Background Coronary artery aneurysms (CAAs) may occur after Kawasaki disease (KD) and lead to important morbidity and mortality. As CAA in patients with KD are rare and heterogeneous lesions, prognostication and risk stratification are difficult. We sought to derive the cumulative risk and associated factors for cardiovascular complications in patients with CAAs after KD. Methods and Results A 34-institution international registry of 1651 patients with KD who had CAAs (maximum CAA Z score ≥2.5) was used. Time-to-event analyses were performed using the Kaplan-Meier method and Cox proportional hazard models for risk factor analysis. In patients with CAA Z scores ≥10, the cumulative incidence of luminal narrowing (>50% of lumen diameter), coronary artery thrombosis, and composite major adverse cardiovascular complications at 10 years was 20±3%, 18±2%, and 14±2%, respectively. No complications were observed in patients with a CAA Z score <10. Higher CAA Z score and a greater number of coronary artery branches affected were associated with increased risk of all types of complications. At 10 years, normalization of luminal diameter was noted in 99±4% of patients with small (2.5≤Z<5.0), 92±1% with medium (5.0≤Z<10), and 57±3% with large CAAs (Z≥10). CAAs in the left anterior descending and circumflex coronary artery branches were more likely to normalize. Risk factor analysis of coronary artery branch level outcomes was performed with a total of 893 affected branches with Z score ≥10 in 440 patients. In multivariable regression models, hazards of luminal narrowing and thrombosis were higher for patients with CAAs of the right coronary artery and left anterior descending branches, those with CAAs that had complex architecture (other than isolated aneurysms), and those with CAAs with Z scores ≥20. Conclusions For patients with CAA after KD, medium-term risk of complications is confined to those with maximum CAA Z scores ≥10. Further risk stratification and close follow-up, including advanced imaging, in patients with large CAAs is warranted.

Coronary artery thrombosis is the major risk associated with Kawasaki disease (KD). Long-term management of KD patients with persistent aneurysms requires a thrombotic risk assessment and clinical decisions regarding the administration of anticoagulation therapy. Computational fluid dynamics has demonstrated that abnormal KD coronary artery hemodynamics can be associated with thrombosis. However, the underlying mechanisms of clot formation are not yet fully understood. Here we present a new model incorporating data from patient-specific simulated velocity fields to track platelet activation and accumulation. We use a system of Reaction-Advection-Diffusion equations solved with a stabilized finite element method to describe the evolution of non-activated platelets and activated platelet concentrations [AP], local concentrations of adenosine diphosphate (ADP) and poly-phosphate (PolyP). The activation of platelets is modeled as a function of shear-rate exposure and local concentration of agonists. We compared the distribution of activated platelets in a healthy coronary case and six cases with coronary artery aneurysms caused by KD, including three with confirmed thrombosis. Results show spatial correlation between regions of higher concentration of activated platelets and the reported location of the clot, suggesting predictive capabilities of this model towards identifying regions at high risk for thrombosis. Also, the concentration levels of ADP and PolyP in cases with confirmed thrombosis are higher than the reported critical values associated with platelet aggregation (ADP) and activation of the intrinsic coagulation pathway (PolyP). These findings suggest the potential initiation of a coagulation pathway even in the absence of an extrinsic factor. Finally, computational simulations show that in regions of flow stagnation, biochemical activation, as a result of local agonist concentration, is dominant. Identifying the leading factors to a pro-coagulant environment in each case-mechanical or biochemical-could help define improved strategies for thrombosis prevention tailored for each patient.

Multiple myeloma (MM) is a haematological malignancy associated with kidney injury resulting from cast nephropathy, which can be caused by monoclonal free light chains (FLC). It has been demonstrated that early reduction of FLC can lead to a higher proportion of patients recovering renal function with a better outcome, especially if high cut-off haemodialysis (HCO-HD) combined with chemotherapy is used.

Transforaminal lumbar interbody fusion (TLIF) is a commonly used technique for treating lumbar degenerative diseases. In this study, we developed a fully computer-supported pipeline to predict both the cage height and the degree of lumbar lordosis subtraction from the pelvic incidence (PI-LL) after TLIF surgery, utilizing preoperative X-ray images. The automated pipeline comprised two primary stages. First, the pretrained BiLuNet deep learning model was employed to extract essential features from X-ray images. Subsequently, five machine learning algorithms were trained using a five-fold cross-validation technique on a dataset of 311 patients to identify the optimal models to predict interbody cage height and postoperative PI-LL. LASSO regression and support vector regression demonstrated superior performance in predicting interbody cage height and postoperative PI-LL, respectively. For cage height prediction, the root mean square error (RMSE) was calculated as 1.01, and the model achieved the highest accuracy at a height of 12 mm, with exact prediction achieved in 54.43% (43/79) of cases. In most of the remaining cases, the prediction error of the model was within 1 mm. Additionally, the model demonstrated satisfactory performance in predicting PI-LL, with an RMSE of 5.19 and an accuracy of 0.81 for PI-LL stratification. In conclusion, our results indicate that machine learning models can reliably predict interbody cage height and postoperative PI-LL.