Septicemia mortality reduction in neonates in a heart rate characteristics monitoring trial

Apnea, Intermittent Hypoxemia, and Bradycardia Events Predict Late-Onset Sepsis in Infants Born Extremely Preterm

OBJECTIVE

The objective of this study was to examine the association of cardiorespiratory events, including apnea, periodic breathing, intermittent hypoxemia (IH), and bradycardia, with late-onset sepsis for extremely preterm infants (<29 weeks of gestational age) on vs off invasive mechanical ventilation.

STUDY DESIGN

This is a retrospective analysis of data from infants enrolled in Pre-Vent (ClinicalTrials.gov identifier NCT03174301), an observational study in 5 level IV neonatal intensive care units. Clinical data were analyzed for 737 infants (mean gestational age: 26.4 weeks, SD 1.71). Monitoring data were available and analyzed for 719 infants (47 512 patient-days); of whom, 109 had 123 sepsis events. Using continuous monitoring data, we quantified apnea, periodic breathing, bradycardia, and IH. We analyzed the relationships between these daily measures and late-onset sepsis (positive blood culture >72 hours after birth and ≥5-day antibiotics).

RESULTS

For infants not on a ventilator, apnea, periodic breathing, and bradycardia increased before sepsis diagnosis. During times on a ventilator, increased sepsis risk was associated with longer events with oxygen saturation <80% (IH80) and more bradycardia events before sepsis. IH events were associated with higher sepsis risk but did not dynamically increase before sepsis, regardless of ventilator status. A multivariable model including postmenstrual age, cardiorespiratory variables (apnea, periodic breathing, IH80, and bradycardia), and ventilator status predicted sepsis with an area under the receiver operator characteristic curve of 0.783.

CONCLUSION

We identified cardiorespiratory signatures of late-onset sepsis. Longer IH events were associated with increased sepsis risk but did not change temporally near diagnosis. Increases in bradycardia, apnea, and periodic breathing preceded the clinical diagnosis of sepsis.

Framework for Considering Abnormal Heart Rate Characteristics and Other Signs of Sepsis in Very Low Birth Weight Infants

OBJECTIVE

 A heart rate characteristics index (HeRO score), incorporating low variability and superimposed decelerations, was developed as a sepsis risk indicator for preterm infants in the neonatal intensive care unit (NICU). A rise in the risk score should prompt consideration of other clinical changes that may be signs of sepsis to decide whether a workup and antibiotics are needed. We aimed to develop a framework to systematically consider signs potentially indicating sepsis in very low birth weight (VLBW) infants.

STUDY DESIGN

 We developed easy-recall acronyms for 10 signs of sepsis in VLBW infants. Over 12 months in a level IV NICU, neonatology fellows completed a brief survey after each shift to document changes prompting sepsis workups. We analyzed associations between survey data, hourly heart rate characteristic data, and the diagnosis of the workup, grouped as culture-positive sepsis (CXSEP, positive blood or urine culture), clinical sepsis (CLINSEP, negative cultures treated with antibiotics ≥5 days), or sepsis ruled out (SRO, negative cultures and <3 days antibiotics).

RESULTS

 We analyzed 93 sepsis workups in 48 VLBW infants (35 CXSEP, 20 CLINSEP, and 38 SRO). The most frequently cited changes prompting the workups were heart rate patterns and respiratory deterioration, which were common in all three categories. Low blood pressure and poor perfusion were uncommonly cited but were more likely to be associated with CXSEP than the other signs. A rise in the HeRO score ≥1 from 0 to 12 hours before compared with 12to 72 hours prior the blood culture occurred in 31% of workups diagnosed as CXSEP, 16% CLINSEP, and 31% SRO.

CONCLUSION

 The HeRO score can alert clinicians to VLBW infants at high or increasing risk of a sepsis-like illness, but heart rate characteristic patterns are highly variable in individual babies. The easy-recall NeoSEP-10 framework can assist clinicians in considering other clinical changes when making decisions about sepsis workups and the duration of antibiotics.

KEY POINTS

· Abnormal heart rate characteristics can indicate sepsis or other pathologies in preterm infants.. · We developed a simple bedside tool to consider clinical signs potentially associated with sepsis.. · Considering vital sign trends together with clinical changes is a key to right-timing antibiotics..

The Finnegan Score for Neonatal Opioid Withdrawal Revisited With Routine Electronic Data: Retrospective Study

Background

The severity of neonatal abstinence syndrome (NAS) may be assessed with the Finnegan score (FS). Since the FS is laborious and subjective, alternative ways of assessment may improve quality of care.

Objective

In this pilot study, we examined associations between the FS and routine monitoring data obtained from the electronic health record system.

Methods

The study included 205 neonates with NAS after intrauterine (n=23) or postnatal opioid exposure (n=182). Routine monitoring data were analyzed at 60±10 minutes (t-1) and 120±10 minutes (t-2) before each FS assessment. Within each time period, the mean for each variable was calculated. Readings were also normalized to individual baseline data for each patient and parameter. Mixed effects models were used to assess the effect of different variables.

Results

Plots of vital parameters against the FS showed heavily scattered data. When controlling for several variables, the best-performing mixed effects model displayed significant effects of individual baseline-controlled mean heart rate (estimate 0.04, 95% CI 0.02-0.07) and arterial blood pressure (estimate 0.05, 95% CI 0.01-0.08) at t-1 with a goodness of fit (R2m) of 0.11.

Conclusions

Routine electronic data can be extracted and analyzed for their correlation with FS data. Mixed effects models show small but significant effects after normalizing vital parameters to individual baselines.

Apnea, Intermittent Hypoxemia, and Bradycardia Events Predict Late-Onset Sepsis in Extremely Preterm Infants

Objectives

Detection of changes in cardiorespiratory events, including apnea, periodic breathing, intermittent hypoxemia (IH), and bradycardia, may facilitate earlier detection of sepsis. Our objective was to examine the association of cardiorespiratory events with late-onset sepsis for extremely preterm infants (<29 weeks' gestational age (GA)) on versus off invasive mechanical ventilation.

Study Design

Retrospective analysis of data from infants enrolled in Pre-Vent (ClinicalTrials.gov identifier NCT03174301), an observational study in five level IV neonatal intensive care units. Clinical data were analyzed for 737 infants (mean GA 26.4w, SD 1.71). Monitoring data were available and analyzed for 719 infants (47,512 patient-days), of whom 109 had 123 sepsis events. Using continuous monitoring data, we quantified apnea, periodic breathing, bradycardia, and IH. We analyzed the relationships between these daily measures and late-onset sepsis (positive blood culture >72h after birth and ≥5d antibiotics).

Results

For infants not on a ventilator, apnea, periodic breathing, and bradycardia increased before sepsis diagnosis. During times on a ventilator, increased sepsis risk was associated with longer IH80 events and more bradycardia events before sepsis. IH events were associated with higher sepsis risk, but did not dynamically increase before sepsis, regardless of ventilator status. A multivariable model predicted sepsis with an AUC of 0.783.

Conclusion

We identified cardiorespiratory signatures of late-onset sepsis. Longer IH events were associated with increased sepsis risk but did not change temporally near diagnosis. Increases in bradycardia, apnea, and periodic breathing preceded the clinical diagnosis of sepsis.

Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants

Objective

Highly comparative time series analysis (HCTSA) is a novel approach involving massive feature extraction using publicly available code from many disciplines. The Prematurity-Related Ventilatory Control (Pre-Vent) observational multicenter prospective study collected bedside monitor data from > 700 extremely preterm infants to identify physiologic features that predict respiratory outcomes. We calculated a subset of 33 HCTSA features on > 7M 10-minute windows of oxygen saturation (SPO2) and heart rate (HR) from the Pre-Vent cohort to quantify predictive performance. This subset included representatives previously identified using unsupervised clustering on > 3500 HCTSA algorithms. Performance of each feature was measured by individual area under the receiver operating curve (AUC) at various days of life and binary respiratory outcomes. These were compared to optimal PreVent physiologic predictor IH90 DPE, the duration per event of intermittent hypoxemia events with threshold of 90%.

Main Results

The top HCTSA features were from a cluster of algorithms associated with the autocorrelation of SPO2 time series and identified low frequency patterns of desaturation as high risk. These features had comparable performance to and were highly correlated with IH90_DPE but perhaps measure the physiologic status of an infant in a more robust way that warrants further investigation. The top HR HCTSA features were symbolic transformation measures that had previously been identified as strong predictors of neonatal mortality. HR metrics were only important predictors at early days of life which was likely due to the larger proportion of infants whose outcome was death by any cause. A simple HCTSA model using 3 top features outperformed IH90_DPE at day of life 7 (.778 versus .729) but was essentially equivalent at day of life 28 (.849 versus .850). These results validated the utility of a representative HCTSA approach but also provides additional evidence supporting IH90_DPE as an optimal predictor of respiratory outcomes.

Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities

Artificial intelligence (AI) offers tremendous potential to transform neonatology through improved diagnostics, personalized treatments, and earlier prevention of complications. However, there are many challenges to address before AI is ready for clinical practice. This review defines key AI concepts and discusses ethical considerations and implicit biases associated with AI. Next we will review literature examples of AI already being explored in neonatology research and we will suggest future potentials for AI work. Examples discussed in this article include predicting outcomes such as sepsis, optimizing oxygen therapy, and image analysis to detect brain injury and retinopathy of prematurity. Realizing AI's potential necessitates collaboration between diverse stakeholders across the entire process of incorporating AI tools in the NICU to address testability, usability, bias, and transparency. With multi-center and multi-disciplinary collaboration, AI holds tremendous potential to transform the future of neonatology.

Early heart rate variability changes during acute fetal inflammatory response syndrome: An experimental study in a fetal sheep model

INTRODUCTION

Fetal infection during labor with fetal inflammatory response syndrome (FIRS) is associated with neurodevelopmental disabilities, cerebral palsy, neonatal sepsis, and mortality. Current methods to diagnose FIRS are inadequate. Thus, the study aim was to explore whether fetal heart rate variability (HRV) analysis can be used to detect FIRS.

MATERIAL AND METHODS

In chronically instrumented near-term fetal sheep, lipopolysaccharide (LPS) was injected intravenously to model FIRS. A control group received saline solution injection. Hemodynamic, blood gas analysis, interleukin-6 (IL-6), and 14 HRV indices were recorded for 6 h. In both groups, comparisons were made between the stability phase and the 6 h following injection (H1-H6, respectively) and between LPS and control groups.

RESULTS

Fifteen lambs were instrumented. In the LPS group (n = 8), IL-6 increased significantly after LPS injection (p < 0.001), confirming the FIRS model. Fetal heart rate increased significantly after H5 (p < 0.01). In our FIRS model without shock or cardiovascular decompensation, five HRV measures changed significantly after H2 until H4 in comparison to baseline. Moreover, significant differences between LPS and control groups were observed in HRV measures between H2 and H4. These changes appear to be mediated by an increase of global variability and a loss of signal complexity.

CONCLUSION

As significant HRV changes were detected before FHR increase, these indices may be valuable for early detection of acute FIRS.

Development and clinical impact assessment of a machine-learning model for early prediction of late-onset sepsis

BACKGROUND AND AIM

Preterm infants are prone to neonatal infections such as late-onset sepsis (LOS). The consequences of LOS can be severe and potentially life-threatening. Unfortunately, LOS often presents with unspecific symptoms, and early screening laboratory tests have limited diagnostic value and are often late. This study aimed to build a predictive algorithm to aid doctors in the early detection of LOS in very preterm infants.

METHODS

In a retrospective cohort study, all consecutively admitted preterm infants (GA ≤ 32 weeks) from 2008 until 2019 were included. They were classified as LOS or control according to blood culture results, currently the gold standard. To generate features, routine and continuously measured oxygen saturation and heart rate data with a minute-by-minute sampling rate were extracted from electronic medical records. Care was taken not to include variables indicative of existing LOS suspicion. The timing of a positive blood culture served as a proxy for LOS-onset. An equivalent timestamp was generated in gestational-age-matched control patients without a positive blood culture. Three machine learning (ML) techniques (generalized additive models, logistic regression, and XGBoost) were used to build a classification algorithm. To simulate the performance of the algorithm in clinical practice, a simulation using multiple alarm thresholds was performed on hourly predictions for the total hospitalization period.

RESULTS

292 infants with LOS were matched to 1497 controls. The median gestational age before matching was 28.1 and 30.3 weeks, respectively. Evaluation of the overall discriminative power of the LR algorithm yielded an AUC of 0.73 (p < 0.05) at the moment of clinical suspicion (t = 0). In the longitudinal simulation, our algorithm detects LOS in at least 47% of the patients before clinical suspicion without exceeding the alarm fatigue threshold of 3 alarms per day. Furthermore, medical experts evaluated the algorithm as clinically relevant regarding the feature contributions in the model explanations.

CONCLUSIONS

An ML algorithm was trained for the early detection of LOS. Performance was evaluated on both prediction horizons and in a clinical impact simulation. To the best of our knowledge, our assessment of clinical impact with a retrospective simulation on longitudinal data is the most extensive in the literature on LOS prediction to date. The clinically relevant algorithm, based on routinely collected data, can potentially accelerate clinical decisions in the early detection of LOS, even with limited inputs.

Impact of race on heart rate characteristics monitoring in very low birth weight infants

BACKGROUND

A multicenter RCT showed that displaying a heart rate characteristics index (HRCi) predicting late-onset sepsis reduced mortality for VLBW infants. We aimed to assess whether HRCi display had a differential impact for Black versus White infants.

METHODS

We performed secondary data analysis of Black and White infants enrolled in the HeRO RCT. We evaluated the predictive performance of the HRCi for infants with Black or White maternal race. Using models adjusted for birth weight, we assessed outcomes and interventions for a race × randomization interaction.

RESULTS

Among 2607 infants, Black infants had lower birth weight, gestational age, length of stay, and ventilator days, while sepsis and mortality were similar. The HRCi performed equally for sepsis prediction in Black and White infants. We found no differential effect of randomization by race on sepsis, mortality, antibiotic days, length of stay, or ventilator days. However, there was a differential randomization effect by race for blood cultures per patient: White RR 1.11 (95% CrI 1.04-1.18), Black RR 1.00 (0.93-1.07).

CONCLUSIONS

The HRCi performed similarly for sepsis prediction in Black and White infants. Randomization to HRCi display increased blood cultures in White but not in Black infants, while the impact on other outcomes or interventions was similar.

IMPACT

Predictive analytics, such as heart rate characteristics (HRC) monitoring for late-onset neonatal sepsis, should have equal impact among patients of different race. Infants with Black or White maternal race randomized to HRC display had similar outcomes, but randomization to the study arm increased a related clinical intervention, blood cultures, in White but not in Black infants. This study provides evidence of a differential effect of predictive models on clinical care by race. The work will promote consideration and analysis of equity in the implementation of predictive analytics.

Heart Rate Characteristics Monitoring for Late-Onset Sepsis in Preterm Infants: A Systematic Review

BACKGROUND

Early diagnosis of late-onset sepsis (LOS) and necrotizing enterocolitis (NEC) by monitoring heart rate characteristics (HRC) of preterm infants might reduce the risk of death and morbidities. We aimed to systematically assess the effects of HRC monitoring on death, LOS, and NEC.

METHODS

A systematic search was performed in MEDLINE, Embase, Cochrane Library, and Web of Science.

RESULTS

Fifteen papers were included in this review. Three of these papers reported results from the only identified randomized controlled trial (RCT). This RCT showed that HRC monitoring resulted in a small but significant reduction in mortality (absolute risk reduction 2.1% [95% confidence interval 0.01-4.14]) without any differences in neurodevelopmental impairment. The risk of bias was rated high due to performance and detection bias and failure to correct for multiple testing. Most diagnostic cohort studies showed high discriminating accuracy in predicting LOS but lacked sufficient quality and generalizability. No studies for the detection of NEC were identified.

CONCLUSION

Supported by multiple observational cohort studies, the RCT identified in this systematic review showed that HRC monitoring as an early warning system for LOS might reduce the risk of death in preterm infants. However, methodological weaknesses and limited generalizability do not justify implementation of HRC in clinical care. A large international RCT is warranted.

Vital sign-based detection of sepsis in neonates using machine learning

AIM

Sepsis is a leading cause of morbidity and mortality in neonates. Early diagnosis is key but difficult due to non-specific signs. We investigate the predictive value of machine learning-assisted analysis of non-invasive, high frequency monitoring data and demographic factors to detect neonatal sepsis.

METHODS

Single centre study, including a representative cohort of 325 infants (2866 hospitalisation days). Personalised event timelines including interventions and clinical findings were generated. Time-domain features from heart rate, respiratory rate and oxygen saturation values were calculated and demographic factors included. Sepsis prediction was performed using Naïve Bayes algorithm in a maximum a posteriori framework up to 24 h before clinical sepsis suspicion.

RESULTS

Twenty sepsis cases were identified. Combining multiple vital signs improved algorithm performance compared to heart rate characteristics alone. This enabled a prediction of sepsis with an area under the receiver operating characteristics curve of 0.82, up to 24 h before clinical sepsis suspicion. Moreover, 10 h prior to clinical suspicion, the risk of sepsis increased 150-fold.

CONCLUSION

The present algorithm using non-invasive patient data provides useful predictive value for neonatal sepsis detection. Machine learning-assisted algorithms are promising novel methods that could help individualise patient care and reduce morbidity and mortality.

Sepsis and Mortality Prediction in Very Low Birth Weight Infants: Analysis of HeRO and nSOFA

OBJECTIVE

Scores to predict sepsis or define sepsis severity could improve care for very low birth weight (VLBW) infants. The heart rate characteristics (HRC) index (HeRO score) was developed as an early warning system for late-onset sepsis (LOS), and also rises before necrotizing enterocolitis (NEC). The neonatal sequential organ failure assessment (nSOFA) was developed to predict sepsis-associated mortality using respiratory, hemodynamic, and hematologic data. The aim of this study was to analyze the HRC index and nSOFA near blood cultures in VLBW infants relative to diagnosis and sepsis-associated mortality.

STUDY DESIGN

Retrospective, single-center study of VLBW infants from 2011 to 2019. We analyzed HRC index and nSOFA around blood cultures diagnosed as LOS/NEC. In a subgroup of the cohort, we analyzed HRC and nSOFA near the first sepsis-like illness (SLI) or sepsis ruled-out (SRO) compared with LOS/NEC. We compared scores by diagnosis and mortality during treatment.

RESULTS

We analyzed 179 LOS/NEC, 93 SLI, and 96 SRO blood culture events. In LOS/NEC, the HRC index increased before the blood culture, while nSOFA increased at the time of culture. Both scores were higher in nonsurvivors compared with survivors and in LOS/NEC compared with SRO. The nSOFA 12 hours after the time of blood culture predicted mortality during treatment better than any other time point analyzed (area under the curve 0.91).

CONCLUSION

The HRC index provides earlier warning of imminent sepsis, whereas nSOFA after blood culture provides better prediction of mortality.

KEY POINTS

· The HRC index and nSOFA provide complementary information on sepsis risk and sepsis-related mortality risk.. · This study adds to existing literature evaluating these risk scores independently by analyzing them together and in cases of not only proven but also suspected infections.. · The impact of combining risk models could be improved outcomes for premature infants..

Cardiorespiratory signature of neonatal sepsis: development and validation of prediction models in 3 NICUs

BACKGROUND

Heart rate characteristics aid early detection of late-onset sepsis (LOS), but respiratory data contain additional signatures of illness due to infection. Predictive models using cardiorespiratory data may improve early sepsis detection. We hypothesized that heart rate (HR) and oxygenation (SpO₂) data contain signatures that improve sepsis risk prediction over HR or demographics alone.

METHODS

We analyzed cardiorespiratory data from very low birth weight (VLBW, <1500 g) infants admitted to three NICUs. We developed and externally validated four machine learning models to predict LOS using features calculated every 10 m: mean, standard deviation, skewness, kurtosis of HR and SpO₂, and cross-correlation. We compared feature importance, discrimination, calibration, and dynamic prediction across models and cohorts. We built models of demographics and HR or SpO₂ features alone for comparison with HR-SpO₂ models.

RESULTS

Performance, feature importance, and calibration were similar among modeling methods. All models had favorable external validation performance. The HR-SpO₂ model performed better than models using either HR or SpO₂ alone. Demographics improved the discrimination of all physiologic data models but dampened dynamic performance.

CONCLUSIONS

Cardiorespiratory signatures detect LOS in VLBW infants at 3 NICUs. Demographics risk-stratify, but predictive modeling with both HR and SpO₂ features provides the best dynamic risk prediction.

IMPACT

Heart rate characteristics aid early detection of late-onset sepsis, but respiratory data contain signatures of illness due to infection. Predictive models using both heart rate and respiratory data may improve early sepsis detection. A cardiorespiratory early warning score, analyzing heart rate from electrocardiogram or pulse oximetry with SpO₂, predicts late-onset sepsis within 24 h across multiple NICUs and detects sepsis better than heart rate characteristics or demographics alone. Demographics risk-stratify, but predictive modeling with both HR and SpO₂ features provides the best dynamic risk prediction. The results increase understanding of physiologic signatures of neonatal sepsis.

Novel approaches to capturing and using continuous cardiorespiratory physiological data in hospitalized children

Continuous cardiorespiratory physiological monitoring is a cornerstone of care in hospitalized children. The data generated by monitoring devices coupled with machine learning could transform the way we provide care. This scoping review summarizes existing evidence on novel approaches to continuous cardiorespiratory monitoring in hospitalized children. We aimed to identify opportunities for the development of monitoring technology and the use of machine learning to analyze continuous physiological data to improve the outcomes of hospitalized children. We included original research articles published on or after January 1, 2001, involving novel approaches to collect and use continuous cardiorespiratory physiological data in hospitalized children. OVID Medline, PubMed, and Embase databases were searched. We screened 2909 articles and performed full-text extraction of 105 articles. We identified 58 articles describing novel devices or approaches, which were generally small and single-center. In addition, we identified 47 articles that described the use of continuous physiological data in prediction models, but only 7 integrated multidimensional data (e.g., demographics, laboratory results). We identified three areas for development: (1) further validation of promising novel devices; (2) more studies of models integrating multidimensional data with continuous cardiorespiratory data; and (3) further dissemination, implementation, and validation of prediction models using continuous cardiorespiratory data. IMPACT: We performed a comprehensive scoping review of novel approaches to capture and use continuous cardiorespiratory physiological data for monitoring, diagnosis, providing care, and predicting events in hospitalized infants and children, from novel devices to machine learning-based prediction models. We identified three key areas for future development: (1) further validation of promising novel devices; (2) more studies of models integrating multidimensional data with continuous cardiorespiratory data; and (3) further dissemination, implementation, and validation of prediction models using cardiorespiratory data.

From computer to bedside, involving neonatologists in artificial intelligence models for neonatal medicine

In recent years, data have become the main driver of medical innovation. With increased availability and decreased price of storage and computing power, the potential for improvement in care is enormous. Many data-driven explorations have started. However, the actual implementation of artificial intelligence in healthcare remains scarce. We describe essential elements during a computer-to-bedside process in a data science project that support the crucial role of the neonatologist. IMPACT: There is a great potential for data science in neonatal medicine. Multidisciplinary teams form the foundation of a data science project. Domain experts will need to play a pivotal role. We need an open learning environment.

Artificial and human intelligence for early identification of neonatal sepsis

Artificial intelligence may have a role in the early detection of sepsis in neonates. Machine learning can identify patterns that predict high or increasing risk for clinical deterioration from a sepsis-like illness. In developing this potential addition to NICU care, careful consideration should be given to the data and methods used to develop, validate, and evaluate prediction models. When an AI system alerts clinicians to a change in a patient's condition that warrants a bedside evaluation, human intelligence and experience come into play to determine an appropriate course of action: evaluate and treat or wait and watch closely. With intelligently developed, validated, and implemented AI sepsis systems, both clinicians and patients stand to benefit. IMPACT: This narrative review highlights the application of AI in neonatal sepsis prediction. It describes issues in clinical prediction model development specific to this population. This article reviews the methods, considerations, and literature on neonatal sepsis model development and validation. Challenges of AI technology and potential barriers to using sepsis AI systems in the NICU are discussed.

Improving child health through Big Data and data science

Child health is defined by a complex, dynamic network of genetic, cultural, nutritional, infectious, and environmental determinants at distinct, developmentally determined epochs from preconception to adolescence. This network shapes the future of children, susceptibilities to adult diseases, and individual child health outcomes. Evolution selects characteristics during fetal life, infancy, childhood, and adolescence that adapt to predictable and unpredictable exposures/stresses by creating alternative developmental phenotype trajectories. While child health has improved in the United States and globally over the past 30 years, continued improvement requires access to data that fully represent the complexity of these interactions and to new analytic methods. Big Data and innovative data science methods provide tools to integrate multiple data dimensions for description of best clinical, predictive, and preventive practices, for reducing racial disparities in child health outcomes, for inclusion of patient and family input in medical assessments, and for defining individual disease risk, mechanisms, and therapies. However, leveraging these resources will require new strategies that intentionally address institutional, ethical, regulatory, cultural, technical, and systemic barriers as well as developing partnerships with children and families from diverse backgrounds that acknowledge historical sources of mistrust. We highlight existing pediatric Big Data initiatives and identify areas of future research. IMPACT: Big Data and data science can improve child health. This review highlights the importance for child health of child-specific and life course-based Big Data and data science strategies. This review provides recommendations for future pediatric-specific Big Data and data science research.

Assessment of neonatal respiratory rate variability

Accurate measurement of respiratory rate (RR) in neonates is challenging due to high neonatal RR variability (RRV). There is growing evidence that RRV measurement could inform and guide neonatal care. We sought to quantify neonatal RRV during a clinical study in which we compared multiparameter continuous physiological monitoring (MCPM) devices. Measurements of capnography-recorded exhaled carbon dioxide across 60-s epochs were collected from neonates admitted to the neonatal unit at Aga Khan University-Nairobi hospital. Breaths were manually counted from capnograms and using an automated signal detection algorithm which also calculated mean and median RR for each epoch. Outcome measures were between- and within-neonate RRV, between- and within-epoch RRV, and 95% limits of agreement, bias, and root-mean-square deviation. Twenty-seven neonates were included, with 130 epochs analysed. Mean manual breath count (MBC) was 48 breaths per minute. Median RRV ranged from 11.5% (interquartile range (IQR) 6.8-18.9%) to 28.1% (IQR 23.5-36.7%). Bias and limits of agreement for MBC vs algorithm-derived breath count, MBC vs algorithm-derived median breath rate, MBC vs algorithm-derived mean breath rate were - 0.5 (- 2.7, 1.66), - 3.16 (- 12.12, 5.8), and - 3.99 (- 11.3, 3.32), respectively. The marked RRV highlights the challenge of performing accurate RR measurements in neonates. More research is required to optimize the use of RRV to improve care. When evaluating MCPM devices, accuracy thresholds should be less stringent in newborns due to increased RRV. Lastly, median RR, which discounts the impact of extreme outliers, may be more reflective of the underlying physiological control of breathing.

Prevention of severe brain injury in very preterm neonates: A quality improvement initiative

OBJECTIVE

To determine the impact of neuroprotection interventions bundle on the incidence of severe brain injury or early death (intraventricular hemorrhage grade 3/4 or death by 7 days or ventriculomegaly or cystic periventricular leukomalacia on 1-month head ultrasound, primary composite outcome) in very preterm (27^0/7 to ≤ 29^6/7 weeks gestational age) infants.

STUDY DESIGN

Prospective quality improvement initiative, from April 2017-September 2019, with neuroprotection interventions bundle including cerebral NIRS, TcCO₂, and HeRO monitoring-based management algorithm, indomethacin prophylaxis, protocolized bicarbonate and inotropes use, noise reduction, and neutral positioning.

RESULT

There was a decrease in the incidence of the primary composite outcome in the intervention period on unadjusted (N = 11/99, pre-intervention to N = 0/127, intervention period, p < 0.001) and adjusted analysis (adjusted for birthweight and Apgar score <5 at 5 min, aOR = 0.042, 95% CI = 0.003-0.670, p = 0.024).

CONCLUSIONS

Neuroprotection interventions bundle was associated with significant decrease in severe brain injury or early death in very preterm infants.

Cardiorespiratory measures shortly after extubation and extubation outcomes in extremely preterm infants

BACKGROUND

Nasal continuous positive airway pressure, nasal intermittent positive pressure ventilation, and non-invasive neurally adjusted ventilatory assist are modes of non-invasive respiratory support. The objective was to investigate if cardiorespiratory measures performed shortly after extubation are associated with extubation outcomes and predictors of extubation success.

METHODS

Randomized crossover trial of infants with birth weight (BW) ≤ 1250 g undergoing their first extubation. Shortly after extubation, electrocardiogram and electrical activity of the diaphragm (Edi) were recorded during 40 min on each mode. Measures of heart rate variability (HRV), diaphragmatic activity (Edi area, breath area and amplitude), and respiratory variability (RV) were computed on each mode and compared between infants with extubation success or failure (reintubation ≤ 7 days).

RESULTS

Twenty-three extremely preterm infants with median [IQR] gestational age 25.9 weeks [25.2-26.4] and BW 760 g [595-900] were included: 14 success and 9 failures. There were significant differences for HRV (very low-frequency power and sample entropy) and RV parameters (breath areas, amplitudes and expiratory times) between groups, with moderate strength (0.75-0.80 areas under ROC curves) in predicting success. Diaphragmatic activity measures were similar between groups.

CONCLUSIONS

In extremely preterm infants receiving non-invasive respiratory support shortly after extubation, several cardiorespiratory variability parameters were associated with successful extubation with moderate predictive accuracy.

IMPACT

Measures of cardiorespiratory variability, performed in extremely preterm infants while receiving NCPAP, NIPPV, and NIV-NAVA shortly after extubation, were significantly different between patients that succeeded or failed extubation. Cardiorespiratory variability measures had a moderate predictive accuracy for extubation success and can be potentially used as biomarkers, in recently extubated infants. Future investigations in this population may also consider including cardiorespiratory variability measures when assessing types of post-extubation respiratory support and promote individualized care.

Evaluation of Pulse Rate, Oxygen Saturation, and Respiratory Effort after Different Types of Feeding Methods in Preterm Newborns

Background

During the initial days of hospitalization, preterm newborns are given combinations of breastfeeding, spoon/paladai feeding, and/or gavage feeding. Each method of feeding may have a different effect on vital parameters.

Objective

To study changes in vital parameters in relation to different feeding methods and postmenstrual age (PMA) in preterm newborns. Study Design. This prospective observational study was carried out at a tertiary care neonatal unit. Participants. Physiologically stable preterm newborns with PMA less than 37 weeks on full enteral feeds were included in the study. Intervention. None. Outcomes. Respiratory rate (RR), pulse rate (PR), oxygen saturation (SPO2), nasal flaring, and lower chest indrawing were monitored before and up to 3 h after the breastfeeding/spoon (paladai) feeding/gavage feeding or their combinations. These vital parameters were assessed in relation to the feeding methods and PMA groups using ANOVA.

Results

A total of 383 records were analyzed from 110 newborns. No infant developed chest indrawing or nasal flaring after any feeding method. During the 3 h period of monitoring, vital parameters changed significantly except in the gavage feeding group. The mean PR did not change, but the mean RR and SPO2 changed significantly at different PMA.

Conclusion

Vital parameters changed after different types of feeding methods and at different PMA. A further multicentric prospective study is needed to understand the effect of different feeding methods and PMA on vital parameters.

Contemporary Trends in Global Mortality of Sepsis Among Young Infants Less Than 90 Days: A Systematic Review and Meta-Analysis

Objective

Current knowledge on the global burden of infant sepsis is limited to population-level data. We aimed to summarize global case fatality rates (CFRs) of young infants with sepsis, stratified by gross national income (GNI) status and patient-level risk factors.

Methods

We performed a systematic review and meta-analysis on CFRs among young infants < 90 days with sepsis. We searched PubMed, Cochrane Central, Embase, and Web of Science for studies published between January 2010 and September 2019. We obtained pooled CFRs estimates using the random effects model. We performed a univariate analysis at patient-level and a meta-regression to study the associations of gestational age, birth weight, onset of sepsis, GNI, age group and culture-proven sepsis with CFRs.

Results

The search yielded 6314 publications, of which 240 studies (N = 437,796 patients) from 77 countries were included. Of 240 studies, 99 were conducted in high-income countries, 44 in upper-middle-income countries, 82 in lower-middle-income countries, 6 in low-income countries and 9 in multiple income-level countries. Overall pooled CFR was 18% (95% CI, 17-19%). The CFR was highest for low-income countries [25% (95% CI, 7-43%)], followed by lower-middle [25% (95% CI, 7-43%)], upper-middle [21% (95% CI, 18-24%)] and lowest for high-income countries [12% (95% CI, 11-13%)]. Factors associated with high CFRs included prematurity, low birth weight, age less than 28 days, early onset sepsis, hospital acquired infections and sepsis in middle- and low-income countries. Study setting in middle-income countries was an independent predictor of high CFRs. We found a widening disparity in CFRs between countries of different GNI over time.

Conclusion

Young infant sepsis remains a major global health challenge. The widening disparity in young infant sepsis CFRs between GNI groups underscore the need to channel greater resources especially to the lower income regions.

Systematic Review Registration

[www.crd.york.ac.uk/prospero], identifier [CRD42020164321].

Evaluation of a contactless neonatal physiological monitor in Nairobi, Kenya

BACKGROUND

Globally, 2.5 million neonates died in 2018, accounting for 46% of under-5 deaths. Multiparameter continuous physiological monitoring (MCPM) of neonates allows for early detection and treatment of life-threatening health problems. However, neonatal monitoring technology is largely unavailable in low-resource settings.

METHODS

In four evaluation rounds, we prospectively compared the accuracy of the EarlySense under-mattress device to the Masimo Rad-97 pulse CO-oximeter with capnography reference device for heart rate (HR) and respiratory rate (RR) measurements in neonates in Kenya. EarlySense algorithm optimisations were made between evaluation rounds. In each evaluation round, we compared 200 randomly selected epochs of data using Bland-Altman plots and generated Clarke error grids with zones of 20% to aid in clinical interpretation.

RESULTS

Between 9 July 2019 and 8 January 2020, we collected 280 hours of MCPM data from 76 enrolled neonates. At the final evaluation round, the EarlySense MCPM device demonstrated a bias of -0.8 beats/minute for HR and 1.6 breaths/minute for RR, and normalised spread between the 95% upper and lower limits of agreement of 6.2% for HR and 27.3% for RR. Agreement between the two MCPM devices met the a priori-defined threshold of 30%. The Clarke error grids showed that all observations for HR and 197/200 for RR were within a 20% difference.

CONCLUSION

Our research indicates that there is acceptable agreement between the EarlySense and Masimo MCPM devices in the context of large within-subject variability; however, further studies establishing cost-effectiveness and clinical effectiveness are needed before large-scale implementation of the EarlySense MCPM device in neonates.

TRIAL REGISTRATION NUMBER

NCT03920761.

Using Temporal Data Mining on Patient Data for Clinical Decision Making in the Care of the Sick Newborn

BACKGROUND

In a neonatal intensive care unit, streaming healthcare data comes from many sources, but humans are unable to understand relationships between data variables. Data mining and analysis are just beginning to get utilized in critical care. We present a case study using electronic medical record data in the neonatal intensive care unit and explore possible avenues of advancement using temporal data analytics.

CASE PRESENTATION

Electronic medical record data were collected for physiological monitor data. Heart rate, respiratory rate, oxygen saturation and temperature data were retrospectively analyzed by temporal data mining. Three premature babies were selected and data de-identified. The first case of a urinary tract infection showed nursing ability to synthesize data streams coming from a patient. For the second case of necrotizing enterocolitis, Temporal-Data-Mining analysis of combinations of clinical events based on deviations from the mean showed specific heuristic biomarkers related to events before discovery of necrotizing enterocolitis. Specific sequences 6-event and 5-event in length were identified with nursing unease at clinical deterioration, which were 100- and 87-times unlikely to occur randomly with 99.5% confidence. No such sequences were found in the rest of the 37 days for the second case and entire 133 days of stay in the third case of an uneventful premature baby.

CONCLUSION

Temporal data mining is a possible clinical tool in providing useful information in the neonatal intensive care unit for diagnosis of adverse clinical occurrences such as necrotizing enterocolitis. There is the possibility of changing the clinical paradigm of episodic watchfulness to constant vigilance using real-time data gathering.

The principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICU

In 2011, a multicenter group spearheaded at the University of Virginia demonstrated reduced mortality from real-time continuous cardiorespiratory monitoring in the neonatal ICU using what we now call Artificial Intelligence, Big Data, and Machine Learning. The large, randomized heart rate characteristics trial made real, for the first time that we know of, the promise that early detection of illness would allow earlier and more effective intervention and improved patient outcomes. Currently, though, we hear as much of failures as we do of successes in the rapidly growing field of predictive analytics monitoring that has followed. This Perspective aims to describe the principles of how we developed heart rate characteristics monitoring for neonatal sepsis and then applied them throughout adult ICU and hospital medicine. It primarily reflects the work since the 1990s of the University of Virginia group: the theme is that sudden and catastrophic deteriorations can be preceded by subclinical but measurable physiological changes apparent in the continuous cardiorespiratory monitoring and electronic health record.

Intelligent Clinical Decision Support

Early recognition of pathologic cardiorespiratory stress and forecasting cardiorespiratory decompensation in the critically ill is difficult even in highly monitored patients in the Intensive Care Unit (ICU). Instability can be intuitively defined as the overt manifestation of the failure of the host to adequately respond to cardiorespiratory stress. The enormous volume of patient data available in ICU environments, both of high-frequency numeric and waveform data accessible from bedside monitors, plus Electronic Health Record (EHR) data, presents a platform ripe for Artificial Intelligence (AI) approaches for the detection and forecasting of instability, and data-driven intelligent clinical decision support (CDS). Building unbiased, reliable, and usable AI-based systems across health care sites is rapidly becoming a high priority, specifically as these systems relate to diagnostics, forecasting, and bedside clinical decision support. The ICU environment is particularly well-positioned to demonstrate the value of AI in saving lives. The goal is to create AI models embedded in a real-time CDS for forecasting and mitigation of critical instability in ICU patients of sufficient readiness to be deployed at the bedside. Such a system must leverage multi-source patient data, machine learning, systems engineering, and human action expertise, the latter being key to successful CDS implementation in the clinical workflow and evaluation of bias. We present one approach to create an operationally relevant AI-based forecasting CDS system.

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.

Discovery of signatures of fatal neonatal illness in vital signs using highly comparative time-series analysis

To seek new signatures of illness in heart rate and oxygen saturation vital signs from Neonatal Intensive Care Unit (NICU) patients, we implemented highly comparative time-series analysis to discover features of all-cause mortality in the next 7 days. We collected 0.5 Hz heart rate and oxygen saturation vital signs of infants in the University of Virginia NICU from 2009 to 2019. We applied 4998 algorithmic operations from 11 mathematical families to random daily 10 min segments from 5957 NICU infants, 205 of whom died. We clustered the results and selected a representative from each, and examined multivariable logistic regression models. 3555 operations were usable; 20 cluster medoids held more than 81% of the information, and a multivariable model had AUC 0.83. New algorithms outperformed others: moving threshold, successive increases, surprise, and random walk. We computed provenance of the computations and constructed a software library with links to the data. We conclude that highly comparative time-series analysis revealed new vital sign measures to identify NICU patients at the highest risk of death in the next week.

Vital signs as physiomarkers of neonatal sepsis

Neonatal sepsis accounts for significant morbidity and mortality, particularly among premature infants in the Neonatal Intensive Care Unit. Abnormal vital sign patterns serve as physiomarkers of sepsis and provide early warning of illness before overt clinical decompensation. The systemic inflammatory response to pathogens signals the autonomic nervous system, leading to changes in temperature, respiratory rate, heart rate, and blood pressure. In infants with comorbidities of prematurity, vital sign abnormalities often occur in the absence of infection, which confounds sepsis diagnosis. This review will cover the mechanisms of vital sign changes in neonatal sepsis, including the cholinergic anti-inflammatory pathway mediated by the vagus nerve, which is critical to the host response to infectious and inflammatory insults. We will also review the clinical implications of vital sign changes in neonatal sepsis, including their use in early warning scores and systems to direct clinicians to the bedside of infants with physiologic changes that might be due to sepsis. IMPACT: This manuscript summarizes and reviews the relevant literature on the physiological manifestations of neonatal sepsis and how we monitor and analyze these through vital signs and advanced analytics.

Cost-effectiveness analysis of heart rate characteristics monitoring to improve survival for very low birth weight infants

Introduction

Over 50,000 very low birth weight (VLBW) infants are born each year in the United States. Despite advances in care, these premature babies are subjected to long stays in a neonatal intensive care unit (NICU), and experience high rates of morbidity and mortality. In a large randomized controlled trial (RCT), heart rate characteristics (HRC) monitoring in addition to standard monitoring decreased all-cause mortality among VLBW infants by 22%. We sought to understand the cost-effectiveness of HRC monitoring to improve survival among VLBW infants.

Methods

We performed a secondary analysis of cost-effectiveness of heart rate characteristics (HRC) monitoring to improve survival from birth to NICU discharge, up to 120 days using data and outcomes from an RCT of 3,003 VLBW patients. We estimated each patient's cost from a third-party perspective in 2021 USD using the resource utilization data gathered during the RCT (NCT00307333) during their initial stay in the NICU and applied to specific per diem rates. We computed the incremental cost-effectiveness ratio and used non-parametric boot-strapping to evaluate uncertainty.

Results

The incremental cost-effectiveness ratio of HRC-monitoring was $34,720 per life saved. The 95th percentile of cost to save one additional life through HRC-monitoring was $449,291.

Conclusion

HRC-monitoring appears cost-effective for increasing survival among VLBW infants.

Knowledge gaps in late-onset neonatal sepsis in preterm neonates: a roadmap for future research

Late-onset neonatal sepsis (LONS) remains an important threat to the health of preterm neonates in the neonatal intensive care unit. Strategies to optimize care for preterm neonates with LONS are likely to improve survival and long-term neurocognitive outcomes. However, many important questions on how to improve the prevention, early detection, and therapy for LONS in preterm neonates remain unanswered. This review identifies important knowledge gaps in the management of LONS and describe possible methods and technologies that can be used to resolve these knowledge gaps. The availability of computational medicine and hypothesis-free-omics approaches give way to building bedside feedback tools to guide clinicians in personalized management of LONS. Despite advances in technology, implementation in clinical practice is largely lacking although such tools would help clinicians to optimize many aspects of the management of LONS. We outline which steps are needed to get possible research findings implemented on the neonatal intensive care unit and provide a roadmap for future research initiatives. IMPACT: This review identifies knowledge gaps in prevention, early detection, antibiotic, and additional therapy of late-onset neonatal sepsis in preterm neonates and provides a roadmap for future research efforts. Research opportunities are addressed, which could provide the means to fill knowledge gaps and the steps that need to be made before possible clinical use. Methods to personalize medicine and technologies feasible for bedside clinical use are described.

Neonatal sepsis: a systematic review of core outcomes from randomised clinical trials

BACKGROUND

The lack of a consensus definition of neonatal sepsis and a core outcome set (COS) proves a substantial impediment to research that influences policy and practice relevant to key stakeholders, patients and parents.

METHODS

A systematic review of the literature was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. In the included studies, the described outcomes were extracted in accordance with the provisions of the Core Outcome Measures in Effectiveness Trials (COMET) handbook and registered.

RESULTS

Among 884 abstracts identified, 90 randomised controlled trials (RCTs) were included in this review. Only 30 manuscripts explicitly stated the primary and/or secondary outcomes. A total of 88 distinct outcomes were recorded across all 90 studies included. These were then assigned to seven different domains in line with the taxonomy for classification proposed by the COMET initiative. The most frequently reported outcome was survival with 74% (n = 67) of the studies reporting an outcome within this domain.

CONCLUSIONS

This systematic review constitutes one of the initial phases in the protocol for developing a COS in neonatal sepsis. The paucity of standardised outcome reporting in neonatal sepsis hinders comparison and synthesis of data. The final phase will involve a Delphi Survey to generate a COS in neonatal sepsis by consensus recommendation.

IMPACT

This systematic review identified a wide variation of outcomes reported among published RCTs on the management of neonatal sepsis. The paucity of standardised outcome reporting hinders comparison and synthesis of data and future meta-analyses with conclusive recommendations on the management of neonatal sepsis are unlikely. The final phase will involve a Delphi Survey to determine a COS by consensus recommendation with input from all relevant stakeholders.

Introducing heart rate variability monitoring combined with biomarker screening into a level IV NICU: a prospective implementation study

The aim of this study was to investigate the association between the implementation of a local heart rate variability (HRV) monitoring guideline combined with determination of inflammatory biomarkers and mortality, measures of sepsis severity, frequency of sepsis testing, and antibiotic usage, among very preterm neonates. In January 2018, a guideline was implemented for early detection of late-onset neonatal sepsis using HRV monitoring combined with determination of inflammatory biomarkers. Data on all patients admitted with a gestational age at birth of < 32 weeks were reviewed in the period January 2016-June 2020 (n = 1,135; n = 515 pre-implementation, n = 620 post-implementation). Outcomes of interest were (sepsis-related) mortality, sepsis severity (neonatal sequential organ failure assessment (nSOFA)), sepsis testing, and antibiotic usage. Differences before and after implementation of the guideline were assessed using logistic and linear regression analysis for binary and continuous outcomes respectively. All analyses were adjusted for gestational age and sex. Mortality within 10 days of a sepsis episode occurred in 39 (10.3%) and 34 (7.6%) episodes in the pre- and post-implementation period respectively (P = 0.13). The nSOFA course during a sepsis episode was significantly lower in the post-implementation group (P = 0.01). We observed significantly more blood tests for determination of inflammatory biomarkers, but no statistically significant difference in number of blood cultures drawn and in antibiotic usage between the two periods.Conclusion: Implementing HRV monitoring with determination of inflammatory biomarkers might help identify patients with sepsis sooner, resulting in reduced sepsis severity, without an increased use of antibiotics or number of blood cultures.

Neonatal heart rate variability: a contemporary scoping review of analysis methods and clinical applications

BACKGROUND

Neonatal heart rate variability (HRV) is widely used as a research tool. However, HRV calculation methods are highly variable making it difficult for comparisons between studies.

OBJECTIVES

To describe the different types of investigations where neonatal HRV was used, study characteristics, and types of analyses performed.

ELIGIBILITY CRITERIA

Human neonates ≤1 month of corrected age.

SOURCES OF EVIDENCE

A protocol and search strategy of the literature was developed in collaboration with the McGill University Health Center's librarians and articles were obtained from searches in the Biosis, Cochrane, Embase, Medline and Web of Science databases published between 1 January 2000 and 1 July 2020.

CHARTING METHODS

A single reviewer screened for eligibility and data were extracted from the included articles. Information collected included the study characteristics and population, type of HRV analysis used (time domain, frequency domain, non-linear, heart rate characteristics (HRC) parameters) and clinical applications (physiological and pathological conditions, responses to various stimuli and outcome prediction).

RESULTS

Of the 286 articles included, 171 (60%) were small single centre studies (sample size <50) performed on term infants (n=136). There were 138 different types of investigations reported: physiological investigations (n=162), responses to various stimuli (n=136), pathological conditions (n=109) and outcome predictor (n=30). Frequency domain analyses were used in 210 articles (73%), followed by time domain (n=139), non-linear methods (n=74) or HRC analyses (n=25). Additionally, over 60 different measures of HRV were reported; in the frequency domain analyses alone there were 29 different ranges used for the low frequency band and 46 for the high frequency band.

CONCLUSIONS

Neonatal HRV has been used in diverse types of investigations with significant lack of consistency in analysis methods applied. Specific guidelines for HRV analyses in neonates are needed to allow for comparisons between studies.

Neonatal sepsis prediction through clinical decision support algorithms: A systematic review

AIM

To systematically summarise the current evidence of employing clinical decision support algorithms (CDSAs) using non-invasive parameters for sepsis prediction in neonates.

METHODS

A comprehensive search in PubMed, CENTRAL and EMBASE was conducted. Screening, data extraction and risk of bias were performed by two authors. The certainty of the evidence was assessed using GRADE.

PROSPERO ID

CRD42020205143.

RESULTS

After abstract and full-text screening, 36 studies comprising 18,096 infants were included. Most CDSAs evaluated heart rate (HR)-based parameters. Two publications derived from one randomised-controlled trial assessing HR characteristics reported significant reduction in 30-day septicaemia-related mortality. Thirty-four non-randomised studies found promising yet inconclusive results.

CONCLUSION

Heart rate-based parameters are reliable components of CDSAs for sepsis prediction, particularly in combination with additional vital signs and demographics. However, inconclusive evidence and limited standardisation restricts clinical implementation of CDSAs outside of a controlled research environment. Further experimentation and comparison of parameter combinations and testing of new CDSAs are warranted.

Nursing and precision predictive analytics monitoring in the acute and intensive care setting: An emerging role for responding to COVID-19 and beyond

As the global response to COVID-19 continues, nurses will be tasked with appropriately triaging patients, responding to events of clinical deterioration, and developing family-centered plans of care within a healthcare system exceeding capacity. Predictive analytics monitoring, an artificial intelligence (AI)-based tool that translates streaming clinical data into a real-time visual estimation of patient risks, allows for evolving acuity assessments and detection of clinical deterioration while the patient is in pre-symptomatic states. While nurses are on the frontline for the COVID-19 pandemic, the use of AI-based predictive analytics monitoring may help cognitively complex clinical decision-making tasks and pave a pathway for early detection of patients at risk for decompensation. We must develop strategies and techniques to study the impact of AI-based technologies on patient care outcomes and the clinical workflow. This paper outlines key concepts for the intersection of nursing and precision predictive analytics monitoring.

Identification of thresholds for accuracy comparisons of heart rate and respiratory rate in neonates

Background: Heart rate (HR) and respiratory rate (RR) can be challenging to measure accurately and reliably in neonates. The introduction of innovative, non-invasive measurement technologies suitable for resource-constrained settings is limited by the lack of appropriate clinical thresholds for accuracy comparison studies. Methods: We collected measurements of photoplethysmography-recorded HR and capnography-recorded exhaled carbon dioxide across multiple 60-second epochs (observations) in enrolled neonates admitted to the neonatal care unit at Aga Khan University Hospital in Nairobi, Kenya. Trained study nurses manually recorded HR, and the study team manually counted individual breaths from capnograms. For comparison, HR and RR also were measured using an automated signal detection algorithm. Clinical measurements were analyzed for repeatability. Results: A total of 297 epochs across 35 neonates were recorded. Manual HR showed a bias of -2.4 (-1.8%) and a spread between the 95% limits of agreement (LOA) of 40.3 (29.6%) compared to the algorithm-derived median HR. Manual RR showed a bias of -3.2 (-6.6%) and a spread between the 95% LOA of 17.9 (37.3%) compared to the algorithm-derived median RR, and a bias of -0.5 (1.1%) and a spread between the 95% LOA of 4.4 (9.1%) compared to the algorithm-derived RR count. Manual HR and RR showed repeatability of 0.6 (interquartile range (IQR) 0.5-0.7), and 0.7 (IQR 0.5-0.8), respectively. Conclusions: Appropriate clinical thresholds should be selected a priori when performing accuracy comparisons for HR and RR. Automated measurement technologies typically use a smoothing or averaging filter, which significantly impacts accuracy. A wider spread between the LOA, as much as 30%, should be considered to account for the observed physiological nuances and within- and between-neonate variability and different averaging methods. Wider adoption of thresholds by data standards organizations and technology developers and manufacturers will increase the robustness of clinical comparison studies.

Cardiorespiratory effects of NIV-NAVA, NIPPV, and NCPAP shortly after extubation in extremely preterm infants: A randomized crossover trial

OBJECTIVE

Investigate the cardiorespiratory effects of noninvasive neurally adjusted ventilatory assist (NIV-NAVA), nonsynchronized nasal intermittent positive pressure ventilation (NIPPV), and nasal continuous positive airway pressure (NCPAP) shortly after extubation.

HYPOTHESIS

Types of noninvasive pressure support and the presence of synchronization may affect cardiorespiratory parameters.

STUDY DESIGN

Randomized crossover trial.

PATIENT-SUBJECT SELECTION

Infants with birth weight (BW) 1250 g or under, undergoing their first planned extubation were randomly assigned to all three modes using a computer-generated sequence.

METHODOLOGY

Electrocardiogram and electrical activity of the diaphragm (Edi) were recorded for 30 min on each mode. Analysis of heart rate variability (HRV), diaphragmatic activity (Edi area, breath area, amplitude, inspiratory and expiratory times), and respiratory variability were compared between modes.

RESULTS

Twenty-three infants had full data recordings and analysis: Median (IQR) gestational age = 25.9 weeks (25.2-26.4), BW = 760 g (595-900), and postnatal age 7 (4-19) days. There were no differences in HRV between modes. A significantly reduced Edi area and breath amplitude, and increased coefficient of variation (CV) of breath amplitude were observed during NIV-NAVA and NIPPV compared to NCPAP. A higher proportion of assisted breaths (99% vs. 51%; p < .001) provided a higher mean airway pressure (MAP; 9.4 vs. 8.2 cmH₂ O; p = .002) with lower peak inflation pressures (PIPs; 14 vs. 16 cmH₂ O; p < .001) during NIV-NAVA compared to NIPPV.

CONCLUSIONS

NIV-NAVA and NIPPV applied shortly after extubation were associated with lower respiratory efforts and higher respiratory variability. These effects were more evident for NIV-NAVA where optimal patient-ventilator synchronization provided a higher MAP with lower PIPs.

Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring

A new development in the practice of medicine is Artificial Intelligence-based predictive analytics that forewarn clinicians of future deterioration of their patients. This proactive opportunity, though, is different from the reactive stance that clinicians traditionally take. Implementing these tools requires new ideas about how to educate clinician users to facilitate trust and adoption and to promote sustained use. Our real-world hospital experience implementing a predictive analytics monitoring system that uses electronic health record and continuous monitoring data has taught us principles that we believe to be applicable to the implementation of other such analytics systems within the health care environment. These principles are mentioned below:• To promote trust, the science must be understandable.• To enhance uptake, the workflow should not be impacted greatly.• To maximize buy-in, engagement at all levels is important.• To ensure relevance, the education must be tailored to the clinical role and hospital culture.• To lead to clinical action, the information must integrate into clinical care.• To promote sustainability, there should be periodic support interactions after formal implementation.

Heart rate characteristics monitoring and reduction in mortality or neurodevelopmental impairment in extremely low birthweight infants with sepsis

We questioned whether a heart rate characteristics (HRC) sepsis risk score displayed to clinicians would modify 18-22 month neurodevelopmental outcomes for extremely low birthweight infants who develop sepsis. Infants allocated to HRC display with sepsis had a 12% absolute reduction in the composite outcome of death or neurodevelopmental impairment. TRIAL REGISTRATION: NCT00307333.

Predictive Monitoring-Impact in Acute Care Cardiology Trial (PM-IMPACCT): Protocol for a Randomized Controlled Trial

BACKGROUND

Patients in acute care wards who deteriorate and are emergently transferred to intensive care units (ICUs) have poor outcomes. Early identification of patients who are decompensating might allow for earlier clinical intervention and reduced morbidity and mortality. Advances in bedside continuous predictive analytics monitoring (ie, artificial intelligence [AI]-based risk prediction) have made complex data easily available to health care providers and have provided early warning of potentially catastrophic clinical events. We present a dynamic, visual, predictive analytics monitoring tool that integrates real-time bedside telemetric physiologic data into robust clinical models to estimate and communicate risk of imminent events. This tool, Continuous Monitoring of Event Trajectories (CoMET), has been shown in retrospective observational studies to predict clinical decompensation on the acute care ward. There is a need to more definitively study this advanced predictive analytics or AI monitoring system in a prospective, randomized controlled, clinical trial.

OBJECTIVE

The goal of this trial is to determine the impact of an AI-based visual risk analytic, CoMET, on improving patient outcomes related to clinical deterioration, response time to proactive clinical action, and costs to the health care system.

METHODS

We propose a cluster randomized controlled trial to test the impact of using the CoMET display in an acute care cardiology and cardiothoracic surgery hospital floor. The number of admissions to a room undergoing cluster randomization was estimated to be 10,424 over the 20-month study period. Cluster randomization based on bed number will occur every 2 months. The intervention cluster will have the CoMET score displayed (along with standard of care), while the usual care group will receive standard of care only.

RESULTS

The primary outcome will be hours free from events of clinical deterioration. Hours of acute clinical events are defined as time when one or more of the following occur: emergent ICU transfer, emergent surgery prior to ICU transfer, cardiac arrest prior to ICU transfer, emergent intubation, or death. The clinical trial began randomization in January 2021.

CONCLUSIONS

Very few AI-based health analytics have been translated from algorithm to real-world use. This study will use robust, prospective, randomized controlled, clinical trial methodology to assess the effectiveness of an advanced AI predictive analytics monitoring system in incorporating real-time telemetric data for identifying clinical deterioration on acute care wards. This analysis will strengthen the ability of health care organizations to evolve as learning health systems, in which bioinformatics data are applied to improve patient outcomes by incorporating AI into knowledge tools that are successfully integrated into clinical practice by health care providers.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04359641; https://clinicaltrials.gov/ct2/show/NCT04359641.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/29631.

Identification of thresholds for accuracy comparisons of heart rate and respiratory rate in neonates

Background: Heart rate (HR) and respiratory rate (RR) can be challenging to measure accurately and reliably in neonates. The introduction of innovative, non-invasive measurement technologies suitable for resource-constrained settings is limited by the lack of appropriate clinical thresholds for accuracy comparison studies. Methods: We collected measurements of photoplethysmography-recorded HR and capnography-recorded exhaled carbon dioxide across multiple 60-second epochs (observations) in enrolled neonates admitted to the neonatal care unit at Aga Khan University Hospital in Nairobi, Kenya. Trained study nurses manually recorded HR, and the study team manually counted individual breaths from capnograms. For comparison, HR and RR also were measured using an automated signal detection algorithm. Clinical measurements were analyzed for repeatability. Results: A total of 297 epochs across 35 neonates were recorded. Manual HR showed a bias of -2.4 (-1.8%) and a spread between the 95% limits of agreement (LOA) of 40.3 (29.6%) compared to the algorithm-derived median HR. Manual RR showed a bias of -3.2 (-6.6%) and a spread between the 95% LOA of 17.9 (37.3%) compared to the algorithm-derived median RR, and a bias of -0.5 (1.1%) and a spread between the 95% LOA of 4.4 (9.1%) compared to the algorithm-derived RR count. Manual HR and RR showed repeatability of 0.6 (interquartile range (IQR) 0.5-0.7), and 0.7 (IQR 0.5-0.8), respectively. Conclusions: Appropriate clinical thresholds should be selected a priori when performing accuracy comparisons for HR and RR. Automated measurement technologies typically use median values rather than counts, which significantly impacts accuracy. A wider spread between the LOA, as much as 30%, should be considered to account for the observed physiological nuances and within- and between-neonate variability and different averaging methods. Wider adoption of thresholds by data standards organizations and technology developers and manufacturers will increase the robustness of clinical comparison studies.

Predicting mortality risk for preterm infants using random forest

Mortality is an unfortunately common outcome of extremely and very preterm birth. Existing clinical prediction models capture mortality risk at birth but fail to account for the remainder of the hospital course. Infants born < 32 weeks gestation with complete physiologic and clinical data were included in this retrospective study. Mortality risk was quantified by conventional means (clinical factors) using the CRIB-II score and the optimal logistic regression model. A random forest (RF) model was trained using a subset of the cohort, labeling data within 6 h of death as “worry.” The model was then tested using the remaining infants. A total of 275 infants were included in the study with a mean gestational age of 27 weeks, mean birth weight of 929 g, 49% female, and a mortality rate of 21%. The CRIB-II and logistic regression models had acceptable performance with sensitivities of 71% and 80% AUC scores of 0.78 and 0.84, respectively. The RF model had superior performance with a sensitivity of 88% and an AUC of 0.93. A random forest model which incorporates fixed clinical factors with the influence of aberrancies in subsequent physiology has superior performance for mortality prediction compared to conventional models.

Clinical and vital sign changes associated with late-onset sepsis in very low birth weight infants at 3 NICUs

BACKGROUND

In premature infants, clinical changes frequently occur due to sepsis or non-infectious conditions, and distinguishing between these is challenging. Baseline risk factors, vital signs, and clinical signs guide decisions to culture and start antibiotics. We sought to compare heart rate (HR) and oxygenation (SpO2) patterns as well as baseline variables and clinical signs prompting sepsis work-ups ultimately determined to be late-onset sepsis (LOS) and sepsis ruled out (SRO).

METHODS

At three NICUs, we reviewed records of very low birth weight (VLBW) infants around their first sepsis work-up diagnosed as LOS or SRO. Clinical signs prompting the evaluation were determined from clinician documentation. HR-SpO2 data, when available, were analyzed for mean, standard deviation, skewness, kurtosis, and cross-correlation. We used LASSO and logistic regression to assess variable importance and associations with LOS compared to SRO.

RESULTS

We analyzed sepsis work-ups in 408 infants (173 LOS, 235 SRO). Compared to infants with SRO, those with LOS were of lower GA and BW, and more likely to have a central catheter and mechanical ventilation. Clinical signs cited more often in LOS included hypotension, acidosis, abdominal distension, lethargy, oliguria, and abnormal CBC or CRP(p <  0.05). HR-SpO2 data were available in 266 events. Cross-correlation HR-SpO2 before the event was associated with LOS after adjusting for GA, BW, and postnatal age. A model combining baseline, clinical and HR-SpO2 variables had AUC 0.821.

CONCLUSION

In VLBW infants at 3-NICUs, we describe the baseline, clinical, and HR-SpO2 variables associated with LOS versus SRO.

Diagnosis of Neonatal Late-Onset Infection in Very Preterm Infant: Inter-Observer Agreement and International Classifications

Background: The definition of late-onset bacterial sepsis (LOS) in very preterm infants is not unified. The objective was to assess the concordance of LOS diagnosis between experts in neonatal infection and international classifications and to evaluate the potential impact on heart rate variability and rate of “bronchopulmonary dysplasia or death”. Methods: A retrospective (2017–2020) multicenter study including hospitalized infants born before 31 weeks of gestation with intention to treat at least 5-days with antibiotics was performed. LOS was classified as “certain or probable” or “doubtful” independently by five experts and according to four international classifications with concordance assessed by Fleiss’s kappa test. Results: LOS was suspected at seven days (IQR: 5–11) of life in 48 infants. Following expert classification, 36 of them (75%) were considered as “certain or probable” (kappa = 0.41). Following international classification, this number varied from 13 to 46 (kappa = −0.08). Using the expert classification, “bronchopulmonary dysplasia or death” occurred less frequently in the doubtful group (25% vs. 78%, p < 0.001). Differences existed in HRV changes between the two groups. Conclusion: The definition of LOS is not consensual with a low international and moderate inter-observer agreement. This affects the evaluation of associated organ dysfunction and prognosis.

Interpretation of Heart Rate Variability: The Art of Looking Through a Keyhole

The heart may be a mirror of the soul, but the human mind is more than its heart rate variability (HRV). Many techniques to quantify HRV promise to give a view of what is going on in the body or even the psyche of the subject under study. This "Hypothesis" paper gives, on the one hand, a critical view on the field of HRV-analysis and, on the other hand, points out a possible direction of future applications. In view of the inherent variability of HRV and the underlying processes, as lined out here, the best use may be found in serial analysis in a subject/patient, to find changes over time that may help in early discovery of developing pathology. Not every future possibility is bright and shining, though, as demonstrated in a fictional diary excerpt from a future subject, living in a society geared toward preventive medicine. Here implanted biochips watch over the health of the population and artificial intelligence (AI) analyses the massive data flow to support the diagnostic process.

Heart rate variability as an autonomic biomarker in ischemic stroke

Stroke is one of the leading causes of mortality and disability worldwide. Autonomic dysfunction after ischemic stroke is frequently associated with cardiac complications and high mortality. The brain-heart axis is a good model for understanding autonomic interaction between the autonomic central network and the cardiovascular system. Heart rate variability (HRV) analysis is a non-invasive approach for understanding cardiac autonomic regulation. In stroke patients, HRV parameters are altered in the acute and chronic stages of the disease, having a prognostic value. In this literature review we summarize the main concepts about the autonomic nervous system and HRV as autonomic biomarkers in ischemic stroke.

Coronavirus Disease 2019 Calls for Predictive Analytics Monitoring-A New Kind of Illness Scoring System

Coronavirus disease 2019 can lead to sudden and severe respiratory failure that mandates endotracheal intubation, a procedure much more safely performed under elective rather than emergency conditions. Early warning of rising risk of this event could benefit both patients and healthcare providers by reducing the high risk of emergency intubation. Current illness severity scoring systems, which usually update only when clinicians measure vital signs or laboratory values, are poorly suited for early detection of this kind of rapid clinical deterioration. We propose that continuous predictive analytics monitoring, a new approach to bedside management, is more useful. The principles of this new practice anchor in analysis of continuous bedside monitoring data, training models on diagnosis-specific paths of deterioration using clinician-identified events, and continuous display of trends in risks rather than alerts when arbitrary thresholds are exceeded.