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BACKGROUND: Instant messaging services (IMS) are widely used in medical practice. OBJECTIVE: To evaluate perceptions regarding use and usability of IMS within clinical practice and assess users’ knowledge of the ethical and legal context involved in using IMS within medical practice. DESIGN AND SETTING: Cross-sectional study conducted in different hospitals and medical institutions in Minas Gerais, Brazil. METHODS: Medical students, medical residents, primary care physicians and specialist doctors answered an online questionnaire regarding epidemiological data, graduation level and use of IMS for medical communication. Responses were collected over a five-month period and data were assessed using the IBM-SPSS software. RESULTS: 484 people answered the questionnaire: 97.0% declared that they were using IMS for medical-related purposes; 42.0%, to elucidate medical concerns every week; 75.0%, to share imaging or laboratory tests and patients’ medical records; and 90.5%, to participate in clinical case-study private groups. Moreover, only 37.0% declared that they had knowledge of the legislative aspects of use of smartphones within clinical practice. Differences in the frequency of discussion of medical concerns within the daily routine between student/residents and general practitioners/specialists, and in the frequency of image-sharing and patient-guiding/assistance between students and medical doctors, were observed. CONCLUSIONS: Our results provide reliable proof that medical doctors and students use IMS, as a tool for clinical case discussions, interactions between healthcare providers and patients, or dissemination of knowledge and information. Nonetheless, because of limitations to the ethical and legal regulations, evidence-based discussions between authorities, academics and medical institutions are needed in order to fully achieve positive outcomes from such platforms.

ObjectiveTo provide a thorough comparative study among state-of-the-art machine learning methods and statistical methods for determining in-hospital mortality in COVID-19 patients using data upon hospital admission; to study the reliability of the predictions of the most effective methods by correlating the probability of the outcome and the accuracy of the methods; to investigate how explainable are the predictions produced by the most effective methods.Materials and MethodsDe-identified data were obtained from COVID-19 positive patients in 36 participating hospitals, from March 1 to September 30, 2020. Demographic, comorbidity, clinical presentation and laboratory data were used as training data to develop COVID-19 mortality prediction models. Multiple machine learning and traditional statistics models were trained on this prediction task using a folded cross-validation procedure, from which we assessed performance and interpretability metrics.ResultsThe Stacking of machine learning models improved over the previous state-of-the-art results by more than 26% in predicting the class of interest (death), achieving 87.1% of AUROC and macro F1 of 73.9%. We also show that some machine learning models can be very interpretable and reliable, yielding more accurate predictions while providing a good explanation for the ‘why’.ConclusionThe best results were obtained using the meta-learning ensemble model – Stacking. State-of the art explainability techniques such as SHAP-values can be used to draw useful insights into the patterns learned by machine-learning algorithms. Machine-learning models can be more explainable than traditional statistics models while also yielding highly reliable predictions.

ObjectiveTo develop and validate a rapid scoring system at hospital admission for predicting in-hospital mortality in patients hospitalized with coronavirus disease 19 (COVID-19), and to compare this score with other existing ones.DesignCohort studySettingThe Brazilian COVID-19 Registry has been conducted in 36 Brazilian hospitals in 17 cities. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients that were admitted between March-July, 2020. The model was then validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients.ParticipantsConsecutive symptomatic patients (≥18 years old) with laboratory confirmed COVID-19 admitted to participating hospitals. Patients who were transferred between hospitals and in whom admission data from the first hospital or the last hospital were not available were excluded, as well those who were admitted for other reasons and developed COVID-19 symptoms during their stay.Main outcome measuresIn-hospital mortalityResultsMedian (25th-75th percentile) age of the model-derivation cohort was 60 (48-72) years, 53.8% were men, in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. From 20 potential predictors, seven significant variables were included in the in-hospital mortality risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2ratio, platelet count and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829 to 0.859), which was confirmed in the Brazilian (0.859) and Spanish (0.899) validation cohorts. Our ABC2-SPH score showed good calibration in both Brazilian cohorts, but, in the Spanish cohort, mortality was somewhat underestimated in patients with very high (>25%) risk. The ABC2-SPH score is implemented in a freely available online risk calculator (https://abc2sph.com/).ConclusionsWe designed and validated an easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation, for early stratification for in-hospital mortality risk of patients with COVID-19.Summary boxesWhat is already known on this topic?Rapid scoring systems may be very useful for fast and effective assessment of COVID-19 patients in the emergency department.The majority of available scores have high risk of bias and lack benefit to clinical decision making.Derivation and validation studies in low- and middle-income countries, including Latin America, are scarce.What this study addsABC2-SPH employs seven well defined variables, routinely assessed upon hospital presentation: age, number of comorbidities, blood urea nitrogen, C reactive protein, Spo2/FiO2 ratio, platelets and heart rate.This easy-to-use risk score identified four categories at increasing risk of death with a high level of accuracy, and displayed better discrimination ability than other existing scores.A free web-based calculator is available and may help healthcare practitioners to estimate the expected risk of mortality for patients at hospital presentation.

This study aims to assess the prevalence of electrocardiographic abnormalities based on the Minnesota code from a large sample of 1.01 m adult Brazilian primary care patients and to evaluate its association with age, sex, and cardiovascular risk factors. A large electrocardiogram (ECG) database of such patients aged ≥18 years (mean age 52 ± 17 years-old with 60.2% female). was analyzed and the results were stratified by sex and age. The prevalence of abnormalities was more common in men and the elderly population. Major electrocardiographic abnormalities were observed in 20.2% of adults and 43.1% of elderly patients, and minor abnormalities were found in 46.0% 39.4%, respectively. For adult patients, the number and percentage of major abnormalities rise depending on the number of cardiovascular risk factors. For elderly patients, the correlation between these variables is not clear, and the percentage of major abnormalities remains stable independently of the number of risk factors.

BACKGROUND: Electrocardiograms (ECGs) are an essential examination for identification and management of cardiovascular emergencies.The aim of this study was to report on the frequency and recognition of cardiovascular emergencies in primary care units. DESIGN AND SETTING: Observational retrospective study assessing consecutive patients whose digital ECGs were sent for analysis to the team of the Telehealth Network of Minas Gerais. METHODS: Data from patients diagnosed with cardiological emergencies in the primary care setting of 750 municipalities in Minas Gerais, Brazil, between March and September 2015, were collected via telephone contact with the healthcare practitioner who performed the ECG. After collection, the data were subjected to statistical analysis. RESULTS: Over the study period, 304 patients with cardiovascular emergencies were diagnosed within primary care. Only 73.4% of these were recognized by the local physicians. Overall, the most frequent ECG abnormalities were acute ischemic patterns (44.7%) and the frequency of such patterns was higher among the ECGs assigned as emergency priority (P = 0.03). It was possible to obtain complete information on 231 patients (75.9%). Among these, the mean age was 65 ± 14.4 years, 57.1% were men and the most prevalent comorbidity was hypertension (68.4%). In total, 77.9% were referred to a unit caring for cases of higher complexity and 11.7% of the patients died. CONCLUSION: In this study, cardiovascular emergencies were misdiagnosed in primary care settings, acute myocardial ischemia was the most frequent emergency and the mortality rate was high.