Search

Digital health is currently booming, providing major innovations, particularly in terms of changing the practices of the stakeholders in the healthcare system as a whole. It allows our healthcare system to draw on new synergies between independent, hospital and medico-social professionals, as well as on high-performance digital tools for the benefit of all, users, patients and professionals. These tools, or digital solutions, have a strong potential to improve the healthcare system but also a strong potential for economic development. In this respect, the great diversity of existing and future digital solutions, as well as their vast fields of application, are prompting public and private stakeholders in the sector to question their integration into our healthcare system. The resulting challenges concern the identification of the targets they are intended for, the values they embody and, as a result, the methods of funding and evaluation. At a time when the first reimbursement terms for digital solutions are taking shape in the context of the Social Security Financing Bill for 2022, the roundtable wished to propose 8 recommendations to help structure exchanges between the various stakeholders and initiate avenues of work around the integration of digital solutions into the healthcare system. The main orientations are based on the proposal of a common and transparent reflection methodology around the technical scope of these solutions, the values they bring and the funding mechanisms. Other work will be necessary beyond the points addressed by the round table in order to go into greater depth on certain themes such as the adaptation of existing funding methods to the momentum and specificities of digital technology or the development of research work on the evaluation of the value claimed by these digital solutions.

Background Implantable Loop Recorders (ILRs) are increasingly used and generate a high workload for timely adjudication of ECG recordings. In particular, the excessive false positive rate leads to a significant review burden. Purpose A novel machine learning algorithm was developed to reclassify ILR episodes in order to decrease by 80% the False Positive rate while maintaining 99% sensitivity. This study aims to evaluate the impact of this algorithm to reduce the number of abnormal episodes reported in Medtronic ILRs. Methods Among 20 European centers, all Medtronic ILR patients were enrolled during the 2nd semester of 2020. Using a remote monitoring platform, every ILR transmitted episode was collected and anonymised. For every ILR detected episode with a transmitted ECG, the new algorithm reclassified it applying the same labels as the ILR (asystole, brady, AT/AF, VT, artifact, normal). We measured the number of episodes identified as false positive and reclassified as normal by the algorithm, and their proportion among all episodes. Results In 370 patients, ILRs recorded 3755 episodes including 305 patient-triggered and 629 with no ECG transmitted. 2821 episodes were analyzed by the novel algorithm, which reclassified 1227 episodes as normal rhythm. These reclassified episodes accounted for 43% of analyzed episodes and 32.6% of all episodes recorded. Conclusion A novel machine learning algorithm significantly reduces the quantity of episodes flagged as abnormal and typically reviewed by healthcare professionals. Funding Acknowledgement Type of funding sources: None. Figure 1. ILR episodes analysis

Graphical abstract Incidence of ventricular arrhythmias per week compared with the number of new coronavirus disease 2019 (COVID-19) cases per week in France (blue line) and the daily percentage of COVID-19 information on 24-hour television information channels per week (red line). TV: television; VF: ventricular fibrillation; VT: ventricular tachycardia., Background The coronavirus disease 2019 (COVID-19) has been a fast-growing worldwide pandemic. Aims We aimed to investigate the incidence of cardiac arrhythmias among a large French cohort of implantable cardioverter defibrillator recipients over the first 5 months of 2020. Methods Five thousand nine hundred and fifty-four implantable cardioverter defibrillator recipients were followed by remote monitoring during the COVID-19 period (from 01 January to 31 May 2020). Data were obtained from automated remote follow-up of implantable cardioverter defibrillators utilizing the Implicity® platform. For all patients, the type of arrhythmia (atrial fibrillation, ventricular tachycardia or ventricular fibrillation), the number of ventricular arrhythmia episodes and the type of implantable cardioverter defibrillator-delivered therapy were recorded. Results A total of 472 (7.9%) patients presented 4917 ventricular arrhythmia events. An increase in ventricular arrhythmia incidence was observed after the first COVID-19 case in France, and especially during weeks #10 and #11, at the time of major governmental measures, with an increase in the incidence of antitachycardia pacing delivered therapy. During the 11 weeks before the lockdown order, the curve of the percentage of live-stream television coverage of COVID-19 information matched the ventricular arrhythmia incidence. During the lockdown, the incidence of ventricular arrhythmia decreased significantly compared with baseline (0.05 ± 0.7 vs. 0.09 ± 1.2 episodes per patient per week, respectively; P

Background: Automated electrocardiogram (ECG) interpretations may be erroneous, and lead to erroneous overreads, including for atrial fibrillation (AF). We compared the accuracy of the first version of a new deep neural network 12-Lead ECG algorithm (Cardiologs®) to the conventional Veritas algorithm in interpretation of AF. Methods: 24,123 consecutive 12-lead ECGs recorded over 6 months were interpreted by 1) the Veritas® algorithm, 2) physicians who overread Veritas® (Veritas® + physician), and 3) Cardiologs® algorithm. We randomly selected 500 out of 858 ECGs with a diagnosis of AF according to either algorithm, then compared the algorithms' interpretations, and Veritas® + physician, with expert interpretation. To assess sensitivity for AF, we analyzed a separate database of 1473 randomly selected ECGs interpreted by both algorithms and by blinded experts. Results: Among the 500 ECGs selected, 399 had a final classification of AF; 101 (20.2%) had ≥1 false positive automated interpretation. Accuracy of Cardiologs® (91.2%; CI: 82.4–94.4) was higher than Veritas® (80.2%; CI: 76.5–83.5) (p

L’augmentation massive de la quantite de donnees issues de sources heterogenes motive le developpement d’outils de traitement de l’information permettant leur interoperabilite semantique. Les ontologies appliquees ont ete developpees dans ce but. Nous montrerons, dans cet article, en quoi l’ontologie philosophique a un role central a jouer dans l’ontologie appliquee, notamment biomedicale ; et reciproquement, en quoi l’ontologie appliquee eclaire certaines problematiques classiques d’ontologie philosophique, en prenant pour exemple la question suivante : la maladie est-elle une espece naturelle ? The massive increase of data generated by heterogeneous sources requires the development of computer tools enabling their semantic interoperability. Applied ontologies aim at fulfilling such needs. We will show in this article the central role that philosophical ontology can play for applied ontology, with a focus on biomedical ontologies; and reciprocally, how applied ontology can enlighten some classical issues in philosophical ontology, by considering the following question: Is disease a natural kind?

Aims Implantable cardioverter-defibrillators (ICDs) reduce sudden cardiac death in selected patients but inappropriate ICD shocks have been associated with increased mortality. The THORN registry aims to describe the rate of inappropriate ventricular arrhythmia diagnoses and therapies in patients followed by remote monitoring, as well as the following delay to next patient contact (DNPC). Methods and results One thousand eight hundred and eighty-two patients issued from a large remote monitoring database first implanted with an ICD for primary or secondary prevention in 110 French hospitals from 2007 to 2014 constitute the THORN population. Among them, 504 patients were additionally followed prospectively for evaluation of the DNPC. Eight hundred and ninety-five out of 1551 (58%) patients had ischaemic heart disease and 358/771 (46%) were implanted for secondary prevention. During 13.7 ± 3.4 months of follow-up, the prevalence of first inappropriate diagnosis in a ventricular arrhythmia zone with enabled therapy was 162/1882 (9%). Among those patients, 122/162 (75%) suffered at least one inappropriate therapy and 58/162 (36%) at least one inappropriate shock. Eighty-three out of 162 (51%) of first inappropriate diagnosis occurred during the first 4 months following implantation. The median DNPC was 8 days (interquartile range 1–26). At least one other day with recording of an inappropriate diagnosis of the same cause occurred in 13/43 (30%) of available DNPC periods, with an inappropriate therapy in 7/13 (54%). Conclusion Inappropriate diagnoses occurred in 9% of patients implanted with an ICD during the first 14 months. The DNPC after inadequate ventricular arrhythmia diagnoses remains long in daily practice and should be optimized. ClinicalTrials.gov Identifier NCT01594112.

Goal To evaluate routine Remote Monitoring (RM) of Cardiovascular Implantable Device (CID). Method A multiple choice questionnaire was e-mailed to 100 French physicians Implanters of CID in November 2017. Results A total of 73 answers were obtained (73%). Seventy five percent work in a public center, twenty three percent in a private center. The rate of Internal Cardiac Defibrillator implantation (ICD)/year per center is > 100 for 63%, 50–100 for 24,7%, 1000 (9.6%), 500–1000 (27.4%), 200–500 (41.1%), Conclusion Remote monitoring of CID is a current practice in France especially for ICD follow up. Collaboration with paramedical staff is partial. Patients are informed but not systematically. Data are archived but partially. Absence of funding may explain incomplete and heterogeneous RM activity in France.

International audience; AIMS: Remote monitoring of cardiac implantable electronic devices is a growing standard; yet, remote follow-up and management of alerts represents a time-consuming task for physicians or trained staff. This study evaluates an automatic mechanism based on artificial intelligence tools to filter atrial fibrillation (AF) alerts based on their medical significance.METHODS AND RESULTS: We evaluated this method on alerts for AF episodes that occurred in 60 pacemaker recipients. AKENATON prototype workflow includes two steps: natural language-processing algorithms abstract the patient health record to a digital version, then a knowledge-based algorithm based on an applied formal ontology allows to calculate the CHA2DS2-VASc score and evaluate the anticoagulation status of the patient. Each alert is then automatically classified by importance from low to critical, by mimicking medical reasoning. Final classification was compared with human expert analysis by two physicians. A total of 1783 alerts about AF episode \textgreater5 min in 60 patients were processed. A 1749 of 1783 alerts (98%) were adequately classified and there were no underestimation of alert importance in the remaining 34 misclassified alerts.CONCLUSION: This work demonstrates the ability of a pilot system to classify alerts and improves personalized remote monitoring of patients. In particular, our method allows integration of patient medical history with device alert notifications, which is useful both from medical and resource-management perspectives. The system was able to automatically classify the importance of 1783 AF alerts in 60 patients, which resulted in an 84% reduction in notification workload, while preserving patient safety

Background Inappropriate shocks (IS) are a major side effect of implantable cardioverter defibrillators (ICD). Remote monitoring (RM) may reduce inappropriate diagnoses (ID) and subsequent inappropriate therapies (IT). Purpose The purpose of THORN study was to determine the ability of ICD RM to early identify ID of ventricular arrhythmias and prompt physician reaction, in order to reduce the risk of recurrent IT. Methods THORN is an observational multicenter study of RM ICD patients, separated in a retrospective cohort (R), issued from a large RM database collected since 2007 by Biotronik SE & Co.KG and a prospective cohort (P) of patients implanted since 2012. The primary objective was to determine: 1/in the retrospective cohort, the relative proportion of patients experiencing at least one IT during a 15-month follow-up period, and, 2/in the prospective cohort, the medical reaction time (MRT) and ID recurrence after an initial ID. Results A total of 1891 patients (R: 1379, P: 512), implanted with a CRT-D (28.1%) or conventional ICD, (single chamber: 45.1%; dual chamber: 26.8%) were enrolled (83.6% men, 62.9 ± 12.8 y), 31.8% in primary prevention. During 13.7 ± 3.5 months of follow-up, 8.6% (R: 9.2%, P: 6.9%, P = 0.05) of the patients experienced at least one ID, of whom, 75.3% suffered at least one IT (R: 74%, P: 80%, P = NS) and 35.8% at least one IS. The median MRT was 9 days (0 to 145 days). It was 4 times longer for ID due to supraventricular tachyarrhythmias (16 days) compared to those due to abnormal sensing (4 days) (P = 0.04). ID recurrence occurred in 37% of ID patients, with a substantial part (25.7%) within the MRT. Among the 6.5% of patients experiencing at least one IT, 36.1% had an IT recurrence. Conclusions The rate of patients experiencing ID/IT and recurrences seems to decrease over time. Though RM is intended to take action earlier, the MRT is sometimes long and could be reduced as ID/IT recurrences are not infrequent within the MRT period.