Assessment of Ecological State of Surface Waters in ARROW Project: Robust Multivariate Predictive Models

The ARROW (www.cba.muni.cz/arrow/eng - Assessment and Reference Reports of Water monitoring) project is the project of implementation of EU Water Framework Directive in the monitoring of surface waters of the Czech Republic and covers all aspects of this problem from data sampling to informatics solution (focusing on the ecostat). The project is based on long term development of this field in the Czech Republic and it is carried out under the supervision of the Ministry of the Environment of the CR. The important part of the ARROW project is the development and implementation of a Czech approach to the evaluation of the ecological state of surface waters using an analysis of monitoring data. The main idea of the system is an approach based on a network of reference sites and robust multivariate modeling of expected environmental and biological conditions (could be called “RIVPACS type”). The approach presented is compatible with the EU WFD and will be implemented in the national-wide information system of the ecological state of surface waters of the CR. The evaluation of ecological state in ARROW project is based on two former projects; the direct predecessor is TRITON project aimed on analysis of biomonitoring data from small watercourses under collaboration with Agricultural Water Management Authority of the Czech Republic which has been developed since 1999. The other source of inspiration and especially reference dataset is also PERLA system which should be considered as a scientific background for biomonitoring activities in the CR. The methodology is flexible and robust and could be adopted for different types of data and biological communities. All levels of the process are covered by objective statistical methodology and monitored by experts; their computations are based on robust multivariate and multimetric methods, some of them newly developed

Alcohol dose in septal ablation for hypertrophic obstructive cardiomyopathy

Background: The aim of this study was to evaluate short- and long-term outcomes related to dose of alcohol administered during alcohol septal ablation (ASA) in patients with hypertrophic obstructive cardiomyopathy (HOCM). Current guidelines recommend using 1–3 mL of alcohol administered in the target septal perforator artery, but this recommendation is based more on practical experience of interventionalists rather than on systematic evidence. Methods: We included 1448 patients and used propensity score to match patients who received a low-dose (1.0–1.9 mL) versus a high-dose (2.0–3.8 mL) of alcohol during ASA. Results: The matched cohort analysis comprised 770 patients (n = 385 in both groups). There was a similar occurrence of 30-day post-procedural adverse events (13% vs. 12%; p = 0.59), and similar all-cause mortality rates (0.8% vs. 0.5%; p = 1) in the low-dose group and the high-dose group, respectively. In the long-term follow-up (5.4 ± 4.5 years), a total of 110 (14%) patients died representing 2.58 deaths and 2.64 deaths per 100 patient-years in the low dose and the high dose group (logrank, p = 0.92), respectively. There were no significant differences in the long-term dyspnea and left ventricular outflow gradient between the two groups. Patients treated with a low-dose of alcohol underwent more subsequent septal reduction procedures (logrank, p = 0.04). Conclusions: Matched HOCM patients undergoing ASA with a low-dose (1.0–1.9 mL) or a high-dose (2.0–3.8 mL) of alcohol had similar short- and long-term outcomes. A higher rate of repeated septal reduction procedures was observed in the group treated with a low-dose of alcohol. © 2021 The Author

Sudden cardiac death after myocardial infarction: individual participant data from pooled cohorts

Abstract Background and Aims: Risk stratification of sudden cardiac death after myocardial infarction and prevention by defibrillator rely on left ventricular ejection fraction (LVEF). Improved risk stratification across the whole LVEF range is required for decision-making on defibrillator implantation. Methods: The analysis pooled 20 data sets with 140 204 post-myocardial infarction patients containing information on demographics, medical history, clinical characteristics, biomarkers, electrocardiography, echocardiography, and cardiac magnetic resonance imaging. Separate analyses were performed in patients (i) carrying a primary prevention cardioverter-defibrillator with LVEF ≤ 35% [implantable cardioverter-defibrillator (ICD) patients], (ii) without cardioverter-defibrillator with LVEF ≤ 35% (non-ICD patients ≤ 35%), and (iii) without cardioverter-defibrillator with LVEF > 35% (non-ICD patients >35%). Primary outcome was sudden cardiac death or, in defibrillator carriers, appropriate defibrillator therapy. Using a competing risk framework and systematic internal–external cross-validation, a model using LVEF only, a multivariable flexible parametric survival model, and a multivariable random forest survival model were developed and externally validated. Predictive performance was assessed by random effect meta-analysis. Results: There were 1326 primary outcomes in 7543 ICD patients, 1193 in 25 058 non-ICD patients ≤35%, and 1567 in 107 603 non-ICD patients >35% during mean follow-up of 30.0, 46.5, and 57.6 months, respectively. In these three subgroups, LVEF poorly predicted sudden cardiac death (c-statistics between 0.50 and 0.56). Considering additional parameters did not improve calibration and discrimination, and model generalizability was poor. Conclusions: More accurate risk stratification for sudden cardiac death and identification of low-risk individuals with severely reduced LVEF or of high-risk individuals with preserved LVEF was not feasible, neither using LVEF nor using other predictors.Abstract Background and Aims: Risk stratification of sudden cardiac death after myocardial infarction and prevention by defibrillator rely on left ventricular ejection fraction (LVEF). Improved risk stratification across the whole LVEF range is required for decision-making on defibrillator implantation. Methods: The analysis pooled 20 data sets with 140 204 post-myocardial infarction patients containing information on demographics, medical history, clinical characteristics, biomarkers, electrocardiography, echocardiography, and cardiac magnetic resonance imaging. Separate analyses were performed in patients (i) carrying a primary prevention cardioverter-defibrillator with LVEF ≤ 35% [implantable cardioverter-defibrillator (ICD) patients], (ii) without cardioverter-defibrillator with LVEF ≤ 35% (non-ICD patients ≤ 35%), and (iii) without cardioverter-defibrillator with LVEF > 35% (non-ICD patients >35%). Primary outcome was sudden cardiac death or, in defibrillator carriers, appropriate defibrillator therapy. Using a competing risk framework and systematic internal–external cross-validation, a model using LVEF only, a multivariable flexible parametric survival model, and a multivariable random forest survival model were developed and externally validated. Predictive performance was assessed by random effect meta-analysis. Results: There were 1326 primary outcomes in 7543 ICD patients, 1193 in 25 058 non-ICD patients ≤35%, and 1567 in 107 603 non-ICD patients >35% during mean follow-up of 30.0, 46.5, and 57.6 months, respectively. In these three subgroups, LVEF poorly predicted sudden cardiac death (c-statistics between 0.50 and 0.56). Considering additional parameters did not improve calibration and discrimination, and model generalizability was poor. Conclusions: More accurate risk stratification for sudden cardiac death and identification of low-risk individuals with severely reduced LVEF or of high-risk individuals with preserved LVEF was not feasible, neither using LVEF nor using other predictors

898 The impact of micrometastases in cervical cancer patients – a retrospective study of the SCCAN (Surveillance in Cervical CANcer) project

n/

Sudden cardiac death after myocardial infarction: individual participant data from pooled cohorts

BACKGROUND AND AIMS: Risk stratification of sudden cardiac death after myocardial infarction and prevention by defibrillator rely on left ventricular ejection fraction (LVEF). Improved risk stratification across the whole LVEF range is required for decision-making on defibrillator implantation. METHODS: The analysis pooled 20 data sets with 140 204 post-myocardial infarction patients containing information on demographics, medical history, clinical characteristics, biomarkers, electrocardiography, echocardiography, and cardiac magnetic resonance imaging. Separate analyses were performed in patients (i) carrying a primary prevention cardioverter-defibrillator with LVEF ≤ 35% [implantable cardioverter-defibrillator (ICD) patients], (ii) without cardioverter-defibrillator with LVEF ≤ 35% (non-ICD patients ≤ 35%), and (iii) without cardioverter-defibrillator with LVEF > 35% (non-ICD patients >35%). Primary outcome was sudden cardiac death or, in defibrillator carriers, appropriate defibrillator therapy. Using a competing risk framework and systematic internal-external cross-validation, a model using LVEF only, a multivariable flexible parametric survival model, and a multivariable random forest survival model were developed and externally validated. Predictive performance was assessed by random effect meta-analysis. RESULTS: There were 1326 primary outcomes in 7543 ICD patients, 1193 in 25 058 non-ICD patients ≤35%, and 1567 in 107 603 non-ICD patients >35% during mean follow-up of 30.0, 46.5, and 57.6 months, respectively. In these three subgroups, LVEF poorly predicted sudden cardiac death (c-statistics between 0.50 and 0.56). Considering additional parameters did not improve calibration and discrimination, and model generalizability was poor. CONCLUSIONS: More accurate risk stratification for sudden cardiac death and identification of low-risk individuals with severely reduced LVEF or of high-risk individuals with preserved LVEF was not feasible, neither using LVEF nor using other predictors

Sudden cardiac death after myocardial infarction: individual participant data from pooled cohorts

Background and AimsRisk stratification of sudden cardiac death after myocardial infarction and prevention by defibrillator rely on left ventricular ejection fraction (LVEF). Improved risk stratification across the whole LVEF range is required for decision-making on defibrillator implantation.MethodsThe analysis pooled 20 data sets with 140 204 post-myocardial infarction patients containing information on demographics, medical history, clinical characteristics, biomarkers, electrocardiography, echocardiography, and cardiac magnetic resonance imaging. Separate analyses were performed in patients (i) carrying a primary prevention cardioverter-defibrillator with LVEF ≤ 35% [implantable cardioverter-defibrillator (ICD) patients], (ii) without cardioverter-defibrillator with LVEF ≤ 35% (non-ICD patients ≤ 35%), and (iii) without cardioverter-defibrillator with LVEF > 35% (non-ICD patients >35%). Primary outcome was sudden cardiac death or, in defibrillator carriers, appropriate defibrillator therapy. Using a competing risk framework and systematic internal–external cross-validation, a model using LVEF only, a multivariable flexible parametric survival model, and a multivariable random forest survival model were developed and externally validated. Predictive performance was assessed by random effect meta-analysis.ResultsThere were 1326 primary outcomes in 7543 ICD patients, 1193 in 25 058 non-ICD patients ≤35%, and 1567 in 107 603 non-ICD patients >35% during mean follow-up of 30.0, 46.5, and 57.6 months, respectively. In these three subgroups, LVEF poorly predicted sudden cardiac death (c-statistics between 0.50 and 0.56). Considering additional parameters did not improve calibration and discrimination, and model generalizability was poor.ConclusionsMore accurate risk stratification for sudden cardiac death and identification of low-risk individuals with severely reduced LVEF or of high-risk individuals with preserved LVEF was not feasible, neither using LVEF nor using other predictors.</div