Variability Analysis and the Diagnosis, Management, and Treatment of Sepsis

Early changes in skin surface temperature predict body temperature increases in patients with fever: A pilot study

OBJECTIVE

To investigate the correlation between body temperature and skin surface temperature in intensive care unit patients and to identify specific indicators of skin surface temperature for early fever detection.

RESEARCH METHODOLOGY/DESIGN

This pilot study was a prospective, observational investigation conducted at National Cheng Kung University Hospital in Tainan, Taiwan. A total of 54 patients admitted to the Surgical Intensive Care Unit of a tertiary hospital between April and August 2020 were included. Patients utilized the wearable device HEARThremoTM to continuously monitor skin surface temperature and heart rate. Analysis of Variance was applied to identify the association of skin surface temperature with different body temperature groups. The comparison between skin surface temperature and fever over eight time intervals was studied using a generalized estimating equation.

RESULTS

In 34 patients (63 %) with a fever (≥38 °C), skin surface temperature increased (P < 0.001) when body temperature increased. The maximum skin surface temperature was significantly associated with fever 180-210 min before the fever events occurred (OR: 2.22, 95 % CI: 1.30-3.80). The mean skin surface temperature was associated with fever 120-150 min before the fever events (OR: 8.70, 95 % CI: 2.08-36.36).

CONCLUSIONS

Skin surface temperature can be an important early predictive sign before the onset of fever. Continuous temperature monitoring can detect fever early and initiate treatment in advance. This study serves as a preliminary exploration in this area, laying the groundwork for future comprehensive research.

IMPLICATIONS FOR CLINICAL PRACTICE

Continuous monitoring of skin surface temperature empowers nurses to swiftly detect fever, transcending conventional methods. This proactive approach allows for the early identification of physiological abnormalities, facilitating the prompt initiation of further physical assessments and relevant examinations for early treatment commencement.

Changepoint Detection in Heart Rate Variability Indices in Older Patients Without Cancer at End of Life Using Ballistocardiography Signals: Preliminary Retrospective Study

BACKGROUND

In an aging society such as Japan, where the number of older people continues to increase, providing in-hospital end-of-life care for all deaths, and end-of-life care outside of hospitals, such as at home or in nursing homes, will be difficult. In end-of-life care, monitoring patients is important to understand their condition and predict survival time; this information gives family members and caregivers time to prepare for the end of life. However, with no clear indicators, health care providers must subjectively decide if an older patient is in the end-of-life stage, considering factors such as condition changes and decreased food intake. This complicates decisions for family members, especially during home-based care.

OBJECTIVE

The purpose of this preliminary retrospective study was to determine whether and how changes in heart rate variability (HRV) indices estimated from ballistocardiography (BCG) occur before the date of death in terminally ill older patients, and ultimately to predict the date of death from the changepoint.

METHODS

This retrospective pilot study assessed the medical records of 15 older patients admitted to a special nursing home between August 2019 and December 2021. Patient characteristics and time-domain HRV indices such as the average normal-to-normal (ANN) interval, SD of the normal-to-normal (SDNN) interval, and root mean square of successive differences (RMSSD) from at least 2 months before the date of death were collected. Overall trends of indices were examined by drawing a restricted cubic spline curve. A repeated measures ANOVA was performed to evaluate changes in the indices over the observation period. To explore more detailed changes in HRV, a piecewise regression analysis was conducted to estimate the changepoint of HRV indices.

RESULTS

The 15 patients included 8 men and 7 women with a median age of 93 (IQR 91-96) years. The cubic spline curve showed a gradual decline of indices from approximately 30 days before the patients' deaths. The repeated measures ANOVA showed that when compared with 8 weeks before death, the ratio of the geometric mean of ANN (0.90, 95% CI 0.84-0.98; P=.005) and RMSSD (0.83, 95% CI 0.70-0.99; P=.03) began to decrease 3 weeks before death. The piecewise regression analysis estimated the changepoints for ANN, SDNN, and RMSSD at -34.5 (95% CI -42.5 to -26.5; P<.001), -33.0 (95% CI -40.9 to -25.1; P<.001), and -35.0 (95% CI -42.3 to -27.7; P<.001) days, respectively, before death.

CONCLUSIONS

This preliminary study identified the changepoint of HRV indices before death in older patients at end of life. Although few data were examined, our findings indicated that HRV indices from BCG can be useful for monitoring and predicting survival time in older patients at end of life. The study and results suggest the potential for more objective and accurate prognostic tools in predicting end-of-life outcomes.

Assessing the effects of oxytocin in changes of core body temperature during LPS-induced endotoxemia: A novel approach using Extended Poincaré Plot Analysis

Oxytocin has shown cardioprotective effects during inflammation and may modify the core body temperature changes in LPS-induced endotoxemia. Notably, the time series analysis of core body temperature fluctuations may indicate thermoregulation alterations. This study aims to assess the effects of oxytocin on changes in the core body temperature by analyzing the fluctuations of the temperature time series of endotoxemic rats. Twelve hours of continuous core body temperature fluctuations time series were obtained from adult male Dark Agouti rats implanted with a telemetric transmitter under the following treatment: lipopolysaccharide (LPS); oxytocin (O); lipopolysaccharide + oxytocin (LPS + O), and vehicle or control (C). The temperature fluctuations time series were analyzed using the Extended Poincaré Plot Analysis (EPPA), a novel approach for measuring nonlinear features, to compute the autocorrelation by Pearson's correlation coefficient r, the standard deviation perpendicular to the line of identity (SD1), and the standard deviation parallel to the line of identity (SD2). The autocorrelation of the temperature fluctuations assessed by Pearson's coefficient was significantly higher in the LPS group compared to control rats (C). Likewise, the co-administration of oxytocin during endotoxemia (LPS + O) significantly reduced the autocorrelation and increased the short-term variability (SD1) of temperature fluctuations compared to those recorded with a single dose of LPS. Thus, we concluded that oxytocin may introduce thermoregulatory changes under LPS-induced endotoxemia. The EPPA is a simple and powerful approach to assess physiological variability that can provide valuable insights into changes in thermoregulation.

Morphic Sensors for Respiratory Parameters Estimation: Validation against Overnight Polysomnography

Effective monitoring of respiratory disturbances during sleep requires a sensor capable of accurately capturing chest movements or airflow displacement. Gold-standard monitoring of sleep and breathing through polysomnography achieves this task through dedicated chest/abdomen bands, thermistors, and nasal flow sensors, and more detailed physiology, evaluations via a nasal mask, pneumotachograph, and airway pressure sensors. However, these measurement approaches can be invasive and time-consuming to perform and analyze. This work compares the performance of a non-invasive wearable stretchable morphic sensor, which does not require direct skin contact, embedded in a t-shirt worn by 32 volunteer participants (26 males, 6 females) with sleep-disordered breathing who performed a detailed, overnight in-laboratory sleep study. Direct comparison of computed respiratory parameters from morphic sensors versus traditional polysomnography had approximately 95% (95 ± 0.7) accuracy. These findings confirm that novel wearable morphic sensors provide a viable alternative to non-invasively and simultaneously capture respiratory rate and chest and abdominal motions.

Predicting deterioration of patients with early sepsis at the emergency department using continuous heart rate variability analysis: a model-based approach

BACKGROUND

Sepsis is a life-threatening disease with an in-hospital mortality rate of approximately 20%. Physicians at the emergency department (ED) have to estimate the risk of deterioration in the coming hours or days and decide whether the patient should be admitted to the general ward, ICU or can be discharged. Current risk stratification tools are based on measurements of vital parameters at a single timepoint. Here, we performed a time, frequency, and trend analysis on continuous electrocardiograms (ECG) at the ED to try and predict deterioration of septic patients.

METHODS

Patients were connected to a mobile bedside monitor that continuously recorded ECG waveforms from triage at the ED up to 48 h. Patients were post-hoc stratified into three groups depending on the development of organ dysfunction: no organ dysfunction, stable organ dysfunction or progressive organ dysfunction (i.e., deterioration). Patients with de novo organ dysfunction and those admitted to the ICU or died were also stratified to the group of progressive organ dysfunction. Heart rate variability (HRV) features over time were compared between the three groups.

RESULTS

In total 171 unique ED visits with suspected sepsis were included between January 2017 and December 2018. HRV features were calculated over 5-min time windows and summarized into 3-h intervals for analysis. For each interval, the mean and slope of each feature was calculated. Of all analyzed features, the average of the NN-interval, ultra-low frequency, very low frequency, low frequency and total power were different between the groups at multiple points in time.

CONCLUSIONS

We showed that continuous ECG recordings can be automatically analyzed and used to extract HRV features associated with clinical deterioration in sepsis. The predictive accuracy of our current model based on HRV features derived from the ECG only shows the potential of HRV measurements at the ED. Unlike other risk stratification tools employing multiple vital parameters this does not require manual calculation of the score and can be used on continuous data over time. Trial registration The protocol of this study is published by Quinten et al., 2017.

Embracing complexity in sepsis

Sepsis involves the dynamic interplay between a pathogen, the host response, the failure of organ systems, medical interventions and a myriad of other factors. This together results in a complex, dynamic and dysregulated state that has remained ungovernable thus far. While it is generally accepted that sepsis is very complex indeed, the concepts, approaches and methods that are necessary to understand this complexity remain underappreciated. In this perspective we view sepsis through the lens of complexity theory. We describe the concepts that support viewing sepsis as a state of a highly complex, non-linear and spatio-dynamic system. We argue that methods from the field of complex systems are pivotal for a fuller understanding of sepsis, and we highlight the progress that has been made over the last decades in this respect. Still, despite these considerable advancements, methods like computational modelling and network-based analyses continue to fly under the general scientific radar. We discuss what barriers contribute to this disconnect, and what we can do to embrace complexity with regards to measurements, research approaches and clinical applications. Specifically, we advocate a focus on longitudinal, more continuous biological data collection in sepsis. Understanding the complexity of sepsis will require a huge multidisciplinary effort, in which computational approaches derived from complex systems science must be supported by, and integrated with, biological data. Such integration could finetune computational models, guide validation experiments, and identify key pathways that could be targeted to modulate the system to the benefit of the host. We offer an example for immunological predictive modelling, which may inform agile trials that could be adjusted throughout the trajectory of disease. Overall, we argue that we should expand our current mental frameworks of sepsis, and embrace nonlinear, system-based thinking in order to move the field forward.

Application of probability theory to neonatal cardiac evaluation

Based on probability theory, a methodology that allows diagnosing neonatal cardiac dynamics was previously developed; however, diagnostic applications of this method are required to validate it to the neonatal cardiac dynamics was conducted, allowing to differentiate normal from pathological dynamics. The hourly maximum and minimum heart rate values from 39 continuous and ambulatory electrocardiographic records with a minimum length of 21 hours were taken, from newborns between 0 and 10 days of life, 9 clinically within normality limits and 30 with cardiac pathologies. The probability of occurrence of heart rates in ranges of 5 beats/minute was calculated. The distributions of probability were analysed, and finally the diagnosis was determined by the physical-mathematical methodology. Then, a statistical validation of sensitivity, specificity, and diagnostic agreement was performed. Normal registries showed probability distributions with absent or minimal presence of heart rates of the ranges between 125 and 135 beats/minute, while the abnormal ones had values within these ranges, as well as absence or minimal presence of heart rates from 75 beats/minute to 85 beats/minute. The sensitivity and specificity were 100%, and the Kappa coefficient had a value of 1. Hereby, it is concluded that through an application of a physical-mathematical methodology of neonatal cardiac diagnosis, it is possible to differentiate normality from disease.

Possible effects of Treponema pallidum infection on human vascular endothelial cells

Pathogens can affect host cells in various ways, and the same effect can be found in the Treponema pallidum acting on the endothelium of host vessels, and the mechanism is often complex and multiple. Based on the existing T. pallidum of a cognitive framework, the first concerns involving T. pallidum or the bacteria protein directly acted on vascular endothelial cells of the host, the second concerns mainly involved in the process of T. pallidum infection in vivo blood lipid change, secretion of cytokines and the interactions between immune cells indirectly. Through both direct and indirect influence, this study explores the role of host by T. pallidum infect in the process of the vascular endothelium.

Selenocompounds and Sepsis: Redox Bypass Hypothesis for Early Diagnosis and Treatment: Part A-Early Acute Phase of Sepsis: An Extraordinary Redox Situation (Leukocyte/Endothelium Interaction Leading to Endothelial Damage)

Significance: Sepsis is a health disaster. In sepsis, an initial, beneficial local immune response against infection evolves rapidly into a generalized, dysregulated response or a state of chaos, leading to multiple organ failure. Use of life-sustaining supportive therapies creates an unnatural condition, enabling the complex cascades of the sepsis response to develop in patients who would otherwise die. Multiple attempts to control sepsis at an early stage have been unsuccessful. Recent Advances: Major events in early sepsis include activation and binding of leukocytes and endothelial cells in the microcirculation, damage of the endothelial surface layer (ESL), and a decrease in the plasma concentration of the antioxidant enzyme, selenoprotein-P. These events induce an increase in intracellular redox potential and lymphocyte apoptosis, whereas apoptosis is delayed in monocytes and neutrophils. They also induce endothelial mitochondrial and cell damage. Critical Issues: Neutrophil production increases dramatically, and aggressive immature forms are released. Leukocyte cross talk with other leukocytes and with damaged endothelial cells amplifies the inflammatory response. The release of large quantities of reactive oxygen, halogen, and nitrogen species as a result of the leukocyte respiratory burst, endothelial mitochondrial damage, and ischemia/reperfusion processes, along with the marked decrease in selenoprotein-P concentrations, leads to peroxynitrite damage of the ESL, reducing flow and damaging the endothelial barrier. Future Directions: Endothelial barrier damage by activated leukocytes is a time-sensitive event in sepsis, occurring within hours and representing the first step toward organ failure and death. Reducing or stopping this event is necessary before irreversible damage occurs.

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.

An Innovative Wearable Device For Monitoring Continuous Body Surface Temperature (HEARThermo): Instrument Validation Study

Background

Variations in body temperature are highly informative during an illness. To date, there are not many adequate studies that have investigated the feasibility of a wearable wrist device for the continuous monitoring of body surface temperatures in humans.

Objective

The objective of this study was to validate the performance of HEARThermo, an innovative wearable device, which was developed to continuously monitor the body surface temperature in humans.

Methods

We implemented a multi-method research design in this study, which included 2 validation studies—one in the laboratory and one with human subjects. In validation study I, we evaluated the test-retest reliability of HEARThermo in the laboratory to measure the temperature and to correct the values recorded by each HEARThermo by using linear regression models. We conducted validation study II on human subjects who wore HEARThermo for the measurement of their body surface temperatures. Additionally, we compared the HEARThermo temperature recordings with those recorded by the infrared skin thermometer simultaneously. We used intraclass correlation coefficients (ICCs) and Bland-Altman plots to analyze the criterion validity and agreement between the 2 measurement tools.

Results

A total of 66 participants (age range, 10-77 years) were recruited, and 152,881 completed data were analyzed in this study. The 2 validation studies in the laboratory and on human skin indicated that HEARThermo showed a good test-retest reliability (ICC 0.96-0.98) and adequate criterion validity with the infrared skin thermometer at room temperatures of 20°C-27.9°C (ICC 0.72, P<.001). The corrected measurement bias averaged –0.02°C, which was calibrated using a water bath ranging in temperature from 16°C to 40°C. The values of each HEARThermo improved by the regression models were not significantly different from the temperature of the water bath (P=.19). Bland-Altman plots showed no visualized systematic bias. HEARThermo had a bias of 1.51°C with a 95% limit of agreement between –1.34°C and 4.35°C.

Conclusions

The findings of our study show the validation of HEARThermo for the continuous monitoring of body surface temperatures in humans.

Heart rate variability measures for prediction of severity of illness and poor outcome in ED patients with sepsis

INTRODUCTION

This study evaluates the utility of heart rate variability (HRV) for assessment of severity of illness and poor outcome in Emergency Department (ED) patients with sepsis. HRV measures evaluated included low frequency (LF) signal, high frequency (HF) signal, and deviations in LF and HF signal from age-adjusted reference values.

METHODS

This was a prospective, observational study. Seventy-two adult ED patients were assessed within 6 h of arrival.

RESULTS

Severity of illness as defined by sepsis subtype correlated with decreased LF signal (sepsis: 70.68 ± 22.95, severe sepsis: 54.00 ± 28.41, septic shock: 45.54 ± 23.31, p = 0.02), increased HF signal (sepsis: 27.87 ± 19.42, severe sepsis: 44.63 ± 27.29, septic shock: 47.66 ± 20.98, p = 0.01), increasingly negative deviations in LF signal (sepsis: 0.41 ± 24.53, severe sepsis: -21.43 ± 30.09, septic shock -30.39 ± 26.09, p = 0.005) and increasingly positive deviations in HF signal (sepsis: -1.86 ± 21.09, severe sepsis: 20.07 ± 29.03, septic shock: 23.6 ± 24.17, p = 0.004). Composite poor outcome correlated with decreased LF signal (p = 0.008), increased HF signal (p = 0.03), large negative deviations in LF signal (p = 0.004) and large positive deviations in HF signal (p = 0.02). Deviations in LF and HF signal from age-adjusted reference values correlated with individual measures of poor outcome with greater consistency than LF or HF signal.

DISCUSSION

Accounting for the influence of age on baseline HRV signal improves the predictive value of HRV measures in ED patients with sepsis.

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.

Monitoring skin temperature at the wrist in hospitalised patients may assist in the detection of infection

Background

Measuring temperature has always been a key observation in the diagnosis of infection. No studies have examined the usefulness of measuring temperature at the wrist to detect infection.

Aim

We sought to determine whether a watch measuring wrist temperature could accurately identify patients who are infected.

Methods

Prospective cross‐sectional pilot study of temperature monitoring in an unselected patients in a tertiary referral adult nephrology unit.

Results

One hundred and four data recording sessions revealed 88 useful data sets, with recording failures in the others. Patients were retrospectively classified as having no infection (Group A, n = 60), clinically diagnosed infection with less than 24 h of treatment with antibiotics (Group B, n = 5), and clinically diagnosed infection with greater than 24 h on antibiotics (Group C, n = 23). There was a significantly higher average maximum temperature in Group B (mean (SEM)) 38°C (0.6) compared with Groups A (36.1°C (0.1)) and C (36.3°C (0.3)). Based on receiver operating characteristics (ROC) a cut‐off temperature of ≥37.5°C gave sensitivity 80% and specificity 98%. Mean electrodermal activity was significantly higher in Groups B and C.

Conclusions

ROC of peripheral skin temperature measurements suggest that such a device may identify many patients requiring treatment for infection. This proof of principle study showed value in using a wearable device in the detection of infection and its potential as an early warning or monitoring device.

Heart rate variability: Measurement and emerging use in critical care medicine

Variation in the time interval between consecutive R wave peaks of the QRS complex has long been recognised. Measurement of this RR interval is used to derive heart rate variability. Heart rate variability is thought to reflect modulation of automaticity of the sinus node by the sympathetic and parasympathetic components of the autonomic nervous system. The clinical application of heart rate variability in determining prognosis post myocardial infarction and the risk of sudden cardiac death is well recognised. More recently, analysis of heart rate variability has found utility in predicting foetal deterioration, deterioration due to sepsis and impending multiorgan dysfunction syndrome in critically unwell adults. Moreover, reductions in heart rate variability have been associated with increased mortality in patients admitted to the intensive care unit. It is hypothesised that heart rate variability reflects and quantifies the neural regulation of organ systems such as the cardiovascular and respiratory systems. In disease states, it is thought that there is an 'uncoupling' of organ systems, leading to alterations in 'inter-organ communication' and a clinically detectable reduction in heart rate variability. Despite the increasing evidence of the utility of measuring heart rate variability, there remains debate as to the methodology that best represents clinically relevant outcomes. With continuing advances in technology, our understanding of the physiology responsible for heart rate variability evolves. In this article, we review the current understanding of the physiological basis of heart rate variability and the methods available for its measurement. Finally, we review the emerging use of heart rate variability analysis in intensive care medicine and conditions in which heart rate variability has shown promise as a potential physiomarker of disease.

Influencing outcomes with automated time zero for sepsis through statistical validation and process improvement

BACKGROUND

Sepsis is a life threating complication of infection acquired by more than 1.5 million people in the United State annually. Each year, sepsis claims the lives of at least 250,000 people. Real-time, automated surveillance for sepsis among hospitalized patients is of critical importance, given that one in three people who die in hospitals have sepsis. The early identification and treatment of sepsis is associated with reduced mortality and costly intensive care bed days. The objective of this analysis was to improve the performance of an electronic medical record based sepsis algorithm (early identification) and improve evidence based bundle compliance (early intervention) with the addition of a real-time, automated time zero calculation.

METHODS

Data from our enterprise-wide health information systems were landed in a data lake platform and was used to statistically validate existing sepsis algorithms. An additional algorithm calculating time zero was introduced and a post-hoc comparison of measures of test performance, alert timing, bundle compliance, ICU length of stay, and all-hospital mortality were performed.

RESULTS

A total of 55,918 alerts for sepsis were generated over the one-year study period across 30 inpatient facilities. The addition of an automated time zero algorithm improved several key indicators including superior positive predictive value (37% to 52%), enhanced timing of the alert (79% occurred within six hours, 77% within the critical 180-minute SEP-1 window, 47% within an hour of time zero), a 14% increase in bundle compliance, a 10% reduction in ICU length of stay, and a decrease in mortality from sepsis.

CONCLUSIONS

The addition of a real-time, automated sepsis time zero calculation improved the performance and timeliness of a predictive sepsis alert provided through a system developed mobile application for clinicians and administrators.

KEYWORDS

Sepsis; validation studies; decision making; computer assisted.

Repeated vital sign measurements in the emergency department predict patient deterioration within 72 hours: a prospective observational study

BACKGROUND

More than one in five patients presenting to the emergency department (ED) with (suspected) infection or sepsis deteriorate within 72 h from admission. Surprisingly little is known about vital signs in relation to deterioration, especially in the ED. The aim of our study was to determine whether repeated vital sign measurements in the ED can differentiate between patients who will deteriorate within 72 h and patients who will not deteriorate.

METHODS

We performed a prospective observational study in patients presenting with (suspected) infection or sepsis to the ED of our tertiary care teaching hospital. Vital signs (heart rate, mean arterial pressure (MAP), respiratory rate and body temperature) were measured in 30-min intervals during the first 3 h in the ED. Primary outcome was patient deterioration within 72 h from admission, defined as the development of acute kidney injury, liver failure, respiratory failure, intensive care unit admission or in-hospital mortality. We performed a logistic regression analysis using a base model including age, gender and comorbidities. Thereafter, we performed separate logistic regression analyses for each vital sign using the value at admission, the change over time and its variability. For each analysis, the odds ratios (OR) and area under the receiver operator curve (AUC) were calculated.

RESULTS

In total 106 (29.5%) of the 359 patients deteriorated within 72 h from admission. Within this timeframe, 18.3% of the patients with infection and 32.9% of the patients with sepsis at ED presentation deteriorated. Associated with deterioration were: age (OR: 1.02), history of diabetes (OR: 1.90), heart rate (OR: 1.01), MAP (OR: 0.96) and respiratory rate (OR: 1.05) at admission, changes over time of MAP (OR: 1.04) and respiratory rate (OR: 1.44) as well as the variability of the MAP (OR: 1.06). Repeated measurements of heart rate and body temperature were not associated with deterioration.

CONCLUSIONS

Repeated vital sign measurements in the ED are better at identifying patients at risk for deterioration within 72 h from admission than single vital sign measurements at ED admission.

The Central Role of the Inflammatory Response in Understanding the Heterogeneity of Sepsis-3

In sepsis-3, in contrast with sepsis-1, the definition "systemic inflammatory response" has been replaced with "dysregulated host response", and "systemic inflammatory response syndrome" (SIRS) has been replaced with "sequential organ failure assessment" (SOFA). Although the definition of sepsis has changed, the debate regarding its nature is ongoing. What are the fundamental processes controlling sepsis-induced inflammation, immunosuppression, or organ failure? In this review, we discuss the heterogeneity of sepsis-3 and address the central role of inflammation in the pathogenesis of sepsis. An unbalanced pro- and anti-inflammatory response, inflammatory resolution disorder, and persistent inflammation play important roles in the acute and/or chronic phases of sepsis. Moreover, powerful links exist between inflammation and other host responses (such as the neuroendocrine response, coagulation, and immunosuppression). We suggest that a comprehensive evaluation of the role of the inflammatory response will improve our understanding of the heterogeneity of sepsis.

Protocol of the sepsivit study: a prospective observational study to determine whether continuous heart rate variability measurement during the first 48 hours of hospitalisation provides an early warning for deterioration in patients presenting with infection or sepsis to the emergency department of a Dutch academic teaching hospital

INTRODUCTION

One in five patients with sepsis deteriorates within 48 hours after hospital admission. Regrettably, a clear tool for the early detection of deterioration is still lacking. The SepsiVit study aims to determine whether continuous heart rate variability (HRV) measurement can provide an early warning for deterioration in patients presenting with suspected infection or sepsis to the emergency department (ED).

METHODS AND ANALYSIS

The protocol of a prospective observational study in the ED. We will include 171 adult medical patients presenting with suspected infection or sepsis and at least two systemic inflammatory response syndrome criteria. Patients with known pregnancy, cardiac transplantation or not admitted to our hospital are excluded.High sample frequency ECG signals (500 Hz), respiratory rate, blood pressure and peripheral oxygen saturation will be recorded continuously during the first 48 hours of hospitalisation using a bedside patient monitor (Philips IntelliVue MP70). Primary endpoint is patient deterioration, defined as the development of organ dysfunction, unplanned intensive care unit admission or in-hospital mortality. The ECG data will be used for offline HRV analysis. We will compare the HRV between two groups (deterioration/no deterioration) and analyse whether HRV provides an early warning for deterioration. Furthermore, we will create a multivariate predictive model for deterioration based on heart rate, respiratory rate and HRV. As planned secondary analyses, we (1) perform a subgroup analysis for patients with pneumosepsis and urosepsis and (2) determine whether HRV using lower sample frequencies (1 Hz or less) suffices to predict deterioration.

ETHICS AND DISSEMINATION

The Institutional Review Board of the University Medical Center Groningen granted a waiver for the study (METc 2015/164). Results will be disseminated through international peer-reviewed publications and conference presentations. A lay summary of the results will be provided to the study participants.

TRIAL REGISTRATION NUMBER

NTR6168; Pre-results.

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.

Monitoring Severity of Multiple Organ Dysfunction Syndrome: New Technologies

OBJECTIVE

To describe new technologies (biomarkers and tests) used to assess and monitor the severity and progression of multiple organ dysfunction syndrome in children as discussed as part of the Eunice Kennedy Shriver National Institute of Child Health and Human Development MODS Workshop (March 26-27, 2015).

DATA SOURCES

Literature review, research data, and expert opinion.

STUDY SELECTION

Not applicable.

DATA EXTRACTION

Moderated by an experienced expert from the field, investigators developing and assessing new technologies to improve the care and understanding of critical illness presented their research and the relevant literature.

DATA SYNTHESIS

Summary of presentations and discussion supported and supplemented by relevant literature.

CONCLUSIONS

There are many innovative tools and techniques with the potential application for the assessment and monitoring of severity of multiple organ dysfunction syndrome. If the reliability and added value of these candidate technologies can be established, they hold promise to enhance the understanding, monitoring, and perhaps, treatment of multiple organ dysfunction syndrome in children.

Neural Control of Inflammation: Implications for Perioperative and Critical Care

Inflammation and immunity are regulated by neural reflexes. Recent basic science research has demonstrated that a neural reflex, termed the inflammatory reflex, modulates systemic and regional inflammation in a multiplicity of clinical conditions encountered in perioperative medicine and critical care. In this review, the authors describe the anatomic and physiologic basis of the inflammatory reflex and review the evidence implicating this pathway in the modulation of sepsis, ventilator-induced lung injury, postoperative cognitive dysfunction, myocardial ischemia-reperfusion injury, and traumatic hemorrhage. The authors conclude with a discussion of how these new insights might spawn novel therapeutic strategies for the treatment of inflammatory diseases in the context of perioperative and critical care medicine.

Trends in vital signs and routine biomarkers in patients with sepsis during resuscitation in the emergency department: a prospective observational pilot study

OBJECTIVES

Sepsis lacks a reliable and readily available measure of disease activity. Thereby, it remains unclear how to monitor response to treatment. Research on numerous (new) biomarkers associated with sepsis provided disappointing results and little is known about changes in vital signs during sepsis resuscitation. We hypothesised that trends in vital signs together with routine biomarker levels during resuscitation might provide information about the response to treatment at a very early stage of sepsis in the emergency department (ED). We therefore explore trends in vital signs and routine biomarker levels during sepsis resuscitation in the ED.

DESIGN

Prospective observational pilot study.

SETTING

ED of a tertiary care teaching hospital.

PARTICIPANTS

99 Adult non-trauma patients with suspected infection and 2 or more systemic inflammatory response syndrome criteria admitted to the ED.

PRIMARY AND SECONDARY OUTCOME MEASURES

Vital signs and biomarker levels at admittance (T0) and after 3 h in the ED (T1).

RESULTS

In total, data of 99 patients were analysed. Of these patients, 63 presented with sepsis, 30 with severe sepsis and 6 with septic shock. All vital signs decreased, except for peripheral oxygen saturation which increased. Almost all routine biomarker levels decreased during resuscitation, except for C reactive protein, bands, potassium, troponin T and direct bilirubin which remained stable. Sodium, chloride and N-terminal prohormone of brain natriuretic peptide increased slightly.

CONCLUSIONS

Vital signs and biomarker levels showed descending trends during resuscitation, except for parameters directly affected by treatment modalities. Despite these trends, most patients improved clinically. Trends in vital signs and routine biomarkers might be helpful in predicting clinical course and response to treatment in patients with sepsis during early resuscitation.

A multiscale entropy-based tool for scoring severity of systemic inflammation

OBJECTIVE

Early detection and start of appropriate treatment are highly correlated with survival of sepsis and septic shock, but the currently available predictive tools are not sensitive enough to identify patients at risk.

DESIGN

Linear (time and frequency domain) and nonlinear (unifractal and multiscale complexity dynamics) measures of beat-to-beat interval variability were analyzed in two mouse models of inflammatory shock to determine if they are sensitive enough to predict outcome.

SETTING

University research laboratory.

SUBJECTS

Blood pressure transmitter-implanted female C57BL/6J mice.

INTERVENTIONS

IV administration of tumor necrosis factor (n = 11) or lipopolysaccharide (n = 14).

MEASUREMENTS AND MAIN RESULTS

Contrary to linear indices of variability, unifractal dynamics, and absolute heart rate or blood pressure, quantification of complex beat-to-beat dynamics using multiscale entropy was able to predict survival outcome starting as early as 40 minutes after induction of inflammatory shock. Based on these results, a new and clinically relevant index of multiscale entropy was developed that scores the key features of a multiscale entropy profile. Contrary to multiscale entropy, multiscale entropy scoring can be followed as a function of time to monitor disease progression with limited loss of information.

CONCLUSIONS

Analysis of multiscale complexity of beat-to-beat dynamics at high temporal resolution has potential as a sensitive prognostic tool with translational power that can predict survival outcome in systemic inflammatory conditions such as sepsis and septic shock.

Coordinated Molecular Cross-Talk between Staphylococcus aureus, Endothelial Cells and Platelets in Bloodstream Infection

Staphylococcus aureus is an opportunistic pathogen often carried asymptomatically on the human body. Upon entry to the otherwise sterile environment of the cardiovascular system, S. aureus can lead to serious complications resulting in organ failure and death. The success of S. aureus as a pathogen in the bloodstream is due to its ability to express a wide array of cell wall proteins on its surface that recognise host receptors, extracellular matrix proteins and plasma proteins. Endothelial cells and platelets are important cells in the cardiovascular system and are a major target of bloodstream infection. Endothelial cells form the inner lining of a blood vessel and provide an antithrombotic barrier between the vessel wall and blood. Platelets on the other hand travel throughout the cardiovascular system and respond by aggregating around the site of injury and initiating clot formation. Activation of either of these cells leads to functional dysregulation in the cardiovascular system. In this review, we will illustrate how S. aureus establish intimate interactions with both endothelial cells and platelets leading to cardiovascular dysregulation.

Dysregulation of the endothelium following Staphylococcus aureus infection

The cardiovascular system is typically a sterile environment; however entry of a microorganism into the circulation can cause potentially life threatening cardiac and/or vascular disease. Staphylococcus aureus endothelial cell interactions are arguably the most important interactions in the pathogenesis of cardiovascular infection. These interactions can trigger cardiac valve destruction in the case of endocarditis, multi-organ dysfunction in the case of sepsis and coagulopathy. Here, we review the interactions between S. aureus and endothelial cells and discuss the implications of these interactions in the progression of cardiovascular infection.

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.

Sepsis: current dogma and new perspectives

Sepsis, a clinical syndrome occurring in patients following infection or injury, is a leading cause of morbidity and mortality worldwide. Current immunological mechanisms do not explain the basis of cellular dysfunction and organ failure, the ultimate cause of death. Here we review current dogma and argue that it is time to delineate novel immunometabolic and neurophysiological mechanisms underlying the altered cellular bioenergetics and failure of epithelial and endothelial barriers that produce organ dysfunction and death. These mechanisms might hold the key to future therapeutic strategies.

Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients?

Introduction

Prolonged ventilation and failed extubation are associated with increased harm and cost. The added value of heart and respiratory rate variability (HRV and RRV) during spontaneous breathing trials (SBTs) to predict extubation failure remains unknown.

Methods

We enrolled 721 patients in a multicenter (12 sites), prospective, observational study, evaluating clinical estimates of risk of extubation failure, physiologic measures recorded during SBTs, HRV and RRV recorded before and during the last SBT prior to extubation, and extubation outcomes. We excluded 287 patients because of protocol or technical violations, or poor data quality. Measures of variability (97 HRV, 82 RRV) were calculated from electrocardiogram and capnography waveforms followed by automated cleaning and variability analysis using Continuous Individualized Multiorgan Variability Analysis (CIMVA™) software. Repeated randomized subsampling with training, validation, and testing were used to derive and compare predictive models.

Results

Of 434 patients with high-quality data, 51 (12%) failed extubation. Two HRV and eight RRV measures showed statistically significant association with extubation failure (P <0.0041, 5% false discovery rate). An ensemble average of five univariate logistic regression models using RRV during SBT, yielding a probability of extubation failure (called WAVE score), demonstrated optimal predictive capacity. With repeated random subsampling and testing, the model showed mean receiver operating characteristic area under the curve (ROC AUC) of 0.69, higher than heart rate (0.51), rapid shallow breathing index (RBSI; 0.61) and respiratory rate (0.63). After deriving a WAVE model based on all data, training-set performance demonstrated that the model increased its predictive power when applied to patients conventionally considered high risk: a WAVE score >0.5 in patients with RSBI >105 and perceived high risk of failure yielded a fold increase in risk of extubation failure of 3.0 (95% confidence interval (CI) 1.2 to 5.2) and 3.5 (95% CI 1.9 to 5.4), respectively.

Conclusions

Altered HRV and RRV (during the SBT prior to extubation) are significantly associated with extubation failure. A predictive model using RRV during the last SBT provided optimal accuracy of prediction in all patients, with improved accuracy when combined with clinical impression or RSBI. This model requires a validation cohort to evaluate accuracy and generalizability.

Trial registration

ClinicalTrials.gov NCT01237886. Registered 13 October 2010.

Cognitive Processes in Anesthesiology Decision Making

The quality and safety of health care are under increasing scrutiny. Recent studies suggest that medical errors, practice variability, and guideline noncompliance are common, and that cognitive error contributes significantly to delayed or incorrect diagnoses. These observations have increased interest in understanding decision-making psychology.

Many nonrational (i.e., not purely based in statistics) cognitive factors influence medical decisions and may lead to error. The most well-studied include heuristics, preferences for certainty, overconfidence, affective (emotional) influences, memory distortions, bias, and social forces such as fairness or blame.

Although the extent to which such cognitive processes play a role in anesthesia practice is unknown, anesthesia care frequently requires rapid, complex decisions that are most susceptible to decision errors. This review will examine current theories of human decision behavior, identify effects of nonrational cognitive processes on decision making, describe characteristic anesthesia decisions in this context, and suggest strategies to improve decision making.

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.

Galactofuranose-rich polysaccharides from Trebouxia sp. induce inflammation and exacerbate lethality by sepsis in mice

Trebouxia sp. is a genus of green algae that is a symbiotic partner of lichenized fungi. Previous studies conduced demonstrated that Trebouxia sp. is able to produce galactofuranose-rich polysaccharides (β-d-galactofuranan, mannogalactofuranan), which were able to activate macrophages in vitro. The present study was proposed to investigate the effects of SK10 polysaccharides fraction from Trebouxia sp. on the model of polymicrobial sepsis induced by cecal ligation and puncture in mice in vivo. The subcutaneous administration of SK10 increased the late mortality rate by 20%, stimulated neutrophil accumulation in lungs (indirectly measured through myeloperoxidase activity) and also Interleukin-1β, creatinine and glucose serum levels. Moreover this study demonstrates the in vivo proinflammatory effects of polymers of galactofuranose and that they can act as pathogen-associated molecular patterns being highly recognized by the immune system of mammals, even if they come from a non-pathogenic microorganism.

Body temperature patterns as a predictor of hospital-acquired sepsis in afebrile adult intensive care unit patients: a case-control study

Introduction

Early treatment of sepsis improves survival, but early diagnosis of hospital-acquired sepsis, especially in critically ill patients, is challenging. Evidence suggests that subtle changes in body temperature patterns may be an early indicator of sepsis, but data is limited. The aim of this study was to examine whether abnormal body temperature patterns, as identified by visual examination, could predict the subsequent diagnosis of sepsis in afebrile critically ill patients.

Methods

Retrospective case-control study of 32 septic and 29 non-septic patients in an adult medical and surgical ICU. Temperature curves for the period starting 72 hours and ending 8 hours prior to the clinical suspicion of sepsis (for septic patients) and for the 72-hour period prior to discharge from the ICU (for non-septic patients) were rated as normal or abnormal by seven blinded physicians. Multivariable logistic regression was used to compare groups in regard to maximum temperature, minimum temperature, greatest change in temperature in any 24-hour period, and whether the majority of evaluators rated the curve to be abnormal.

Results

Baseline characteristics of the groups were similar except the septic group had more trauma patients (31.3% vs. 6.9%, p = .02) and more patients requiring mechanical ventilation (75.0% vs. 41.4%, p = .008). Multivariable logistic regression to control for baseline differences demonstrated that septic patients had significantly larger temperature deviations in any 24-hour period compared to control patients (1.5°C vs. 1.1°C, p = .02). An abnormal temperature pattern was noted by a majority of the evaluators in 22 (68.8%) septic patients and 7 (24.1%) control patients (adjusted OR 4.43, p = .017). This resulted in a sensitivity of 0.69 (95% CI [confidence interval] 0.50, 0.83) and specificity of 0.76 (95% CI 0.56, 0.89) of abnormal temperature curves to predict sepsis. The median time from the temperature plot to the first culture was 9.40 hours (IQR [inter-quartile range] 8.00, 18.20) and to the first dose of antibiotics was 16.90 hours (IQR 8.35, 34.20).

Conclusions

Abnormal body temperature curves were predictive of the diagnosis of sepsis in afebrile critically ill patients. Analysis of temperature patterns, rather than absolute values, may facilitate decreased time to antimicrobial therapy.

From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system

OBJECTIVE

To develop a decision support system to identify patients at high risk for hyperlactatemia based upon routinely measured vital signs and laboratory studies.

MATERIALS AND METHODS

Electronic health records of 741 adult patients at the University of California Davis Health System who met at least two systemic inflammatory response syndrome criteria were used to associate patients' vital signs, white blood cell count (WBC), with sepsis occurrence and mortality. Generative and discriminative classification (naïve Bayes, support vector machines, Gaussian mixture models, hidden Markov models) were used to integrate heterogeneous patient data and form a predictive tool for the inference of lactate level and mortality risk.

RESULTS

An accuracy of 0.99 and discriminability of 1.00 area under the receiver operating characteristic curve (AUC) for lactate level prediction was obtained when the vital signs and WBC measurements were analysed in a 24 h time bin. An accuracy of 0.73 and discriminability of 0.73 AUC for mortality prediction in patients with sepsis was achieved with only three features: median of lactate levels, mean arterial pressure, and median absolute deviation of the respiratory rate.

DISCUSSION

This study introduces a new scheme for the prediction of lactate levels and mortality risk from patient vital signs and WBC. Accurate prediction of both these variables can drive the appropriate response by clinical staff and thus may have important implications for patient health and treatment outcome.

CONCLUSIONS

Effective predictions of lactate levels and mortality risk can be provided with a few clinical variables when the temporal aspect and variability of patient data are considered.

Initial fractal exponent of heart rate variability is associated with success of early resuscitation in patients with severe sepsis or septic shock: a prospective cohort study

INTRODUCTION

Heart rate variability (HRV) reflects autonomic nervous system tone as well as the overall health of the baroreflex system. We hypothesized that loss of complexity in HRV upon intensive care unit (ICU) admission would be associated with unsuccessful early resuscitation of sepsis.

METHODS

We prospectively enrolled patients admitted to ICUs with severe sepsis or septic shock from 2009 to 2011. We studied 30 minutes of electrocardiogram, sampled at 500 Hz, at ICU admission and calculated heart rate complexity via detrended fluctuation analysis. Primary outcome was vasopressor independence at 24 hours after ICU admission. Secondary outcome was 28-day mortality.

RESULTS

We studied 48 patients, of whom 60% were vasopressor independent at 24 hours. Five (10%) died within 28 days. The ratio of fractal alpha parameters was associated with both vasopressor independence and 28-day mortality (P = .04) after controlling for mean heart rate. In the optimal model, Sequential Organ Failure Assessment score and the long-term fractal α parameter were associated with vasopressor independence.

CONCLUSIONS

Loss of complexity in HRV is associated with worse outcome early in severe sepsis and septic shock. Further work should evaluate whether complexity of HRV could guide treatment in sepsis.

Do physiological and pathological stresses produce different changes in heart rate variability?

Although physiological (e.g., exercise) and pathological (e.g., infection) stress affecting the cardiovascular system have both been documented to be associated with a reduction in overall heart rate variability (HRV), it remains unclear if loss of HRV is ubiquitously similar across different domains of variability analysis or if distinct patterns of altered HRV exist depending on the stressor. Using Continuous Individualized Multiorgan Variability Analysis (CIMVA™) software, heart rate (HR) and four selected measures of variability were measured over time (windowed analysis) from two datasets, a set (n = 13) of patients who developed systemic infection (i.e., sepsis) after bone marrow transplant (BMT), and a matched set of healthy subjects undergoing physical exercise under controlled conditions. HR and the four HRV measures showed similar trends in both sepsis and exercise. The comparison through Wilcoxon sign-rank test of the levels of variability at baseline and during the stress (i.e., exercise or after days of sepsis development) showed similar changes, except for LF/HF, ratio of power at low (LF) and high (HF) frequencies (associated with sympathovagal modulation), which was affected by exercise but did not show any change during sepsis. Furthermore, HRV measures during sepsis showed a lower level of correlation with each other, as compared to HRV during exercise. In conclusion, this exploratory study highlights similar responses during both exercise and infection, with differences in terms of correlation and inter-subject fluctuations, whose physiologic significance merits further investigation.

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

PURPOSE

The purpose of this study is to evaluate the utility of using continuous heart rate variability (HRV) and respiratory rate variability (RRV) monitoring for (a) tracking daily organ dysfunction in critically ill patients and (b) identifying patterns of variability changes during onset of shock and resolution of respiratory failure.

MATERIALS AND METHODS

Thirty-three critically ill patients experiencing respiratory and/or cardiac failure underwent continuous recording of their electrocardiogram and capnogram (CO2) waveforms from admission or intubation until discharge (maximum 14 days). HRV and RRV were computed in 5-minute overlapping windows, using Continuous Individualized Multi-organ Variability Analysis software. Multiple organ dysfunction scores were recorded daily. HRV and RRV trajectories were characterized during onset of shock and resolution of respiratory failure.

RESULTS

Both HRV and RRV decreased with increasing severity of multiple organ dysfunction scores for a variety of variability metrics. A decline in several measures of HRV and no decline in RRV were observed before onset of shock (n=6). In contrast, during resolution of respiratory failure, an increase in RRV was observed in patients who successfully passed extubation (n=12), with no change in RRV in those who subsequently failed extubation (n=2).

CONCLUSIONS

There is an association between reduced HRV and RRV and increasing organ dysfunction in critically ill patients. The significance of observing trends of decreasing HRV (with onset of shock) and increasing RRV (with resolution of respiratory failure) merits further investigation.

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.