Wireless monitoring and real-time adaptive predictive indicator of deterioration

A novel digital health approach to improving global pediatric sepsis care in Bangladesh using wearable technology and machine learning

Sepsis is the leading cause of child death globally with low- and middle-income countries (LMICs) bearing a disproportionate burden of pediatric sepsis deaths. Limited diagnostic and critical care capacity and health worker shortages contribute to delayed recognition of advanced sepsis (severe sepsis, septic shock, and/or multiple organ dysfunction) in LMICs. The aims of this study were to 1) assess the feasibility of a wearable device for physiologic monitoring of septic children in a LMIC setting and 2) develop machine learning models that utilize readily available wearable and clinical data to predict advanced sepsis in children. This was a prospective observational study of children with sepsis admitted to an intensive care unit in Dhaka, Bangladesh. A wireless, wearable device linked to a smartphone was used to collect continuous recordings of physiologic data for the duration of each patient's admission. The correlation between wearable device-collected vital signs (heart rate [HR], respiratory rate [RR], temperature [T]) and manually collected vital signs was assessed using Pearson's correlation coefficients and agreement was assessed using Bland-Altman plots. Clinical and laboratory data were used to calculate twice daily pediatric Sequential Organ Failure Assessment (pSOFA) scores. Ridge regression was used to develop three candidate models for advanced sepsis (pSOFA > 8) using combinations of clinical and wearable device data. In addition, the lead time between the models' detection of advanced sepsis and physicians' documentation was compared. 100 children were enrolled of whom 41% were female with a mean age of 15.4 (SD 29.6) months. In-hospital mortality rate was 24%. Patients were monitored for an average of 2.2 days, with > 99% data capture from the wearable device during this period. Pearson's r was 0.93 and 0.94 for HR and RR, respectively) with r = 0.72 for core T). Mean difference (limits of agreement) was 0.04 (-14.26, 14.34) for HR, 0.29 (-5.91, 6.48) for RR, and -0.0004 (-1.48, 1.47) for core T. Model B, which included two manually measured variables (mean arterial pressure and SpO2:FiO2) and wearable device data had excellent discrimination, with an area under the Receiver-Operating Curve (AUC) of 0.86. Model C, which consisted of only wearable device features, also performed well, with an AUC of 0.78. Model B was able to predict the development of advanced sepsis more than 2.5 hours earlier compared to clinical documentation. A wireless, wearable device was feasible for continuous, remote physiologic monitoring among children with sepsis in a LMIC setting. Additionally, machine-learning models using wearable device data could discriminate cases of advanced sepsis without any laboratory tests and minimal or no clinician inputs. Future research will develop this technology into a smartphone-based system which can serve as both a low-cost telemetry monitor and an early warning clinical alert system, providing the potential for high-quality critical care capacity for pediatric sepsis in resource-limited settings.

Early detection of deteriorating patients in general wards through continuous contactless vital signs monitoring

Objective

To assess the efficacy of continuous contactless vital signs monitoring with an automated Early Warning System (EWS) in detecting clinical deterioration among patients in general wards.

Methods

A prospective observational cohort study was conducted in the medical unit of a tertiary care hospital in India, involving 706 patients over 84,448 monitoring hours. The study used a contactless ballistocardiography system (Dozee system) to continuously monitor heart rate, respiratory rate, and blood pressure. The study assessed total, mean, and median alerts at 24, 48, 72, 96, 120 h, and length of stay (LOS) before patient deterioration or discharge. It analyzed alert sensitivity and specificity, average time from initial alert to deterioration, and healthcare practitioners (HCP) activity. Study was registered with the Clinical Trials Registry-India CTRI/2022/10/046404.

Results

Out of 706 patients, 33 (5%) experienced clinical deterioration, while 673 (95%) did not. The deterioration group consistently had a higher number of alerts compared to those who were discharged normally, across all time-points. On average, the time between the initial alert and clinical deterioration was 16 h within the last 24 h preceding the event. The sensitivity of the Dozee-EWS varied between 67% and 94%. HCP spend 10% of their time on vital signs check and documentation.

Conclusions

This study suggests that utilizing contactless continuous vital signs monitoring with Dozee-EWS in general ward holds promise for enhancing the early detection of clinical deterioration. Further research is essential to evaluate the effectiveness across a wider range of clinical settings.

Role of Machine Learning and Artificial Intelligence in Arrhythmias and Electrophysiology

Machine learning (ML), a subset of artificial intelligence (AI) centered on machines learning from extensive datasets, stands at the forefront of a technological revolution shaping various facets of society. Cardiovascular medicine has emerged as a key domain for ML applications, with considerable efforts to integrate these innovations into routine clinical practice. Within cardiac electrophysiology, ML applications, especially in the automated interpretation of electrocardiograms, have garnered substantial attention in existing literature. However, less recognized are the diverse applications of ML in cardiac electrophysiology and arrhythmias, spanning basic science research on arrhythmia mechanisms, both experimental and computational, as well as contributions to enhanced techniques for mapping cardiac electrical function and translational research related to arrhythmia management. This comprehensive review delves into various ML applications within the scope of this journal, organized into 3 parts. The first section provides a fundamental understanding of general ML principles and methodologies, serving as a foundational resource for readers interested in exploring ML applications in arrhythmia research. The second part offers an in-depth review of studies in arrhythmia and electrophysiology that leverage ML methodologies, showcasing the broad potential of ML approaches. Each subject is thoroughly outlined, accompanied by a review of notable ML research advancements. Finally, the review delves into the primary challenges and future perspectives surrounding ML-driven cardiac electrophysiology and arrhythmias research.

Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association

A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.

ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient Monitoring

Addressing the increasing demand for remote patient monitoring, especially among the elderly and mobility-impaired, this study proposes the "ScalableDigitalHealth" (SDH) framework. The framework integrates smart digital health solutions with latency-aware edge computing autoscaling, providing a novel approach to remote patient monitoring. By leveraging IoT technology and application autoscaling, the "SDH" enables the real-time tracking of critical health parameters, such as ECG, body temperature, blood pressure, and oxygen saturation. These vital metrics are efficiently transmitted in real time to AWS cloud storage through a layered networking architecture. The contributions are two-fold: (1) establishing real-time remote patient monitoring and (2) developing a scalable architecture that features latency-aware horizontal pod autoscaling for containerized healthcare applications. The architecture incorporates a scalable IoT-based architecture and an innovative microservice autoscaling strategy in edge computing, driven by dynamic latency thresholds and enhanced by the integration of custom metrics. This work ensures heightened accessibility, cost-efficiency, and rapid responsiveness to patient needs, marking a significant leap forward in the field. By dynamically adjusting pod numbers based on latency, the system optimizes system responsiveness, particularly in edge computing's proximity-based processing. This innovative fusion of technologies not only revolutionizes remote healthcare delivery but also enhances Kubernetes performance, preventing unresponsiveness during high usage.

The effect of technical filtering and clinical criteria on alert rates from continuous vital sign monitoring in the general ward

OBJECTIVES

Continuous vital sign monitoring at the general hospital ward has major potential advantages over intermittent monitoring but generates many alerts with risk of alert fatigue. We hypothesized that the number of alerts would decrease using different filters.

METHODS

This study was an exploratory analysis of the alert reducing effect from adding two different filters to continuously collected vital sign data (peripheral oxygen saturation, blood pressure, heart rate, and respiratory rate) in patients admitted after major surgery or severe medical disease. Filtered data were compared to data without artifact removal. Filter one consists of artifact removal, filter two consists of artifact removal plus duration criteria adjusted for severity of vital sign deviation. Alert thresholds were based on the National Early Warning Score (NEWS) threshold.

RESULTS

A population of 716 patients admitted for severe medical disease or major surgery with continuous wireless vital sign monitoring at the general ward with a mean monitoring time of 75.8 h, were included for the analysis. Without artifact removal, we found a median of 137 [IQR: 87-188] alerts per patient/day, artifact removal resulted in a median of 101 [IQR: 56-160] alerts per patient/day and with artifact removal combined with a duration-severity criterion, we found a median of 19 [IQR: 9-34] alerts per patient/day. Reduction of alerts was 86.4% (p < 0.001) for values without artifact removal (137 alerts) vs. the duration criteria and a reduction (19 alerts) of 81.5% (p < 0.001) for the criteria with artifact removal (101 alerts) vs. the duration criteria (19 alerts).

CONCLUSION

We conclude that a combination of artifact removal and duration-severity criteria approach substantially reduces alerts generated by continuous vital sign monitoring.

Wireless monitoring devices in hospitalized children: a scoping review

The purpose of this study is to provide a structured overview of existing wireless monitoring technologies for hospitalized children. A systematic search of the literature published after 2010 was conducted in Medline, Embase, Scielo, Cochrane, and Web of Science. Two investigators independently reviewed articles to determine eligibility for inclusion. Information on study type, hospital setting, number of participants, use of a reference sensor, type and number of vital signs monitored, duration of monitoring, type of wireless information transfer, and outcomes of the wireless devices was extracted. A descriptive analysis was applied. Of the 1130 studies identified from our search, 42 met eligibility for subsequent analysis. Most included studies were observational studies with sample sizes of 50 or less published between 2019 and 2022. Common problems pertaining to study methodology and outcomes observed were short duration of monitoring, single focus on validity, and lack information on wireless transfer and data management.  Conclusion: Research on the use of wireless monitoring for children in hospitals has been increasing in recent years but often limited by methodological problems. More rigorous studies are necessary to establish the safety and accuracy of novel wireless monitoring devices in hospitalized children. What is Known: • Continuous monitoring of vital signs using wired sensors is the standard of care for hospitalized pediatric patients. However, the use of wires may pose significant challenges to optimal care. What is New: • Interest in wireless monitoring for hospitalized pediatric patients has been rapidly growing in recent years. • However, most devices are in early stages of clinical testing and are limited by inconsistent clinical and technological reporting.

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.

Agreement between standard and continuous wireless vital sign measurements after major abdominal surgery: a clinical comparison study

Objective. Continuous wireless monitoring outside the post-anesthesia or intensive care units may enable early detection of patient deterioration, but good accuracy of measurements is required. We aimed to assess the agreement between vital signs recorded by standard and novel wireless devices in postoperative patients.Approach. In 20 patients admitted to the post-anesthesia care unit, we compared heart rate (HR), respiratory rate (RR), peripheral oxygen saturation (SpO₂), and systolic and diastolic blood pressure (SBP and DBP) as paired data. The primary outcome measure was the agreement between standard wired and wireless monitoring, assessed by mean bias and 95% limits of agreement (LoA). LoA was considered acceptable for HR and PR, if within ±5 beats min^-1(bpm), while RR, SpO2, and BP were deemed acceptable if within ±3 breaths min^-1(brpm), ±3%-points, and ±10 mmHg, respectively.Main results.The mean bias between standard versus wireless monitoring was -0.85 bpm (LoA -6.2 to 4.5 bpm) for HR, -1.3 mmHg (LoA -19 to 17 mmHg) for standard versus wireless SBP, 2.9 mmHg (LoA -17 to 22) for standard versus wireless DBP, and 1.7% (LoA -1.4 mmHg to 4.8 mmHg) for SpO₂, comparing standard versus wireless monitoring. The mean bias of arterial blood gas analysis versus wireless SpO₂measurements was 0.02% (LoA -0.02% to 0.06%), while the mean bias of direct observation of RR compared to wireless measurements was 0.0 brpm (LoA -2.6 brpm to 2.6 brpm). 80% of all values compared were within predefined clinical limits.Significance.The agreement between wired and wireless HR, RR, and PR recordings in postoperative patients was acceptable, whereas the agreement for SpO₂recordings (standard versus wireless) was borderline. Standard wired and wireless BP measurements may be used interchangeably in the clinical setting.

Parents' experiences and perceptions of the acceptability of a whole-hospital, pro-active electronic pediatric early warning system (the DETECT study): A qualitative interview study

BACKGROUND

Failure to recognize and respond to clinical deterioration in a timely and effective manner is an urgent safety concern, driving the need for early identification systems to be embedded in the care of children in hospital. Pediatric early warning systems (PEWS) or PEW scores alert health professionals (HPs) to signs of deterioration, trigger a review and escalate care as needed. PEW scoring allows HPs to record a child's vital signs and other key data including parent concern.

AIM

This study aimed to explore the experiences and perceptions of parents about the acceptability of a newly implemented electronic surveillance system (the DETECT surveillance system), and factors that influenced acceptability and their awareness around signs of clinical deterioration and raising concern.

METHODS

Descriptive, qualitative semi-structured telephone interviews were undertaken with parents of children who had experienced a critical deterioration event (CDE) (n = 19) and parents of those who had not experienced a CDE (non-CDE parents) (n = 17). Data were collected between February 2020 and February 2021.

RESULTS

Qualitative data were analyzed using generic thematic analysis. Analysis revealed an overarching theme of trust as a key factor that underpinned all aspects of children's vital signs being recorded and monitored. The main themes reflect three domains of parents' trust: trust in themselves, trust in the HPs, and trust in the technology.

CONCLUSION

Parents' experiences and perceptions of the acceptability of a whole-hospital, pro-active electronic pediatric early warning system (The DETECT system) were positive; they found it acceptable and welcomed the use of new technology to support the care of their child.

The impact of wearable continuous vital sign monitoring on deterioration detection and clinical outcomes in hospitalised patients: a systematic review and meta-analysis

BACKGROUND

Timely recognition of the deteriorating inpatient remains challenging. Wearable monitoring systems (WMS) may augment current monitoring practices. However, there are many barriers to implementation in the hospital environment, and evidence describing the clinical impact of WMS on deterioration detection and patient outcome remains unclear.

OBJECTIVE

To assess the impact of vital-sign monitoring on detection of deterioration and related clinical outcomes in hospitalised patients using WMS, in comparison with standard care.

METHODS

A systematic search was conducted in August 2020 using MEDLINE, Embase, CINAHL, Cochrane Database of Systematic Reviews, CENTRAL, Health Technology Assessment databases and grey literature. Studies comparing the use of WMS against standard care for deterioration detection and related clinical outcomes in hospitalised patients were included. Deterioration related outcomes (primary) included unplanned intensive care admissions, rapid response team or cardiac arrest activation, total and major complications rate. Other clinical outcomes (secondary) included in-hospital mortality and hospital length of stay. Exploratory outcomes included alerting system parameters and clinical trial registry information.

RESULTS

Of 8706 citations, 10 studies with different designs met the inclusion criteria, of which 7 were included in the meta-analyses. Overall study quality was moderate. The meta-analysis indicated that the WMS, when compared with standard care, was not associated with significant reductions in intensive care transfers (risk ratio, RR 0.87; 95% confidence interval, CI 0.66-1.15), rapid response or cardiac arrest team activation (RR 0.84; 95% CI 0.69-1.01), total (RR 0.77; 95% CI 0.44-1.32) and major (RR 0.55; 95% CI 0.24-1.30) complications prevalence. There was also no statistically significant association with reduced mortality (RR 0.48; 95% CI 0.18-1.29) and hospital length of stay (mean difference, MD - 0.09; 95% CI - 0.43 to 0.44).

CONCLUSION

This systematic review indicates that there is no current evidence that implementation of WMS impacts early deterioration detection and associated clinical outcomes, as differing design/quality of available studies and diversity of outcome measures make it difficult to reach a definite conclusion. Our narrative findings suggested that alarms should be adjusted to minimise false alarms and promote rapid clinical action in response to deterioration. PROSPERO Registration number: CRD42020188633 .

A data-driven performance dashboard for surgical dissection

Surgical error and resulting complication have significant patient and economic consequences. Inappropriate exertion of tool-tissue force is a common variable for such error, that can be objectively monitored by sensorized tools. The rich digital output establishes a powerful skill assessment and sharing platform for surgical performance and training. Here we present SmartForceps data app incorporating an Expert Room environment for tracking and analysing the objective performance and surgical finesse through multiple interfaces specific for surgeons and data scientists. The app is enriched by incoming geospatial information, data distribution for engineered features, performance dashboard compared to expert surgeon, and interactive skill prediction and task recognition tools to develop artificial intelligence models. The study launches the concept of democratizing surgical data through a connectivity interface between surgeons with a broad and deep capability of geographic reach through mobile devices with highly interactive infographics and tools for performance monitoring, comparison, and improvement.

Agreement between wireless and standard measurements of vital signs in acute exacerbation of chronic obstructive pulmonary disease: a clinical validation study

Objective.Wireless sensors for continuous monitoring of vital signs have potential to improve patient care by earlier detection of deterioration in general ward patients. We aimed to assess agreement between wireless and standard (wired) monitoring devices in patients hospitalized with acute exacerbation of chronic obstructive pulmonary disease (AECOPD).Approach.Paired measurements of vital signs were recorded with 15 min intervals for two hours. The primary outcome was agreement between wireless and standard monitor measurements using the Bland and Altman method to calculate bias with 95% limits of agreement (LoA). We considered LoA of less than ±5 beats min^-1(bpm) acceptable for heart rate (HR), whereas agreement of peripheral oxygen saturation (SpO₂), respiratory rate (RR), and blood pressure (BP) were acceptable if within ±3%-points, ±3 breaths min^-1(brpm), and ±10 mmHg, respectively.Main results.180 sample-pairs of vital signs from 20 with AECOPD patients were recorded for comparison. The wireless versus standard monitor bias was 0.03 (LoA -3.2 to 3.3) bpm for HR measurements, 1.4% (LoA -0.7% to 3.6%) for SpO₂, -7.8 (LoA -22.3 to 6.8) mmHg for systolic BP and -6.2 (LoA -16.8 to 4.5) mmHg for diastolic BP. The wireless versus standard monitor bias for RR measurements was 0.75 (LoA -6.1 to 7.6) brpm.Significance.Commercially available wireless monitors could accurately measure HR in patients admitted with AECOPD compared to standard wired monitoring. Agreement for SpO₂were borderline acceptable while agreement for RR and BP should be interpreted with caution.

Machine Learning in Arrhythmia and Electrophysiology

Machine learning (ML), a branch of artificial intelligence, where machines learn from big data, is at the crest of a technological wave of change sweeping society. Cardiovascular medicine is at the forefront of many ML applications, and there is a significant effort to bring them into mainstream clinical practice. In the field of cardiac electrophysiology, ML applications have also seen a rapid growth and popularity, particularly the use of ML in the automatic interpretation of ECGs, which has been extensively covered in the literature. Much lesser known are the other aspects of ML application in cardiac electrophysiology and arrhythmias, such as those in basic science research on arrhythmia mechanisms, both experimental and computational; in the development of better techniques for mapping of cardiac electrical function; and in translational research related to arrhythmia management. In the current review, we examine comprehensively such ML applications as they match the scope of this journal. The current review is organized in 3 parts. The first provides an overview of general ML principles and methodologies that will afford readers of the necessary information on the subject, serving as the foundation for inviting further ML applications in arrhythmia research. The basic information we provide can serve as a guide on how one might design and conduct an ML study. The second part is a review of arrhythmia and electrophysiology studies in which ML has been utilized, highlighting the broad potential of ML approaches. For each subject, we outline comprehensively the general topics, while reviewing some of the research advances utilizing ML under the subject. Finally, we discuss the main challenges and the perspectives for ML-driven cardiac electrophysiology and arrhythmia research.