Risk of hospitalization for heart failure in patients with type 2 diabetes newly treated with DPP-4 inhibitors or other oral glucose-lowering medications: A retrospective registry study on 127,555 patients from the Nationwide OsMed Health-DB Database

Aims Oral glucose-lowering medications are associated with excess risk of heart failure (HF). Given the absence of comparative data among drug classes, we performed a retrospective study in 32 Health Services of 16 Italian regions accounting for a population of 18 million individuals, to assess the association between HF risk and use of sulphonylureas, DPP-4i, and glitazones. Methods and results We extracted data on patients with type 2 diabetes who initiated treatment with DPP-4i, thiazolidinediones, or sulphonylureas alone or in combination with metformin during an accrual time of 2 years. The endpoint was hospitalization for HF (HHF) occurring after the first 6 months of therapy, and the observation was extended for up to 4 years. A total of 127 555 patients were included, of whom 14.3% were on DPP-4i, 72.5% on sulphonylurea, 13.2% on thiazolidinediones, with average 70.7% being on metformin as combination therapy. Patients in the three groups differed significantly for baseline characteristics: age, sex, Charlson index, concurrent medications, and previous cardiovascular events. During an average 2.6-year follow-up, after adjusting for measured confounders, use of DPP-4i was associated with a reduced risk of HHF compared with sulphonylureas [hazard ratio (HR) 0.78; 95% confidence interval (CI) 0.62-0.97; P = 0.026]. After propensity matching, the analysis was restricted to 39 465 patients, and the use of DPP-4i was still associated with a lower risk of HHF (HR 0.70; 95% CI 0.52-0.94; P = 0.018). Conclusion In a very large observational study, the use of DPP-4i was associated with a reduced risk of HHF when compared with sulphonylureas

First drug utilization data related to an anticholinergic agent recently marketed in Italy

A retrospective drug utilisation study was conducted, concerning a new bronchodilator (tiotropium, ATC code: R03BB04) in the first six months after its launch in Italy. The source of data was the administrative database run by a Local Health Unit located in Northern Italy. All patients (920) were selected with at least one prescription for tiotropium between September and December 2004, and data about their use of health resources (hospitalisations, class ATC R03 drugs, lab tests – only drugs and test prescribed in outpatient setting) was collected. Starting from such initial sample, further sub-samples were created (in particular to focus on patients affected by COPD – Chronic Obstructive Pulmonary Disease, for which tiotropium has got the therapeutic indication by the Italian Health Service), for the purpose of different levels of analysis. Results reported in this abstract are referred to the first level (702 patients, aged 40 years or more, still living at the end of the observation period, already with COPD in the previous year); they are expressed as average data per patient on a six-month period. Prescriptions of R03 drugs were 7.8, including 2.4 specifically for tiotropium. To such prescriptions, 348 and 103 DDDs (Defined Daily Doses) respectively corresponded; and, analogously, a cost of 487 and 205 euros. The cost for hospitalisations was 525 euros and the cost for lab tests was 28.5 euros. The other analysis levels (sub-samples with fewer patients) produced not very different outcomes. Evidence given here should prove the potential interest of such kind of studies

Development and representation of health indicators with thematic maps

Italian Local Health Care Agencies (ASLs) have the role of managing the public healthcare resources in their area of competence. To this end, the ASL of Pavia has implemented a data warehouse, which collects and integrates health data of more than 500,000 people since 2004. We have exploited such data repository to compute a variety of yearly health indicators, which have been represented on thematic maps of the area. Thanks to a Web-based application, the ASL decision-makers can monitor the area with a fine-grained spatial detail, dissecting the epidemiological, economical and pharmaceutical factors underlying citizens' health and patients' care. The implemented tool is currently up-and-running and has been evaluated with a usability questionnaire on a small number of users

On The Correlation Between Geo-Referenced Clinical Data And Remotely Sensed Air Pollution Maps

This work presents an analysis framework enabling the integration of a clinical-administrative dataset of Type 2 Diabetes (T2D) patients with environmental information derived from air quality maps acquired from remote sensing data. The research has been performed within the EU project MOSAIC, which gathers T2D patients' data coming from Fondazione S. Maugeri (FSM) hospital and the Pavia local health care agency (ASL). The proposed analysis is aimed to highlight the complexity of the domain, showing the different perspectives that can be adopted when applying a data-driven approach to large variety of temporal, geo-localized data. We investigated a set of 899 patients, located in the Pavia area, and detected several patterns depicting how clinical facts and air pollution variations may be related

Temporal Data Mining for the Assessment of the Costs Related to Diabetes Mellitus Pharmacological Treatment

Diabetes care and chronic disease management represent data-intensive contexts which allow Local Healthcare Agencies (ASL) to collect a huge amount of information. Time is often an essential component of such information, given the strong importance of the temporal evolution of the considered disease and of its treatment. In this paper we show the application of a temporal data mining technique to extract temporal association rules over an integrated repository including both administrative and clinical data related to a sample of diabetic patients. We will show how the method can be used to highlight cases and conditions which lead to the highest pharmaceutical costs. Considering the perspective of a Regional Healthcare Agency, this method could be properly exploited to assess the overall standards and quality of care, while lowering costs

Influence of initial glucocorticoid co-medication on mortality and hospitalization in early inflammatory arthritis: an investigation by record linkage of clinical and administrative databases

Background While low-dose oral glucocorticoids (GCs) are recommended in the management of early arthritis, their impact on mortality is unclear. The aim of this study is to evaluate the effect of GCs on mortality in patients with early arthritis, by linking clinical and administrative databases. Methods The study included patients with new-onset rheumatoid arthritis (RA) or undifferentiated arthritis (2005-2010), who received DMARDs (MTX in RA or UA with poor prognosis, hydroxychloroquine in UA) and were alive at the second year of follow-up. Low-dose GCs could be prescribed. Clinical and administrative data were linked from Administrative Health Databases (AHD) of the corresponding province, which provided us with information on drug delivery, comorbidities, hospitalization, and mortality. The effect of GCs in the first year was defined using a dichotomous variable or a 3-level categorization (not delivered, 7.5 mg/day of prednisone) on all-cause mortality, assessed with Cox regression, either crude or adjusted for age, gender, Charlson Comorbidity Index (CCI) or single comorbidities, ACPA, HAQ, and MTX in the first year. A secondary analysis of the effect of GCs on related hospitalizations (for cardiovascular events, diabetes, serious infections, osteoporotic fractures) was also carried. Results Four hundred forty-nine patients were enrolled (mean age 58.59, RA 65.03%) of which 51 (11.36%) died during the study. The median (IQR) follow-up was equal to 103.91 (88.03-126.71) months. Treatments with GCs were formally prescribed to 198 patients (44.10%) at 7.5 mg/day. In adjusted analyses, the GC delivery (HR, 95% CI 1.35 (0.74, 2.47)) did not significantly predict mortality - both at a low (HR, 95% CI 1.41 (0.73, 2.71)) and at a high (HR, 95% CI 1.23 (0.52, 2.92)) dosage. When "all-cause hospitalization" was used as an outcome, the analysis did not show a difference between patients receiving GC and patients not receiving GC. Conclusion In patients with early inflammatory arthritis, the initial GC dose was higher than that prescribed by rheumatologists; however, on background treatment with DMARDs, GC treatments did not seem to increase mortality and hospitalizations

Integration of Administrative, Clinical, and Environmental Data to Support the Management of Type 2 Diabetes Mellitus:From Satellites to Clinical Care

A very interesting perspective of "big data" in diabetes management stands in the integration of environmental information with data gathered for clinical and administrative purposes, to increase the capability of understanding spatial and temporal patterns of diseases. Within the MOSAIC project, funded by the European Union with the goal to design new diabetes analytics, we have jointly analyzed a clinical-administrative dataset of nearly 1.000 type 2 diabetes patients with environmental information derived from air quality maps acquired from remote sensing (satellite) data. Within this context we have adopted a general analysis framework able to deal with a large variety of temporal, geo-localized data. Thanks to the exploitation of time series analysis and satellite images processing, we studied whether glycemic control showed seasonal variations and if they have a spatiotemporal correlation with air pollution maps. We observed a link between the seasonal trends of glycated hemoglobin and air pollution in some of the considered geographic areas. Such findings will need future investigations for further confirmation. This work shows that it is possible to successfully deal with big data by implementing new analytics and how their exploration may provide new scenarios to better understand clinical phenomena