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Background: Enhanced nocturnal heart rate variability (HRV) has been evoked in sleep-related breathing disorders. However, its capacity to detect obstructive sleep apnea syndrome (OSAS) has not been systematically determined. Thus, we evaluated the discriminant power of HRV parameters in a first group of patients (G1) and validated their discriminant capacity in a second group (G2)., Methods and Results: In G1, 39 of 91 patients (42.8%) were identified as diseased by polysomnography, as were 24 of 52 patients (46%) in G2. Time-domain HRV variables (SD of NN intervals [SDNN], mean of the standard deviations of all NN intervals for all consecutive 5-minute segments of the recording [SDNN index], square root of the mean of the sum of the squares of differences between adjacent normal RR intervals [r-MSSD], and SD of the averages of NN intervals in all 5-minute segments of the recording [SDANN]) were calculated for daytime and nighttime periods, as well as the differences between daytime and nighttime values (Delta[D/N]). Correlations between HRV variables and OSAS status were analyzed in G1 by use of receiver-operating characteristic (ROC) curves and logistic regression analysis. By ROC curve analysis, 7 variables were significantly associated with OSAS. After adjustment for other variables through multiple logistic regression analysis, Delta[D/N]SDNN index and Delta[D/N] r-MSSD remained significant independent predictors of OSAS, with ORs of 8.22 (95% CI, 3.16 to 21.4) and 2.86 (95% CI, 1.21 to 6.75), respectively. The classification and regression tree methodology demonstrated a sensitivity reaching 89.7% (95% CI, 73.7 to 97.7) with Delta[D/N] SDNN index and a specificity of 98.1% (95% CI, 86.4 to 100) with Delta[D/N] SDNN using appropriate thresholds. These thresholds, applied to G2, yielded a sensitivity of 83% using Delta[D/N] SDNN index and a specificity of 96.5% using Delta[D/N] SDNN., Conclusions: Time-domain HRV analysis may represent an accurate and inexpensive screening tool in clinically suspected OSAS patients and may help focus resources on those at the highest risk.

Background: The interpretation of exercise stress testing for coronary artery disease detection is affected by the many differences in chosen variables and mathematical methods. We conducted a prospective trial to evaluate a global muscle fatigue parameter--the blood lactate level achieved at maximal exercise--as a method of distinguishing between diseased and nondiseased coronary status., Methods and Results: We evaluated 236 consecutive male patients without previous myocardial infarction who had been referred for the diagnosis of coronary artery disease. None of the patients had cardiomyopathy, severe cardiac heart failure, or valvular heart disease. Blood lactate concentration at maximal exercise was measured as well as other classic variables. Correlations between variables and coronary status as assessed by coronary arteriography were described using receiver operating characteristic (ROC) curves and logistic regression analysis. The first four most powerful variables (lactate level, maximal power output, exercise duration, and percentage of maximal predicted heart rate), which are directly representative of the global functional capacity, showed values of 0.777, 0.775, 0.760, and 0.740, respectively, by ROC curve analysis. Mean +/- SD blood lactate level at peak exercise reached 7.68 +/- 2.70 mmol/L in the 153 diseased and 10.56 +/- 2.75 mmol/L in the 83 nondiseased patients (P < .0001). After adjustment for other variables, blood lactate level remained a significant predictor of coronary artery disease by logistic regression analysis (adjusted odds ratio, 1.2; confidence interval, 1.04 to 1.4)., Conclusions: Global muscle fatigue as assessed by lactate levels in the blood at maximal exercise appears to be a powerful distinguisher of diseased and nondiseased coronary status.