Feasibility and performance of a device for automatic self-detection of symptomatic acute coronary artery occlusion in outpatients with coronary artery disease : a multicentre observational study

Background Time delay between onset of symptoms and seeking medical attention is a major determinant of mortality and morbidity in patients with acute coronary artery occlusion. Response time might be reduced by reliable self-detection. We aimed to formally assess the proof-of-concept and accuracy of self-detection of acute coronary artery occlusion by patients during daily life situations and during the very early stages of acute coronary artery occlusion. Methods In this multicentre, observational study, we tested the operational feasibility, specificity, and sensitivity of our RELF method, a three-lead detection system with an automatic algorithm built into a mobile handheld device, for detection of acute coronary artery occlusion. Patients were recruited continuously by physician referrals from three Belgian hospitals until the desired sample size was achieved, had been discharged with planned elective percutaneous coronary intervention, and were able to use a smartphone; they were asked to perform random ambulatory selfrecordings for at least 1 week. A similar self-recording was made before percutaneous coronary intervention and at 60 s of balloon occlusion. Patients were clinically followed up until 1 month after discharge. We quantitatively assessed the operational feasibility with an automated dichotomous quality check of self-recordings. Performance was assessed by analysing the receiver operator characteristics of the ST difference vector magnitude. This trial is registered with ClinicalTrials.gov, number NCT02983396. Findings From Nov 18, 2016, to April 25, 2018, we enrolled 64 patients into the study, of whom 59 (92%) were eligible for self-applications. 58 (91%) of 64 (95% CI 81.0-95.6) patients were able to perform ambulatory self-recordings. Of all 5011 self-recordings, 4567 (91%) were automatically classified as successful within 1 min. In 65 balloon occlusions, 63 index tests at 60 s of occlusion in 55 patients were available. The mean specificity of daily life recordings was 0.96 (0.95-0.97). The mean false positive rate during daily life conditions was 4.19% (95% CI 3.29-5.10). The sensitivity for the target conditions was 0.87 (55 of 63; 95% CI 0.77-0.93) for acute coronary artery occlusion, 0.95 (54 of 57; 0.86-0.98) for acute coronary artery occlusion with electrocardiogram (ECG) changes, and 1.00 (35 of 35) for acute coronary artery occlusion with ECG changes and ST-segment elevation myocardial infarction criteria (STEMI). The index test was more sensitive to detect a 60 s balloon occlusion than the STEMI criteria on 12-lead ECG (87% vs 56%; p<0.0001). The proportion of total variation in study estimates due to heterogeneity between patients (I-2) was low (12.6%). The area under the receiver operator characteristics curve was 0.973 (95% CI 0.956-0.990) for acute coronary artery occlusion at different cutoff values of the magnitude of the ST difference vector. No patients died during the study. Interpretation Self-recording with our RELF device is feasible for most patients with coronary artery disease. The sensitivity and specificity for automatic detection of the earliest phase of acute coronary artery occlusion support the concept of our RELF device for patient empowerment to reduce delay and increase Survival without overloading emergency services. Copyright (C) 2019 The Author(s). Published by Elsevier Ltd

Advanced observation and telemetry heart system utilizing wearable ECG device and a Cloud platform

Short lived chest pain episodes of post PCI patients represent the most common clinical scenario treated in the Accidents and Emergency Room. Continuous ECG monitoring could substantially diminish such hospital admissions and related ambulance calls. Delivering community based, easy-To-handle, easy to wear, real time electrocardiography systems is still a quest, despite the existence of electronic electrocardiography systems for several decades. The PATRIOT system serves this challenge via a 12-channel, easy to wear, easy to carry, mobile linked, miniaturized automatic ECG device and a Cloud platform. The system may deliver high quality electrocardiograms of a patient to medical personnel either on the spot or remotely both in a synchronous or asynchronous mode, enhancing autonomy, mobility, quality of life and safety of recently treated coronary artery disease patients

Adaptive Process Management in Cyber-Physical Domains

The increasing application of process-oriented approaches in new challenging cyber-physical domains beyond business computing (e.g., personalized healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex processes in such domains. A cyber-physical domain is characterized by the presence of a cyber-physical system coordinating heterogeneous ICT components (PCs, smartphones, sensors, actuators) and involving real world entities (humans, machines, agents, robots, etc.) that perform complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and processes enacted in cyber-physical domains must be robust to unexpected conditions and adaptable to unanticipated exceptions. This demands a more flexible approach in process design and enactment, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. In this chapter, we tackle the above issue and we propose a general approach, a concrete framework and a process management system implementation, called SmartPM, for automatically adapting processes enacted in cyber-physical domains in case of unanticipated exceptions and exogenous events. The adaptation mechanism provided by SmartPM is based on declarative task specifications, execution monitoring for detecting failures and context changes at run-time, and automated planning techniques to self-repair the running process, without requiring to predefine any specific adaptation policy or exception handler at design-time

Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest

The automatic detection of pulse during out-of-hospital cardiac arrest (OHCA) is necessary for the early recognition of the arrest and the detection of return of spontaneous circulation (end of the arrest). The only signal available in every single defibrillator and valid for the detection of pulse is the electrocardiogram (ECG). In this study we propose two deep neural network (DNN) architectures to detect pulse using short ECG segments (5 s), i.e., to classify the rhythm into pulseless electrical activity (PEA) or pulse-generating rhythm (PR). A total of 3914 5-s ECG segments, 2372 PR and 1542 PEA, were extracted from 279 OHCA episodes. Data were partitioned patient-wise into training (80%) and test (20%) sets. The first DNN architecture was a fully convolutional neural network, and the second architecture added a recurrent layer to learn temporal dependencies. Both DNN architectures were tuned using Bayesian optimization, and the results for the test set were compared to state-of-the art PR/PEA discrimination algorithms based on machine learning and hand crafted features. The PR/PEA classifiers were evaluated in terms of sensitivity (Se) for PR, specificity (Sp) for PEA, and the balanced accuracy (BAC), the average of Se and Sp. The Se/Sp/BAC of the DNN architectures were 94.1%/92.9%/93.5% for the first one, and 95.5%/91.6%/93.5% for the second one. Both architectures improved the performance of state of the art methods by more than 1.5 points in BAC.This work was supported by: The Spanish Ministerio de Economía y Competitividad, TEC2015-64678-R, jointly with the Fondo Europeo de Desarrollo Regional (FEDER), UPV/EHU via GIU17/031 and the Basque Government through the grant PRE_2018_2_0260

216 Jewish Hospital of St. Louis

https://digitalcommons.wustl.edu/bjc_216/1087/thumbnail.jp

A new approach to scoring systems to improve identification of acute medical admissions that will require critical care

Removal of the intensive care unit (ICU) at the Vale of Leven Hospital mandated the identification and transfer out of those acute medical admissions with a high risk of requiring ICU. The aim of the study was to develop triaging tools that identified such patients and compare them with other scoring systems. The methodology included a retrospective analysis of physiological and arterial gas measurements from 1976 acute medical admissions produced PREEMPT-1 (PRE-critical Emergency Medical Patient Triage). A simpler one for ambulance use (PREAMBLE-1 [PRE-Admission Medical Blue-Light Emergency]) was produced by the addition of peripheral oxygen saturation to a modification of MEWS (Modified Early Warning Score). Prospective application of these tools produced a larger database of 4447 acute admissions from which logistic regression models produced PREEMPT-2 and PREAMBLE-2, which were then compared with the original systems and seven other early warning scoring systems. Results showed that in patients with arterial gases, the area under the receiver operator characteristic curve was significantly higher in PREEMPT-2 (89·1%) and PREAMBLE-2 (84.4%) than all other scoring systems. Similarly, in all patients, it was higher in PREAMBLE-2 (92·4%) than PREAMBLE-1 (88·1%) and the other scoring systems. In conclusion, risk of requiring ICU can be more accurately predicted using PREEMPT-2 and PREAMBLE-2, as described here, than by other early warning scoring systems developed over recent years

Characterization of health care utilization in patients receiving implantable cardioverter-defibrillator therapies: An analysis of the managed ventricular pacing trial.

BACKGROUND: Implantable cardioverter-defibrillators (ICDs) are effective in terminating lethal arrhythmias, but little is known about the degree of health care utilization (HCU) after ICD therapies. OBJECTIVE: Using data from the managed ventricular pacing trial, we sought to identify the incidence and types of HCU in ICD patients after receiving ICD therapy (shocks or antitachycardia pacing [ATP]). METHODS: We analyzed HCU events (ventricular tachyarrhythmia [VTA]-related, heart failure-related, ICD implant procedure-related, ICD system-related, or other) and their association with ICD therapies (shocked ventricular tachycardia episode, ATP-terminated ventricular tachycardia episode, and inappropriately shocked episode). RESULTS: A total of 1879 HCUs occurred in 695 of 1030 subjects (80% primary prevention) and were classified as follows: 133 (7%) VTA-related, 373 (20%) heart failure-related, 97 (5%) implant procedure-related, 115 (6%) system-related, and 1160 (62%) other. Of 2113 treated VTA episodes, 1680 (80%) received ATP only and 433 (20%) received shocks. Stratifying VTA-related HCUs on the basis of the type of ICD therapy delivered, there were 25 HCUs per 100 shocked VTA episodes compared with 1 HCU per 100 ATP-terminated episodes. Inappropriate ICD shocks occurred in 8.7% of the subjects and were associated with 115 HCUs. The majority of HCUs (52%) began in the emergency department, and 66% of all HCUs resulted in hospitalization. CONCLUSION: For VTA-related HCUs, shocks are associated with a 25-fold increase in HCUs compared to VTAs treated by ATP only. Application of evidence-based strategies and automated device-based algorithms to reduce ICD shocks (higher rate cutoffs, use of ATP, and arrhythmia detection) may help reduce HCUs

Medical technology advances from space research

Details of medical research and development programs, particularly an integrated medical laboratory, as derived from space technology are given. The program covers digital biotelemetry systems, automatic visual field mapping equipment, sponge electrode caps for clinical electroencephalograms, and advanced respiratory analysis equipment. The possibility of using the medical laboratory in ground based remote areas and regional health care facilities, as well as long duration space missions is discussed

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 153)

This bibliography lists 175 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1976