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13. Registered prodromal symptoms of out-of-hospital cardiac arrest among patients calling the medical helpline services

Author

Zylyftari, Nertila, Lee, Christina Ji-Young, Gnesin, Filip, Møller, Amalie Lykkemark, Mills, Elisabeth Helen Anna, Møller, Sidsel G., Jensen, Britta, Ringgren, Kristian Bundgaard, Kragholm, Kristian, Christensen, Helle Collatz, Blomberg, Stig Nikolaj Fasmer, Tan, Hanno L., Folke, Fredrik, Køber, Lars, Gislason, Gunnar, and Torp-Pedersen, Christian

Published
2023
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14. Smartwatch based automatic detection of out-of-hospital cardiac arrest: Study rationale and protocol of the HEART-SAFE project

Author

Schober, Patrick, van den Beuken, Wisse M.F., Nideröst, Beat, Kooy, Tom A., Thijssen, Steve, Bulte, Carolien S.E., Huisman, Bregje A.A., Tuinman, Pieter R., Nap, Alexander, Tan, Hanno L., Loer, Stephan A., Franschman, Gaby, Lettinga, Roelof G., Demirtas, Derya, Eberl, Susanne, van Schuppen, Hans, and Schwarte, Lothar A.

Published
2022
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17. Associations of Clinical Characteristics With Sudden Cardiac Arrest in People With Type 2 Diabetes With and Without Cardiovascular Disease: A Longitudinal Case-Control Study Using Routine Primary Care Data.

Author

Harms, Peter P., van Dongen, Laura H., Bennis, Frank, Swart, Karin M.A., Hoogendoorn, Mark, Beulens, Joline W.J., Tan, Hanno L., Elders, Petra P.J.M., and Blom, Marieke T.

Subjects
TYPE 2 diabetes, ELECTRONIC health records, CARDIAC arrest, HEART failure, ANTIPSYCHOTIC agents
Abstract

OBJECTIVE: To assess longitudinal associations with sudden cardiac arrest (SCA) of clinical characteristics recorded in primary care in people with type 2 diabetes (T2D), both with and without cardiovascular disease (CVD). RESEARCH DESIGN AND METHODS: We performed a case-control study, with SCA case subjects with T2D from the Amsterdam Resuscitation Studies (ARREST) registry of out-of-hospital resuscitation attempts in the Dutch Noord-Holland region (2010–2020) and up to five matched (age, sex, T2D, general practitioner [GP] practice) non-SCA control subjects. We collected relevant clinical measurements, medication use, and medical history from GPs' electronic health care records. We analyzed the associations of clinical characteristics and medication use with SCA in the total sample and in subgroups with or without CVD using multivariable time-dependent Cox regression (hazard ratios, 95% confidence intervals). RESULTS: We included 689 SCA case subjects and 3,230 non-SCA control subjects. In multivariable models, low fasting glucose (<4.5 mmol/mol: 1.91 [1.00–3.64]), antihypertensive (1.80 [1.39–2.33]), glucose lowering (oral only: 1.32 [1.06–1.63]; insulin only: 2.31 [1.71–3.12]; oral and insulin: 1.64 [1.21–2.22]), heart failure (1.91 [1.55–2.35]), and QTc-prolonging prokinetic (1.78 [1.27–2.50]), antibiotic (1.35 [1.05–1.73]), and antipsychotic (2.10 [1.42–3.09]) medication were associated with SCA in the total sample. In subgroup effect modification analyses, QTc-prolonging antibiotic (1.82 [1.26–2.63]) and antipsychotic (3.10 [2.09–4.59]) medication use were associated with SCA only in those without CVD. CONCLUSIONS: In people with T2D, low fasting glucose and QTc-prolonging prokinetic, antibiotic, or antipsychotic medication use and a history of heart failure are associated with SCA risk. Subgroup analyses indicate antibiotic and antipsychotic medication use increases SCA risk specifically in those without CVD. [ABSTRACT FROM AUTHOR]

Published
2025
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18. A comprehensive evaluation of the genetic architecture of sudden cardiac arrest

Author

Ashar, Foram N, Mitchell, Rebecca N, Albert, Christine M, Newton-Cheh, Christopher, Brody, Jennifer A, Müller-Nurasyid, Martina, Moes, Anna, Meitinger, Thomas, Mak, Angel, Huikuri, Heikki, Junttila, M Juhani, Goyette, Philippe, Pulit, Sara L, Pazoki, Raha, Tanck, Michael W, Blom, Marieke T, Zhao, XiaoQing, Havulinna, Aki S, Jabbari, Reza, Glinge, Charlotte, Tragante, Vinicius, Escher, Stefan A, Chakravarti, Aravinda, Ehret, Georg, Coresh, Josef, Li, Man, Prineas, Ronald J, Franco, Oscar H, Kwok, Pui-Yan, Lumley, Thomas, Dumas, Florence, McKnight, Barbara, Rotter, Jerome I, Lemaitre, Rozenn N, Heckbert, Susan R, O’Donnell, Christopher J, Hwang, Shih-Jen, Tardif, Jean-Claude, VanDenburgh, Martin, Uitterlinden, André G, Hofman, Albert, Stricker, Bruno HC, de Bakker, Paul IW, Franks, Paul W, Jansson, Jan-Hakan, Asselbergs, Folkert W, Halushka, Marc K, Maleszewski, Joseph J, Tfelt-Hansen, Jacob, Engstrøm, Thomas, Salomaa, Veikko, Virmani, Renu, Kolodgie, Frank, Wilde, Arthur AM, Tan, Hanno L, Bezzina, Connie R, Eijgelsheim, Mark, Rioux, John D, Jouven, Xavier, Kääb, Stefan, Psaty, Bruce M, Siscovick, David S, Arking, Dan E, and Sotoodehnia, Nona

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Heart Disease - Coronary Heart Disease, Cardiovascular, Genetics, Human Genome, Prevention, Heart Disease, Clinical Research, 2.1 Biological and endogenous factors, Good Health and Well Being, Arrhythmias, Cardiac, Body Mass Index, Coronary Artery Disease, Death, Sudden, Cardiac, Female, Genome-Wide Association Study, Heart Conduction System, Humans, Male, Mendelian Randomization Analysis, Polymorphism, Single Nucleotide, Risk Assessment, Risk Factors, Sex Factors, Sudden cardiac arrest, Genome-wide association study, Mendelian randomization, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract

AimsSudden cardiac arrest (SCA) accounts for 10% of adult mortality in Western populations. We aim to identify potential loci associated with SCA and to identify risk factors causally associated with SCA.Methods and resultsWe carried out a large genome-wide association study (GWAS) for SCA (n = 3939 cases, 25 989 non-cases) to examine common variation genome-wide and in candidate arrhythmia genes. We also exploited Mendelian randomization (MR) methods using cross-trait multi-variant genetic risk score associations (GRSA) to assess causal relationships of 18 risk factors with SCA. No variants were associated with SCA at genome-wide significance, nor were common variants in candidate arrhythmia genes associated with SCA at nominal significance. Using cross-trait GRSA, we established genetic correlation between SCA and (i) coronary artery disease (CAD) and traditional CAD risk factors (blood pressure, lipids, and diabetes), (ii) height and BMI, and (iii) electrical instability traits (QT and atrial fibrillation), suggesting aetiologic roles for these traits in SCA risk.ConclusionsOur findings show that a comprehensive approach to the genetic architecture of SCA can shed light on the determinants of a complex life-threatening condition with multiple influencing factors in the general population. The results of this genetic analysis, both positive and negative findings, have implications for evaluating the genetic architecture of patients with a family history of SCA, and for efforts to prevent SCA in high-risk populations and the general community.

Published
2018

19. Harmonization of the definition of sudden cardiac death in longitudinal cohorts of the European Sudden Cardiac Arrest network – towards Prevention, Education, and New Effective Treatments (ESCAPE-NET) consortium

Author

Warming, Peder Emil, Ågesen, Frederik Nybye, Lynge, Thomas Hadberg, Jabbari, Reza, Smits, Robin L.A., van Valkengoed, Irene G.M., Welten, Sabrina J.G.C., van der Heijden, Amber A., Elders, Petra J., Blom, Marieke T., Jouven, Xavier, Schwartz, Peter J., Albert, Christine M., Beulens, Joline W., Rutters, Femke, Tan, Hanno L., Empana, Jean-Philippe, and Tfelt-Hansen, Jacob

Published
2022
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22. Genome-wide association analyses identify new Brugada syndrome risk loci and highlight a new mechanism of sodium channel regulation in disease susceptibility

Author

Barc, Julien, Tadros, Rafik, Glinge, Charlotte, Chiang, David Y., Jouni, Mariam, Simonet, Floriane, Jurgens, Sean J., Baudic, Manon, Nicastro, Michele, Potet, Franck, Offerhaus, Joost A., Walsh, Roddy, Choi, Seung Hoan, Verkerk, Arie O., Mizusawa, Yuka, Anys, Soraya, Minois, Damien, Arnaud, Marine, Duchateau, Josselin, Wijeyeratne, Yanushi D., Muir, Alison, Papadakis, Michael, Castelletti, Silvia, Torchio, Margherita, Ortuño, Cristina Gil, Lacunza, Javier, Giachino, Daniela F., Cerrato, Natascia, Martins, Raphaël P., Campuzano, Oscar, Van Dooren, Sonia, Thollet, Aurélie, Kyndt, Florence, Mazzanti, Andrea, Clémenty, Nicolas, Bisson, Arnaud, Corveleyn, Anniek, Stallmeyer, Birgit, Dittmann, Sven, Saenen, Johan, Noël, Antoine, Honarbakhsh, Shohreh, Rudic, Boris, Marzak, Halim, Rowe, Matthew K., Federspiel, Claire, Le Page, Sophie, Placide, Leslie, Milhem, Antoine, Barajas-Martinez, Hector, Beckmann, Britt-Maria, Krapels, Ingrid P., Steinfurt, Johannes, Winkel, Bo Gregers, Jabbari, Reza, Shoemaker, Moore B., Boukens, Bas J., Škorić-Milosavljević, Doris, Bikker, Hennie, Manevy, Federico C., Lichtner, Peter, Ribasés, Marta, Meitinger, Thomas, Müller-Nurasyid, Martina, Veldink, Jan H., van den Berg, Leonard H., Van Damme, Philip, Cusi, Daniele, Lanzani, Chiara, Rigade, Sidwell, Charpentier, Eric, Baron, Estelle, Bonnaud, Stéphanie, Lecointe, Simon, Donnart, Audrey, Le Marec, Hervé, Chatel, Stéphanie, Karakachoff, Matilde, Bézieau, Stéphane, London, Barry, Tfelt-Hansen, Jacob, Roden, Dan, Odening, Katja E., Cerrone, Marina, Chinitz, Larry A., Volders, Paul G., van de Berg, Maarten P., Laurent, Gabriel, Faivre, Laurence, Antzelevitch, Charles, Kääb, Stefan, Arnaout, Alain Al, Dupuis, Jean-Marc, Pasquie, Jean-Luc, Billon, Olivier, Roberts, Jason D., Jesel, Laurence, Borggrefe, Martin, Lambiase, Pier D., Mansourati, Jacques, Loeys, Bart, Leenhardt, Antoine, Guicheney, Pascale, Maury, Philippe, Schulze-Bahr, Eric, Robyns, Tomas, Breckpot, Jeroen, Babuty, Dominique, Priori, Silvia G., Napolitano, Carlo, de Asmundis, Carlo, Brugada, Pedro, Brugada, Ramon, Arbelo, Elena, Brugada, Josep, Mabo, Philippe, Behar, Nathalie, Giustetto, Carla, Molina, Maria Sabater, Gimeno, Juan R., Hasdemir, Can, Schwartz, Peter J., Crotti, Lia, McKeown, Pascal P., Sharma, Sanjay, Behr, Elijah R., Haissaguerre, Michel, Sacher, Frédéric, Rooryck, Caroline, Tan, Hanno L., Remme, Carol A., Postema, Pieter G., Delmar, Mario, Ellinor, Patrick T., Lubitz, Steven A., Gourraud, Jean-Baptiste, Tanck, Michael W., George, Jr., Alfred L., MacRae, Calum A., Burridge, Paul W., Dina, Christian, Probst, Vincent, Wilde, Arthur A., Schott, Jean-Jacques, Redon, Richard, and Bezzina, Connie R.

Published
2022
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30. European first responder systems and differences in return of spontaneous circulation and survival after out-of-hospital cardiac arrest: A study of registry cohorts

Author

Oving, Iris, de Graaf, Corina, Masterson, Siobhan, Koster, Rudolph W., Zwinderman, Aeilko H., Stieglis, Remy, AliHodzic, Hajriz, Baldi, Enrico, Betz, Susanne, Cimpoesu, Diana, Folke, Fredrik, Rupp, Dennis, Semeraro, Federico, Truhlar, Anatolij, Tan, Hanno L., and Blom, Marieke T.

Published
2021
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32. Relationship between activity and sleep, as measured through a wearable accelerometer, and appropriate cardioverter defibrillator interventions: a prospective SafeHeart substudy.

Author

Frodi, Diana M, Kolk, Maarten Z H, Diederichsen, Søren Z, Langford, Joss, Knops, Reinoud E, Tan, Hanno L, Andersen, Tariq O, Jacobsen, Peter Karl, Risum, Niels, Tjong, Fleur V Y, and Svendsen, Jesper Hastrup

Abstract

Aims Physical activity has shown association with ventricular arrhythmia, however, the role of specific behavioral patterns over a 24 h cycle remains unknown. Therefore, we aimed to explore associations between physical behavior and appropriate implantable cardioverter defibrillator (ICD) therapy. Methods and results We included patients with an ICD at two European sites, who wore wrist-based accelerometers capturing 24 h movement and sleep behaviours for 28 days. Behavioural measures included activity volume, duration and intensity, sleep duration, and efficiency. Participants were followed for 12 months for the outcome of appropriate ICD therapy. Cox proportional hazard models with restricted cubic splines were used for the analysis. Lastly, the predictive capacity was tested. A total of 253 ICD patients were included (mean age 63.5 (±10.2), 48 (19.0%) female). During follow-up, 40 participants (15.8%) received appropriate ICD therapy; 32 anti-tachycardia pacing (ATP) only (12.6%), 5 shock only (2.0%), and 3 combined ATP and shock (1.2%). In the adjusted model, high inactive duration (HR 1.40 (95% 1.10–1.78)), peak walking cadence (HR 1.07 (95% 1.03–1.12)), and total sleep duration (HR 1.50 (1.02–2.22)) were associated with the outcome. The dose–response relationship was U-shaped for inactive duration with a cut-off at 16 h, and linear for peak cadence and sleep. The prediction model reached an area under the receiver operating characteristic curve of 0.70 ± 0.03, with highest accuracy in the first months. Conclusion Wearable-derived 24 h movement and sleep behaviours collected over 28 days were associated with later appropriate ICD therapy risk. Testing of the predictive value of digital biomarkers for enhanced risk stratification of ventricular arrhythmia warrants larger prospective studies. Clinical Trial Registration National Trial Registration (NL9218, http://onderzoekmetmensen.nl/). [ABSTRACT FROM AUTHOR]

Published
2024
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42. Behavioural digital biomarkers enable real-time monitoring of patient-reported outcomes:a substudy of the multicentre, prospective observational SafeHeart study

Author

Kolk, Maarten Z. H., Frodi, Diana M., Langford, Joss, Meskers, Caroline J., Andersen, Tariq O., Jacobsen, Peter Karl, Risum, Niels, Tan, Hanno L., Svendsen, Jesper H., Knops, Reinoud E., Diederichsen, Søren Z., Tjong, Fleur V. Y., Kolk, Maarten Z. H., Frodi, Diana M., Langford, Joss, Meskers, Caroline J., Andersen, Tariq O., Jacobsen, Peter Karl, Risum, Niels, Tan, Hanno L., Svendsen, Jesper H., Knops, Reinoud E., Diederichsen, Søren Z., and Tjong, Fleur V. Y.

Abstract

Introduction Patient-reported outcome measures (PROMs) serve multiple purposes, including shared decision-making and patient communication, treatment monitoring and health-technology assessment. Patient monitoring using PROMs is constrained by recall and non-response bias, respondent burden and missing data. We evaluated the potential of behavioural digital biomarkers obtained from a wearable accelerometer to achieve personalised predictions of PROMs Methods Data from the multicenter, prospective SafeHeart study conducted at Amsterdam University Medical Center in the Netherlands and Copenhagen University Hospital, Rigshospitalet in Copenhagen, Denmark, was used. The study enrolled patients with an implantable cardioverter defibrillator (ICD) between May 2021 and September 2022 who then wore wearable devices with raw acceleration output to capture digital biomarkers reflecting physical behaviour. To collect PROMs, patients received the KCCQ and EQ5D-5 L questionnaire at two instances; baseline and after 6 months. Multivariable Tobit regression models were used to explore associations between digital biomarkers and PROMs, specifically whether digital biomarkers could enable PROM prediction. Results The study population consisted of 303 patients (mean age 62.9 ± 10.9 years, 81.2% male). Digital biomarkers showed significant correlations to patient-reported physical and social limitations, severity and frequency of symptoms and quality of life. Prospective validation of the Tobit models indicated moderate correlations between the observed and predicted scores for KCCQ (concordance correlation coefficient (CCC) = 0.49, mean difference: 1.07 points) and EQ5D-5 L (CCC = 0.38, mean difference 0.02 points). Conclusion Wearable digital biomarkers correlate with PROMs, and may be leveraged for real-time prediction. These findings hold promise for monitoring of PROMs through wearable accelerometers.

Published
2024

43. Relationship between activity and sleep, as measured through a wearable accelerometer, and appropriate cardioverter defibrillator interventions:a prospective SafeHeart substudy

Author

Frodi, Diana M., Kolk, Maarten Z.H., Diederichsen, Søren Z., Langford, Joss, Knops, Reinoud E., Tan, Hanno L., Andersen, Tariq O., Jacobsen, Peter Karl, Risum, Niels, Tjong, Fleur V.Y., Svendsen, Jesper Hastrup, Frodi, Diana M., Kolk, Maarten Z.H., Diederichsen, Søren Z., Langford, Joss, Knops, Reinoud E., Tan, Hanno L., Andersen, Tariq O., Jacobsen, Peter Karl, Risum, Niels, Tjong, Fleur V.Y., and Svendsen, Jesper Hastrup

Abstract

Aims: Physical activity has shown association with ventricular arrhythmia, however, the role of specific behavioral patterns over a 24 h cycle remains unknown. Therefore, we aimed to explore associations between physical behavior and appropriate implantable cardioverter defibrillator (ICD) therapy. Methods and results: We included patients with an ICD at two European sites, who wore wrist-based accelerometers capturing 24 h movement and sleep behaviours for 28 days. Behavioural measures included activity volume, duration and intensity, sleep duration, and efficiency. Participants were followed for 12 months for the outcome of appropriate ICD therapy. Cox proportional hazard models with restricted cubic splines were used for the analysis. Lastly, the predictive capacity was tested. A total of 253 ICD patients were included (mean age 63.5 (±10.2), 48 (19.0%) female). During follow-up, 40 participants (15.8%) received appropriate ICD therapy; 32 anti-tachycardia pacing (ATP) only (12.6%), 5 shock only (2.0%), and 3 combined ATP and shock (1.2%). In the adjusted model, high inactive duration (HR 1.40 (95% 1.10-1.78)), peak walking cadence (HR 1.07 (95% 1.03-1.12)), and total sleep duration (HR 1.50 (1.02-2.22)) were associated with the outcome. The dose-response relationship was U-shaped for inactive duration with a cut-off at 16 h, and linear for peak cadence and sleep. The prediction model reached an area under the receiver operating characteristic curve of 0.70 ± 0.03, with highest accuracy in the first months. Conclusion: Wearable-derived 24 h movement and sleep behaviours collected over 28 days were associated with later appropriate ICD therapy risk. Testing of the predictive value of digital biomarkers for enhanced risk stratification of ventricular arrhythmia warrants larger prospective studies.

Published
2024

44. Deep behavioural representation learning reveals risk profiles for malignant ventricular arrhythmias

Author

Kolk, Maarten Z H, Frodi, Diana My, Langford, Joss, Andersen, Tariq O, Jacobsen, Peter Karl, Risum, Niels, Tan, Hanno L, Svendsen, Jesper Hastrup, Knops, Reinoud E, Diederichsen, Søren Zöga, Tjong, Fleur V Y, Kolk, Maarten Z H, Frodi, Diana My, Langford, Joss, Andersen, Tariq O, Jacobsen, Peter Karl, Risum, Niels, Tan, Hanno L, Svendsen, Jesper Hastrup, Knops, Reinoud E, Diederichsen, Søren Zöga, and Tjong, Fleur V Y

Abstract

We aimed to identify and characterise behavioural profiles in patients at high risk of SCD, by using deep representation learning of day-to-day behavioural recordings. We present a pipeline that employed unsupervised clustering on low-dimensional representations of behavioural time-series data learned by a convolutional residual variational neural network (ResNet-VAE). Data from the prospective, observational SafeHeart study conducted at two large tertiary university centers in the Netherlands and Denmark were used. Patients received an implantable cardioverter-defibrillator (ICD) between May 2021 and September 2022 and wore wearable devices using accelerometer technology during 180 consecutive days. A total of 272 patients (mean age of 63.1 ± 10.2 years, 81% male) were eligible with a total sampling of 37,478 days of behavioural data (138 ± 47 days per patient). Deep representation learning identified five distinct behavioural profiles: Cluster A (n = 46) had very low physical activity levels and a disturbed sleep pattern. Cluster B (n = 70) had high activity levels, mainly at light-to-moderate intensity. Cluster C (n = 63) exhibited a high-intensity activity profile. Cluster D (n = 51) showed above-average sleep efficiency. Cluster E (n = 42) had frequent waking episodes and poor sleep. Annual risks of malignant ventricular arrhythmias ranged from 30.4% in Cluster A to 9.8% and 9.5% for Clusters D-E, respectively. Compared to low-risk profiles (D-E), Cluster A demonstrated a three-to-four fold increased risk of malignant ventricular arrhythmias adjusted for clinical covariates (adjusted HR 3.63, 95% CI 1.54–8.53, p < 0.001). These behavioural profiles may guide more personalised approaches to ventricular arrhythmia and SCD prevention., We aimed to identify and characterise behavioural profiles in patients at high risk of SCD, by using deep representation learning of day-to-day behavioural recordings. We present a pipeline that employed unsupervised clustering on low-dimensional representations of behavioural time-series data learned by a convolutional residual variational neural network (ResNet-VAE). Data from the prospective, observational SafeHeart study conducted at two large tertiary university centers in the Netherlands and Denmark were used. Patients received an implantable cardioverter-defibrillator (ICD) between May 2021 and September 2022 and wore wearable devices using accelerometer technology during 180 consecutive days. A total of 272 patients (mean age of 63.1 ± 10.2 years, 81% male) were eligible with a total sampling of 37,478 days of behavioural data (138 ± 47 days per patient). Deep representation learning identified five distinct behavioural profiles: Cluster A (n = 46) had very low physical activity levels and a disturbed sleep pattern. Cluster B (n = 70) had high activity levels, mainly at light-to-moderate intensity. Cluster C (n = 63) exhibited a high-intensity activity profile. Cluster D (n = 51) showed above-average sleep efficiency. Cluster E (n = 42) had frequent waking episodes and poor sleep. Annual risks of malignant ventricular arrhythmias ranged from 30.4% in Cluster A to 9.8% and 9.5% for Clusters D-E, respectively. Compared to low-risk profiles (D-E), Cluster A demonstrated a three-to-four fold increased risk of malignant ventricular arrhythmias adjusted for clinical covariates (adjusted HR 3.63, 95% CI 1.54-8.53, p < 0.001). These behavioural profiles may guide more personalised approaches to ventricular arrhythmia and SCD prevention.

Published
2024

45. Long-term adherence to a wearable for continuous behavioural activity measuring in the SafeHeart implantable cardioverter defibrillator population

Author

Frodi, Diana My, Kolk, Maarten Z. H., Langford, Joss, Knops, Reinoud, Tan, Hanno L., Andersen, Tariq Osman, Jacobsen, Peter Karl, Risum, Niels, Svendsen, Jesper Hastrup, Tjong, Fleur V. Y., Diederichsen, Søren Zöga, Frodi, Diana My, Kolk, Maarten Z. H., Langford, Joss, Knops, Reinoud, Tan, Hanno L., Andersen, Tariq Osman, Jacobsen, Peter Karl, Risum, Niels, Svendsen, Jesper Hastrup, Tjong, Fleur V. Y., and Diederichsen, Søren Zöga

Abstract

Aims Wearable health technologies are increasingly popular. Yet, wearable monitoring only works when devices are worn as intended, and adherence reporting lacks standardization. In this study, we aimed to explore the long-term adherence to a wrist-worn activity tracker in the prospective SafeHeart study and identify patient characteristics associated with adherence. Methods and results This study enrolled 303 participants, instructed to wear a wrist-worn accelerometer day and night for 6 months. Long-term adherence was defined as valid days (≥22 h of wear time) divided by expected days, and daily adherence as mean hours of wear time per 24 h period. Optimal, moderate, and low long-term and daily adherence groups were defined as long-term adherence above or below 95 and 75% and daily adherence above or below 90 and 75%. Regression models were used to identify patient characteristics associated with long-term adherence. In total, 296 participants [median age 64 years; interquartile range (IQR) 57–72; 19% female] were found eligible, yielding 44 003 days for analysis. The median long-term adherence was 88.2% (IQR 74.6–96.5%). A total of 83 (28%), 127 (42.9%), and 86 (29.1%) participants had optimal, moderate, and low long-term adherence, and 163 (55.1%), 87 (29.4%), and 46 (15.5%) had optimal, moderate, and low daily adherence, respectively. Age and smoking habits differed significantly between adherence levels, and increasing changeover intervals improved the degree of long-term adherence. Conclusion Long-term adherence to a wearable activity tracker was 88.2% over a 6-month period. Older age and longer changeover interval were positively associated with long-term adherence. This serves as a benchmark for future studies that rely on wearable devices.

Published
2024

46. Sudden cardiac arrest in infants and children:proposal for a diagnostic workup to identify the etiology. An 18-year multicenter evaluation in the Netherlands

Author

Bakker, Ashley M, Albrecht, Marijn, Verkaik, Bas J, de Jonge, Rogier C J, Buysse, Corinne M P, Blom, Nico A, Rammeloo, Lukas A J, Verhagen, Judith M A, Riedijk, Maaike A, Yap, Sing C, Tan, Hanno L, Kammeraad, Janneke A E, Bakker, Ashley M, Albrecht, Marijn, Verkaik, Bas J, de Jonge, Rogier C J, Buysse, Corinne M P, Blom, Nico A, Rammeloo, Lukas A J, Verhagen, Judith M A, Riedijk, Maaike A, Yap, Sing C, Tan, Hanno L, and Kammeraad, Janneke A E

Abstract

Sudden cardiac arrest (SCA) studies are often population-based, limited to sudden cardiac death, and excluding infants. To guide prevention opportunities, it is essential to be informed of pediatric SCA etiologies. Unfortunately, etiologies frequently remain unresolved. The objectives of this study were to determine paediatric SCA etiology, and to evaluate the extent of post-SCA investigations and to assess the performance of previous cardiac evaluation in detecting conditions predisposing to SCA. In a retrospective cohort (2002–2019), all children 0–18 years with out-of-hospital cardiac arrest (OHCA) referred to Erasmus MC Sophia Children's Hospital or the Amsterdam UMC (tertiary-care university hospitals), with cardiac or unresolved etiologies were eligible for inclusion. SCA etiologies, cardiac and family history and etiologic investigations in unresolved cases were assessed. The etiology of arrest could be determined in 52% of 172 cases. Predominant etiologies in children ≥ 1 year (n = 99) were primary arrhythmogenic disorders (34%), cardiomyopathies (22%) and unresolved (32%). Events in children < 1 year (n = 73) were largely unresolved (70%) or caused by cardiomyopathy (8%), congenital heart anomaly (8%) or myocarditis (7%). Of 83 children with unresolved etiology a family history was performed in 51%, an autopsy in 51% and genetic testing in 15%. Pre-existing cardiac conditions presumably causative for SCA were diagnosed in 9%, and remained unrecognized despite prior evaluation in 13%. Conclusion: SCA etiology remained unresolved in 83 of 172 cases (48%) and essential diagnostic investigations were often not performed. Over one-fifth of SCA patients underwent prior cardiac evaluation, which did not lead to recognition of a cardiac condition predisposing to SCA in all of them. The diagnostic post-SCA approach should be improved and the proposed standardized pediatric post-SCA diagnostics protocol may ensure a consistent and systematic evaluation process

Published
2024

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