A nomogram to predict ventricular thrombus in dilated cardiomyopathy patients.

BACKGROUND: VT (Ventricular Thrombus) is a serious complication of dilated cardiomyopathy (DCM). Our goal is to develop a nomogram for personalized prediction of incident VT in DCM patients. METHODS: 1267 patients (52.87 ± 11.75 years old, 73.8% male) were analyzed retrospectively from January 01, 2015, to December 31, 2020. A nomogram model for VT risk assessment was established using minimum absolute contraction and selection operator (LASSO) and multivariate logistic regression analysis, and its effectiveness was validated by internal guidance. The model was evaluated by the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). We compared the performance in predicting VT between nomogram and CHA2DS2, CHA2DS2- VASc or ATRIA by AUC, akaike information criterion (AIC), bayesian information criterion (BIC), net reclassification index (NRI), and integrated discrimination index (IDI). RESULTS: 89 patients (7.02%) experienced VT. Multivariate logistic regression analysis revealed that age, left ventricular ejection fraction (LVEF), uric acid (UA), N-terminal precursor B-type diuretic peptide (NT-proBNP), and D-dimer (DD) were important independent predictors of VT. The nomogram model correctly separates patients with and without VT, with an optimistic C score of 0.92 (95%CI: 0.90-0.94) and good calibration (Hosmer-Lemeshow χ2 = 11.51, P = 0.12). Our model showed improved prediction of VT compared to CHA2DS2, CHA2DS2-VASc or ATRIA (all P < 0.05). CONCLUSIONS: The novel nomogram demonstrated better than presenting scores and showed an improvement in predicting VT in DCM patients.

Development and validation of nomogram for unplanned ICU admission in patients with dilated cardiomyopathy.

Objective: Unplanned admission to the intensive care unit (ICU) is the major in-hospital adverse event for patients with dilated cardiomyopathy (DCM). We aimed to establish a nomogram of individualized risk prediction for unplanned ICU admission in DCM patients. Methods: A total of 2,214 patients diagnosed with DCM from the First Affiliated Hospital of Xinjiang Medical University from January 01, 2010, to December 31, 2020, were retrospectively analyzed. Patients were randomly divided into training and validation groups at a 7:3 ratio. The least absolute shrinkage and selection operator and multivariable logistic regression analysis were used for nomogram model development. The area under the receiver operating characteristic curve, calibration curves, and decision curve analysis (DCA) were used to evaluate the model. The primary outcome was defined as unplanned ICU admission. Results: A total of 209 (9.44%) patients experienced unplanned ICU admission. The variables in our final nomogram included emergency admission, previous stroke, New York Heart Association Class, heart rate, neutrophil count, and levels of N-terminal pro b-type natriuretic peptide. In the training group, the nomogram showed good calibration (Hosmer-Lemeshow χ 2 = 14.40, P = 0.07) and good discrimination, with an optimal-corrected C-index of 0.76 (95% confidence interval: 0.72-0.80). DCA confirmed the clinical net benefit of the nomogram model, and the nomogram maintained excellent performances in the validation group. Conclusion: This is the first risk prediction model for predicting unplanned ICU admission in patients with DCM by simply collecting clinical information. This model may assist physicians in identifying individuals at a high risk of unplanned ICU admission for DCM inpatients.

Genetic polymorphism of the Dab2 gene and its association with Type 2 Diabetes Mellitus in the Chinese Uyghur population.

Objective: The human Disabled-2 (Dab2) protein is an endocytic adaptor protein, which plays an essential role in endocytosis of transmembrane cargo, including low-density lipoprotein cholesterol (LDL-C). As a candidate gene for dyslipidemia, Dab2 is also involved in the development of type 2 diabetes mellitus(T2DM). The aim of this study was to investigate the effects of genetic variants of the Dab2 gene on the related risk of T2DM in the Uygur and Han populations of Xinjiang, China. Methods: A total of 2,157 age- and sex-matched individuals (528 T2DM patients and 1,629 controls) were included in this case-control study. Four high frequency SNPs (rs1050903, rs2255280, rs2855512 and rs11959928) of the Dab2 gene were genotyped using an improved multiplex ligation detection reaction (iMLDR) genotyping assay, and the forecast value of the SNP for T2DM was assessed by statistical analysis of clinical data profiles and gene frequencies. Results: We found that in the Uygur population studied, for both rs2255280 and rs2855512, there were significant differences in the distribution of genotypes (AA/CA/CC), and the recessive model (CC vs. CA + AA) between T2DM patients and the controls (P < 0.05). After adjusting for confounders, the recessive model (CC vs. CA + AA) of both rs2255280 and rs2855512 remained significantly associated with T2DM in this population (rs2255280: OR = 5.303, 95% CI [1.236 to -22.755], P = 0.025; rs2855512: OR = 4.892, 95% CI [1.136 to -21.013], P = 0.033). The genotypes (AA/CA/CC) and recessive models (CC vs. CA + AA) of rs2855512 and rs2255280 were also associated with the plasma glucose and HbA1c levels (all P < 0.05) in this population. There were no significant differences in genotypes, all genetic models, or allele frequencies between the T2DM and control group in the Han population group (all P > 0.05). Conclusions: The present study suggests that the variation of the Dab2 gene loci rs2255280 and rs2855512 is related to the incidence of T2DM in the Uygur population, but not in the Han population. In this study, these variations in Dab2 were an independent predictor for T2DM in the Uygur population of Xinjiang, China.