Using photoplethysmography data to estimate heart rate variability and its association with organ dysfunction in pediatric oncology patients

Evaluating RGB channels in remote photoplethysmography: a comparative study with contact-based PPG

Remote photoplethysmography (rPPG) provides a non-contact method for measuring blood volume changes. In this study, we compared rPPG signals obtained from video cameras with traditional contact-based photoplethysmography (cPPG) to assess the effectiveness of different RGB channels in cardiac signal extraction. Our objective was to determine the most effective RGB channel for detecting blood volume changes and estimating heart rate. We employed dynamic time warping, Pearson's correlation coefficient, root-mean-square error, and Beats-per-minute Difference to evaluate the performance of each RGB channel relative to cPPG. The results revealed that the green channel was superior, outperforming the blue and red channels in detecting volumetric changes and accurately estimating heart rate across various activities. We also observed that the reliability of RGB signals varied based on recording conditions and subject activity. This finding underscores the importance of understanding the performance nuances of RGB inputs, crucial for constructing rPPG signals in algorithms. Our study is significant in advancing rPPG research, offering insights that could benefit clinical applications by improving non-contact methods for blood volume assessment.

Pediatric Critical Care in the Twenty-first Century and Beyond

Pediatric critical care addresses prevention, diagnosis, and treatment of organ dysfunction in the setting of increasingly complex patients, therapies, and environments. Soon burgeoning data science will enable all aspects of intensive care: driving facilitated diagnostics, empowering a learning health-care environment, promoting continuous advancement of care, and informing the continuum of critical care outside the intensive care unit preceding and following critical illness/injury. Although novel technology will progressively objectify personalized critical care, humanism, practiced at the bedside, defines the essence of pediatric critical care now and in the future.

Novel approaches to capturing and using continuous cardiorespiratory physiological data in hospitalized children

Continuous cardiorespiratory physiological monitoring is a cornerstone of care in hospitalized children. The data generated by monitoring devices coupled with machine learning could transform the way we provide care. This scoping review summarizes existing evidence on novel approaches to continuous cardiorespiratory monitoring in hospitalized children. We aimed to identify opportunities for the development of monitoring technology and the use of machine learning to analyze continuous physiological data to improve the outcomes of hospitalized children. We included original research articles published on or after January 1, 2001, involving novel approaches to collect and use continuous cardiorespiratory physiological data in hospitalized children. OVID Medline, PubMed, and Embase databases were searched. We screened 2909 articles and performed full-text extraction of 105 articles. We identified 58 articles describing novel devices or approaches, which were generally small and single-center. In addition, we identified 47 articles that described the use of continuous physiological data in prediction models, but only 7 integrated multidimensional data (e.g., demographics, laboratory results). We identified three areas for development: (1) further validation of promising novel devices; (2) more studies of models integrating multidimensional data with continuous cardiorespiratory data; and (3) further dissemination, implementation, and validation of prediction models using continuous cardiorespiratory data. IMPACT: We performed a comprehensive scoping review of novel approaches to capture and use continuous cardiorespiratory physiological data for monitoring, diagnosis, providing care, and predicting events in hospitalized infants and children, from novel devices to machine learning-based prediction models. We identified three key areas for future development: (1) further validation of promising novel devices; (2) more studies of models integrating multidimensional data with continuous cardiorespiratory data; and (3) further dissemination, implementation, and validation of prediction models using cardiorespiratory data.

Quality Assessment and Morphological Analysis of Photoplethysmography in Daily Life

The photoplethysmographic (PPG) signal has been applied in various research fields, with promising results for its future clinical application. However, there are several sources of variability that, if not adequately controlled, can hamper its application in pervasive monitoring contexts. This study assessed and characterized the impact of several sources of variability, such as physical activity, age, sex, and health state on PPG signal quality and PPG waveform parameters (Rise Time, Pulse Amplitude, Pulse Time, Reflection Index, Delta T, and DiastolicAmplitude). We analyzed 31 24 h recordings by as many participants (19 healthy subjects and 12 oncological patients) with a wristband wearable device, selecting a set of PPG pulses labeled with three different quality levels. We implemented a Multinomial Logistic Regression (MLR) model to evaluate the impact of the aforementioned factors on PPG signal quality. We then extracted six parameters only on higher-quality PPG pulses and evaluated the influence of physical activity, age, sex, and health state on these parameters with Generalized Linear Mixed Effects Models (GLMM). We found that physical activity has a detrimental effect on PPG signal quality quality (94% of pulses with good quality when the subject is at rest vs. 9% during intense activity), and that health state affects the percentage of available PPG pulses of the best quality (at rest, 44% for healthy subjects vs. 13% for oncological patients). Most of the extracted parameters are influenced by physical activity and health state, while age significantly impacts two parameters related to arterial stiffness. These results can help expand the awareness that accurate, reliable information extracted from PPG signals can be reached by tackling and modeling different sources of inaccuracy.

Vital signs in pediatric oncology patients assessed by continuous recording with a wearable device, NCT04134429

Pediatric patients with cancer are at high risk for severe infections. Changes in vital signs, triggered by infections, may be detected earlier by continuous recording with a wearable device than with discrete measurements. This prospective, observational single-center feasibility study consecutively recruited pediatric patients undergoing chemotherapy for cancer. The WD Everion® was used for 14 days in each of the 20 patients on study to continuously record vital signs. Nine different vital signs and health indicators derived from them, plus six quality scores. This resulted in 274 study days (6576 hours) with 85’854 measuring points, which are a total of 772’686 measurements of vital signs and health indicators, plus 515’124 quality scores. Additionally, non-WD data like side effects, acceptability of the WD and effort for investigators were collected. In this manuscript, we present the methods of acquisition and explanations to the complete data set, which have been made publically available on open access and which can be used to study feasibility of continuous multi-parameter recording of vital signs by a WD.

Measurement(s)	Vital signs continiously
Technology Type(s)	Wearable Device
Factor Type(s)	Core temperature • Galvanic skin response • Health score • Heart rate • Heart rate variability • Oxygen saturation • Skin temperature • Skin blood perfusion
Sample Characteristic - Organism	Homo sapiens

Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review

Recent research indicates that Photoplethysmography (PPG) signals carry more information than oxygen saturation level (SpO2) and can be utilized for affordable, fast, and noninvasive healthcare applications. All these encourage the researchers to estimate its feasibility as an alternative to many expansive, time-wasting, and invasive methods. This systematic review discusses the current literature on diagnostic features of PPG signal and their applications that might present a potential venue to be adapted into many health and fitness aspects of human life. The research methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines 2020. To this aim, papers from 1981 to date are reviewed and categorized in terms of the healthcare application domain. Along with consolidated research areas, recent topics that are growing in popularity are also discovered. We also highlight the potential impact of using PPG signals on an individual’s quality of life and public health. The state-of-the-art studies suggest that in the years to come PPG wearables will become pervasive in many fields of medical practices, and the main domains include cardiology, respiratory, neurology, and fitness. Main operation challenges, including performance and robustness obstacles, are identified.

The biology of stress in cancer: Applying the biobehavioral framework to adolescent and young adult oncology research

The stress response influences the development and trajectory of cancer through a host of complex neuroimmune mechanisms. Basic, translational, and clinical research has elucidated these biobehavioral connections and offers a new paradigm for scientific investigation and patient care. Using a biobehavioral approach could offer new diagnostic and therapeutic opportunities in oncology, and this approach will be particularly impactful for adolescent and young adult (AYA) patients with cancer. To date, nearly all biobehavioral oncology research has been done in the adult population. And yet, AYAs have traditionally poorer mental health and cancer-related outcomes, and thus represent a population that could benefit from parallel psychosocial and biomedical intervention. Future biobehavioral work in oncology should focus on the AYA population, integrating new cancer therapies and technology into the next generation of research.

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Translational research efforts are clarifying the role of stress in cancer biology and patient outcomes.

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AYAs have poorer cancer-related and mental health outcomes, but nearly all biobehavioral oncology studies are in adults.

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Future work in immunotherapy, digital health technology, and interdisciplinary cooperation will advance the field.

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A translational biobehavioral approach brings the paradigm of precision medicine to psychosocial care in cancer.

Objectifying the Subjective: The Use of Heart Rate Variability as a Psychosocial Symptom Biomarker in Hospice and Palliative Care Research

Measuring psychosocial symptoms in hospice and palliative care research is critical to understanding the patient and caregiver experience. Subjective patient-reported outcome tools have been the primary method for collecting these data in palliative care, and the growing field of biobehavioral research offers new tools that could deepen our understanding of psychosocial symptomatology. Here we describe one psychosocial biomarker, heart rate variability (HRV), and simple techniques for measurement in an adolescent and young adult cancer population that are applicable to palliative care studies. Complementing self-reported measures with objective biomarkers like HRV could facilitate a more nuanced understanding of physiologic and perceived well-being in patients with serious or life-limiting illness and inform future "precision supportive care" in hospice and palliative medicine.

Development of a Heart Rate Variability Risk Score to Predict Organ Dysfunction and Death in Critically Ill Children

OBJECTIVES

Determine whether the Heart Rate Variability Dysfunction score, a novel age-normalized measure of autonomic nervous system dysregulation, is associated with the development of new or progressive multiple organ dysfunction syndrome or death in critically ill children.

DESIGN, SETTING, AND PATIENTS

This was a retrospective, observational cohort study from 2012 to 2018. Patients admitted to the PICU with at least 12 hours of continuous heart rate data available from bedside monitors during the first 24 hours of admission were included in the analysis.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Heart rate variability was measured using the integer heart rate variability, which is the sd of the heart rate sampled every 1 second over 5 consecutive minutes. The Heart Rate Variability Dysfunction score was derived from age-normalized values of integer heart rate variability and transformed, so that higher scores were indicative of lower integer heart rate variability and a proxy for worsening autonomic nervous system dysregulation. Heart Rate Variability Dysfunction score performance as a predictor of new or progressive multiple organ dysfunction syndrome and 28-day mortality were determined using the area under the receiver operating characteristic curve. Of the 7,223 patients who met inclusion criteria, 346 patients (4.8%) developed new or progressive multiple organ dysfunction syndrome, and 103 (1.4%) died by day 28. For every one-point increase in the median Heart Rate Variability Dysfunction score in the first 24 hours of admission, there was a 25% increase in the odds of new or progressive multiple organ dysfunction syndrome and a 51% increase in the odds of mortality. The median Heart Rate Variability Dysfunction score in the first 24 hours had an area under the receiver operating characteristic curve to discriminate new or progressive multiple organ dysfunction syndrome of 0.67 and to discriminate mortality of 0.80. These results were reproducible in a temporal validation cohort.

CONCLUSIONS

The Heart Rate Variability Dysfunction score, an age-adjusted proxy for autonomic nervous system dysregulation derived from bedside monitor data is independently associated with new or progressive multiple organ dysfunction syndrome and mortality in PICU patients. The Heart Rate Variability Dysfunction score could potentially be used as a single continuous physiologic biomarker or as part of a multivariable prediction model to increase awareness of at-risk patients and augment clinical decision-making.

Predicting cardiac disease from interactions of simultaneously-acquired hemodynamic and cardiac signals

BACKGROUND AND OBJECTIVE

Coronary artery disease (CAD) and heart failure are the most common cardiovascular diseases. Non-invasive diagnostic testing for CAD requires radiation, heart rate acceleration, and imaging infrastructure. Early detection of left ventricular dysfunction is critical in heart failure management, the best measure of which is an elevated left ventricular end-diastolic pressure (LVEDP) that can only be measured using invasive cardiac catheterization. There exists a need for non-invasive, safe, and fast diagnostic testing for CAD and elevated LVEDP. This research employs nonlinear dynamics to assess for significant CAD and elevated LVEDP using non-invasively acquired photoplethysmographic (PPG) and three-dimensional orthogonal voltage gradient (OVG) signals. PPG (variations of the blood volume perfusing the tissue) and OVG (mechano-electrical activity of the heart) signals represent the dynamics of the cardiovascular system.

METHODS

PPG and OVG were simultaneously acquired from two cohorts, (i) symptomatic subjects that underwent invasive cardiac catheterization, the gold standard test (408 CAD positive with stenosis≥ 70% and 186 with LVEDP≥ 20 mmHg) and (ii) asymptomatic healthy controls (676). A set of Poincaré-based synchrony features were developed to characterize the interactions between the OVG and PPG signals. The extracted features were employed to train machine learning models for CAD and LVEDP. Five-fold cross-validation was used and the best model was selected based on the average area under the receiver operating characteristic curve (AUC) across 100 runs, then assessed using a hold-out test set.

RESULTS

The Elastic Net model developed on the synchrony features can effectively classify CAD positive subjects from healthy controls with an average validation AUC=0.90±0.03 and an AUC= 0.89 on the test set. The developed model for LVEDP can discriminate subjects with elevated LVEDP from healthy controls with an average validation AUC=0.89±0.03 and an AUC=0.89 on the test set. The feature contributions results showed that the selection of a proper registration point for Poincaré analysis is essential for the development of predictive models for different disease targets.

CONCLUSIONS

Nonlinear features from simultaneously-acquired signals used as inputs to machine learning can assess CAD and LVEDP safely and accurately with an easy-to-use, portable device, utilized at the point-of-care without radiation, contrast, or patient preparation.

A Vital Sign-Based Model to Predict Clinical Deterioration in Hospitalized Children

OBJECTIVES

Clinical deterioration in hospitalized children is associated with increased risk of mortality and morbidity. A prediction model capable of accurate and early identification of pediatric patients at risk of deterioration can facilitate timely assessment and intervention, potentially improving survival and long-term outcomes. The objective of this study was to develop a model utilizing vital signs from electronic health record data for predicting clinical deterioration in pediatric ward patients.

DESIGN

Observational cohort study.

SETTING

An urban, tertiary-care medical center.

PATIENTS

Patients less than 18 years admitted to the general ward during years 2009-2018.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The primary outcome of clinical deterioration was defined as a direct ward-to-ICU transfer. A discrete-time logistic regression model utilizing six vital signs along with patient characteristics was developed to predict ICU transfers several hours in advance. Among 31,899 pediatric admissions, 1,375 (3.7%) experienced the outcome. Data were split into independent derivation (yr 2009-2014) and prospective validation (yr 2015-2018) cohorts. In the prospective validation cohort, the vital sign model significantly outperformed a modified version of the Bedside Pediatric Early Warning System score in predicting ICU transfers 12 hours prior to the event (C-statistic 0.78 vs 0.72; p < 0.01).

CONCLUSIONS

We developed a model utilizing six commonly used vital signs to predict risk of deterioration in hospitalized children. Our model demonstrated greater accuracy in predicting ICU transfers than the modified Bedside Pediatric Early Warning System. Our model may promote opportunities for timelier intervention and risk mitigation, thereby decreasing preventable death and improving long-term health.

Autonomic Nervous System Dysfunction in Pediatric Sepsis

The autonomic nervous system (ANS) plays a major role in maintaining homeostasis through key adaptive responses to stress, including severe infections and sepsis. The ANS-mediated processes most relevant during sepsis include regulation of cardiac output and vascular tone, control of breathing and airway resistance, inflammation and immune modulation, gastrointestinal motility and digestion, and regulation of body temperature. ANS dysfunction (ANSD) represents an imbalanced or maladaptive response to injury and is prevalent in pediatric sepsis. Most of the evidence on ANSD comes from studies of heart rate variability, which is a marker of ANS function and is inversely correlated with organ dysfunction and mortality. In addition, there is evidence that other measures of ANSD, such as respiratory rate variability, skin thermoregulation, and baroreflex and chemoreflex sensitivity, are associated with outcomes in critical illness. The relevance of understanding ANSD in the context of pediatric sepsis stems from the fact that it might play an important role in the pathophysiology of sepsis, is associated with outcomes, and can be measured continuously and noninvasively. Here we review the physiology and dysfunction of the ANS during critical illness, discuss methods for measuring ANS function in the intensive care unit, and review the diagnostic, prognostic, and therapeutic value of understanding ANSD in pediatric sepsis.