A narrative review of heart rate and variability in sepsis

Parsimonious Waveform-derived Features consisting of Pulse Arrival Time and Heart Rate Variability Predicts the Onset of Septic Shock

Sepsis is a major public health emergency and one of the leading causes of morbidity and mortality in critically ill patients. For each hour treatment is delayed, shock-related mortality increases, so early diagnosis and intervention is of utmost importance. However, earlier recognition of shock requires active monitoring, which may be delayed due to subclinical manifestations of the disease at the early phase of onset. Machine learning systems can increase timely detection of shock onset by exploiting complex interactions among continuous physiological waveforms. We use a dataset consisting of high-resolution physiological waveforms from intensive care unit (ICU) of a tertiary hospital system. We investigate the use of mean arterial blood pressure (MAP), pulse arrival time (PAT), heart rate variability (HRV), and heart rate (HR) for the early prediction of shock onset. Using only five minutes of the aforementioned vital signals from 239 ICU patients, our developed models can accurately predict septic shock onset 6 to 36 hours prior to clinical recognition with area under the receiver operating characteristic (AUROC) of 0.84 and 0.8 respectively. This work lays foundations for a robust, efficient, accurate and early prediction of septic shock onset which may help clinicians in their decision-making processes. This study introduces machine learning models that provide fast and accurate predictions of septic shock onset times up to 36 hours in advance. BP, PAT and HR dynamics can independently predict septic shock onset with a look-back period of only 5 mins.

Cardiac index and heart rate as prognostic indicators for mortality in septic shock: A retrospective cohort study from the MIMIC-IV database

Background

Septic shock is a life-threatening condition that can lead to organ dysfunction and death. In the ICU, monitoring of cardiac index (CI) and heart rate (HR) is commonly used to guide management and predict outcomes in septic shock patients. However, there is a lack of research on the association between CI and HR and the risk of mortality in this patient population. Therefore, the aim of this study was to investigate the relationship between different levels of CI and HR and mortality in septic shock patients.

Methods

Data analysis was obtained from the MIMIC-IV version 2.0 database. Sepsis and septic shock were primarily defined by sepsis-3, the third international consensus on sepsis and septic shock. CI was computed using cardiac output (CO) and body surface area (BSA). To evaluate the incidence of CI with respect to each endpoint (7-, 14-, 21-, and 28-day mortality), a restricted cubic spline curve function (RCS) was used. The optimal cutoff value for predicted mortality was determined using the Youden index. Analyses of KM curves, cox regression, and logistic regression were conducted separately to determine the relationship between various CI and HR and 28-day mortality.

Results

This study included 1498 patients with septic shock. A U-shaped relationship between CI levels and risk of mortality in septic shock was found by RCS analysis (p < 0.001). CI levels within the intermediate range of 1.85-2.8 L/min/m2 were associated with a mortality hazard ratio (HR) < 1. In contrast, low CI (HR = 1.87 95% CI: 1.01-3.49) and high CI (HR = 1.93 95% CI: 1.26-2.97) had a significantly increased risk of mortality. The AUC for heart rate prediction of mortality by Youden index analysis was 0.70 95%CI:0.64-0.76 with a cut-off value of 93.63 bpm. According to the characteristics of HR and CI, patients were divided into six subgroups HR↓+CI intermediate group (n = 772), HR↓+CI↓ group (n = 126), HR↓+CI↑ group (n = 294), HR↑+CI intermediate group (n = 132), HR↑+CI↓ group (n = 24), and HR↑+CI↑ group (n = 150). The KM curves, COX regression, and logistic regression analysis showed that the survival rates the of HR↓+CI intermediate group, HR↓+CI↓ group, and HR↓+CI↑ were higher than the other groups. The risk factors of HR↑+CI intermediate group, HR↑+CI↓, and HR↑+CI↑ with ICU 28-day mortality were HR = 2.91 (95% CI: 1.39-5.97), HR = 3.67 (95% CI: 1.39-11.63), and HR = 5.77 (95% CI: 2.98-11.28), respectively.

Conclusion

Our retrospective study shows that monitoring cardiac index and heart rate in patients with septic shock may help predict the organismal response and hemodynamic consequences, as well as the prognosis. Thus, healthcare providers should carefully monitor changes in these parameters in septic shock patients transferred to the ICU for treatment.

Heart rate/temperature ratio: A practical prognostic indicator for critically ill patients with sepsis

Background

We hypothesize that the heart rate/temperature ratio can predict intensive care unit (ICU) mortality in critical ill patients with sepsis. We aimed to explore the association between the heart rate/temperature ratio and ICU mortality in patients with sepsis.

Methods

We conducted this study utilizing a comprehensive critical care medicine database. The primary endpoint assessed was ICU mortality. A multivariable logistic regression model was employed to determine the independent impact of the heart rate to temperature ratio on ICU mortality.

Results

The study included 12,321 patients. A nonlinear relationship was observed between the heart rate/temperature ratio and ICU mortality, with an inflection point identified at 2.22. The results from the Multivariable logistic regression analysis revealed that the heart rate/temperature ratio independently contributed to the risk of ICU mortality. In model II, there was a 55 % higher ICU mortality rate with a heart rate/temperature ratio greater than 2.22 than with that less than 2.22 (odds ratio [OR] = 1.55, 95 % confidence interval [CI] 1.35-1.77). Moreover, an elevated heart rate/temperature ratio as a continuous variable showed a positive association with ICU mortality (OR = 2.14; 95 % CI: 1.87-2.45). The impact of the heart rate/temperature ratio on ICU mortality remained consistent across all subgroup variables. The sensitivity analysis results consistently supported the primary outcome, with an E value of 2.47. This suggests that the influence of unmeasured confounders on the observed outcomes was minimal, thereby confirming the robustness of the findings.

Conclusions

The heart rate/temperature ratio is a readily available and convenient indicator in a clinical setting. Elevated heart rate/temperature ratios, particularly those exceeding 2.22, are strongly linked to a high ICU mortality rate among critically ill sepsis patients.

A time series driven model for early sepsis prediction based on transformer module

Sepsis remains a critical concern in intensive care units due to its high mortality rate. Early identification and intervention are paramount to improving patient outcomes. In this study, we have proposed predictive models for early sepsis prediction based on time-series data, utilizing both CNN-Transformer and LSTM-Transformer architectures. By collecting time-series data from patients at 4, 8, and 12 h prior to sepsis diagnosis and subjecting it to various network models for analysis and comparison. In contrast to traditional recurrent neural networks, our model exhibited a substantial improvement of approximately 20%. On average, our model demonstrated an accuracy of 0.964 (± 0.018), a precision of 0.956 (± 0.012), a recall of 0.967 (± 0.012), and an F1 score of 0.959 (± 0.014). Furthermore, by adjusting the time window, it was observed that the Transformer-based model demonstrated exceptional predictive capabilities, particularly within the earlier time window (i.e., 12 h before onset), thus holding significant promise for early clinical diagnosis and intervention. Besides, we employed the SHAP algorithm to visualize the weight distribution of different features, enhancing the interpretability of our model and facilitating early clinical diagnosis and intervention.

Eleven years of experience in operating a pediatric rapid response system at a children's hospital in South Korea

BACKGROUND

Various rapid response systems have been developed to detect clinical deterioration in patients. Few studies have evaluated single-parameter systems in children compared to scoring systems. Therefore, in this study we evaluated a single-parameter system called the acute response system (ARS).

METHODS

This retrospective study was performed at a tertiary children's hospital. Patients under 18 years old admitted from January 2012 to August 2023 were enrolled. ARS parameters such as systolic blood pressure, heart rate, respiratory rate, oxygen saturation, and whether the ARS was activated were collected. We divided patients into two groups according to activation status and then compared the occurrence of critical events (cardiopulmonary resuscitation or unexpected intensive care unit admission). We evaluated the ability of ARS to predict critical events and calculated compliance. We also analyzed the correlation between each parameter that activates ARS and critical events.

RESULTS

The critical events prediction performance of ARS has a specificity of 98.5%, a sensitivity of 24.0%, a negative predictive value of 99.6%, and a positive predictive value of 8.1%. The compliance rate was 15.6%. Statistically significant increases in the risk of critical events were observed for all abnormal criteria except low heart rate. There was no significant difference in the incidence of critical events.

CONCLUSIONS

ARS, a single parameter system, had good specificity and negative predictive value for predicting critical events; however, sensitivity and positive predictive value were not good, and medical staff compliance was poor.

HIRA: Heart Rate Interval based Rapid Alert score to characterize autonomic dysfunction among patients with sepsis-related acute respiratory failure (ARF)

Objective. To examine whether heart rate interval based rapid alert (HIRA) score derived from a combination model of heart rate variability (HRV) and modified early warning score (MEWS) is a surrogate for the detection of acute respiratory failure (ARF) in critically ill sepsis patients.Approach. Retrospective HRV analysis of sepsis patients admitted to Emory healthcare intensive care unit (ICU) was performed between sepsis-related ARF and sepsis controls without ARF. HRV measures such as time domain, frequency domain, and nonlinear measures were analyzed up to 24 h after patient admission, 1 h before the onset of ARF, and a random event time in the sepsis controls. Statistical significance was computed by the Wilcoxon Rank Sum test. Machine learning algorithms such as eXtreme Gradient Boosting and logistic regression were developed to validate the HIRA score model. The performance of HIRA and early warning score models were evaluated using the area under the receiver operating characteristic (AUROC).Main Results. A total of 89 (ICU) patients with sepsis were included in this retrospective cohort study, of whom 31 (34%) developed sepsis-related ARF and 58 (65%) were sepsis controls without ARF. Time-domain HRV for Electrocardiogram (ECG) Beat-to-Beat RR intervals strongly distinguished ARF patients from controls. HRV measures for nonlinear and frequency domains were significantly altered (p< 0.05) among ARF compared to controls. The HIRA score AUC: 0.93; 95% confidence interval (CI): 0.88-0.98) showed a higher predictive ability to detect ARF when compared to MEWS (AUC: 0.71; 95% CI: 0.50-0.90).Significance. HRV was significantly impaired across patients who developed ARF when compared to controls. The HIRA score uses non-invasively derived HRV and may be used to inform diagnostic and therapeutic decisions regarding the severity of sepsis and earlier identification of the need for mechanical ventilation.

Prognostic implications of ultra-short heart rate variability indices in hospitalized patients with infective endocarditis

BACKGROUND

Infective endocarditis (IE) is a disease that poses a serious health risk. It is important to identify high-risk patients early in the course of their treatment. In the current study, we evaluated the prognostic value of ultra-short heart-rate variability (HRV), an index of vagal nerve activity, in IE.

METHODS

Retrospective analysis was performed on adult patients admitted to a tertiary hospital due to IE. A logistic regression (LR) was used to determine whether clinical, laboratory, and HRV parameters were predictive of specific clinical features (valve type, staphylococcal infection) or severe short-term complications (cardiac, metastatic infection, and death). The accuracy of the model was evaluated through the measurement of the area under the curve (AUC) of the receiver operating characteristic curve (ROC). An analysis of survival was conducted using Cox regression. A number of HRV indices were calculated, including the standard deviation of normal heart-beat intervals (SDNN) and the root mean square of successive differences (RMSSD).

RESULTS

75 patients, aged 60.3(±18.6) years old, were examined. When compared with published age- and gender-adjusted HRV norms, SDNN and RMSSD were found to be relatively low in our cohort (75%-76% lower than the median; 33%-41% lower than the 2nd percentile). 26(34.6%) patients developed a metastatic infection, with RMSSD<7.03ms (adjusted odds ratio (aOR) 9.340, p = 0.002), incorporated in a multivariate LR model (AUC 0.833). Furthermore, 27(36.0%) patients were diagnosed with Staphylococcus IE, with SDNN<4.92ms (aOR 5.235, p = 0.004), a major component of the multivariate LR model (AUC 0.741). Multivariate Cox regression survival model, included RMSSD (HR 1.008, p = 0.012).

CONCLUSION

SDNN, and particularly RMSSD, derived from ultra-short ECG recordings, may provide prognostic information about patients presenting with IE.

Using a continuous electrocardiographic patch with heart rhythm analysis in the subacute stage of aneurysmal subarachnoid hemorrhage: The feasibility verification

OBJECTIVE

Continuous cardiac monitoring on patients with aneurysmal subarachnoid hemorrhage (aSAH) is difficult out of intensive care unit (ICU) in the subacute stage. Therefore, we verified the feasibility of a novel electrocardiography (ECG) patch device to record long-term heart rhythm.

METHODS

The ECG patches were applied on aSAH patients during their stay in general ward. Any types of significant arrythmia were identified, and heart rate variability (HRV) measures were calculated in time and frequency domains. We analyzed the correlation between heart rhythm with Hunt and Hess scale and modified Fisher scale as well as the occurrence of secondary complications.

RESULTS

Twenty-six patients used the devices on median day 6 after aSAH onset, with put on and take down time average as 137 s and 45 s, respectively. Mean record time was 221.7 h, and no adverse event presented within the period. Hunt and Hess II/III subgroup had higher percentage of HRV high frequency band than IV/V subgroup (9.1 % vs 3.5 %, p = 0.043), whereas ultra low frequency band presented more in the later subgroup (50.4 % vs 61.4 %, p = 0.035). The very low frequency percentage significantly decreased (p = 0.025) at an average of 3 days prior to the occurrence of secondary complications compared to the days without complications.

CONCLUSION

For aSAH patients in general ward during subacute stage, the ECG patch is a safe and feasible tool. The correlation of long-term heart rhythm with prognosis is worthy to be investigated on larger sample size using this device in the future.

Adding heart rate n-variability (HRnV) to clinical assessment potentially improves prediction of serious bacterial infections in young febrile infants at the emergency department: a prospective observational study

Background

We aim to investigate the utility of heart rate variability (HRV) and heart rate n-variability (HRnV) in addition to vital signs and blood biomarkers, among febrile young infants at risk of serious bacterial infections (SBIs).

Methods

We performed a prospective observational study between December 2017 and November 2021 in a tertiary paediatric emergency department (ED). We included febrile infants <90 days old with a temperature ≥38 ℃. We obtained HRV and HRnV parameters via a single lead electrocardiogram. HRV measures beat-to-beat (R-R) oscillation and reflects autonomic nervous system (ANS) regulation. HRnV includes overlapping and non-overlapping R-R intervals and provides additional physiological information. We defined SBIs as meningitis, bacteraemia and urinary tract infections (UTIs). We performed area under curve (AUC) analysis to assess predictive performance.

Results

We recruited 330 and analysed 312 infants. The median age was 35.5 days (interquartile range 13.0-61.0); 74/312 infants (23.7%) had SBIs with the most common being UTIs (66/72, 91.7%); 2 infants had co-infections. No patients died and 32/312 (10.3%) received fluid resuscitation. Adding HRV and HRnV to demographics and vital signs at ED triage successively improved the AUC from 0.765 [95% confidence interval (CI): 0.705-0.825] to 0.776 (95% CI: 0.718-0.835) and 0.807 (95% CI: 0.752-0.861) respectively. The final model including demographics, vital signs, HRV, HRnV and blood biomarkers had an AUC of 0.874 (95% CI: 0.828-0.921).

Conclusions

Addition of HRV and HRnV to current assessment tools improved the prediction of SBIs among febrile infants at ED triage. We intend to validate our findings and translate them into tools for clinical care in the ED.

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.

Prospective multicenter study of heart rate variability with ANI monitor as predictor of mortality in critically ill patients with COVID-19

The purpose of this study is to demonstrate that the most critically ill patients with COVID-19 have greater autonomic nervous system dysregulation and assessing the heart rate variability, allows us to predict severity and 30-day mortality. This was a multicentre, prospective, cohort study. Patients were divided into two groups depending on the 30-day mortality. The heart rate variability and more specifically the relative parasympathetic activity (ANIm), and the SDNN (Energy), were measured. To predict severity and mortality multivariate analyses of ANIm, Energy, SOFA score, and RASS scales were conducted. 112 patients were collected, the survival group (n = 55) and the deceased group (n = 57). The ANIm value was higher (p = 0.013) and the Energy was lower in the deceased group (p = 0.001); Higher Energy was correlated with higher survival days (p = 0.009), and a limit value of 0.31 s predicted mortalities with a sensitivity of 71.9% and a specificity of 74.5%. Autonomic nervous system and heart rate variability monitoring in critically ill patients with COVID-19 allows for predicting survival days and 30-day mortality through the Energy value. Those patients with greater severity and mortality showed higher sympathetic depletion with a predominance of relative parasympathetic activity.

Machine learning for early prediction of sepsis-associated acute brain injury

Background

Sepsis-associated encephalopathy (SAE) is defined as diffuse brain dysfunction associated with sepsis and leads to a high mortality rate. We aimed to develop and validate an optimal machine-learning model based on clinical features for early predicting sepsis-associated acute brain injury.

Methods

We analyzed adult patients with sepsis from the Medical Information Mart for Intensive Care (MIMIC III) clinical database. Candidate models were trained using random forest, support vector machine (SVM), decision tree classifier, gradients boosting machine (GBM), multiple layer perception (MLP), extreme gradient boosting (XGBoost), light gradients boosting machine (LGBM) and a conventional logistic regression model. These methods were applied to develop and validate the optimal model based on its accuracy and area under curve (AUC).

Results

In total, 12,460 patients with sepsis met inclusion criteria, and 6,284 (50.4%) patients suffered from sepsis-associated acute brain injury. Compared other models, the LGBM model achieved the best performance. The AUC for both train set and test set indicated excellent validity (Trainset AUC 0.91, Testset AUC 0.87). Feature importance analysis showed that glucose, age, mean arterial pressure, heart rate, hemoglobin, and length of ICU stay were the top 6 important clinical factors to predict occurrence of sepsis-associated acute brain injury.

Conclusion

Almost half of patients admitted to ICU with sepsis had sepsis-associated acute brain injury. The LGBM model better identify patients with sepsis-associated acute brain injury than did other machine-learning models. Glucose, age, and mean arterial pressure were the three most important clinical factors to predict occurrence of sepsis-associated acute brain injury.

Do paediatric early warning systems reduce mortality and critical deterioration events among children? A systematic review and meta-analysis

Aim

We conducted a systematic review and meta-analysis to answer the question: Does the implementation of Paediatric Early Warning Systems (PEWS) in the hospital setting reduce mortality, cardiopulmonary arrests, unplanned codes and critical deterioration events among children, as compared to usual care without PEWS?

Methods

We conducted a comprehensive search using Medline, EMBASE, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature and Web of Science. We included studies published between January 2006 and April 2022 on children <18 years old performed in inpatient units and emergency departments, and compared patient populations with PEWS to those without PEWS. We excluded studies without a comparator, case control studies, systematic reviews, and studies published in non-English languages. We employed a random effects meta-analysis and synthesised the risk and rate ratios from individual studies. We used the Scottish Intercollegiate Guidelines Network (SIGN) to appraise the risk of bias.

Results

Among 911 articles screened, 15 were included for descriptive analysis. Fourteen of the 15 studies were pre- versus post-implementation studies and one was a multi-centre cluster randomised controlled trial (RCT). Among 10 studies (580,604 hospital admissions) analysed for mortality, we found an increased risk (pooled RR 1.18, 95% CI 1.01–1.38, p = 0.036) in the group without PEWS compared to the group with PEWS. The sensitivity analysis performed without the RCT (436,065 hospital admissions) showed a non-significant relationship (pooled RR 1.17, 95% CI 0.98–1.40, p = 0.087). Among four studies (168,544 hospital admissions) analysed for unplanned code events, there was an increased risk in the group without PEWS (pooled RR 1.73, 95%CI 1.01–2.96, p = 0.046) There were no differences in the rate of cardiopulmonary arrests or critical deterioration events between groups. Our findings were limited by potential confounders and imprecision among included studies.

Conclusions

Healthcare systems that implemented PEWS were associated with reduced mortality and code rates. We recognise that these gains vary depending on resource availability and efferent response systems.

PROSPERO registration: CRD42021269579.

The potential therapeutic benefits of low frequency haemodynamic oscillations

Haemodynamic oscillations occurring at frequencies below the rate of respiration have been observed experimentally for more than a century. Much of the research regarding these oscillations, observed in arterial pressure and blood flow, has focused on mechanisms of generation and methods of quantification. However, examination of the physiological role of these oscillations has been limited. Multiple studies have demonstrated that oscillations in arterial pressure and blood flow are associated with the protection in tissue oxygenation or functional capillary density during conditions of reduced tissue perfusion. There is also evidence that oscillatory blood flow can improve clearance of interstitial fluid, with a growing number of studies demonstrating a role for oscillatory blood flow to aid in clearance of debris from the brain. The therapeutic potential of these haemodynamic oscillations is an important new area of research which may have beneficial impact in treating conditions such as stroke, cardiac arrest, blood loss injuries, sepsis, or even Alzheimer's disease and vascular dementia.

Beyond Vital Signs: Pediatric Sepsis Screening that Includes Organ Failure Assessment Detects Patients with Worse Outcomes

Pediatric sepsis screening is recommended. The 2005 Goldstein criteria, the basis of our institutional sepsis screening tool (ISST), correlate poorly with clinically diagnosed sepsis. The study objective was to retrospectively evaluate the ISST sensitivity compared with the Pediatric Sequential Organ Failure Assessment (pSOFA). This was a single-center retrospective cohort study. The primary outcome was pSOFA score and ISST sensitivity for severe sepsis. Secondary outcomes included clinical outcome measures. In this severe sepsis cohort (N = 491), pSOFA and ISST sensitivity were 57.6 and 61.1%, respectively. In regression analysis for a positive pSOFA, positive blood culture (odds ratio [OR] 2.2, 95% confidence interval [CI] 1.1–4.3, p = 0.025), older age (OR 1.006, 95% CI 1.003–1.009, p < 0.001), and pulmonary infectious source (OR 3.3, 95% CI 1.6–6.5, p = 0.001) demonstrated independent association. In regression analysis for a positive ISST, older age (OR 1.003, 95% CI 1–1.006, p = 0.031) and intra-abdominal infectious source (OR 0.3, 95% CI 0.1–0.8, p = 0.014) demonstrated independent association. A negative ISST was associated with higher intensive care unit (ICU) admission prevalence (p = 0.01) and fewer ICU-free days (p = 0.018). A positive pSOFA score was associated with higher ICU admission prevalence, vasopressor requirement, and vasopressor days as well as fewer ICU, hospital, and mechanical ventilation-free days (all p < 0.001). Exploratory analysis combining the ISST and pSOFA into a hybrid screen demonstrated superior sensitivity (84.3%) and outcome discrimination. The pSOFA demonstrated noninferior sensitivity to a Goldstein-based institutional sepsis screening model. Further, pSOFA was a better discriminator of poor clinical outcomes. An exploratory hybrid screening model shows superior performance but will require prospective study.

Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data

Mounting clinical evidence suggests that viral infections can lead to detectable changes in an individual's normal physiologic and behavioral metrics, including heart and respiration rates, heart rate variability, temperature, activity, and sleep prior to symptom onset, potentially even in asymptomatic individuals. While the ability of wearable devices to detect viral infections in a real-world setting has yet to be proven, multiple recent studies have established that individual, continuous data from a range of biometric monitoring technologies can be easily acquired and that through the use of machine learning techniques, physiological signals and warning signs can be identified. In this review, we highlight the existing knowledge base supporting the potential for widespread implementation of biometric data to address existing gaps in the diagnosis and treatment of viral illnesses, with a particular focus on the many important lessons learned from the coronavirus disease 2019 pandemic.

Leveraging Continuous Vital Sign Measurements for Real-Time Assessment of Autonomic Nervous System Dysfunction After Brain Injury: A Narrative Review of Current and Future Applications

Subtle and profound changes in autonomic nervous system (ANS) function affecting sympathetic and parasympathetic homeostasis occur as a result of critical illness. Changes in ANS function are particularly salient in neurocritical illness, when direct structural and functional perturbations to autonomic network pathways occur and may herald impending clinical deterioration or intervenable evolving mechanisms of secondary injury. Sympathetic and parasympathetic balance can be measured quantitatively at the bedside using multiple methods, most readily by extracting data from electrocardiographic or photoplethysmography waveforms. Work from our group and others has demonstrated that data-analytic techniques can identify quantitative physiologic changes that precede clinical detection of meaningful events, and therefore may provide an important window for time-sensitive therapies. Here, we review data-analytic approaches to measuring ANS dysfunction from routine bedside physiologic data streams and integrating this data into multimodal machine learning-based model development to better understand phenotypical expression of pathophysiologic mechanisms and perhaps even serve as early detection signals. Attention will be given to examples from our work in acute traumatic brain injury on detection and monitoring of paroxysmal sympathetic hyperactivity and prediction of neurologic deterioration, and in large hemispheric infarction on prediction of malignant cerebral edema. We also discuss future clinical applications and data-analytic challenges and future directions.

NRF2 Inhibits Cardiomyocyte Pyroptosis Via Regulating CTRP1 in Sepsis-Induced Myocardial Injury

ABSTRACT

C1q/tumor necrosis factor-related protein 1 (CTRP1) has been demonstrated as a crucial regulator in myocardial injury (MI). The present study aims to evaluate the mechanism of CTRP1 in sepsis-induced MI. The septic mouse model was established via cecal ligation and puncture and the in vitro cell model was established via lipopolysaccharide treatment. The mouse survival rate within 96 h was recorded. Morphologic changes of cardiomyocytes were observed and cell viability and cardiac functions were detected. CTRP1 and nuclear factor erythroid 2-related factor (Nrf2) expressions, creatine troponin-T, and creatine phosphokinase isoenzyme levels, and expressions of pyroptotic markers were determined. The binding relationship between Nrf2 and the CTRP1 promotor was predicted and verified. Rescue experiments were designed to confirm the role of CTRP1. CTRP1 was poorly expressed in septic mice. CTRP1 overexpression inhibited cardiomyocyte pyroptosis and improved cardiac functions, MI, and survival rate in septic mice. Nrf2was decreased in cecal ligation and puncture -treated mice. Nrf2 overexpression promoted CTRP1 expression via binding to the CTRP1 promotor and suppressed cardiomyocyte pyroptosis. CTRP1 downregulation abolished the inhibitory effect of Nrf2 overexpression on cardiomyocyte pyroptosis. Overall, Nrf2 promoted CTRP1 expression via binding to the CTRP1 promotor to inhibit cardiomyocyte pyroptosis, thereby alleviating MI in septic mice.

Classification of Blood Volume Decompensation State via Machine Learning Analysis of Multi-Modal Wearable-Compatible Physiological Signals

This paper presents a novel computational algorithm to estimate blood volume decompensation state based on machine learning (ML) analysis of multi-modal wearable-compatible physiological signals. To the best of our knowledge, our algorithm may be the first of its kind which can not only discriminate normovolemia from hypovolemia but also classify hypovolemia into absolute hypovolemia and relative hypovolemia. We realized our blood volume classification algorithm by (i) extracting a multitude of features from multi-modal physiological signals including the electrocardiogram (ECG), the seismocardiogram (SCG), the ballistocardiogram (BCG), and the photoplethysmogram (PPG), (ii) constructing two ML classifiers using the features, one to classify normovolemia vs. hypovolemia and the other to classify hypovolemia into absolute hypovolemia and relative hypovolemia, and (iii) sequentially integrating the two to enable multi-class classification (normovolemia, absolute hypovolemia, and relative hypovolemia). We developed the blood volume decompensation state classification algorithm using the experimental data collected from six animals undergoing normovolemia, relative hypovolemia, and absolute hypovolemia challenges. Leave-one-subject-out analysis showed that our classification algorithm achieved an F1 score and accuracy of (i) 0.93 and 0.89 in classifying normovolemia vs. hypovolemia, (ii) 0.88 and 0.89 in classifying hypovolemia into absolute hypovolemia and relative hypovolemia, and (iii) 0.77 and 0.81 in classifying the overall blood volume decompensation state. The analysis of the features embedded in the ML classifiers indicated that many features are physiologically plausible, and that multi-modal SCG-BCG fusion may play an important role in achieving good blood volume classification efficacy. Our work may complement existing computational algorithms to estimate blood volume compensatory reserve as a potential decision-support tool to provide guidance on context-sensitive hypovolemia therapeutic strategy.

Vital signs as physiomarkers of neonatal sepsis

Neonatal sepsis accounts for significant morbidity and mortality, particularly among premature infants in the Neonatal Intensive Care Unit. Abnormal vital sign patterns serve as physiomarkers of sepsis and provide early warning of illness before overt clinical decompensation. The systemic inflammatory response to pathogens signals the autonomic nervous system, leading to changes in temperature, respiratory rate, heart rate, and blood pressure. In infants with comorbidities of prematurity, vital sign abnormalities often occur in the absence of infection, which confounds sepsis diagnosis. This review will cover the mechanisms of vital sign changes in neonatal sepsis, including the cholinergic anti-inflammatory pathway mediated by the vagus nerve, which is critical to the host response to infectious and inflammatory insults. We will also review the clinical implications of vital sign changes in neonatal sepsis, including their use in early warning scores and systems to direct clinicians to the bedside of infants with physiologic changes that might be due to sepsis. IMPACT: This manuscript summarizes and reviews the relevant literature on the physiological manifestations of neonatal sepsis and how we monitor and analyze these through vital signs and advanced analytics.

Diagnosis of Neonatal Sepsis: The Role of Inflammatory Markers

This is a narrative review on the role of biomarkers in the diagnosis of neonatal sepsis. We describe the difficulties to obtain standardized definitions in neonatal sepsis and discuss the limitations of published evidence of cut-off values and their sensitivities and specificities. Maternal risk factors influence the results of inflammatory markers as do gestational age, the time of sampling, the use of either cord blood or neonatal peripheral blood, and some non-infectious causes. Current evidence suggests that the use of promising diagnostic markers such as CD11b, CD64, IL-6, IL-8, PCT, and CRP, either alone or in combination, might enable clinicians discontinuing antibiotics confidently within 24-48 h. However, none of the current diagnostic markers is sensitive and specific enough to support the decision of withholding antibiotic treatment without considering clinical findings. It therefore seems to be justified that antibiotics are often initiated in ill term and especially preterm infants. Early markers like IL-6 and later markers like CRP are helpful in the diagnosis of neonatal sepsis considering the clinical aspect of the neonate, the gestational age, maternal risk factors and the time (age of the neonate regarding early-onset sepsis) of blood sampling.

Assessment of Sepsis in the ICU by Linear and Complex Characterization of Cardiovascular Dynamics

Sepsis is one of the pathological conditions with the highest incidence in intensive care units. Sepsis-induced cardiac and autonomic dysfunction are well-known effects, among others, caused by a dysregulated host response to infection. In this context, we investigate the role of complex cardiovascular dynamics quantified through sample entropy indices from the inter-beat interval, systolic and diastolic blood pressure time series as well as the cross-entropy between heartbeat and systolic blood pressure in patients with sepsis in the first hour of intensive care when compared with non-septic subjects. Results show a significant (p<0.05) reduction in the probability of being septic for a unitary increase in entropy for systolic and diastolic time series (odds equal to 0.038 and 0.264, respectively) when adjusting for confounding factors. A significant (p<0.001) odds ratio (0.248) is observed also in cross-entropy, showing a reduced probability of being septic for an increase in heartbeat and systolic pressure asynchrony. The inclusion of our measures of complexity also determines an increase in the predictive ability (+0.03) of a logistic regression model reaching an area under the receiving operating and precision recall curves both equal to 0.95.Clinical relevance The study demonstrates the ability of information theory in catching a reduction of complex cardiovascular dynamics from vital signs commonly recorded in ICU. The considered complexity measures contribute to characterize sepsis development by showing a general loss of the interaction between heartbeat and pressure regulation. The extracted measures also improve the ability to identify sepsis in the first hour of intensive care.

Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset

IMPORTANCE

Currently, there are no presymptomatic screening methods to identify individuals infected with a respiratory virus to prevent disease spread and to predict their trajectory for resource allocation.

OBJECTIVE

To evaluate the feasibility of using noninvasive, wrist-worn wearable biometric monitoring sensors to detect presymptomatic viral infection after exposure and predict infection severity in patients exposed to H1N1 influenza or human rhinovirus.

DESIGN, SETTING, AND PARTICIPANTS

The cohort H1N1 viral challenge study was conducted during 2018; data were collected from September 11, 2017, to May 4, 2018. The cohort rhinovirus challenge study was conducted during 2015; data were collected from September 14 to 21, 2015. A total of 39 adult participants were recruited for the H1N1 challenge study, and 24 adult participants were recruited for the rhinovirus challenge study. Exclusion criteria for both challenges included chronic respiratory illness and high levels of serum antibodies. Participants in the H1N1 challenge study were isolated in a clinic for a minimum of 8 days after inoculation. The rhinovirus challenge took place on a college campus, and participants were not isolated.

EXPOSURES

Participants in the H1N1 challenge study were inoculated via intranasal drops of diluted influenza A/California/03/09 (H1N1) virus with a mean count of 106 using the median tissue culture infectious dose (TCID50) assay. Participants in the rhinovirus challenge study were inoculated via intranasal drops of diluted human rhinovirus strain type 16 with a count of 100 using the TCID50 assay.

MAIN OUTCOMES AND MEASURES

The primary outcome measures included cross-validated performance metrics of random forest models to screen for presymptomatic infection and predict infection severity, including accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC).

RESULTS

A total of 31 participants with H1N1 (24 men [77.4%]; mean [SD] age, 34.7 [12.3] years) and 18 participants with rhinovirus (11 men [61.1%]; mean [SD] age, 21.7 [3.1] years) were included in the analysis after data preprocessing. Separate H1N1 and rhinovirus detection models, using only data on wearble devices as input, were able to distinguish between infection and noninfection with accuracies of up to 92% for H1N1 (90% precision, 90% sensitivity, 93% specificity, and 90% F1 score, 0.85 [95% CI, 0.70-1.00] AUC) and 88% for rhinovirus (100% precision, 78% sensitivity, 100% specificity, 88% F1 score, and 0.96 [95% CI, 0.85-1.00] AUC). The infection severity prediction model was able to distinguish between mild and moderate infection 24 hours prior to symptom onset with an accuracy of 90% for H1N1 (88% precision, 88% sensitivity, 92% specificity, 88% F1 score, and 0.88 [95% CI, 0.72-1.00] AUC) and 89% for rhinovirus (100% precision, 75% sensitivity, 100% specificity, 86% F1 score, and 0.95 [95% CI, 0.79-1.00] AUC).

CONCLUSIONS AND RELEVANCE

This cohort study suggests that the use of a noninvasive, wrist-worn wearable device to predict an individual's response to viral exposure prior to symptoms is feasible. Harnessing this technology would support early interventions to limit presymptomatic spread of viral respiratory infections, which is timely in the era of COVID-19.

Auricular Neuromodulation for Mass Vagus Nerve Stimulation: Insights From SOS COVID-19 a Multicentric, Randomized, Controlled, Double-Blind French Pilot Study

Importance: An exacerbated inflammatory response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is believed to be one of the major causes of the morbidity and mortality of the coronavirus disease 2019 (COVID-19). Neuromodulation therapy, based on vagus nerve stimulation, was recently hypothesized to control both the SARS-CoV-2 replication and the ensuing inflammation likely through the inhibition of the nuclear factor kappa-light-chain-enhancer of activated B cells pathway and could improve the clinical outcomes as an adjunct treatment. We proposed to test it by the stimulation of the auricular branch of the vagus nerve, i.e., auricular neuromodulation (AN), a non-invasive procedure through the insertion of semipermanent needles on the ears.

Objective: The aim of this study was to assess the effect of AN on the clinical outcomes in patients affected by COVID-19.

Design, Setting, and Participants: A multicenter, randomized, placebo-controlled, double-blind clinical trial included 31 patients with respiratory failure due to COVID-19 requiring hospitalization. Within 72 h after admission, patients received either AN (n = 14) or sham neuromodulation (SN, n = 15) in addition to the conventional treatments.

Main Outcome and Measures: The primary endpoint of the study was the rate of a clinical benefit conferred by AN at Day 14 (D14) as assessed by a 7-point Clinical Progression Scale. The secondary endpoint of the study was the impact of AN on the rate of transfer to the intensive care unit (ICU) and on the survival rate at D14.

Results: The AN procedure was well-tolerated without any reported side effects but with no significant improvement for the measures of both primary (p > 0.3) and secondary (p > 0.05) endpoints at the interim analysis. None of the AN-treated patients died but one in the SN group did (81 years). Two AN-treated patients (73 and 79 years, respectively) and one SN-treated patient (59 years) were transferred to ICU. Remarkably, AN-treated patients were older with more representation by males than in the SN arm (i.e., the median age of 75 vs. 65 years, 79% male vs. 47%).

Conclusion: The AN procedure, which was used within 72 h after the admission of patients with COVID-19, was safe and could be successfully implemented during the first two waves of COVID-19 in France. Nevertheless, AN did not significantly improve the outcome of the patients in our small preliminary study. It is pertinent to explore further to validate AN as the non-invasive mass vagal stimulation solution for the forthcoming pandemics.

Clinical Trial Registration: [https://clinicaltrials.gov/], identifier [NCT04341415].

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.