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

Sepsis-induced inflammatory demyelination in medullary visceral zone and cholinergic anti-inflammatory pathway: Insights from a Rat's model study

Background

Our previous studies have demonstrated that the activated Cholinergic Anti-inflammatory Pathway (CAP) effectively suppresses systemic inflammation and immunity in early sepsis. Some parameters of Heart Rate Variability (HRV) could be used to reflect the regulatory activity of CAP. However, in the early stages of severe sepsis of some patients, the inflammatory storm can still result in multiple organs dysfunction and even death, suggesting they lose CAP's modulation ability. Since CAP is part of the vagus nerve and is directly innervated by the Medullary Visceral Zone (MVZ), we can reasonably concluded that pathological changes induced by MVZ's neuroinflammation should be responsible for CAP's dysfunction in modulating systemic inflammation in early sepsis.

Methods

We conducted two independent septic experiments, the sepsis model rats were prepared by cecum ligation and puncture (CLP) method. In the first experiment, A total of 64 adult male Sprague-Dawley rats were included. Under the condition of sepsis and CAP's pharmacological activation or blockade, we investigated the MVZ's pathological changes, the functional state of key neurons including catecholaminergic and cholinergic neurons, key genes' expression such as Oligodendrocyte Transcription Factor 2 (Olig-2) mRNA, glial fibrillary acidic protein (GFAP) mRNA, and matrix metalloprotein (MMP) -9 mRNA, and CAP's activities reflected by HRV. The second experiment involved in 56 rats, through central anti-inflammation by feeding with 10 mg/ml minocycline sucrose solution as the only water source, or right vagus transection excepting for central anti-inflammation as a mean of the CAP's functional cancel, we confirmed that the neuroinflammation in MVZ affected systemic inflammation through CAP in sepsis.

Results

In the first experiment, cholinergic and catecholaminergic neurons showed significant apoptosis with reduced expressions of TH, but the expression of CHAT remained relatively unaffected in MVZ in sepsis. HRV parameters representing the tone of the vagus nerve, such as SDNN, RMSSD, HF, SD1, and SD2, did not show significant differences among the three Septic Groups, although they all decreased significantly compared to the Control Group. The expressions of GFAP mRNA and MMP-9 mRNA were up-regulated, while the expression of Olig-2 mRNA was down-regulated in the Septic Groups. Intervention of CAP had a significant effect on cholinergic and catecholaminergic neurons' apoptosis, as well as the expressions of TH/CHAT and these key genes, but had little effect on HRV in sepsis. In the second experiment, the levels of TNF-α, IL-6, in serum and MVZ were significantly increased in sepsis. Central anti-inflammatory treatment reversed these changes. However, right vagotomy abolished the central anti-inflammatory effect.

Conclusions

Our study uncovered that MVZ's neuroinflammation may play a crucial role in the uncontrolled systemic inflammation through inflammatory demyelination in MVZ, which disrupts CAP's modulation on the systemic inflammation in early 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.

Cardiovascular Autonomic Dysfunction in Hospitalized Patients with a Bacterial Infection: A Longitudinal Observational Pilot Study in the UK

PURPOSE

A temporal reduction in the cardiovascular autonomic responses predisposes patients to cardiovascular instability after a viral infection and therefore increases the risk of associated complications. These findings have not been replicated in a bacterial infection. This pilot study will explore the prevalence of cardiovascular autonomic dysfunction (CAD) in hospitalized patients with a bacterial infection.

METHODS

A longitudinal observational pilot study was conducted. Fifty participants were included: 13 and 37 participants in the infection group and healthy group, respectively. Recruitment and data collection were carried out during a two-year period. Participants were followed up for 6 weeks: all participants' cardiovascular function was assessed at baseline (week 1) and reassessed subsequently at week 6 so that the progression of the autonomic function could be evaluated over that period of time. The collected data were thereafter analyzed using STATA/SE version 16.1 (StataCorp). The Fisher Exact test, McNemar exact test, Mann-Whitney test and Wilcoxon test were used for data analysis.

RESULTS

32.4% of the participants in the healthy group were males (n = 12) and 67.6% were females (n = 25). Participants' age ranged from 33 years old to 76 years old with the majority being 40-60 years of age (62.1%) (Mean age 52.4 SD = 11.4). Heart rate variability (HRV) in response to Valsalva Maneuver, metronome breathing, standing and sustained handgrip in the infection group was lower than in the healthy group throughout the weeks. Moreover, both the HRV in response to metronome breathing and standing up showed a statistically significant difference when the mean values were compared between both groups in week 1 (p = 0.03 and p = 0.013). The prevalence of CAD was significantly higher in the infection group compared to healthy volunteers, both at the beginning of the study (p = 0.018) and at the end of follow up (p = 0.057), when all patients had been discharged.

CONCLUSIONS

CAD, as assessed by the HRV, is a common finding during the recovery period of a bacterial infection, even after 6 weeks post-hospital admission. This may increase the risk of complications and cardiovascular instability. It may therefore be of value to conduct a wider scale study to further evaluate this aspect so recommendations can be made for the cardiovascular autonomic assessment of patients while they are recovering from a bacterial infectious process.

Association between early manifestations of infection or sepsis and prognosis in a high complexity hospital in the city of Medellín

OBJECTIVE

To identify the first symptoms and signs of patients with suspected infection or sepsis and their association with the composite outcome of admission to the Intensive Care Unit (ICU) or mortality.

DESIGN

Prospective cohort study between June 2019 and March 2020.

SETTING

Hospital Universitario San Vicente Fundación, Colombia.

PATIENTS

Over 18 years of age with suspicion or confirmation of sepsis, which required hospitalization.

INTERVENTIONS

None.

MAIN VARIABLES OF INTEREST

Symptoms and signs associated with infection, with their time of evolution, specified in the study.

RESULTS

From 1005 eligible patients, 261 were included. After multivariable adjustment with a logistic regression model, the main factors for ICU admission or mortality were heart rate (OR 1.04 with 95% CI 1.04-3.7), respiratory rate (OR 1.19 with 95% CI 1.0-1.4) and capillary refill time (OR 3.4 with 95% CI 1.9-6.1).

CONCLUSIONS

Heart rate, respiratory rate, and capillary refill may behave as early predictors of ICU admission and mortality in cases of sepsis.

Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis

BACKGROUND AND OBJECTIVES

Sepsis is accompanied by a considerably high risk of mortality in the short term, despite the availability of recommended mortality risk assessment tools. However, these risk assessment tools seem to have limited predictive value. With the gradual integration of machine learning into clinical practice, some researchers have attempted to employ machine learning for early mortality risk prediction in sepsis patients. Nevertheless, there is a lack of comprehensive understanding regarding the construction of predictive variables using machine learning and the value of various machine learning methods. Thus, we carried out this systematic review and meta-analysis to explore the predictive value of machine learning for sepsis-related death at different time points.

METHODS

PubMed, Embase, Cochrane, and Web of Science databases were searched until August 9th, 2022. The risk of bias in predictive models was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). We also performed subgroup analysis according to time of death and type of model and summarized current predictive variables used to construct models for sepsis death prediction.

RESULTS

Fifty original studies were included, covering 104 models. The combined Concordance index (C-index), sensitivity, and specificity of machine learning models were 0.799, 0.81, and 0.80 in the training set, and 0.774, 0.71, and 0.68 in the validation set, respectively. Machine learning outperformed conventional clinical scoring tools and showed excellent C-index, sensitivity, and specificity in different subgroups. Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) are the preferred machine learning models because they showed more favorable accuracy with similar modeling variables. This study found that lactate was the most frequent predictor but was seriously ignored by current clinical scoring tools.

CONCLUSION

Machine learning methods demonstrate relatively favorable accuracy in predicting the mortality risk in sepsis patients. Given the limitations in accuracy and applicability of existing prediction scoring systems, there is an opportunity to explore updates based on existing machine learning approaches. Specifically, it is essential to develop or update more suitable mortality risk assessment tools based on the specific contexts of use, such as emergency departments, general wards, and intensive care units.

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.

Norepinephrine dose, lactate or heart rate: what impacts prognosis in sepsis and septic shock? Results from a prospective, monocentric registry

OBJECTIVE

The study comprehensively investigates the prognostic value of norepinephrine (NE) dose, lactate and heart rate in patients with sepsis and septic shock.

BACKGROUND

Limited data regarding the prognostic value of NE dose, lactate and heart rate in patients meeting the sepsis-3 criteria is available.

METHODS

Consecutive patients with sepsis and septic shock from 2019 to 2021 were included. The prognostic value of NE dose, lactate and heart rate was tested for 30-day all-cause mortality. Statistical analyses included univariable t-tests, Spearman's correlations, C-statistics, Kaplan-Meier analyses, as well as one-factorial repeated measures analysis of variance (ANOVA) and Cox proportional regression analyses.

RESULTS

339 patients with sepsis or septic shock were included. With an area under the curve (AUC) of up to 0.638 and 0.685, NE dose and lactate revealed moderate prognostic accuracy for 30-day all-cause mortality, whereas heart rate was not associated with prognosis. Very high NE doses (i.e. > 1.0 mcg/kg/min) (HR = 2.938; 95% CI 1.933 - 4.464; p = .001) and lactate levels (i.e. ≥ 4 mmol/l) (HR = 2.963; 95% CI 2.095 - 4.191; p = .001) on admission were associated with highest risk of death. Finally, increasing NE doses and lactate levels from day 1 to 3 indicated increased risk of death, which was consistent after multivariable adjustment.

CONCLUSION

Both very high NE doses and lactate levels - but not heart rate - were associated with increased risk of 30-d all-cause mortality in patients with sepsis and septic shock.

Embracing complexity in sepsis

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

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.

The Neuroimmune Response to Surgery – An Exploratory Study of Trauma-Induced Changes in Innate Immunity and Heart Rate Variability

Surgery triggers a systemic inflammatory response that ultimately impacts the brain and associates with long-term cognitive impairment. Adequate regulation of this immune surge is pivotal for a successful surgical recovery. We explored the temporal immune response in a surgical cohort and its associations with neuroimmune regulatory pathways and cognition, in keeping with the growing body of evidence pointing towards the brain as a regulator of peripheral inflammation. Brain-to-immune communication acts through cellular, humoral and neural pathways. In this context, the vagal nerve and the cholinergic anti-inflammatory pathway (CAP) have been shown to modify peripheral immune cell activity in both acute and chronic inflammatory conditions. However, the relevance of neuroimmune regulatory mechanisms following a surgical trauma is not yet elucidated. Twenty-five male patients undergoing elective laparoscopic abdominal surgery were included in this observational prospective study. Serial blood samples with extensive immune characterization, assessments of heart rate variability (HRV) and cognitive tests were performed before surgery and continuing up to 6 months post-surgery. Temporal immune responses revealed biphasic reaction patterns with most pronounced changes at 5 hours after skin incision and 14 days following surgery. Estimations of cardiac vagal nerve activity through HRV recordings revealed great individual variations depending on the pre-operative HRV baseline. A principal component analysis displayed distinct differences in systemic inflammatory biomarker trajectories primarily based on pre-operative HRV, with potiential consequences for long-term surgical outcomes. In conclusion, individual pre-operative HRV generates differential response patterns that associate with distinct inflammatory trajectories following surgery. Long-term surgical outcomes need to be examined further in larger studies with mixed gender cohorts.

The Association between Inflammatory Biomarkers and Cardiovascular Autonomic Dysfunction after Bacterial Infection

Heart rate variability (HRV) is a known measure of cardiac autonomic function. A cardiovascular autonomic dysfunction (CAD), measured as changes in HRV, is usually presented after an infectious process. The aim of the present study is to assess the association between serum inflammatory markers and CAD. For this purpose, 50 volunteers (13 of them recovering from an infection) were recruited and followed-up for 6 weeks. Their serum inflammatory biomarkers (CRP, IL1, IL4, IL6, IL10, and TNFalpha) were quantified throughout those weeks, along with their HRV resting, in response to the Valsalva maneuver, metronome breathing, standing and sustained handgrip. The correlation of within-subject changes in both HRV and inflammatory biomarkers was assessed to evaluate the concurrent changes. An inverse within-subject correlation was found between CRP and HRV in response to the Valsalva maneuver (rho (95% CI): −0.517 (−0.877 to −0.001); p = 0.032) and HRV standing (rho (95% CI): −0.490 (−0.943 to −0.036); p = 0.034). At the beginning, increased values of CRP are found along with reduced levels of HRV. Then, the CRP was reduced, accompanied by an improvement (increase) in HRV. These results suggest that CRP is a potential marker of CAD. Whether it is the cause, the consequence or a risk indicator non-causally associated is still to be determined.

Exploration of autonomic regulation reflecting on pathophysiological change of sepsis: a prospective observational study

Aim

It remains unclear how autonomic regulation modulates pathophysiological changes of sepsis. This study aims to analyze and clarify those in patients with suspected sepsis.

Methods

In this single‐centered, prospective, observational study, adult patients who had an infection, a quick Sequential Organ Failure Assessment score of 2 or more at the emergency department, and underwent intensive care were screened. Heart rate variability (HRV) and serum adrenaline were measured immediately after arrival. The primary outcome was defined as vasopressor dependence during 48 h after arrival.

Results

A total of 63 patients were included. All the patients had SOFA score of 2 or more on admission. Vasopressor dependence, renal replacement therapy, and in‐hospital mortality were associated with higher adrenaline (which reflects sympathetic adrenergic system activity). Bacteremia was associated with lower high‐frequency components of HRV (parasympathetic nerve activity). The HRV parameter of sympathetic nerve activity had no significant association with the outcomes. In the multivariate logistic regression model adjusted for age and sex, vasopressor dependence remained associated with higher adrenaline (cut‐off 0.11 ng/mL, odds ratio 9.71, 95% confidence interval 2.55–37; P = 0.000874), and lower high‐frequency components with bacteremia (17.2 ms², odds ratio 4.86, 95% confidence interval 1.36–17.4; P = 0.0152). There were no significant correlations between parameters of HRV and serum adrenaline.

Conclusion

Hypoperfusion, organ dysfunction, and in‐hospital mortality were associated with an increased sympathetic adrenergic activity. Bacteremia was associated with decreased parasympathetic nerve activity. The autonomic regulator may involve a multilayered and differentiated modulating process for sepsis.

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.

Comparison of qSOFA and Hospital Early Warning Scores for prognosis in suspected sepsis in emergency department patients: a systematic review

BACKGROUND

Sepsis is a major cause of morbidity and mortality and many tools exist to facilitate early recognition. This review compares two tools: the quick Sequential Organ Failure Assessment (qSOFA) and Early Warning Scores (National/Modified Early Warning Scores (NEWS/MEWS)) for predicting intensive care unit (ICU) admission and mortality when applied in the emergency department.

METHODS

A literature search was conducted using Medline, CINAHL, Embase and Cochrane Library, handsearching of references and a grey literature search with no language or date restrictions. Two authors selected studies and quality assessment completed using QUADAS-2. Area under the receiver operating characteristic curve (AUROC), sensitivities and specificities were compared.

RESULTS

13 studies were included, totalling 403 865 patients. All reported mortality and six reported ICU admission.The ranges for AUROC estimates varied from little better than chance to good prediction of mortality (NEWS: 0.59-0.88; qSOFA: 0.57-0.79; MEWS 0.56-0.75), however, individual papers generally reported higher AUROC values for NEWS than qSOFA. NEWS values demonstrated a tendency towards better sensitivity for ICU admission (NEWS ≥5, 46%-91%; qSOFA ≥2, 12%-53%) and mortality (NEWS ≥5, 51%-97%; qSOFA ≥2, 14%-71%) but lower specificity (ICU: NEWS ≥5, 25%-91%; qSOFA ≥2, 67%-99%; mortality: NEWS ≥5, 22%-91%; qSOFA ≥2, 58%-99%).

CONCLUSION

The wide range of AUROC estimates and high heterogeneity limit our conclusions. Allowing for this, the NEWS AUROC was consistently higher than qSOFA within individual papers. Both scores allow threshold setting, determined by the preferred compromise between sensitivity and specificity. At established thresholds NEWS tended to higher sensitivity while qSOFA tended to a higher specificity.

PROSPERO REGISTRATION NUMBER

CRD42019131414.

A critical appraisal of the prognostic predictive models for patients with sepsis: Which model can be applied in clinical practice?

BACKGROUND

Sepsis is associated with high mortality and predictive models can help in clinical decision-making. The objective of this study was to carry out a systematic review of these models.

METHODS

In 2019, we conducted a systematic review in MEDLINE and EMBASE (CDR42018111121:PROSPERO) of articles that developed predictive models for mortality in septic patients (inclusion criteria). We followed the CHARMS recommendations (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies), extracting the information from its 11 domains (Source of data, Participants, etc). We determined the risk of bias and applicability (participants, outcome, predictors and analysis) through PROBAST (Prediction model Risk Of Bias ASsessment Tool).

RESULTS

A total of 14 studies were included. In the CHARMS extraction, the models found showed great variability in its 11 domains. Regarding the PROBAST checklist, only one article had an unclear risk of bias as it did not indicate how missing data were handled while the others all had a high risk of bias. This was mainly due to the statistical analysis (inadequate sample size, handling of continuous predictors, missing data and selection of predictors), since 13 studies had a high risk of bias. Applicability was satisfactory in six articles. Most of the models integrate predictors from routine clinical practice. Discrimination and calibration were assessed for almost all the models, with the area under the ROC curve ranging from 0.59 to 0.955 and no lack of calibration. Only three models were externally validated and their maximum discrimination values in the derivation were from 0.712 and 0.84. One of them (Osborn) had undergone multiple validation studies.

DISCUSSION

Despite most of the studies showing a high risk of bias, we very cautiously recommend applying the Osborn model, as this has been externally validated various times.

Early prediction of serious infections in febrile infants incorporating heart rate variability in an emergency department: a pilot study

BACKGROUND

Early differentiation of febrile young infants with from those without serious infections (SIs) remains a diagnostic challenge. We sought to (1) compare vital signs and heart rate variability (HRV) parameters between febrile infants with versus without SIs, (2) assess the performance of HRV and vital signs with reference to current triage tools and (3) compare HRV and vital signs to HRV, vital signs and blood biomarkers, when predicting for the presence of SIs.

METHODS

Using a prospective observational design, we recruited patients <3 months old presenting to a tertiary paediatric ED in Singapore from December 2018 through November 2019. We obtained patient demographic characteristics, triage assessment (including the Severity Index Score (SIS)), HRV parameters (time, frequency and non-linear domains) and laboratory results. We performed multivariable logistic regression analyses to predict the presence of an SI, using area under the curve (AUC) with the corresponding 95% CI to assess predictive capability.

RESULTS

Among 203 infants with a mean age of 38.4 days (SD 27.6), 67 infants (33.0%) had an SI. There were significant differences in the time, frequency and non-linear domains of HRV parameters between infants with versus without SIs. In predicting SIs, gender, temperature and the HRV non-linear parameter Poincaré plot SD2 (AUC 0.78, 95% CI 0.71 to 0.84) performed better than SIS alone (AUC 0.61, 95% CI 0.53 to 0.68). Model performance improved with the addition of absolute neutrophil count and C reactive protein (AUC 0.82, 95% CI 0.76 to 0.89).

CONCLUSION

An exploratory prediction model incorporating HRV and biomarkers improved prediction of SIs. Further research is needed to assess if HRV can identify which young febrile infants have an SI at ED triage.

TRIAL REGISTRATION NUMBER

NCT04103151.

Quick sequential organ failure assessment versus systemic inflammatory response syndrome criteria for emergency department patients with suspected infection

Previous studies have shown inconsistent prognostic accuracy for mortality with both quick sequential organ failure assessment (qSOFA) and the systemic inflammatory response syndrome (SIRS) criteria. We aimed to validate the accuracy of qSOFA and the SIRS criteria for predicting in-hospital mortality in patients with suspected infection in the emergency department. A prospective study was conducted including participants with suspected infection who were hospitalised or died in 34 emergency departments in Japan. Prognostic accuracy of qSOFA and SIRS criteria for in-hospital mortality was assessed by the area under the receiver operating characteristic (AUROC) curve. Of the 1060 participants, 402 (37.9%) and 915 (86.3%) had qSOFA ≥ 2 and SIRS criteria ≥ 2 (given thresholds), respectively, and there were 157 (14.8%) in-hospital deaths. Greater accuracy for in-hospital mortality was shown with qSOFA than with the SIRS criteria (AUROC: 0.64 versus 0.52, difference + 0.13, 95% CI [+ 0.07, + 0.18]). Sensitivity and specificity for predicting in-hospital mortality at the given thresholds were 0.55 and 0.65 based on qSOFA and 0.88 and 0.14 based on SIRS criteria, respectively. To predict in-hospital mortality in patients visiting to the emergency department with suspected infection, qSOFA was demonstrated to be modestly more accurate than the SIRS criteria albeit insufficiently sensitive.Clinical Trial Registration: The study was pre-registered in the University Hospital Medical Information Network Clinical Trials Registry (UMIN000027258).

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

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

Autonomic Rehabilitation: Adapting to Change

Adaption to changes of external environment or internal health, the body-mind connection, or autonomic nervous system must be flexible and healthy. Population health studies with wearable technology and remote monitoring will lead to paradigm shifts in how to approach the physiology of emotion. Heart rate variability as a whole health biomarker could emerge as a foundation for a process beginning with objective habits and skills of real-time modulation with focused breathing for healthier decision making and autonomic health trajectory change. Physical medicine and rehabilitation is uniquely poised to refine an autonomic rehabilitation process in an integrative manner to help individuals adapt.

Neurotransmitter system aberrations in patients with drug addiction

Drug dependence may affect the neurotransmitter system levels in the human body. This study recruited 113 healthy control subjects, 118 heroin-dependent patients and 118 methamphetamine-dependent patients and examined the serum 5-HT, dopamine, glutamate and norepinephrine levels in the 349 volunteers. ELISA assays demonstrated that the serum 5-HT levels were significantly reduced in the drug-dependent patients, whereas the serum dopamine and glutamate levels were both significantly increased in the drug-dependent patients when compared with control subjects. In contrast, the norepinephrine levels did not exhibit a significant difference between the drug-dependent and control subjects. We also used qRT-PCR to analyze the transcriptional expression levels of 5-HT_1A, 5-HT_1B, dopmaine-D1 and dopamine-D2 receptors in the blood of drug-dependent patients and controls, and the results show that only 5-HT_1B receptor levels were dysfunctional in the heroin abusers. In addition, our results suggest that serum 5-HT, dopamine, and glutamate levels had the potential to differ between drug abusers and controls, and combining those three potential biomarkers provided an accurate means to differentiate between the drug-dependent and control subjects. Taken together, our study reveals a differential profile of neurotransmitters in the heroin-dependent patients and methamphetamine-dependent patients, and this revelation may contribute to understanding the pathophysiology of drug addiction.

A narrative review of heart rate and variability in sepsis

Clinicians face challenges in the timely diagnosis and management of pediatric sepsis. Pediatric heart rate has been incorporated into early warning systems and studied as a predictor for critical illness. We aim to review: (I) the role of heart rate in pediatric warning systems and (II) the role of heart rate variability (HRV) in adult and neonatal sepsis, with a focus on its potential applications in pediatrics. We conducted a literature search for papers published up to December 2019 on the utility of heart rate and HRV analysis in the diagnosis and management of sepsis, using four medical databases: PubMed, Google Scholar, EMBASE and Web of Science. This review demonstrates that the clinical utility of pediatric heart rate in predicting clinical deterioration is limited by the lack of consensus among warning systems, consensus-based guidelines, and evidence-based studies as to what constitutes abnormal heart rate in the pediatric age group. Current studies demonstrate that abnormal heart rate itself does not adequately discriminate children with sepsis from those without. HRV analysis provides a quick and non-invasive method of assessment and can provide more information than traditional heart rate. HRV analysis has the potential to add value in identification and prognostication of adult and neonatal sepsis. With further studies to explore its role, HRV analysis has the potential to add to current tools in the diagnosis and prognosis of pediatric sepsis.

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.

Serial Heart Rate Variability Measures for Risk Prediction of Septic Patients in the Emergency Department

In this study, we used serial heart rate variability (HRV) measures over 2 hours to improve the prediction of 30-day in-hospital mortality among septic patients in the emergency department (ED). We presented a generalizable methodology for processing and analysing HRV time series (HRVTS) data which may be noisy and incomplete. Feature sets were created from the HRVTS data of 162 patients with suspected sepsis using aggregation-based, deltabased and regression-based series-to-point transformations, and modelled over 100 random stratified splits. An optimized feature set comprising 12 selected HRVTS features performed better than baseline feature sets which only included patient demographics, vital signs and single time-point HRV measures taken at triage. This improved risk stratification approach could be used in the ED to identify high-risk septic patients for appropriate management and disposition.

A Personalized Signature and Chronotherapy-Based Platform for Improving the Efficacy of Sepsis Treatment

Sepsis remains a major therapeutic challenge and is associated with a high rate of morbidity and mortality. It is a dynamic condition in which multiple parameters change over time, rendering it difficult to overcome the various injurious responses, which worsen the prognosis in these patients. The prognosis of sepsis is associated with a disbalance of compensatory responses to infectious triggers, part of which can be deleterious. Marked inter- and intra-patient variability characterizes the mechanisms that underlie sepsis progression and determine the response to therapy. In this paper, we review some of the data on the use of chronopharmacological approaches for the treatment of patients with sepsis and discuss the role of the autonomic nervous system in the mechanisms associated with immune response and chronotherapy in these patients. We describe the implementation of an individualized platform that is based on the personalized autonomic nervous system, immune, and chronobiology-derived parameters for generating a patient-tailored therapeutic regimen. The notion of overcoming the deleterious compensatory response in a highly dynamic system in sepsis is presented to ensure an improved response to current therapies.

First Evaluation of an Index of Low Vagally-Mediated Heart Rate Variability as a Marker of Health Risks in Human Adults: Proof of Concept

Multiple studies have demonstrated low vagally-mediated heart rate variability (HRV) being associated with a range of risk factors for heart disease and stroke, including inflammation, hyperglycemia, hyperlipidemia, and hypertension. Yet, no cut point exists that indicates elevated risk. In the present study we sought to identify a cut point-value for HRV that is associated with elevated risk across a range of known risk factors. Methods: A total of 9550 working adults from 19 study sites took part in a health assessment that included measures of inflammation, hyperglycemia, hyperlipidemia, and hypertension and vagally-mediated HRV (Root mean square of successive differences between normal heartbeats (RMSSD)). Multiple age and sex adjusted logistic regressions were calculated per risk factor (normal versus clinical range), with RMSSD being entered in binary at different cut points ranging from 15–39 msec with a 2 msec increment. Results: For daytime RMSSD, values below 25 ± 4 indicated elevated risk (odds ratios (OR) 1.5–3.5 across risk factors). For nighttime RMSSD, values below 29 ± 4 indicated elevated risk (OR 1.2–2.0). Conclusion: These results provide the first evidence that a single value of RMSSD may be associated with elevated risk across a range of established cardiovascular risk factors and may present an easy to assess novel marker of cardiovascular risk.

Validation of the mortality in emergency department sepsis (MEDS) score in a Singaporean cohort

The emergency department (ED) serves as the first point of hospital contact for most septic patients. Early mortality risk stratification using a quick and accurate triage tool would have great value in guiding management. The mortality in emergency department sepsis (MEDS) score was developed to risk stratify patients presenting to the ED with suspected sepsis, and its performance in the literature has been promising. We report in this study the first utilization of the MEDS score in a Singaporean cohort.In this retrospective observational cohort study, adult patients presenting to the ED with suspected sepsis and fulfilling systemic inflammatory response syndrome (SIRS) criteria were recruited. Primary outcome was 30-day in-hospital mortality (IHM) and secondary outcome was 72-hour mortality. MEDS, acute physiology and chronic health evaluation II (APACHE II), and sequential organ failure assessment (SOFA) scores were compared for prediction of primary and secondary outcomes. Receiver operating characteristic (ROC) analysis was conducted to compare predictive performance.Of the 249 patients included in the study, 46 patients (18.5%) met 30-day IHM. MEDS score achieved an area under the ROC curve (AUC) of 0.87 (95% confidence interval [CI], 0.82-0.93), outperforming the APACHE II score (0.77, 95% CI 0.69-0.85) and SOFA score (0.78, 95% CI 0.71-0.85). On secondary analysis, MEDS score was superior to both APACHE II and SOFA scores in predicting 72-hour mortality, with AUC of 0.88 (95% CI 0.82-0.95), 0.81 (95% CI 0.72-0.89), and 0.79 (95% CI 0.71-0.87), respectively. In predicting 30-day IHM, MEDS score ≥12, APACHE II score ≥23, and SOFA score ≥5 performed at sensitivities of 76.1%, 67.4%, and 76.1%, and specificities of 83.3%, 73.9%, and 65.0%, respectively.The MEDS score performed well in its ability for mortality risk stratification in a Singaporean ED cohort.

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.

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.