Severe hypoglycemia (SH) can be a significant problem for patients around the world with Type 1 Diabetes Mellitus (T1DM). To avoid SH, patients need to better manage, and reduce the occurrence of, preceding mild hypoglycemia. Hypoglycemia Anticipation, Awareness and Treatment Training (HAATT), developed in the United States specifically to address such issues, was evaluated at short- and long-term follow-up in a medically, economically and culturally different setting; Bulgaria. Sixty adults with T1DM and a history of recurrent SH (20 each from Sofia, Russe, and Varna, Bulgaria) were randomized to Self-Monitoring of Blood Glucose (SMBG) or SMBG+ HAATT. For 6 months before and 1 to 6 and 13 to 18 months after intervention, participants recorded occurrence of moderate, severe, and nocturnal hypoglycemia. For 1-month pre- and post-intervention, participants completed daily diaries concerning their diabetes management. Relative to SMBG, HAATT produced significant improvement in occurrence of low BG, moderate, severe, and nocturnal hypoglycemia, and detection and treatment of low BG (p values < .05 to < .001), with no compromise in metabolic control. At long-term follow-up, HAATT participants continued to have significantly fewer episodes of moderate and severe hypoglycemia. These findings suggest that a structured, specialized psycho-educational treatment program (HAATT) can be highly effective in managing hypoglycemia.

Background: Acromegaly and its comorbidities affect the patients' quality of life, each healthcare system and the society. This study aimed to evaluate clinical characteristics and treatment patterns and the economic burden of acromegaly.Materials and methods: All patients with acromegaly treated with expensive medicines and regularly followed up at the main expert clinical center for acromegaly in the country were included in this nationwide, retrospective, observational, population-based study. Patient characteristics, treatment patterns, healthcare resource use, and costs were assessed for 1-year period (01. 01.2018-31.12.2018). Results were processed through statistical analysis using MedCalc software version 16.4.1.Results: A total of 191 acromegaly patients were observed. Approximately 67% were female, 45.5% were between 41 and 60 years and the mean age at diagnosis was 40.73 years. Surgical treatment was preferred as a first-line therapy among almost 89% of all diagnosed patients. The level of comorbidities was very high as more than 95% suffered from at least one concomitant disease. The most frequent comorbidities were other endocrine and metabolic diseases (96.7%), followed by cardiovascular diseases (70.7%). The most common first-line pharmacotherapy was long-acting somatostatin analogs (SSA) (38%) followed by dual combination SSA + pegvisomant (21%). The total economic burden of acromegaly was estimated to be 2,674,499.90 e in 2018 as the direct costs (medication costs, hospitalization costs covered by the patients and the National Health Insurance Fund) outnumbered indirect costs (loss of productivity due to hospitalization): 2,630,568.58 e vs. 43,931.32 e. The average annual per-patient direct and indirect costs were 14,002.62 e. Conclusions:The current study demonstrates a significant clinical and socio-economic burden of acromegaly in the country. Proper diagnosing and regular follow up of acromegaly patients in a specialized pituitary center ensure appropriate innovative pharmacotherapy with achievement of disease control.

DAs are efficient in less than 20% of non-irradiated patients. They are a good cost-effective alternative for carefully selected patients.

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Background: Studying comorbidities of disorders is important for detection and prevention. For discovering frequent patterns of diseases we can use retrospective analysis of population data, by filtering events with common properties and similar significance. Most frequent pattern mining methods do not consider contextual information about extracted patterns. Further data mining developments might enable more efficient applications in specific tasks like comorbidities identification. Methods:We propose a cascade data mining approach for frequent pattern mining enriched with context information, including a new algorithm MIxCO for maximal frequent patterns mining. Text mining tools extract entities from free text and deliver additional context attributes beyond the structured information about the patients. Results:The proposed approach was tested using pseudonymised reimbursement requests (outpatient records) submitted to the Bulgarian National Health Insurance Fund in 2010-2016 for more than 5 million citizens yearly. Experiments were run on 3 data collections. Some known comorbidities of Schizophrenia, Hyperprolactinemia and Diabetes Mellitus Type 2 are confirmed; novel hypotheses about stable comorbidities are generated. The evaluation shows that MIxCO is efficient for big dense datasets. Conclusion:Explicating maximal frequent itemsets enables to build hypotheses concerning the relationships between the exogeneous and endogeneous factors triggering the formation of these sets. MixCO will help to identify risk groups of patients with a predisposition to develop socially-significant disorders like diabetes. This will turn static archives like the Diabetes Register in Bulgaria to a powerful alerting and predictive framework.