LDL-C/HDL-C is associated with ischaemic stroke in patients with non-valvular atrial fibrillation: a case-control study.

BACKGROUND: This study explored the relationships between the low-/high-density lipoprotein cholesterol ratio (LDL-C/HDL-C) and other clinical indicators and ischaemic stroke (IS) in patients with non-valvular atrial fibrillation (NVAF) in Xinjiang. The findings could provide a theoretical and therapeutic basis for NVAF patients. METHODS: NVAF patients who were admitted to 10 medical centres across Xinjiang were divided into stroke (798 patients) and control (2671 patients) groups according to the occurrence of first acute IS. Univariate and multivariate logistic regression analysis were used to examine the independent risk factors for IS in NVAF patients. Factor analysis and principal component regression analysis were used to analyse the main factors influencing IS. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminatory ability of LDL-C/HDL-C for predicting the occurrence of IS. RESULTS: The stroke group had an average age of 71.64 ± 9.96 years and included 305 females (38.22%). The control group had a mean age of 67.30 ± 12.01 years and included 825 females (30.89%). Multivariate logistic regression showed that the risk of IS in the highest LDL-C/HDL-C quartile (≥2.73) was 16.23-fold that of the lowest quartile (< 1.22); IS risk was 2.27-fold higher in obese patients than in normal-weight subjects; IS risk was 3.15-fold higher in smoking patients than in non-smoking patients. The area under the ROC curve of LDL-C/HDL-C was 0.76, the optimal critical value was 2.33, the sensitivity was 63.53%, and the specificity was 76.34%. Principal component regression analysis showed that LDL-C/HDL-C, age, smoking, drinking, LDL-C and hypertension were risk factors for IS in NVAF patients. CONCLUSIONS: LDL-C/HDL-C > 1.22, smoking, BMI ≥24 kg/m2 and CHA2DS2-VASc score were independent risk factors for IS in NVAF patients; LDL-C/HDL-C was the main risk factor.

KCNQ1 G219E and TRPM4 T160M polymorphisms are involved in the pathogenesis of long QT syndrome: A case report.

RATIONALE: Long QT syndrome (LQTS) is an inheritable disease characterized by prolonged QT interval on the electrocardiogram. The pathogenesis of LQTS is related to mutations in LQTS-susceptible genes encoding cardiac ion channel proteins or subunits. PATIENT CONCERNS: Here, we reported a 37-year-old female Uygur patient with palpitation and loss of consciousness. DIAGNOSES: At the time of admission, a 12-lead electrocardiogram showed a QTc interval of 514 ms. Genetic analysis revealed KCNQ1 G219E and TRPM4 T160M mutations. INTERVENTIONS: Although beta-blockers remain the mainstay in treating LQTS, the patient underwent implantation of an automatic cardioverter defibrillator due to life-threatening arrhythmias. OUTCOMES: To explore the effect of the calcium ion antagonist verapamil on ion channels, we generated human induced pluripotent stem cell cardiomyocytes (hiPSC-CMs) from the peripheral blood mononuclear cells of the patient. The changes of action potential duration in response to verapamil were observed. LESSONS: Our results showed that patient-derived hiPSC-CMs could recapitulate the electrophysiological features of LQTS and display pharmaceutical responses to verapamil.

Establishment of a predictive model for inpatient sudden cardiac death in a Chinese cardiac department population: a retrospective study.

BACKGROUND: Little is known about the risk factors for sudden cardiac death (SCD) in the overall hospitalized cardiac department population. This study was conducted to investigate the risk factors and develop a predictive model for SCD in a hospitalized cardiac department population. METHODS: We conducted a retrospective study of patients admitted to the cardiac department of the First Affiliated Hospital of Xinjiang Medical University from June 2015 to February 2017. We collected the clinical data from medical records. Multiple stepwise logistic regression analysis was carried out to confirm the risk factors for SCD and develop a predictive risk model. The risk score was assessed by the area under receiver operating characteristic (AUROC) curve and the Hosmer-Lemeshow goodness-of-fit test. RESULTS: A total of 262 patients with SCD and 4485 controls were enrolled in our study. Logistic regression modeling identified eight significant risk factors for in-hospital SCD: age, main admitting diagnosis, diabetes, corrected QT interval, QRS duration, ventricular premature beat burden, left ventricular ejection fraction, and estimated glomerular filtration rate. A predictive risk score including these variables showed an AUROC curve of 0.774 (95% confidence interval: 0.744-0.805). The Hosmer-Lemeshow goodness-of-fit test showed the chi-square value was 2.527 (P = 0.640). The incidence of in-hospital SCD was 1.3%, 4.1%, and 18.6% for scores of 0 to 2, 3 to 5 and ≥6, respectively (P < 0.001). CONCLUSIONS: Age, main admitting diagnosis, diabetes, QTc interval, QRS duration, ventricular premature beat burden, left ventricular ejection fraction, and estimated glomerular filtration rate are factors related to in-hospital SCD in a hospitalized cardiac department population. We developed a predictive risk score including these factors that could identify patients who are predisposed to in-hospital SCD.