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7. Computer versus cardiologist: Is a machine learning algorithm able to outperform an expert in diagnosing a phospholamban p.Arg14del mutation on the electrocardiogram?

Author

Bleijendaal, Hidde, Ramos, Lucas A., Lopes, Ricardo R., Verstraelen, Tom E., Baalman, Sarah W.E., Oudkerk Pool, Marinka D., Tjong, Fleur V.Y., Melgarejo-Meseguer, Francisco M., Gimeno-Blanes, F. Javier, Gimeno-Blanes, Juan R., Amin, Ahmad S., Winter, Michiel M., Marquering, Henk A., Kok, Wouter E.M., Zwinderman, Aeilko H., Wilde, Arthur A.M., and Pinto, Yigal M.

Published
2021
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8. Digital consults to optimize guideline‐directed therapy: design of a pragmatic multicenter randomized controlled trial

Author

Man, Jelle P., primary, Dijkgraaf, Marcel G.W., additional, Handoko, M. Louis, additional, de Lange, Frederik J., additional, Winter, Michiel M., additional, Schijven, Marlies P., additional, Stienen, Susan, additional, Meregalli, Paola, additional, Kok, Wouter E.M., additional, Kuipers, Dorianne I., additional, van der Harst, Pim, additional, Koole, Maarten A.C., additional, Chamuleau, Steven A.J., additional, and Schuuring, Mark J., additional

Published
2023
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12. Digital consults to optimize guideline‐directed therapy: design of a pragmatic multicenter randomized controlled trial.

Author

Man, Jelle P., Dijkgraaf, Marcel G.W., Handoko, M. Louis, de Lange, Frederik J., Winter, Michiel M., Schijven, Marlies P., Stienen, Susan, Meregalli, Paola, Kok, Wouter E.M., Kuipers, Dorianne I., van der Harst, Pim, Koole, Maarten A.C., Chamuleau, Steven A.J., and Schuuring, Mark J.

Subjects
PATIENT satisfaction, VENTRICULAR ejection fraction, PATIENT education, MEDICAL care costs, PATIENT safety, HEART failure
Abstract

Aims: Many heart failure (HF) patients do not receive optimal guideline‐directed medical therapy (GDMT) despite clear benefit on morbidity and mortality outcomes. Digital consults (DCs) have the potential to improve efficiency on GDMT optimization to serve the growing HF population. The investigator‐initiated ADMINISTER trial was designed as a pragmatic multicenter randomized controlled open‐label trial to evaluate efficacy and safety of DC in patients on HF treatment. Methods and results: Patients (n = 150) diagnosed with HF with a reduced ejection fraction will be randomized to DC or standard care (1:1). The intervention group receives multifaceted DCs including (i) digital data sharing (e.g. exchange of pharmacotherapy use and home‐measured vital signs), (ii) patient education via an e‐learning, and (iii) digital guideline recommendations to treating clinicians. The consults are performed remotely unless there is an indication to perform the consult physically. The primary outcome is the GDMT prescription rate score, and secondary outcomes include time till full GDMT optimization, patient and clinician satisfaction, time spent on healthcare, and Kansas City Cardiomyopathy Questionnaire. Results will be reported in accordance to the CONSORT statement. Conclusions: The ADMINISTER trial will offer the first randomized controlled data on GDMT prescription rates, time till full GDMT optimization, time spent on healthcare, quality of life, and patient and clinician satisfaction of the multifaceted patient‐ and clinician‐targeted DC for GDMT optimization. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Digital consults in heart failure care: a randomized controlled trial

Author

Man, Jelle P., Koole, Maarten A. C., Meregalli, Paola G., Handoko, M. Louis, Stienen, Susan, de Lange, Frederik J., Winter, Michiel M., Schijven, Marlies P., Kok, Wouter E. M., Kuipers, Dorianne I., van der Harst, Pim, Asselbergs, Folkert W., Zwinderman, Aeilko H., Dijkgraaf, Marcel G. W., Chamuleau, Steven A. J., and Schuuring, Mark J.

Abstract

Guideline-directed medical therapy (GDMT) has clear benefits on morbidity and mortality in patients with heart failure; however, GDMT use remains low. In the multicenter, open-label, investigator-initiated ADMINISTER trial, patients (n= 150) diagnosed with heart failure and reduced ejection fraction (HFrEF) were randomized (1:1) to receive usual care or a strategy using digital consults (DCs). DCs contained (1) digital data sharing from patient to clinician (pharmacotherapy use, home-measured vital signs and Kansas City Cardiomyopathy Questionnaires); (2) patient education via a text-based e-learning; and (3) guideline recommendations to all treating clinicians. All remotely gathered information was processed into a digital summary that was available to clinicians in the electronic health record before every consult. All patient interactions were standardly conducted remotely. The primary endpoint was change in GDMT score over 12 weeks (ΔGDMT); this GDMT score directly incorporated all non-conditional class 1 indications for HFrEF therapy with equal weights. The ADMINISTER trial met its primary outcome of achieving a higher GDMT in the DC group after a follow-up of 12 weeks (ΔGDMT score in the DC group: median 1.19, interquartile range (0.25, 2.3) arbitrary units versus 0.08 (0.00, 1.00) in usual care; P< 0.001). To our knowledge, this is the first multicenter randomized controlled trial that proves a DC strategy is effective to achieve GDMT optimization. ClinicalTrials.gov registration: NCT05413447.

Published
2024
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19. Clinical applicability of artificial intelligence for patients with an inherited heart disease: a scoping review

Author

Bleijendaal, Hidde, Croon, Philip, and Winter, Michiel

Subjects
cardiovascular diseases
Abstract

The number of inherited heart disease (IHD) studies using artificial intelligence (AI) has increased rapidly over the last years. In this scoping review, we aimed to present an overview of the current literature available on the applicability of AI, within the field of IHD. The literature search resulted in eighteen articles in which an AI model was trained and tested, mostly for diagnostic and predictive purposes. The areas under the receiver operating characteristic curves ranged from 0.78-0.96, but varied between IHD types, used methods and outcome measures. Only three out of eighteen did perform validation on an external dataset and most studies did not use explainable deep learning models. To be able to integrate AI as a tool to aid physicians in their diagnoses and clinical decisions within the IHD field, generalizability has to be better evaluated and explainability of DL models has to be increased.

Published
2022
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21. Ultra-low-dose CT versus chest X-ray for patients suspected of pulmonary disease at the emergency department: a multicentre randomised clinical trial

Author

van den Berk, Inge A H, primary, Kanglie, Maadrika M N P, additional, van Engelen, Tjitske S R, additional, Altenburg, Josje, additional, Annema, Jouke T, additional, Beenen, Ludo F M, additional, Boerrigter, Bart, additional, Bomers, Marije K, additional, Bresser, Paul, additional, Eryigit, Elvin, additional, Groenink, Maarten, additional, Hochheimer, Suzanne M R, additional, Holleman, Frits, additional, Kooter, Jos A J, additional, van Loon, Ramon B, additional, Keijzers, Mitran, additional, van der Lee, Ivo, additional, Luijendijk, Paul, additional, Meijboom, Lilian J, additional, Middeldorp, Saskia, additional, Schijf, Laura J, additional, Soetekouw, Robin, additional, Sprengers, Ralf W, additional, Montauban van Swijndregt, Alexander D, additional, de Monyé, Wouter, additional, Ridderikhof, Milan L, additional, Winter, Michiel M, additional, Bipat, Shandra, additional, Dijkgraaf, Marcel G W, additional, Bossuyt, Patrick M M, additional, Prins, Jan M, additional, and Stoker, Jaap, additional

Published
2022
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22. PO-631-03 DEEP LEARNING FOR THE IDENTIFICATION OF PATIENTS WITH A HIGH RISK FOR IDIOPATHIC VENTRICULAR FIBRILLATION

Author

Bleijendaal, Hidde, primary, Boersma, Jussi, additional, Lopes, Ricardo Ricci, additional, van der Ree, Martijn H., additional, Meijborg, Veronique M.F., additional, Amin, Ahmad S., additional, Winter, Michiel M., additional, Marquering, Henk, additional, Schomaker, Lambert, additional, zwinderman, Aeilko H., additional, Pinto, Yigal M., additional, Wilde, Arthur A.M., additional, and Postema, Pieter G., additional

Published
2022
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24. Artificial Intelligence in Patients with Congenital Heart Disease: Where Do We Stand?

Author

Oudkerk Pool, Marinka D., Kauw, Dirkjan, Bleijendaal, Hidde, Mulder, Barbara J. M., Pinto, Yigal, Bouma, Berto J., Winter, Michiel M., Graduate School, Cardiology, Amsterdam Cardiovascular Sciences, ACS - Heart failure & arrhythmias, APH - Personalized Medicine, APH - Aging & Later Life, and ACS - Pulmonary hypertension & thrombosis

Abstract

Life expectancy of patients with congenital heart disease (CHD) has increased in recent decades; however, late complications remain frequent and difficult to predict. Progress in data science has spurred the development of decision support systems and could aid physicians in predicting clinical deterioration and in the management of CHD patients. Newly developed artificial intelligence (AI) algorithms have shown performances comparable to humans in clinical diagnostics using statistical and computational algorithms and are expected to partly surpass human intelligence in the near future. Although much research on AI has been performed in patients with acquired heart disease, little data is available with respect to research on AI in patients with CHD. Learning algorithms in patients with CHD have shown to be promising in the interpretation of ECG, cardiac imaging, and the prediction of surgical outcome. However, current learning algorithms are not accurate enough to be implemented into daily clinical practice. Data on AI possibilities remain scarce in patients with CHD, and studies on large data sets are warranted to increase sensitivity, specificity, accuracy, and clinical relevance of these algorithms.

Published
2020

31. Ultra-low-dose CT versus chest X-ray for patients suspected of pulmonary disease at the emergency department: a multicentre randomised clinical trial

Author

van den Berk, Inge A H, Kanglie, Maadrika M N P, van Engelen, Tjitske S R, Altenburg, Josje, Annema, Jouke T, Beenen, Ludo F M, Boerrigter, Bart, Bomers, Marije K, Bresser, Paul, Eryigit, Elvin, Groenink, Maarten, Hochheimer, Suzanne M R, Holleman, Frits, Kooter, Jos A J, van Loon, Ramon B, Keijzers, Mitran, van der Lee, Ivo, Luijendijk, Paul, Meijboom, Lilian J, Middeldorp, Saskia, Schijf, Laura J, Soetekouw, Robin, Sprengers, Ralf W, Montauban van Swijndregt, Alexander D, de Monyé, Wouter, Ridderikhof, Milan L, Winter, Michiel M, Bipat, Shandra, Dijkgraaf, Marcel G W, Bossuyt, Patrick M M, Prins, Jan M, and Stoker, Jaap

Abstract

BackgroundChest CT displays chest pathology better than chest X-ray (CXR). We evaluated the effects on health outcomes of replacing CXR by ultra-low-dose chest-CT (ULDCT) in the diagnostic work-up of patients suspected of non-traumatic pulmonary disease at the emergency department.MethodsPragmatic, multicentre, non-inferiority randomised clinical trial in patients suspected of non-traumatic pulmonary disease at the emergency department. Between 31 January 2017 and 31 May 2018, every month, participating centres were randomly allocated to using ULDCT or CXR. Primary outcome was functional health at 28 days, measured by the Short Form (SF)-12 physical component summary scale score (PCS score), non-inferiority margin was set at 1 point. Secondary outcomes included hospital admission, hospital length of stay (LOS) and patients in follow-up because of incidental findings.Results2418 consecutive patients (ULDCT: 1208 and CXR: 1210) were included. Mean SF-12 PCS score at 28 days was 37.0 for ULDCT and 35.9 for CXR (difference 1.1; 95% lower CI: 0.003). After ULDCT, 638/1208 (52.7%) patients were admitted (median LOS of 4.8 days; IQR 2.1–8.8) compared with 659/1210 (54.5%) patients after CXR (median LOS 4.6 days; IQR 2.1–8.8). More ULDCT patients were in follow-up because of incidental findings: 26 (2.2%) versus 4 (0.3%).ConclusionsShort-term functional health was comparable between ULDCT and CXR, as were hospital admissions and LOS, but more incidental findings were found in the ULDCT group. Our trial does not support routine use of ULDCT in the work-up of patients suspected of non-traumatic pulmonary disease at the emergency department.Trial registration numberNTR6163.

Published
2023
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36. Classifying the diagnosis of study participants in clinical trials: a structured and efficient approach

Author

van Engelen, Tjitske S. R., Kanglie, Maadrika M. N. P., van den Berk, Inge A. H., Bouwman, Merel L. J., Suhooli, Hind J. M., Heckert, Sascha L., Stoker, Jaap, Bossuyt, Patrick M. M., Prins, Jan M., Annema, Jouke, Beenen, Ludo F. M., Bipat, Shandra, Bresser, Paul, Dijkgraaf, Marcel, Donker, Jos, Frankemölle, Betty, Groenink, Maarten, Hochheimer, Suzanne M. R., Holleman, Frits, Hulzebosch, Dorine, Keijzers, Mitran, van der Lee, Ivo, Leenhouts, Peter, Luitse, Jan, Meijboom, Lilian J., Middeldorp, Saskia, Montauban van Swijndregt, Alexander, de Monyé, Wouter, Otker, Jacqueline, Ridderikhof, Milan, Romijn, Johannes A., Schoonderwoerd, Antoinet J. N., Sprengers, Ralf W., Taal, Elizabeth M., Winter, Michiel, Graduate School, APH - Quality of Care, AGEM - Amsterdam Gastroenterology Endocrinology Metabolism, Radiology and Nuclear Medicine, APH - Methodology, APH - Personalized Medicine, Epidemiology and Data Science, Infectious diseases, Pulmonology, ARD - Amsterdam Reproduction and Development, ACS - Pulmonary hypertension & thrombosis, ACS - Microcirculation, Cardiology, ACS - Heart failure & arrhythmias, Surgery, Vascular Medicine, AMS - Amsterdam Movement Sciences, Emergency Department, AMS - Musculoskeletal Health, ACS - Diabetes & metabolism, ACS - Amsterdam Cardiovascular Sciences, Internal medicine, and Radiology and nuclear medicine

Subjects
lcsh:Medical physics. Medical radiology. Nuclear medicine, Adult, Lung Diseases, Male, medicine.medical_specialty, Students, Medical, lcsh:R895-920, Concordance, Guidelines as Topic, Computed tomography, 030204 cardiovascular system & hematology, Radiation Dosage, 03 medical and health sciences, 0302 clinical medicine, Methods, Humans, Medicine, Radiology, Nuclear Medicine and imaging, 030212 general & internal medicine, Netherlands, Neuroradiology, Emergency service (hospital), medicine.diagnostic_test, Tomography x-ray, business.industry, Radiography (thoracic), Internship and Residency, Interventional radiology, Workload, Emergency department, computed, Confidence interval, Clinical trial, Observer variation, Family medicine, Female, Radiography, Thoracic, Original Article, Clinical Competence, Emergency Service, Hospital, Tomography, X-Ray Computed, business
Abstract

Background A challenge in imaging research is a diagnostic classification of study participants. We hypothesised that a structured approach would be efficient and that classification by medical students, residents, and an expert panel whenever necessary would be as valid as classification of all patients by experts. Methods OPTIMACT is a randomised trial designed to evaluate the effectiveness of replacing chest x-ray for ultra-low-dose chest computed tomography (CT) at the emergency department. We developed a handbook with diagnostic guidelines and randomly selected 240 cases from 2,418 participants enrolled in OPTIMACT. Each case was independently classified by two medical students and, if they disagreed, by the students and a resident in a consensus meeting. Cases without consensus and cases classified as complex were assessed by a panel of medical specialists. To evaluate the validity, 60 randomly selected cases not referred to the panel by the students and the residents were reassessed by the specialists. Results Overall, the students and, if necessary, residents were able to assign a diagnosis in 183 of the 240 cases (76% concordance; 95% confidence interval [CI] 71–82%). We observed agreement between students and residents versus medical specialists in 50/60 cases (83% concordance; 95% CI 74–93%). Conclusions A structured approach in which study participants are assigned diagnostic labels by assessors with increasing levels of medical experience was an efficient and valid classification method, limiting the workload for medical specialists. We presented a viable option for classifying study participants in large-scale imaging trials (Netherlands National Trial Register number NTR6163).

Published
2020

41. Mobile health in cardiac patients: an overview on experiences and challenges of stakeholders involved in daily use and development

Author

Kauw, Dirkjan, primary, Huisma, Piter R, additional, Medlock, Stephanie K, additional, Koole, Maarten A C, additional, Wierda, Eric, additional, Abu-Hanna, Ameen, additional, Schijven, Marlies P, additional, Mulder, Barbara J M, additional, Bouma, Berto J, additional, Winter, Michiel M, additional, and Schuuring, Mark J, additional

Published
2020
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42. Protecting patient privacy in digital health technology: the Dutch m-Health infrastructure of Hartwacht as a learning case

Author

Wierda, Eric, primary, Blok, Sebastiaan, additional, Somsen, G Aernout, additional, van der Velde, Enno T, additional, Tulevski, Igor I, additional, Stavrov, Borut, additional, de Wildt, Maud C C, additional, van den Born, Bert J H, additional, Breukel, Laura, additional, de Mol, Bas A J M, additional, Ploem, M Corrette, additional, and Winter, Michiel M, additional

Published
2020
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46. Abstraction in argumentation: necessary but dangerous

Author

Prakken, H., de Winter, Michiel, Modgil, S.J., Budzynska, K., Lawrence, J., Sub Intelligent Systems, and Intelligent Systems

Subjects
Nature ofattack, Structure of arguments, Abstract argumentation frameworks
Abstract

While work on abstract argumentation frameworks has greatly advanced the study of argumentation in AI, its use is not without danger. One danger is that the direct modelling of examples in abstract frameworks instead of through a theory of the structure of arguments and the nature of attacks leads to ad-hoc modellings. Another danger is that it may be overlooked that abstract accounts of argumentation can implicitly make assumptions that are not shared by many of their instantiations. A variant of this is where assumptions valid for specific argumentation contexts are incorrectly generalised by abstracting away from the context. This paper gives examples of both dangers. A lesson drawn from this is that abstraction in AI research, although necessary for understanding the essentials of the object of study, can oversimplify in ways that are not easily noticed without an explicit account of the structure of arguments and the nature of attack.

Published
2018

48. Long-term clinical outcomes of valsartan in patients with a systemic right ventricle: Follow-up of a multicenter randomized controlled trial

Author

van Dissel, Alexandra C., primary, Winter, Michiel M., additional, van der Bom, Teun, additional, Vliegen, Hubert W., additional, van Dijk, Arie P.J., additional, Pieper, Petronella G., additional, Sieswerda, Gertjan T., additional, Roos-Hesselink, Jolien W., additional, Zwinderman, Aeilko H., additional, Mulder, Barbara J.M., additional, and Bouma, Berto J., additional

Published
2019
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49. Evaluating the systemic right ventricle by CMR: the importance of consistent and reproducible delineation of the cavity

Author

van Dijk Arie PJ, Bouma Berto J, Groenink Maarten, Bernink Flip JP, Winter Michiel M, Helbing Willem A, Tijssen Jan GP, and Mulder Barbara JM

Subjects
Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Abstract Background The method used to delineate the boundary of the right ventricle (RV), relative to the trabeculations and papillary muscles in cardiovascular magnetic resonance (CMR) ventricular volume analysis, may matter more when these structures are hypertrophied than in individuals with normal cardiovascular anatomy. This study aimed to compare two methods of cavity delineation in patients with systemic RV. Methods Twenty-nine patients (mean age 34.7 ± 12.4 years) with a systemic RV (12 with congenitally corrected transposition of the great arteries (ccTGA) and 17 with atrially switched (TGA) underwent CMR. We compared measurements of systemic RV volumes and function using two analysis protocols. The RV trabeculations and papillary muscles were either included in the calculated blood volume, the boundary drawn immediately within the apparently compacted myocardial layer, or they were manually outlined and excluded. RV stroke volume (SV) calculated using each method was compared with corresponding left ventricular (LV) SV. Additionally, we compared the differences in analysis time, and in intra- and inter-observer variability between the two methods. Paired samples t-test was used to test for differences in volumes, function and analysis time between the two methods. Differences in intra- and inter-observer reproducibility were tested using an extension of the Bland-Altman method. Results The inclusion of trabeculations and papillary muscles in the ventricular volume resulted in higher values for systemic RV end diastolic volume (mean difference 28.7 ± 10.6 ml, p < 0.001) and for end systolic volume (mean difference 31.0 ± 11.5 ml, p < 0.001). Values for ejection fraction were significantly lower (mean difference -7.4 ± 3.9%, p < 0.001) if structures were included. LV SV did not differ significantly from RV SV for both analysis methods (p = NS). Including structures resulted in shorter analysis time (p < 0.001), and showed better inter-observer reproducibility for ejection fraction (p < 0.01). Conclusion The choice of method for systemic RV cavity delineation significantly affected volume measurements, given the CMR acquisition and analysis systems used. We recommend delineation outside the trabeculations for routine clinical measurements of systemic RV volumes as this approach took less time and gave more reproducible measurements.

Published
2008
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50. Protecting patient privacy in digital health technology: the Dutch m-Health infrastructure of Hartwacht as a learning case.

Author

Wierda, Eric, Blok, Sebastiaan, Somsen, G. Aernout, van der Velde, Enno T., Tulevski, Igor I., Stavrov, Borut, de Wildt, Maud C. C., van den Born, Bert J. H., Breukel, Laura, de Mol, Bas A. J. M., Ploem, M. Corrette, and Winter, Michiel M.

Subjects
DIFFUSION of innovations, MEDICAL ethics, MEDICAL technology, PRIVACY, TELEMEDICINE, WEARABLE technology, MOBILE apps, ETHICS
Abstract

Innovative ways of healthcare delivery like m-Health, the practice of medicine by mobile devices and wearable devices are the promising new technique that may lead to improvement in quality of care at lower costs. While fully acknowledging the importance of m-Health development, there are challenges on privacy legislation. We address the legal framework, especially the General Data Protection Regulation, applied to m-Health and its implications for m-Health developments in Europe. We discuss how these rules are applied using a representative example of an m-Health programme with remote monitoring in the Netherlands. We consider informing patients about the data processing and obtaining their explicit consent as main responsibilities of healthcare providers introducing m-Health in their practice. [ABSTRACT FROM AUTHOR]

Published
2020
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