Development of a novel risk prediction model for sudden cardiac death in childhood hypertrophic cardiomyopathy (HCM Risk-Kids)

Importance Sudden cardiac death (SCD) is the most common mode of death in childhood hypertrophic cardiomyopathy (HCM), but there is no validated algorithm to identify those at highest risk. Objective To develop and validate an SCD risk prediction model that provides individualized risk estimates. Design, Setting, and Participants A prognostic model was developed from a retrospective, multicenter, longitudinal cohort study of 1024 consecutively evaluated patients aged 16 years or younger with HCM. The study was conducted from January 1, 1970, to December 31, 2017. Exposures The model was developed using preselected predictor variables (unexplained syncope, maximal left-ventricular wall thickness, left atrial diameter, left-ventricular outflow tract gradient, and nonsustained ventricular tachycardia) identified from the literature and internally validated using bootstrapping. Main Outcomes and Measures A composite outcome of SCD or an equivalent event (aborted cardiac arrest, appropriate implantable cardioverter defibrillator therapy, or sustained ventricular tachycardia associated with hemodynamic compromise). Results Of the 1024 patients included in the study, 699 were boys (68.3%); mean (interquartile range [IQR]) age was 11 (7-14) years. Over a median follow-up of 5.3 years (IQR, 2.6-8.3; total patient years, 5984), 89 patients (8.7%) died suddenly or had an equivalent event (annual event rate, 1.49; 95% CI, 1.15-1.92). The pediatric model was developed using preselected variables to predict the risk of SCD. The model’s ability to predict risk at 5 years was validated; the C statistic was 0.69 (95% CI, 0.66-0.72), and the calibration slope was 0.98 (95% CI, 0.59-1.38). For every 10 implantable cardioverter defibrillators implanted in patients with 6% or more of a 5-year SCD risk, 1 patient may potentially be saved from SCD at 5 years. Conclusions and Relevance This new, validated risk stratification model for SCD in childhood HCM may provide individualized estimates of risk at 5 years using readily obtained clinical risk factors. External validation studies are required to demonstrate the accuracy of this model's predictions in diverse patient populations

Науки о Земле и Цивилизация

Коллективная монография, подготовленная по материалам XI Международной конференции «Науки о Земле и Цивилизация», посвящена проблемам отношений окружающей среды и общества. Адресуется специалистам в области наук о Земле и естественнонаучного образования, студентам, аспирантам и преподавателям вузов

Landscape Genomics: Understanding Relationships Between Environmental Heterogeneity and Genomic Characteristics of Populations

Landscape genomics is a rapidly advancing research field that combines population genomics, landscape ecology, and spatial analytical techniques to explicitly quantify the effects of environmental heterogeneity on neutral and adaptive genetic variation and underlying processes. Landscape genomics has tremendous potential for addressing fundamental and applied research questions in various research fields, including ecology, evolution, and conservation biology. However, the unique combination of different scientific disciplines and analytical approaches also constitute a challenge to most researchers wishing to apply landscape genomics. Here, we present an introductory overview of important concepts and methods used in current landscape genomics. For this, we first define the field and explain basic concepts and methods to capture different hypotheses of landscape influences on neutral genetic variation. Next, we highlight established and emerging genomic tools for quantifying adaptive genetic variation in landscape genomic studies. To illustrate the covered topics and to demonstrate the potential of landscape genomics, we provide empirical examples addressing a variety of research question, i.e., the investigation of evolutionary processes driving population differentiation, the landscape genomics of range expanding species, and landscape genomic patterns in organisms of special interest, including species inhabiting aquatic and terrestrial environments. We conclude by outlining remaining challenges and future research avenues in landscape genomics