Background Nonalcoholic fatty liver disease is associated with an increased cardiovascular disease (CVD) risk, although the exact mechanism(s) are less clear. Moreover, the relationship between newly redefined metabolic-associated fatty liver disease (MAFLD) and CVD risk has been poorly investigated. Data-driven machine learning (ML) techniques may be beneficial in discovering the most important risk factors for CVD in patients with MAFLD. Methods In this observational study, the patients with MAFLD underwent subclinical atherosclerosis assessment and blood biochemical analysis. Patients were split into two groups based on the presence of CVD (defined as at least one of the following: coronary artery disease; myocardial infarction; coronary bypass grafting; stroke; carotid stenosis; lower extremities artery stenosis). The ML techniques were utilized to construct a model which could identify individuals with the highest risk of CVD. We exploited the multiple logistic regression classifier operating on the most discriminative patient’s parameters selected by univariate feature ranking or extracted using principal component analysis (PCA). Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for the investigated classifiers, and the optimal cut-point values were extracted from the ROC curves using the Youden index, the closest to (0, 1) criteria and the Index of Union methods. Results In 191 patients with MAFLD (mean age: 58, SD: 12 years; 46% female), there were 47 (25%) patients who had the history of CVD. The most important clinical variables included hypercholesterolemia, the plaque scores, and duration of diabetes. The five, ten and fifteen most discriminative parameters extracted using univariate feature ranking and utilized to fit the ML models resulted in AUC of 0.84 (95% confidence interval [CI]: 0.77–0.90, p < 0.0001), 0.86 (95% CI 0.80–0.91, p < 0.0001) and 0.87 (95% CI 0.82–0.92, p < 0.0001), whereas the classifier fitted over 10 principal components extracted using PCA followed by the parallel analysis obtained AUC of 0.86 (95% CI 0.81–0.91, p < 0.0001). The best model operating on 5 most discriminative features correctly identified 114/144 (79.17%) low-risk and 40/47 (85.11%) high-risk patients. Conclusion A ML approach demonstrated high performance in identifying MAFLD patients with prevalent CVD based on the easy-to-obtain patient parameters.

Splenosis is a heterotopic autotransplantation of splenic tissue after rupture of the splenic capsule caused by injury or during elective splenectomy in the management of haematological diseases. Splenosis might cause nonspecific abdominal symptoms but is usually asymptomatic, and it is diagnosed accidentally as an abdominal tumour. It is rarely diagnosed in the paediatric population. We describe a 12-year-old female patient with hereditary spherocytosis admitted to the hospital 3 years after elective splenectomy and cholecystectomy. In medical imaging abdominal and pelvic tumours together with enlarged lymph nodes were found. During laparoscopic surgery many nodules were found with morphology similar to splenic tissue in the omentum, on the surface of small intestine, colon and peritoneum. Results of histopathological examination confirmed splenosis. Splenosis should be considered in differential diagnosis of abdominal tumours in paediatric patients with a history of elective splenectomy or spleen injury.

There is no consistency in the literature between observational and interventional studies regarding vitamin D deficiency (VDD) and both cardiovascular disease (CVD) and glycemic control of diabetes. The outcomes of this observational study among Polish patients with type 2 diabetes revealed that 36% of participants have VDD. We did not find an association between VDD and CVD and the difference between groups in glycemic control of diabetes was insignificant.However we found that patients with VDD had higher prevalence of treatment with sodiumglucose co-transporter-2 inhibitors (SGLT-2i) compared to patients with vitamin D (VD) concentrations within the reference range, which is, to the best of our knowledge, a novel finding. This is of high importance given the concerns that SGLT-2i may possibly affect the bone turnover through a negative influence on VD concentration.