Karin Kopitowski

Estimation of the burden of cardiovascular disease attributable to modifiable risk factors and cost-effectiveness analysis of preventative interventions to reduce this burden in Argentina

BackgroundCardiovascular disease (CVD) is the primary cause of mortality and morbidity in Argentina representing 34.2% of deaths and 12.6% of potential years of life lost (PYLL). The aim of the study was to estimate the burden of acute coronary heart disease (CHD) and stroke and the cost-effectiveness of preventative population-based and clinical interventions.MethodsAn epidemiological model was built incorporating prevalence and distribution of high blood pressure, high cholesterol, hyperglycemia, overweight and obesity, smoking, and physical inactivity, obtained from the Argentine Survey of Risk Factors dataset. Population Attributable Fraction (PAF) of each risk factor was estimated using relative risks from international sources. Total fatal and non-fatal events, PYLL and Disability Adjusted Life Years (DALY) were estimated. Costs of event were calculated from local utilization databases and expressed in international dollars (I

Estimación de la carga de las enfermedades cardiovasculares atribuible a factores de riesgo modificables en Argentina

). Incremental cost-effectiveness ratios (ICER) were estimated for six interventions: reducing salt in bread, mass media campaign to promote tobacco cessation, pharmacological therapy of high blood pressure, pharmacological therapy of high cholesterol, tobacco cessation therapy with bupropion, and a multidrug strategy for people with an estimated absolute risk > 20% in 10 years.ResultsAn estimated total of 611,635 DALY was lost due to acute CHD and stroke for 2005. Modifiable risk factors explained 71.1% of DALY and more than 80% of events. Two interventions were cost-saving: lowering salt intake in the population through reducing salt in bread and multidrug therapy targeted to persons with an absolute risk above 20% in 10 years; three interventions had very acceptable ICERs: drug therapy for high blood pressure in hypertensive patients not yet undergoing treatment (I

Development and validation of various phenotyping algorithms for Diabetes Mellitus using data from electronic health records

2,908 per DALY saved), mass media campaign to promote tobacco cessation amongst smokers (I

Deep Bidirectional Recurrent Neural Networks as End-To-End Models for Smoking Status Extraction from Clinical Notes in Spanish

3,186 per DALY saved), and lowering cholesterol with statin drug therapy (I

Translation, transcultural adaptation, and validation of two questionnaires on shared decision making

14,432 per DALY saved); and one intervention was not found to be cost-effective: tobacco cessation with bupropion (I

Excesivo rastreo de osteoporosis en mujeres menores de 65 años: estudio de corte transversal

59,433 per DALY saved)ConclusionsMost of the interventions selected were cost-saving or very cost-effective. This study aims to inform policy makers on resource-allocation decisions to reduce the burden of CVD in Argentina.

A multimodal strategy based on pay-per-performance to improve quality of care of family practitioners in Argentina.

OBJECTIVE Estimate the burden of disease, the proportion attributable to the principal modifiable cardiovascular risk factors, and the direct medical cost of hospitalization associated with coronary heart disease and stroke in Argentina. METHODOLOGY An analitical model was prepared using Argentinas 2005 mortality data and the prevalence of the principal cardiovascular risk factors (hypertension, hypercholesterolemia, overweight, obesity, hyperglycemia, current and past smoking, sedentary lifestyle, and inadequate intake of fruits and vegetables). The burden of disease-years of potential life lost (YPLL) and years of healthy life lost (YHLL)- and hospitalization costs for the cardiovascular diseases analyzed were estimated. RESULTS In 2005 over 600 000 YHL were lost in Argentina and the number of YPLL due to heart disease and stroke was calculated at 400 000; 71.1% of the YHLL, 73.9% of the YPLL, and 76.0% of the associated costs were attributable to modifiable risk factors. Hypertension was the risk factor with the greatest impact in both men and in women, responsible for 37.3% of the total cost, 37.5% of the YPLL, and 36.6% of the YHLL. CONCLUSIONS Most of the burden of disease from cardiovascular disease in Argentina is associated with modifiable, and therefore preventable, risk factors and could be reduced through population-based and clinical interventions that employ a risk approach; such interventions have already proven to be cost effective, accessible, and feasible in countries like Argentina.

Adherencia a la prevención secundaria de la enfermedad coronaria

BACKGROUND AND OBJECTIVE Recent progression towards precision medicine has encouraged the use of electronic health records (EHRs) as a source for large amounts of data, which is required for studying the effect of treatments or risk factors in more specific subpopulations. Phenotyping algorithms allow to automatically classify patients according to their particular electronic phenotype thus facilitating the setup of retrospective cohorts. Our objective is to compare the performance of different classification strategies (only using standardized problems, rule-based algorithms, statistical learning algorithms (six learners) and stacked generalization (five versions)), for the categorization of patients according to their diabetic status (diabetics, not diabetics and inconclusive; Diabetes of any type) using information extracted from EHRs. METHODS Patient information was extracted from the EHR at Hospital Italiano de Buenos Aires, Buenos Aires, Argentina. For the derivation and validation datasets, two probabilistic samples of patients from different years (2005: n = 1663; 2015: n = 800) were extracted. The only inclusion criterion was age (≥40 & <80 years). Four researchers manually reviewed all records and classified patients according to their diabetic status (diabetic: diabetes registered as a health problem or fulfilling the ADA criteria; non-diabetic: not fulfilling the ADA criteria and having at least one fasting glycemia below 126 mg/dL; inconclusive: no data regarding their diabetic status or only one abnormal value). The best performing algorithms within each strategy were tested on the validation set. RESULTS The standardized codes algorithm achieved a Kappa coefficient value of 0.59 (95% CI 0.49, 0.59) in the validation set. The Boolean logic algorithm reached 0.82 (95% CI 0.76, 0.88). A slightly higher value was achieved by the Feedforward Neural Network (0.9, 95% CI 0.85, 0.94). The best performing learner was the stacked generalization meta-learner that reached a Kappa coefficient value of 0.95 (95% CI 0.91, 0.98). CONCLUSIONS The stacked generalization strategy and the feedforward neural network showed the best classification metrics in the validation set. The implementation of these algorithms enables the exploitation of the data of thousands of patients accurately.

What makes pediatric clinical research ethical

Although natural language processing (NLP) tools have been available in English for quite some time, it is not the case for many other languages, particularly for context specific texts like clinical notes. This poses a challenge for tasks like text classification in languages other than English. In the absence of basic NLP tools, manually engineering features that capture semantic information of the documents is a potential solution. Nevertheless, it is very time consuming. Deep neural networks, particularly deep recurrent neural networks (RNN), have been proposed as End-to-End models that learn both features and parameters jointly, thus avoiding the need to manually encode the features. We compared the performance of two classifiers for labeling 14718 clinical notes in Spanish according to the patients’ smoking status: a bag-of-words model involving heavy manual feature engineering and a bidirectional long-short-term-memory (LSTM) deep recurrent neural network (RNN) with GloVe word embeddings. The RNN slightly outperforms the bag-of-words model, but with 80% less overall development time. Such algorithms can facilitate the exploitation of clinical notes in languages in which NLP tools are not as developed as in English.

Toma de decisiones compartidas: centrando los cuidados médicos realmente en nuestros pacientes

To translate, transcultural adapt, and validate the “CollaboRATE” measure and the “Ask 3 Questions” intervention in Argentina, allowing us to quantify the degree of use and implementation of shared decision making (SDM).