Reliable Collection of Real-Time Patient Physiologic Data from less Reliable Networks: a "Monitor of Monitors" System (MoMs)

Utilizing ultra-early continuous physiologic data to develop automated measures of clinical severity in a traumatic brain injury population

Determination of prognosis in the triage process after traumatic brain injury (TBI) is difficult to achieve. Current severity measures like the Trauma and injury severity score (TRISS) and revised trauma score (RTS) rely on additional information from the Glasgow Coma Scale (GCS) and the Injury Severity Score (ISS) which may be inaccurate or delayed, limiting their usefulness in the rapid triage setting. We hypothesized that machine learning based estimations of GCS and ISS obtained through modeling of continuous vital sign features could be used to rapidly derive an automated RTS and TRISS. We derived variables from electrocardiograms (ECG), photoplethysmography (PPG), and blood pressure using continuous data obtained in the first 15 min of admission to build machine learning models of GCS and ISS (ML-GCS and ML-ISS). We compared the TRISS and RTS using ML-ISS and ML-GCS and its value using the actual ISS and GCS in predicting in-hospital mortality. Models were tested in TBI with systemic injury (head abbreviated injury scale (AIS) ≥ 1), and isolated TBI (head AIS ≥ 1 and other AIS ≤ 1). The area under the receiver operating characteristic curve (AUROC) was used to evaluate model performance. A total of 21,077 cases (2009-2015) were in the training set. 6057 cases from 2016 to 2017 were used for testing, with 472 (7.8%) severe TBI (GCS 3-8), 223 (3.7%) moderate TBI (GCS 9-12), and 5913 (88.5%) mild TBI (GCS 13-15). In the TBI with systemic injury group, ML-TRISS had similar AUROC (0.963) to TRISS (0.965) in predicting mortality. ML-RTS had AUROC (0.823) and RTS had AUROC 0.928. In the isolated TBI group, ML-TRISS had AUROC 0.977, and TRISS had AUROC 0.983. ML-RTS had AUROC 0.790 and RTS had AUROC 0.957. Estimation of ISS and GCS from machine learning based modeling of vital sign features can be utilized to provide accurate assessments of the RTS and TRISS in a population of TBI patients. Automation of these scores could be utilized to enhance triage and resource allocation during the ultra-early phase of resuscitation.

A Novel Continuous Real-Time Vital Signs Viewer for Intensive Care Units: Design and Evaluation Study

BACKGROUND

Clinicians working in intensive care units (ICUs) are immersed in a cacophony of alarms and a relentless onslaught of data. Within this frenetic environment, clinicians make high-stakes decisions using many data sources and are often oversaturated with information of varying quality. Traditional bedside monitors only depict static vital signs data, and these data are not easily viewable remotely. Clinicians must rely on separate nursing charts-handwritten or electric-to review physiological patterns, including signs of potential clinical deterioration. An automated physiological data viewer has been developed to provide at-a-glance summaries and to assist with prioritizing care for multiple patients who are critically ill.

OBJECTIVE

This study aims to evaluate a novel vital signs viewer system in a level 1 trauma center by subjectively assessing the viewer's utility in a high-volume ICU setting.

METHODS

ICU attendings were surveyed during morning rounds. Physicians were asked to conduct rounds normally, using data reported from nurse charts and briefs from fellows to inform their clinical decisions. After the physician finished their assessment and plan for the patient, they were asked to complete a questionnaire. Following completion of the questionnaire, the viewer was presented to ICU physicians on a tablet personal computer that displayed the patient's physiologic data (ie, shock index, blood pressure, heart rate, temperature, respiratory rate, and pulse oximetry), summarized for up to 72 hours. After examining the viewer, ICU physicians completed a postview questionnaire. In both questionnaires, the physicians were asked questions regarding the patient's stability, status, and need for a higher or lower level of care. A hierarchical clustering analysis was used to group participating ICU physicians and assess their general reception of the viewer.

RESULTS

A total of 908 anonymous surveys were collected from 28 ICU physicians from February 2015 to June 2017. Regarding physicians' perception of whether the viewer enhanced the ability to assess multiple patients in the ICU, 5% (45/908) strongly agreed, 56.6% (514/908) agreed, 35.3% (321/908) were neutral, 2.9% (26/908) disagreed, and 0.2% (2/908) strongly disagreed.

CONCLUSIONS

Morning rounds in a trauma center ICU are conducted in a busy environment with many data sources. This study demonstrates that organized physiologic data and visual assessment can improve situation awareness, assist clinicians with recognizing changes in patient status, and prioritize care.

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.

Adjunctive virtual reality pain relief following traumatic injury: protocol for a randomised within-subjects clinical trial

INTRODUCTION

The annual mortality and national expense of the opioid crisis continue to rise in the USA (130 deaths/day, $50 billion/year). Opioid use disorder usually starts with the prescription of opioids for a medical condition. Its risk is associated with greater pain intensity and coping strategies characterised by pain catastrophising. Non-pharmacological analgesics in the hospital setting are critical to abate the opioid epidemic. One promising intervention is virtual reality (VR) therapy. It has performed well as a distraction tool and pain modifier during medical procedures; however, little is known about VR in the acute pain setting following traumatic injury. Furthermore, no studies have investigated VR in the setting of traumatic brain injury (TBI). This study aims to establish the safety and effect of VR therapy in the inpatient setting for acute traumatic injuries, including TBI.

METHODS AND ANALYSIS

In this randomised within-subjects clinical study, immersive VR therapy will be compared with two controls in patients with traumatic injury, including TBI. Affective measures including pain catastrophising, trait anxiety and depression will be captured prior to beginning sessions. Before and after each session, we will capture pain intensity and unpleasantness, additional affective measures and physiological measures associated with pain response, such as heart rate and variability, pupillometry and respiratory rate. The primary outcome is the change in pain intensity of the VR session compared with controls.

ETHICS AND DISSEMINATION

Dissemination of this protocol will allow researchers and funding bodies to stay abreast in their fields through exposure to research not otherwise widely publicised. Study protocols are compliant with federal regulation and University of Maryland Baltimore's Human Research Protections and Institutional Review Board (protocol number HP-00090603). Study results will be published on completion of enrolment and analysis, and deidentified data can be shared by request to the corresponding author.

TRIAL REGISTRATION NUMBER

NCT04356963; Pre-results.

A telediagnosis assistance system for multiple-lead electrocardiography

The diffusion of telemedicine opens-up a new perspective for the development of technologies furthered by Biomedical Engineering. In particular, herein we deal with those related to telediagnosis through multiple-lead electrocardiographic signals. This study focuses on the proof-of-concept of an internet-based telemedicine system as a use case that attests to the feasibility for the development, within the university environment, of techniques for remote processing of biomedical signals for adjustable detection of myocardial ischemia episodes. At each signal lead, QRS complexes are detected and delimited with the J-point marking. The same procedure to detect the complex is used to identify the respective T wave, then the area over the ST segment is applied to detect ischemia-related elevations. The entire system is designed on web-based telemedicine services using multiuser, remote access technologies, and database. The measurements for sensitivity and precision had their respective averages calculated at 11.79 and 24.21% for the leads of lower noise. The evaluations regarding the aspects of user friendliness and the usefulness of the application, resulted in 88.57 and 89.28% of broad or total acceptance, respectively. They are robust enough to enable scalability and can be offered by cloud computing, besides enabling the development of new biomedical signal processing techniques within the concept of distance services, using a modular architecture with collaborative bias.

A practical approach to storage and retrieval of high-frequency physiological signals

OBJECTIVE

Storage of physiological waveform data for retrospective analysis presents significant challenges. Resultant data can be very large, and therefore becomes expensive to store and complicated to manage. Traditional database approaches are not appropriate for large scale storage of physiological waveforms. Our goal was to apply modern time series compression and indexing techniques to the problem of physiological waveform storage and retrieval.

APPROACH

We deployed a vendor-agnostic data collection system and developed domain-specific compression approaches that allowed long term storage of physiological waveform data and other associated clinical and medical device data. The database (called AtriumDB) also facilitates rapid retrieval of retrospective data for high-performance computing and machine learning applications.

MAIN RESULTS

A prototype system has been recording data in a 42-bed pediatric critical care unit at The Hospital for Sick Children in Toronto, Ontario since February 2016. As of December 2019, the database contains over 720,000 patient-hours of data collected from over 5300 patients, all with complete waveform capture. One year of full resolution physiological waveform storage from this 42-bed unit can be losslessly compressed and stored in less than 300 GB of disk space. Retrospective data can be delivered to analytical applications at a rate of up to 50 million time-value pairs per second.

SIGNIFICANCE

Stored data are not pre-processed or filtered. Having access to a large retrospective dataset with realistic artefacts lends itself to the process of anomaly discovery and understanding. Retrospective data can be replayed to simulate a realistic streaming data environment where analytical tools can be rapidly tested at scale.

Linking Big Data and Prediction Strategies: Tools, Pitfalls, and Lessons Learned

OBJECTIVES

Modern critical care amasses unprecedented amounts of clinical data-so called "big data"-on a minute-by-minute basis. Innovative processing of these data has the potential to revolutionize clinical prognostics and decision support in the care of the critically ill but also forces clinicians to depend on new and complex tools of which they may have limited understanding and over which they have little control. This concise review aims to provide bedside clinicians with ways to think about common methods being used to extract information from clinical big datasets and to judge the quality and utility of that information.

DATA SOURCES

We searched the free-access search engines PubMed and Google Scholar using the MeSH terms "big data", "prediction", and "intensive care" with iterations of a range of additional potentially associated factors, along with published bibliographies, to find papers suggesting illustration of key points in the structuring and analysis of clinical "big data," with special focus on outcomes prediction and major clinical concerns in critical care.

STUDY SELECTION

Three reviewers independently screened preliminary citation lists.

DATA EXTRACTION

Summary data were tabulated for review.

DATA SYNTHESIS

To date, most relevant big data research has focused on development of and attempts to validate patient outcome scoring systems and has yet to fully make use of the potential for automation and novel uses of continuous data streams such as those available from clinical care monitoring devices.

CONCLUSIONS

Realizing the potential for big data to improve critical care patient outcomes will require unprecedented team building across disparate competencies. It will also require clinicians to develop statistical awareness and thinking as yet another critical judgment skill they bring to their patients' bedsides and to the array of evidence presented to them about their patients over the course of care.

No wire? No problem: Resuscitative endovascular balloon occlusion of the aorta can be performed effectively and more rapidly with a wire-free device

BACKGROUND

A wire-free device is available for resuscitative endovascular balloon occlusion of the aorta (REBOA) providing aortic occlusion (AO) without lengthy platform guide-wires and large sheaths.

METHODS

This was a retrospective, single-institution review of patients who received REBOA from May 2014 to September 2017. Timing of procedural steps was measured in seconds using time-stamped videography.

RESULTS

Seventy-four patients received REBOA: 29 with a platform guidewire, 12-Fr sheath, and balloon catheter (W group), and 45 with a 7-Fr sheath and wire-free device (WF group). Mean age (p = 0.22) and ISS (p = 0.80) were similar between groups. Fifty-nine patients received REBOA at Zone 1, 15 patients at Zone 3. There was no difference in median [interquartile range] time to common femoral artery (CFA) access between the WF (194 [98-313] seconds) and W (193 [126-280] seconds) groups (p = 0.96). Both median time to AO after CFA access (WF, 158 [109-264] seconds vs. W, 307 [222-390] seconds, p < 0.001) and median total procedural time (WF, 366 [263-596] seconds vs. W, 511 [441-597] seconds; p = 0.012) were significantly shorter with the wire-free system. The rates of percutaneous versus open CFA access was not different between groups (p = 0.48). Both groups had a similar physiologic response to AO as measured by pre- and post-AO SBP (p = 0.86). Overall mortality rate was 74%, 90% in the W group, and 64% in the WF group (p = 0.027). The procedure-related complication rate was not significantly different between groups with regard to compartment syndrome (W, 3% vs. WF, 4%, p = 1.0), access-related complications (W, 0 vs. WF, 6%, p = 0.28), or systemic complication (W, 0 vs. WF, 9%, p = 0.15).

CONCLUSION

Once CFA access is obtained, AO with a smaller wire-free device reduces procedural time by approximately 50%. When perfusion to proximal organs is essential, the seconds saved to achieve AO may contribute to improved mortality. Time to obtain CFA access is not dependent on introducer sheath size.

LEVEL OF EVIDENCE

Therapeutic, level V.

Examination of hemodynamics in patients in hemorrhagic shock undergoing Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA)

BACKGROUND

The objective of this study was to investigate the hemodynamic effects of aortic occlusion (AO) during Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA) using a sophisticated continuous vital sign (CVS) monitoring tool.

METHODS

Patients admitted between February 2013 and May 2017 at a tertiary center that received REBOA were included. Patients in cardiac arrest before or at the time of REBOA were excluded. Time of AO was documented by time-stamped videography and correlated with CVS data.

RESULTS

28 patients were included, mean (standard deviation) ISS was 38 (11). 18 received Zone 1 (distal thoracic aorta) and 10 received Zone 3 (distal abdominal aorta) AO. Among Zone 1 patients the pre-AO systolic blood pressure (SBP) nadir was 64 (19) mmHg, which increased to a mean of 124 (29) mmHg within 5 min after AO (p < 0.01). Among Zone 3 patients the pre-AO SBP nadir was 75 (19) mmHg, which increased to a mean of 98 (14) mmHg within 5 min after AO (p < 0.01). 72% of Zone 1 patients had episodes during AO where SBP was less than 90 mmHg as compared to 80% of Zone 3 patients (p = 0.51). 100% of Zone 1 patients had periods during AO where SBP was greater than 140 mmHg as compared to 70% Zone 3 patients (p = 0.04). The overall mean decrease in SBP after balloon deflation was 13 (20) mmHg (p < 0.01), with similar decreases among groups (14 (21) mmHg vs 12 (18) mmHg for Zone 1 and 3 patients, respectively (p = 0.85)). Patients undergoing Zone 1 AO were more likely to have an acute change (increase or decrease) in their heart rate immediately after AO as compared to Zone 3 AO (p = 0.048).

CONCLUSIONS

Significant hemodynamic alterations occur before, during, and after AO. The effects of Zone 1 AO on blood pressure and heart rate appear different than Zone 3 AO. This may have important implications for cardiac or cerebral function and perfusion goals, particularly with concomitant injuries such as cardiac contusion or traumatic brain injury.

Traumatic cardiac arrest and resuscitative endovascular balloon occlusion of the aorta (REBOA): a preliminary analysis utilizing high fidelity invasive blood pressure recording and videography

PURPOSE

Aortic occlusion (AO) increases proximal perfusion and may improve rates of return of spontaneous circulation (ROSC). The objective of this study was to investigate the hemodynamic effects of cardiopulmonary resuscitation (CPR) and AO by REBOA on patients in traumatic cardiac arrest.

METHODS

Patients admitted between February 2013 and May 2017 at a tertiary center who suffered traumatic arrest, had an arterial line placed during resuscitation, and received CPR and REBOA which were included. In-hospital CPR data were obtained from videography. Arterial waveforms were recorded at 240 Hz.

RESULTS

11 consecutive patients were included, 82% male; mean (± SD) age 37 ± 19 years. 55% suffered blunt trauma and the remaining penetrating injuries. 64% arrested out of hospital. During compressions with AO, the mean systolic blood pressure (SBP) was 70 ± 22 mmHg, mean arterial pressure (MAP) 43 ± 19 mmHg, and diastolic blood pressure (DBP) 26 ± 17 mmHg. Nine (82%) had ROSC, with eight having multiple periods of ROSC and arrest in the initial period. In-hospital mortality was 82%. Cardiac ultrasonography was used during arrest in 73%. In two patients with arterial line data before and after AO, SBP (mmHg) improved from 51 to 73 and 55 to 96 during arrest after AO.

CONCLUSIONS

High-quality chest compressions coupled with aortic occlusion may generate adequate perfusion pressures to increase the rate of ROSC. New technology capable of transducing central arterial pressure may help us to understand the effectiveness of CPR with and without aortic occlusion. REBOA may be a useful adjunct to high-quality chest compressions during arrest.

Design and Evaluation of a Real Time Physiological Signals Acquisition System Implemented in Multi-Operating Rooms for Anesthesia

With critical importance of medical healthcare, there exist urgent needs for in-depth medical studies that can access and analyze specific physiological signals to provide theoretical support for practical clinical care. As a consequence, obtaining the valuable medical data with minimal cost and impacts on hospital work comes as the first concern of researchers. Anesthesia plays a widely recognized role in surgeries, which attracts people to undertake relevant research. In this paper, a real-time physiological medical signal data acquisition system (PMSDA) for the multi-operating room applications is proposed with high universality of the hospital practical settings and research requirements. By utilizing a wireless communication approach, it provides an easily accessible network platform for collection of physiological medical signals such as photoplethysmogram (PPG), electrocardiograph (ECG) and electroencephalogram (EEG) during the surgery. In addition, the raw data is stored on a server for safe backup and further analysis of depth of anesthesia (DoA). Results show that the PMSDA exhibits robust, high quality performance and efficiently reduces costs compared to previously manual methods and allows seamless integration into hospital environment, independent of its routine work. Overall, it provides a pragmatic and flexible surgery-data acquisition system model with low impact and resource cost applicable to research in critical and practical medical circumstances.

Real-Time Fault-Tolerant mHealth System: Comprehensive Review of Healthcare Services, Opens Issues, Challenges and Methodological Aspects

The burden on healthcare services in the world has increased substantially in the past decades. The quality and quantity of care have to increase to meet surging demands, especially among patients with chronic heart diseases. The expansion of information and communication technologies has led to new models for the delivery healthcare services in telemedicine. Therefore, mHealth plays an imperative role in the sustainable delivery of healthcare services in telemedicine. This paper presents a comprehensive review of healthcare service provision. It highlights the open issues and challenges related to the use of the real-time fault-tolerant mHealth system in telemedicine. The methodological aspects of mHealth are examined, and three distinct and successive phases are presented. The first discusses the identification process for establishing a decision matrix based on a crossover of 'time of arrival of patient at the hospital/multi-services' and 'hospitals' within mHealth. The second phase discusses the development of a decision matrix for hospital selection based on the MAHP method. The third phase discusses the validation of the proposed system.

Life over Limb: Lower Extremity Ischemia in the Setting of Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA)

Resuscitative endovascular balloon occlusion of the aorta (REBOA) is a temporizing maneuver used to decrease hemorrhage, and thus perfusion, below the level of aortic occlusion (AO). We sought to investigate lower extremity ischemia in patients who received REBOA. Between February 2013 and September 2016 patients at a tertiary center that received REBOA and survived more than six hours were enrolled. Thirty-one patients were identified, the mean ISS was 40 ± 14 and inhospital mortality was 39 per cent. Twenty received REBOA in zone 1 (distal thoracic aorta). Three (15%) developed lower extremity compartment syndrome (LECS) after zone 1 REBOA. Injury of iliofemoral arteries and veins was each associated with calf fasciotomies (both P = 0.005). A longer duration of AO at zone 1 was associated with calf and thigh fasciotomy (P = 0.046 and P = 0.048, respectively). Iliofemoral arterial injury was associated with thigh fasciotomy (P = 0.04). Eleven patients received REBOA in zone 3 (distal abdominal aorta). Five (45%) patients underwent fasciotomy; four (36%) due to LECS. Femoral arterial injury was associated with calf fasciotomies (P = 0.02). There was no association with sheath size or laterality and need for fas-ciotomy. Neither groin access for REBOA or AO solely caused limb loss or LECS. The contribution to distal ischemia by REBOA remains unclear in patients with lower extremity injury.