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Key Clinical Message: CHARGE syndrome is a rare genetic disorder characterized by several distinct features. The presence of fetal ear abnormalities could be the early indicator of CHARGE syndrome. Subsequent prenatal diagnosis is essential to confirm the disorder. This is significant because the patient may receive genetic counseling and appropriate disposal based on the accurate diagnosis. Abstract: CHARGE syndrome is a rare genetic disorder with multiple specific clinical features. The prenatal diagnosis is crucial but rarely achieved. We report a fetus with fetal external ear abnormality detected by ultrasound at 22nd week of gestation. Postnatal examination revealed an external ear abnormality, a mild atrial septal defect, and other clinical signs of CHARGE syndrome. A de novo pathogenic nonsense mutation in the CHD7 gene (c.406C > T, p.Q136X in exon 2) was identified to cause the disorder. Our study demonstrated that prenatal diagnosis and genetic testing were recommended to obtain a solid diagnosis of CHARGE syndrome when fetal external ear abnormality was detected by ultrasound examination.
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Background: Previous results on the association between the estimated glomerular filtration rate (eGFR) and stroke are mixed. Most studies derived the eGFR from serum creatinine, which is affected by non-kidney determinants and thus has possibly biased the association with stroke risk. Methods: In this cohort study, we included 429 566 UK Biobank participants (94.5% white, 54% women, age 56 ± 8 years) free of stroke at enrollment. The eGFRcys and eGFRcr were calculated with serum cystatin C and creatinine, respectively. Outcomes of interest were risk of total stroke and subtypes. We investigated the linear and nonlinear associations using Cox proportional hazards models and restricted cubic splines, corrected for regression dilution bias. Results: During an average follow-up of 10.11 years, 4427 incident strokes occurred, among which 3447 were ischemic and 1163 were hemorrhagic. After adjustment for confounders, the regression dilution-corrected hazard ratios (95% confidence intervals) for every 10 mL/min/1.73 m2 decrement in eGFRcys were 1.10 (1.05-1.14) for total stroke and 1.11 (1.08-1.15) for ischemic stroke. A similar pattern was observed with eGFRcr, although the association was weaker. When either type of eGFR was below 75 mL/min/1.73 m2, the risks of total and ischemic stroke increased exponentially as eGFR decreased. A U-shaped relationship was witnessed if eGFRcr was used instead. There was a null association between eGFR and hemorrhagic stroke. Conclusions: The risks of total stroke and ischemic stroke increased exponentially when the eGFRcys fell below 75 mL/min/1.73 m2.
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Background: Death due to cardiovascular diseases (CVD) increased significantly in China. One possible way to reduce CVD is to identify people at risk and provide targeted intervention. We aim to develop and validate a CVD risk prediction model for Chinese males (CVDMCM) to help clinicians identify those males at risk of CVD and provide targeted intervention. Methods: We conducted a retrospective cohort study of 2,331 Chinese males without CVD at baseline to develop and internally validate the CVDMCM. These participants had a baseline physical examination record (2008-2016) and at least one revisit record by September 2019. With the full cohort, we conducted three models: A model with Framingham CVD risk model predictors; a model with predictors selected by univariate cox proportional hazard model adjusted for age; and a model with predictors selected by LASSO algorithm. Among them, the optimal model, CVDMCM, was obtained based on the Akaike information criterion, the Brier's score, and Harrell's C statistic. Then, CVDMCM, the Framingham CVD risk model, and the Wu's simplified model were all validated and compared. All the validation was carried out by bootstrap resampling strategy (TRIPOD statement type 1b) with the full cohort with 1,000 repetitions. Results: CVDMCM's Harrell's C statistic was 0.769 (95% CI: 0.738-0.799), and D statistic was 4.738 (95% CI: 3.270-6.864). The results of Harrell's C statistic, D statistic and calibration plot demonstrated that CVDMCM outperformed the Framingham CVD model and Wu's simplified model for 4-year CVD risk prediction. Conclusions: We developed and internally validated CVDMCM, which predicted 4-year CVD risk for Chinese males with a better performance than Framingham CVD model and Wu's simplified model. In addition, we developed a web calculator-calCVDrisk for physicians to conveniently generate CVD risk scores and identify those males with a higher risk of CVD.
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Background: Coronary heart disease (CHD) and cerebral ischemic stroke (CIS) are two major types of cardiovascular disease (CVD) that are increasingly exerting pressure on the healthcare system worldwide. Machine learning holds great promise for improving the accuracy of disease prediction and risk stratification in CVD. However, there is currently no clinically applicable risk stratification model for the Asian population. This study developed a machine learning-based CHD and CIS model to address this issue. Methods: A case-control study was conducted based on 8,624 electronic medical records from 2008 to 2019 at the Tongji Hospital in Wuhan, China. Two machine learning methods (the random down-sampling method and the random forest method) were integrated into 2 ensemble models (the CHD model and the CIS model). The trained models were then interpreted using Shapley Additive exPlanations (SHAP). Results: The CHD and CIS models achieved good performance with the areas under the receiver operating characteristic curve (AUC) of 0.895 and 0.884 in random testing, and 0.905 and 0.889 in sequential testing, respectively. We identified 4 common factors between CHD and CIS: age, brachial-ankle pulse wave velocity, hypertension, and low-density lipoprotein cholesterol (LDL-C). Moreover, carcinoembryonic antigen (CEA) was identified as an independent indicator for CHD. Conclusions: Our ensemble models can provide risk stratification for CHD and CIS with clinically applicable performance. By interpreting the trained models, we provided insights into the common and unique indicators in CHD and CIS. These findings may contribute to a better understanding and management of risk factors associated with CVD.
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BACKGROUND Thromboelastography (TEG) is a novel blood viscoelasticity detection method revealing blood coagulation status and has been reported to be helpful in predicting clinical outcomes in patients with cardiovascular diseases (CVD). In this study, we aimed to investigate the association between TEG and CVD. MATERIAL AND METHODS A single-center case-control study was performed. Individuals who took TEG tests at Tongji Hospital in Wuhan, China from 2015 to 2019 were included. The nearest-neighbor Mahalanobis matching with replacement, within propensity score calipers of 0.25 was used to control the covariate imbalance between CVD patients and controls. Logistic regression analyses were conducted to assess the relationship between TEG and CVD. Subgroup and sensitivity analyses were performed to evaluate the robustness of the association between TEG and CVD. RESULTS After matching, a total of 151 participants were included in this study, with 83 patients having CVD (49 patients having coronary heart disease [CHD] and 34 patients having an ischemic stroke). By comparison, CHD patients had a significantly higher maximum amplitude (MA) (P=0.02) than controls. After multivariable adjustment, MA (OR 1.11, 95% CI 1.01-1.24, P=0.04) was independently associated with CHD. The association between MA and CHD remained robust across subgroups and in sensitivity analyses. CONCLUSIONS The current study suggests that MA is significantly associated with CHD. Enhanced platelet reactivity as described by high MA might be associated with risk of CHD. The exact role of MA in the measurement of CHD risk needs to be further examined in large-scale prospective cohort studies.
