The Netherlands Arrhythmogenic Cardiomyopathy Registry: design and status update

Background Clinical research on arrhythmogenic cardiomyopathy (ACM) is typically limited by small patient numbers, retrospective study designs, and inconsistent definitions. Aim To create a large national ACM patient cohort with a vast amount of uniformly collected high-quality data that is readily available for future research. Methods This is a multicentre, longitudinal, observational cohort study that includes (1) patients with a definite ACM diagnosis, (2) at-risk relatives of ACM patients, and (3) ACM-associated mutation carriers. At baseline and every follow-up visit, a medical history as well information regarding (non-)invasive tests is collected (e. g. electrocardiograms, Holter recordings, imaging and electrophysiological studies, pathology reports, etc.). Outcome data include (non-)sustained ventricular and atrial arrhythmias, heart failure, and (cardiac) death. Data are collected on a research electronic data capture (REDCap) platform in which every participating centre has its own restricted data access group, thus empowering local studies while facilitating data sharing. Discussion The Netherlands ACM Registry is a national observational cohort study of ACM patients and relatives. Prospective and retrospective data are obtained at multiple time points, enabling both cross-sectional and longitudinal research in a hypothesis-generating approach that extends beyond one specific research question. In so doing, this registry aims to (1) increase the scientific knowledge base on disease mechanisms, genetics, and novel diagnostic and treatment strategies of ACM; and (2) provide education for physicians and patients concerning ACM, e. g. through our website (www.acmregistry.nl) and patient conferences

NK cell-dependent antibody-mediated immunotherapy is improved in vitro and in vivo when combined with agonists for toll-like receptor 2 in head and neck cancer models

The immunosuppressive character of head and neck cancers may explain the relatively low response rates to antibody therapy targeting a tumor antigen, such as cetuximab, and anti-PD-1 checkpoint inhibition. Immunostimulatory agents that overcome tumor-derived inhibitory signals could augment therapeutic efficacy, thereby enhancing tumor elimination and improving patient survival. Here, we demonstrate that cetuximab treatment combined with immunostimulatory agonists for Toll-like receptor (TLR) 2 induces profound immune responses. Natural killer (NK) cells, isolated from healthy individuals or patients with head and neck cancer, harbored enhanced cytotoxic capacity and increased tumor-killing potential in vitro. Additionally, combination treatment increased the release of several pro-inflammatory cytokines and chemokines by NK cells. Tumor-bearing mice that received cetuximab and the TLR2 ligand Pam3CSK4 showed increased infiltration of immune cells into the tumors compared to mice that received cetuximab monotherapy, resulting in a significant delay in tumor growth or even complete tumor regression. Moreover, combination treatment resulted in improved overall survival in vivo. In conclusion, combining tumor-targeting antibody-based immunotherapy with TLR stimulation represents a promising treatment strategy to improve the clinical outcomes of cancer patients. This treatment could well be applied together with other therapeutic strategies such as anti-PD-(L)1 checkpoint inhibition to further overcome immunosuppression.Transplantation and immunomodulatio

Electrocardiogram-based mortality prediction in patients with COVID-19 using machine learning

Background and purpose: The electrocardiogram (ECG) is frequently obtained in the work-up of COVID-19 patients. So far, no study has evaluated whether ECG-based machine learning models have added value to predict in-hospital mortality specifically in COVID-19 patients. / Methods: Using data from the CAPACITY-COVID registry, we studied 882 patients admitted with COVID-19 across seven hospitals in the Netherlands. Raw format 12-lead ECGs recorded within 72 h of admission were studied. With data from five hospitals (n = 634), three models were developed: (a) a logistic regression baseline model using age and sex, (b) a least absolute shrinkage and selection operator (LASSO) model using age, sex and human annotated ECG features, and (c) a pre-trained deep neural network (DNN) using age, sex and the raw ECG waveforms. Data from two hospitals (n = 248) was used for external validation. / Results: Performances for models a, b and c were comparable with an area under the receiver operating curve of 0.73 (95% confidence interval [CI] 0.65–0.79), 0.76 (95% CI 0.68–0.82) and 0.77 (95% CI 0.70–0.83) respectively. Predictors of mortality in the LASSO model were age, low QRS voltage, ST depression, premature atrial complexes, sex, increased ventricular rate, and right bundle branch block. / Conclusion: This study shows that the ECG-based prediction models could be helpful for the initial risk stratification of patients diagnosed with COVID-19, and that several ECG abnormalities are associated with in-hospital all-cause mortality of COVID-19 patients. Moreover, this proof-of-principle study shows that the use of pre-trained DNNs for ECG analysis does not underperform compared with time-consuming manual annotation of ECG features

The Netherlands Arrhythmogenic Cardiomyopathy Registry:design and status update

BACKGROUND: Clinical research on arrhythmogenic cardiomyopathy (ACM) is typically limited by small patient numbers, retrospective study designs, and inconsistent definitions. AIM: To create a large national ACM patient cohort with a vast amount of uniformly collected high-quality data that is readily available for future research. METHODS: This is a multicentre, longitudinal, observational cohort study that includes (1) patients with a definite ACM diagnosis, (2) at-risk relatives of ACM patients, and (3) ACM-associated mutation carriers. At baseline and every follow-up visit, a medical history as well information regarding (non-)invasive tests is collected (e. g. electrocardiograms, Holter recordings, imaging and electrophysiological studies, pathology reports, etc.). Outcome data include (non-)sustained ventricular and atrial arrhythmias, heart failure, and (cardiac) death. Data are collected on a research electronic data capture (REDCap) platform in which every participating centre has its own restricted data access group, thus empowering local studies while facilitating data sharing. DISCUSSION: The Netherlands ACM Registry is a national observational cohort study of ACM patients and relatives. Prospective and retrospective data are obtained at multiple time points, enabling both cross-sectional and longitudinal research in a hypothesis-generating approach that extends beyond one specific research question. In so doing, this registry aims to (1) increase the scientific knowledge base on disease mechanisms, genetics, and novel diagnostic and treatment strategies of ACM; and (2) provide education for physicians and patients concerning ACM, e. g. through our website ( www.acmregistry.nl ) and patient conferences

Process Mining for Six Sigma

Process mining offers a set of techniques for gaining data-based insights into business processes from event logs. The literature acknowledges the potential benefits of using process mining techniques in Six Sigma-based process improvement initiatives. However, a guideline that is explicitly dedicated on how process mining can be systematically used in Six Sigma initiatives is lacking. To address this gap, the Process Mining for Six Sigma (PMSS) guideline has been developed to support organizations in systematically using process mining techniques aligned with the DMAIC (Define-Measure-Analyze-Improve-Control) model of Six Sigma. Following a design science research methodology, PMSS and its tool support have been developed iteratively in close collaboration with experts in Six Sigma and process mining, and evaluated by means of focus groups, demonstrations and interviews with industry experts. The results of the evaluations indicate that PMSS is useful as a guideline to support Six Sigma-based process improvement activities. It offers a structured guideline for practitioners by extending the DMAIC-based standard operating procedure. PMSS can help increasing the efficiency and effectiveness of Six Sigma-based process improving efforts. This work extends the body of knowledge in the fields of process mining and Six Sigma, and helps closing the gap between them. Hence, it contributes to the broad field of quality management