to 18 years, were assessed for SIRS by the CDSS and by physicians during clinical routine. Sensitivity and specificity (when compared to the reference standard) with 95% Wald confidence intervals (CI) were estimated on the level of patients and patient-days.

Results

Sensitivity and specificity was 91.7% (95% CI 85.5–95.4%) and 54.1% (95% CI 45.4–62.5%) on patient level, and 97.5% (95% CI 95.1–98.7%) and 91.5% (95% CI 89.3–93.3%) on the level of patient-days. Physicians’ SIRS recognition during clinical routine was considerably less accurate (sensitivity of 62.0% (95% CI 56.8–66.9%)/specificity of 83.3% (95% CI 80.4–85.9%)) when measurd on the level of patient-days. Evaluation revealed valuable insights for the general design of the CDSS as well as specific rule modifications. Despite a lower than expected specificity, diagnostic accuracy was higher than the one in daily routine ratings, thus, demonstrating high potentials of using our CDSS to help to detect SIRS in clinical routine.

Conclusions

We successfully evaluated an interoperable CDSS for SIRS detection in PICU. Our study demonstrated the general feasibility and potentials of the implemented algorithms but also some limitations. In the next step, the CDSS will be optimized to overcome these limitations and will be evaluated in a multi-center study.

Trial registration: NCT03661450 (ClinicalTrials.gov); registered September 7, 2018.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01428-7.

Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective

Sepsis is a major cause of death worldwide. Over the past years, prediction of clinically relevant events through machine learning models has gained particular attention. In the present perspective, we provide a brief, clinician-oriented vision on the following relevant aspects concerning the use of machine learning predictive models for the early detection of sepsis in the daily practice: (i) the controversy of sepsis definition and its influence on the development of prediction models; (ii) the choice and availability of input features; (iii) the measure of the model performance, the output, and their usefulness in the clinical practice. The increasing involvement of artificial intelligence and machine learning in health care cannot be disregarded, despite important pitfalls that should be always carefully taken into consideration. In the long run, a rigorous multidisciplinary approach to enrich our understanding in the application of machine learning techniques for the early recognition of sepsis may show potential to augment medical decision-making when facing this heterogeneous and complex syndrome.

Barriers and Proposed Solutions to a Successful Implementation of Pediatric Sepsis Protocols

The implementation of managed protocols contributes to a systematized approach to the patient and continuous evaluation of results, focusing on improving clinical practice, early diagnosis, treatment, and outcomes. Advantages to the adoption of a pediatric sepsis recognition and treatment protocol include: a reduction in time to start fluid and antibiotic administration, decreased kidney dysfunction and organ dysfunction, reduction in length of stay, and even a decrease on mortality. Barriers are: absence of a written protocol, parental knowledge, early diagnosis by healthcare professionals, venous access, availability of antimicrobials and vasoactive drugs, conditions of work, engagement of healthcare professionals. There are challenges in low-middle-income countries (LMIC). The causes of sepsis and resources differ from high-income countries. Viral agent such as dengue, malaria are common in LMIC and initial approach differ from bacterial infections. Some authors found increased or no impact in mortality or increased length of stay associated with the implementation of the SCC sepsis bundle which reinforces the importance of adapting it to most frequent diseases, disposable resources, and characteristics of healthcare professionals. Conclusions: (1) be simple; (2) be precise; (3) education; (5) improve communication; (5) work as a team; (6) share and celebrate results.

Multiplexed Plasma Immune Mediator Signatures Can Differentiate Sepsis From NonInfective SIRS: American Surgical Association 2020 Annual Meeting Paper

OBJECTIVES

Sepsis and sterile both release "danger signals' that induce the systemic inflammatory response syndrome (SIRS). So differentiating infection from SIRS can be challenging. Precision diagnostic assays could limit unnecessary antibiotic use, improving outcomes.

METHODS

After surveying human leukocyte cytokine production responses to sterile damage-associated molecular patterns (DAMPs), bacterial pathogen-associated molecular patterns, and bacteria we created a multiplex assay for 31 cytokines. We then studied plasma from patients with bacteremia, septic shock, "severe sepsis," or trauma (ISS ≥15 with circulating DAMPs) as well as controls. Infections were adjudicated based on post-hospitalization review. Plasma was studied in infection and injury using univariate and multivariate means to determine how such multiplex assays could best distinguish infective from noninfective SIRS.

RESULTS

Infected patients had high plasma interleukin (IL)-6, IL-1α, and triggering receptor expressed on myeloid cells-1 (TREM-1) compared to controls [false discovery rates (FDR) <0.01, <0.01, <0.0001]. Conversely, injury suppressed many mediators including MDC (FDR <0.0001), TREM-1 (FDR <0.001), IP-10 (FDR <0.01), MCP-3 (FDR <0.05), FLT3L (FDR <0.05), Tweak, (FDR <0.05), GRO-α (FDR <0.05), and ENA-78 (FDR <0.05). In univariate studies, analyte overlap between clinical groups prevented clinical relevance. Multivariate models discriminated injury and infection much better, with the 2-group random-forest model classifying 11/11 injury and 28/29 infection patients correctly in out-of-bag validation.

CONCLUSIONS

Circulating cytokines in traumatic SIRS differ markedly from those in health or sepsis. Variability limits the accuracy of single-mediator assays but machine learning based on multiplexed plasma assays revealed distinct patterns in sepsis- and injury-related SIRS. Defining biomarker release patterns that distinguish specific SIRS populations might allow decreased antibiotic use in those clinical situations. Large prospective studies are needed to validate and operationalize this approach.

Role of Artificial Intelligence in Fighting Antimicrobial Resistance in Pediatrics

Artificial intelligence (AI) is a field of science and engineering concerned with the computational understanding of what is commonly called intelligent behavior. AI is extremely useful in many human activities including medicine. The aim of our narrative review is to show the potential role of AI in fighting antimicrobial resistance in pediatric patients. We searched for PubMed articles published from April 2010 to April 2020 containing the keywords “artificial intelligence”, “machine learning”, “antimicrobial resistance”, “antimicrobial stewardship”, “pediatric”, and “children”, and we described the different strategies for the application of AI in these fields. Literature analysis showed that the applications of AI in health care are potentially endless, contributing to a reduction in the development time of new antimicrobial agents, greater diagnostic and therapeutic appropriateness, and, simultaneously, a reduction in costs. Most of the proposed AI solutions for medicine are not intended to replace the doctor’s opinion or expertise, but to provide a useful tool for easing their work. Considering pediatric infectious diseases, AI could play a primary role in fighting antibiotic resistance. In the pediatric field, a greater willingness to invest in this field could help antimicrobial stewardship reach levels of effectiveness that were unthinkable a few years ago.

Prediction of perinatal death using machine learning models: a birth registry-based cohort study in northern Tanzania

OBJECTIVE

We aimed to determine the key predictors of perinatal deaths using machine learning models compared with the logistic regression model.

DESIGN

A secondary data analysis using the Kilimanjaro Christian Medical Centre (KCMC) Medical Birth Registry cohort from 2000 to 2015. We assessed the discriminative ability of models using the area under the receiver operating characteristics curve (AUC) and the net benefit using decision curve analysis.

SETTING

The KCMC is a zonal referral hospital located in Moshi Municipality, Kilimanjaro region, Northern Tanzania. The Medical Birth Registry is within the hospital grounds at the Reproductive and Child Health Centre.

PARTICIPANTS

Singleton deliveries (n=42 319) with complete records from 2000 to 2015.

PRIMARY OUTCOME MEASURES

Perinatal death (composite of stillbirths and early neonatal deaths). These outcomes were only captured before mothers were discharged from the hospital.

RESULTS

The proportion of perinatal deaths was 3.7%. There were no statistically significant differences in the predictive performance of four machine learning models except for bagging, which had a significantly lower performance (AUC 0.76, 95% CI 0.74 to 0.79, p=0.006) compared with the logistic regression model (AUC 0.78, 95% CI 0.76 to 0.81). However, in the decision curve analysis, the machine learning models had a higher net benefit (ie, the correct classification of perinatal deaths considering a trade-off between false-negatives and false-positives)-over the logistic regression model across a range of threshold probability values.

CONCLUSIONS

In this cohort, there was no significant difference in the prediction of perinatal deaths between machine learning and logistic regression models, except for bagging. The machine learning models had a higher net benefit, as its predictive ability of perinatal death was considerably superior over the logistic regression model. The machine learning models, as demonstrated by our study, can be used to improve the prediction of perinatal deaths and triage for women at risk.

Updates on pediatric sepsis

Sepsis, defined as an infection with dysregulated host response leading to life-threatening organ dysfunction, continues to carry a high potential for morbidity and mortality in children. The recognition of sepsis in children in the emergency department (ED) can be challenging, related to the high prevalence of common febrile infections, poor specificity of discriminating features, and the capacity of children to compensate until advanced stages of shock. Sepsis outcomes are strongly dependent on the timeliness of recognition and treatment, which has led to the successful implementation of quality improvement programs, increasing the reliability of sepsis treatment in many US institutions. We review clinical, laboratory, and technical modalities that can be incorporated into ED practice to facilitate the recognition, treatment, and reassessment of children with suspected sepsis. The 2020 updated pediatric sepsis guidelines are reviewed and framed in the context of ED interventions, including guidelines for antibiotic administration, fluid resuscitation, and the use of vasoactive agents. Despite a large body of literature on pediatric sepsis epidemiology in recent years, the evidence base for treatment and management components remains limited, implying an urgent need for large trials in this field. In conclusion, although the burden and impact of pediatric sepsis remains substantial, progress in our understanding of the disease and its management have led to revised guidelines and the available data emphasizes the importance of local quality improvement programs.

Duplex Shiny app quantification of the sepsis biomarkers C-reactive protein and interleukin-6 in a fast quantum dot labeled lateral flow assay

Fast point-of-care (POC) diagnostics represent an unmet medical need and include applications such as lateral flow assays (LFAs) for the diagnosis of sepsis and consequences of cytokine storms and for the treatment of COVID-19 and other systemic, inflammatory events not caused by infection. Because of the complex pathophysiology of sepsis, multiple biomarkers must be analyzed to compensate for the low sensitivity and specificity of single biomarker targets. Conventional LFAs, such as gold nanoparticle dyed assays, are limited to approximately five targets-the maximum number of test lines on an assay. To increase the information obtainable from each test line, we combined green and red emitting quantum dots (QDs) as labels for C-reactive protein (CRP) and interleukin-6 (IL-6) antibodies in an optical duplex immunoassay. CdSe-QDs with sharp and tunable emission bands were used to simultaneously quantify CRP and IL-6 in a single test line, by using a single UV-light source and two suitable emission filters for readout through a widely available BioImager device. For image and data processing, a customized software tool, the MultiFlow-Shiny app was used to accelerate and simplify the readout process. The app software provides advanced tools for image processing, including assisted extraction of line intensities, advanced background correction and an easy workflow for creation and handling of experimental data in quantitative LFAs. The results generated with our MultiFlow-Shiny app were superior to those generated with the popular software ImageJ and resulted in lower detection limits. Our assay is applicable for detecting clinically relevant ranges of both target proteins and therefore may serve as a powerful tool for POC diagnosis of inflammation and infectious events.

Biomarkers in pediatric sepsis: a review of recent literature

Sepsis remains the leading cause of death in infants and children worldwide. Prompt diagnosis and monitoring of infection is pivotal to guide therapy and optimize outcomes. No single biomarker has so far been identified to accurately diagnose sepsis, monitor response and predict severity. We aimed to assess existing evidence of available sepsis biomarkers, and their utility in pediatric population. C-reactive protein and procalcitonin remain the most extensively evaluated and used biomarkers. However, biomarkers related to endothelial damage, vasodilation, oxidative stress, cytokines/chemokines and cell bioproducts have also been identified, often with regard to the site of infection and etiologic pathogen; still, with controversial utility. A multi-biomarker model driven by genomic tools could establish a personalized approach in future disease management.

Diagnostic and Prognostic Value of IL-6 and sTREM-1 in SIRS and Sepsis in Children

Purpose

The aim of this study was to evaluate the diagnostic and prognostic value of IL-6 and sTREM-1 in the course of SIRS and sepsis in children with reference to routinely used CRP and PCT.

Methods

A prospective study included 180 patients at the ages from 2 months to 18 years hospitalized due to fever from November 2015 to January 2017. Forty-nine children without fever hospitalized due to noninfectious causes formed the control group. IL-6 and sTREM-1 serum concentrations were assessed with the enzyme-linked immunosorbent assay method.

Results

The mean serum concentrations of all the analyzed biomarkers were statistically significantly higher in the study group compared to the control group. Mean IL-6, sTREM-1, and PCT serum concentrations were statistically significantly higher in the group of patients with SIRS/sepsis compared to the group of feverish patients without diagnosed SIRS (N-SIRS). Based on the ROC curve analysis, it was shown that of all the biomarkers tested, only two—IL-6 and procalcitonin—had potential usefulness in the diagnosis of SIRS/sepsis in children with fever.

Conclusion

Elevated levels of IL-6 and PCT are important risk factors for the development of SIRS/sepsis in children with fever. It seems that elevated IL-6 baseline serum level may predict a more severe course of febrile illness in children, because based on the ROC curve analysis, it was found that IL-6 is a statistically significant prognostic marker of prolonged fever ≥ 3 days and prolonged hospitalization > 10 days. The assessment of the usefulness of sTREM-1 in the diagnosis of SIRS/sepsis in feverish children requires further research.

Intermediate monocytes exhibit higher levels of TLR2, TLR4 and CD64 early after congenital heart surgery

INTRODUCTION

Congenital heart surgery with cardiopulmonary bypass (CPB) initiates an immune response which frequently leads to organ dysfunction and a systemic inflammatory response. Complications associated with exacerbated immune responses may severely impact the postoperative recovery. The objective was to describe the characteristics of monocyte subpopulations and neutrophils at the level of pattern recognition receptors (PRR) and the cytokine response after CPB in infants.

METHODS

An observational cohort study was conducted between June 2016 and June 2017 of infants < 2 years of age, electively admitted for surgical correction of acyanotic congenital heart defects using CPB. Fourteen blood samples were collected sequentially and processed immediately during and up to 48 h following cardiac surgery for each patient. Flow cytometry analysis comprised monocytic and granulocytic surface expression of CD14, CD16, CD64, TLR2, TLR4 and Dectin-1 (CLEC7A). Monocyte subpopulations were further defined as classical (CD14⁺⁺/CD16^-), intermediate (CD14⁺⁺/CD16⁺) and nonclassical (CD14⁺/CD16⁺⁺) monocytes. Plasma concentrations of 14 cytokines, including G-CSF, GM-CSF, IL-1β, IL-1RA, IL-4, IL-6, IL-8, IL-10, IL-12p40, IL-12p70, TNF-α, IFN-γ, MIP-1β (CCL4) and TGF-β1, were measured using multiplex immunoassay for seven points in time.

RESULTS

Samples from 21 infants (median age 7.4 months) were analyzed by flow cytometry and from 11 infants, cytokine concentrations were measured. Classical and intermediate monocytes showed first receptor upregulation with an increase in CD64 expression four hours post CPB. CD64-expression on intermediate monocytes almost tripled 48 h post CPB (p < 0.0001). TLR4 was only increased on intermediate monocytes, occurring 12 h post CPB (p = 0.0406) along with elevated TLR2 levels (p = 0.0002). TLR4 expression on intermediate monocytes correlated with vasoactive-inotropic score (r_s = 0.642, p = 0.0017), duration of ventilation (r_s = 0.485, p = 0.0259), highest serum creatinine (r_s = 0.547, p = 0.0102), postsurgical transfusion (total volume per kg bodyweight) (r_s = 0.469, p = 0.0321) and lowest mean arterial pressure (r_s = -0.530, p = 0.0135). Concentrations of IL-10, MIP-1β, IL-8, G-CSF and IL-6 increased one hour post CPB. Methylprednisolone administration in six patients had no significant influence on the studied surface receptors but led to lower IL-8 and higher IL-10 plasma concentrations.

CONCLUSIONS

Congenital heart surgery with CPB induces a systemic inflammatory process including cytokine response and changes in PRR expression. Intermediate monocytes feature specific inflammatory characteristics in the 48 h after pediatric CPB and TLR4 correlates with poorer clinical course, which might provide a potential diagnostic or even therapeutic target.

Inflammatory markers in saliva for diagnosis of sepsis of hospitalizes patients

BACKGROUND

Inflammatory/immunological serum markers are useful for the early detection of organ dysfunction, helping the diagnosis of sepsis. Although the detection of blood biomarkers is a standard practice, the use of noninvasive samples (eg saliva) would be beneficial.

AIM

To investigate the saliva of hospitalized patients with and without sepsis and identify the levels of inflammatory markers such as C-reactive protein (CRP), procalcitonin (PCT), interleukin 6 (IL-6) and nitric oxide (NO).

METHODS

Saliva samples were collected from 26 patients in intensive care unit with diagnosis of sepsis and from 26 without sepsis (control). The levels of CRP were determined by using latex agglutination test, whereas those of procalcitonin and IL-6 by ELISA and NO by the Griess reaction.

RESULTS

Of 26 patients with sepsis, 14 were males (54%) with a mean age of 63.81 ± 3.48 years. The control group had the same distribution for gender, with mean age 65.04 ± 4.07 years. Sepsis group showed higher salivary concentrations of CRP, PCT, IL-6 and NO, with only levels of IL-6 being statistically different (P = .0001).

CONCLUSIONS

Patients with sepsis had significantly higher levels of IL-6 in their saliva, suggesting that this biological sample could be useful in the diagnosis of this condition.

Role of procalcitonin use in the management of sepsis

Important aspects of sepsis management include early diagnosis as well as timely and specific treatment in the first few hours of triage. However, diagnosis and differentiation from non-infectious causes often cause uncertainties and potential time delays. Correct use of antibiotics still represents a major challenge, leading to increased risk for opportunistic infections, resistances to multiple antimicrobial agents and toxic side effects, which in turn increase mortality and healthcare costs. Optimized procedures for reliable diagnosis and management of antibiotic therapy has great potential to improve patient care. Herein, biomarkers have been shown to improve infection diagnosis, help in early risk stratification and provide prognostic information which helps optimizing therapeutic decisions ("antibiotic stewardship"). In this context, the use of the blood infection marker procalcitonin (PCT) has gained much attention. There is still no gold standard for the detection of sepsis and use of conventional diagnostic approaches are restricted by some limitations. Therefore, additional tests are necessary to enable early and reliable diagnosis. PCT has good discriminatory properties to differentiate between bacterial and viral inflammations with rapidly available results. Further, PCT adds to risk stratification and prognostication, which may influence appropriate use of health-care resources and therapeutic options. PCT kinetics over time also improves the monitoring of critically ill patients with sepsis and thus influences decisions regarding de-escalation of antibiotics. Most importantly, PCT helps in guiding antibiotic use in patients with respiratory infection and sepsis by limiting initiation and by shortening treatment duration. To date, PCT is the best studied biomarker regarding antibiotic stewardship. Still, further research is needed to understand optimal use of PCT, also in combination with other remerging diagnostic tests for most efficient sepsis care.

Use of artificial intelligence in infectious diseases

Infectious diseases are caused by microorganisms belonging to the class of bacteria, viruses, fungi, or parasites. These pathogens are transmitted, directly or indirectly, and can lead to epidemics or even pandemics. The resulting infection may lead to mild-to-severe symptoms such as life-threatening fever or diarrhea. Infectious diseases may be asymptomatic in some individuals but may lead to disastrous effects in others. Despite the advances in medicine, infectious diseases are a leading cause of death worldwide, especially in low-income countries. With the advent of mathematical tools, scientists are now able to better predict epidemics, understand the specificity of each pathogen, and identify potential targets for drug development. Artificial intelligence and its components have been widely publicized for their ability to better diagnose certain types of cancer from imaging data. This chapter aims at identifying potential applications of machine learning in the field of infectious diseases. We are deliberately focusing on key aspects of infection: diagnosis, transmission, response to treatment, and resistance. We are proposing the use of extreme values as an avenue of interest for future developments in the field of infectious diseases. This chapter covers a series of applications selectively chosen to showcase how artificial intelligence is moving the field of infectious disease further and how it helps institutions to better tackles them, especially in low-income countries.

Thromboelastography Variables, Immune Markers, and Endothelial Factors Associated With Shock and NPMODS in Children With Severe Sepsis

Objective: Evaluate hemostatic dysfunction in pediatric severe sepsis by thromboelastography (TEG) and determine if TEG parameters are associated with new or progressive multiple organ dysfunction syndrome (NPMODS) or shock, defined as a lactate ≥2mmol/L. We explored the relationship between TEG variables, selective cytokines, and endothelial factors. Design: Prospective observational. Setting: Single-center, quaternary care pediatric intensive care unit. Patients: Children aged 6- months to 14- years with severe sepsis with expected PICU stay for >72 h. Interventions: None. Measurements and Main Results: Twenty-eight children were enrolled with median (IQR) age of 7.3 years (4.4-11.4), PELOD score (study day-1) of 11(1.25-13), and PICU length of stay of 10 days (5-28). TEG-defined hypercoagulable state occurred most commonly in 73% (94/129) of samples, followed by hypocoagulable state in 7.8% (10/129) and mixed coagulation state in 1.5% (2/129) of samples in the study cohort. In contrast, hypocoagulable state occurred most commonly in 66% (98/148) of samples based on standard coagulation parameters. In the seven children who developed shock with NPMODS compared to eight patients with shock without NPMODS and 12 patients with severe sepsis only, we found more profound coagulopathy [thrombocytopenia (p = 0.04), elevated INR (p = 0.038), low fibrinogen level (p = 0.049), and low TEG-G value (p = 0.01)] and higher peak of interleukin-6 (p = 0.0014) and IL-10 (p = 0.007). Peak lactate in the first 5 study days had moderate correlation with standard coagulation assays, TEG parameters, and selective cytokines. Peak lactate did not correlate with markers of endothelial activation. Lowest TEG -G value had moderate correlation with peak IL-10 (ρ -0.442, p =0.019), peak VCAM (ρ - 0.495, p = 0.007), and peak lactate (ρ -0.542, p = 0.004) in the first 5 study days. A combination of TEG-G value and IL-6 concentration best discriminated children with shock and NPMODS [AUC 0.979 (95%CI 0.929-1.00), p < 0.001]. Conclusion: This exploratory analysis of hemostasis dysfunction on TEG in pediatric severe sepsis suggests that while hypercoagulability is more common, a hypocoagulable state is associated with shock and NPMODS. In addition, TEG abnormalities are also associated with immune and endothelial factors. A larger cohort study is needed to validate these findings.

Clinical applications of artificial intelligence in sepsis: A narrative review

Many studies have been published on a variety of clinical applications of artificial intelligence (AI) for sepsis, while there is no overview of the literature. The aim of this review is to give an overview of the literature and thereby identify knowledge gaps and prioritize areas with high priority for further research. A literature search was conducted in PubMed from inception to February 2019. Search terms related to AI were combined with terms regarding sepsis. Articles were included when they reported an area under the receiver operator characteristics curve (AUROC) as outcome measure. Fifteen articles on diagnosis of sepsis with AI models were included. The best performing model reached an AUROC of 0.97. There were also seven articles on prognosis, predicting mortality over time with an AUROC of up to 0.895. Finally, there were three articles on assistance of treatment of sepsis, where the use of AI was associated with the lowest mortality rates. Of the articles, twenty-two were judged to be at high risk of bias or had major concerns regarding applicability. This was mostly because predictor variables in these models, such as blood pressure, were also part of the definition of sepsis, which led to overestimation of the performance. We conclude that AI models have great potential for improving early identification of patients who may benefit from administration of antibiotics. Current AI prediction models to diagnose sepsis are at major risks of bias when the diagnosis criteria are part of the predictor variables in the model. Furthermore, generalizability of these models is poor due to overfitting and a lack of standardized protocols for the construction and validation of the models. Until these problems have been resolved, a large gap remains between the creation of an AI algorithm and its implementation in clinical practice.

Machine learning for clinical decision support in infectious diseases: a narrative review of current applications

BACKGROUND

Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID).

OBJECTIVES

We aim to inform clinicians about the use of ML for diagnosis, classification, outcome prediction and antimicrobial management in ID.

SOURCES

References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, ACM Digital Library, arXiV and IEEE Xplore Digital Library up to July 2019.

CONTENT

We found 60 unique ML-clinical decision support systems (ML-CDSS) aiming to assist ID clinicians. Overall, 37 (62%) focused on bacterial infections, 10 (17%) on viral infections, nine (15%) on tuberculosis and four (7%) on any kind of infection. Among them, 20 (33%) addressed the diagnosis of infection, 18 (30%) the prediction, early detection or stratification of sepsis, 13 (22%) the prediction of treatment response, four (7%) the prediction of antibiotic resistance, three (5%) the choice of antibiotic regimen and two (3%) the choice of a combination antiretroviral therapy. The ML-CDSS were developed for intensive care units (n = 24, 40%), ID consultation (n = 15, 25%), medical or surgical wards (n = 13, 20%), emergency department (n = 4, 7%), primary care (n = 3, 5%) and antimicrobial stewardship (n = 1, 2%). Fifty-three ML-CDSS (88%) were developed using data from high-income countries and seven (12%) with data from low- and middle-income countries (LMIC). The evaluation of ML-CDSS was limited to measures of performance (e.g. sensitivity, specificity) for 57 ML-CDSS (95%) and included data in clinical practice for three (5%).

IMPLICATIONS

Considering comprehensive patient data from socioeconomically diverse healthcare settings, including primary care and LMICs, may improve the ability of ML-CDSS to suggest decisions adapted to various clinical contexts. Currents gaps identified in the evaluation of ML-CDSS must also be addressed in order to know the potential impact of such tools for clinicians and patients.

Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host–pathogen–protein interactions, combined with a better understanding of a host’s immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination.