Background and ObjectivesPatients with severe kidney function impairment often have autonomic dysfunction, which could be evaluated noninvasively by heart rate variability (HRV) analysis. Nonlinear HRV parameters such as detrended fluctuation analysis (DFA) has been demonstrated to be an important outcome predictor in patients with cardiovascular diseases. Whether cardiac autonomic dysfunction measured by DFA is also a useful prognostic factor in patients with end-stage renal disease (ESRD) receiving peritoneal dialysis (PD) remains unclear. The purpose of the present study was designed to test the hypothesis.Materials and MethodsPatients with ESRD receiving PD were included for the study. Twenty-four hour Holter monitor was obtained from each patient together with other important traditional prognostic makers such as underlying diseases, left ventricular ejection fraction (LVEF) and serum biochemistry profiles. Short-term (DFAα1) and long-term (DFAα2) DFA as well as other linear HRV parameters were calculated.ResultsA total of 132 patients (62 men, 72 women) with a mean age of 53.7±12.5 years were recruited from July 2007 to March 2009. During a median follow-up period of around 34 months, eight cardiac and six non-cardiac deaths were observed. Competing risk analysis demonstrated that decreased DFAα1 was a strong prognostic predictor for increased cardiac and total mortality. ROC analysis showed that the AUC of DFAα1 (<0.95) to predict mortality was 0.761 (95% confidence interval (CI). = 0.617–0.905). DFAα1≧ 0.95 was associated with lower cardiac mortality (Hazard ratio (HR) 0.062, 95% CI = 0.007–0.571, P = 0.014) and total mortality (HR = 0.109, 95% CI = 0.033–0.362, P = 0.0003).ConclusionCardiac autonomic dysfunction evaluated by DFAα1 is an independent predictor for cardiac and total mortality in patients with ESRD receiving PD.

Heart rhythm complexity analysis has been shown to have good prognostic power in patients with cardiovascular disease. The aim of this study was to analyze serial changes in heart rhythm complexity from the acute to chronic phase of acute myocardial infarction (MI). We prospectively enrolled 27 patients with anterior wall ST segment elevation myocardial infarction (STEMI) and 42 control subjects. In detrended fluctuation analysis (DFA), the patients had significantly lower DFAα2 in the acute stage (within 72 hours) and lower DFAα1 at 3 months and 12 months after MI. In multiscale entropy (MSE) analysis, the patients had a lower slope 5 in the acute stage, which then gradually increased during the follow-up period. The areas under the MSE curves for scale 1 to 5 (area 1–5) and 6 to 20 (area 6–20) were lower throughout the chronic stage. Area 6–20 had the greatest discriminatory power to differentiate the post-MI patients (at 1 year) from the controls. In both the net reclassification improvement and integrated discrimination improvement models, MSE parameters significantly improved the discriminatory power of the linear parameters to differentiate the post-MI patients from the controls. In conclusion, the patients with STEMI had serial changes in cardiac complexity.

Current evidence suggests that beta-blocker lower the risk of development of atrial fibrillation (AF) and in-hospital stroke after cardiac surgery. This study was to assess whether beta-blockers could decrease incidence of new-onset AF in patients with end stage renal disease (ESRD). We identified patients from a nation-wide database called Registry for Catastrophic Illness, which encompassed almost 100% of the patients receiving dialysis therapy in Taiwan from 1995 to 2008. Propensity score matching and Cox’s proportional hazards regression model were used to estimate hazard ratios (HRs) for new-onset AF. Among 100066 patients, 41.7% received beta-blockers. After a median follow-up of 1500 days, the incidence of new-onset AF significantly decreased in patients treated with beta-blockers (HR = 0.483, 95% confidence interval = 0.437-0.534). The prevention of new-onset AF was significantly better in patients taking longer duration of beta-blockers therapy (P for time trend <0.001). The AF prevention effect remains robust in subgroup analyses. In conclusion, beta-blockers seem effective in the primary prevention of AF in ESRD patients. Hence, beta-blockers may be the target about upstream treatment of AF.

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combination use of digoxin and other medications might lead to worse outcomes in patients with atrial fibrillation (AF). We sought to investigate whether digoxin-amiodarone combination would lead to worse outcome than digoxin alone in patients with AF. Adult patients with AF and received digoxin treatment from random samples of 1,000,000 individuals covered by the National Health Insurance in Taiwan were included. Baseline characteristics including risk factors and medications were matched by propensity score (PS) in those with and without addition of amiodarone treatment. A total of 5,040 AF patients taking digoxin therapy was included. PS matching identified 1,473 patients receiving digoxin-amiodarone combination and 2,660 patients receiving digoxin with a median follow-up of 1,331 days. Digoxin-amiodarone combination was associated with increased all-cause mortality (adjusted hazard ratio (HR): 1.640, 95% confidence interval (CI): 1.470-1.829, P < 0.001). The risk of mortality increased regardless of duration of combination. Risk of sudden cardiac death was not increased in the combination group (HR: 1.304, 95% CI: 1.049-1.622, P = 0.017). Death due to non-arrhythmic cardiac disease, cerebrovascular disease, and other vascular disease were higher in the combination group than the digoxin group. In conclusion, in patients with AF, digoxin-amiodarone combination therapy is associated with excess mortality than digoxin alone.Digoxin is one of the oldest drugs in cardiovascular (CV) medicine, traditionally used in treating patients with atrial fibrillation (AF) and heart failure (HF) 1 , and one of the most frequently prescribed drugs in AF. In the Stroke Prevention using an ORal Thrombin Inhibitor in atrial Fibrillation (SPOTIF) study, 53% of patients were taking digoxin 2 . Digoxin is effective for long-term rate control at rest through slowing down atrioventricular conduction 3 . However, from meta-analysis and cohort study, use of digoxin might be associated with excess mortality in AF patients 2,4,5 .In clinical practice, digoxin is frequently used in combination with other drugs, and many drugs interact with digoxin 6 . This may cause serum digoxin concentration (SDC) to exceed its therapeutic range, and according to the Digitalis Investigation Group (DIG) trial 7 , higher SDC resulted in less neurohormonal-inhibiting properties and higher rate of CV and all-cause mortality. Therefore, when interpretation of harmful effect of digoxin, concomitant drugs in use and their interactions with digoxin should be taken into consideration.Plenty of medication were known to interact with digoxin. For example, bench and mice study had showed that adding quinidine would inhibited P-glycoprotein-mediated digoxin transport, and increased plasma digoxin concentration 19 . In cohort studies, addition of quinidine resulted in a mean 2.5-fold increase in SDC (from 0.98 ± 0.37 to 2.47 ± 0.71 ng/ml), P < 0.001) and the rate of digoxin toxicity 20 . Suggestion of a reduction of 30 to 50 percent of the digoxin dose when quinidine ...

Background Atrial fibrillation (AF) is the most common arrhythmia, and its paroxysmal and short duration nature makes its detection challenging. The most important limitation of current smartwatches is that patients need to touch to the sensor of the watch to record signals when patients feel discomfort. We developed a wearable smart watch and evaluated its accuracy to differentiate AF from sinus rhythm, which can continuously detecting heart rhythm without hand touching the device. Methods and results A wearable smart watch with PPG sensor and electrocardiogram (ECG) recording function was used for signal acquisition. A total 399 patients with a mean age of 67 years old were enrolled in the study, of whom 237 (81.5%) were male, and 101 have been diagnosed with AF. Pulse wave extracted from the green light spectrum of the signal and ECG were recorded for about 10 minutes for each patient. Pulse-to-pulse intervals (PPI) were automatically identified. All ECG signals were verified by two cardiologists. The correlation between R-to-R interval on ECG and PPI were excellent, with a correlation coefficient R >0.99 (p<0.05). An entropy-based algorithm which combined Shannon entropy of successive difference of PPI and sample entropy of PPI was used to discriminate between AF and sinus rhythm. This method had high sensitivity and specificity (96% and 98%, respectively), the area under receiver operating characteristic curve reached 0.98. Conclusions We developed an entropy-based algorithm for AF detection with PPG signal recorded by a wearable smart watch. This algorithm discriminates AF from sinus rhythm accurately. This advance in technology overcomes an important clinical obstacle and can increase the AF detection rate tremendously.