Mechanocardiography in the Detection of Acute ST Elevation Myocardial Infarction: The MECHANO-STEMI Study

Novel means to minimize treatment delays in patients with ST elevation myocardial infarction (STEMI) are needed. Using an accelerometer and gyroscope on the chest yield mechanocardiographic (MCG) data. We investigated whether STEMI causes changes in MCG signals which could help to detect STEMI. The study group consisted of 41 STEMI patients and 49 control patients referred for elective coronary angiography and having normal left ventricular function and no valvular heart disease or arrhythmia. MCG signals were recorded on the upper sternum in supine position upon arrival to the catheterization laboratory. In this study, we used a dedicated wearable sensor equipped with 3-axis accelerometer, 3-axis gyroscope and 1-lead ECG in order to facilitate the detection of STEMI in a clinically meaningful way. A supervised machine learning approach was used. Stability of beat morphology, signal strength, maximum amplitude and its timing were calculated in six axes from each window with varying band-pass filters in 2–90 Hz range. In total, 613 features were investigated. Using logistic regression classifier and leave-one-person-out cross validation we obtained a sensitivity of 73.9%, specificity of 85.7% and AUC of 0.857 (SD = 0.005) using 150 best features. As a result, mechanical signals recorded on the upper chest wall with the accelerometers and gyroscopes differ significantly between STEMI patients and stable patients with normal left ventricular function. Future research will show whether MCG can be used for the early screening of STEMI.</p

Staging evaluation of posttraumatic stress disorder : a machine learning study

Os transtornos de estresse relacionados a um evento traumático, como o transtorno de estresse agudo (TEA) e o transtorno de estresse pós-traumático (TEPT), são caracterizados por alta morbidade e prejuízo social significativo. No Brasil, estima-se que 80% da população já foi exposta a pelo menos um evento traumático ao longo da vida em grandes centros urbanos, como São Paulo e Rio de Janeiro; o crescente problema da violência urbana mostra-se fator importante para a gênese dos transtornos relacionados ao trauma. Devido à etiologia do TEPT ser multicausal e complexa, técnicas de Machine Learning (Aprendizado de Máquina – ML) tem sido usadas para desenvolver escores de risco, para predição diagnóstica e para definição de tratamento. Contudo, considerando sua heterogeneidade clínica e etiológica, realizar o diagnóstico e definir um tratamento adequado pode ser muitas vezes desafiador. O uso do estadiamento clínico surge como um método mais refinado de diagnóstico, procurando definir a progressão do transtorno em momentos específicos durante o continuum da enfermidade. Esta abordagem pode auxiliar em um diagnóstico mais aprimorado, conhecer melhor o prognóstico e escolher o melhor tratamento de acordo com o estágio do transtorno. Assim, o TEPT aparece como um exemplo importante de como um método de estadiamento pode trazer benefícios. O objetivo desta tese é avaliar como os aspectos pessoais, clínicos e relacionados ao trauma dos pacientes atendidos em ambulatórios especializados em trauma psíquico podem estar relacionados à predição do estadiamento clínico de TEPT usando técnicas de ML.Stress disorders related to a traumatic event, such as acute stress disorder (ASD) and posttraumatic stress disorder (PTSD), are characterized by high morbidity and significant social impairment. In Brazil, it is estimated that 80% of the population has already been exposed to at least one traumatic event throughout life in large urban centers, such as São Paulo and Rio de Janeiro; the growing problem of urban violence proves to be an important factor in the genesis of trauma-related disorders. The etiology of PTSD is multicausal and complex; techniques of Machine Learning (ML) have been used to develop PTSD risk scores, to predict its diagnosis and to choose better treatments. However, considering its clinical and etiological heterogeneity, making the diagnosis and defining an appropriate treatment can often be challenging. The use of clinical staging appears as a refined method of diagnosis, aiming to define the progression of the disorder at specific times during the continuum of the illness. This approach may provide improved diagnosis, better understand the prognosis and choose the best treatment according to the stage of the disorder. Thus, PTSD appears as an important example of how a staging method can bring benefits. The objective of this thesis is to evaluate how the personal, clinical and trauma-related aspects of patients who sought care at outpatient clinics specialized in emotional trauma can be related to the prediction of the PTSD staging using ML techniques

The Health consequences of smoking; cardiovascular disease : a report of the Surgeon General

Prepared by the Dept. of Health and Human Services under the general editorship of the Office on Smoking and Health; managing editor: Donald R. Shopland.Includes bibliographies and index.1983704