Incretins and SGLT-2i Therapy of Type 2 Diabetes - Real Life Study of Their Therapeutic and Economic Effects.

AIM: Incretins [dipeptidyl peptidase-4 inhibitors (DPP-4i) and glucagon-like peptide 1 RA (GLP-1 RA)] and sodium-glucose cotransporter-2 inhibitors (SGLT-2i) groups are now routinely used for type 2 diabetes therapy and comprise a large number of medicinal products. The long term therapeutic and economic effect of the incretins' and SGLT-2i in real life setting is not well documented. The goal of the current study is to analyze the cost and results of incretins and SGLT-2i based therapy for type 2 diabetes in Bulgaria. METHODS: The study uses information about the changes in glycated hemoglobin (HbA1c) level from the National diabetes register for 6122 patients and cost paid by the National Health Insurance Fund (NHIF) for diabetes complications, and medicine prices. RESULTS: The results show that after the therapy patients achieved excellent diabetes control. There were no HbA1c values less than 6% before treatment. After the therapy, 3356 people showed values less than 7% HbA1c. It is considered very good diabetic control. The number of people with HbA1c above 8% is decreasing significantly. The number of people with values above 9% is decreasing by almost four times. HbA1c level decreases with the highest percentage for the patients treated with GLP-1 RA, followed by those treated with DPP-4i and SGLT-2i. For a year NHIF reimbursed 5.25 million BGN for incretins and SGLT-2i therapy. NHIF can save between 306 and 510 thousand BGN from incidents that have not occurred as a result of 5 years of therapy. CONCLUSION: Incretins [dipeptidyl peptidase-4 inhibitors (DPP-4i) and glucagon-like peptide 1 receptor agonists (GLP-1 RA)] and sodium-glucose linked transporter-2 inhibitors (SGLT-2i) therapy steadily decreases the HbA1c level, and risk of developing diabetic incidents is reduced to between 333 and 465 cases among 6122 treated patients. Avoided cost for therapy of diabetes incidents account for between 305 and 510 thousand BGN.

Mining comorbidity patterns using retrospective analysis of big collection of outpatient records.

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.

Completion of structured patient descriptions by semantic mining.

This paper presents experiments in automatic Information Extraction of medication events, diagnoses, and laboratory tests form hospital patient records, in order to increase the completeness of the description of the episode of care. Each patient record in our hospital information system contains structured data and text descriptions, including full discharge letters. From these letters, we extract automatically information about the medication just before and in the time of hospitalization, especially for the drugs prescribed to the patient, but not delivered by the hospital pharmacy; we also extract values of lab tests not performed and not registered in our laboratory as well as all non-encoded diagnoses described only in the free text of discharge letters. Thus we increase the availability of suitable and accurate information about the hospital stay and the outpatient segment of care before the hospitalization. Information Extraction also helps to understand the clinical and organizational decisions concerning the patient without increasing the complexity of the structured health record.