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Resumo Fundamento Embora muitos modelos de risco tenham sido desenvolvidos para prever o prognóstico na insuficiência cardíaca (IC), esses modelos raramente são úteis para o clínico, pois incluem múltiplas variáveis que podem ser demoradas para serem obtidas, são geralmente difíceis de calcular e podem sofrer de overfitting estatístico. Objetivos Investigar se um modelo mais simples, nomeadamente o escore ACEF-MDRD, poderia ser usado para prever a mortalidade em um ano em pacientes com IC. Métodos 748 casos do registro SELFIE-HF tinham dados completos para calcular o escore ACEF-MDRD. Os pacientes foram agrupados em tercis para análise. Para todos os testes, um valor de p <0,05 foi aceito como significativo. Resultados Significativamente mais pacientes dentro do tercil ACEF-MDRD alto (30,0%) morreram dentro de um ano, em comparação com outros tercis (10,8% e 16,1%, respectivamente, para ACEF-MDRD baixo e ACEF-MDRD med , p<0,001 para ambas as comparações). Houve uma diminuição gradual na sobrevida em um ano à medida que o escore ACEF-MDRD aumentou (log-rank p<0,001). ACEF-MDRD foi preditor independente de sobrevida após ajuste para outras variáveis (OR: 1,14, IC95%:1,04 - 1,24, p=0,006). O escore ACEF-MDRD ofereceu precisão semelhante ao escore GWTG-HF para prever a mortalidade em um ano (p=0,14). Conclusões ACEF-MDRD é um preditor de mortalidade em pacientes com IC e sua utilidade é comparável a modelos semelhantes, porém mais complicados.
Abstract Background While many risk models have been developed to predict prognosis in heart failure (HF), these models are rarely useful for the clinical practitioner as they include multiple variables that might be time-consuming to obtain, they are usually difficult to calculate, and they may suffer from statistical overfitting. Objectives To investigate whether a simpler model, namely the ACEF-MDRD score, could be used for predicting one-year mortality in HF patients. Methods 748 cases within the SELFIE-HF registry had complete data to calculate the ACEF-MDRD score. Patients were grouped into tertiles for analyses. For all tests, a p-value <0.05 was accepted as significant. Results Significantly more patients within the ACEF-MDRD high tertile (30.0%) died within one year, as compared to other tertiles (10.8% and 16.1%, respectively, for ACEF-MDRD low and ACEF-MDRD med , p<0.001 for both comparisons). There was a stepwise decrease in one-year survival as the ACEF-MDRD score increased (log-rank p<0.001). ACEF-MDRD was an independent predictor of survival after adjusting for other variables (OR: 1.14, 95%CI:1.04 - 1.24, p=0.006). ACEF-MDRD score offered similar accuracy to the GWTG-HF score for predicting one-year mortality (p=0.14). Conclusions ACEF-MDRD is a predictor of mortality in patients with HF, and its usefulness is comparable to similar yet more complicated models.
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Background WHO has targeted that medicines to prevent recurrent cardiovascular disease be available in 80% of communities and used by 50% of eligible individuals by 2025. We have previously reported that use of these medicines is very low, but now aim to assess how such low use relates to their lack of availability or poor affordability. Methods We analysed information about availability and costs of cardiovascular disease medicines (aspirin, β blockers, angiotensin-converting enzyme inhibitors, and statins) in pharmacies gathered from 596 communities in 18 countries participating in the Prospective Urban Rural Epidemiology (PURE) study. Medicines were considered available if present at the pharmacy when surveyed, and affordable if their combined cost was less than 20% of household capacity-to-pay. We compared results from high-income, upper middle-income, lower middle-income, and low-income countries. Data from India were presented separately given its large, generic pharmaceutical industry. Findings Communities were recruited between Jan 1, 2003, and Dec 31, 2013. All four cardiovascular disease medicines were available in 61 (95%) of 64 urban and 27 (90%) of 30 rural communities in high-income countries, 53 (80%) of 66 urban and 43 (73%) of 59 rural communities in upper middle-income countries, 69 (62%) of 111 urban and 42 (37%) of 114 rural communities in lower middle-income countries, eight (25%) of 32 urban and one (3%) of 30 rural communities in low-income countries (excluding India), and 34 (89%) of 38 urban and 42 (81%) of 52 rural communities in India...