Continuous multiorgan variability analysis to track severity of organ failure in critically ill patients

Heart rate variability as a marker of multiple organ dysfunction syndromes: a systematic review

Multiple organ dysfunction syndrome (MODS) can be caused by many factors. Assessments of the severity of MODS are currently based on occasional measurements of several clinical variables (laboratory data, vital signs, etc.). The analysis of heart rate variability (HRV) as a guide to autonomic nervous system activity might be of value in the continuous assessment of the severity of MODS. We systematically reviewed publications on the value of HRV variables for the diagnosis of MODS in patients of any age admitted to the ICU. Two investigators independently searched the PubMed, Embase, Cochrane and Science Direct databases for articles in English or French published between 2004 and 2024. Ten studies were included and rated for endpoint bias (MODS or mortality), using the revised Quality Assessment of Diagnostic Accuracy Studies. Nine studies assessed MODS, and six assessed mortality. All the studies evidenced low HRV in patients with MODS and in non-survivors. The results of our review show that HRV indices are influenced by the severity of MODS and might serve as a tool for predicting mortality in patients with MODS. However, patient characteristics, and treatments and HRV processing methods must be taken into account when interpreting the results. In order to clarify the impact of MODS on HRV variables, methodologically rigorous studies are now needed.

Liberation from mechanical ventilation using Extubation Advisor Decision Support (LEADS): protocol for a multicentre pilot trial

INTRODUCTION

Timely successful liberation from invasive ventilation has the potential to minimise critically ill patients' exposure to invasive ventilation, save costs and improve outcomes; yet no trials have evaluated strategies to better inform extubation decision-making. The Liberation from mechanical ventilation using Extubation Advisor (EA) Decision Support (LEADS) Pilot Trial will assess the feasibility of a trial of a novel extubation decision support tool on feasibility metrics. The primary feasibility outcome will reflect our ability to recruit the desired population. Secondary feasibility outcomes will assess rates of (1) consent, (2) randomisation, (3) intervention adherence, (4) bidirectional crossovers and the (5) completeness of clinical outcomes collected. We will also evaluate physicians' perceptions of the usefulness of the EA tool and measure costs related to EA implementation.

METHODS AND ANALYSIS

We will include critically ill adults who are invasively ventilated for ≥48 hours and who are ready to undergo a spontaneous breathing trial (SBT) with a view to extubation. Patients in the intervention arm will undergo an EA assessment that measures respiratory rate variability to derive an estimate of extubation readiness. Treating clinicians (respiratory therapists, attending physicians and intensive care unit fellows) will receive an EA report for each SBT conducted. The EA report will assist, rather than direct, extubation decision-making. Patients in the control arm will receive standard care. SBTs will be directed by clinicians, using current best evidence, without EA assessments or reports. We aim to recruit 1 to 2 patients/month in approximately 10 centres, and to achieve >75% consent rate, >95% randomisation among consented patients, >80% of EA reports generated and delivered (intervention arm), <10% crossovers (both arms) and >90% of patients with complete clinical outcomes. We will also report physician point-of-care perceptions of the usefulness of the EA tool.

ETHICS AND DISSEMINATION

The LEADS Pilot Trial is approved by the Research Ethics Boards of all participating centres and Clinical Trials Ontario (4008). We will disseminate the LEADS trial findings through conference presentations and publication.

TRIAL REGISTRATION NUMBER

NCT05506904.

PROTOCOL VERSION

24 April 2024.

Roadmap for the evolution of monitoring: developing and evaluating waveform-based variability-derived artificial intelligence-powered predictive clinical decision support software tools

BACKGROUND

Continuous waveform monitoring is standard-of-care for patients at risk for or with critically illness. Derived from waveforms, heart rate, respiratory rate and blood pressure variability contain useful diagnostic and prognostic information; and when combined with machine learning, can provide predictive indices relating to severity of illness and/or reduced physiologic reserve. Integration of predictive models into clinical decision support software (CDSS) tools represents a potential evolution of monitoring.

METHODS

We perform a review and analysis of the multidisciplinary steps required to develop and rigorously evaluate predictive clinical decision support tools based on monitoring.

RESULTS

Development and evaluation of waveform-based variability-derived predictive models involves a multistep, multidisciplinary approach. The stepwise processes involves data science (data collection, waveform processing, variability analysis, statistical analysis, machine learning, predictive modelling), CDSS development (iterative research prototype evolution to commercial tool), and clinical research (observational and interventional implementation studies, followed by feasibility then definitive randomized controlled trials), and poses unique challenges (including technical, analytical, psychological, regulatory and commercial).

CONCLUSIONS

The proposed roadmap provides guidance for the development and evaluation of novel predictive CDSS tools with potential to help transform monitoring and improve care.

Ventilator Weaning and Extubation

Increasing evidence supports specific approaches to liberate patients from invasive ventilation including the use of liberation protocols, inspiratory assistance during spontaneous breathing trials (SBTs), early extubation of patients with chronic obstructive pulmonary disease to noninvasive ventilation, and prophylactic use of noninvasive support strategies after extubation. Additional research is needed to elucidate the best criteria to identify patients who are ready to undergo an SBT and to inform optimal screening frequency, the best SBT technique and duration, extubation assessments, and extubation decision-making. Additional clarity is also needed regarding the optimal timing to measure and report extubation success.

Autonomic dysfunction as a predictor of infection in neurocritical care unit: a prospective cohort study

PURPOSE

Infection in the neurocritical care unit ( NCCU) can cause significant mortality and morbidity. Autonomic nervous system plays an important role in defense against infection. Autonomic dysfunction causing inflammatory dysregulation can potentiate infection. We aimed to study the relationship between autonomic dysfunction and occurrence of infection in neurologically ill patients.

METHODS

Fifty one patients who were on mechanical ventilation were prospectively enrolled in this study. Autonomic dysfunction was measured for three consecutive days on admission to NCCU using Ansiscope. Patients were followed up for seven days to see the occurrence of infection. Infection was defined as per centre of disease control definition.

RESULTS

A total of 386 patients were screened for eligibility. 68 patients satisfied the eligibility criteria and 51 patients were finally included in the study. The incidence of infection was 74.5%. The commonest infection was pulmonary infection (38.8%) followed by urinary tract infection (33.3%), blood stream infection(14.8%), central nervous system infection (11.1%) and wound site infection (3.7%). The degree of autonomic dysfunction (AD) percentage was more in infection group (37.7% (25.2-49.7)) compared to non infection group (23.5% (18-33.5)) and maximal on day 3 (P = 0.02). Patients with increasing trend of AD% from day 1 to day 3 had the highest infection rates. The length of NCCU stay (20(10-23) days and mortality (42.1%) was higher in infection group (p < 0.001).

CONCLUSION

AD assessment can be used as a tool to predict development of infection in NCCU. This can help triage and institute early investigation and treatment.

Predicting Time to Death After Withdrawal of Life-Sustaining Measures Using Vital Sign Variability: Derivation and Validation

To develop a predictive model using vital sign (heart rate and arterial blood pressure) variability to predict time to death after withdrawal of life-supporting measures.

Early heart rate variability evaluation enables to predict ICU patients' outcome

Heart rate variability (HRV) is a mean to evaluate cardiac effects of autonomic nervous system activity, and a relation between HRV and outcome has been proposed in various types of patients. We attempted to evaluate the best determinants of such variation in survival prediction using a physiological data-warehousing program. Plethysmogram tracings (PPG) were recorded at 75 Hz from the standard monitoring system, for a 2 h period, during the 24 h following ICU admission. Physiological data recording was associated with metadata collection. HRV was derived from PPG in either the temporal and non-linear domains. 540 consecutive patients were recorded. A lower LF/HF, SD2/SD1 ratios and Shannon entropy values on admission were associated with a higher ICU mortality. SpO2/FiO2 ratio and HRV parameters (LF/HF and Shannon entropy) were independent correlated with mortality in the multivariate analysis. Machine-learning using neural network (kNN) enabled to determine a simple decision tree combining the three best determinants (SDNN, Shannon Entropy, SD2/SD1 ratio) of a composite outcome index. HRV measured on admission enables to predict outcome in the ICU or at Day-28, independently of the admission diagnosis, treatment and mechanical ventilation requirement.

Trial registration: ClinicalTrials.gov identifier NCT02893462.

Development of a Heart Rate Variability Risk Score to Predict Organ Dysfunction and Death in Critically Ill Children

OBJECTIVES

Determine whether the Heart Rate Variability Dysfunction score, a novel age-normalized measure of autonomic nervous system dysregulation, is associated with the development of new or progressive multiple organ dysfunction syndrome or death in critically ill children.

DESIGN, SETTING, AND PATIENTS

This was a retrospective, observational cohort study from 2012 to 2018. Patients admitted to the PICU with at least 12 hours of continuous heart rate data available from bedside monitors during the first 24 hours of admission were included in the analysis.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Heart rate variability was measured using the integer heart rate variability, which is the sd of the heart rate sampled every 1 second over 5 consecutive minutes. The Heart Rate Variability Dysfunction score was derived from age-normalized values of integer heart rate variability and transformed, so that higher scores were indicative of lower integer heart rate variability and a proxy for worsening autonomic nervous system dysregulation. Heart Rate Variability Dysfunction score performance as a predictor of new or progressive multiple organ dysfunction syndrome and 28-day mortality were determined using the area under the receiver operating characteristic curve. Of the 7,223 patients who met inclusion criteria, 346 patients (4.8%) developed new or progressive multiple organ dysfunction syndrome, and 103 (1.4%) died by day 28. For every one-point increase in the median Heart Rate Variability Dysfunction score in the first 24 hours of admission, there was a 25% increase in the odds of new or progressive multiple organ dysfunction syndrome and a 51% increase in the odds of mortality. The median Heart Rate Variability Dysfunction score in the first 24 hours had an area under the receiver operating characteristic curve to discriminate new or progressive multiple organ dysfunction syndrome of 0.67 and to discriminate mortality of 0.80. These results were reproducible in a temporal validation cohort.

CONCLUSIONS

The Heart Rate Variability Dysfunction score, an age-adjusted proxy for autonomic nervous system dysregulation derived from bedside monitor data is independently associated with new or progressive multiple organ dysfunction syndrome and mortality in PICU patients. The Heart Rate Variability Dysfunction score could potentially be used as a single continuous physiologic biomarker or as part of a multivariable prediction model to increase awareness of at-risk patients and augment clinical decision-making.

Early Warning of Infection in Patients Undergoing Hematopoietic Stem Cell Transplantation Using Heart Rate Variability and Serum Biomarkers

Early warning of infection is critical to reduce the risk of deterioration and mortality, especially in neutropenic patients following hematopoietic stem cell transplantation (HCT). Given that heart rate variability (HRV) is a sensitive and early marker for infection, and that serum inflammatory biomarkers can have high specificity for infection, we hypothesized their combination may be useful for accurate early warning of infection. In this study, we developed and evaluated a composite predictive model using continuous HRV with daily serum biomarker measurements to provide risk stratification of future deterioration in HCT recipients. A total of 116 ambulatory outpatients about to undergo HCT consented to collection of prospective demographic, clinical (daily vital signs), HRV (continuous electrocardiography [ECG] monitoring, laboratory [daily serum samples frozen at -80 °C]), and infection outcome variables (defined as the time of escalation of antibiotics), all from 24 hours pre-HCT to the onset of infection or 14 days post-HCT. Indications for antibiotic escalation were adjudicated as "true infection" or not by 2 blinded HCT clinicians. A composite time series of 8 HRV metrics was created for each patient, and the probability of deterioration within the next 72 hours was estimated using logistic regression modeling of composite HRV and serum biomarkers using a rule-based naïve Bayes model if the HRV-based probability exceeded a median threshold. Thirty-five patients (30%) withdrew within <24 hours owing to intolerability of ECG monitoring, leaving 81 patients, of whom 48 (59%) had antibiotic escalation adjudicated as true infection. The combined HRV and biomarker (TNF-α, IL-6, and IL-7) predictive model began increasing at ∼48 hours on average before the diagnosis of infection, could distinguish between high risk of impending infection (>90% incidence of subsequent infection within 72 hours), average risk (∼50%), and low risk (<10%), with an area under the receiver operating characteristic curve of 0.87. However, given that prophylactic predictive ECG monitoring and daily serum collection proved challenging for many patients, further refinement in measurement is necessary for further study.

Optimizing Our Patients' Entropy Production as Therapy? Hypotheses Originating from the Physics of Physiology

Understanding how nature drives entropy production offers novel insights regarding patient care. Whilst energy is always preserved and energy gradients irreversibly dissipate (thus producing entropy), increasing evidence suggests that they do so in the most optimal means possible. For living complex non-equilibrium systems to create a healthy internal emergent order, they must continuously produce entropy over time. The Maximum Entropy Production Principle (MEPP) highlights nature's drive for non-equilibrium systems to augment their entropy production if possible. This physical drive is hypothesized to be responsible for the spontaneous formation of fractal structures in space (e.g., multi-scale self-similar tree-like vascular structures that optimize delivery to and clearance from an organ system) and time (e.g., complex heart and respiratory rate variability); both are ubiquitous and essential for physiology and health. Second, human entropy production, measured by heat production divided by temperature, is hypothesized to relate to both metabolism and consciousness, dissipating oxidative energy gradients and reducing information into meaning and memory, respectively. Third, both MEPP and natural selection are hypothesized to drive enhanced functioning and adaptability, selecting states with robust basilar entropy production, as well as the capacity to enhance entropy production in response to exercise, heat stress, and illness. Finally, a targeted focus on optimizing our patients' entropy production has the potential to improve health and clinical outcomes. With the implications of developing a novel understanding of health, illness, and treatment strategies, further exploration of this uncharted ground will offer value.

Complex dynamics of skin sympathetic nerve activities as a prognostic predictor for critically ill patients

BACKGROUND

The skin sympathetic nerve activity (SKNA) is a new method to measure sympathetic nerve activity by using conventional ECG electrodes. We developed a novel approach to analyze the complexity of SKNA time series under different time scales and showed its prognostic significance in patients receiving critical care.

METHODS

This study measured SKNA in patients admitted to an intensive care unit (ICU). Each recording is 10-minute long with 10000Hz sampling rate. Multi-scale fluctuation analysis (MSFA) was developed to quantify the variation within each time scale after removing the linear trend. The prognostic value of SKNA was combined with traditional prognostics scoring system to improve the predictive values.

RESULTS

155 patients were recruited. After 30 and 90 days, 30 and 48 patients expired. MSFA was significantly higher in survival group than mortality group for 30-day (0.487 ± 0.185 vs 0.401 ± 0.045, p = 0.018) and 90-day (0.499 ± 0.196 vs 0.414 ± 0.061, p = 0.001) follow-up. Sequential Organ Failure Assessment (SOFA) score was significantly lower in the survival group compared to the expired group for 30-day and 90-day (4.1 ± 2.9 vs. 5.5 ± 4.1, p = 0.032 and 3.9 ± 3.0 vs. 5.4 ± 3.5, p = 0.012). The Kaplan-Meier survival analysis showed MSFA lower than 0.401 (log-rank test:4.96, p = 0.03) or with SOFA score lower than 5 (log-rank test:5.49, p = 0.019) have a significantly higher mortality rate. A multivariate Cox regression model showed that the MSFA is an independent predictor for 30-day mortality (HR = 2.35, 1.08-5.09, p = 0.031) and 90-day mortality (HR = 1.96, 1.08-3.58, p = 0.027).

CONCLUSION

MSFA was a significant prognostic predictor for critically ill patients. MSFA adding to SOFA score could help improve risk prediction.

Heart Rate Variability, Clinical and Laboratory Measures to Predict Future Deterioration in Patients Presenting With Sepsis

BACKGROUND

Risk stratification of patients presenting to the emergency department (ED) with sepsis can be challenging. We derived and evaluated performance of a predictive model containing clinical, laboratory, and heart rate variability (HRV) measures to quantify risk of deterioration in this population.

METHODS

ED patients aged 21 and older satisfying the 1992 consensus conference criteria for sepsis and able to consent (directly or through a surrogate) were enrolled (n = 1,247). Patients had clinical, laboratory, and HRV data recorded within 1 h of ED presentation, and were followed to identify deterioration within 72 h.

RESULTS

Eight hundred thirty-two patients had complete data, of whom 68 (8%) reached at least one endpoint. Optimal predictive performance was derived from a combination of laboratory values and HRV metrics with an area under the receiver-operating curve (AUROC) of 0.80 (95% CI, 0.65-0.92). This combination of variables was superior to clinical (AUROC = 0.69, 95% CI, 0.54-0.83), laboratory (AUROC = 0.77, 95% CI, 0.63-0.90), and HRV measures (AUROC = 0.76, 95% CI, 0.61-0.90) alone. The HRV+LAB model identified a high-risk cohort of patients (14% of all patients) with a 4.3-fold (95% CI, 3.2-5.4) increased risk of deterioration (incidence of deterioration: 35%), as well as a low-risk group (61% of all patients) with 0.2-fold (95% CI 0.1-0.4) risk of deterioration (incidence of deterioration: 2%).

CONCLUSIONS

A model that combines HRV and laboratory values may help ED physicians evaluate risk of deterioration in patients with sepsis and merits validation and further evaluation.

Dynamic properties of glucose complexity during the course of critical illness: a pilot study

Methods to control the blood glucose (BG) levels of patients in intensive care units (ICU) improve the outcomes. The development of continuous BG levels monitoring devices has also permitted to optimize these processes. Recently it was shown that a complexity loss of the BG signal is linked to poor clinical outcomes. Thus, it becomes essential to decipher this relation to design efficient BG level control methods. In previous studies the BG signal complexity was calculated as a single index for the whole ICU stay. Although, these approaches did not grasp the potential variability of the BG signal complexity. Therefore, we setup this pilot study using a continuous monitoring of central venous BG levels in ten critically ill patients (EIRUS platform, Maquet Critical CARE AB, Solna, Sweden). Data were processed and the complexity was assessed by the detrended fluctuation analysis and multiscale entropy (MSE) methods. Finally, recordings were split into 24 h overlapping intervals and a MSE analysis was applied to each of them. The MSE analysis on time intervals revealed an entropy variation and allowed periodic BG signal complexity assessments. To highlight differences of MSE between each time interval we calculated the MSE complexity index defined as the area under the curve. This new approach could pave the way to future studies exploring new strategies aimed at restoring blood glucose complexity during the ICU stay.

Diffusing an Innovation: Clinician Perceptions of Continuous Predictive Analytics Monitoring in Intensive Care

BACKGROUND

The purpose of this article is to describe neonatal intensive care unit clinician perceptions of a continuous predictive analytics technology and how those perceptions influenced clinician adoption. Adopting and integrating new technology into care is notoriously slow and difficult; realizing expected gains remain a challenge.

METHODS

Semistructured interviews from a cross-section of neonatal physicians (n = 14) and nurses (n = 8) from a single U.S. medical center were collected 18 months following the conclusion of the predictive monitoring technology randomized control trial. Following qualitative descriptive analysis, innovation attributes from Diffusion of Innovation Theory-guided thematic development.

RESULTS

Results suggest that the combination of physical location as well as lack of integration into work flow or methods of using data in care decisionmaking may have delayed clinicians from routinely paying attention to the data. Once data were routinely collected, documented, and reported during patient rounds and patient handoffs, clinicians came to view data as another vital sign. Through clinicians' observation of senior physicians and nurses, and ongoing dialogue about data trends and patient status, clinicians learned how to integrate these data in care decision making (e.g., differential diagnosis) and came to value the technology as beneficial to care delivery.

DISCUSSION

The use of newly created predictive technologies that provide early warning of illness may require implementation strategies that acknowledge the risk-benefit of treatment clinicians must balance and take advantage of existing clinician training methods.

Autonomic Nervous System Dysfunction in Pediatric Sepsis

The autonomic nervous system (ANS) plays a major role in maintaining homeostasis through key adaptive responses to stress, including severe infections and sepsis. The ANS-mediated processes most relevant during sepsis include regulation of cardiac output and vascular tone, control of breathing and airway resistance, inflammation and immune modulation, gastrointestinal motility and digestion, and regulation of body temperature. ANS dysfunction (ANSD) represents an imbalanced or maladaptive response to injury and is prevalent in pediatric sepsis. Most of the evidence on ANSD comes from studies of heart rate variability, which is a marker of ANS function and is inversely correlated with organ dysfunction and mortality. In addition, there is evidence that other measures of ANSD, such as respiratory rate variability, skin thermoregulation, and baroreflex and chemoreflex sensitivity, are associated with outcomes in critical illness. The relevance of understanding ANSD in the context of pediatric sepsis stems from the fact that it might play an important role in the pathophysiology of sepsis, is associated with outcomes, and can be measured continuously and noninvasively. Here we review the physiology and dysfunction of the ANS during critical illness, discuss methods for measuring ANS function in the intensive care unit, and review the diagnostic, prognostic, and therapeutic value of understanding ANSD in pediatric sepsis.

Autonomic nervous system monitoring in intensive care as a prognostic tool. Systematic review

Objective

To present a systematic review of the use of autonomic nervous system monitoring as a prognostic tool in intensive care units by assessing heart rate variability.

Methods

Literature review of studies published until July 2016 listed in PubMed/Medline and conducted in intensive care units, on autonomic nervous system monitoring, via analysis of heart rate variability as a prognostic tool (mortality study). The following English terms were entered in the search field: ("autonomic nervous system" OR "heart rate variability") AND ("intensive care" OR "critical care" OR "emergency care" OR "ICU") AND ("prognosis" OR "prognoses" OR "mortality").

Results

There was an increased likelihood of death in patients who had a decrease in heart rate variability as analyzed via heart rate variance, cardiac uncoupling, heart rate volatility, integer heart rate variability, standard deviation of NN intervals, root mean square of successive differences, total power, low frequency, very low frequency, low frequency/high frequency ratio, ratio of short-term to long-term fractal exponents, Shannon entropy, multiscale entropy and approximate entropy.

Conclusion

In patients admitted to intensive care units, regardless of the pathology, heart rate variability varies inversely with clinical severity and prognosis.

Increases in Heart Rate Variability Signal Improved Outcomes in Rapid Response Team Consultations: A Cohort Study

Background

Reduced heart rate variability (HRV) indicates dominance of the sympathetic system and a state of “physiologic stress.” We postulated that, in patients with critical illness, increases in HRV might signal successful resuscitation and improved prognosis.

Methods

We carried out a prospective observational study of HRV on all patients referred to the rapid response team (RRT) and correlated with serial vital signs, lactate clearance, ICU admission, and mortality.

Results

Ninety-one patients were studied. Significantly higher HRV was observed in patients who achieved physiological stability and did not need ICU admission: ASDNN 19 versus 34.5, p=0.032; rMSSD 13.5 versus 25, p=0.046; mean VLF 9.4 versus 17, p=0.021; mean LF 5.8 versus 12.4, p=0.018; and mean HF 4.7 versus 10.5, p=0.017. ROC curves confirmed the change in very low frequencies at 2 hours as a strong predictor for ICU admission with an AUC of 0.772 (95% CI 0.633, 0.911, p=0.001) and a cutoff value of −0.65 associated with a sensitivity of 78.6% and a specificity of 61%.

Conclusions

Reduced HRV, specifically VLF, appears closely related to greater severity of critical illness, identifies unsuccessful resuscitation, and can be used to identify consultations that need early ICU admission.

Use of the low-frequency/high-frequency ratio of heart rate variability to predict short-term deterioration in emergency department patients with sepsis

OBJECTIVE

To examine the ability of the low-frequency/high-frequency (LF/HF) ratio of heart rate variability (HRV) analysis to identify patients with sepsis at risk of early deterioration.

METHODS

This is a prospective observational cohort study of patients with sepsis presenting to the Montefiore Medical Center ED from December 2014 through September 2015. On presentation, a single ECG Holter recording was obtained and analysed to obtain the LF/HF ratio of HRV. Initial Sequential Organ Failure Assessment (SOFA) scores were computed. Patients were followed for 72 hours to identify those with early deterioration.

RESULTS

466 patients presenting to the ED with sepsis were analysed. Thirty-two (7%) reached at least one endpoint within 72 hours. An LF/HF ratio <1 had a sensitivity and specificity of 34% (95% CI (19% to 53%)) and 82% (95% CI (78% to 85%)), respectively, with positive and negative likelihood ratios of 1.9 (95% CI (1.1 to 3.2)) and 0.8 (95% CI (0.6 to 1.0)). An initial SOFA score ≥3 had a sensitivity and specificity of 38% (95% CI (22% to 56%)) and 92% (95% CI (89% to 95%)), with positive and negative likelihood ratios of 4.9 (95% CI (2.8 to 8.6)) and 0.7 (95% CI (0.5 to 0.9)). The composite measure of HRV+SOFA had improved sensitivity (56%, 95% CI (38% to 73%)) but at the expense of specificity (77%, 95% CI (72% to 80%)), with positive and negative likelihood ratios of 2.4 (95% CI (1.7 to 3.4)) and 0.6 (95% CI (0.4 to 0.9)). Receiver operating characteristic analysis did not identify a superior alternate threshold for the LF/HF ratio. Kaplan-Meier survival functions differed significantly (p=0.02) between low (<1) and high (≥1) LF/HF groups.

CONCLUSIONS

While we found a statistically significant relationship between HRV, SOFA and HRV+SOFA, and early deterioration, none reliably functioned as a clinical predictive tool. More complex multivariable models will likely be required to construct models with clinical utility.

Heart rate variability in critical care medicine: a systematic review

BACKGROUND

Heart rate variability (HRV) has been used to assess cardiac autonomic activity in critically ill patients, driven by translational and biomarker research agendas. Several clinical and technical factors can interfere with the measurement and/or interpretation of HRV. We systematically evaluated how HRV parameters are acquired/processed in critical care medicine.

METHODS

PubMed, MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials (1996-2016) were searched for cohort or case-control clinical studies of adult (>18 years) critically ill patients using heart variability analysis. Duplicate independent review and data abstraction. Study quality was assessed using two independent approaches: Newcastle-Ottowa scale and Downs and Black instrument. Conduct of studies was assessed in three categories: (1) study design and objectives, (2) procedures for measurement, processing and reporting of HRV, and (3) reporting of relevant confounding factors.

RESULTS

Our search identified 31/271 eligible studies that enrolled 2090 critically ill patients. A minority of studies (15; 48%) reported both frequency and time domain HRV data, with non-normally distributed, wide ranges of values that were indistinguishable from other (non-critically ill) disease states. Significant heterogeneity in HRV measurement protocols was observed between studies; lack of adjustment for various confounders known to affect cardiac autonomic regulation was common. Comparator groups were often omitted (n = 12; 39%). This precluded meaningful meta-analysis.

CONCLUSIONS

Marked differences in methodology prevent meaningful comparisons of HRV parameters between studies. A standardised set of consensus criteria relevant to critical care medicine are required to exploit advances in translational autonomic physiology.

Systems Biology and Clinical Practice in Respiratory Medicine. The Twain Shall Meet

Respiratory diseases are highly complex, being driven by host-environment interactions and manifested by inflammatory, structural, and functional abnormalities that vary over time. Traditional reductionist approaches have contributed vastly to our knowledge of biological systems in health and disease to date; however, they are insufficient to provide an understanding of the behavior of the system as a whole. In this Pulmonary Perspective, we discuss systems biology approaches, especially but not limited to the study of the lung as a complex system. Such integrative approaches take into account the large number of dynamic subunits and their interactions found in biological systems. Borrowing methods from physics and mathematics, it is possible to study the collective behavior of these systems over time and in a multidimensional manner. We first examine the physiological basis for complexity in the respiratory system and its implications for disease. We then expand on the potential applications of systems biology methods to study complex systems, within the context of diagnosis and monitoring of respiratory diseases including asthma, chronic obstructive pulmonary disease (COPD), and critical illness. We summarize the significant advances made in recent years using systems approaches for disease phenotyping, applied to data ranging from the molecular to clinical level, obtained from large-scale asthma and COPD networks. We describe new studies using temporal complexity patterns to characterize asthma and COPD and predict exacerbations as well as predict adverse outcomes in critical care. We highlight new methods that are emerging with this approach and discuss remaining questions that merit greater attention in the field.

Implementation of a novel postoperative monitoring system using automated Modified Early Warning Scores (MEWS) incorporating end-tidal capnography

Modified Early Warning Scores (MEWS) provide real-time vital sign (VS) trending and reduce ICU admissions in post-operative patients. These early warning calculations classically incorporate oxygen saturation, heart rate, respiratory rate, systolic blood pressure, and temperature but have not previously included end-tidal CO2 (EtCO₂), more recently identified as an independent predictor of critical illness. These systems may be subject to failure when physiologic data is incorrectly measured, leading to false alarms and increased workload. This study investigates whether the implementation of automated devices that utilize ongoing vital signs monitoring and MEWS calculations, inclusive of a score for end-tidal CO₂ (EtCO₂), can be feasibly implemented on the general care hospital floor and effectively identify derangements in a post-operative patient's condition while limiting the amount of false alarms that would serve to increase provider workload. From July to November 2014, post-operative patients meeting the inclusion criteria (BMI > 30 kg/m², history of obstructive sleep apnea, or the use of patient-controlled analgesia (PCA) or epidural narcotics) were monitored using automated devices that record minute-by-minute VS included in classic MEWS calculations as well as EtCO₂. Automated messages via pagers were sent to providers for instances when the device measured elevated MEWS, abnormal EtCO₂, and oxygen desaturations below 85 %. Data, including alarm and message details from the first 133 patients, were recorded and analyzed. Overall, 3.3 alarms and pages sounded per hour of monitoring. Device-only alarms sounded 2.7 times per hour-21 % were technical alarms. The remaining device-only alarms for concerning VS sounded 2.0/h, 70 % for falsely recorded VS. Pages for abnormal EtCO₂ sounded 0.4/h (82 % false recordings) while pages for low blood oxygen saturation sounded 0.1/h (55 % false alarms). 143 times (0.1 pages/h) the devices calculated a MEWS warranting a page (rise in MEWS by 2 or 5 or greater)-62 % were false scores inclusive of falsely recorded VS. An abnormal EtCO₂ value resulted in or added to an elevated MEWS score in 29 % of notifications, but 50 % of these included a falsely abnormal EtCO₂ value. To date, no adverse events have occurred. There were no statistically significant demographic, post-operative condition, or pre-existing comorbidity differences between patients who had a majority of true alarms from those who had mostly false-positive alarms. Although not statistically significant, the group of patients in whom automated MEWS suggested greater utility included those with a history of hypertension (p = 0.072) and renal disease (p = 0.084). EtCO₂ monitoring was more likely to be useful in patients with a history of type 2 diabetes, coronary artery disease, and obstructive sleep apnea (p < 0.05). These patients were also more likely to have been on a PCA post-operatively (p < 0.05). Overall, non-invasive physiologic monitoring incorporating an automated MEWS system, modified to include end-tidal CO2 can be feasibly implemented in a hospital ward. Further study is needed to evaluate its clinical utility, including an end-tidal CO₂ score, is feasibly implemented and can be useful in monitoring select post-operative patients for derangements in physiologic metrics. Like any other monitoring system, false alarms may occur at high rates. While further study is needed to determine the additive utility of EtCO₂ in MEWS calculations, this study suggests utility of EtCO₂ in select post-operative patients.

Using what you get: dynamic physiologic signatures of critical illness

The development and resolution of cardiopulmonary instability take time to become clinically apparent, and the treatments provided take time to have an impact. The characterization of dynamic changes in hemodynamic and metabolic variables is implicit in physiologic signatures. When primary variables are collected with high enough frequency to derive new variables, this data hierarchy can be used to develop physiologic signatures. The creation of physiologic signatures requires no new information; additional knowledge is extracted from data that already exist. It is possible to create physiologic signatures for each stage in the process of clinical decompensation and recovery to improve outcomes.

Correlating multidimensional fetal heart rate variability analysis with acid-base balance at birth

Fetal monitoring during labour currently fails to accurately detect acidemia. We developed a method to assess the multidimensional properties of fetal heart rate variability (fHRV) from trans-abdominal fetal electrocardiogram (fECG) during labour. We aimed to assess this novel bioinformatics approach for correlation between fHRV and neonatal pH or base excess (BE) at birth.We enrolled a prospective pilot cohort of uncomplicated singleton pregnancies at 38-42 weeks' gestation in Milan, Italy, and Liverpool, UK. Fetal monitoring was performed by standard cardiotocography. Simultaneously, with fECG (high sampling frequency) was recorded. To ensure clinician blinding, fECG information was not displayed. Data from the last 60 min preceding onset of second-stage labour were analyzed using clinically validated continuous individualized multiorgan variability analysis (CIMVA) software in 5 min overlapping windows. CIMVA allows simultaneous calculation of 101 fHRV measures across five fHRV signal analysis domains. We validated our mathematical prediction model internally with 80:20 cross-validation split, comparing results to cord pH and BE at birth.The cohort consisted of 60 women with neonatal pH values at birth ranging from 7.44 to 6.99 and BE from -0.3 to -18.7 mmol L(-1). Our model predicted pH from 30 fHRV measures (R(2) = 0.90, P < 0.001) and BE from 21 fHRV measures (R(2) = 0.77, P < 0.001).Novel bioinformatics approach (CIMVA) applied to fHRV derived from trans-abdominal fECG during labor correlated well with acid-base balance at birth. Further refinement and validation in larger cohorts are needed. These new measurements of fHRV might offer a new opportunity to predict fetal acid-base balance at birth.

The impact of noise on the reliability of heart-rate variability and complexity analysis in trauma patients

This study focused on the impact of noise on the reliability of heart-rate variability and complexity (HRV, HRC) to discriminate between different trauma patients and to monitor individual patients. Life-saving interventions (LSIs) were chosen as an endpoint because performance of LSIs is a critical aspect of trauma patient care. Noise was modeled and simulated by modifying original R-R interval (RRI) sequences via decimation, concatenation, and division of RRIs, as well as R-wave detection using the electrocardiogram. Results showed that under increasing simulated noise, entropy and autocorrelation measures can still effectively discriminate between LSI and non-LSI patients and monitor individuals over time.

Reliable real-time calculation of heart-rate complexity in critically ill patients using multiple noisy waveform sources

Heart-rate complexity (HRC) has been proposed as a new vital sign for critical care medicine. The purpose of this research was to develop a reliable method for determining HRC continuously in real time in critically ill patients using multiple waveform channels that also compensates for noisy and unreliable data. Using simultaneously acquired electrocardiogram (Leads I, II, V) and arterial blood pressure waveforms sampled at 360 Hz from 250 patients (over 375 h of patient data), we evaluated a new data fusion framework for computing HRC in real time. The framework employs two algorithms as well as signal quality indices. HRC was calculated (via the method of sample entropy), and equivalence tests were then performed. Bland-Altman plots and box plots of differences between mean HRC values were also obtained. Finally, HRC differences were analyzed by paired t tests. The gold standard for obtaining true means was manual verification of R waves and subsequent entropy calculations. Equivalence tests between mean HRC values derived from manually verified sequences and those derived from automatically detected peaks showed that the "Fusion" values were the least statistically different from the gold standard. Furthermore, the fusion of waveform sources produced better error density distributions than those derived from individual waveforms. The data fusion framework was shown to provide in real-time a reliable continuously streamed HRC value, derived from multiple waveforms in the presence of noise and artifacts. This approach will be validated and tested for assessment of HRC in critically ill patients.