Periodic repolarization dynamics: Different methods for quantifying low-frequency oscillations of repolarization.

BACKGROUND: Periodic repolarization dynamics (PRD) is an electrocardiographic biomarker that quantifies low-frequency (LF) instabilities of repolarization. PRD is a strong predictor of mortality in patients with ischaemic and non-ischaemic cardiomyopathy. Until recently, two methods for calculating PRD have been proposed. The wavelet analysis has been widely tested and quantifies PRD in deg2 units by application of continuous wavelet transformation (PRDwavelet). The phase rectified signal averaging method (PRDPRSA) is an algebraic method, which quantifies PRD in deg. units. The correlation, as well as a conversion formula between the two methods remain unknown. METHOD: The first step for quantifying PRD is to calculate the beat-to-beat change in the direction of repolarization, called dT°. PRD is subsequently quantified by means of either wavelet or PRSA-analysis. We simulated 1.000.000 dT°-signals. For each simulated signal we calculated PRD using the wavelet and PRSA-method. We calculated the ratio between PRDwavelet and PRDPRSA for different values of dT° and RR-intervals and applied this ratio in a real-ECG validation cohort of 455 patients after myocardial infarction (MI). We finally calculated the correlation coefficient between real and calculated PRDwavelet. PRDwavelet was dichotomized at the established cut-off value of ≥5.75 deg2. RESULTS: The ratio between PRDwavelet and PRDPRSA increased with increasing heart-rate and mean dT°-values (p < 0.001 for both). The correlation coefficient between PRDwavelet and PRDPRSA in the validation cohort was 0.908 (95% CI 0.891-0.923), which significantly (p < 0.001) improved to 0.945 (95% CI 0.935-0.955) after applying the formula considering the ratio between PRDwavelet and PRDPRSA obtained from the simulation cohort. The calculated PRDwavelet correctly classified 98% of the patients as low-risk and 87% of the patients as high-risk and correctly identified 97% of high-risk patients, who died within the follow-up period. CONCLUSION: This is the first analytical investigation of the different methods used to calculate PRD using simulated and clinical data. In this article we propose a novel algorithm for converting PRDPRSA to the widely validated PRDwavelet, which could unify the calculation methods and cut-offs for PRD.

Interacción entre el estadio de la enfermedad renal crónica y la diabetes mellitus como factores asociados con mortalidad en pacientes con enfermedad renal crónica: un estudio de cohortes externas / Interaction between the stage of chronic kidney disease and diabetes mellitus as factors associated with mortality in chronic kidney disease patients: An external cohort study

Introducción: La enfermedad renal diabética es la principal causa de enfermedad renal crónica (ERC) en el mundo y tanto la diabetes mellitus (DMT2) como la ERC son importantes factores de riesgo para mortalidad. Sin embargo, se desconoce si la presencia simultánea de ambas enfermedades modifica el riesgo de muerte. Objetivo: Evaluar la presencia de interacción entre DMT2 y estadio ERC respecto a la mortalidad en una población representativa de un país latinoamericano. Métodos: Estudio analítico en dos cohortes de pacientes con diagnóstico de ERC con cuatro años de seguimiento entre 2004 y 2008. Se calculó la tasa de incidencia, progresión, supervivencia (Kaplan-Meier), interacción (aditiva y multiplicativa) e impacto de la presencia de los diferentes estadios de ERC en pacientes con y sin DMT2 mediante un análisis de riesgos proporcionales de Cox. Resultados: En esta población de estudio de 5.663 pacientes, tanto la DMT2 como el estadio de ERC son factores de riesgo para mortalidad (p < 0,001). La diferencia en la supervivencia entre diabéticos y no diabéticos en estadios 3 – 4 – 5 fue estadísticamente significativa (Log-rank p = 0,0076). Se encontró una interacción estadísticamente significativa en las escalas aditiva y multiplicativa entre la presencia de DMT2 y el estadio de ERC, con respecto a la mortalidad (p = 0,005). Se confirmó el impacto de la diabetes como factor de riesgo de mortalidad (Hazard Ratio 1,61 p < 0,001), pero en los pacientes con DMT2 solamente la edad, la dislipidemia y los nefroprotectores estuvieron asociados significativamente con la mortalidad. Conclusión: La interacción entre ERC y DMT2 modifica de forma negativa el riesgo de muerte de ambas enfermedades. Es decir que el efecto conjunto observado es menor al esperado. (AU)

Introduction: Diabetic kidney disease is the main cause of chronic kidney disease (CKD) worldwide. Both CKD and diabetes mellitus (DMT2) are important risk factors for mortality. However, it is still unknown if the risk of death is modified by the simultaneous presence of these diseases. Objective: To evaluate the presence of an interaction between DMT2 and CKD for mortality in a representative population of a Latin American country. Methods: It is an analytical cohort study of patients with CKD, who were followed for 4 years (between 2004 and 2009). We calculated the incidence rate, progression, survival (using Kaplan–Meier curves), interaction (on the additive and multiplicative scales) and impact of the different stages of CKD in patients with and without DMT2 (using a cox proportional hazards model). Results: In this population of 5663 individuals, both DMT2 and CKD are risk factors for mortality (P < 0.001). We found a statistically significant difference in mortality between individuals with and without DMT2, who also had CKD stages 3 – 4 – 5 (Log-rank P = 0.0076). Additionally, we found a statistically significant interaction for mortality in both the additive and multiplicative scales between DMT2 and CKD (P = 0.005). DMT2 was found to be a risk factor for mortality (Hazard Ratio 1.61 P < 0.001), but in individuals with DMT2, the only risks significantly associated with mortality, were age, dyslipidemia and nephroprotective drugs. Conclusión: The interaction between CKD and DMT2 negatively modifies the risk of death of both diseases. This means that when the two diseases are present, the risk of mortality is lower than expected. (AU)

Interaction between the stage of chronic kidney disease and diabetes mellitus as factors associated with mortality in chronic kidney disease patients: An external cohort study.

INTRODUCTION: Diabetic kidney disease is the main cause of chronic kidney disease (CKD) worldwide. Both CKD and diabetes mellitus (DMT2) are important risk factors for mortality. However, it is still unknown if the risk of death is modified by the simultaneous presence of these diseases. OBJECTIVE: To evaluate the presence of an interaction between DMT2 and CKD for mortality in a representative population of a Latin American country. METHODS: It is an analytical cohort study of patients with CKD, who were followed for 4 years (between 2004 and 2009). We calculated the incidence rate, progression, survival (using Kaplan-Meier curves), interaction (on the additive and multiplicative scales) and impact of the different stages of CKD in patients with and without DMT2 (using a cox proportional hazards model). RESULTS: In this population of 5663 individuals, both DMT2 and CKD are risk factors for mortality (p<0.001). We found a statistically significant difference in mortality between individuals with and without DMT2, who also had CKD stages 3-4 -5 (Log-rank p=0.0076). Additionally, we found a statistically significant interaction for mortality in both the additive and multiplicative scales between DMT2 and CKD (p=0.005). DMT2 was found to be a risk factor for mortality (Hazard Ratio 1.61 p<0.001), but in individuals with DMT2, the only risks significantly associated with mortality, were age, dyslipidemia and nephroprotective drugs. CONCLUSIóN: The interaction between CKD and DMT2 negatively modifies the risk of death of both diseases. This means that when the two diseases are present, the risk of mortality is lower than expected.

Interaction between the stage of chronic kidney disease and diabetes mellitus as factors associated with mortality in chronic kidney disease patients: An external cohort study. / Interacción entre el estadio de la enfermedad renal crónica y la diabetes mellitus como factores asociados con mortalidad en pacientes con enfermedad renal crónica: un estudio de cohortes externas.

INTRODUCTION: Diabetic kidney disease is the main cause of chronic kidney disease (CKD) worldwide. Both CKD and diabetes mellitus (DMT2) are important risk factors for mortality. However, it is still unknown if the risk of death is modified by the simultaneous presence of these diseases. OBJECTIVE: To evaluate the presence of an interaction between DMT2 and CKD for mortality in a representative population of a Latin American country. METHODS: It is an analytical cohort study of patients with CKD, who were followed for 4 years (between 2004 and 2009). We calculated the incidence rate, progression, survival (using Kaplan-Meier curves), interaction (on the additive and multiplicative scales) and impact of the different stages of CKD in patients with and without DMT2 (using a cox proportional hazards model). RESULTS: In this population of 5663 individuals, both DMT2 and CKD are risk factors for mortality (P < 0.001). We found a statistically significant difference in mortality between individuals with and without DMT2, who also had CKD stages 3 - 4 - 5 (Log-rank P = 0.0076). Additionally, we found a statistically significant interaction for mortality in both the additive and multiplicative scales between DMT2 and CKD (P = 0.005). DMT2 was found to be a risk factor for mortality (Hazard Ratio 1.61 P < 0.001), but in individuals with DMT2, the only risks significantly associated with mortality, were age, dyslipidemia and nephroprotective drugs. CONCLUSIóN: The interaction between CKD and DMT2 negatively modifies the risk of death of both diseases. This means that when the two diseases are present, the risk of mortality is lower than expected.

Genetically Determined ABO Blood Group and its Associations With Health and Disease.

OBJECTIVE: To determine the spectrum of phenotypes linked to the ABO blood group system, using genetic determinants of the ABO blood group system. Approach and Results: We assessed the risk of 41 health and disease outcomes, and 36 linear traits associated with the ABO blood group system in the UK Biobank cohort. A total of 406 755 unrelated individuals were included in this study. Blood groups A, B, and O were determined based on allele combinations of previously established single-nucleotide polymorphisms rs8176746, rs8176719 in the ABO gene. Group AB was excluded because of its relative small sample size. Overall, 187 387 (46%) were male with a mean (SD) age of 57±8.1 years and a median total exposure of 64 person-years (interquartile range, 57-70). Of 406 755 individuals, 182 621 (44.9%) participants had blood group O, 182 786 (44.9%) had blood group A, and 41 348 (10.2%) had blood group B. ABO blood groups were associated with 11 health and disease outcomes (P<2.19×10-4). ABO blood groups were primarily associated with cardiovascular outcomes. Compared with individuals with blood group O, blood groups A and B were associated with increased odds of up to 1.56 (95% CI, 1.43-1.69) for thromboembolic events and decreased odds for hypertension (0.94 [95% CI, 0.92-0.97]). CONCLUSIONS: The ABO blood group system is associated with several parameters of healthy aging and disease development. Knowledge of ABO blood groups might be of interest for more personalized approaches towards health maintenance and the prevention of diseases.