Stock Identification Methods Working Group (SIMWG)

The Stock Identification Methods Working Group (SIMWG) reviews new methods for the defi-nition and investigation of stock structure and provides advice to other ICES expert groups on how to interpret patterns of population structure. The identification of the spatial boundaries of exploited stocks is a fundamental requirement before any stock assessment or modelling can be contemplated, and therefore lies at the heart of resource management. SIMWG continues to provide annual updates on recent applications of stock identification methods to species assessed by ICES and on advances in stock identification methods. Based on the wide expertise of SIMWG members, the group provides reviews of recent literature on genetics, growth marks in calcified structures, life history parameters, morphometrics/ me-ristics, tagging, otolith shape, otolith chemistry, parasites and interdisciplinary approaches. A key activity of SIMWG is to address requests by ICES working groups for technical advice on issues of stock identity. In 2020, the working group reviewed the outcome of the Workshop on Stock Identification of North Sea Cod (WKNSCodID). SIMWG contributes to the general understanding of the biological features of the north Atlantic ecosystem through its work to describe fish population structure. Additionally, SIMWG’s annual reviews on advances in stock identification methods keeps ICES members abreast of best prac-tices in this field of study. SIMWG expert reviews on questions of stock structure for particular ICES species are directly relevant to the appropriate definition of stock and contribute to the accuracy of stock assessment and effectiveness of management actions. We see an important role for SIMWG in the future as ICES copes with the shifting distributions of fishery resources and questions regarding the appropriate definition of fish stocks. Understanding stock structure is a fundamental requirement before any assessment or modelling on a stock can be contemplated and SIMWG will continue to work with ICES expert groups to address pressing stock identifica-tion issues

Arrhythmic Risk Stratification in Arrhythmogenic Right Ventricular Cardiomyopathy

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an heritable cardiomyopathy characterized by a predominantly arrhythmic presentation. It represents the leading cause of sudden cardiac death (SCD) among athletes and poses a significant morbidity treat in the general population. As a causative treatment for ARVC is still not available, the placement of an implantable cardioverter defibrillator (ICD) represent the current cornerstone for SCD prevention in this setting. Thanks to international ARVC-dedicated efforts, significant steps have been achieved in recent years towards an individualized, patient-centered risk stratification approach. A novel risk calculator algorithm estimating the 5 year risk of arrhythmias of patients with ARVC have been introduced in clinical practice and subsequently validated. The purpose of this article is to summarize the body of evidence that has allowed the development of this tool and to discuss the best way to implement its use in the care of an individual patient

Integrating genetic analysis of mixed populations with a spatially explicit population dynamics model

1. Inferring the dynamics of populations in time and space is a central challenge in ecology. Intra-specific structure (for example genetically distinct sub-populations or meta-populations) may require methods that can jointly infer the dynamics of multiple populations. This is of particular importance for harvested species, for which management must balance utilization of productive populations with protection of weak ones. 2. Here we present a novel method for simultaneous learning about the spatio-temporal dynamics of multiple populations that combines genetic data with prior information about abundance and movement, akin to an integrated population modelling approach. We apply the Bayesian genetic mixed stock analysis to 17 wild and 10 hatchery-reared Baltic salmon (S. salar) stocks, quantifying uncertainty in stock composition in time and space, and in population dynamics parameters such as migration timing and speed. 3. The genetic data were informative about stock-specific movement patterns, updating priors for migration path, timing and speed. Use of a population dynamics model allowed robust interpolation of expected catch composition at areas and times with no genetic observations. Our results indicate that the commonly used "equal prior probabilities" assumption may not be appropriate for all mixed stock analyses: incorporation of prior information about stock abundance and movement resulted in more plausible and precise estimates of mixture compositions in time and space. 4. The model we present here forms the basis for optimizing the spatial and temporal allocation of harvest to support the management of mixed populations of migratory species.Peer reviewe

Diagnosing arrhythmogenic right ventricular cardiomyopathy by 2010 Task Force Criteria: clinical performance and simplified practical implementation

AIMS: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is diagnosed by a complex set of clinical tests as per 2010 Task Force Criteria (TFC). Avoiding misdiagnosis is crucial to prevent sudden cardiac death as well as unnecessary implantable cardioverter-defibrillator implantations. This study aims to validate the overall performance of the TFC in a real-world cohort of patients referred for ARVC evaluation. METHODS AND RESULTS: We included patients consecutively referred to our centres for ARVC evaluation. Patients were diagnosed by consensus of three independent clinical experts. Using this as a reference standard, diagnostic performance was measured for each individual criterion as well as the overall TFC classification. Of 407 evaluated patients (age 38 ± 17 years, 51% male), the expert panel diagnosed 66 (16%) with ARVC. The clinically observed TFC was false negative in 7/66 (11%) patients and false positive in 10/69 (14%) patients. Idiopathic outflow tract ventricular tachycardia was the most common alternative diagnosis. While the TFC performed well overall (sensitivity and specificity 92%), signal-averaged electrocardiogram (SAECG, P = 0.43), and several family history criteria (P ≥ 0.17) failed to discriminate. Eliminating these criteria reduced false positives without increasing false negatives (net reclassification improvement 4.3%, P = 0.019). Furthermore, all ARVC patients met at least one electrocardiogram (ECG) or arrhythmia criterion (sensitivity 100%). CONCLUSION: The TFC perform well but are complex and can lead to misdiagnosis. Simplification by eliminating SAECG and several family history criteria improves diagnostic accuracy. Arrhythmogenic right ventricular cardiomyopathy can be ruled out using ECG and arrhythmia criteria alone, hence these tests may serve as a first-line screening strategy among at-risk individuals

Comparing clinical performance of current implantable cardioverter-defibrillator implantation recommendations in arrhythmogenic right ventricular cardiomyopathy

AIMS: Arrhythmogenic right ventricular cardiomyopathy (ARVC) patients have an increased risk of ventricular arrhythmias (VA). Four implantable cardioverter-defibrillator (ICD) recommendation algorithms are available The International Task Force Consensus (‘ITFC’), an ITFC modification by Orgeron et al. (‘mITFC’), the AHA/HRS/ACC guideline for VA management (‘AHA’), and the HRS expert consensus statement (‘HRS’). This study aims to validate and compare the performance of these algorithms in ARVC. METHODS AND RESULTS: We classified 617 definite ARVC patients (38.5 ± 15.1 years, 52.4% male, 39.2% prior sustained VA) according to four algorithms. Clinical performance was evaluated by sensitivity, specificity, ROC-analysis, and decision curve analysis for any sustained VA and for fast VA (>250 b.p.m.). During 6.4 [2.8–11.5] years follow-up, 282 (45.7%) patients experienced any sustained VA, and 63 (10.2%) fast VA. For any sustained VA, ITFC and mITFC provide higher sensitivity than AHA and HRS (94.0–97.8% vs. 76.7–83.5%), but lower specificity (15.9–32.0% vs. 42.7%-60.1%). Similarly, for fast VA, ITFC and mITFC provide higher sensitivity than AHA and HRS (95.2–97.1% vs. 76.7–78.4%) but lower specificity (42.7–43.1 vs. 76.7–78.4%). Decision curve analysis showed ITFC and mITFC to be superior for a 5-year sustained VA risk ICD indication threshold between 5–25% or 2–9% for fast VA. CONCLUSION: The ITFC and mITFC provide the highest protection rates, whereas AHA and HRS decrease unnecessary ICD placements. ITFC or mITFC should be used if we consider the 5-year threshold for ICD indication to lie within 5–25% for sustained VA or 2–9% for fast VA. These data will inform decision-making for ICD placement in ARVC

Stock Identification Methods Working Group (SIMWG). 2021

The Stock Identification Methods Working Group (SIMWG) reviews new methods for the definition and investigation of stock structure and provides recommendations to other ICES expert groups on how to interpret patterns of population structure. In 2021, SIMWG continued providing annual updates on recent applications of stock identification methods to species assessed by ICES and on advances in stock identification methods. Based on the wide expertise of SIMWG members, the group provides reviews of recent literature on genetics, growth marks in calcified structures, life history parameters, morphometrics/ meristics, tagging, otolith shape, otolith chemistry, parasites and interdisciplinary approaches. The key activity of SIMWG is to address requests by ICES working groups for technical advice on issues of stock identity. In 2021, SIMWG reviewed the report of a project on herring stock structure upon request by the ICES Herring Assessment Working Group (HAWG). SIMWG contributes to the general understanding of the biological features of the north Atlantic ecosystem through its work to describe fish population structure. Additionally, SIMWG annual reviews on advances in stock identification methods keep ICES members abreast of best practices in this field of study. SIMWG expert reviews on questions of stock structure for particular ICES species are directly relevant to the appropriate definition of stock and contribute to the accuracy of stock assessment and effectiveness of management actions. We see an important role for SIMWG in the future as ICES is coping with the shifting distributions of fishery resources and questions regarding the appropriate definition of fish stocks. Understanding stock structure is a fundamental requirement before any assessment or modelling on a stock can be contemplated and SIMWG will continue to work with ICES expert groups to address pressing stock identification issues

A new prediction model for ventricular arrhythmias in arrhythmogenic right ventricular cardiomyopathy

Aims Arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVC) is characterized by ventricular arrhythmias (VAs) and sudden cardiac death (SCD). We aimed to develop a model for individualized prediction of incident VA/SCD in ARVC patients. Methods and results Five hundred and twenty-eight patients with a definite diagnosis and no history of sustained VAs/SCD at baseline, aged 38.2 ± 15.5 years, 44.7% male, were enrolled from five registries in North America and Europe. Over 4.83 (interquartile range 2.44–9.33) years of follow-up, 146 (27.7%) experienced sustained VA, defined as SCD, aborted SCD, sustained ventricular tachycardia, or appropriate implantable cardioverter-defibrillator (ICD) therapy. A prediction model estimating annual VA risk was developed using Cox regression with internal validation. Eight potential predictors were pre-specified: age, sex, cardiac syncope in the prior 6 months, non-sustained ventricular tachycardia, number of premature ventricular complexes in 24 h, number of leads with T-wave inversion, and right and left ventricular ejection fractions (LVEFs). All except LVEF were retained in the final model. The model accurately distinguished patients with and without events, with an optimism-corrected C-index of 0.77 [95% confidence interval (CI) 0.73–0.81] and minimal over-optimism [calibration slope of 0.93 (95% CI 0.92–0.95)]. By decision curve analysis, the clinical benefit of the model was superior to a current consensus-based ICD placement algorithm with a 20.6% reduction of ICD placements with the same proportion of protected patients (P < 0.001). Conclusion Using the largest cohort of patients with ARVC and no prior VA, a prediction model using readily available clinical parameters was devised to estimate VA risk and guide decisions regarding primary prevention ICD