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Purpose: The risk factors of chronic kidney disease were analyzed by using the region of interest quantitative technology of color doppler combined with QLab software, and a Nomogram was established to conduct an individualized assessment of patients with chronic kidney disease.Methods: A total of 500 patients with chronic kidney disease diagnosed in our hospital from June 2019 to March 2021 were selected as the chronic kidney disease group, and 300 healthy patients during the same period were selected as the control group. Univariate analysis was performed on the test indexes(Fasting blood glucose, total cholesterol, triglyceride, urea nitrogen , creatinine, uric acid, albumin, red blood cell count, glomerular ltration rate, urinary protein) and the vascularity index, ow index, and vascularization ow index measured by the color doppler region of interest quantitative technique. The above meaningful indicators were included in the Logistics regression analysis to obtain the independent risk factors of early chronic kidney disease. The independent risk factors were imported into R software to draw a nomogram model for predicting early chronic kidney disease and evaluate the model.Results: Single factor analysis results suggest age, hypertension, diabetes, hyperlipidemia, disease of heart head blood-vessel, body mass index, vascularity index, ow index, and vascularization ow index, fasting blood sugar, triglyceride, total cholesterol, urea nitrogen, creatinine, uric acid, glomerular ltration rate differences statistically signi cant (P < 0.05), while gender, renal length to diameter, the thickness of the cortex, Resistance index and peak systolic velocity and albumin difference has no statistical signi cance (P > 0.05). Logistics regression analysis showed that hypertension, diabetes, ow index, and vascularization ow index, urea nitrogen and albumin were independent risk factors for the early occurrence of chronic kidney disease. The C index of this nomogram using independent risk factors is 0.896 (95%CI: 0.862-0.930), which indicates that the nomogram has good discriminant power. The receiver operating curve of the histograph was AUC(area under the curve) 0.884 (95%CI: 0.860-0.908), with the optimal threshold of 0.663, speci city of 88.7%, sensitivity of 78.0%, accuracy of 82.0%, and positive predictive value of 91.9%. The ROC(receiver operator characteristic curve) of urea nitrogen, albumin , ow index, and vascularization ow index were evaluated. The results indicated that the best cutoff value of urea nitrogen was 5.9mmol/L, ow index was 14.67, vascularization ow index was 4.6, and albumin was 40.26g/L. Conclusion: In the prediction of chronic kidney disease I-II stage, the quantitative technique of color Doppler region of interest has certain diagnostic value. The model established in this study has good discriminative power and can be applied to clinical practice, giving certain indicative signi cance.

PurposeStudies have shown that gout can increase the risk of cardiovascular disease. Three‐dimensional speckle‐tracking echocardiography (3D‐STE), a sensitive imaging technology, enables the detection of subtle myocardial dysfunctions. Our aim is to evaluate the left ventricular (LV) functions in patients with gout using 3D‐STE.Methods80 subjects: 40 with gout and 40 as normal controls were involved. We obtained and analyzed these parameters from the dynamic images of a 3D full‐volume dataset: global longitudinal strain (GLS), global circumferential strain (GCS), global radial strain (GRS), Twist, 16‐segmental time‐to‐peak longitudinal strain (TTP) and systolic dyssynchrony index (SDI)besides other relevant parameters.ResultsCompared with the normal group, gout patients were more likely to have left ventricular remodeling. The patients with gout showed decreased Em, increased E/Em and larger volume index of the left atrium (LAVI) indicating reduced diastolic function. The peak GLS (−17.42 ± 2.02 vs. −22.40 ± 2.57, P < 0.001), GCS (−27.04 ± 3.75 vs. −34.85 ± 4.99, P < 0.001), GRS (38.22 ± 4.28 vs. 46.15 ± 5.17, P < 0.001), and Twist (15.18 ± 5.45 vs. 19.02 ± 5.29, P = 0.015) were significantly lower in patients with gout than in healthy participants. The SDI (5.57 ± 1.46 vs. 4.91 ± 1.19, P = 0.016) was significantly increased in patients with gout compared with normal controls. There was no significant between‐group difference in TTP (P = 0.43). The systolic GLS, GRS and GCS peak values increased gradually from the base to the apex, with the lowest values in the basal segment in patients with gout. Receiver‐operating characteristic curve analysis revealed among these strains GLS has the largest area under the curve (AUC: 0.93, P < 0.001), the cutoff value of −18.97% with a sensitivity and specificity of 80.0% and 92.0%, respectively, for differentiating two groups. A multivariate linear regression analysis shows that the relationship between gout and strain parameters including GLS, GRS, and GCS is statistically significant (P < 0.001).ConclusionAlthough patients with gout having a normal ejection fraction, structural remodeling of the left ventricle and subclinical LV deformation may occur. 3D‐STE can detect subtle cardiac dysfunctions in patients with gout at an early stage.

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