Temporal trends of lipid control in very high cardiovascular risk patients

Introduction: Since 2011, the European guidelines have included a specific low-density lipoprotein cholesterol (LDL-C) target, <70 mg/dl, for very high cardiovascular risk (CVR) patients. However, registries have shown unsatisfactory results in obtaining this level of adequate lipid control. Objectives: To assess temporal trends in the use of lipid-lowering therapy (LLT) and attainment of adequate control in very high CVR patients since 2011. Methods: We performed a retrospective observational study including very high CVR patients admitted in two periods: the first two years since the 2011 guidelines (2011/2012) and five years later (2016/2017). Lipid values, LLT, clinical variables and adequate lipid control rates were analyzed. Results: A total of 1314 patients were reviewed (2011/2012: 638; 2016/2017: 676). Overall, 443 patients (33.7%) were not under LLT and only a slight improvement in drug prescription was observed from 2011/2012 to 2016/2017. In LLT users, the proportion of high-intensity LLT increased significantly in the later years (6.4% vs. 24.0%; p<0.001), but this was not associated with adequate lipid control. Overall, mean LDL-C was 95.4±37.2 mg/dl and adequate control was achieved in 320 patients (24.4%), without significant differences between 2011/2012 and 2016/2017 (p=0.282). Independent predictors of adequate control were male gender, older age, diabetes, chronic kidney disease, prior acute coronary syndrome, prior stroke and LLT, while stable coronary artery disease was associated with higher risk of failure. Conclusion: Even after the introduction of specific LDL-C targets, these are still not reached in most patients. Over a five-year period, LLT prescription only improved slightly, while adequate lipid control rates remained unchanged. Resumo: Introdução: Desde 2011, as guidelines europeias introduziram um alvo específico de colesterol de lipoproteínas de baixa densidade (C-LDL) para pacientes de risco cardiovascular muito alto (RCVMA): C-LDL <70 mg/dL. No entanto, os registos demonstraram resultados insatisfatórios na obtenção desse controlo lipídico adequado (CLAd). Objetivos: Avaliar tendências temporais no uso de terapêutica hipolipemiante (TH) e no alcance de CLAd em pacientes de RCVMA desde 2011. Métodos: Estudo observacional retrospetivo, incluindo pacientes de RCVMA admitidos em dois períodos: dois primeiros anos desde as guidelines de 2011 (2011/2012) e cinco anos depois (2016/2017). Os valores lipídicos, TH, variáveis clínicas e taxas de CLAd foram analisados. Resultados: Foram revistos 1314 pacientes (2011/2012: 638; 2016/2017: 676). No geral, 443 pacientes (33,7%) não estavam sob TH e foi observada apenas uma ligeira melhoria na prescrição destes fármacos de 2011/2012 a 2016/2017. Em pacientes sob TH, a proporção destes com terapêutica de alta intensidade aumentou significativamente nos últimos anos (6,4% versus 24,0%; p<0,001), mas não foi associada ao CLAd. No geral, o C-LDL médio foi de 95,4±37,2 mg/dL e o CLAd foi alcançado em 320 pacientes (24,4%), sem diferenças significativas entre 2011/2012 e 2016/2017 (p=0,282). Os preditores independentes de CLAd foram: género masculino, idade avançada, diabetes, doença renal crónica, síndrome coronária aguda prévia, acidente vascular cerebral prévio e TH, enquanto a doença coronária estável foi associada a maior risco de falhar esse objetivo. Conclusão: Mesmo após a introdução de metas específicas de C-LDL, estas ainda não são atingidas na maioria dos pacientes. Durante um período de cinco anos, a prescrição de TH apenas melhorou ligeiramente, enquanto as taxas de obtenção de CLAd permanecerem inalteradas

A Machine Learning Application Based in Random Forest for Integrating Mass Spectrometry-Based Metabolomic Data: A Simple Screening Method for Patients With Zika Virus

Recent Zika outbreaks in South America, accompanied by unexpectedly severe clinical complications have brought much interest in fast and reliable screening methods for ZIKV (Zika virus) identification. Reverse-transcriptase polymerase chain reaction (RT-PCR) is currently the method of choice to detect ZIKV in biological samples. This approach, nonetheless, demands a considerable amount of time and resources such as kits and reagents that, in endemic areas, may result in a substantial financial burden over affected individuals and health services veering away from RT-PCR analysis. This study presents a powerful combination of high-resolution mass spectrometry and a machine-learning prediction model for data analysis to assess the existence of ZIKV infection across a series of patients that bear similar symptomatic conditions, but not necessarily are infected with the disease. By using mass spectrometric data that are inputted with the developed decision-making algorithm, we were able to provide a set of features that work as a “fingerprint” for this specific pathophysiological condition, even after the acute phase of infection. Since both mass spectrometry and machine learning approaches are well-established and have largely utilized tools within their respective fields, this combination of methods emerges as a distinct alternative for clinical applications, providing a diagnostic screening—faster and more accurate—with improved cost-effectiveness when compared to existing technologies