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Computerized Analysis of the Ventricular Fibrillation Waveform Allows Identification of Myocardial Infarction: A Proof-of-Concept Study for Smart Defibrillator Applications in Cardiac Arrest.

ABSTRACT: Background In cardiac arrest, computerized analysis of the ventricular fibrillation (VF) waveform provides prognostic information, while its diagnostic potential is subject of study. Animal studies suggest that VF morphology is affected by prior myocardial infarction (MI), and even more by acute MI. This experimental in-human study reports on the discriminative value of VF waveform analysis to identify a prior MI. Outcomes may provide support for in-field studies on acute MI. Methods and Results We conducted a prospective registry of implantable cardioverter defibrillator recipients with defibrillation testing (2010-2014). From 12-lead surface ECG VF recordings, we calculated 10 VF waveform characteristics. First, we studied detection of prior MI with lead II, using one key VF characteristic (amplitude spectrum area [AMSA]). Subsequently, we constructed diagnostic machine learning models: model A, lead II, all VF characteristics; model B, 12-lead, AMSA only; and model C, 12-lead, all VF characteristics. Prior MI was present in 58% (119/206) of patients. The approach using the AMSA of lead II demonstrated a C-statistic of 0.61 (95% CI, 0.54-0.68). Model A performance was not significantly better: 0.66 (95% CI, 0.59-0.73), P=0.09 versus AMSA lead II. Model B yielded a higher C-statistic: 0.75 (95% CI, 0.68-0.81), P<0.001 versus AMSA lead II. Model C did not improve this further: 0.74 (95% CI, 0.67-0.80), P=0.66 versus model B. Conclusions This proof-of-concept study provides the first in-human evidence that MI detection seems feasible using VF waveform analysis. Information from multiple ECG leads rather than from multiple VF characteristics may improve diagnostic accuracy. These results require additional experimental studies and may serve as pilot data for in-field smart defibrillator studies, to try and identify acute MI in the earliest stages of cardiac arrest.

SUBMITTER: Thannhauser J

PROVIDER: S-EPMC7792424 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Computerized Analysis of the Ventricular Fibrillation Waveform Allows Identification of Myocardial Infarction: A Proof-of-Concept Study for Smart Defibrillator Applications in Cardiac Arrest.

Thannhauser Jos J Nas Joris J Rebergen Dennis J DJ Westra Sjoerd W SW Smeets Joep L R M JLRM Van Royen Niels N Bonnes Judith L JL Brouwer Marc A MA

Journal of the American Heart Association 20201002 19

Background In cardiac arrest, computerized analysis of the ventricular fibrillation (VF) waveform provides prognostic information, while its diagnostic potential is subject of study. Animal studies suggest that VF morphology is affected by prior myocardial infarction (MI), and even more by acute MI. This experimental in-human study reports on the discriminative value of VF waveform analysis to identify a prior MI. Outcomes may provide support for in-field studies on acute MI. Methods and Results ...[more]

PMID: 33003984

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Project description:BACKGROUND:Quantitative measures of the ventricular fibrillation (VF) ECG waveform can assess myocardial physiology and predict cardiac arrest outcomes, making these measures a candidate to help guide resuscitation. Chest compressions are typically paused for waveform measure calculation because compressions cause ECG artifact. However, such pauses contradict resuscitation guideline recommendations to minimize cardiopulmonary resuscitation interruptions. We evaluated a comprehensive group of VF measures with and without ongoing compressions to determine their performance under both conditions for predicting functionally-intact survival, the study's primary outcome. METHODS:Five-second VF ECG segments were collected with and without chest compressions before 2755 defibrillation shocks from 1151 out-of-hospital cardiac arrest patients. Twenty-four individual measures and 3 combination measures were implemented. Measures were optimized to predict functionally-intact survival (Cerebral Performance Category score ?2) using 460 training cases, and their performance evaluated using 691 independent test cases. RESULTS:Measures predicted functionally-intact survival on test data with an area under the receiver operating characteristic curve ranging from 0.56 to 0.75 (median, 0.73) without chest compressions and from 0.53 to 0.75 (median, 0.69) with compressions ( P<0.001 for difference). Of all measures evaluated, the support vector machine model ranked highest both without chest compressions (area under the receiver operating characteristic curve, 0.75; 95% CI, 0.73-0.78) and with compressions (area under the receiver operating characteristic curve, 0.75; 95% CI, 0.72-0.78; P=0.75 for difference). CONCLUSIONS:VF waveform measures predict functionally-intact survival when calculated during chest compressions, but prognostic performance is generally reduced compared with compression-free analysis. However, support vector machine models exhibited similar performance with and without compressions while also achieving the highest area under the receiver operating characteristic curve. Such machine learning models may, therefore, offer means to guide resuscitation during uninterrupted cardiopulmonary resuscitation.

Project description:OBJECTIVES:Cognitive behavioural therapy (CBT) can improve symptoms of anxiety and depression, but also reduces the risk of future relapse after therapy completion. However, current CBT relapse prevention methods are resource-intensive and can be limited in clinical practice. This paper investigates a personalized means of reducing relapse using smart-messaging in two settings: research and routine care. DESIGN:Study 1 presents a cohort study comparing a cohort of smart-messaging users versus non-users. Study 2 presents time series follow-up data from a case series of smart-messaging users from clinical practice. METHODS:Fifteen of 56 CBT completers who participated in a trial for the treatment of health anxiety wrote advice they would want if in future they were doing well, experiencing early warning signs of relapse, or experiencing full relapse. Following CBT, participants received weekly text-message requests to rate their well-being. Dependent upon their response, participants received tailored advice they had written, appropriate to the well-being level reported after recovery from health anxiety. Smart-messaging was also trialled in a routine practice sample of 14 CBT completers with anxiety and depression. RESULTS:Across a 12-month follow-up, participants receiving smart-messaging showed greater health improvements than those who did not. Well-being scores showed stability between CBT completion and 6-month follow-up among routine care patients. CONCLUSIONS:These findings suggest that a low-intensity, personalized relapse prevention method can have a clinical benefit following CBT for common mental health problems. PRACTITIONER POINTS:Post-treatment outcomes may be improved using personalized smart-messaging to prevent relapse following cognitive behavioural therapy (CBT) for health anxiety. In clinical practice, post-treatment smart-messaging can be well-used by patients and may help maintain stable well-being in the 6 months after CBT ends. This evidence supports the clinical utility of a brief tailored digital intervention, which can be integrated within routine clinical practice with minimal therapist input. Overall, longer-term post-CBT outcomes may be improved by integrating a smart-messaging intervention at the end of therapy.

Project description:BackgroundRecent advancements in digital pathology resulting from advances in imaging and digitization have increased the convenience and usability of pathology for disease diagnosis, especially in oncology, urology, and gastroenteric diagnosis. However, despite the possibilities to include low-cost diagnosis and viable telemedicine, digital pathology is not yet accessible owing to expensive storage, data security requirements, and network bandwidth limitations to transfer high-resolution images and associated data. The increase in storage, transmission, and security complexity concerning data collection and diagnosis makes it even more challenging to use artificial intelligence algorithms for machine-assisted disease diagnosis. We designed and prototyped a digital pathology system that uses blockchain-based smart contracts using the nonfungible token (NFT) standard and the Interplanetary File System for data storage. Our design remediates shortcomings in the existing digital pathology systems infrastructure, which is centralized. The proposed design is extendable to other fields of medicine that require high-fidelity image and data storage. Our solution is implemented in data systems that can improve access quality of care and reduce the cost of access to specialized pathological diagnosis, reducing cycle times for diagnosis.ObjectiveThe main objectives of this study are to highlight the issues in digital pathology and suggest that a software architecture-based blockchain and the Interplanetary File System create a low-cost data storage and transmission technology.MethodsWe used the design science research method consisting of 6 stages to inform our design overall. We innovated over existing public-private designs for blockchains but using a 2-layered approach that separates actual file storage from metadata and data persistence.ResultsHere, we identified key challenges to adopting digital pathology, including challenges concerning long-term storage and the transmission of information. Next, using accepted frameworks in NFT-based intelligent contracts and recent innovations in distributed secure storage, we proposed a decentralized, secure, and privacy-preserving digital pathology system. Our design and prototype implementation using Solidity, web3.js, Ethereum, and node.js helped us address several challenges facing digital pathology. We demonstrated how our solution, which combines NFT smart contract standard with persistent decentralized file storage, solves most of the challenges of digital pathology and sets the stage for reducing costs and improving patient care and speed of diagnosis.ConclusionsWe identified technical limitations that increase costs and reduce the mass adoption of digital pathology. We presented several design innovations using NFT decentralized storage standards to prototype a system. We also presented the implementation details of a unique security architecture for a digital pathology system. We illustrated how this design can overcome privacy, security, network-based storage, and data transmission limitations. We illustrated how improving these factors sets the stage for improving data quality and standardized application of machine learning and artificial intelligence to such data.

Project description:BACKGROUND:Despite the high rate of sudden death after myocardial infarction among patients with a low ejection fraction, implantable cardioverter-defibrillators are contraindicated until 40 to 90 days after myocardial infarction. Whether a wearable cardioverter-defibrillator would reduce the incidence of sudden death during this high-risk period is unclear. METHODS:We randomly assigned (in a 2:1 ratio) patients with acute myocardial infarction and an ejection fraction of 35% or less to receive a wearable cardioverter-defibrillator plus guideline-directed therapy (the device group) or to receive only guideline-directed therapy (the control group). The primary outcome was the composite of sudden death or death from ventricular tachyarrhythmia at 90 days (arrhythmic death). Secondary outcomes included death from any cause and nonarrhythmic death. RESULTS:Of 2302 participants, 1524 were randomly assigned to the device group and 778 to the control group. Participants in the device group wore the device for a median of 18.0 hours per day (interquartile range, 3.8 to 22.7). Arrhythmic death occurred in 1.6% of the participants in the device group and in 2.4% of those in the control group (relative risk, 0.67; 95% confidence interval [CI], 0.37 to 1.21; P=0.18). Death from any cause occurred in 3.1% of the participants in the device group and in 4.9% of those in the control group (relative risk, 0.64; 95% CI, 0.43 to 0.98; uncorrected P=0.04), and nonarrhythmic death in 1.4% and 2.2%, respectively (relative risk, 0.63; 95% CI, 0.33 to 1.19; uncorrected P=0.15). Of the 48 participants in the device group who died, 12 were wearing the device at the time of death. A total of 20 participants in the device group (1.3%) received an appropriate shock, and 9 (0.6%) received an inappropriate shock. CONCLUSIONS:Among patients with a recent myocardial infarction and an ejection fraction of 35% or less, the wearable cardioverter-defibrillator did not lead to a significantly lower rate of the primary outcome of arrhythmic death than control. (Funded by the National Institutes of Health and Zoll Medical; VEST ClinicalTrials.gov number, NCT01446965 .).

Dataset Information

Computerized Analysis of the Ventricular Fibrillation Waveform Allows Identification of Myocardial Infarction: A Proof-of-Concept Study for Smart Defibrillator Applications in Cardiac Arrest.

Publications

Computerized Analysis of the Ventricular Fibrillation Waveform Allows Identification of Myocardial Infarction: A Proof-of-Concept Study for Smart Defibrillator Applications in Cardiac Arrest.

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