Abnormal heart rate characteristics are associated with neonatal mortality

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..

Sample entropy correlates with intraventricular hemorrhage and mortality in premature infants early in life

BACKGROUND

Mortality and intraventricular hemorrhage (IVH) are common adverse outcomes in preterm infants and are challenging to predict clinically. Sample entropy (SE), a measure of heart rate variability (HRV), has shown predictive power for sepsis and other morbidities in neonates. We evaluated associations between SE and mortality and IVH in the first week of life.

METHODS

Participants were 389 infants born before 32 weeks of gestation for whom bedside monitor data were available. A total of 29 infants had IVH grade 3 or 4 and 31 infants died within 2 weeks of life. SE was calculated with the PhysioNet open-source benchmark. Logistic regressions assessed associations between SE and IVH and/or mortality with and without common clinical covariates over various hour of life (HOL) censor points.

RESULTS

Lower SE was associated with mortality by 4 HOL, but higher SE was very strongly associated with IVH and mortality at 24-96 HOL. Bootstrap testing confirmed SE significantly improved prediction using clinical variables at 96 HOL.

CONCLUSION

SE is a significant predictor of IVH and mortality in premature infants. Given IVH typically occurs in the first 24-72 HOL, affected infants may initially have low SE followed by a sustained period of high SE.

IMPACT

SE correlates with IVH and mortality in preterm infants early in life. SE combined with clinical factors yielded ROC AUCs well above 0.8 and significantly outperformed the clinical model at 96 h of life. Previous studies had not shown predictive power over clinical models. First study using the PhysioNet Cardiovascular Toolbox benchmark in young infants. Relative to the generally accepted timing of IVH in premature infants, we saw lower SE before or around the time of hemorrhage and a sustained period of higher SE after. Higher SE after acute events has not been reported previously.

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.

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..

Signatures of illness in children requiring unplanned intubation in the pediatric intensive care unit: A retrospective cohort machine-learning study

Acute respiratory failure requiring the initiation of invasive mechanical ventilation remains commonplace in the pediatric intensive care unit (PICU). Early recognition of patients at risk for respiratory failure may provide clinicians with the opportunity to intervene and potentially improve outcomes. Through the development of a random forest model to identify patients at risk for requiring unplanned intubation, we tested the hypothesis that subtle signatures of illness are present in physiological and biochemical time series of PICU patients in the early stages of respiratory decompensation. We included 116 unplanned intubation events as recorded in the National Emergency Airway Registry for Children in 92 PICU admissions over a 29-month period at our institution. We observed that children have a physiologic signature of illness preceding unplanned intubation in the PICU. Generally, it comprises younger age, and abnormalities in electrolyte, hematologic and vital sign parameters. Additionally, given the heterogeneity of the PICU patient population, we found differences in the presentation among the major patient groups - medical, cardiac surgical, and non-cardiac surgical. At four hours prior to the event, our random forest model demonstrated an area under the receiver operating characteristic curve of 0.766 (0.738 for medical, 0.755 for cardiac surgical, and 0.797 for non-cardiac surgical patients). The multivariable statistical models that captured the physiological and biochemical dynamics leading up to the event of urgent unplanned intubation in a PICU can be repurposed for bedside risk prediction.

Monitoring of heart rate characteristics to detect neonatal sepsis

BACKGROUND

Monitoring of heart rate characteristics (HRC) index may improve outcomes of late-onset neonatal sepsis (LOS) through early detection. We aimed at describing the association between LOS and elevated HRC index.

METHODS

This single-center retrospective case-control study included neonates who presented with blood culture-proven hospital-acquired LOS. Controls were matched to cases (ratio 1:2) based on gestational age, postnatal age, and birthweight. We compared the highest HRC indexes in the 48 h preceding blood culture sampling in LOS cases to the highest HRC indexes at the same postnatal days in controls.

RESULTS

In 59 LOS cases and 123 controls, an HRC index > 2 was associated with LOS (OR 7.1, 95% CI 2.6-19.0). Sensitivity and specificity of an HRC index > 2 to predict LOS were 53% (32/59) and 79% (98/123). Sensitivity increased from 25% in infants born > 32 weeks to 76% in infants born < 28 weeks. Specificity decreased from 97% in infants > 32 weeks to 63% in those born < 28 weeks.

CONCLUSIONS

An increase of HRC index > 2 has a significant association with the diagnosis of LOS, supporting the use of HRC monitoring to assist early detection of LOS. Clinicians using HRC monitoring should be aware of its diagnostic accuracy and limitations in different gestational age groups.

IMPACT

There is a paucity of data regarding the predictive value of heart rate characteristics (HRC) monitoring for early diagnosis of late-onset neonatal sepsis (LOS) in daily clinical practice. Monitoring of heart rate characteristics provides valuable information to assist the early diagnosis of LOS across all gestational age groups. However, the strong influence of gestational age on positive and negative predictive values adds complexity to the interpretation of HRC indexes.

Individual illness dynamics: An analysis of children with sepsis admitted to the pediatric intensive care unit

Illness dynamics and patterns of recovery may be essential features in understanding the critical illness course. We propose a method to characterize individual illness dynamics in patients who experienced sepsis in the pediatric intensive care unit. We defined illness states based on illness severity scores generated from a multi-variable prediction model. For each patient, we calculated transition probabilities to characterize movement among illness states. We calculated the Shannon entropy of the transition probabilities. Using the entropy parameter, we determined phenotypes of illness dynamics based on hierarchical clustering. We also examined the association between individual entropy scores and a composite variable of negative outcomes. Entropy-based clustering identified four illness dynamic phenotypes in a cohort of 164 intensive care unit admissions where at least one sepsis event occurred. Compared to the low-risk phenotype, the high-risk phenotype was defined by the highest entropy values and had the most ill patients as defined by a composite variable of negative outcomes. Entropy was significantly associated with the negative outcome composite variable in a regression analysis. Information-theoretical approaches to characterize illness trajectories offer a novel way of assessing the complexity of a course of illness. Characterizing illness dynamics with entropy offers additional information in conjunction with static assessments of illness severity. Additional attention is needed to test and incorporate novel measures representing the dynamics of illness.

Fetal heart rate variability is a biomarker of rapid but not progressive exacerbation of inflammation in preterm fetal sheep

Perinatal infection/inflammation can trigger preterm birth and contribute to neurodevelopmental disability. There are currently no sensitive, specific methods to identify perinatal infection. We investigated the utility of time, frequency and non-linear measures of fetal heart rate (FHR) variability (FHRV) to identify either progressive or more rapid inflammation. Chronically instrumented preterm fetal sheep were randomly assigned to one of three different 5d continuous i.v. infusions: 1) control (saline infusions; n = 10), 2) progressive lipopolysaccharide (LPS; 200 ng/kg over 24 h, doubled every 24 h for 5d, n = 8), or 3) acute-on-chronic LPS (100 ng/kg over 24 h then 250 ng/kg/24 h for 4d plus 1 μg boluses at 48, 72, and 96 h, n = 9). Both LPS protocols triggered transient increases in multiple measures of FHRV at the onset of infusions. No FHRV or physiological changes occurred from 12 h after starting progressive LPS infusions. LPS boluses during the acute-on-chronic protocol triggered transient hypotension, tachycardia and an initial increase in multiple time and frequency domain measures of FHRV, with an asymmetric FHR pattern of predominant decelerations. Following resolution of hypotension after the second and third LPS boluses, all frequencies of FHRV became suppressed. These data suggest that FHRV may be a useful biomarker of rapid but not progressive preterm infection/inflammation.

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.

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.

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.

Trajectories of the heart rate characteristics index, a physiomarker of sepsis in premature infants, predict Neonatal ICU mortality

OBJECTIVE

Trajectories of physiomarkers over time can be useful to define phenotypes of disease progression and as predictors of clinical outcomes. The aim of this study was to identify phenotypes of the time course of late-onset sepsis in premature infants in Neonatal Intensive Care Units.

METHODS

We examined the trajectories of a validated continuous physiomarker, abnormal heart rate characteristics, using functional data analysis and clustering techniques.

PARTICIPANTS

We analyzed continuous heart rate characteristics data from 2989 very low birth weight infants (<1500 grams) from nine NICUs from 2004-2010.

RESULT

Despite the relative homogeneity of the patients, we found extreme variability in the physiomarker trajectories. We identified phenotypes that were indicative of seven and 30 day mortality beyond that predicted by individual heart rate characteristics values or baseline demographic information.

CONCLUSION

Time courses of a heart rate characteristics physiomarker reveal snapshots of illness patterns, some of which were more deadly than others.

Influence of nasal CPAP on cardiorespiratory control in healthy neonate

The present study aimed to further unravel the effects of nasal continuous positive airway pressure (nCPAP) on the cardiovascular and respiratory systems in the neonatal period. Six-hour polysomnographic recordings were first performed in seven healthy newborn lambs, aged 2-3 days, without and with nCPAP application at 6 cmH₂O (nCPAP-6), in randomized order. The effects of nCPAP-6 on heart rate variability, respiratory rate variability, and cardiorespiratory interrelations were analyzed using a semiautomatic signal processing approach applied to ECG and respiration recordings. Thereafter, a cardiorespiratory mathematical model was adapted to the experimental conditions to gain further physiological interpretation and to simulate higher nCPAP levels (8 and 10 cmH₂O). Results from the signal processing approach suggest that nCPAP-6 applied in newborns with healthy lungs: 1) increases heart rate and decreases the time and frequency domain indices of heart rate variability, especially those representing parasympathetic activity, while increasing the complexity of the RR-interval time series; 2) prolongs the respiratory cycle and expiration duration and decreases respiratory rate variability; and 3) slightly impairs cardiorespiratory interrelations. Model-based analysis revealed that nCPAP-6 increases the heart rate and decreases respiratory sinus arrhythmia amplitude, in association with a reduced parasympathetic efferent activity. These results were accentuated when simulating an increased CPAP level. Overall, our results provide a further understanding of the effects of nCPAP in neonates, in the absence of lung disease.NEW & NOTEWORTHY Application of nasal continuous positive airway pressure (CPAP) at 6 cmH₂O, a level very frequently used in newborns, alters heart and respiratory rate variability, as well as cardiorespiratory interrelations in a full-term newborn model without lung disease. Moreover, whereas nasal CPAP at 6 cmH₂O decreases parasympathetic efferent activity, there is no change in sympathetic efferent activity.

Preterm Stress Behaviors, Autonomic Indices, and Maternal Perceptions of Infant Colic

BACKGROUND

While biological and behavioral stress response systems are intact in early gestation, preterm infants' behaviors are often more subtle and difficult to interpret compared with full-term infants. They are also more vulnerable for regulatory issues (ie, colic) that are known to impact caregiver-infant interactions. Biobehavioral measures such as behavioral responsivity and heart rate variability (HRV), particularly cardiac vagal tone, may help elucidate preterm infants' stress/regulatory systems.

PURPOSE

To test the hypotheses that preterm infants' consoling behaviors and high-frequency (HF) HRV in the first week of life are significantly associated and they are inverse correlates of future colic risk.

METHODS/SEARCH STRATEGY

Thirty preterm (mean ± SE = 32.7 ± 0.3 weeks postmenstrual age [PMA]) infants underwent direct NIDCAP (Newborn Individualized Development and Assessment Program) observation during routine care and had HRV measurements during their first week postbirth. Sixty-three percent of mothers completed the Infant Colic Scale at 6 to 8 weeks adjusted postnatal age. Nonparametric tests were used to determine associations among behaviors, HRV, and maternal perceptions of infant colic.

FINDINGS/RESULTS

Self-consoling behaviors were positively associated with HF-HRV (vagal tone). In addition, stress behaviors were positively associated with low-frequency/high-frequency HRV (sympathetic dominance). Infants who displayed more stress behaviors also demonstrated more self-consoling behaviors. No significant associations were found with colic.

IMPLICATIONS FOR PRACTICE

HF-HRV provides information on the infant's capacity to modulate stress and is a useful, noninvasive measure when behaviors are more difficult to discern.

IMPLICATIONS FOR RESEARCH

Further study in a larger sample is needed to determine whether behavioral stress measures and HF-HRV may be useful to determine colic risk.

Dynamics and complexity of body temperature in preterm infants nursed in incubators

BACKGROUND

Poor control of body temperature is associated with mortality and major morbidity in preterm infants. We aimed to quantify its dynamics and complexity to evaluate whether indices from fluctuation analyses of temperature time series obtained within the first five days of life are associated with gestational age (GA) and body size at birth, and presence and severity of typical comorbidities of preterm birth.

METHODS

We recorded 3h-time series of body temperature using a skin electrode in incubator-nursed preterm infants. We calculated mean and coefficient of variation of body temperature, scaling exponent alpha (Talpha) derived from detrended fluctuation analysis, and sample entropy (TSampEn) of temperature fluctuations. Data were analysed by multilevel multivariable linear regression.

RESULTS

Data of satisfactory technical quality were obtained from 285/357 measurements (80%) in 73/90 infants (81%) with a mean (range) GA of 30.1 (24.0-34.0) weeks. We found a positive association of Talpha with increasing levels of respiratory support after adjusting for GA and birth weight z-score (p<0.001; R2 = 0.38).

CONCLUSION

Dynamics and complexity of body temperature in incubator-nursed preterm infants show considerable associations with GA and respiratory morbidity. Talpha may be a useful marker of autonomic maturity and severity of disease in preterm infants.

Early Heart Rate Characteristics Predict Death and Morbidities in Preterm Infants

OBJECTIVES

To determine whether an early heart rate characteristics (HRC) index (HeRO score), measured in the first day and week after birth predicts death and morbidities compared with established illness severity scores.

STUDY DESIGN

For all very low birth weight infants in a single neonatal intensive care unit from 2004-2014, the average first day HRC index was calculated within 24 hours of birth (aHRC-24h) and the average first week HRC index within 7 days of birth (aHRC-7d). The Score for Neonatal Acute Physiology (SNAP-II) and Clinical Risk Indicator for Babies (CRIB-II) were calculated when data were available. The aHRC was compared with the SNAP-II and CRIB-II for predicting death, late-onset septicemia, necrotizing enterocolitis, bronchopulmonary dysplasia, severe intraventricular hemorrhage, or severe retinopathy of prematurity.

RESULTS

All 4 scores were associated with death and severe intraventricular hemorrhage (P < .01). The OR and 95% CI for every 1-point increase in aHRC for predicting mortality, adjusted for gestational age, was 1.59 (1.25-2.00) for aHRC-24h and 2.61 (1.58-4.33) for aHRC-7d. High aHRC-7d, SNAP-II, and CRIB-II were associated with bronchopulmonary dysplasia (P < .001). High aHRC-7d was associated with late-onset septicemia (P < .05). None of the scores predicted necrotizing enterocolitis or severe retinopathy of prematurity.

CONCLUSIONS

HRC assessed in the first day or first week after birth compares favorably to established risk scores to predict death and morbidities in very low birth weight infants.

Heart Rate Variability in Porcine Progressive Peritonitis-Induced Sepsis

Accumulating evidence suggests that heart rate variability (HRV) alterations could serve as an indicator of sepsis progression and outcome, however, the relationships of HRV and major pathophysiological processes of sepsis remain unclear. Therefore, in this experimental study HRV was investigated in a clinically relevant long-term porcine model of severe sepsis/septic shock. HRV was analyzed by several methods and the parameters were correlated with pathophysiological processes of sepsis. In 16 anesthetized, mechanically ventilated, and instrumented domestic pigs of either gender, sepsis was induced by fecal peritonitis. Experimental subjects were screened up to the refractory shock development or death. ECG was continuously recorded throughout the experiment, afterwards RR intervals were detected and HRV parameters computed automatically using custom made measurement and analysis MATLAB routines. In all septic animals, progressive hyperdynamic septic shock developed. The statistical measures of HRV, geometrical measures of HRV and Poincaré plot analysis revealed a pronounced reduction of HRV that developed quickly upon the onset of sepsis and was maintained throughout the experiment. The frequency domain analysis demonstrated a decrease in the high frequency component and increase in the low frequency component together with an increase of the low/high frequency component ratio. The reduction of HRV parameters preceded sepsis-associated hemodynamic changes including heart rate increase or shock progression. In a clinically relevant porcine model of peritonitis-induced progressive septic shock, reduction of HRV parameters heralded sepsis development. HRV reduction was associated with a pronounced parasympathetic inhibition and a shift of sympathovagal balance. Early reduction of HRV may serve as a non-invasive and sensitive marker of systemic inflammatory syndrome, thereby widening the therapeutic window for early interventions.

Predictive monitoring for sepsis and necrotizing enterocolitis to prevent shock

Despite vigilant clinical assessment of infants in the neonatal intensive care unit (NICU), diagnosis of sepsis and necrotizing enterocolitis often does not occur until an infant has significant hemodynamic compromise. Predictive monitoring involves analysis of vital signs and other clinical data to identify infants at highest risk and to detect early-stage illness, leading to timelier treatment and improved outcomes. The first vital-sign predictive monitoring device developed for sepsis detection in babies in the NICU is the heart rate characteristics index (HeRO) monitor, which continuously analyzes the electrocardiogram signal for low heart rate variability and transient decelerations. Use of this monitor in very low birth weight infants (<1500 g) was shown in a large multicenter randomized clinical trial to significantly reduce mortality. The purpose of this review is (1) to summarize the physiologic changes in neonatal sepsis and progression to shock, (2) to review efforts toward risk stratification for sepsis shortly after birth based on demographic and physiologic scoring systems, (3) to describe development and implementation of heart rate characteristics monitoring and other important aspects of sepsis early warning systems, and (4) to provide an overview of current research analyzing multiple vital signs and other clinical variables in an attempt to develop even more effective predictive monitoring devices and systems.

Are vital signs indicative for bacteremia in newborns?

OBJECTIVE

Neonatal systemic infection is a leading cause of morbidity and mortality both in industrialized and developing countries. The aim of this prospective study was to evaluate if vital signs had a predictive power in neonatal sepsis as an early marker.

METHODS

This study was designed as a matched case-control study. Vital signs were monitorized prior to infection in newborns that had healthcare-associated blood stream infection (BSI). Maximum and minimum values of the vital signs (blood pressure, heart rate, respiratory rate and temperature) of the babies at rest were recorded from the nurse observation charts five days prior to clinical sepsis and compared with vital signs of healthy, age-matched babies.

RESULTS

Maximum mean heart rates, respiratory rates and systolic blood pressure levels of the patients in BSI group were significantly higher than the control group in the past three days prior to clinical deterioration.

CONCLUSION

Monitoring vital signs closely might be helpful in a newborn infant to define a BSI. In future, a respiratory and blood pressure predictive monitoring system such as heart rate variability index may be developed for newborn patients with sepsis.

Local dynamics of heart rate: detection and prognostic implications

The original observation that reduced heart rate variability (HRV) confers poor prognosis after myocardial infarction has been followed by many studies of heart rate dynamics. We tested the hypothesis that an entropy-based local dynamics measure gave prognostic information in ambulatory patients undergoing 24-h electrocardiography. In this context, entropy is the probability that short templates will find matches in the time series. We studied RR interval time series from 24-h Holter monitors of 1564 consecutive patients over age 39. We generated histograms of the count of templates as a function of the number of templates matches in short RR interval time series, and found characteristic appearance of histograms for atrial fibrillation, sinus rhythm with normal HRV, and sinus rhythm with reduced HRV and premature ventricular contractions (PVCs). We developed statistical models to detect the abnormal dynamic phenotype of reduced HRV with PVCs and fashioned a local dynamics score (LDs) that, after controlling for age, added more prognostic information than other standard risk factors and common HRV metrics, including, to our surprise, the PVC count and the HRV of normal-to-normal intervals. Addition of the LDs to a predictive model using standard risk factors significantly increased the ROC area and the net reclassification improvement was 27%. We conclude that abnormal local dynamics of heart rate confer adverse prognosis in patients undergoing 24-h ambulatory electrocardiography.

Infection and other clinical correlates of abnormal heart rate characteristics in preterm infants

OBJECTIVE

To identify clinical conditions associated with a large increase (spike) in the heart rate characteristics index in very low birth weight (VLBW) infants.

STUDY DESIGN

Retrospective medical record review within a day of all large heart rate characteristics index spikes (increase of ≥3 from the previous 5-day average) in VLBW infants at a single center enrolled from 2007 to 2010 in a multicenter trial of heart rate characteristics monitoring. In the trial, infants were randomized to having their heart rate characteristics index displayed to clinicians or not displayed.

RESULTS

Of 274 eligible infants, 224 large heart rate characteristics spikes occurred in 105 infants. Thirty-three spikes were associated with surgery or procedures requiring anesthetic or anticholinergic medications, and infection-related conditions were the most common clinical association with the other spikes. Of the first spikes in 47 infants randomized to conventional monitoring (heart rate characteristics index not displayed to clinicians), 53% were associated with suspected or proven infection. Respiratory deterioration without suspected infection occurred with 34%, and no association was identified in 13%. Infants randomized to having their heart rate characteristics index displayed were more likely to have antibiotics initiated around the time of a large heart rate characteristics index spike.

CONCLUSIONS

Sepsis, other infectious or systemic inflammatory conditions, respiratory deterioration, and surgical procedures are the most common clinical associations with a large increase in the heart rate characteristics index in VLBW infants. This information may improve use of heart rate characteristics monitors in patients in the neonatal intensive care unit.

Heart rate variability in patients being treated for dengue viral infection: new insights from mathematical correction of heart rate

INTRODUCTION

Severe dengue hemorrhagic fever (DHF) is a viral infection that acts to increase permeability of capillaries, resulting in internal hemorrhage. Linear frequency domain Fourier spectral analysis represents the most published noninvasive tool for diagnosing and assessing health status via calculated heart rate variability (HRV). As such, HRV may be useful in assessing clinical status in DHF patients, but is prone to erroneous results and conclusions due to the influence of the average HR during the time period of HRV assessment (defined as the "prevailing" HR). We tested the hypothesis that alterations in HRV calculated with linear frequency analysis would be minimal when mathematically corrected for prevailing HR following dengue viral infection.

METHODS

Male (N = 16) and female (N = 11) patients between the ages of 6 months and 15 years of age (10 ± 6 SD years) were tracked through the progression of the dengue viral infection with treatment following the abatement of a fever (defervescence). Electrocardiographic recordings were collected and analyzed for HRV.

RESULTS

High frequency (HF), low frequency (LF), and LF/HF ratio were unaffected by correction for prevailing HR.

CONCLUSION

HRV corrected for changes in HR did not alter the interpretation of our data. Therefore, we conclude that cardiac parasympathetic activity (based on HF frequency) is responsible for the majority of the HR reduction following defervescence in patients with dengue viral infection.

Complex signals bioinformatics: evaluation of heart rate characteristics monitoring as a novel risk marker for neonatal sepsis

PURPOSES

Heart rate characteristics monitoring for early detection of late-onset neonatal sepsis was first described in 2003. This technique, which uses mathematical methods to report the fold-increase in the risk of imminent neonatal sepsis, adds independent information to laboratory tests and clinical findings, and, in a large randomized trial, reduced NICU mortality of very low birth weight infants. Through re-analysis and new secondary analyses of published studies, we have systematically evaluated the utility of this new risk marker for screening the growing population of premature infants.

METHODS

We followed the guidelines proposed by Hlatky et al. (Circulation, 119:2408-2416, 2009), reviewed past works, and re-analyzed data from 1,489 patients receiving conventional monitoring alone, 348 of whom had 488 episodes of proven sepsis, in the large randomized trial.

RESULTS

Heart rate characteristics monitoring passed all phases of risk marker development from proof of concept to improvement of clinical outcomes. The predictiveness curve affirmed good calibration, and addition of the heart rate characteristics index to predictive models using standard risk factors favorably impacted the receiver operating characteristic curve area (increase of 0.030), continuous net reclassification index (0.389) and the integrated discrimination index (0.008), and compares well to other modern risk factors.

CONCLUSION

Heart rate characteristics monitoring is a validated risk marker for sepsis in the NICU.

Predictive monitoring for early detection of sepsis in neonatal ICU patients

PURPOSE OF REVIEW

Predictive monitoring is an exciting new field involving analysis of physiologic data to detect abnormal patterns associated with critical illness. The first example of predictive monitoring being taken from inception (proof of concept) to reality (demonstration of improved outcomes) is the use of heart rate characteristics (HRC) monitoring to detect sepsis in infants in the neonatal ICU. The commercially available 'HeRO' monitor analyzes electrocardiogram data from existing bedside monitors for decreased HR variability and transient decelerations associated with sepsis, and converts these changes into a score (the HRC index or HeRO score). This score is the fold increase in probability that a patient will have a clinical deterioration from sepsis within 24 h. This review focuses on HRC monitoring and discusses future directions in predictive monitoring of ICU patients.

RECENT FINDINGS

In a randomized trial of 3003 very low birthweight infants, display of the HeRO score reduced mortality more than 20%. Ongoing research aims to combine respiratory and HR analysis to optimize care of ICU patients.

SUMMARY

Predictive monitoring has recently been shown to save lives. Harnessing and analyzing the vast amounts of physiologic data constantly displayed in ICU patients will lead to improved algorithms for early detection, prognosis, and therapy of critical illnesses.

Predictive monitoring for respiratory decompensation leading to urgent unplanned intubation in the neonatal intensive care unit

BACKGROUND

Infants admitted to the neonatal intensive care unit (NICU), and especially those born with very low birth weight (VLBW; <1,500 g), are at risk for respiratory decompensation requiring endotracheal intubation and mechanical ventilation. Intubation and mechanical ventilation are associated with increased morbidity, particularly in urgent unplanned cases.

METHODS

We tested the hypothesis that the systemic response associated with respiratory decompensation can be detected from physiological monitoring and that statistical models of bedside monitoring data can identify infants at increased risk of urgent unplanned intubation. We studied 287 VLBW infants consecutively admitted to our NICU and found 96 events in 51 patients, excluding intubations occurring within 12 h of a previous extubation.

RESULTS

In order of importance in a multivariable statistical model, we found that the characteristics of reduced O(2) saturation, especially as heart rate was falling; increased heart rate correlation with respiratory rate; and the amount of apnea were all significant independent predictors. The predictive model, validated internally by bootstrap, had a receiver-operating characteristic area of 0.84 ± 0.04.

CONCLUSION

We propose that predictive monitoring in the NICU for urgent unplanned intubation may improve outcomes by allowing clinicians to intervene noninvasively before intubation is required.

Lipopolysaccharide prolongs action potential duration in HL-1 mouse cardiomyocytes

Sepsis has deleterious effects on cardiac function including reduced contractility. We have shown previously that lipopolysaccharides (LPS) directly affect HL-1 cardiac myocytes by inhibiting Ca(2+) regulation and by impairing pacemaker "funny" current, I(f). We now explore further cellular mechanisms whereby LPS inhibits excitability in HL-1 cells. LPS (1 μg/ml) derived from Salmonella enteritidis decreased rate of firing of spontaneous action potentials in HL-1 cells, and it increased their pacemaker potential durations and decreased their rates of depolarization, all measured by whole cell current clamp. LPS also increased action potential durations and decreased their amplitude in cells paced at 1 Hz with 0.1 nA, and 20 min were necessary for maximal effect. LPS decreased the amplitude of a rapidly inactivating inward current attributed to Na(+) and of an outward current attributed to K(+); both were measured by whole cell voltage clamp. The K(+) currents displayed a resurgent outward tail current, which is characteristic of the rapid delayed-rectifier K(+) current, I(Kr). LPS accordingly reduced outward currents measured with pipette Cs(+) substituted for K(+) to isolate I(Kr). E-4031 (1 μM) markedly inhibited I(Kr) in HL-1 cells and also increased action potential duration; however, the direct effects of E-4031 occurred minutes faster than the slow effects of LPS. We conclude that LPS increases action potential duration in HL-1 mouse cardiomyocytes by inhibition of I(Kr) and decreases their rate of firing by inhibition of I(Na.) This protracted time course points toward an intermediary metabolic event, which either decreases available mouse ether-a-go-go (mERG) and Na(+) channels or potentiates their inactivation.

Predictive monitoring for early detection of subacute potentially catastrophic illnesses in critical care

We wish to save lives of patients admitted to ICUs. Their mortality is high enough based simply on the severity of the original injury or illness, but is further raised by events during their stay. We target those events that are subacute but potentially catastrophic, such as infection. Sepsis, for example, is a bacterial infection of the bloodstream, that is common in ICU patients and has a >25% risk of death. Logically, early detection and treatment with antibiotics should improve outcomes. Our fundamental precepts are (1) some potentially catastrophic medical and surgical illnesses have subclinical phases during which early diagnosis and treatment might have life-saving effects, (2) these phases are characterized by changes in the normal highly complex but highly adaptive regulation and interaction of the nervous system and other organs such as the heart and lungs, (3) teams of clinicians and quantitative scientists can work together to identify clinically important abnormalities of monitoring data, to develop algorithms that match the clinicians' eye in detecting abnormalities, and to undertake the clinical trials to test their impact on outcomes.

Improved entropy rate estimation in physiological data

Calculating entropy rate in physiologic signals has proven very useful in many settings. Common entropy estimates for this purpose are sample entropy (SampEn) and its less robust elder cousin, approximate entropy (ApEn). Both approaches count matches within a tolerance r for templates of length m consecutive observations. When physiologic data records are long and well-behaved, both approaches work very well for a wide range of m and r. However, more attention to the details of the estimation algorithm is needed for short records and signals with anomalies. In addition, interpretation of the magnitude of these estimates is highly dependent on how r is chosen and precludes comparison across studies with even slightly different methodologies. In this paper, we summarize recent novel approaches to improve the accuracy of entropy estimation. An important (but not necessarily new) alternative to current approaches is to develop estimates that convert probabilities to densities by normalizing by the matching region volume. This approach leads to a novel concept introduced here of reporting entropy rate in equivalent Gaussian white noise units. Another approach is to allow r to vary so that a pre-specified number of matches are found, called the minimum numerator count, to ensure confident probability estimation. The approaches are illustrated using a simple example of detecting abnormal cardiac rhythms in heart rate records.

Cytokine screening identifies NICU patients with Gram-negative bacteremia

INTRODUCTION

Biomarkers and physiomarkers may be useful adjunct tests for sepsis detection in neonatal intensive care unit (NICU) patients. We studied whether measuring plasma cytokines at the time of suspected sepsis could identify patients with bacteremia in centers in which patients were undergoing continuous physiomarker screening using a heart rate characteristics (HRC) index monitor.

RESULTS

Six cytokines were higher in Gram-negative bacteremia (GNB) than in Gram-positive bacteremia or candidemia (GPBC). A cytokine score using thresholds for granulocyte colony-stimulating factor (G-CSF), interleukin (IL)-6, IL-8, and tumor necrosis factor (TNF)-α had 100% sensitivity and 69% positive predictive value (PPV) for GNB. A single cytokine marker, IL-6 < 130 pg/ml, had 100% sensitivity and 52% PPV for sepsis ruled out (SRO). The average HRC index was abnormal in this cohort of patients with clinical suspicion of sepsis and did not discriminate between the final sepsis designations.

DISCUSSION

In summary, in NICU patients with suspected late-onset sepsis, plasma cytokines can identify those with SRO and those with GNB, potentially aiding in decisions regarding therapy.

METHODS

Seven cytokines were measured in 226 plasma samples from patients >3 d old with sepsis suspected based on clinical signs, abnormal HRC index, or both. Cases were classified as SRO, clinical sepsis (CS), GPBC, or GNB.

A new algorithm for detecting central apnea in neonates

Apnea of prematurity is an important and common clinical problem, and is often the rate-limiting process in NICU discharge. Accurate detection of episodes of clinically important neonatal apnea using existing chest impedance (CI) monitoring is a clinical imperative. The technique relies on changes in impedance as the lungs fill with air, a high impedance substance. A potential confounder, however, is blood coursing through the heart. Thus, the cardiac signal during apnea might be mistaken for breathing. We report here a new filter to remove the cardiac signal from the CI that employs a novel resampling technique optimally suited to remove the heart rate signal, allowing improved apnea detection. We also develop an apnea detection method that employs the CI after cardiac filtering. The method has been applied to a large database of physiological signals, and we prove that, compared to the presently used monitors, the new method gives substantial improvement in apnea detection.

Mortality reduction by heart rate characteristic monitoring in very low birth weight neonates: a randomized trial

OBJECTIVE

To test the hypothesis that heart rate characteristics (HRC) monitoring improves neonatal outcomes.

STUDY DESIGN

We conducted a two-group, parallel, individually randomized controlled clinical trial of 3003 very low birth weight infants in 9 neonatal intensive care units. In one group, HRC monitoring was displayed; in the other, it was masked. The primary outcome was number of days alive and ventilator-free in the 120 days after randomization. Secondary outcomes were mortality, number of ventilator days, neonatal intensive care unit stay, and antibiotic use.

RESULTS

The mortality rate was reduced in infants whose HRC monitoring was displayed, from 10.2% to 8.1% (hazard ratio, 0.78; 95% CI, 0.61-0.99; P = .04; number needed to monitor = 48), and there was a trend toward increased days alive and ventilator-free (95.9 of 120 days compared with 93.6 in control subjects, P = .08). The mortality benefit was concentrated in infants with a birth weight <1000 g (hazard ratio, 0.74; 95% CI, 0.57-0.95; P = .02; number needed to monitor = 23). There were no significant differences in the other outcomes.

CONCLUSION

HRC monitoring can reduce the mortality rate in very low birth weight infants.

Cardiovascular oscillations at the bedside: early diagnosis of neonatal sepsis using heart rate characteristics monitoring

We have applied principles of statistical signal processing and nonlinear dynamics to analyze heart rate time series from premature newborn infants in order to assist in the early diagnosis of sepsis, a common and potentially deadly bacterial infection of the bloodstream. We began with the observation of reduced variability and transient decelerations in heart rate interval time series for hours up to days prior to clinical signs of illness. We find that measurements of standard deviation, sample asymmetry and sample entropy are highly related to imminent clinical illness. We developed multivariable statistical predictive models, and an interface to display the real-time results to clinicians. Using this approach, we have observed numerous cases in which incipient neonatal sepsis was diagnosed and treated without any clinical illness at all. This review focuses on the mathematical and statistical time series approaches used to detect these abnormal heart rate characteristics and present predictive monitoring information to the clinician.

Lung and brainstem cytokine levels are associated with breathing pattern changes in a rodent model of acute lung injury

Acute lung injury evokes a pulmonary inflammatory response and changes in the breathing pattern. The inflammatory response has a centrally mediated component which depends on the vagi. We hypothesize that the central inflammatory response, complimentary to the pulmonary inflammatory response, is expressed in the nuclei tractus solitarii (nTS) and that the expression of cytokines in the nTS is associated with breathing pattern changes. Adult, male Sprague-Dawley rats (n=12) received intratracheal instillation of either bleomycin (3units in 120μl of saline) or saline (120μl). Respiratory pattern changed by 24h. At 48h, bronchoalveolar lavage fluid and lung tissue had increased IL-1β and TNF-α levels, but not IL-6. No changes in these cytokines were noted in serum. Immunocytochemical analysis of the brainstem indicated increased expression of IL-1β in the nTS commissural subnucleus that was localized to neurons. We conclude that breathing pattern changes in acute lung injury were associated with increased levels of IL-1β in brainstem areas which integrate cardio-respiratory sensory input.

Hydrocortisone at stress-associated concentrations helps maintain human heart rate variability during subsequent endotoxin challenge

PURPOSE

We evaluated the differential impact of stress-associated vs high pharmacologic concentrations of hydrocortisone pretreatment on heart rate variability (HRV) during a subsequent systemic inflammatory stimulus.

MATERIALS AND METHODS

Healthy volunteers were randomized to receive placebo (Control) and hydrocortisone at 1.5 μg/kg per minute (STRESS) or at 3.0 μg/kg per minute (PHARM) as a 6-hour infusion. The STRESS dose was chosen to replicate the condition of physiologic adrenal cortical output during acute systemic stress. The PHARM dose was chosen to induce a supraphysiologic concentration of cortisol. The next day, all subjects received 2 ng/kg Escherichia coli endotoxin (lipopolysaccharide). Heart rate variability was analyzed with the statistic approximate entropy (ApEn). A lower ApEn correlates with decreased HRV.

RESULTS

At the 3-hour nadir, the decrease in ApEn in the STRESS group was significantly less compared to placebo (P < .03), whereas ApEn in the PHARM group was not statistically different. We also found that the maximal decrease in ApEn preceded maximal increase in heart rate in all groups. The decrease in R-R interval was maximal at 4 hours, whereas the ApEn nadir was 1 hour earlier at 3 hours.

CONCLUSIONS

Pretreatment with a stress dose of hydrocortisone but not a higher pharmacologic dose maintained a significantly higher ApEn after endotoxin exposure when compared to a placebo. In addition, decreases in ApEn preceded increases in heart rate.

Accurate estimation of entropy in very short physiological time series: the problem of atrial fibrillation detection in implanted ventricular devices

Entropy estimation is useful but difficult in short time series. For example, automated detection of atrial fibrillation (AF) in very short heart beat interval time series would be useful in patients with cardiac implantable electronic devices that record only from the ventricle. Such devices require efficient algorithms, and the clinical situation demands accuracy. Toward these ends, we optimized the sample entropy measure, which reports the probability that short templates will match with others within the series. We developed general methods for the rational selection of the template length m and the tolerance matching r. The major innovation was to allow r to vary so that sufficient matches are found for confident entropy estimation, with conversion of the final probability to a density by dividing by the matching region volume, 2r(m). The optimized sample entropy estimate and the mean heart beat interval each contributed to accurate detection of AF in as few as 12 heartbeats. The final algorithm, called the coefficient of sample entropy (COSEn), was developed using the canonical MIT-BIH database and validated in a new and much larger set of consecutive Holter monitor recordings from the University of Virginia. In patients over the age of 40 yr old, COSEn has high degrees of accuracy in distinguishing AF from normal sinus rhythm in 12-beat calculations performed hourly. The most common errors are atrial or ventricular ectopy, which increase entropy despite sinus rhythm, and atrial flutter, which can have low or high entropy states depending on dynamics of atrioventricular conduction.

Heart rate characteristics: physiomarkers for detection of late-onset neonatal sepsis

Early detection of late-onset neonatal sepsis, before the onset of obvious and potentially catastrophic clinical signs, is an important goal in neonatal medicine. Sepsis causes a well-known series of physiologic changes including abnormalities of blood pressure, respiration, temperature, and heart rate, and less well-known changes in heart rate variability. Although vital signs are frequently or continuously monitored in patients in the neonatal intensive care unit (NICU), changes in these parameters are subtle in the early phase of sepsis and difficult to interpret using traditional NICU monitoring tools. A new tool, continuous monitoring of heart rate characteristics (HRC), is now available for clinical use. Recent research has established that 2 abnormalities of HRC that have long been used by obstetricians to identify fetal compromise, reduced heart rate variability and transient decelerations, occur early in the course of sepsis in patients in the NICU, often before clinical signs of illness. Through mathematical modeling of electrocardiogram data from hundreds of patients in the NICU, an HRC index that represents the fold increase in risk that a neonate will be diagnosed with clinical or culture-proven sepsis within the next 24 hours was derived. The effect of continuous HRC monitoring on outcomes in preterm very low birth weight infants is the subject of a multicenter randomized clinical trial of 3000 patients, which will be complete in 2010. Further research into mechanisms of abnormal HRC and regulation of autonomic nervous system function in sepsis and other disease processes will shed light on additional applications of this exciting new technology.

Clinical review: a review and analysis of heart rate variability and the diagnosis and prognosis of infection

Bacterial infection leading to organ failure is the most common cause of death in critically ill patients. Early diagnosis and expeditious treatment is a cornerstone of therapy. Evaluating the systemic host response to infection as a complex system provides novel insights: however, bedside application with clinical value remains wanting. Providing an integrative measure of an altered host response, the patterns and character of heart rate fluctuations measured over intervals-in-time may be analysed with a panel of mathematical techniques that quantify overall fluctuation, spectral composition, scale-free variation, and degree of irregularity or complexity. Using these techniques, heart rate variability (HRV) has been documented to be both altered in the presence of systemic infection, and correlated with its severity. In this review and analysis, we evaluate the use of HRV monitoring to provide early diagnosis of infection, document the prognostic implications of altered HRV in infection, identify current limitations, highlight future research challenges, and propose improvement strategies. Given existing evidence and potential for further technological advances, we believe that longitudinal, individualized, and comprehensive HRV monitoring in critically ill patients at risk for or with existing infection offers a means to harness the clinical potential of this bedside application of complex systems science.

Heart rate characteristics and neurodevelopmental outcome in very low birth weight infants

BACKGROUND

Sepsis in very low birth weight (VLBW) infants has been associated with an increased risk of adverse developmental outcome. We have identified abnormal heart rate characteristics (HRCs) that are predictive of impending sepsis, and we have developed a summary measure of an infant's abnormal HRCs during the neonatal hospitalization that we refer to as the cumulative HRC score (cHRC).

OBJECTIVE

In this study, we tested the hypothesis that increasing cHRC is associated with an increasing risk of adverse neurodevelopmental outcome in VLBW infants.

METHOD

Data were collected on 65 VLBW infants whose HRCs were monitored while in the neonatal intensive care unit and who were examined at 12 to 18 months adjusted age. Using the Bayley Scale of Infant Development-II, we identified delays in early cognitive function (i.e., Mental Developmental Index <70) and psychomotor development (i.e., Psychomotor Developmental Index <70). Cerebral palsy (CP) was diagnosed using a standard neurological examination.

RESULT

Increasing cHRC score was associated with an increased risk of CP (odds ratio per 1 standard deviation increase in cHRC: 2.6, 95% confidence limits: 1.42, 5.1) and delayed early cognitive development [odds ratio: 2.3 (1.3; 4.3)]. These associations remain statistically significant when adjusted for major cranial ultrasound abnormality. There was an association of increasing cHRC and delayed psychomotor development, which did not reach statistical significance [odds ratio: 1.7 (1.0, 3.0)].

CONCLUSION

Among VLBW infants, the cumulative frequency of abnormal HRCs, which can be assessed non-invasively in the neonatal intensive care unit, is associated with an increased risk of adverse neurodevelopmental outcome.

Probing the fetal cardiac signal for antecedents of brain injury

Obstetric care providers and researchers have long relied on analysis of the fetal heart rate tracing for insight into the fetal neurologic status. Although a normal fetal heart rate tracing does provide reassurance of intact neurologic function, an abnormal pattern is a very poor predictor of newborn brain injury. Indeed, if the clinical end point of interest is cerebral palsy, a non-reassuring fetal heart rate tracing has a 99% false positive rate. More recent analyses of fetal heart rate variability and fetal ECG waveforms, however, hold promise for improved diagnostic accuracy.

Endotoxin depresses heart rate variability in mice: cytokine and steroid effects

Heart rate variability (HRV) falls in humans with sepsis, but the mechanism is not well understood. We utilized a mouse model of endotoxemia to test the hypothesis that cytokines play a role in abnormal HRV during sepsis. Adult male C57BL/6 mice underwent surgical implantation of probes to continuously monitor electrocardiogram and temperature or blood pressure via radiotelemetry. Administration of high-dose LPS (Escherichia coli LPS, 10 mg/kg, n = 10) caused a biphasic response characterized by an early decrease in temperature and heart rate at 1 h in some mice, followed by a prolonged period of depressed HRV in all mice. Further studies showed that LPS doses as low as 0.01 mg/kg evoked a significant decrease in HRV. With high-dose LPS, the initial drops in temperature and HR were temporally correlated with peak expression of TNFalpha 1 h post-LPS, whereas maximal depression in HRV coincided with peak levels of multiple other cytokines 3-9 h post-LPS. Neither hypotension nor hypothermia explained the HRV response. Pretreatment with dexamethasone prior to LPS significantly blunted expression of 7 of the 10 cytokines studied and shortened the duration of depressed HRV by about half. Interestingly, dexamethasone treatment alone caused a dramatic increase in both low- and high-frequency HRV. Administration of recombinant TNFalpha caused a biphasic response in HR and HRV similar to that caused by LPS. Understanding the role of cytokines in abnormal HRV during sepsis could lead to improved strategies for detecting life-threatening nosocomial infections in intensive care unit patients.

Nearest-neighbor and logistic regression analyses of clinical and heart rate characteristics in the early diagnosis of neonatal sepsis

OBJECTIVES

To test the hypothesis that nearest-neighbor analysis adds to logistic regression in the early diagnosis of late-onset neonatal sepsis.

DESIGN

The authors tested methods to make the early diagnosis of neonatal sepsis using continuous physiological monitoring of heart rate characteristics and intermittent measurements of laboratory values. First, the hypothesis that nearest-neighbor analysis makes reasonable predictions about neonatal sepsis with performance comparable to an existing logistic regression model was tested. The most parsimonious model was systematically developed by excluding the least efficacious clinical data. Second, the authors tested the hypothesis that a combined nearest-neighbor and logistic regression model gives an outcome prediction that is more plausible than either model alone. Training and test data sets of heart rate characteristics and laboratory test results over a 4-y period were used to create and test predictive models.

MEASUREMENTS

Nearest-neighbor, regression, and combination models were evaluated for discrimination using receiver-operating characteristic areas and for fit using the Wald statistic.

RESULTS

Both nearest-neighbor and regression models using heart rate characteristics and available laboratory test results were significantly associated with imminent sepsis, and each kind of model added independent information to the other. The best predictive strategy employed both kinds of models.

CONCLUSION

The authors propose nearest-neighbor analysis in addition to regression in the early diagnosis of subacute, potentially catastrophic illnesses such as neonatal sepsis, and they recommend it as an approach to the general problem of predicting a clinical event from a multivariable data set.

Heart rate variability: impact of differences in outlier identification and management strategies on common measures in three clinical populations

Heart rate variability (HRV) is reported increasingly in pediatric research, but different strategies used to identify and manage potential outlier beats impact HRV parameter values in adults and animals. Do they in pediatrics? To compare the impact of different strategies to identifying and managing outliers, we used interbeat interval (IBI) data from three different populations: 10 stable premature infants, 33 stable pediatric oncology patients, and 15 healthy adults. Five commonly reported HRV parameters were compared using three identification and two management strategies to filter potential outliers. The three populations had different resting heart rates: 155 +/- 9 beats per minute (bpm) in infants, 105 +/- 17 bpm in children, and 87 +/- 12 bpm in adults. All three identification strategies flagged fewer than 2% of IBIs; the threshold identification strategy, excluding IBIs denoting heart rates <30 or >300 bpm, identified significantly fewer outliers than the other two strategies and generated higher HRV parameters in all populations (p < 0.001). There were no significant differences in HRV parameters calculated by managing identified outliers by "tossing" them versus "interpolating" values. Different strategies for identifying potential outliers are associated with significant differences in HRV parameters. Pediatric researchers who report HRV should detail their outlier filtering strategies.

Heart rate characteristics and clinical signs in neonatal sepsis

To test the hypothesis that heart rate characteristic (HRC) monitoring adds information to clinical signs of illness in diagnosing neonatal sepsis, we prospectively recorded clinical data and the HRC index in 76 episodes of proven sepsis and 80 episodes of clinical sepsis in 337 infants in the University of Virginia NICU more than 7 d old. We devised an illness severity score based on clinical findings and tests relevant to sepsis. Point scores were derived from coefficients of multivariable regression models, and we internally validated a total score. We determined relationships of the HRC index with individual clinical signs, laboratory tests, and the total score. We found highly significant correlations of the clinical score and individual clinical signs with the HRC index. The clinical score and HRC index added independent information in predicting sepsis, and were similar in clinical and proven sepsis. The clinical score and the HRC index rose before sepsis, and the HRC index rose first. We conclude that clinical signs of illness and HRC monitoring add independent information to one another in the diagnosis of neonatal sepsis.

Temporal abstraction in intelligent clinical data analysis: a survey

OBJECTIVE

Intelligent clinical data analysis systems require precise qualitative descriptions of data to enable effective and context sensitive interpretation to take place. Temporal abstraction (TA) provides the means to achieve such descriptions, which can then be used as input to a reasoning engine where they are evaluated against a knowledge base to arrive at possible clinical hypotheses. This paper surveys previous research into the development of intelligent clinical data analysis systems that incorporate TA mechanisms and presents research synergies and trends across the research reviewed, especially those associated with the multi-dimensional nature of real-time patient data streams. The motivation for this survey is case study based research into the development of an intelligent real-time, high-frequency patient monitoring system to provide detection of temporal patterns within multiple patient data streams.

RESULTS

The survey was based on factors that are of importance to broaden research into temporal abstraction and on characteristics we believe will assume an increasing level of importance for future clinical IDA systems. These factors were: aspects of the data that is abstracted such as source domain and sample frequency, complexity available within abstracted patterns, dimensionality of the TA and data environment and the knowledge and reasoning underpinning TA processes.

CONCLUSION

It is evident from the review that for intelligent clinical data analysis systems to progress into the future where clinical environments are becoming increasingly data-intensive, the ability for managing multi-dimensional aspects of data at high observation and sample frequencies must be provided. Also, the detection of complex patterns within patient data requires higher levels of TA than are presently available. The conflicting matters of computational tractability and temporal reasoning within a real-time environment present a non-trivial problem for investigation in regard to these matters. Finally, to be able to fully exploit the value of learning new knowledge from stored clinical data through data mining and enable its application to data abstraction, the fusion of data mining and TA processes becomes a necessity.

Early dynamic changes in pulse oximetry signals in preterm newborns with histologic chorioamnionitis

OBJECTIVE

No reliable clinical markers of histologic chorioamnionitis (HCA), a major and often subclinical cause of prematurity leading to high neonatal morbidity and mortality, are available to date. Increasing evidence indicates myocardial dysfunctions in affected fetuses and newborns. We sought to assess the value of nonlinear dynamics from pulse oximetry signals in identifying affected newborns.

DESIGN

Prospective case-control study.

SETTING

Tertiary level neonatal intensive care unit, Brindisi Hospital.

PATIENTS AND INTERVENTION

Pulse oximetry-derived signals (pulse rate, oxygen saturation, and perfusion index), recorded within the first 1.5 hrs of life, were analyzed for 110 very low-birth-weight infants, of whom 54 had histopathological evidence of HCA.

MEASUREMENTS AND MAIN RESULTS

Four different time series parameters were determined for nonlinear dynamical (NLD) analysis. Significantly decreased Lempel-Ziv, Lyapunov largest exponent, and correlation dimension, with significantly increased Hurst values for heart rate and perfusion index (p < .00001), were observed in newborns with HCA. Heart rate Lempel-Ziv </=0.218 showed 100% sensitivity (95% confidence interval, 98.8-100) and 100% specificity (95% confidence interval, 98.6-100) in distinguishing cases from controls, with positive and negative predictive values of 100% and 95.7%, respectively.

CONCLUSIONS

Our findings indicate that early autonomic tone balance abnormalities are present in newborns with HCA and suggest that early dynamic analysis of pulse oximetry signals could be useful in identifying affected infants.

Heart Rate Characteristics Monitoring for Neonatal Sepsis

While heart rate variability has been measured in many clinical settings and has offered insights into how HR is controlled, rarely has it offered unique information that has led to changes in patient management. We review our experience in developing continuous HR characteristics monitoring to aid in the early diagnosis of sepsis in premature infants in the neonatal intensive care unit. A predictive algorithm, developed at one center and validated at another, has led to diagnosis and treatment of this subacute and potentially catastrophic illness prior to appearance of symptoms of severe illness.

Evaluating illness severity for very low birth weight infants: CRIB or CRIB-II?

BACKGROUND

Estimating the risk of in-hospital mortality provides essential information in the neonatal intensive care unit (NICU). The clinical risk index for babies (CRIB) is a widely used, risk-adjustment instrument to determine illness severity in infants of gestational age <or=31 wks, or birth weight <or=1,500 g, recently updated and simplified into a five-items scoring system (CRIB-II).Aim. The accuracy values of CRIB and CRIB-II scores in predicting in-hospital mortality were compared in a tertiary level, minimal intubation policy NICU setting.

METHODS

A total of 147 very low birth weight (VLBW) infants were examined. Both CRIB and CRIB-II scores were calculated for each newborn, and death before hospital discharge was selected as the outcome measure. Comparisons were performed by receiver-operating characteristic (ROC) curve analysis, and the area under the curve (AUC) was used as a measure of predictor accuracy.

RESULTS

Mean AUCs for CRIB, CRIB-II, gestational age and birth weight in identifying neonatal mortality in VLBW infants ranged from 0.924 (CRIB) to 0.869 (gestational age). No significant differences were found for the AUCs of CRIB versus CRIB-II, CRIB versus gestational age, CRIB versus birth weight, CRIB-II versus gestational age, or CRIB-II versus birth weight.

CONCLUSIONS

Our findings show that; 1) CRIB and CRIB-II show similar accuracy values in predicting in-hospital neonatal mortality in VLBW infants; and 2) neither score offers an advantage in predicting mortality, as compared to gestational age or birth weight, thus suggesting that treatment modalities may modify predictive accuracy.