Introduction Mobile-technology-based interventions are promising strategies for promoting behavioural change in obese patients. The aims of this study were to evaluate the feasibility of implementing a text message intervention, and to assess the effects of the intervention on body mass index (BMI) and self-reported behavioural change. Methods TELEFIT was a three-phase feasibility study comprising the following stages: (a) the development of text messages; (b) testing; and (c) a quasi-experimental pilot study in which patients who were engaged in obesity/overweight educational groups in public primary care centres in Belo Horizonte, Brazil, were recruited. A bank of text messages was drafted and reviewed by an expert panel, text message delivery software was developed and tested, and a pilot study assessed patients before and after receiving the intervention using validated questionnaires and body measures. The data were analysed using the Wilcoxon test. Results A total of 46 patients completed the follow-up; 93.5% were women and the median age was 42 years (interquartile range (IQR) 34-52 years). At four months, participants had a significant reduction in BMI (median 31.3 (IQR 28.2-34.6) vs. 29.9 (IQR 27.2-34.6) kg/m, p < 0.001), systolic (median 125 (IQR 120-132) vs. 120 (IQR 110-130) mmHg, p = 0.013) and diastolic blood pressure (median 80 (IQR 70-100) vs. 80 (IQR 70-80) mmHg, p = 0.006), when compared to baseline. All patients reported to be satisfied and willing to continue receiving the intervention, and 93.3% felt that the intervention helped them change their behaviours. Discussion This study has shown that a text message intervention to promote behavioural change and weight loss was feasible and effective in a short-term period. Participants were satisfied and willing to continue receiving the SMS messages.

Objective: To 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. Design: Cohort study Setting: The 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. Participants: Consecutive 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 measures: In-hospital mortality Results: Median (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/FiO2 ratio, 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/). Conclusions: We 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.

Use of telemedicine has become more frequent and more convenient for addressing medical issues. 1 It is perfectly suited for use in the modern world because of its cost-efficiency, its availability in remote and rural areas, the improved access to care that it provides and its shorter response time. It can also improve the consistency and quality of healthcare. 2 In 2018, data from the International Telecommunications Union showed that there were more than 3.9 billion active mobile phone (cellphone) subscriptions worldwide. 3 More than 165,000 health-related applications (apps) have been designed and 62% of smartphone holders use their phone to obtain health advice. 4,5 Social media and smartphone-based instant messaging services (IMS) have exploded in popularity over recent years. Instant messaging services, such as WhatsApp and iMessage, have become a very common way to communicate, for personal and professional purposes. 6 The use of these services has become progressively more popular within the field of medicine, and they serve to connect doctors to patients, to other doctors and to other healthcare professionals. 7 This paradigm shift in medicine, created through popular communication applications, is of relevance both to developed and developing countries because of the economic, political and social issues that arise through use of these means of communication. Within the context of telemedicine programs, it is important to consider the legal perspective relating to contact between healthcare professionals and their patients or between these professionals and their colleagues. There is a need to avoid problems relating to privacy issues and medical malpractice, as well as to avoid fraud and abuse.

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.

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The COVID-19 pandemic caused unprecedented pressure over health care systems worldwide. Hospital-level data that may influence the prognosis in COVID-19 patients still needs to be better investigated. Therefore, this study analyzed regional socioeconomic, hospital, and intensive care units (ICU) characteristics associated with in-hospital mortality in COVID-19 patients admitted to Brazilian institutions. This multicenter retrospective cohort study is part of the Brazilian COVID-19 Registry. We enrolled patients ≥ 18 years old with laboratory-confirmed COVID-19 admitted to the participating hospitals from March to September 2020. Patients’ data were obtained through hospital records. Hospitals’ data were collected through forms filled in loco and through open national databases. Generalized linear mixed models with logit link function were used for pooling mortality and to assess the association between hospital characteristics and mortality estimates. We built two models, one tested general hospital characteristics while the other tested ICU characteristics. All analyses were adjusted for the proportion of high-risk patients at admission. Thirty-one hospitals were included. The mean number of beds was 320.4 ± 186.6. These hospitals had eligible 6556 COVID-19 admissions during the study period. Estimated in-hospital mortality ranged from 9.0 to 48.0%. The first model included all 31 hospitals and showed that a private source of funding ( β = − 0.37; 95% CI − 0.71 to − 0.04; p = 0.029) and location in areas with a high gross domestic product (GDP) per capita ( β = − 0.40; 95% CI − 0.72 to − 0.08; p = 0.014) were independently associated with a lower mortality. The second model included 23 hospitals and showed that hospitals with an ICU work shift composed of more than 50% of intensivists ( β = − 0.59; 95% CI − 0.98 to − 0.20; p = 0.003) had lower mortality while hospitals with a higher proportion of less experienced medical professionals had higher mortality ( β = 0.40; 95% CI 0.11–0.68; p = 0.006). The impact of those association increased according to the proportion of high-risk patients at admission. In-hospital mortality varied significantly among Brazilian hospitals. Private-funded hospitals and those located in municipalities with a high GDP had a lower mortality. When analyzing ICU-specific characteristics, hospitals with more experienced ICU teams had a reduced mortality. Supplementary Information The online version contains supplementary material available at 10.1007/s11739-022-03092-9.

The majority of early prediction scores and methods to predict COVID-19 mortality are bound by methodological flaws and technological limitations (e.g., the use of a single prediction model). Our aim is to provide a thorough comparative study that tackles those methodological issues, considering multiple techniques to build mortality prediction models, including modern machine learning (neural) algorithms and traditional statistical techniques, as well as meta-learning (ensemble) approaches. This study used a dataset from a multicenter cohort of 10,897 adult Brazilian COVID-19 patients, admitted from March/2020 to November/2021, including patients [median age 60 (interquartile range 48–71), 46% women]. We also proposed new original population-based meta-features that have not been devised in the literature. Stacking has shown to achieve the best results reported in the literature for the death prediction task, improving over previous state-of-the-art by more than 46% in Recall for predicting death, with AUROC 0.826 and MacroF1 of 65.4%. The newly proposed meta-features were highly discriminative of death, but fell short in producing large improvements in final prediction performance, demonstrating that we are possibly on the limits of the prediction capabilities that can be achieved with the current set of ML techniques and (meta-)features. Finally, we investigated how the trained models perform on different hospitals, showing that there are indeed large differences in classifier performance between different hospitals, further making the case that errors are produced by factors that cannot be modeled with the current predictors.

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