Osteoporotic hip fractures: Bisphosphonates sales and observed turning point in trend. A population-based retrospective study

The aim is to examine the temporal trends of hip fracture incidence in Portugal by sex and age groups, and explore the relation with anti-osteoporotic medication. From the National Hospital Discharge Database, we selected from 1st January 2000 to 31st December 2008, 77,083 hospital admissions (77.4% women) caused by osteoporotic hip fractures (low energy, patients over 49 years-age), with diagnosis codes 820.x of ICD 9-CM. The 2001 Portuguese population was used as standard to calculate direct age-standardized incidence rates (ASIR) (100,000 inhabitants). Generalized additive and linear models were used to evaluate and quantify temporal trends of age specific rates (AR), by sex. We identified 2003 as a turning point in the trend of ASIR of hip fractures in women. After 2003, the ASIR in women decreased on average by 10.3 cases/100,000 inhabitants, 95% CI (− 15.7 to − 4.8), per 100,000 anti-osteoporotic medication packages sold. For women aged 65–69 and 75–79 we identified the same turning point. However, for women aged over 80, the year 2004 marked a change in the trend, from an increase to a decrease. Among the population aged 70–74 a linear decrease of incidence rate (95% CI) was observed in both sexes, higher for women: − 28.0% (− 36.2 to − 19.5) change vs − 18.8%, (− 32.6 to − 2.3). The abrupt turning point in the trend of ASIR of hip fractures in women is compatible with an intervention, such as a medication. The trends were different according to gender and age group, but compatible with the pattern of bisphosphonates sales.

Machine Learning Algorithms at Myocardial Perfusion Imaging - a Preliminary Study

Introduction: A major focus of data mining process - especially machine learning researches - is to automatically learn to recognize complex patterns and help to take the adequate decisions strictly based on the acquired data. Since imaging techniques like MPI – Myocardial Perfusion Imaging on Nuclear Cardiology, can implicate a huge part of the daily workflow and generate gigabytes of data, there could be advantages on Computerized Analysis of data over Human Analysis: shorter time, homogeneity and consistency, automatic recording of analysis results, relatively inexpensive, etc.Objectives: The aim of this study relates with the evaluation of the efficacy of this methodology on the evaluation of MPI Stress studies and the process of decision taking concerning the continuation – or not – of the evaluation of each patient. It has been pursued has an objective to automatically classify a patient test in one of three groups: “Positive”, “Negative” and “Indeterminate”. “Positive” would directly follow to the Rest test part of the exam, the “Negative” would be directly exempted from continuation and only the “Indeterminate” group would deserve the clinician analysis, so allowing economy of clinician’s effort, increasing workflow fluidity at the technologist’s level and probably sparing time to patients. Methods: WEKA v3.6.2 open source software was used to make a comparative analysis of three WEKA algorithms (“OneR”, “J48” and “Naïve Bayes”) - on a retrospective study using the comparison with correspondent clinical results as reference, signed by nuclear cardiologist experts - on “SPECT Heart Dataset”, available on University of California – Irvine, at the Machine Learning Repository. For evaluation purposes, criteria as “Precision”, “Incorrectly Classified Instances” and “Receiver Operating Characteristics (ROC) Areas” were considered. Results: The interpretation of the data suggests that the Naïve Bayes algorithm has the best performance among the three previously selected algorithms. Conclusions: It is believed - and apparently supported by the findings - that machine learning algorithms could significantly assist, at an intermediary level, on the analysis of scintigraphic data obtained on MPI, namely after Stress acquisition, so eventually increasing efficiency of the entire system and potentially easing both roles of Technologists and Nuclear Cardiologists. In the actual continuation of this study, it is planned to use more patient information and significantly increase the population under study, in order to allow improving system accuracy.

Pilot monitoring study of ibuprofen in surface waters of north of Portugal

Ibuprofen is amongst the most worldwide consumed pharmaceuticals. The present work presents the first data in the occurrence of ibuprofen in Portuguese surface waters, focusing in the north area of the country, which is one of the most densely populated areas of Portugal. Analysis of ibuprofen is based on pre-concentration of the analyte with solid phase extraction and subsequent determination with liquid chromatography coupled to fluorescence detection. A total of 42 water samples, including surface waters, landfill leachates,Wastewater Treatment Plant (WWTP), and hospital effluents, were analyzed in order to evaluate the occurrence of ibuprofen in the north of Portugal. In general, the highest concentrations were found in the river mouths and in the estuarine zone. The maximum concentrations found were 48,720 ngL−1 in the landfill leachate, 3,868 ngL−1 in hospital effluent, 616 ngL−1 in WWTP effluent, and 723 ngL−1 in surface waters (Lima river). Environmental risk assessment was evaluated and at the measured concentrations only landfill leachates reveal potential ecotoxicological risk for aquatic organisms. Owing to a high consumption rate of ibuprofen among Portuguese population, as prescribed and nonprescribed medicine, the importance of hospitals, WWTPs, and landfills as sources of entrance of pharmaceuticals in the environment was pointed out. Landfill leachates showed the highest contribution for ibuprofen mass loading into surface waters. On the basis of our findings, more studies are needed as an attempt to assess more vulnerable areas.