Numerical investigation of arterial stenosis location affecting hemodynamics considering microcirculation function

Contrast-enhanced ultrasound evaluation of renal perfusion before angioplasty and its predictive value for hypertension

BACKGROUND

Atherosclerotic renal artery stenosis (ARAS) is a common disease in the elderly population.

OBJECTIVE

The aim was to develop a contrast-enhanced ultrasound (CEUS)-based model for predicting post-angioplasty improvement in hypertension in patients with severe ARAS.

METHODS

Thirty-five patients with severe ARAS (⩾ 70%) were included in this study, and 42 renal arteries received percutaneous transluminal renal arterial stenting. An optimal integral formula was developed from pre-interventional color-coded duplex sonography (CCDS) and CEUS parameters using least absolute shrinkage and selection operator (LASSO) regression and receiver operating characteristic (ROC) curve analysis. A model for predicting short-term hypertension improvement was established using the integral formula and clinical risk factors. Bootstrapping was used for internal validation.

RESULTS

Two integral formulas, LASSO.CCDS and LASSO.CEUS, were established. ROC curves of the two integral formulas showed that LASSO.CEUS was the better formula for predicting hypertension improvement (AUC 0.816, specificity 78.6%). Univariate and multivariate regression analyses showed that duration of hypertension (OR 0.841, P= 0.027), diabetes (OR = 0.019, P= 0.010), and LASSO.CEUS (OR 7.641, P= 0.052) were predictors of short-term hypertension improvement after interventional therapy. Using LASSO.CEUS combined with clinical risk factors, the following prediction model was established: logit (short-term improvement in hypertension) = 1.879-0.173 × hypertension duration - 3.961 × diabetes + 2.034 × LASSO.CEUS (AUC 0.939).

CONCLUSIONS

The model established using CEUS parameters and clinical risk factors could predict hypertension improvement after interventional therapy, but further research and verification are needed.

A study on the prevention of thrombosis after simultaneous pancreas-kidney transplantation

BACKGROUND

Renal failure is one of the most common chronic complications of diabetes. Simultaneous pancreas-kidney transplantation (SPK) is considered the preferred treatment for individuals with diabetes and chronic renal failure. This procedure has demonstrated efficacy in enhancing the quality of life for patients and minimizing the complications associated with diabetes.

OBJECTIVE

In this study, we analyzed the incidence and safety of complications in different thrombosis prevention techniques post simultaneous pancreas-kidney transplantation (SPK).

METHODS

Patients who underwent SPK between January 2019 and December 2022 were selectively categorized into two groups: the heparin group and the non-heparin group depending on the utilization of low molecular weight heparin. The occurrence of complications and clinical outcomes were subsequently calculated in each group.

RESULTS

In this study, we included a total of 58 recipients who underwent SPK, with 36 in the heparin group and 22 in the non-heparin group. Among the 58 participants, there were 3 cases of pancreatic thrombosis complications, with 2 cases (5.6%) in the heparin group and 1 case (4.6%) in the non-heparin group, and the differences were not statistically significant (P> 0.05). Regarding gastrointestinal bleeding, there were 17 cases out of the total 58 patients, with 14 cases (38.9%) in the heparin group and 3 cases (13.6%) in the non-heparin group, and the difference was statistically significant (P< 0.05).

CONCLUSION

After surgery, the use of low molecular weight heparin anticoagulation may increase the likelihood of experiencing gastrointestinal bleeding. Prior to the surgery, a comprehensive evaluation of the coagulation status and medical history of the patient should be performed, enabling stratification of risks involved. Based on this assessment, either low-molecular-weight heparin or aspirin should be selected as a preventive measure against thrombosis.

Software that combines deep learning, 3D reconstruction and CFD to analyze the state of carotid arteries from ultrasound imaging

BACKGROUND

Ultrasound is one of the non-invasive techniques that are used in clinical diagnostics of carotid artery disease.

OBJECTIVE

This paper presents software methodology that can be used in combination with this imaging technique to provide additional information about the state of patient-specific artery.

METHODS

Overall three modules are combined within the proposed methodology. A clinical dataset is used within the deep learning module to extract the contours of the carotid artery. This data is then used within the second module to perform the three-dimensional reconstruction of the geometry of the carotid bifurcation and ultimately this geometry is used within the third module, where the hemodynamic analysis is performed. The obtained distributions of hemodynamic quantities enable a more detailed analysis of the blood flow and state of the arterial wall and could be useful to predict further progress of present abnormalities in the carotid bifurcation.

RESULTS

The performance of the deep learning module was demonstrated through the high values of relevant common classification metric parameters. Also, the accuracy of the proposed methodology was shown through the validation of results for the reconstructed parameters against the clinically measured values.

CONCLUSION

The presented methodology could be used in combination with standard clinical ultrasound examination to quickly provide additional quantitative and qualitative information about the state of the patient's carotid bifurcation and thus ensure a treatment that is more adapted to the specific patient.