Waveform prototype-based feature learning for automatic detection of the early repolarization pattern in ECG signals

Objective: Our aim was to develop an automated detection method, for prescreening purposes, of early repolarization (ER) pattern with slur/notch configuration in electrocardiogram (ECG) signals using a waveform prototype-based feature vector for supervised classification. Approach: The feature vectors consist of fragments of the ECG signal where the ER pattern is located, instead of abstract descriptive variables of ECG waveforms. The tested classifiers included linear discriminant analysis, k-nearest neighbor algorithm, and support vector machine (SVM). Main results: SVM showed the best performance in Friedman tests in our test data including 5676 subjects representing 45408 leads. Accuracies of the different classifiers showed results well over 90%, indicating that the waveform prototype-based feature vector is an effective representation of the differences between ECG signals with and without the ER pattern. The accuracy of inferior ER was 92.74% and 92.21% for lateral ER. The sensitivity achieved was 91.80% and specificity was 92.73%. Significance: The algorithm presented here showed good performance results, indicating that it could be used as a prescreening tool of ER, and it provides an additional identification of critical cases based on the distances to the classifier decision boundary, which are close to the 0.1 mV threshold and are difficult to label.Peer reviewe

Waveform prototype-based feature learning for automatic detection of the early repolarization pattern in ECG signals

Objective: Our aim was to develop an automated detection method, for prescreening purposes, of early repolarization (ER) pattern with slur/notch configuration in electrocardiogram (ECG) signals using a waveform prototype-based feature vector for supervised classification. Approach: The feature vectors consist of fragments of the ECG signal where the ER pattern is located, instead of abstract descriptive variables of ECG waveforms. The tested classifiers included linear discriminant analysis, k-nearest neighbor algorithm, and support vector machine (SVM). Main results: SVM showed the best performance in Friedman tests in our test data including 5676 subjects representing 45408 leads. Accuracies of the different classifiers showed results well over 90%, indicating that the waveform prototype-based feature vector is an effective representation of the differences between ECG signals with and without the ER pattern. The accuracy of inferior ER was 92.74% and 92.21% for lateral ER. The sensitivity achieved was 91.80% and specificity was 92.73%. Significance: The algorithm presented here showed good performance results, indicating that it could be used as a prescreening tool of ER, and it provides an additional identification of critical cases based on the distances to the classifier decision boundary, which are close to the 0.1 mV threshold and are difficult to label.Peer reviewe

Prediction of sudden cardiac death with automated high-throughput analysis of heterogeneity in standard resting 12-lead electrocardiograms

BACKGROUND Heterogeneity of depolarization and repolarization underlies the development of lethal arrhythmias. OBJECTIVE We investigated whether quantification of spatial depolarization and repolarization heterogeneity identifies individuals at risk for sudden cardiac death (SCD). METHODS Spatial R-, J-, and T-wave heterogeneity (RWH, JWH, and TWH, respectively) was analyzed using automated second central moment analysis of standard digital 12-lead electrocardiograms in 5618 adults (2588, 46% men; mean +/- SEM age 50.9 +/- 0.2 years), who took part in the epidemiological Health 2000 Survey as representative of the entire Finnish adult population. RESULTS During the follow-up period of 7.7 +/- 0.2 years, a total of 72 SCDs occurred (1.3%), with an average yearly incidence rate of 0.17% per year. Increased RWH, JWH, and TWH in left precordial leads (V-4-V-6) were univariately associated with SCD (P = 102 mu V) was associated with a 1.7-fold adjusted relative risk for SCD (95% confidence interval [CI] 1.0-2.9; P = .048) and increased JWH (>= 123 mu V) with a 2.0-fold adjusted relative risk for SCD (95% CI 1.2-3.3; P = .006). When both TWH and JWH were above the threshold, the adjusted relative risk for SCD was 2.9-fold (95% CI 1.5-5.7; P = .002). When RWH (>= 470 mu V), JWH, and TWH were all above the threshold, the adjusted relative risk for SCD was 3.2-fold (95% CI 1.4-7.1; P = .009). CONCLUSION Second central moment analysis of standard resting 12-lead electrocardiographic morphology provides an ultrarapid means for the automated measurement of spatial RWH, JWH, and TWH, enabling analysis of high subject volumes and screening for SCD risk in the general population.Peer reviewe

Anticoagulation Therapy After Biologic Aortic Valve Replacement

Objectives: Thromboembolism prophylaxis after biologic aortic valve replacement (BAVR) is recommended for 3 months postoperatively. We examined the continuation of oral anticoagulation (OAC) treatment and its effect on the long-term prognosis after BAVR.Methods: We used nation-wide register data from 4,079 individuals who underwent BAVR. We examined the association between warfarin and the non-vitamin K antagonist oral anticoagulant use with death, stroke and major bleeding in 2010 – 2016.Results: The risk of stroke was higher (HR 2.39, 95% CI 1.62 – 3.53, p p = 0.016) in OAC-users compared to individuals without OAC. We observed no significant associations between OAC use and bleeding risk.Conclusion: OAC use after BAVR was associated with increased risk of stroke and decreased risk of death. These observational findings warrant validation in randomized controlled trials before any clinical conclusions can be drawn.</p

Risk of sudden cardiac death associated with QRS, QTc, and JTc intervals in the general population

BACKGROUND QRS duration and corrected QT (QTc) interval have been associated with sudden cardiac death (SCD), but no data are available on the significance of repolarization component (JTc interval) of the QTc interval as an independent risk marker in the general population. OBJECTIVE In this study, we sought to quantify the risk of SCD associated with QRS, QTc, and JTc intervals. METHODS This study was conducted using data from 3 population cohorts from different eras, comprising a total of 20,058 individuals. The follow-up period was limited to 10 years and age at baseline to 30-61 years. QRS duration and QT interval (Bazett's) were measured from standard 12-lead electrocardiograms at baseline. JTc interval was defined as QTc interval - QRS duration. Cox proportional hazards models that controlled for confounding clinical factors identified at baseline were used to estimate the relative risk of SCD. RESULTS During a mean period of 9.7 years, 207 SCDs occurred (1.1 per 1000 person-years). QRS duration was associated with a significantly increased risk of SCD in each cohort (pooled hazard ratio [HR] 1.030 per 1-ms increase; 95% confidence interval [CI] 1.017-1.043). The QTc interval had borderline to significant associations with SCD and varied among cohorts (pooled HR 1.007; 95% CI 1.001-1.012). JTc interval as a continuous variable was not associated with SCD (pooled HR 1.001; 95% CI 0.996-1.007). CONCLUSION Prolonged QRS durations and QTc intervals are associated with an increased risk of SCD. However, when the QTc interval is deconstructed into QRS and JTc intervals, the repolarization component (JTc) appears to have no independent prognostic value.Peer reviewe

Population trends in aortic valve surgery in Finland between 2001 and 2016

Objectives. To investigate nationwide changes in procedure rates, patient selection, and prognosis after all surgical aortic valve replacements. Design. Patients undergoing primary surgical aortic valve replacement between 2001 and 2016 were identified from three nationwide registers with compulsory reporting to examine trends in aortic valve surgery over four four-year time periods. Results. A total of 12,139 surgical aortic valve replacement procedures (mean age 61.9 +/- 11.8 years, 39.1% women) were performed. The total number of biological valves increased from 1001 (42.9%) to 2526 (75.5%) from 2001-2004 to 2013-2016 (p Conclusions. The use of biologic aortic valve prosthesis has increased from 2001 to 2016. The proportion of women has declined markedly. The short-term mortality has decreased and the long-term mortality has stayed constant despite increasing comorbidity burden.</div

Anticoagulation Therapy After Biologic Aortic Valve Replacement

Objectives: Thromboembolism prophylaxis after biologic aortic valve replacement (BAVR) is recommended for 3 months postoperatively. We examined the continuation of oral anticoagulation (OAC) treatment and its effect on the long-term prognosis after BAVR.Methods: We used nation-wide register data from 4,079 individuals who underwent BAVR. We examined the association between warfarin and the non-vitamin K antagonist oral anticoagulant use with death, stroke and major bleeding in 2010 – 2016.Results: The risk of stroke was higher (HR 2.39, 95% CI 1.62 – 3.53, p &lt; 0.001) and the risk of death was lower (HR 0.79, 95% CI 0.65 – 0.96, p = 0.016) in OAC-users compared to individuals without OAC. We observed no significant associations between OAC use and bleeding risk.Conclusion: OAC use after BAVR was associated with increased risk of stroke and decreased risk of death. These observational findings warrant validation in randomized controlled trials before any clinical conclusions can be drawn

Risk of sudden cardiac death associated with QRS, QTc, and JTc intervals in the general population

BackgroundQRS duration and corrected QT (QTc) interval have been associated with sudden cardiac death (SCD), but no data are available on the significance of repolarization component (JTc interval) of the QTc interval as an independent risk marker in the general population.ObjectiveIn this study, we sought to quantify the risk of SCD associated with QRS, QTc, and JTc intervals.MethodsThis study was conducted using data from 3 population cohorts from different eras, comprising a total of 20,058 individuals. The follow-up period was limited to 10 years and age at baseline to 30–61 years. QRS duration and QT interval (Bazett’s) were measured from standard 12-lead electrocardiograms at baseline. JTc interval was defined as QTc interval – QRS duration. Cox proportional hazards models that controlled for confounding clinical factors identified at baseline were used to estimate the relative risk of SCD.ResultsDuring a mean period of 9.7 years, 207 SCDs occurred (1.1 per 1000 person-years). QRS duration was associated with a significantly increased risk of SCD in each cohort (pooled hazard ratio [HR] 1.030 per 1-ms increase; 95% confidence interval [CI] 1.017–1.043). The QTc interval had borderline to significant associations with SCD and varied among cohorts (pooled HR 1.007; 95% CI 1.001–1.012). JTc interval as a continuous variable was not associated with SCD (pooled HR 1.001; 95% CI 0.996–1.007).ConclusionProlonged QRS durations and QTc intervals are associated with an increased risk of SCD. However, when the QTc interval is deconstructed into QRS and JTc intervals, the repolarization component (JTc) appears to have no independent prognostic value.</p

Hundreds of variants clustered in genomic loci and biological pathways affect human height

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

Quality of dietary fat and genetic risk of type 2 diabetes: individual participant data meta-analysis.

OBJECTIVE: To investigate whether the genetic burden of type 2 diabetes modifies the association between the quality of dietary fat and the incidence of type 2 diabetes. DESIGN: Individual participant data meta-analysis. DATA SOURCES: Eligible prospective cohort studies were systematically sourced from studies published between January 1970 and February 2017 through electronic searches in major medical databases (Medline, Embase, and Scopus) and discussion with investigators. REVIEW METHODS: Data from cohort studies or multicohort consortia with available genome-wide genetic data and information about the quality of dietary fat and the incidence of type 2 diabetes in participants of European descent was sought. Prospective cohorts that had accrued five or more years of follow-up were included. The type 2 diabetes genetic risk profile was characterized by a 68-variant polygenic risk score weighted by published effect sizes. Diet was recorded by using validated cohort-specific dietary assessment tools. Outcome measures were summary adjusted hazard ratios of incident type 2 diabetes for polygenic risk score, isocaloric replacement of carbohydrate (refined starch and sugars) with types of fat, and the interaction of types of fat with polygenic risk score. RESULTS: Of 102 305 participants from 15 prospective cohort studies, 20 015 type 2 diabetes cases were documented after a median follow-up of 12 years (interquartile range 9.4-14.2). The hazard ratio of type 2 diabetes per increment of 10 risk alleles in the polygenic risk score was 1.64 (95% confidence interval 1.54 to 1.75, I2=7.1%, τ2=0.003). The increase of polyunsaturated fat and total omega 6 polyunsaturated fat intake in place of carbohydrate was associated with a lower risk of type 2 diabetes, with hazard ratios of 0.90 (0.82 to 0.98, I2=18.0%, τ2=0.006; per 5% of energy) and 0.99 (0.97 to 1.00, I2=58.8%, τ2=0.001; per increment of 1 g/d), respectively. Increasing monounsaturated fat in place of carbohydrate was associated with a higher risk of type 2 diabetes (hazard ratio 1.10, 95% confidence interval 1.01 to 1.19, I2=25.9%, τ2=0.006; per 5% of energy). Evidence of small study effects was detected for the overall association of polyunsaturated fat with the risk of type 2 diabetes, but not for the omega 6 polyunsaturated fat and monounsaturated fat associations. Significant interactions between dietary fat and polygenic risk score on the risk of type 2 diabetes (P>0.05 for interaction) were not observed. CONCLUSIONS: These data indicate that genetic burden and the quality of dietary fat are each associated with the incidence of type 2 diabetes. The findings do not support tailoring recommendations on the quality of dietary fat to individual type 2 diabetes genetic risk profiles for the primary prevention of type 2 diabetes, and suggest that dietary fat is associated with the risk of type 2 diabetes across the spectrum of type 2 diabetes genetic risk.The EPIC-InterAct study received funding from the European Union (Integrated Project LSHM-CT-2006-037197 in the Framework Programme 6 of the European Community). We thank all EPIC participants and staff for their contribution to the study. We thank Nicola Kerrison (MRC Epidemiology Unit, University of Cambridge, Cambridge, UK) for managing the data for the InterAct Project. In addition, InterAct investigators acknowledge funding from the following agencies: MT: Health Research Fund (FIS) of the Spanish Ministry of Health; the CIBER en Epidemiología y Salud Pública (CIBERESP), Spain; Murcia Regional Government (N° 6236); JS: JS was supported by a Heisenberg-Professorship (SP716/2-1), a Clinical Research Group (KFO218/1) and a research group (Molecular Nutrition to JS) of the Bundesministerium für Bildung und Forschung (BMBF); YTvdS, JWJB, PHP, IS: Verification of diabetes cases was additionally funded by NL Agency grant IGE05012 and an Incentive Grant from the Board of the UMC Utrecht; HBBdM: Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); MDCL: Health Research Fund (FIS) of the Spanish Ministry of Health; Murcia Regional Government (N° 6236); FLC: Cancer Research UK; PD: Wellcome Trust; LG: Swedish Research Council; GH: The county of Västerbotten; RK: Deutsche Krebshilfe; TJK: Cancer Research UK; KK: Medical Research Council UK, Cancer Research UK; AK: Medical Research Council (Cambridge Lipidomics Biomarker Research Initiative); CN: Health Research Fund (FIS) of the Spanish Ministry of Health; Murcia Regional Government (N° 6236); KO: Danish Cancer Society; OP: Faculty of Health Science, 47 University of Aarhus, Denmark; JRQ: Asturias Regional Government; LRS: Asturias Regional Government; AT: Danish Cancer Society; RT: AIRE-ONLUS Ragusa, AVIS-Ragusa, Sicilian Regional Government; DLvdA, WMMV: Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); MMC: Wellcome Trust (083270/Z/07/Z), MRC (G0601261)