Heart rate variability measures as predictors of in-hospital mortality in ED patients with sepsis

Exploration of COVID-19 associated bradycardia using heart rate variability analysis in a case-control study of ARDS patients

BACKGROUND

Bradycardia and dysautonomia observed during SARS-Cov2 infection suggests involvement of the autonomic nervous system (ANS). Limited data exists on ANS dysregulation and its association with outcomes in patients with acute respiratory distress syndrome (ARDS) related to COVID-19 (C-ARDS) or other etiologies (NC-ARDS).

OBJECTIVES

We aimed to explore sympathovagal balance, assessed by heart rate variability (HRV), and its clinical prognostic value in C-ARDS compared with NC-ARDS.

METHODS

A single-center, prospective case-control study was conducted. Consecutive patients meeting ARDS criteria between 2020 and 2022 were included. HRV was assessed using 1-hour electrographic tracing during a stable, daytime period.

RESULTS

Twenty-four patients with C-ARDS and 19 with NC-ARDS were included. Age, sex and ARDS severity were similar between groups. The median heart rate was markedly lower in the C-ARDS group than in the NC-ARDS group (60 [53-72] versus 101 [91-112] bpm, p<.001). Most of HRV parameters were significantly increased in patients with C-ARDS. HRV correlated with heart rate only in patients with C-ARDS. A positive correlation was found between the low-to high-frequency ratio (LF/HF) and length of intensive care unit stay (r = 0.576, p<.001).

CONCLUSION

This study confirmed that C-ARDS was associated with marked bradycardia and severe ANS impairment, suggesting a sympathovagal imbalance with vagal overtone. Poor outcomes appeared to be more related to sympathetic rather than parasympathetic hyperactivation.

Heart rate variability biofeedback for critical illness polyneuropathy: a randomized sham-controlled study

BACKGROUND AND PURPOSE

Critical illness polyneuropathy (CIP) has been linked to neurocardiac dysfunction mediated by autonomic nervous system dysregulation, which increases mortality. We aimed to assess if heart rate variability (HRV) biofeedback could improve neurocardiac function in CIP.

METHODS

We randomly allocated (1:1) patients with electrophysiologically confirmed CIP undergoing early inpatient neurological rehabilitation to additional HRV or sham biofeedback over 14 days. We evaluated neurocardiac function via standard deviation of normal-to-normal intervals (SDNN) as the primary outcome, as well as HRV frequency domains, sympathetic cutaneous sudomotor and vasomotor functions and disability at baseline, post intervention and 4 weeks later. The study is registered on the German Clinical Trials Register (DRKS00028911).

RESULTS

We included 30 patients with CIP (40% females, median [interquartile range] age 64.6 [56, 72] years). We observed an increase in SDNN and the predominantly parasympathetic high frequency domain post intervention (ß = 16.4, 95% confidence interval [CI] 0.2, 32.6 [p = 0.047] and ß = 1179.2, 95% CI 119.9, 2158.5 [p = 0.018]), which was sustained at the 4-week follow-up (ß = 25.7, 95% CI 6.0, 45.4 [p = 0.011] and ß = 25.7, 95% CI 6.0, 45.4 [p = 0.011]). Patients who underwent HRV biofeedback displayed a higher adjusted Barthel index, indicating less severe disability 4 weeks after the intervention compared to those in the sham group (ß = 23.3, 95% CI 5.5, 41.1 [p = 0.014]). Low frequency and sympathetic skin functions did not differ between groups (p = nonsignificant).

CONCLUSIONS

Our study provides pilot data suggesting that, in patients with CIP, HRV biofeedback can improve neurocardiac function with a predominant effect on the parasympathetic nervous system and has a beneficial effect on functional recovery.

Effectiveness analysis of deceleration capacity and traditional heart rate variability in diagnosing vasovagal syncope

Background

Vasovagal syncope (VVS) is a prevalent medical condition with a lack of efficient methods for its detection.

Aim

This study aimed to explore an objective clinical indicator in diagnosing VVS.

Methods

The retrospective analysis involved clinical data of 243 syncope patients from 1 June 2020 to 31 July 2023. Among them, 108 patients had a negative result in the tilt test (TTT), while the remaining 135 patients had a positive result in the TTT. Relevant statistical methods were utilized to examine the correlation between VVS and different indicators of heart rate variability.

Results

After screening, 354 patients being considered for VVS were evaluated, resulting in a final sample size of 243. Sex, age, deceleration capacity (DC), and standard deviation of all normal-to-normal intervals (SDNNs) were the variables that showed statistical significance between the TTT(-) group and the TTT(+) group. Independent risk factors identified by multivariate logistic regression were DC [odds ratio (OR) 1.710, 95% confidence interval (CI) 1.388-2.106, P < 0.001] and SDNN (OR 1.033, 95% CI 1.018-1.049, P < 0.001). Comparing the groups, receiver operating characteristic analysis revealed a notable distinction in both DC and SDNN [the respective areas under the curve were 0.789 (95% CI 0.730-0.848) and 0.702 (95% CI 0.637-0.767); the cutoff values were 7.15 and 131.42; P < 0.001, respectively].

Conclusion

In summary, DC can function as an impartial and easily accessible clinical marker for differentiating VVS. A value exceeding 7.15 ms might suggest a higher likelihood of syncope.

Physiological network approach to prognosis in cirrhosis: A shifting paradigm

Decompensated liver disease is complicated by multi-organ failure and poor prognosis. The prognosis of patients with liver failure often dictates clinical management. Current prognostic models have focused on biomarkers considered as individual isolated units. Network physiology assesses the interactions among multiple physiological systems in health and disease irrespective of anatomical connectivity and defines the influence or dependence of one organ system on another. Indeed, recent applications of network mapping methods to patient data have shown improved prediction of response to therapy or prognosis in cirrhosis. Initially, different physical markers have been used to assess physiological coupling in cirrhosis including heart rate variability, heart rate turbulence, and skin temperature variability measures. Further, the parenclitic network analysis was recently applied showing that organ systems connectivity is impaired in patients with decompensated cirrhosis and can predict mortality in cirrhosis independent of current prognostic models while also providing valuable insights into the associated pathological pathways. Moreover, network mapping also predicts response to intravenous albumin in patients hospitalized with decompensated cirrhosis. Thus, this review highlights the importance of evaluating decompensated cirrhosis through the network physiologic prism. It emphasizes the limitations of current prognostic models and the values of network physiologic techniques in cirrhosis.

Autonomic Nerve Function Predicts Risk of Early Death after Discharge in Acute Medical Disease

BACKGROUND

Heart rate variability, a marker of autonomic function, has shown promising prognostic results in specific populations, but has not been tested in a general medical population. We hypothesized that heart rate variability identifies high-risk medical patients early after admission to the hospital.

METHODS

This was a single-center prospective cohort study of acutely admitted medical patients aged ≥18 years with a life expectancy ≥3 months, included between 2019-2023. Unstable patients needing direct admission to the intensive care unit were excluded. Heart rate variability was recorded within 24 hours of admission for 10 minutes. The standard deviation of normal-normal beats (SDNN) was the primary heart rate variability marker. Low SDNN was defined as the lowest tertile (≤22 ms). The primary outcome was 30-day all-cause mortality. The secondary outcome was 30-day readmission or mortality.

RESULTS

Among 721 patients included, low SDNN carried an 8-fold greater risk of 30-day mortality in univariate analysis (hazard ratio [HR] 8.3; P = .001); in multivariate analyses a 4-fold greater risk (HR 3.8; P = .037). Low SDNN was associated with the combined outcome of 30-day mortality or readmission (HR 1.5; P = .03) in multivariate analysis. In receiver operating characteristics analyses, low SDNN improved the predictive accuracy of early warning score for 30-day mortality or readmission from 0.63 to 0.71 (P = .008) but did not improve the accuracy for 30-day mortality alone.

CONCLUSIONS

In patients admitted due to acute medical illness, low heart rate variability predicted 30-day mortality and readmission, suggesting heart rate variability as a tool to identify patients at high and low risk of relevant endpoints.

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.

Evidence of increased cardiovascular disease risk in left-handed individuals

Background

Approximately 10% of the world is left-handed (LH). Research suggests that LH individuals may have shorter lifespans compared to right-handed (RH) individuals. LH individuals also appear to have more cardiovascular disease (CVD) related conditions like diabetes and cancer. Thus, the present study sought to test the hypothesis that vascular function and heart rate variability (HRV), both key indicators of CVD risk, would be lower in LH compared to RH individuals.

Methods

Three hundred seventy-nine participants, 18-50 years old, were enrolled. Flow-mediated dilation (FMD), a bioassay of vascular endothelial function and standard deviation of R-R interval (SDNN), a parameter of HRV, were evaluated as indices of CVD risk. Data are reported as mean ± SD.

Results

12.1% of the participants were LH. No differences in demographics or clinical laboratory values were observed between groups, except high-density lipoprotein (HDL) was higher (p = 0.033) in RH. FMD was significantly (p = 0.043) lower in LH (6.1% ± 3.2%) compared to RH (7.6% ± 3.8%), independent of age, sex, race, BMI, and HDL. Total power (p = 0.024) and low-frequency power (p = 0.003) were lower in LH compared to RH. Additionally, SDNN was lower (p = 0.041) in LH (47.4 ± 18.8 ms) compared to RH (54.7 ± 22.3 ms). A negative correlation between FMD and mean arterial pressure (r = -0.517; p < 0.001) was observed in LH; no relationships were observed in RH (all p > 0.05).

Conclusion

Vascular endothelial function and HRV are lower in LH compared to RH. In addition, relationships between FMD and traditional CVD risk factors were only observed in LH. These data support an increased risk of CVD in LH.

The validity and applications of the analgesia nociception index: a narrative review

Pain refers to the subjective, unpleasant experience that is related to illness or injury. In contrast to pain, nociception refers to the physiological neural processing of noxious stimuli, such as intra-operative surgical stimuli. One novel device, the Analgesia Nociception Index (ANI), aims to objectively measure intra-operative nociception by analyzing the heart rate variability in patients undergoing surgery. Through this method of nociceptive monitoring, the ANI device aims to provide an objective, continuous evaluation of patient comfort levels and allow anesthesiologists to better manage surgical stress and patient analgesia, perhaps with even better efficacy than current practices used to assess nociception. Additionally, ANI may have clinical application in settings outside of the operating room, such as in the intensive care unit. In this narrative review, we compiled and summarized the findings of many studies that have investigated ANI's validity and applications in different clinical settings. Currently, the literature appears mostly supportive of ANI's ability to detect nociception in both surgical and non-surgical settings. However, the ability for ANI to provide clinical benefits, such as decreased intra-operative opioid use, post-operative opioid use, and post-operative pain compared to standard practices appear controversial. Because of the wide variety of methodology, clinical settings, patient populations, and limitations in these studies, more investigation of ANI is needed before any firm conclusions can be drawn on its clinical benefits.

Lipopolysaccharide-induced sepsis impairs M2R-GIRK signaling in the mouse sinoatrial node

Sepsis has emerged as a global health burden associated with multiple organ dysfunction and 20% mortality rate in patients. Numerous clinical studies over the past two decades have correlated the disease severity and mortality in septic patients with impaired heart rate variability (HRV), as a consequence of impaired chronotropic response of sinoatrial node (SAN) pacemaker activity to vagal/parasympathetic stimulation. However, the molecular mechanism(s) downstream to parasympathetic inputs have not been investigated yet in sepsis, particularly in the SAN. Based on electrocardiography, fluorescence Ca2+ imaging, electrophysiology, and protein assays from organ to subcellular level, we report that impaired muscarinic receptor subtype 2-G protein-activated inwardly-rectifying potassium channel (M2R-GIRK) signaling in a lipopolysaccharide-induced proxy septic mouse model plays a critical role in SAN pacemaking and HRV. The parasympathetic responses to a muscarinic agonist, namely IKACh activation in SAN cells, reduction in Ca2+ mobilization of SAN tissues, lowering of heart rate and increase in HRV, were profoundly attenuated upon lipopolysaccharide-induced sepsis. These functional alterations manifested as a direct consequence of reduced expression of key ion-channel components (GIRK1, GIRK4, and M2R) in the mouse SAN tissues and cells, which was further evident in the human right atrial appendages of septic patients and likely not mediated by the common proinflammatory cytokines elevated in sepsis.

HEART RATE VARIABILITY PARAMETERS WERE NOT ASSOCIATED WITH 30-DAY ALL-CAUSE MORTALITY IN INTENSIVE CARE UNIT PATIENTS WITH OR WITHOUT ATRIAL FIBRILLATION: A RETROSPECTIVE STUDY OF THE MIMIC-IV DATABASE

ABSTRACT

Objective: Our study aims to evaluate the association between heart rate variability (HRV) and short- and long-term prognosis in patients admitted to intensive care unit (ICU). Methods and Results: Adult patients continuously monitored for over 24 h in ICUs from the the American Medical Information Mart for Intensive Care (MIMIC)-IV Waveform Database were recruited in our study. Twenty HRV-related variables (8 time domain, 6 frequency domain, and 6 nonlinear variables) were calculated based on RR intervals. The association between HRV and all-cause mortality was assessed. Ninety-three patients met the inclusion criteria and were classified into atrial fibrillation (AF) and sinus rhythm (SR) groups, which were further divided into 30-day survivor group and nonsurvivor\groups based on their survival status. The 30-day all-cause mortality rates in AF and SR groups were 36.3% and 14.6%, respectively. All the time domain, frequency domain, and nonlinear HRV parameters did not differ significantly between survivors and nonsurvivors with or without AF (all P > 0.05). Presence of renal failure, malignancy, and elevated blood urea nitrogen level were associated with increased 30-day all-cause mortality in SR patients, while presence of sepsis, infection, higher platelet count, and magnesium level were associated with increased 30-day all-cause mortality in AF patients. Conclusions: Heart rate variability variables were not associated with increased 30-day all-cause mortality in ICU patients with or without AF.

Correlation between heart rate variability and cerebral autoregulation in septic patients

BACKGROUND

Heart rate variability (HRV) may provide an estimation of the autonomous nervous system (ANS) integrity in critically ill patients. Disturbances of cerebral autoregulation (CAR) may share common pathways of ANS dysfunction.

AIM

To explore whether changes in HRV and CAR index correlate in critically ill septic patients.

METHODS

Prospectively collected data on septic adult (> 18 years) patients admitted into a mixed Intensive Care between February 2016 and August 2019 with a recorded transcranial doppler CAR assessment. CAR was assessed calculating the Pearson's correlation coefficient (i.e. mean flow index, Mxa) between the left middle cerebral artery flow velocity (FV), insonated with a 2-MHz probe, and invasive blood pressure (BP) signal, both recorded simultaneously through a Doppler Box (DWL, Germany). MATLAB software was used for CAR assessment using a validated script; a Mxa >0.3 was considered as impaired CAR. HRV was assessed during the same time period using a specific software (Kubios HRV 3.2.0) and analyzed in both time-domain and frequency domain methods. Correlation between HRV-derived variables and Mxa were assessed using the Spearman's coefficient.

RESULTS

A total of 141 septic patients was studied; median Mxa was 0.35 [0.13-0.60], with 77 (54.6 %) patients having an impaired CAR. Mxa had a significant although weak correlation with HRV time domain (SDNN, r = 0.17, p = 0.04; RMSSD, r = 0.18, p = 0.03; NN50, r = 0.23, p = 0.006; pNN50, r = 0.23, p = 0.007), frequency domain (FFT-HF, r = 0.21; p = 0.01; AR-HF, r = 0.19; p = 0.02), and non-linear domain (SD1, r = 0.18, p = 0.03) parameters. Impaired CAR patients had also all of these HRV-derived parameters higher than those with intact CAR.

CONCLUSIONS

In this exploratory study, a potential association of ANS dysfunction and impaired CAR during sepsis was observed.

Prediction of intrapartum fever using continuously monitored vital signs and heart rate variability

Objectives

Neonatal early onset sepsis (EOS), bacterial infection during the first seven days of life, is difficult to diagnose because presenting signs are non-specific, but early diagnosis before birth can direct life-saving treatment for mother and baby. Specifically, maternal fever during labor from placental infection is the strongest predictor of EOS. Alterations in maternal heart rate variability (HRV) may precede development of intrapartum fever, enabling incipient EOS detection. The objective of this work was to build a predictive model for intrapartum fever.

Methods

Continuously measured temperature, heart rate, and beat-to-beat RR intervals were obtained from wireless sensors on women (n = 141) in labor; traditional manual vital signs were taken every 3-6 hours. Validated measures of HRV were calculated in moving 5-minute windows of RR intervals: standard deviation of normal-to-normal intervals (SDNN) and root mean square of successive differences (RMSSD) between normal heartbeats.

Results

Fever (>38.0 °C) was detected by manual or continuous measurements in 48 women. Compared to afebrile mothers, average SDNN and RMSSD in febrile mothers decreased significantly (p < 0.001) at 2 and 3 hours before fever onset, respectively. This observed HRV divergence and raw recorded vitals were applied to a logistic regression model at various time horizons, up to 4-5 hours before fever onset. Model performance increased with decreasing time horizons, and a model built using continuous vital signs as input variables consistently outperformed a model built from episodic vital signs.

Conclusions

HRV-based predictive models could identify mothers at risk for fever and infants at risk for EOS, guiding maternal antibiotic prophylaxis and neonatal monitoring.

Autonomic Dysfunction during Acute SARS-CoV-2 Infection: A Systematic Review

Although autonomic dysfunction (AD) after the recovery from Coronavirus disease 2019 (COVID-19) has been thoroughly described, few data are available regarding the involvement of the autonomic nervous system (ANS) during the acute phase of SARS-CoV-2 infection. The primary aim of this review was to summarize current knowledge regarding the AD occurring during acute COVID-19. Secondarily, we aimed to clarify the prognostic value of ANS involvement and the role of autonomic parameters in predicting SARS-CoV-2 infection. According to the PRISMA guidelines, we performed a systematic review across Scopus and PubMed databases, resulting in 1585 records. The records check and the analysis of included reports’ references allowed us to include 22 articles. The studies were widely heterogeneous for study population, dysautonomia assessment, and COVID-19 severity. Heart rate variability was the tool most frequently chosen to analyze autonomic parameters, followed by automated pupillometry. Most studies found ANS involvement during acute COVID-19, and AD was often related to a worse outcome. Further studies are needed to clarify the role of autonomic parameters in predicting SARS-CoV-2 infection. The evidence emerging from this review suggests that a complex autonomic nervous system imbalance is a prominent feature of acute COVID-19, often leading to a poor prognosis.

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

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

Trial registration: ClinicalTrials.gov identifier NCT02893462.

Heart rate n-variability (HRnV) measures for prediction of mortality in sepsis patients presenting at the emergency department

Sepsis is a potentially life-threatening condition that requires prompt recognition and treatment. Recently, heart rate variability (HRV), a measure of the cardiac autonomic regulation derived from short electrocardiogram tracings, has been found to correlate with sepsis mortality. This paper presents using novel heart rate n-variability (HRnV) measures for sepsis mortality risk prediction and comparing against current mortality prediction scores. This study was a retrospective cohort study on patients presenting to the emergency department of a tertiary hospital in Singapore between September 2014 to April 2017. Patients were included if they were above 21 years old and were suspected of having sepsis by their attending physician. The primary outcome was 30-day in-hospital mortality. Stepwise multivariable logistic regression model was built to predict the outcome, and the results based on 10-fold cross-validation were presented using receiver operating curve analysis. The final predictive model comprised 21 variables, including four vital signs, two HRV parameters, and 15 HRnV parameters. The area under the curve of the model was 0.77 (95% confidence interval 0.70–0.84), outperforming several established clinical scores. The HRnV measures may have the potential to allow for a rapid, objective, and accurate means of patient risk stratification for sepsis severity and mortality. Our exploration of the use of wealthy inherent information obtained from novel HRnV measures could also create a new perspective for data scientists to develop innovative approaches for ECG analysis and risk monitoring.

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.

Early Detection of Late Onset Sepsis in Premature Infants Using Visibility Graph Analysis of Heart Rate Variability

OBJECTIVE

This study was designed to test the diagnostic value of visibility graph features derived from the heart rate time series to predict late onset sepsis (LOS) in preterm infants using machine learning.

METHODS

The heart rate variability (HRV) data was acquired from 49 premature newborns hospitalized in neonatal intensive care units (NICU). The LOS group consisted of patients who received more than five days of antibiotics, at least 72 hours after birth. The control group consisted of infants who did not receive antibiotics. HRV features in the days prior to the start of antibiotics (LOS group) or in a randomly selected period (control group) were compared against a baseline value calculated during a calibration period. After automatic feature selection, four machine learning algorithms were trained. All the tests were done using two variants of the feature set: one only included traditional HRV features, and the other additionally included visibility graph features. Performance was studied using area under the receiver operating characteristics curve (AUROC).

RESULTS

The best performance for detecting LOS was obtained with logistic regression, using the feature set including visibility graph features, with AUROC of 87.7% during the six hours preceding the start of antibiotics, and with predictive potential (AUROC above 70%) as early as 42 h before start of antibiotics.

CONCLUSION

These results demonstrate the usefulness of introducing visibility graph indexes in HRV analysis for sepsis prediction in newborns.

SIGNIFICANCE

The method proposed the possibility of non-invasive, real-time monitoring of risk of LOS in a NICU setting.

Is the heart rate variability monitoring using the analgesia nociception index a predictor of illness severity and mortality in critically ill patients with COVID-19? A pilot study

Introduction

The analysis of heart rate variability (HRV) has proven to be an important tool for the management of autonomous nerve system in both surgical and critically ill patients. We conducted this study to show the different spectral frequency and time domain parameters of HRV as a prospective predictor for critically ill patients, and in particular for COVID-19 patients who are on mechanical ventilation. The hypothesis is that most severely ill COVID-19 patients have a depletion of the sympathetic nervous system and a predominance of parasympathetic activity reflecting the remaining compensatory anti-inflammatory response.

Materials and methods

A single-center, prospective, observational pilot study which included COVID-19 patients admitted to the Surgical Intensive Care Unit was conducted. The normalized high-frequency component (HFnu), i.e. ANIm, and the standard deviation of RR intervals (SDNN), i.e. Energy, were recorded using the analgesia nociception index monitor (ANI). To estimate the severity and mortality we used the SOFA score and the date of discharge or date of death.

Results

A total of fourteen patients were finally included in the study. ANIm were higher in the non-survivor group (p = 0.003) and were correlated with higher IL-6 levels (p = 0.020). Energy was inversely correlated with SOFA (p = 0.039) and fewer survival days (p = 0.046). A limit value at 80 of ANIm, predicted mortalities with a sensitivity of 100% and specificity of 85.7%. In the case of Energy, a limit value of 0.41 ms predicted mortality with all predictive values of 71.4%.

Conclusion

A low autonomic nervous system activity, i.e. low SDNN or Energy, and a predominance of the parasympathetic system, i.e. low HFnu or ANIm, due to the sympathetic depletion in COVID-19 patients are associated with a worse prognosis, higher mortality, and higher IL-6 levels.

Stratified Management for Bacterial Infections in Late Preterm and Term Neonates: Current Strategies and Future Opportunities Toward Precision Medicine

Bacterial infections remain a major cause of morbidity and mortality in the neonatal period. Therefore, many neonates, including late preterm and term neonates, are exposed to antibiotics in the first weeks of life. Data on the importance of inter-individual differences and disease signatures are accumulating. Differences that may potentially influence treatment requirement and success rate. However, currently, many neonates are treated following a "one size fits all" approach, based on general protocols and standard antibiotic treatment regimens. Precision medicine has emerged in the last years and is perceived as a new, holistic, way of stratifying patients based on large-scale data including patient characteristics and disease specific features. Specific to sepsis, differences in disease susceptibility, disease severity, immune response and pharmacokinetics and -dynamics can be used for the development of treatment algorithms helping clinicians decide when and how to treat a specific patient or a specific subpopulation. In this review, we highlight the current and future developments that could allow transition to a more precise manner of antibiotic treatment in late preterm and term neonates, and propose a research agenda toward precision medicine for neonatal bacterial infections.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

Heart rate variability as an independent predictor for 8-year mortality among chronic hemodialysis patients

The repeated measurements of heart rate variability (HRV) is more relevant than a single HRV measurement in predicting patient prognosis but is less addressed previously. This prospective study aimed to investigate the association between repeated measurements of HRV and long-term mortality in chronic hemodialysis patients. The 164 patients (65.0 ± 13.1 years; woman, 57.3%) were enrolled from June 1, 2010, to August 31, 2010, and received four HRV measurements (before and during the index hemodialysis session) after the enrollment. The baseline characteristic and clinical variables, including mortality, were documented. The joint modeling method and Cox regression were used for statistical analyses. After an 8-year follow-up, 79 patients expired, and 85 patients survived. We found that higher normalized high-frequency (nHF) (hazard ratio [HR] 1.033) as well as lower very-low-frequency (HR 0.990), Variance (HR 0.991), normalized low-frequency (HR 0.999, P = 0.006), and low-frequency/high-frequency ratio (HR 0.796) were independent predictors for cardiovascular mortality. Whereas the independent predictors for infection-associated mortality included higher nHF (HR 1.033) as well as higher age (HR 19.29) and lower serum albumin (HR 0.01, P = 0.001). (all P < 0.001 unless otherwise stated) In conclusion, HRV measurement predicts long-term mortality among hemodialysis patients.

Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction

Objective

Achieving accurate prediction of sepsis detection moment based on bedside monitor data in the intensive care unit (ICU). A good clinical outcome is more probable when onset is suspected and treated on time, thus early insight of sepsis onset may save lives and reduce costs.

Methodology

We present a novel approach for feature extraction, which focuses on the hypothesis that unstable patients are more prone to develop sepsis during ICU stay. These features are used in machine learning algorithms to provide a prediction of a patient's likelihood to develop sepsis during ICU stay, hours before it is diagnosed.

Results

Five machine learning algorithms were implemented using R software packages. The algorithms were trained and tested with a set of 4 features which represent the variability in vital signs. These algorithms aimed to calculate a patient's probability to become septic within the next 4 hours, based on recordings from the last 8 hours. The best area under the curve (AUC) was achieved with Support Vector Machine (SVM) with radial basis function, which was 88.38%.

Conclusions

The high level of predictive accuracy along with the simplicity and availability of input variables present great potential if applied in ICUs. Variability of a patient's vital signs proves to be a good indicator of one's chance to become septic during ICU stay.

Spectral Analysis of Heart Rate Variability: Time Window Matters

Spectral analysis of heart rate variability (HRV) is a valuable tool for the assessment of cardiovascular autonomic function. Fast Fourier transform and autoregressive based spectral analysis are two most commonly used approaches for HRV analysis, while new techniques such as trigonometric regressive spectral (TRS) and wavelet transform have been developed. Short-term (on ECG of several minutes) and long-term (typically on ECG of 1–24 h) HRV analyses have different advantages and disadvantages. This article reviews the characteristics of spectral HRV studies using different lengths of time windows. Short-term HRV analysis is a convenient method for the estimation of autonomic status, and can track dynamic changes of cardiac autonomic function within minutes. Long-term HRV analysis is a stable tool for assessing autonomic function, describe the autonomic function change over hours or even longer time spans, and can reliably predict prognosis. The choice of appropriate time window is essential for research of autonomic function using spectral HRV analysis.

Combining Heart Rate Variability with Disease Severity Score Variables for Mortality Risk Stratification in Septic Patients Presenting at the Emergency Department

The emergency department (ED) serves as the first point of hospital contact for many septic patients, where risk-stratification would be invaluable. We devised a combination model incorporating demographic, clinical, and heart rate variability (HRV) parameters, alongside individual variables of the Sequential Organ Failure Assessment (SOFA), Acute Physiology and Chronic Health Evaluation II (APACHE II), and Mortality in Emergency Department Sepsis (MEDS) scores for mortality risk-stratification. ED patients fulfilling systemic inflammatory response syndrome criteria were recruited. National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), quick SOFA (qSOFA), SOFA, APACHE II, and MEDS scores were calculated. For the prediction of 30-day in-hospital mortality, combination model performed with an area under the receiver operating characteristic curve of 0.91 (95% confidence interval (CI): 0.88–0.95), outperforming NEWS (0.70, 95% CI: 0.63–0.77), MEWS (0.61, 95% CI 0.53–0.69), qSOFA (0.70, 95% CI 0.63–0.77), SOFA (0.74, 95% CI: 0.67–0.80), APACHE II (0.76, 95% CI: 0.69–0.82), and MEDS scores (0.86, 95% CI: 0.81–0.90). The combination model had an optimal sensitivity and specificity of 91.4% (95% CI: 81.6–96.5%) and 77.9% (95% CI: 72.6–82.4%), respectively. A combination model incorporating clinical, HRV, and disease severity score variables showed superior predictive ability for the mortality risk-stratification of septic patients presenting at the ED.

Combining quick sequential organ failure assessment score with heart rate variability may improve predictive ability for mortality in septic patients at the emergency department

Background

Although the quick Sequential Organ Failure Assessment (qSOFA) score was recently introduced to identify patients with suspected infection/sepsis, it has limitations as a predictive tool for adverse outcomes. We hypothesized that combining qSOFA score with heart rate variability (HRV) variables improves predictive ability for mortality in septic patients at the emergency department (ED).

Methods

This was a retrospective study using the electronic medical record of a tertiary care hospital in Singapore between September 2014 and February 2017. All patients aged 21 years or older who were suspected with infection/sepsis in the ED and received electrocardiography monitoring with ZOLL X Series Monitor (ZOLL Medical Corporation, Chelmsford, MA) were included. We fitted a logistic regression model to predict the 30-day mortality using one of the HRV variables selected from one of each three domains those previously reported as strong association with mortality (i.e. standard deviation of NN [SDNN], ratio of low frequency to high frequency power [LF/HF], detrended fluctuation analysis α-2 [DFA α-2]) in addition to the qSOFA score. The predictive accuracy was assessed with other scoring systems (i.e. qSOFA alone, National Early Warning Score, and Modified Early Warning Score) using the area under the receiver operating characteristic curve.

Results

A total of 343 septic patients were included. Non-survivors were significantly older (survivors vs. non-survivors, 65.7 vs. 72.9, p <0.01) and had higher qSOFA (0.8 vs. 1.4, p <0.01) as compared to survivors. There were significant differences in HRV variables between survivors and non-survivors including SDNN (23.7s vs. 31.8s, p = 0.02), LF/HF (2.8 vs. 1.5, p = 0.02), DFA α-2 (1.0 vs. 0.7, P < 0.01). Our prediction model using DFA-α-2 had the highest c-statistic of 0.76 (95% CI, 0.70 to 0.82), followed by qSOFA of 0.68 (95% CI, 0.62 to 0.75), National Early Warning Score at 0.67 (95% CI, 0.61 to 0.74), and Modified Early Warning Score at 0.59 (95% CI, 0.53 to 0.67).

Conclusions

Adding DFA-α-2 to the qSOFA score may improve the accuracy of predicting in-hospital mortality in septic patients who present to the ED. Further multicenter prospective studies are required to confirm our results.

Noninvasive measurements of hemodynamic, autonomic and endothelial function as predictors of mortality in sepsis: A prospective cohort study

Background and aim

Sepsis is associated with marked alterations in hemodynamic responses, autonomic dysfunction and impaired vascular function. However, to our knowledge, analysis of noninvasive markers to identify greater risk of death has not yet been investigated. Thus, our aim was to explore the prognostic utility of cardiac output (CO), stroke volume (SV), indices of vagal modulation (RMSSD and SD1), total heart rate variability (HRV) indices and FMD of brachial artery (%FMD), all measured noninvasively, in the first 24 hours of the diagnosis of sepsis.

Methods

60 patients were recruited at ICU between 2015 and 2017 and followed by 28 days. CO, SV, RR intervals were measurement. Doppler ultrasound was used to assess brachial artery FMD and the hyperemic response were obtained (%FMD). Patients were divided by survivors (SG) and nonsurvivors groups (NSG).

Results

A total of 60 patients were analysed (SG = 21 and NSG = 39). Survivors were younger (41±15 years vs. 55±11 years) and used less vasoactive drugs. As expected, APACHE and SOFA scores were lower in NSG compared to SG. In addition, higher SD1, triangular index, % FMD, velocity baseline and hyperemia flow velocity as well as lower HR values were observed in the SG, compared to NSG (P<0.05). Interestingly, RMSSD and SD1 indices were independent predictors of %FMD, ΔFMD and FMD_peak. RMSSD threshold of 10.8ms and %FMD threshold of -1 were optimal at discriminatomg survivors and nonsurvivors.

Conclusion

Noninvasive measurements of autonomic and endotelial function may be important markers of sepsis mortality, which can be easily obtained in the early stages of sepsis at the bedside.

Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department

Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving outcomes. We compared the performance of machine learning models against conventional risk stratification tools, namely the Quick Sequential Organ Failure Assessment (qSOFA), National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), and our previously described Singapore ED Sepsis (SEDS) model, in the prediction of 30-day in-hospital mortality (IHM) among suspected sepsis patients in the ED.Adult patients who presented to Singapore General Hospital (SGH) ED between September 2014 and April 2016, and who met ≥2 of the 4 Systemic Inflammatory Response Syndrome (SIRS) criteria were included. Patient demographics, vital signs and heart rate variability (HRV) measures obtained at triage were used as predictors. Baseline models were created using qSOFA, NEWS, MEWS, and SEDS scores. Candidate models were trained using k-nearest neighbors, random forest, adaptive boosting, gradient boosting and support vector machine. Models were evaluated on F1 score and area under the precision-recall curve (AUPRC).A total of 214 patients were included, of whom 40 (18.7%) met the outcome. Gradient boosting was the best model with a F1 score of 0.50 and AUPRC of 0.35, and performed better than all the baseline comparators (SEDS, F1 0.40, AUPRC 0.22; qSOFA, F1 0.32, AUPRC 0.21; NEWS, F1 0.38, AUPRC 0.28; MEWS, F1 0.30, AUPRC 0.25).A machine learning model can be used to improve prediction of 30-day IHM among suspected sepsis patients in the ED compared to traditional risk stratification tools.

Heart rate variability as predictor of mortality in sepsis: A systematic review

BACKGROUND

Autonomic dysregulation is one of the recognized pathophysiological mechanisms in sepsis, generating the hypothesis that heart rate variability (HRV) can be used to predict mortality in sepsis.

METHODS

This was a systematic review of studies evaluating HRV as a predictor of death in patients with sepsis. The search was performed by independent researchers in PubMed, LILACS and Cochrane, including papers in English, Portuguese or Spanish, indexed until August 20th, 2017 with at least 10 patients. Study quality was assessed by Newcastle-Ottawa Scale. To analyze the results, we divided the articles between those who measured HRV for short-term recordings (≤ 1 hour), and those who did long-term recordings (≥ 24 hours).

RESULTS

Nine studies were included with a total of 536 patients. All of them were observational studies. Studies quality varied from 4 to 7 stars in Newcastle-Ottawa Scale. The mortality rate in the studies ranged from 8 to 61%. Seven studies performed HRV analysis in short-term recordings. With the exception of one study that did not explain which group had the lowest results, all other studies showed reduction of several HRV parameters in the non-survivors in relation to the surviving septic patients. SDNN (Standard deviation of the Normal to Normal interval), TP (Total Power), VLF (Very Low Frequency Power), LF (Low Frequency Power), LF/HF (Low Frequency Power / High Frequency Power), nLF (Normalized Low Frequency Power), α1/α2 (short-term and long-term fractal scaling coefficients from DFA) and r-MSSD (Square root of the squared mean of the difference of successive NN-intervals) of the non-survivor group were reduced in relation to the survivors in at least one study. Two studies found that SDNN is associated with mortality in sepsis, even after adjusting for possible confounding factors. Three studies performed HRV analysis using long-term recordings. Only one of these studies found difference between surviving and non-surviving groups, and even so, in only one HRV parameter: LogHF.

CONCLUSIONS

Several HRV parameters are reduced in nonsurviving septic patients in short-term recording. Two studies have found that SDNN is associated with mortality in sepsis, even after adjusting for possible confounding factors.

Autonomic Nervous System Activity for Risk Stratification of Emergency Patients With Pneumonia

BACKGROUND

Community-acquired pneumonia (CAP) causes appreciable morbidity and mortality in adults, especially in those ≥65 years of age. At hospital admission, an immediate and reliable risk assessment is necessary to detect patients with possible fatal outcome.

OBJECTIVE

We aimed to evaluate markers of the autonomic nervous system based on an electrocardiogram to predict mortality in patients with CAP.

METHODS

For this purpose, the deceleration capacity (DC) of heart rate was calculated in 253 patients who presented to the emergency department with CAP. The 30-day mortality rate was defined as the primary endpoint (PEP). The secondary endpoint was the total mortality within 180 days.

RESULTS

PEP was reached in 33 patients (13%). The DC, measured in milliseconds, was significantly lower in patients who reached the PEP than in those who did not (2.3 ± 1.5 ms vs. 3.6 ± 2.3 ms, p = 0.004). The DC was also significantly lower in nonsurvivors than in survivors at the time of the secondary endpoint (2.3 ± 1.5 ms vs. 3.7 ± 2.4 ms, p < 0.001). Our results indicate that DC is an independent predictor of 30- and 180-day mortality.

CONCLUSION

DC was independently associated with death from CAP in our study. As a practical consequence, DC could be useful in triage decisions. Patients with certain high risks could benefit from adjuvant treatment and special medical attention.

Cardiorespiratory interactions in humans and animals: rhythms for life

The cardiorespiratory system exhibits oscillations from a range of sources. One of the most studied oscillations is heart rate variability, which is thought to be beneficial and can serve as an index of a healthy cardiovascular system. Heart rate variability is dampened in many diseases including depression, autoimmune diseases, hypertension, and heart failure. Thus, understanding the interactions that lead to heart rate variability, and its physiological role, could help with prevention, diagnosis, and treatment of cardiovascular diseases. In this review, we consider three types of cardiorespiratory interactions: respiratory sinus arrhythmia (variability in heart rate at the frequency of breathing), cardioventilatory coupling (synchronization between the heart beat and the onset of inspiration), and respiratory stroke volume synchronization (the constant phase difference between the right and the left stroke volumes over one respiratory cycle). While the exact physiological role of these oscillations continues to be debated, the redundancies in the mechanisms responsible for its generation and its strong evolutionary conservation point to the importance of cardiorespiratory interactions. The putative mechanisms driving cardiorespiratory oscillations as well as the physiological significance of these oscillations will be reviewed. We suggest that cardiorespiratory interactions have the capacity to both dampen the variability in systemic blood flow as well as improve the efficiency of work done by the heart while maintaining physiological levels of arterial CO2. Given that reduction in variability is a prognostic indicator of disease, we argue that restoration of this variability via pharmaceutical or device-based approaches may be beneficial in prolonging life.

A novel heart rate variability based risk prediction model for septic patients presenting to the emergency department

A quick, objective, non-invasive means of identifying high-risk septic patients in the emergency department (ED) can improve hospital outcomes through early, appropriate management. Heart rate variability (HRV) analysis has been correlated with mortality in critically ill patients. We aimed to develop a Singapore ED sepsis (SEDS) predictive model to assess the risk of 30-day in-hospital mortality in septic patients presenting to the ED. We used demographics, vital signs, and HRV parameters in model building and compared it with the modified early warning score (MEWS), national early warning score (NEWS), and quick sequential organ failure assessment (qSOFA) score.Adult patients clinically suspected to have sepsis in the ED and who met the systemic inflammatory response syndrome (SIRS) criteria were included. Routine triage electrocardiogram segments were used to obtain HRV variables. The primary endpoint was 30-day in-hospital mortality. Multivariate logistic regression was used to derive the SEDS model. MEWS, NEWS, and qSOFA (initial and worst measurements) scores were computed. Receiver operating characteristic (ROC) analysis was used to evaluate their predictive performances.Of the 214 patients included in this study, 40 (18.7%) met the primary endpoint. The SEDS model comprises of 5 components (age, respiratory rate, systolic blood pressure, mean RR interval, and detrended fluctuation analysis α2) and performed with an area under the ROC curve (AUC) of 0.78 (95% confidence interval [CI]: 0.72-0.86), compared with 0.65 (95% CI: 0.56-0.74), 0.70 (95% CI: 0.61-0.79), 0.70 (95% CI: 0.62-0.79), 0.56 (95% CI: 0.46-0.66) by qSOFA (initial), qSOFA (worst), NEWS, and MEWS, respectively.HRV analysis is a useful component in mortality risk prediction for septic patients presenting to the ED.

High-frequency power of heart rate variability can predict the outcome of thoracic surgical patients with acute respiratory distress syndrome on admission to the intensive care unit: a prospective, single-centric, case-controlled study

BACKGROUND

The morbidity and mortality of acute respiratory distress syndrome (ARDS) remains high, and the strategic focus of ARDS research has shifted toward identifying patients at high risk of mortality early in the course of illness. This study intended to identify the heart rate variability (HRV) measure that can predict the outcome of patients with ARDS on admission to the surgical intensive care unit (SICU).

METHODS

Patients who had lung or esophageal cancer surgery were included either in the ARDS group (n = 21) if they developed ARDS after surgery or in the control group (n = 11) if they did not. The ARDS patients were further stratified into survivors and non-survivors subgroups according to their outcomes. HRV measures of the patients were used for statistical analysis.

RESULTS

The mean RR interval (mRRI), high-frequency power (HFP) and product of low-/high-frequency power ratio tidal volume and tidal volume (LHR*VT) were significantly lower (p < 0.05), while the normalized HFP to VT ratio (nHFP/VT) was significantly higher in the ARDS patients (p = 0.011). The total power (TP), low-frequency power (LFP), HFP and HFP/VT were all significantly higher in the non-survived ARDS patients, whereas Richmond Agitation-Sedation Scale (RASS) was significantly lower in the non-survived ARDS patients. After adjustment for RASS, age and gender, firth logistic regression analysis identified the HFP, TP as the significant independent predictors of mortality for ARDS patients.

CONCLUSIONS

The vagal modulation of thoracic surgical patients with ARDS was enhanced as compared to that of non-ARDS patients, and the non-survived ARDS patients had higher vagal activity than those of survived ARDS patients. The vagal modulation-related parameters such as TP and HFP were independent predictors of mortality in patients with ARDS on admission to the SICU, and the HFP was found to be the best predictor of mortality for those ARDS patients. Increased vagal modulation might be an indicator for poor prognosis in critically ill patients following thoracic surgery.

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

Background

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

Methods

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

Results

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

Conclusions

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

Heart Rate Variability Analysis in Patients Who Have Bradycardia Presenting to the Emergency Department with Chest Pain

BACKGROUND

Heart rate variability (HRV) is a noninvasive method to measure the function of the autonomic nervous system. It has been used to risk stratify patients with undifferentiated chest pain in the emergency department (ED). However, bradycardia can have a modifying effect on HRV.

OBJECTIVE

In this study, we aimed to determine how bradycardia affected HRV analysis in patients who presented with chest pain to the ED.

METHODS

Adult patients presenting to the ED at Singapore General Hospital with chest pain were included in the study. Patients with non-sinus rhythm on electrocardiogram (ECG) were excluded. HRV parameters, including time domain, frequency domain, and nonlinear variables, were analyzed from a 5-min ECG segment. Occurrence of a major adverse cardiac event ([MACE], e.g., acute myocardial infarction, percutaneous coronary intervention, coronary artery bypass graft, or mortality) within 30 days of presentation to the ED was also recorded.

RESULTS

A total of 797 patients were included for analysis with 248 patients (31.1%) with 30-day MACE and 135 patients with bradycardia (16.9%). Compared to non-bradycardic patients, bradycardic patients had significant differences in all HRV parameters suggesting an increased parasympathetic component. Among non-bradycardic patients, comparing those who did and did not have 30-day MACE, there were significant differences predominantly in time domain variables, suggesting decreased HRV. In bradycardic patients, the same analysis revealed significant differences in predominantly frequency-domain variables suggesting decreased parasympathetic input.

CONCLUSIONS

Chest pain patients with bradycardia have increased HRV compared to those without bradycardia. This may have important implications on HRV modeling strategies for risk stratification of bradycardic and non-bradycardic chest pain patients.

Prospective Observational Study on Heart Rate Variability in Emergency Department Patients with Sepsis

Objective

To study the predictive value of heart rate variability in septic patients presented to the emergency department.

Design

Cross sectional study.

Setting

Emergency department.

Methods

Septic patients in the emergency department were recruited according to criteria. Heart rate variability data on time domain and frequency domain were generated from Holter records. Sequential Organ Failure Assessment, clinical progress and laboratory values were used to access the outcomes.

Results

Spectral power of total power (TP), low frequency (LF), very low frequency (VLF) and normalised low frequency (nLF) are shown to be significantly reduced in patients with sepsis who deteriorated (p=0.0070, 0.0032, 0.0005 and 0.0109 respectively). Cut off value 172.5 of VLF can identify all septic patients with potential deterioration.

Conclusions

Application of heart rate variability recording in emergency department is feasible and helpful in early identification of potentially deteriorating septic patients.

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.

Predictors of aging based on the analysis of heart rate variability

BACKGROUND

The current significant progress in the use of heart rate variability in the solution of many diagnostic and therapeutic problems is determined by the availability of standardized methods of measurement and physiological interpretation of heart rate variability indices on the one hand and the high technological level of state-of-the-art electronic measuring equipment that is used for automatic registration and computer processing of cardio-signals.

METHODS

A retrospective analysis of anonymized cardio screening results of 22,433 adult residents of 565 settlements (cities and villages) across all 20 administrative districts of the Khmelnytskyi Region (Ukraine) was conducted to find a statistically significant connection between individual heart rate variability parameters and the age of people.

RESULTS

Primary statistical analysis and visualization showed a correlation between the selected heart rate variability parameters and the age and sex of the examined persons. The study found values of the predicted age slightly over estimation versus the actual age for very young test subjects and below estimation for elderly subjects.

CONCLUSION

The use of neural network computations and the modification of the algorithm through the construction of individual training samples for different age intervals, and the creation of individual ensembles of classification neural networks, therefore achieved a prediction of the age of examined persons based on the values of their time and frequency domain heart rate variability indices, with 87% accuracy for women and 85% accuracy for men in the 66-85 years age interval and at least 85% for age groups across the entire sample.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Heart rate variability in critical care medicine: a systematic review

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Heart rate variability as predictor of mortality in sepsis: A prospective cohort study

Background

Sepsis is a serious medical condition with increasing prevalence and high mortality. The role of the autonomic nervous system in pathophysiology of sepsis has been increasingly researched. The objective of this study is to evaluate the Heart rate variability (HRV) as a predictor of mortality in septic patients.

Methods

This was a prospective cohort of patients diagnosed with sepsis. Patient recruitment was carried out at ICU in tertiary university hospital between March 2012 and February 2014. Clinical data and laboratory exams were collected at admission. Each patient underwent a 20-minute Holter and a 24-hour Holter on the first day of enrollment. The primary outcome was the 28-day all-cause mortality.

Results

A total of 63 patients were included. Patients were categorized into nonsurvivor group (n = 16) or survivor group (n = 47) depending on this endpoint. Survivors were younger (48.6 years vs. 63.0 years), had better renal function and lower values in severity scores (APACHE II and SOFA) compared to nonsurvivors. In the 20-minute Holter, SDNN, Total Power, VLF Power, LF Power and LF/HF of nonsurvivors were significantly lower than those of survivors (p = <0.001, p = 0.003, p = 0.002, p = 0.006, p = 0.009 respectively). ROC curve of SDNN was built, showing area under the curve of 0.772 (0.638–0.906) for mortality. The value of 17ms was chosen as best SDNN cutoff to discriminate survivors and nonsurvivors. In the Cox proportional regression, adjusted for SOFA score and for APACHE II, a SDNN ≤ 17ms was associated with a greater risk of death, with hazard ratios of 6.3 (1.4–28.0; p = 0.015) and 5.5 (1,2–24,8; p = 0.027), respectively. The addition of the dichotomized SDNN to the SOFA model reduced AIC and increased the concordance statistic and the R², indicating that predictive power of the SDNN + SOFA model is better than predictive power of SOFA only.

Conclusions

Several HRV parameters are reduced in nonsurviving septic patients. SDNN ≤17 is a risk factor for death in septic patients, even after adjusting for severity scores.

Relative Bradycardia in Patients With Septic Shock Requiring Vasopressor Therapy

OBJECTIVES

Tachycardia is common in septic shock, but many patients with septic shock are relatively bradycardic. The prevalence, determinants, and implications of relative bradycardia (heart rate, < 80 beats/min) in septic shock are unknown. To determine mortality associated with patients who are relatively bradycardic while in septic shock.

DESIGN

Retrospective study of patients admitted for septic shock to study ICUs during 2005-2013.

SETTING

One large academic referral hospital and two community hospitals.

PATIENTS

Adult patients with septic shock requiring vasopressors.

INTERVENTION

None.

MEASUREMENTS

Primary outcome was 28-day mortality. We used multivariate logistic regression to evaluate the association between relative bradycardia and mortality, controlling for confounding with inverse probability treatment weighting using a propensity score.

RESULTS

We identified 1,554 patients with septic shock, of whom 686 (44%) met criteria for relative bradycardia at some time. Twenty-eight-day mortality in this group was 21% compared to 34% in the never-bradycardic group (p < 0.001). Relatively bradycardic patients were older (65 vs 60 yr; p < 0.001) and had slightly lower illness severity (Sequential Organ Failure Assessment, 10 vs 11; p = 0.004; and Acute Physiology and Chronic Health Evaluation II, 27 vs 28; p = 0.008). After inverse probability treatment weighting, covariates were balanced, and the association between relative bradycardia and survival persisted (p < 0.001).

CONCLUSIONS

Relative bradycardia in patients with septic shock is associated with lower mortality, even after adjustment for confounding. Our data support expanded investigation into whether inducing relative bradycardia will benefit patients with septic shock.

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.

Multi-complexity measures of heart rate variability and the effect of vasopressor titration: a prospective cohort study of patients with septic shock

Background

Septic shock is a common and often devastating syndrome marked by severe cardiovascular dysfunction commonly managed with vasopressors. Whether markers of heart rate complexity before vasopressor up-titration could be used to predict success of the up-titration is not known.

Methods

We studied patients with septic shock requiring vasopressor, newly admitted to the intensive care unit. We measured the complexity of heart rate variability (using the ratio of fractal exponents from detrended fluctuation analysis) in the 5 min before all vasopressor up-titrations in the first 24 h of an intensive care unit (ICU) admission. A successful up-titration was defined as one that did not require further up-titration (or decrease in mean arterial pressure) for 60 min.

Results

We studied 95 patients with septic shock, with a median APACHE II of 27 (IQR: 20–37). The median number of up-titrations, normalized to 24 h, was 12.2 (IQR: 8–17) with a maximum of 49. Of the up-titrations, the median proportion of successful interventions was 0.28 (IQR: 0.12–0.42). The median of mean arterial pressure (MAP) at the time of a vasopressor up-titration was 66 mmHg; the average infusion rate of norepinephrine at the time of an up-titration was 0.11 mcg/kg/min. The ratio of fractal exponents was not associated with successful up-titration on univariate or multivariate regression. On exploratory secondary analyses, however, the long-term fractal exponent was associated (p = 0.003) with success of up-titration. Independent of heart rate variability, MAP was associated (p < 0.001) with success of vasopressor up-titration, while neither Sequential Organ Failure Assessment (SOFA) nor Acute Physiology and Chronic Health Evaluation II (APACHE II) score was associated with vasopressor titration.

Conclusions

Only a third of vasopressor up-titrations were successful among patients with septic shock. MAP and the long-term fractal exponent were associated with success of up-titration. These two, complementary variables may be important to the development of rational vasopressor titration protocols.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-016-1896-1) contains supplementary material, which is available to authorized users.

Cardiovascular autonomic alterations in hospitalized patients with community-acquired pneumonia

Background

Alterations of cardiac autonomic control (CAC) are associated with poor outcomes in patients with infectious and non-infectious diseases. No evaluation of CAC in patients with community-acquired pneumonia (CAP) has been performed so far. The aim of the study was to assess CAC in patients with CAP and evaluate the impact of its alterations on disease severity and clinical outcomes in a multicenter, prospective, observational study.

Methods

Consecutive patients hospitalized for CAP were enrolled between 2011 and 2013 two university hospitals in Italy. CAC was assessed by linear spectral and non-linear symbolic analysis of heart rate variability. The presence of severe CAP was evaluated on hospital admission. The primary study outcome was time to clinical stability (TCS) during hospitalization.

Results

Among the 75 patients enrolled (median age: 75 years; 57 % males), a significantly lower total variability and reduction of sympathetic rhythmical component with predominant respiratory modulation was detected in comparison to controls. Among CAP patients affected by a severe CAP on admission, CAC showed a lower sympathetic modulation and predominant parasympathetic oscillatory rhythm. At the multivariate analysis, variables independently correlated with a TCS >7 days were total power, as marker of total variability, [OR (95 % CI): 0.997 (0.994–1.000), p = 0.0454] and sympathetic modulation [OR (95 % CI): 0.964 (0.932–0.998), p = 0.0367].

Conclusions

Loss of sympathetic rhythmical oscillation is associated with a more severe disease and worse early clinical outcome in hospitalized patients with CAP.

Electronic supplementary material

The online version of this article (doi:10.1186/s12931-016-0414-8) contains supplementary material, which is available to authorized users.

Heart Rate Variability in Porcine Progressive Peritonitis-Induced Sepsis

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

Impaired heart rate regulation and depression of cardiac chronotropic and dromotropic function in polymicrobial sepsis

The scope of cardiac pathophysiology in sepsis has not been fully defined. Accordingly, we evaluated the effects of sepsis on heart rate (HR), HR variability, and conduction parameters in a murine model of sepsis. Electrocardiograms were recorded noninvasively from conscious mice before and after cecal ligation and puncture (CLP) or sham surgery. Responses of isolated atria to tyramine and isoproterenol were quantified to assess the functional state of sympathetic nerves and postjunctional sensitivity to adrenergic stimulation. Cecal ligation and puncture mice had lower HR compared with sham at 16 to 18 h postsurgery (sham, 741 ± 7 beats/min; CLP, 557 ± 31 beats/min; n = 6/group; P < 0.001), and there was significant prolongation of the PR, QRS, and QTc intervals. Slowing of HR and conduction developed within 4 to 6 h after CLP and were preceded by a decrease in HR variability. Treatment of CLP mice with isoproterenol (5 mg/kg, intraperitoneally) at 25 h after surgery failed to increase HR or decrease conduction intervals. The lack of in vivo response to isoproterenol cannot be attributed to hypothermia because robust chronotropic and inotropic responses to isoproterenol were evoked from isolated atria at 25 °C and 30 °C. These findings demonstrate that impaired regulation of HR (i.e., reduced HR variability) develops before the onset of overt cardiac rate and conduction changes in septic mice. Subsequent time-dependent decreases in HR and cardiac conduction can be attributed to hypothermia and would contribute to decreased cardiac output and organ perfusion. Because isolated atria from septic mice showed normal responsiveness to adrenergic stimulation, we conclude that impaired effectiveness of isoproterenol in vivo can be attributed to reversible effects of systemic factors on adrenergic receptors and/or postreceptor signaling.