Anticoagulant treatment in COVID-19: a narrative review

Journal of Thrombosis and Thrombolysis - The actual Coronavirus Disease (COVID 19) pandemic is due to Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a member of the coronavirus...     

Factors influencing patient delay before primary percutaneous coronary intervention in ST-segment elevation myocardial infarction: The Stent for life initiative in Portugal

Introduction and Aims

Shorter patient delays are associated with a better prognosis for patients diagnosed with ST-segment elevation myocardial infarction (STEMI). This study aimed to identify predictors of patient delay in the Portuguese population.

How general practitioners perceive and grade the cardiovascular risk of their patients.

BACKGROUND: Although risk assessment charts have been proposed to identify patients at high cardiovascular risk, in everyday practice general practitioners (GPs) often use their knowledge of the patients to estimate the risk subjectively. DESIGN: A cross-sectional study aimed to describe how GPs perceive, qualify and grade cardiovascular risk in everyday practice. METHODS: General practitioners had to identify in a random sample of 10% of their contacts the first 20 consecutive patients perceived as being at cardiovascular risk. For each patient essential data were collected on clinical history, physical examination and laboratory tests, for the qualification of risk. At the end of the process GPs subjectively estimated the overall patient's level of risk. General practitioners grading was compared with the risk estimate from a reference chart. RESULTS: Over a mean time of 25 days 3120 patients perceived as being at cardiovascular risk were enrolled. According to the inclusion scheme each GP had contact with more than 200 patients at cardiovascular risk every month. Thirty percent of these patients had atherosclerotic diseases. Up to 72% of patients without any history of atherosclerotic diseases but perceived to be at risk could be classified according to a reference chart as being at moderate to very high risk. Comparing GPs' grading of risk with a chart estimate there was agreement in 42% of the cases. Major determinants of GPs' underestimation of risk were age, sex and smoking habits, while obesity and family history were independently associated with overestimation. CONCLUSIONS: On the basis of their perception GPs properly identify patients at cardiovascular risk in the majority of cases. General practitioners subjective grading of risk level only partially agreed with that given by a chart.     

Identifying Goals of Care Conversations in the Electronic Health Record Using Natural Language Processing and Machine Learning

ContextGoals-of-care discussions are an important quality metric in palliative care. However, goals-of-care discussions are often documented as free text in diverse locations. It is difficult to identify these discussions in the electronic health record (EHR) efficiently.ObjectivesTo develop, train, and test an automated approach to identifying goals-of-care discussions in the EHR, using natural language processing (NLP) and machine learning (ML).MethodsFrom the electronic health records of an academic health system, we collected a purposive sample of 3183 EHR notes (1435 inpatient notes and 1748 outpatient notes) from 1426 patients with serious illness over 2008–2016, and manually reviewed each note for documentation of goals-of-care discussions. Separately, we developed a program to identify notes containing documentation of goals-of-care discussions using NLP and supervised ML. We estimated the performance characteristics of the NLP/ML program across 100 pairs of randomly partitioned training and test sets. We repeated these methods for inpatient-only and outpatient-only subsets.ResultsOf 3183 notes, 689 contained documentation of goals-of-care discussions. The mean sensitivity of the NLP/ML program was 82.3% (SD 3.2%), and the mean specificity was 97.4% (SD 0.7%). NLP/ML results had a median positive likelihood ratio of 32.2 (IQR 27.5–39.2) and a median negative likelihood ratio of 0.18 (IQR 0.16–0.20). Performance was better in inpatient-only samples than outpatient-only samples.ConclusionUsing NLP and ML techniques, we developed a novel approach to identifying goals-of-care discussions in the EHR. NLP and ML represent a potential approach toward measuring goals-of-care discussions as a research outcome and quality metric.     

Human Cytomegalovirus and Epstein-Barr virus specific immunity in patients with ulcerative colitis

Clinical and Experimental Medicine - Human Cytomegalovirus (HCMV) and Epstein-Barr virus (EBV) are endowed with the ability of establishing lifelong latency in human hosts and reactivating in...