RESUMEN
Background: While electrocardiographic parameters of hypertensive left ventricular hypertrophy (H-LVH) are well known, limited data are available regarding hypertrophic cardiomyopathy (HCM). This study was to assess the diagnostic value of electrocardiographic voltage parameters in HCM. Methods: Included patients with HCM treated between March 2015 and May 2023. Voltage parameters (S-L, Cornell, Cornell product, Lewis, Peguero, and modified Cornell voltages) and echocardiography were evaluated. Receiver operating characteristic (ROC) curves were used to assess the diagnostic value of electrocardiogram in HCM. The multiple linear regression was conducted to analyze the correlation between electrocardiogram indicators and cardiac diastolic function. Results: The highest sensitivity for HCM was Peguero voltage (70.4%; 88.6% specificity). The Peguero voltage had high sensitivity in male (63.8%) and female patients (84.9%), those aged <65 years (71.6%) and ≥65 years (68.3%), with non-apical HCM (AHCM) (76.7%), obstructive HCM (82.1%), and non-obstructive HCM (66.9%). The sensitivity of the S-L voltage was high in AHCM (72.2%). The sensitivity for HCM reached 88.7% when the S-L and Peguero voltages were combined. The modified Cornell index had the highest area under the curve (0.88, 95% CI: 0.84-0.91), and its optimal cutoff value was 2.05â mV in males (77.6% sensitivity and 74% specificity) and 1.935â mV in females (90.6% sensitivity and 91.4% specificity). Peguero voltage (beta = 0.154, P = 0.034) and SD (beta = 0.223 P = 0.004) were independently correlated with E/e', an index of left ventricular diastolic function. Conclusion: The Peguero voltage had high sensitivity and specificity for detecting the presence of HCM. It was positively correlated with E/e' in patients with HCM. For AHCM, the S-L voltage was more advantageous. Combining the S-L voltage with the Peguero voltage further improves the sensitivity for HCM and thus could be used to improve the screening of HCM in clinical practice. The SD and modified Cornell voltage also had good diagnostic performance, especially in females.
RESUMEN
OBJECTIVE: To assess the association between ultra-short heart rate variability (US-HRV) and short-term mortality in patients with COVID-19 and develop prognostic prediction models to identify high-risk patients as early as possible. METHODS: A retrospective cohort study was performed on 488 patients diagnosed with COVID-19 and hospitalized in the First Affiliated Hospital of Fujian Medical University from December 2022 to January 2023. 10-s electrocardiogram (ECG) data were available for these patients. The US-HRV parameters including standard deviation of all normal-to-normal R-R intervals (SDNN) and root mean square of successive differences between normal-to-normal R-R intervals (rMSSD) were calculated using Nalong ECG software. The endpoint was short-term mortality, including in-hospital mortality or mortality within 1 week after discharge. RESULTS: Of the 488 patients, 76 (15.6%) died. The SDNN and rMSSD in the death group were significantly lower than those in the survival group (P < 0.001). The area under the receiver operating characteristic (ROC) curve (AUC) for SDNN and rMSSD to predict mortality was 0.761 and 0.715, respectively. The combined use of SDNN and rMSSD had an AUC of 0.774. The mortality rate in the group with SDNN ≤7.5 ms was higher than that of SDNN >7.5 ms group (P < 0.05). With the decrease of SDNN, the mortality of patients showed an upward trend, and the mortality of patients with SDNN ≤2 ms was the highest (66.7%). Multivariate logistic regression analysis identified SDNN as an independent predictor of prognosis (odds ratio (OR) = 5.791, 95% confidential interval (CI) 1.615-20.765, P = 0.007). The AUC of Model 1 (simple model) was 0.866 (95% CI 0.826-0.905). The AUC of Model 2 (comprehensive model) was 0.914 (95% CI 0.881-0.947). CONCLUSION: SDNN was associated with short-term mortality and provided the additional discriminatory power of the risk stratification model for hospitalized COVID-19 patients.
