Early loss of heart rate complexity predicts mortality regardless of mechanism, anatomic location, or severity of injury in 2178 trauma patients

Association between trajectories of systolic blood pressure and frailty outcome in middle-aged and older adults

OBJECTIVES

The association between blood pressure and frailty outcome in the middle-aged and older population remains controversial. This study aimed to examine the relationship between trajectories of systolic blood pressure (SBP) and new-onset frailty.

DESIGN

Cohort study with a 7-year follow-up.

SETTING AND PARTICIPANTS

Data were derived from 4 waves (2011, 2013, 2015 and 2018) of the China Health and Retirement Longitudinal Study and 6168 participants aged ≥45 years were included in the study.

METHODS

The frailty index (FI) was constructed based on 40 scored items, with FI ≥ 0.25 defined as frailty. We identified the 5-year trajectory of SBP by latent class trajectory modeling. The association between SBP trajectories and frailty was explored based on hazard ratios (HR) by four Cox proportional hazards models. Furthermore, we also investigated the relationship between mean SBP and systolic blood pressure variability (SBPV) and frailty.

RESULTS

6168 participants were included in this study with a mean age of 59 years. We identified five trajectories based on SBP, which are maintained low-stable SBP (T0), moderate-stable SBP (T1), remitting then increasing SBP (T2), increasing then remitting SBP (T3), and remaining stable at high SBP levels (T4). During the 7-year follow-up period, frailty outcome occurred in 1415 participants. After adjusting for other confounders, the two trajectories labeled "T2" and "T4" were associated with a higher risk of frailty compared with T0. In addition, elevated SBP and increased SBPV were associated with risk of frailty.

CONCLUSIONS

Higher risk of frailty occurred in two trajectories, remitting then increasing and remaining stable at high SBP levels, were associated with a relatively higher risk of frailty.

Recovery of cardiac electrophysiological alterations by heart rate complexity based on multiscale entropy following liver transplantation

Autonomic nervous dysfunction is a known cardiac sequalae in patients with end-stage liver disease and is associated with a poor prognosis. Heart rate analysis using nonlinear models such as multiscale entropy (MSE) or complexity may identify marked changes in these patients where conventional heart rate variability (HRV) measurements do not. To investigate the application of heart rate complexity (HRC) based on MSE in liver transplantation settings. Thirty adult recipients of elective living donor liver transplantation were enrolled. HRV parameters using conventional HRV analysis and HRC analysis were obtained at the following time points: (1) 1 day before surgery, (2) postoperative day (POD) 7, (3) POD 14, (4) POD 90, and (5) POD 180. Preoperatively, patients with MELD score ≥ 25 had significantly lower HRC compared to patients with lower MELD scores. This difference in HRC disappeared by POD 7 following liver transplantation and subsequent analyses at POD 90 and 180 continued to show no significant difference. Our results indicated a significant negative correlation between HRC based on MSE analysis and liver disease severity preoperatively, which may be more sensitive than conventional linear HRV analysis. HRC in patients with MELD score ≧ 25 improved over time and became comparable to those with MELD < 25 as early as in 7 days.

Heart rate complexity helps mortality prediction in the intensive care unit: A pilot study using artificial intelligence

BACKGROUND

In intensive care units (ICUs), accurate mortality prediction is crucial for effective patient management and resource allocation. The Simplified Acute Physiology Score II (SAPS-2), though commonly used, relies heavily on comprehensive clinical data and blood samples. This study sought to develop an artificial intelligence (AI) model utilizing key hemodynamic parameters to predict ICU mortality within the first 24 h and assess its performance relative to SAPS-2.

METHODS

We conducted an analysis of select hemodynamic parameters and the structure of heart rate curves to identify potential predictors of ICU mortality. A machine-learning model was subsequently trained and validated on distinct patient cohorts. The AI algorithm's performance was then compared to the SAPS-2, focusing on classification accuracy, calibration, and generalizability.

MEASUREMENTS AND MAIN RESULTS

The study included 1298 ICU admissions from March 27th, 2015, to March 27th, 2017. An additional cohort from 2022 to 2023 comprised 590 patients, resulting in a total dataset of 1888 patients. The observed mortality rate stood at 24.0%. Key determinants of mortality were the Glasgow Coma Scale score, heart rate complexity, patient age, duration of diastolic blood pressure below 50 mmHg, heart rate variability, and specific mean and systolic blood pressure thresholds. The AI model, informed by these determinants, exhibited a performance profile in predicting mortality that was comparable, if not superior, to the SAPS-2.

CONCLUSIONS

The AI model, which integrates heart rate and blood pressure curve analyses with basic clinical parameters, provides a methodological approach to predict in-hospital mortality in ICU patients. This model offers an alternative to existing tools that depend on extensive clinical data and laboratory inputs. Its potential integration into ICU monitoring systems may facilitate more streamlined mortality prediction processes.

Use of the cardiopulmonary coupling index based on refined composite multiscale entropy for prognostication of acute type A aortic dissection

Objectives

The aim of this study is to assess the influence of cardiopulmonary coupling (CPC) based on RCMSE on the prediction of complications and death in patients with acute type A aortic dissection (ATAAD).

Background

The cardiopulmonary system may be nonlinearly regulated, and its coupling relationship with postoperative risk stratification in ATAAD patients has not been studied.

Methods

This study was a single-center, prospective cohort study (ChiCTR1800018319). We enrolled 39 patients with ATAAD. The outcomes were in-hospital complications and all-cause readmission or death at 2 years.

Results

Of the 39 participants, 16 (41.0%) developed complications in the hospital, and 15 (38.5%) died or were readmitted to the hospital during the two-year follow-up. When CPC-RCMSE was used to predict in-hospital complications in ATAAD patients, the AUC was 0.853 (p &lt; 0.001). When CPC-RCMSE was used to predict all-cause readmission or death at 2 years, the AUC was 0.731 (p &lt; 0.05). After adjusting for age, sex, ventilator support (days), and special care time (days), CPC-RCMSE remained an independent predictor of in-hospital complications in patients with ATAAD [adjusted OR: 0.8 (95% CI, 0.68–0.94)].

Conclusion

CPC-RCMSE was an independent predictor of in-hospital complications and all-cause readmission or death in patients with ATAAD.

Association between non-acute Traumatic Injury (TI) and Heart Rate Variability (HRV) in adults: A systematic review and meta-analysis

Heart rate variability (HRV) is a non-invasive measure of autonomic function. The relationship between unselected long-term traumatic injury (TI) and HRV has not been investigated. This systematic review examines the impact of non-acute TI (>7 days post-injury) on standard HRV indices in adults. Four electronic databases (CINAHL, Medline, Scopus, and Web of Science) were searched. The quality of studies, risk of bias (RoB), and quality of evidence (QoE) were assessed using Axis, RoBANS and GRADE, respectively. Using the random-effects model, mean difference (MD) for root mean square of successive differences (RMSSD) and standard deviation of NN-intervals (SDNN), and standardized mean difference (SMD) for Low-frequency (LF): High-Frequency (HF) were pooled in RevMan guided by the heterogeneity score (I2). 2152 records were screened followed by full-text retrieval of 72 studies. 31 studies were assessed on the inclusion and exclusion criteria. Only four studies met the inclusion criteria. Three studies demonstrated a high RoB (mean RoBANS score 14.5±3.31) with a low QoE. TI was associated with a significantly higher resting heart rate. Meta-analysis of three cross-sectional studies demonstrated a statistically significant reduction in RMSSD (MD -8.45ms, 95%CI-12.78, -4.12, p<0.0001) and SDNN (MD -9.93ms, 95%CI-14.82, -5.03, p<0.0001) (low QoE) in participants with TI relative to the uninjured control. The pooled analysis of four studies showed a higher LF: HF ratio among injured versus uninjured (SMD 0.20, 95%CI 0.01-0.39, p<0.04) (very low QoE). Albeit low QoE, non-acute TI is associated with attenuated HRV indicating autonomic imbalance. The findings might explain greater cardiovascular risk following TI. Trial registration PROSPERO registration number: CRD: CRD42021298530.

The complexity analysis of cerebral oxygen saturation during pneumoperitoneum and Trendelenburg position: a retrospective cohort study

BACKGROUND

The human brain is a highly complex and nonlinear system, nonlinear complexity measures such as approximate entropy (ApEn) and sample entropy (SampEn) can better reveal characteristics of brain dynamics. However, no studies report complexity of perioperative physiological signals to reveal how brain complexity associates with age, varies along with the development of surgery and postoperative neurological complications.

AIM

This study examined the complexity of intraoperative regional cerebral oxygen saturation (rSO₂), aiming to reveal brain dynamics during surgery.

METHODS

This retrospective cohort study enrolled patients who scheduled for robot-assisted urological surgery. Intraoperative rSO₂ was continuously monitored throughout the surgery. Postoperative delirium (POD) was diagnosed by the Confusion Assessment Method. ApEn and SampEn were used to characterize the complexity of rSO₂. Pearson correlation coefficients were used to measure the correlation between complexity of rSO₂ and age. The association between complexity of rSO₂ and POD was examined using T tests.

RESULTS

A total of 68 patients (mean [SD] age, 63.0 (12.0) years; 47 (69.1%) males) were include in this analysis. There was a significant reverse relationship between the complexity of rSO₂ and age (The correlation coefficients range between - 0.32 and - 0.28, all p < 0.05). Patients  ≥ 75 years showed significantly lower complexity of rSO₂ than the other two groups. Older age remained an independent factor influencing complexity of rSO₂ after adjusting for a number of covariates. Six patients (8.8%) developed POD, and POD patients had lower complexity of rSO₂ compared with non-POD patients.

CONCLUSIONS

The complexity of rSO₂ may serve as a new candidate marker of aging and POD prediction.

Association between non-acute traumatic injury (TI) and heart rate variability (HRV) in adults: A systematic review protocol

Heart Rate Variability (HRV) is an indirect measure of autonomic function. Attenuated HRV is linked to worsening health outcomes including Major Adverse Cardiovascular Events (MACE). The relationship between traumatic injury (TI) and HRV has been limitedly studied. This research protocol has been designed to conduct a systematic review of the existing evidence on the association between non-acute TI and HRV in adults. Four electronic bibliographic databases (Web of Science, CINAHL, Medline, and Scopus) will be searched. The studies on non-acute (>7 days post injury) TI and HRV in adults will be included, followed by title-abstract screening by two reviewers independently. The quality and risk of bias of the included studies will be assessed using Axis and a six-item Risk of Bias Assessment tool for of Non-randomized Studies (RoBANS) respectively. Grading of Recommendations Assessment, Development and Evaluation (GRADE) will assess the quality of evidence. The extracted data will be synthesized using narrative syntheses and a Forest plot with or without meta-analysis- whichever permitted by the pooled data. This will be the first systematic review to examine the relationship between generalized TI and HRV in adults.

Trial registration: (PROPSERO registration number: CRD: CRD42021298530) https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021298530.

Reduced System Complexity of Heart Rate Dynamics in Patients with Hyperthyroidism: A Multiscale Entropy Analysis

Studying heart rate dynamics would help understand the effects caused by a hyperkinetic heart in patients with hyperthyroidism. By using a multiscale entropy (MSE) analysis of heart rate dynamics derived from one-channel electrocardiogram recording, we aimed to compare the system complexity of heart rate dynamics between hyperthyroid patients and control subjects. A decreased MSE complexity index (CI) computed from MSE analysis reflects reduced system complexity. Compared with the control subjects (n = 37), the hyperthyroid patients (n = 37) revealed a significant decrease (p < 0.001) in MSE CI (hyperthyroid patients 10.21 ± 0.37 versus control subjects 14.08 ± 0.21), sample entropy for each scale factor (from 1 to 9), and high frequency power (HF) as well as a significant increase (p < 0.001) in low frequency power (LF) in normalized units (LF%) and ratio of LF to HF (LF/HF). In conclusion, besides cardiac autonomic dysfunction, the system complexity of heart rate dynamics is reduced in hyperthyroidism. This finding implies that the adaptability of the heart rate regulating system is impaired in hyperthyroid patients. Additionally, it might explain the exercise intolerance experienced by hyperthyroid patients. In addition, hyperthyroid patients and control subjects could be distinguished by the MSE CI computed from MSE analysis of heart rate dynamics.

Gait Analyses of Parkinson’s Disease Patients Using Multiscale Entropy

Parkinson’s disease (PD) is a type of neurodegenerative diseases. PD influences gait in many aspects: reduced gait speed and step length, increased axial rigidity, and impaired rhythmicity. Gait-related data used in this study are from PhysioNet. Twenty-one PD patients and five healthy controls (CO) were sorted into four groups: PD without task (PDw), PD with dual task (PDd), control without task (COw), and control with dual task (COd). Since dual task actions are attention demanding, either gait or cognitive function may be affected. To quantify the used walking data, eight pressure sensors installed in each insole are used to measure the vertical ground reaction force. Thus, quantitative measurement analysis is performed utilizing multiscale entropy (MSE) and complexity index (CI) to analyze and differentiate between the ground reaction force of the four different groups. Results show that the CI of patients with PD is higher than that of CO and 11 of the sensor signals are statistically significant (p < 0.05). The COd group has larger CI values at the beginning (p = 0.021) but they get lower at the end of the test (p = 0.000) compared to that in the COw group. The end-of-test CI for the PDw group is lower in one of the feet sensor signals, and in the right total ground reaction force compared to the PDd group counterparts. In conclusion, when people start to adjust their gait due to pathology or stress, CI may increase first and reach a peak, but it decreases afterward when stress or pathology is further increased.

Prehospital Hemorrhage Assessment Criteria: A Concise Review

OBJECTIVE

Early assessment of the clinical status of trauma patients is crucial for guiding the treatment strategy, and it requires a rapid and systematic approach. The aim of this report is to critically review the assessment parameters currently used in the prehospital setting to quantify blood loss in trauma.

DATA SOURCES

Studies regarding hemorrhagic shock in trauma were pooled from PubMed, EMBASE, and Cochrane databases using key words such as "hemorrhagic shock," "vital signs evaluation," "trauma," "blood loss," and "emergency medical service," alone or combined.

STUDY SELECTION

Articles published since 2009 in English and Italian were considered eligible if containing data on assessment parameters in blood loss in adults.

DATA EXTRACTION

Sixteen articles matching the inclusion criteria were considered in our study.

DATA SYNTHESIS

Current prehospital assessment measures lack precise correlation with blood loss.

CONCLUSIONS

Traditional assessment parameters such as heart rate, systolic blood pressure, shock index, and Glasgow Coma Scale score often lag in providing accurate blood loss assessment. The current literature supports the need for a noninvasive, continuously monitored assessment parameter to identify early shock in the prehospital setting.

Heart Rate Complexity in US Army Forward Surgical Teams During Pre Deployment Training

INTRODUCTION

For trauma triage, the US Army has developed a portable heart rate complexity (HRC) monitor, which estimates cardiac autonomic input and the activity of the hypothalamic-pituitary-adrenal (HPA) axis. We hypothesize that autonomic/HPA stress associated with predeployment training in U.S. Army Forward Surgical Teams will cause changes in HRC.

MATERIALS AND METHODS

A prospective observational study was conducted in 80 soldiers and 10 civilians at the U.S. Army Trauma Training Detachment. Heart rate (HR, b/min), cardiac output (CO, L/min), HR variability (HRV, ms), and HRC (Sample Entropy, unitless), were measured using a portable non-invasive hemodynamic monitor during postural changes, a mass casualty (MASCAL) situational training exercise (STX) using live tissue, a mock trauma (MT) STX using moulaged humans, and/or physical exercise.

RESULTS

Baseline HR, CO, HRV, and HRC averaged 72 ± 11b/min, 5.6 ± 1.2 L/min, 48 ± 24 ms, and 1.9 ± 0.5 (unitless), respectively. Supine to sitting to standing caused minimal changes. Before the MASCAL or MT, HR and CO both increased to ~125% baseline, whereas HRV and HRC both decreased to ~75% baseline. Those values all changed an additional ~5% during the MASCAL, but an additional 10 to 30% during the MT. With physical exercise, HR and CO increased to >200% baseline, while HRV and HRC both decreased to 40 to 60% baseline; these changes were comparable to those caused by the MT. All the changes were P < 0.05.

CONCLUSIONS

Various forms of HPA stress during Forward Surgical Team STXs can be objectively quantitated continuously in real time with a portable non-invasive monitor. Differences from resting baseline indicate stress anticipating an impending STX whereas differences between average and peak responses indicate the relative stress between STXs. Monitoring HRC could prove useful to field commanders to rapidly and objectively assess the readiness status of troops during STXs or repeated operational missions. In the future, health care systems and regulatory bodies will likely be held accountable for stress in their trainees and/or obliged to develop wellness options and standardize efforts to ameliorate burnout, so HRC metrics might have a role, as well.

Pre-ictal heart rate variability alterations in focal onset seizures and response to vagus nerve stimulation

PURPOSE

Vagus nerve stimulation (VNS) is an effective and well-known treatment for drug resistant epilepsy (DRE) patients since 1997, yet prediction of treatment response before implantation is subject of ongoing research. Neuroimaging and neurophysiological studies investigating the vagal afferent network in resting state documented that differences in between epilepsy patients were related to treatment response. This study investigated whether an event-related parameter, pre-ictal heart rate variability (HRV) is associated with response to VNS therapy.

METHODS

DRE patients underwent video-electroencephalography (EEG) recording before VNS implantation. HRV parameters (time, non-linear and frequency domain) were assessed for every seizure during two 10 min timeframes: baseline (60 min before seizure onset) and pre-ictal (10 min before seizure onset). Pre-ictal HRV parameter alterations were correlated with VNS response after one year of VNS therapy and seizure characteristics (temporal/extratemporal, left/right or bilateral).

RESULTS

104 seizures from 22 patients were evaluated. Eleven patients were VNS responders with a seizure frequency reduction of ≥ 50 % after one year of VNS. In VNS responders no changes in HRV parameters were found while in VNS non-responders the time domain and non-linear HRV variables decreased significantly (p = 0.024, p = 0.005, p = 0.005) during the pre-ictal time frame. 10/11 VNS non-responders had a seizure lateralization to the left compared to 4/11 VNS responders.

CONCLUSION

VNS non-responders were characterized by a significant decrease of pre-ictal HRV (time domain/non-linear variables) suggesting a sudden autonomic imbalance probably due to an impaired central autonomic function that makes it at the same time unlikely to respond to VNS.

Heart Rate Variability as a Predictor of Mortality in Traumatic Brain Injury: A Systematic Review and Meta-Analysis

OBJECTIVE

To systematically review the medical literature to determine the utility of heart rate variability in predicting mortality for moderate to severe traumatic brain injury.

METHODS

A search for randomized controlled trials, nonrandomized trials, and prospective and retrospective cohort studies was carried out using PubMed, SCOPUS, Cochrane Central Register of Controlled Trials, MEDLINE, and EMBASE. Reference lists of included studies were also searched to identify potentially eligible studies.

RESULTS

Five articles comprising 542 patients met inclusion criteria. Heart rate variability as low-frequency/high-frequency ratio (area under the curve [AUC] receiver operating characteristic [ROC]) for predicting mortality was found to be statistically significant (AUC ROC 0.810, P < 0.001) with high heterogeneity (I² = 61.98%, P = 0.032). Meta-analysis of low-frequency/high-frequency ratio, High frequency peak, and total power were statistically significant for predicting mortality. Odd's ratio for predicting mortality for LF/HF ratio, HF peak, and TP were 16.17, 19.09, 22.59 respectively. High-frequency peak in predicting mortality showed an AUC ROC of 0.986 (P ≤ 0.001) with a low level of heterogeneity. Total power (TP) showed an AUC ROC of 0.93 (P < 0.001) in predicting mortality with a high level of heterogeneity (I² = 83.16%, P = 0.002). Funnel plot analysis to assess the presence of publication bias for TP showed a high level of heterogeneity and asymmetry among studies.

CONCLUSIONS

This meta-analysis predicted high mortality based on odds ratio for variables low-frequency/high-frequency ratio, high-frequency peak, and TP. However, the statistical analysis was weakened owing to the high level of heterogeneity in the included studies. Further research is needed to generate high-quality recommendations regarding heart rate variability as a predictor of mortality after traumatic brain injury.

Heart rhythm complexity as predictors for the prognosis of end-stage renal disease patients undergoing hemodialysis

BACKGROUND

Heart rhythm complexity, a measure of heart rate dynamics and a risk predictor in various clinical diseases, has not been systematically studied in patients with end-stage renal disease. The aim of this study is to investigate the heart rhythm complexity and its prognostic value for mortality in end-stage renal disease patients undergoing hemodialysis.

METHODS

To assess heart rhythm complexity and conventional heart rate variability measures, 4-h continuous electrocardiography for a retrospective cohort of 202 ostensibly healthy control subjects and 51 hemodialysis patients with end-stage renal disease were analyzed. Heart rhythm complexity was quantified by the complexity index from the measurement of the multiscale entropy profile.

RESULTS

During a follow-up of 13 months, 8 people died in the patient group. Values of either traditional heart rate variability measurements or complexity indices were found significantly lower in patients than those in healthy controls. In addition, the complexity indices (Area 1-5, Area 6-15 and Area 6-20) in the mortality group were significantly lower than those in the survival group, while there were no significant differences in traditional heart rate variability parameters between the two groups. In receiver operating characteristic curve analysis, Area 6-20 (AUC = 0.895, p < 0.001) showed the strongest predictive power between mortality and survival groups.

CONCLUSION

The results suggest that heart rhythm complexity is impaired for patients with end-stage renal disease. Furthermore, the complexity index of heart rate variability quantified by multiscale entropy may be a powerful independent predictor of mortality in end-stage renal disease patients undergoing hemodialysis.

Cardiac surgery does not lead to loss of oscillatory components in circulatory signals

The circulatory system is oscillatory in its nature. Oscillatory components linked to physiological processes and underlying regulatory mechanisms are identifiable in circulatory signals. Autonomic regulation is essential for the system's ability to deal with external exposure, and the integrity of oscillations may be considered a hallmark of a healthy system. Loss of complexity is seen as a consequence of several diseases and aging. Heart rate variability is known to decrease after cardiac surgery and remain reduced for up to 6 months. Oscillatory components of circulatory signals are linked to the system's overall complexity. We therefore hypothesize that the frequency distributions of circulatory signals show loss of oscillatory components after cardiac surgery and that the observed changes persist. We investigated the development of the circulatory frequency distributions of eight patients undergoing cardiac surgery by extracting three time series from conventional blood pressure and electrocardiography recordings: systolic blood pressure, heart rate, and amplitude of the electrocardiogram's R-wave. Four 30-min selections, representing key events of the perioperative course, were analyzed with the continuous wavelet transform, and average wavelet power spectra illustrated the circulatory frequency distributions. We identified oscillatory components in all patients and variables. Contrary to our hypothesis, they were randomly distributed through frequencies, patients, and situations, thus, not representing any reduction in the overall complexity. One patient showed loss of a 25-s oscillation after surgery. We present a case where noise is misclassified as an oscillation, raising questions about the robustness of such analyses.

Preoperative Blood Pressure Complexity Indices as a Marker for Frailty in Patients Undergoing Cardiac Surgery

OBJECTIVE

Frailty, a state of decreased physiological reserve, increases the risk of adverse outcomes. There is no standard tool for frailty during perioperative period. Autonomic dysfunction, an underlying process in frailty, could result in hemodynamic fluctuations. Complexity, the physiological adaptability of a system can quantify these fluctuations. The authors hypothesized that complexity could be a marker for frailty and explored their relationship in cardiac surgical patients.

DESIGN

Prospective, observational study.

SETTING

Single-center teaching hospital.

PARTICIPANTS

Three hundred and sixty-four adult patients undergoing cardiac surgery.

INTERVENTION

None.

MEASUREMENTS AND MAIN RESULTS

Preoperative beat-to-beat systolic arterial pressure (SAP) and mean arterial pressure (MAP) time series were obtained. Complexity indices were calculated using multiscale entropy (MSE) analysis. Frailty was assessed from: age >70 years, body mass index <18.5, hematocrit <35%, albumin <3.4 g/dL, and creatinine >2.0 mg/dL. The association between complexity indices and frailty was explored by logistic regression and predictive ability by C-statistics. In total, 190 (52%) patients had frailty. The complexity index (MSE_Σ) median (quartile 1, quartile 3) of SAP and MAP time series decreased significantly in frail patients (SAP: 8.32 [7.27, 9.24] v 9.13 [8.00, 9.72], p < 0.001 and MAP: 8.56 [7.56; 9.27] v 9.18 [8.26; 9.83], p < 0.001). MSE _Σ demonstrated a fair predictive ability of frailty (C-statistic: SAP 0.62 and MAP 0.64).

CONCLUSION

Preoperative BP complexity indices correlate and predict frailty. Impaired autonomic control is the underlying mechanism to explain this finding. A simple automated measure of preoperative BP complexity in the surgeon's office has the potential to reliably assess frailty.

Genomics of Cardiovascular Measures of Autonomic Tone

The autonomic nervous system exerts broad control over the involuntary functions of the human body through complex equilibrium between sympathetic and parasympathetic tone. Imbalance in this equilibrium is associated with a multitude of cardiovascular outcomes, including mortality. The cardiovascular static state of this equilibrium can be quantified using physiological parameters such as heart rate (HR), blood pressure, and by spectral analysis of HR variability. Here, we review the current state of knowledge of the genetic background of cardiovascular measurements of autonomic tone. For most parameters of autonomic tone, a large portion of variability is explained by genetic heritability. Many of the static parameters of autonomic tone have also been studied through candidate-gene approach, yielding some insight into how genotypes of adrenergic receptors affect variables such as HR. Genome-wide approaches in large cohorts similarly exist for static variables such as HR and blood pressure but less is known about the genetic background of the dynamic and more specific measurements, such as HR variability. Furthermore, because most autonomic measures are likely polygenic, pathway analyses and modeling of polygenic effects are critical. Future work will hopefully explain the control of autonomic tone and guide individualized therapeutic interventions.

Autonomic Dysfunction and Associations with Functional and Neurophysiological Outcome in Moderate/Severe Traumatic Brain Injury: A Scoping Review

The quantification and objective documentation of autonomic dysfunction in traumatic brain injury (TBI) is neither well studied nor extensively validated. Most of the descriptions of autonomic dysfunction in the literature are in the form of vague non-specific clinical manifestations. Few studies propose the use of objective measures of assessing the extent of autonomic dysfunction to link them to the outcome of TBI. Our goal was to perform a scoping systematic review of the literature on the objective documentation of autonomic dysfunction in terms of functional and physiological variables to be linked to outcome of TBI. PubMed/MEDLINE^®, BIOSIS, Scopus, Embase, Cochrane Libraries, and Global Health databases were searched. Two reviewers independently screened the results. Full texts for citations passing this initial screen were obtained. Inclusion and exclusion criteria were applied to each article to obtain final articles for review. The initial search yielded 2619 citations. Of 69 articles selected for final review, 14 were chosen based on the inclusion and exclusion criteria and are included in the results of this article. 9 of these articles assessed autonomic dysfunction using functional variables and 7 assessed autonomic dysfunction using physiological variables. Some studies included both functional and physiological variables. Of the nine studies linking autonomic dysfunction to functional variables, nine included heart rate variability (HRV), three included baroreflex sensitivity (BRS), and two included blood pressure variability (BPV). A total of 2714 adult patients were studied. Although the nature of association between autonomic dysfunction and outcome is unclear, the objective quantification of autonomic dysfunction seems to be associated with global patient outcome and other neurophysiological measures. Further studies are needed to validate its use and explore the underlying molecular mechanisms of the described associations.

Heart Rate Variability as an Indicator of Nociceptive Pain in Disorders of Consciousness?

CONTEXT

Heart rate variability is thought to reflect the affective and physiological aspects of pain and is emerging as a possible descriptor of the functional brain organization contributing to homeostasis.

OBJECTIVES

To investigate whether the short-term Complexity Index (CIs), a measure of heart rate variability complexity is useful to discriminate responses to potentially noxious and nonnoxious stimulation in patients with different levels of consciousness.

METHODS

Twenty-two patients (11 minimally conscious state [MCS], 11 vegetative state/unresponsive wakefulness syndrome [VS/UWS]) and 14 healthy controls (HC) were enrolled. We recorded the electrocardiographic response and calculated the CIs before (baseline), during, and after nonnoxious and noxious stimulation. Mann-Whitney and Wilcoxon's tests were used to investigate differences in CIs according to the level of consciousness (i.e., HC vs. patients and VS/UWS vs. MCS) and the three conditions (i.e., baseline, nonnoxious, noxious). The correlation between the three conditions and the Coma Recovery Scale-Revised was investigated by Spearman's correlations.

RESULTS

We observed higher CIs values in HC as compared with patients during the baseline (P < 0.034) and after the noxious stimulation (P < 0.0001). We also found higher values in MCS versus VS/UWS patients after the noxious condition (P < 0.001) and lower values in the noxious versus nonnoxious condition solely for the VS/UWS group (P < 0.007). A correlation was found between CIs in noxious condition and Coma Recovery Scale-Revised scores.

CONCLUSION

Our results suggest a less complex autonomic response to noxious stimuli in VS/UWS patients. Such method may help to better understand sympathovagal response to potentially painful stimulation in brain-injured patients.

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.

Water exercise in coronary artery disease patients, effects on heart rate variability, and body composition: A randomized controlled trial

BACKGROUND AND PURPOSE

It is important to assess the effectiveness and efficiency of water-based training programs in order to prescribe it as an alternative in cardiac rehabilitation for patients who have coronary artery disease (CAD). In these patients, autonomic dysfunction is an important physiological change strongly associated with adverse outcomes, morbidity, and mortality. Given that the beneficial effects of physical training in CAD patients have been traditionally evidenced with programs involving land-based aerobic exercises, this study aims to evaluate the effects of water aerobic exercise training (WAET) on the autonomic modulation of heart rate (HR) and body composition, in the rehabilitation of CAD patients.

METHODS

Twenty-six male subjects with CAD were randomly divided into a training group (n = 14), submitted to the WAET, and a control group (n = 12). The WAET consisted of 3 weekly sessions on alternate days, totalling 48 sessions. The analysis of HR variability was used to evaluate the autonomic modulation of HR, from the recording of R-R intervals for 15 min, at rest in the supine position, and the body composition was evaluated through the bioelectrical impedance analysis.

RESULTS

Only the training group participants had improvement in the HR variability indices; patterns without variation decreased (0V, p = .005) and an increase of patterns of two different variations (p < .001), Shannon entropy (p = .02), and normalized conditional entropy (p = .03), whereas the control group had an increase of 0V (p = .04) and a decrease of normalized conditional entropy (p = .01). All body composition variables remained unchanged.

CONCLUSIONS

The WAET protocol improved the cardiac autonomic modulation of patients with CAD and can be considered as exercise training strategy in cardiac rehabilitation programs.

Complexity of brain signals is associated with outcome in preterm infants

A characteristic feature of complex healthy biological systems is the ability to react and adapt to minute changes in the environment. This 'complexity' manifests itself in highly irregular patterns of various physiological measurements. Here, we apply Multiscale Entropy (MSE) analysis to assess the complexity of systemic and cerebral near-infrared spectroscopy (NIRS) signals in a cohort of 61 critically ill preterm infants born at median (range) gestational age of 26 (23-31) weeks, before 24 h of life. We further correlate the complexity of these parameters with brain injury and mortality. Lower complexity index (CoI) of oxygenated haemoglobin (HbO₂), deoxygenated haemoglobin (Hb) and tissue oxygenation index (TOI) were observed in those infants who developed intraventricular haemorrhage (IVH) compared to those who did not (P = 0.002, P = 0.010 and P = 0.038, respectively). Mean CoI of HbO₂, Hb and total haemoglobin index (THI) were lower in those infants who died compared to those who survived (P = 0.012, P = 0.004 and P = 0.003, respectively). CoI-HbO₂ was an independent predictor of IVH (P = 0.010). Decreased complexity of brain signals was associated with mortality and brain injury. Measurement of brain signal complexity in preterm infants is feasible and could represent a significant advance in the brain-oriented care.

The neurovascular complexity index as a potential indicator of traumatic brain injury severity: A case-series study

BACKGROUND

Multimodal monitoring of brain physiology following a traumatic brain injury (TBI) shows promise as a strategy to improve management and outcomes of TBI patients within civilian and military trauma. Valid and reliable measures of different aspects of brain physiology following a TBI could prove critical to accurately capturing these changes.

METHODS

Using a case-series design with a control subject group comparison, we evaluated a new proprietary algorithm called the Neurovascular Complexity Index (NCI) using transcranial Doppler to noninvasively obtain measures of cerebral blood flow variability. Baseline NCI data from 169 control subjects were compared with 12 patients with moderate to severe TBI.

RESULTS

Patients with TBI exhibited significantly greater mean and variability in NCI scores compared with control subjects (F = 195.48; p < 0.001). The mean absolute deviation (MAD) of NCI scores increased significantly and in a monotonic fashion with severity of injury, where control subjects exhibited a small MAD of 0.44, patients with moderate TBI had a higher MAD of 4.20, and patients with severe TBI had an MAD of 6.51 (p < 0.001).

CONCLUSIONS

Advancement in multimodal monitoring of TBI patients is important in reducing the potential risk of secondary injury. This study reports results indicating that a new noninvasive quantifiable assessment of TBI based on a noninvasive measure of cerebral blood flow variability shows potential for continuous monitoring and early identification of brain-injured patients, deployable in far-forward military environments, to better inform individualized management.

LEVEL OF EVIDENCE

Case series, level IV.

The role of autonomic dysfunction in predicting 1-year mortality after liver transplantation

BACKGROUND & AIMS

Model for end-stage liver disease (MELD) score has been extensively used to prioritize patients for liver transplantation and determine their prognosis, but with limited predictive value. Autonomic dysfunction may correlate with increased mortality after liver transplant. In this study, two autonomic biomarkers, complexity and deceleration capacity, were added to the predicting model for 1-year mortality after liver transplantation.

METHODS

In all, 30 patients with end-stage liver diseases awaiting liver transplantation were included. Complexity and deceleration capacity were calculated by multi-scale entropy and phase-rectified signal averaging, respectively. Different combinations of autonomic factors and MELD score were used to predict mortality rate of liver transplant after 1-year follow-up. Receiver-operating characteristics curve analysis was performed to determine clinical predictability. Area under the receiver-operating characteristics curve represents the overall accuracy.

RESULTS

The 1-year mortality rate was 16.7% (5/30). The overall accuracy of MELD score used for predicting mortality after liver transplantation was 0.752. By adding complexity and deceleration capacity into the predicting model, the accuracy increased to 0.912. Notably, the accuracy of the prediction using complexity and deceleration capacity alone was 0.912.

CONCLUSION

Complexity and deceleration capacity, which represent different dynamical properties of a human autonomic system, are critical factors for predicting mortality rate of liver transplantation. We recommend that these pre-operative autonomic factors may be helpful as critical adjuncts to predicting model of mortality rate in prioritizing organ allocation.

Systolic blood pressure variability in patients with early severe sepsis or septic shock: a prospective cohort study

Background

Severe sepsis and septic shock are often lethal syndromes, in which the autonomic nervous system may fail to maintain adequate blood pressure. Heart rate variability has been associated with outcomes in sepsis. Whether systolic blood pressure (SBP) variability is associated with clinical outcomes in septic patients is unknown. The propose of this study is to determine whether variability in SBP correlates with vasopressor independence and mortality among septic patients.

Methods

We prospectively studied patients with severe sepsis or septic shock, admitted to an intensive care unit (ICU) with an arterial catheter. We analyzed SBP variability on the first 5-min window immediately following ICU admission. We performed principal component analysis of multidimensional complexity, and used the first principal component (PC₁) as input for Firth logistic regression, controlling for mean systolic pressure (SBP) in the primary analyses, and Acute Physiology and Chronic Health Evaluation (APACHE) II score or NEE dose in the ancillary analyses. Prespecified outcomes were vasopressor independence at 24 h (primary), and 28-day mortality (secondary).

Results

We studied 51 patients, 51% of whom achieved vasopressor independence at 24 h. Ten percent died at 28 days. PC₁ represented 26% of the variance in complexity measures. PC₁ was not associated with vasopressor independence on Firth logistic regression (OR 1.04; 95% CI: 0.93–1.16; p = 0.54), but was associated with 28-day mortality (OR 1.16, 95% CI: 1.01–1.35, p = 0.040).

Conclusions

Early SBP variability appears to be associated with 28-day mortality in patients with severe sepsis and septic shock.

Electronic supplementary material

The online version of this article (doi:10.1186/s12871-017-0377-4) contains supplementary material, which is available to authorized users.

Serial heart rhythm complexity changes in patients with anterior wall ST segment elevation myocardial infarction

Heart rhythm complexity analysis has been shown to have good prognostic power in patients with cardiovascular disease. The aim of this study was to analyze serial changes in heart rhythm complexity from the acute to chronic phase of acute myocardial infarction (MI). We prospectively enrolled 27 patients with anterior wall ST segment elevation myocardial infarction (STEMI) and 42 control subjects. In detrended fluctuation analysis (DFA), the patients had significantly lower DFAα2 in the acute stage (within 72 hours) and lower DFAα1 at 3 months and 12 months after MI. In multiscale entropy (MSE) analysis, the patients had a lower slope 5 in the acute stage, which then gradually increased during the follow-up period. The areas under the MSE curves for scale 1 to 5 (area 1–5) and 6 to 20 (area 6–20) were lower throughout the chronic stage. Area 6–20 had the greatest discriminatory power to differentiate the post-MI patients (at 1 year) from the controls. In both the net reclassification improvement and integrated discrimination improvement models, MSE parameters significantly improved the discriminatory power of the linear parameters to differentiate the post-MI patients from the controls. In conclusion, the patients with STEMI had serial changes in cardiac complexity.

Heart rhythm complexity impairment in patients undergoing peritoneal dialysis

Cardiovascular disease is one of the leading causes of death in patients with advanced renal disease. The objective of this study was to investigate impairments in heart rhythm complexity in patients with end-stage renal disease. We prospectively analyzed 65 patients undergoing peritoneal dialysis (PD) without prior cardiovascular disease and 72 individuals with normal renal function as the control group. Heart rhythm analysis including complexity analysis by including detrended fractal analysis (DFA) and multiscale entropy (MSE) were performed. In linear analysis, the PD patients had a significantly lower standard deviation of normal RR intervals (SDRR) and percentage of absolute differences in normal RR intervals greater than 20 ms (pNN20). Of the nonlinear analysis indicators, scale 5, area under the MSE curve for scale 1 to 5 (area 1–5) and 6 to 20 (area 6–20) were significantly lower than those in the control group. In DFA anaylsis, both DFA α1 and DFA α2 were comparable in both groups. In receiver operating characteristic curve analysis, scale 5 had the greatest discriminatory power for two groups. In both net reclassification improvement model and integrated discrimination improvement models, MSE parameters significantly improved the discriminatory power of SDRR, pNN20, and pNN50. In conclusion, PD patients had worse cardiac complexity parameters. MSE parameters are useful to discriminate PD patients from patients with normal renal function.

How Can Music Influence the Autonomic Nervous System Response in Patients with Severe Disorder of Consciousness?

Activations to pleasant and unpleasant musical stimuli were observed within an extensive neuronal network and different brain structures, as well as in the processing of the syntactic and semantic aspects of the music. Previous studies evidenced a correlation between autonomic activity and emotion evoked by music listening in patients with Disorders of Consciousness (DoC). In this study, we analyzed retrospectively the autonomic response to musical stimuli by mean of normalized units of Low Frequency (nuLF) and Sample Entropy (SampEn) of Heart Rate Variability (HRV) parameters, and their possible correlation to the different complexity of four musical samples (i.e., Mussorgsky, Tchaikovsky, Grieg, and Boccherini) in Healthy subjects and Vegetative State/Unresponsive Wakefulness Syndrome (VS/UWS) patients. The complexity of musical sample was based on Formal Complexity and General Dynamics parameters defined by Imberty's semiology studies. The results showed a significant difference between the two groups for SampEn during the listening of Mussorgsky's music and for nuLF during the listening of Boccherini and Mussorgsky's music. Moreover, the VS/UWS group showed a reduction of nuLF as well as SampEn comparing music of increasing Formal Complexity and General Dynamics. These results put in evidence how the internal structure of the music can change the autonomic response in patients with DoC. Further investigations are required to better comprehend how musical stimulation can modify the autonomic response in DoC patients, in order to administer the stimuli in a more effective way.

Optical monitoring of stress-related changes in the brain tissues and vessels associated with hemorrhagic stroke in newborn rats

Stress is a major factor for a risk of cerebrovascular catastrophes. Studying of mechanisms underlying stress-related brain-injures in neonates is crucial for development of strategy to prevent of neonatal stroke. Here, using a model of sound-stress-induced intracranial hemorrhages in newborn rats and optical methods, we found that cerebral veins are more sensitive to the deleterious effect of stress than arteries and microvessels. The development of venous insufficiency with decreased blood outflow from the brain accompanied by hypoxia, reduction of complexity of venous blood flow and high production of beta-arrestin-1 are possible mechanisms responsible for a risk of neonatal hemorrhagic stroke.

Reversible heart rhythm complexity impairment in patients with primary aldosteronism

Excess aldosterone secretion in patients with primary aldosteronism (PA) impairs their cardiovascular system. Heart rhythm complexity analysis, derived from heart rate variability (HRV), is a powerful tool to quantify the complex regulatory dynamics of human physiology. We prospectively analyzed 20 patients with aldosterone producing adenoma (APA) that underwent adrenalectomy and 25 patients with essential hypertension (EH). The heart rate data were analyzed by conventional HRV and heart rhythm complexity analysis including detrended fluctuation analysis (DFA) and multiscale entropy (MSE). We found APA patients had significantly decreased DFAα2 on DFA analysis and decreased area 1-5, area 6-15, and area 6-20 on MSE analysis (all p < 0.05). Area 1-5, area 6-15, area 6-20 in the MSE study correlated significantly with log-transformed renin activity and log-transformed aldosterone-renin ratio (all p < = 0.01). The conventional HRV parameters were comparable between PA and EH patients. After adrenalectomy, all the altered DFA and MSE parameters improved significantly (all p < 0.05). The conventional HRV parameters did not change. Our result suggested that heart rhythm complexity is impaired in APA patients and this is at least partially reversed by adrenalectomy.

Higher vagal activity as related to survival in patients with advanced breast cancer: an analysis of autonomic dysregulation

OBJECTIVE

High levels of high-frequency heart rate variability (HF-HRV), related to parasympathetic-nervous-system functioning, have been associated with longer survival in patients with myocardial infarction and acute trauma and in patients undergoing palliative care. From animal studies linking higher vagal activity with better immune system functioning and reduced metastases, we hypothesized that higher HF-HRV would predict longer survival in patients with metastatic or recurrent breast cancer (MRBC).

METHODS

Eighty-seven patients with MRBC participated in a laboratory task including a 5-minute resting baseline electrocardiogram. HF-HRV was computed as the natural logarithm of the summed power spectral density of R-R intervals (0.15-0.50 Hz). In this secondary analysis of a study testing whether diurnal cortisol slope predicted survival, we tested the association between resting baseline HF-HRV on survival using Cox proportional hazards models.

RESULTS

A total of 50 patients died during a median follow-up of 7.99 years. Higher baseline HF-HRV predicted significantly longer survival, with a hazard ratio of 0.75 (95% confidence interval = 0.60-0.92, p = .006). Visceral metastasis status and baseline heart rate were related to both HF-HRV and survival. However, a combination of HF-HRV and heart rate further improved survival prediction, with a hazard ratio of 0.64 (95% confidence interval = 0.48-0.85, p = .002).

CONCLUSIONS

Vagal activity of patients with MRBC strongly predicted their survival, extending the known predictive window of HF-HRV in cancer beyond palliative care. Vagal activity can be altered by behavioral, pharmacological, and surgical interventions and may be a promising target for extending life expectancy in patients with metastasizing cancer.

Sample entropy predicts lifesaving interventions in trauma patients with normal vital signs

INTRODUCTION

Heart rate complexity, commonly described as a "new vital sign," has shown promise in predicting injury severity, but its use in clinical practice is not yet widely adopted. We previously demonstrated the ability of this noninvasive technology to predict lifesaving interventions (LSIs) in trauma patients. This study was conducted to prospectively evaluate the utility of real-time, automated, noninvasive, instantaneous sample entropy (SampEn) analysis to predict the need for an LSI in a trauma alert population presenting with normal vital signs.

METHODS

Prospective enrollment of patients who met criteria for trauma team activation and presented with normal vital signs was conducted at a level I trauma center. High-fidelity electrocardiogram recording was used to calculate SampEn and SD of the normal-to-normal R-R interval (SDNN) continuously in real time for 2 hours with a portable, handheld device. Patients who received an LSI were compared to patients without any intervention (non-LSI). Multivariable analysis was performed to control for differences between the groups. Treating clinicians were blinded to results.

RESULTS

Of 129 patients enrolled, 38 (29%) received 136 LSIs within 24 hours of hospital arrival. Initial systolic blood pressure was similar in both groups. Lifesaving intervention patients had a lower Glasgow Coma Scale. The mean SampEn on presentation was 0.7 (0.4-1.2) in the LSI group compared to 1.5 (1.1-2.0) in the non-LSI group (P < .0001). The area under the curve with initial SampEn alone was 0.73 (95% confidence interval [CI], 0.64-0.81) and increased to 0.93 (95% CI, 0.89-0.98) after adding sedation to the model. Sample entropy of less than 0.8 yields sensitivity, specificity, negative predictive value, and positive predictive value of 58%, 86%, 82%, and 65%, respectively, with an overall accuracy of 76% for predicting an LSI. SD of the normal-to-normal R-R interval had no predictive value.

CONCLUSIONS

In trauma patients with normal presenting vital signs, decreased SampEn is an independent predictor of the need for LSI. Real-time SampEn analysis may be a useful adjunct to standard vital signs monitoring. Adoption of real-time, instantaneous SampEn monitoring for trauma patients, especially in resource-constrained environments, should be considered.

Complexity of heart rate variability predicts outcome in intensive care unit admitted patients with acute stroke

BACKGROUND

Heart rate variability (HRV) has been proposed as a predictor of acute stroke outcome. This study aimed to evaluate the predictive value of a novel non-linear method for analysis of HRV, multiscale entropy (MSE) and outcome of patients with acute stroke who had been admitted to the intensive care unit (ICU).

METHODS

The MSE of HRV was analysed from 1 h continuous ECG signals in ICU-admitted patients with acute stroke and controls. The complexity index was defined as the area under the MSE curve (scale 1-20). A favourable outcome was defined as modified Rankin scale 0-2 at 3 months after stroke.

RESULTS

The trends of MSE curves in patients with atrial fibrillation (AF) (n=77) were apparently different from those in patients with non-AF stroke (n=150) and controls (n=60). In addition, the values of complexity index were significantly lower in the patients with non-AF stroke than in the controls (25.8±.3 vs. 32.3±4.3, p<0.001). After adjustment for clinical variables, patients without AF who had a favourable outcome were significantly related to higher complexity index values (OR=1.15, 95% CI 1.07 to 1.25, p<0.001). Importantly, the area under the receiver operating characteristic curve for predicting a favourable outcome of patients with non-AF stroke from clinical parameters was 0.858 (95% CI 0.797 to 0.919) and significantly improved to 0.903 (95% CI 0.853 to 0.954) after adding on the parameter of complexity index values (p=0.020).

CONCLUSIONS

In ICU-admitted patients with acute stroke, early assessment of the complexity of HRV by MSE can help in predicting outcomes in patients without AF.

Multi-scale symbolic entropy analysis provides prognostic prediction in patients receiving extracorporeal life support

INTRODUCTION

Extracorporeal life support (ECLS) can temporarily support cardiopulmonary function, and is occasionally used in resuscitation. Multi-scale entropy (MSE) derived from heart rate variability (HRV) is a powerful tool in outcome prediction of patients with cardiovascular diseases. Multi-scale symbolic entropy analysis (MSsE), a new method derived from MSE, mitigates the effect of arrhythmia on analysis. The objective is to evaluate the prognostic value of MSsE in patients receiving ECLS. The primary outcome is death or urgent transplantation during the index admission.

METHODS

Fifty-seven patients receiving ECLS less than 24 hours and 23 control subjects were enrolled. Digital 24-hour Holter electrocardiograms were recorded and three MSsE parameters (slope 5, Area 6-20, Area 6-40) associated with the multiscale correlation and complexity of heart beat fluctuation were calculated.

RESULTS

Patients receiving ECLS had significantly lower value of slope 5, area 6 to 20, and area 6 to 40 than control subjects. During the follow-up period, 29 patients met primary outcome. Age, slope 5, Area 6 to 20, Area 6 to 40, acute physiology and chronic health evaluation II score, multiple organ dysfunction score (MODS), logistic organ dysfunction score (LODS), and myocardial infarction history were significantly associated with primary outcome. Slope 5 showed the greatest discriminatory power. In a net reclassification improvement model, slope 5 significantly improved the predictive power of LODS; Area 6 to 20 and Area 6 to 40 significantly improved the predictive power in MODS. In an integrated discrimination improvement model, slope 5 added significantly to the prediction power of each clinical parameter. Area 6 to 20 and Area 6 to 40 significantly improved the predictive power in sequential organ failure assessment.

CONCLUSIONS

MSsE provides additional prognostic information in patients receiving ECLS.

Decreased complexity of glucose dynamics in diabetes: evidence from multiscale entropy analysis of continuous glucose monitoring system data

Parameters of glucose dynamics recorded by the continuous glucose monitoring system (CGMS) could help in the control of glycemic fluctuations, which is important in diabetes management. Multiscale entropy (MSE) analysis has recently been developed to measure the complexity of physical and physiological time sequences. A reduced MSE complexity index indicates the increased repetition patterns of the time sequence, and, thus, a decreased complexity in this system. No study has investigated the MSE analysis of glucose dynamics in diabetes. This study was designed to compare the complexity of glucose dynamics between the diabetic patients (n = 17) and the control subjects (n = 13), who were matched for sex, age, and body mass index via MSE analysis using the CGMS data. Compared with the control subjects, the diabetic patients revealed a significant increase (P < 0.001) in the mean (diabetic patients 166.0 ± 10.4 vs. control subjects 93.3 ± 1.5 mg/dl), the standard deviation (51.7 ± 4.3 vs. 11.1 ± 0.5 mg/dl), and the mean amplitude of glycemic excursions (127.0 ± 9.2 vs. 27.7 ± 1.3 mg/dl) of the glucose levels; and a significant decrease (P < 0.001) in the MSE complexity index (5.09 ± 0.23 vs. 7.38 ± 0.28). In conclusion, the complexity of glucose dynamics is decreased in diabetes. This finding implies the reactivity of glucoregulation is impaired in the diabetic patients. Such impairment presenting as an increased regularity of glycemic fluctuating pattern could be detected by MSE analysis. Thus, the MSE complexity index could potentially be used as a biomarker in the monitoring of diabetes.

The influence of anesthesia on heart rate complexity during elective and urgent surgery in 128 patients

BACKGROUND

As an emerging "new vital sign," heart rate complexity (by sample entropy [SampEn]) has been shown to be a useful trauma triage tool by predicting occult physiologic compromise and need for life-saving interventions. Sample entropy may be confounded by anesthesia possibly limiting its value intraoperatively. We investigated the effects of anesthesia on SampEn during elective and urgent surgical procedures. We hypothesized that SampEn is reduced by general anesthesia.

METHODS

With institutional review board-approved waiver of informed consent, 128 patients undergoing elective or urgent general surgery were prospectively enrolled. Real-time heart rate complexity was calculated using SampEn through electrocardiogram recordings of 200 consecutive beats in a continuous sliding-window fashion. We recorded SampEn starting 10 minutes before induction until 10 minutes after emergence from anesthesia. The time before induction of anesthesia was categorized as period 1, the time after induction and before emergence as period 2 (intraoperative), and the time after emergence as period 3. We analyzed SampEn changes as patients moved between the different periods and made 3 comparisons: from period 1 with period 2 (comparison A), from period 2 with period 3 (comparison B). We also compared period 1 with period 3 SampEn (comparison C).

RESULTS

The mean SampEn value for all patients before induction of anesthesia was 1.55 ± 0.58. In each 1 of the 3, comparisons there was a decline in SampEn. Comparison A had a mean decrease of 0.53 ± 0.55 (P < .0001), comparison B had a decrease of 0.13 ± 0.52 (P < .0051), and the mean SampEn difference for comparison C was 0.66 ± 0.53 (P < .0001). Certain pharmacologics had significant effect on SampEn as did need for urgent surgery and American Society of Anesthesiologists class.

CONCLUSION

Sample entropy decreases after induction of anesthesia and continues to decrease even immediately after emergence in patients without any immediately life-threatening conditions. This finding may complicate interpretation low complexity as a predictor of life-saving interventions in patients in the perioperative period.

A physiological time series dynamics-based approach to patient monitoring and outcome prediction

Cardiovascular variables such as heart rate (HR) and blood pressure (BP) are regulated by an underlying control system, and therefore, the time series of these vital signs exhibit rich dynamical patterns of interaction in response to external perturbations (e.g., drug administration), as well as pathological states (e.g., onset of sepsis and hypotension). A question of interest is whether "similar" dynamical patterns can be identified across a heterogeneous patient cohort, and be used for prognosis of patients' health and progress. In this paper, we used a switching vector autoregressive framework to systematically learn and identify a collection of vital sign time series dynamics, which are possibly recurrent within the same patient and may be shared across the entire cohort. We show that these dynamical behaviors can be used to characterize the physiological "state" of a patient. We validate our technique using simulated time series of the cardiovascular system, and human recordings of HR and BP time series from an orthostatic stress study with known postural states. Using the HR and BP dynamics of an intensive care unit (ICU) cohort of over 450 patients from the MIMIC II database, we demonstrate that the discovered cardiovascular dynamics are significantly associated with hospital mortality (dynamic modes 3 and 9, p=0.001, p=0.006 from logistic regression after adjusting for the APACHE scores). Combining the dynamics of BP time series and SAPS-I or APACHE-III provided a more accurate assessment of patient survival/mortality in the hospital than using SAPS-I and APACHE-III alone (p=0.005 and p=0.045). Our results suggest that the discovered dynamics of vital sign time series may contain additional prognostic value beyond that of the baseline acuity measures, and can potentially be used as an independent predictor of outcomes in the ICU.

Validation of pulse rate variability as a surrogate for heart rate variability in chronically instrumented rabbits

Heart rate variability (HRV) is a function of cardiac autonomic tone that is widely used in both clinical and animal studies. In preclinical studies, HRV measures are frequently derived using the arterial pulse waveform from an implanted pressure telemetry device, termed pulse rate variability (PRV), instead of the electrocardiogram signal in accordance with clinical guidelines. The acceptability of PRV as a surrogate for HRV in instrumented animals is unknown. Using rabbits implanted with intracardiac leads and chronically implanted pressure transducers, we investigated the correlation and agreement of time-domain, frequency-domain, and nonlinear indexes of HRV and PRV at baseline. We also investigated the effects of ventricular pacing and autonomic blockade on both measures. At baseline, HRV and PRV time- and frequency-domain parameters showed robust correlations and moderate to high agreement, whereas nonlinear parameters showed slightly weaker correlations and varied agreement. Ventricular pacing almost completely eliminated HRV, and spectral analysis of the PRV signal revealed a HRV-independent rhythm. After cardiac autonomic blockade with atropine or metoprolol, the changes in time- and non-normalized frequency-domain measures of PRV continued to show strong correlations and moderate to high agreement with corresponding changes in HRV measures. Blockade-induced changes in nonlinear PRV indexes correlated poorly with HRV changes and showed weak agreement. These results suggest that time- and frequency-domain measures of PRV are acceptable surrogates for HRV even in the context of changing cardiac autonomic tone, but caution should be used when nonlinear measures are a primary end point or when HRV is very low as HRV-independent rhythms may predominate.

Evaluating predictions of critical oxygen desaturation events

This paper presents a new approach for evaluating predictions of oxygen saturation levels in blood ( SpO2). A performance metric based on a threshold is proposed to evaluate  SpO2 predictions based on whether or not they are able to capture critical desaturations in the  SpO2 time series of patients. We use linear auto-regressive models built using historical  SpO2 data to predict critical desaturation events with the proposed metric. In 20 s prediction intervals, 88%-94% of the critical events were captured with positive predictive values (PPVs) between 90% and 99%. Increasing the prediction horizon to 60 s, 46%-71% of the critical events were detected with PPVs between 81% and 97%. In both prediction horizons, more than 97% of the non-critical events were correctly classified. The overall classification capabilities for the developed predictive models were also investigated. The area under ROC curves for 60 s predictions from the developed models are between 0.86 and 0.98. Furthermore, we investigate the effect of including pulse rate (PR) dynamics in the models and predictions. We show no improvement in the percentage of the predicted critical desaturations if PR dynamics are incorporated into the  SpO2 predictive models (p-value = 0.814). We also show that including the PR dynamics does not improve the earliest time at which critical  SpO2 levels are predicted (p-value = 0.986). Our results indicate oxygen in blood is an effective input to the PR rather than vice versa. We demonstrate that the combination of predictive models with frequent pulse oximetry measurements can be used as a warning of critical oxygen desaturations that may have adverse effects on the health of patients.

Real-time heart rate entropy predicts the need for lifesaving interventions in trauma activation patients

BACKGROUND

Heart rate complexity (HRC), commonly described as a "new vital sign," has shown promise in predicting injury severity, but its use in clinical practice has been precluded by the absence of real-time data. This study was conducted to evaluate the utility of real-time, automated, instantaneous, hand-held heart rate entropy analysis in predicting the need for lifesaving interventions (LSIs). We hypothesized that real-time HRC would predict LSIs.

METHODS

Prospective enrollment of patients who met criteria for trauma team activation was conducted at a Level I trauma center (September 2011 to February 2012). A novel, hand-held, portable device was used to measure HRC (by sample entropy) and time-domain heart rate variability continuously in real time for 2 hours after the moment of presentation. Electric impedance cardiography was used to determine cardiac output. Patients who received an LSI were compared with patients without any intervention (non-LSI). Multivariable analysis was performed to control for differences between the groups.

RESULTS

Of 82 patients enrolled, 21 (26%) received 67 LSIs within 24 hours of hospital arrival. Initial systolic blood pressure was similar in both groups. LSI patients had a lower Glasgow Coma Scale (GCS) score (9.2 [5.1] vs. 14.9 [0.2], p < 0.0001). The mean (SD) HRC value on presentation was 0.8 (0.6) in the LSI group compared with 1.5 (0.6) in the non-LSI group (p < 0.0001). With the use of logistic regression, initial HRC was the only significant predictor of LSI. A cutoff value for HRC of 1.1 yields sensitivity, specificity, negative predictive value, and positive predictive value of 86%, 74%, 94%, and 53%, respectively, with an accuracy of 77% for predicting an LSI.

CONCLUSION

Decreased HRC on hospital arrival is an independent predictor of the need for LSI in trauma activation patients. Real-time HRC may be a useful adjunct to standard vital signs monitoring and predicts LSIs.

LEVEL OF EVIDENCE

Prognostic and diagnostic study, level III.

Predicting the need for urgent intubation in a surgical/trauma intensive care unit

BACKGROUND

Analysis and modeling of data monitoring vital signs and waveforms in patients in a surgical/trauma intensive care unit (STICU) may allow for early identification and treatment of patients with evolving respiratory failure.

METHODS

Between February 2011 and March 2012, data of vital signs and waveforms for STICU patients were collected. Every-15-minute calculations (n = 172,326) of means and standard deviations of heart rate (HR), respiratory rate (RR), pulse-oxygen saturation (SpO2), cross-correlation coefficients, and cross-sample entropy for HR-RR, RR-SpO2, and HR-SpO2, and cardiorespiratory coupling were calculated. Urgent intubations were recorded. Univariate analyses were performed for the periods <24 and ≥24 hours before intubation. Multivariate predictive models for the risk of unplanned intubation were developed and validated internally by subsequent sample and bootstrapping techniques.

RESULTS

Fifty unplanned intubations (41 patients) were identified from 798 STICU patients. The optimal multivariate predictive model (HR, RR, and SpO2 means, and RR-SpO2 correlation coefficient) had a receiving operating characteristic (ROC) area of 0.770 (95% confidence interval [CI], 0.712-0.841). For this model, relative risks of intubation in the next 24 hours for the lowest and highest quintiles were 0.20 and 2.95, respectively (15-fold increase, baseline risk 1.46%). Adding age and days since previous extubation to this model increased ROC area to 0.865 (95 % CI, 0.821-0.910).

CONCLUSION

Among STICU patients, a multivariate model predicted increases in risk of intubation in the following 24 hours based on vital sign data available currently on bedside monitors. Further refinement could allow for earlier detection of respiratory decompensation and intervention to decrease preventable morbidity and mortality in surgical/trauma patients.

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.

Altering physiological networks using drugs: steps towards personalized physiology

Background

The rise of personalized medicine has reminded us that each patient must be treated as an individual. One factor in making treatment decisions is the physiological state of each patient, but definitions of relevant states and methods to visualize state-related physiologic changes are scarce. We constructed correlation networks from physiologic data to demonstrate changes associated with pressor use in the intensive care unit.

Methods

We collected 29 physiological variables at one-minute intervals from nineteen trauma patients in the intensive care unit of an academic hospital and grouped each minute of data as receiving or not receiving pressors. For each group we constructed Spearman correlation networks of pairs of physiologic variables. To visualize drug-associated changes we split the networks into three components: an unchanging network, a network of connections with changing correlation sign, and a network of connections only present in one group.

Results

Out of a possible 406 connections between the 29 physiological measures, 64, 39, and 48 were present in each of the three component networks. The static network confirms expected physiological relationships while the network of associations with changed correlation sign suggests putative changes due to the drugs. The network of associations present only with pressors suggests new relationships that could be worthy of study.

Conclusions

We demonstrated that visualizing physiological relationships using correlation networks provides insight into underlying physiologic states while also showing that many of these relationships change when the state is defined by the presence of drugs. This method applied to targeted experiments could change the way critical care patients are monitored and treated.

Modeling physiologic variability in human endotoxemia

The control and management of inflammation is a key aspect of clinical care for critical illnesses such as sepsis. In an ideal reaction to injury, the inflammatory response provokes a strong enough response to heal the injury and then restores homeostasis. When inflammation becomes dysregulated, a persistent inflammatory state can lead to significant deleterious effects and clinical challenges. Thus, gaining a better biological understanding of the mechanisms driving the inflammatory response is of the utmost importance. In this review, we discuss our work with the late Stephen F. Lowry to investigate systemic inflammation through systems biology of human endotoxemia. We present our efforts in modeling the human endotoxemia response with a particular focus on physiologic variability. Through modeling, with a focus ultimately on translational applications, we obtain more fundamental understanding of relevant physiological processes. And by taking advantage of the information embedded in biological rhythms, ranging in time scale from high-frequency autonomic oscillations reflected in heart rate variability to circadian rhythms in inflammatory mediators, we gain insight into the underlying physiology.

Translational applications of evaluating physiologic variability in human endotoxemia

Dysregulation of the inflammatory response is a critical component of many clinically challenging disorders such as sepsis. Inflammation is a biological process designed to lead to healing and recovery, ultimately restoring homeostasis; however, the failure to fully achieve those beneficial results can leave a patient in a dangerous persistent inflammatory state. One of the primary challenges in developing novel therapies in this area is that inflammation is comprised of a complex network of interacting pathways. Here, we discuss our approaches towards addressing this problem through computational systems biology, with a particular focus on how the presence of biological rhythms and the disruption of these rhythms in inflammation may be applied in a translational context. By leveraging the information content embedded in physiologic variability, ranging in scale from oscillations in autonomic activity driving short-term heart rate variability to circadian rhythms in immunomodulatory hormones, there is significant potential to gain insight into the underlying physiology.

Complexity of intracranial pressure correlates with outcome after traumatic brain injury

This study applied multiscale entropy analysis to investigate the correlation between the complexity of intracranial pressure waveform and outcome after traumatic brain injury. Intracranial pressure and arterial blood pressure waveforms were low-pass filtered to remove the respiratory and pulse components and then processed using a multiscale entropy algorithm to produce a complexity index. We identified significant differences across groups classified by the Glasgow Outcome Scale in intracranial pressure, pressure-reactivity index and complexity index of intracranial pressure (P < 0.0001; P = 0.001; P < 0.0001, respectively). Outcome was dichotomized as survival/death and also as favourable/unfavourable. The complexity index of intracranial pressure achieved the strongest statistical significance (F = 28.7; P < 0.0001 and F = 17.21; P < 0.0001, respectively) and was identified as a significant independent predictor of mortality and favourable outcome in a multivariable logistic regression model (P < 0.0001). The results of this study suggest that complexity of intracranial pressure assessed by multiscale entropy was significantly associated with outcome in patients with brain injury.

Linking Inflammation, Cardiorespiratory Variability, and Neural Control in Acute Inflammation via Computational Modeling

Acute inflammation leads to organ failure by engaging catastrophic feedback loops in which stressed tissue evokes an inflammatory response and, in turn, inflammation damages tissue. Manifestations of this maladaptive inflammatory response include cardio-respiratory dysfunction that may be reflected in reduced heart rate and ventilatory pattern variabilities. We have developed signal-processing algorithms that quantify non-linear deterministic characteristics of variability in biologic signals. Now, coalescing under the aegis of the NIH Computational Biology Program and the Society for Complexity in Acute Illness, two research teams performed iterative experiments and computational modeling on inflammation and cardio-pulmonary dysfunction in sepsis as well as on neural control of respiration and ventilatory pattern variability. These teams, with additional collaborators, have recently formed a multi-institutional, interdisciplinary consortium, whose goal is to delineate the fundamental interrelationship between the inflammatory response and physiologic variability. Multi-scale mathematical modeling and complementary physiological experiments will provide insight into autonomic neural mechanisms that may modulate the inflammatory response to sepsis and simultaneously reduce heart rate and ventilatory pattern variabilities associated with sepsis. This approach integrates computational models of neural control of breathing and cardio-respiratory coupling with models that combine inflammation, cardiovascular function, and heart rate variability. The resulting integrated model will provide mechanistic explanations for the phenomena of respiratory sinus-arrhythmia and cardio-ventilatory coupling observed under normal conditions, and the loss of these properties during sepsis. This approach holds the potential of modeling cross-scale physiological interactions to improve both basic knowledge and clinical management of acute inflammatory diseases such as sepsis and trauma.