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BACKGROUND Research efforts to develop strategies to effectively identify patients and to reduce the burden of cardiovascular diseases is essential for the future of the health system. Most research studies have used only coded parts of electronic health records (EHRs) for case-detection, obtaining missed data cases and reducing study quality. Incorporating information from free-text into case-detection through Natural Language Processing (NLP) techniques improves research quality. SAVANA was born as an innovating data-driven system based on NLP and big data techniques designed to retrieve prominent biomedical information from narratives clinic notes and to maximize the huge amount of information contained in Spanish EHRs. OBJECTIVE The aim of this work if to assess the performance of SAVANA when identifying concepts within the cardiovascular domain in Spanish EHRs. METHODS SAVANA is a platform for acceleration of clinical research, based on real-time dynamic exploitation of all the information contained in EHRs corpora that uses its own technology (EHRead) to allow unstructured information contained in EHRs to be analysed and expressed by means of medical concepts that contain the most significant information in the text. RESULTS The evaluation corpus consisted of a stratified random sample of patients from 3 Spanish sites. For site 01, the corpus contained a total of 280 mentions of cardiovascular clinical entities, where 249 were correctly identified, obtaining a P=0.93. In site 02, SAVANA correctly detected 53 mentions of cardiovascular entities among 57 annotations, achieving a P=0.98; and in site 03, among 165 manual annotations, 75 were correctly identified, yielding a P= 0.99. CONCLUSIONS This research clearly demonstrates the ability of SAVANA at identifying mentions of atherosclerotic/cardiovascular clinical phenotype in Spanish EHRs, as well as retrieving patients and records related to this pathology.

Background Many factors involved in the onset and clinical course of the ongoing COVID-19 pandemic are still unknown. Although big data analytics and artificial intelligence are widely used in the realms of health and medicine, researchers are only beginning to use these tools to explore the clinical characteristics and predictive factors of patients with COVID-19. Objective Our primary objectives are to describe the clinical characteristics and determine the factors that predict intensive care unit (ICU) admission of patients with COVID-19. Determining these factors using a well-defined population can increase our understanding of the real-world epidemiology of the disease. Methods We used a combination of classic epidemiological methods, natural language processing (NLP), and machine learning (for predictive modeling) to analyze the electronic health records (EHRs) of patients with COVID-19. We explored the unstructured free text in the EHRs within the Servicio de Salud de Castilla-La Mancha (SESCAM) Health Care Network (Castilla-La Mancha, Spain) from the entire population with available EHRs (1,364,924 patients) from January 1 to March 29, 2020. We extracted related clinical information regarding diagnosis, progression, and outcome for all COVID-19 cases. Results A total of 10,504 patients with a clinical or polymerase chain reaction–confirmed diagnosis of COVID-19 were identified; 5519 (52.5%) were male, with a mean age of 58.2 years (SD 19.7). Upon admission, the most common symptoms were cough, fever, and dyspnea; however, all three symptoms occurred in fewer than half of the cases. Overall, 6.1% (83/1353) of hospitalized patients required ICU admission. Using a machine-learning, data-driven algorithm, we identified that a combination of age, fever, and tachypnea was the most parsimonious predictor of ICU admission; patients younger than 56 years, without tachypnea, and temperature 39 ºC without respiratory crackles) were not admitted to the ICU. In contrast, patients with COVID-19 aged 40 to 79 years were likely to be admitted to the ICU if they had tachypnea and delayed their visit to the emergency department after being seen in primary care. Conclusions Our results show that a combination of easily obtainable clinical variables (age, fever, and tachypnea with or without respiratory crackles) predicts whether patients with COVID-19 will require ICU admission.

Resumen: Una de las características singulares de la enfermedad de Parkinson (EP) es la gran variabilidad clínica en relación con el tratamiento que puede acontecer en un mismo paciente. Esto sucede tanto con el tratamiento específico para la EP como con otra serie de fármacos que pueden empeorar la función motora. Por esta razón, el manejo perioperatorio de la EP requiere experiencia y sobre todo una planificación adecuada. En este artículo se revisan las peculiaridades de la EP y su tratamiento, y se plantea una estrategia para el perioperatorio de estos pacientes. Abstract: One of the particular characteristics of Parkinson's disease (PD) is the wide clinical variation as regards the treatment that can be found in the same patient. This occurs with specific treatment for PD, as well as with other drug groups that can make motor function worse. For this reason, the perioperative management of PD requires experience and above all appropriate planning. In this article, the peculiarities of PD and its treatment are reviewed, and a strategy is set out for the perioperative management of these patients. Palabras clave: Enfermedad de Parkinson, Manejo perioperatorio, Riesgos perioperatorios, Keywords: Parkinson's disease, Perioperative management, Perioperative risks

One of the particular characteristics of Parkinson's disease (PD) is the wide clinical variation as regards the treatment that can be found in the same patient. This occurs with specific treatment for PD, as well as with other drug groups that can make motor function worse. For this reason, the perioperative management of PD requires experience and above all appropriate planning. In this article, the peculiarities of PD and its treatment are reviewed, and a strategy is set out for the perioperative management of these patients. Resumen: Una de las características singulares de la enfermedad de Parkinson (EP) es la gran variabilidad clínica en relación con el tratamiento que puede acontecer en un mismo paciente. Esto sucede tanto con el tratamiento específico para la EP como con otra serie de fármacos que pueden empeorar la función motora. Por esta razón, el manejo perioperatorio de la EP requiere experiencia y sobre todo una planificación adecuada. En este artículo se revisan las peculiaridades de la EP y su tratamiento, y se plantea una estrategia para el perioperatorio de estos pacientes. Keywords: Parkinson's disease, Perioperative management, Perioperative risks, Palabras clave: Enfermedad de Parkinson, Manejo perioperatorio, Riesgos perioperatorios

Descompressive craniectomy (DC) for treatment of malignant infarction of the middle cerebral artery (MIMCA) reduces mortality and increases the probability of favourable outcome.To present the experience in daily practice after implantation of a clinical protocol of DC.Prospective register of patients with MIMCA treated with DC. Age, stroke severity -National Institute of Health Stroke Scale (NIHSS) score-, basal characteristics, delay until DC, mortality, modified Rankin Scale (mRS) score at three and twelve months and questionnaire of satisfaction are recorded.From February 2008 to December 2010, 15 patients were treated. Mean age: 60 years (range: 35-69); basal NIHSS, median (p25/p75): 17 (15.5/21), NIHSS before craniectomy: 20 (18/23.5). DC was performed within 48 hours in 8 patients (53.3%). 5 subjects (33.3%) died during in-hospital stay. Mortality was 25% among those treated within 48 hours and 42.9% among those treated later and 37.5% among patients= 60 years vs 28.6% in older patients. All survivors scored= 4 in the mRS at three months and expressed satisfaction with surgery despite sequelae. Rate of favourable outcome (mRS= 3) was higher among patients under 60 (63%) and among those treated before 48 h (50%).DC is safe in clinical practice and reduces mortality after MIMCA, especially if it is performed within 48 hours from stroke onset. Benefit appears to be greater in younger patients, but older people may benefit also. The decision to perform DC should be made on an individual basis.