A machine learning COVID-19 mass screening based on symptoms and a simple olfactory test.

The early detection of symptoms and rapid testing are the basis of an efficient screening strategy to control COVID-19 transmission. The olfactory dysfunction is one of the most prevalent symptom and in many cases is the first symptom. This study aims to develop a machine learning COVID-19 predictive tool based on symptoms and a simple olfactory test, which consists of identifying the smell of an aromatized hydroalcoholic gel. A multi-centre population-based prospective study was carried out in the city of Reus (Catalonia, Spain). The study included consecutive patients undergoing a reverse transcriptase polymerase chain reaction test for presenting symptoms suggestive of COVID-19 or for being close contacts of a confirmed COVID-19 case. A total of 519 patients were included, 386 (74.4%) had at least one symptom and 133 (25.6%) were asymptomatic. A classification tree model including sex, age, relevant symptoms and the olfactory test results obtained a sensitivity of 0.97 (95% CI 0.91-0.99), a specificity of 0.39 (95% CI 0.34-0.44) and an AUC of 0.87 (95% CI 0.83-0.92). This shows that this machine learning predictive model is a promising mass screening for COVID-19.

Type 2 Diabetes Mellitus and COVID-19: A Narrative Review.

Type-2 diabetes mellitus (T2DM) is a chronic metabolic disorder. The incidence and prevalence of patients with T2DM are increasing worldwide, even reaching epidemic values in most high- and middle-income countries. T2DM could be a risk factor of developing complications in other diseases. Indeed, some studies suggest a bidirectional interaction between T2DM and COVID-19. A growing body of evidence shows that COVID-19 prognosis in individuals with T2DM is worse compared with those without. Moreover, various studies have reported the emergence of newly diagnosed patients with T2DM after SARS-CoV-2 infection. The most common treatments for T2DM may influence SARS-CoV-2 and their implication in infection is briefly discussed in this review. A better understanding of the link between TD2M and COVID-19 could proactively identify risk factors and, as a result, develop strategies to improve the prognosis for these patients.

[Patient safety incidents reported before and after the start of the COVID-19 pandemic in Primary Care in Tarragona]. / Incidentes de seguridad del paciente notificados antes y después del inicio de la pandemia de COVID-19 en atención primaria en Tarragona.

OBJECTIVE: To analyse and compare the epidemiology of patient safety incidents reported in Primary Health Care, before and after the start of the COVID-19 pandemic. DESIGN AND SETTING: Analytical descriptive study comparing reported incidents from March 1st 2019 to February 28th 2020, and from March 1st 2020 to February 28th 2021, notified through the TPSC Cloud™ platform accessible from the Intranet corporative in 25 Primary Health Care centres from Tarragona district, in Catalonia (Spain). MEASUREMENTS: Data obtained from voluntary notifications, through electronic, standardized and anonymized forms. VARIABLES: Centre, professional, incident type, risk matrix, causal factors and contributing factors, and avoidability. STATISTICAL ANALYSIS: Every notification was included in descriptive analysis, and another one specifically for adverse events, comparing both periods. RESULTS: 2231 incidents were reported. Comparing both periods, during the pandemic a reduction in the number of reported incidents was observed (only represented 20% of the total). However, the percentage of reported notifications from health care professionals and adverse events that required observation were increased. Causal factors related to attendance and diagnosis were incremented whereas the causal factors related to medication were decreased. In addition, an increase in contributing factors related to the professional was observed. Avoidability was high (>95%) in both periods. CONCLUSIONS: During the pandemic, fewer patient safety incidents have been reported, but proportionally more adverse events, most of which are preventable. The professional himself becomes the main contributing factor.

Effectiveness of Telephone Monitoring in Primary Care to Detect Pneumonia and Associated Risk Factors in Patients with SARS-CoV-2.

Improved technology facilitates the acceptance of telemedicine. The aim was to analyze the effectiveness of telephone follow-up to detect severe SARS-CoV-2 cases that progressed to pneumonia. A prospective cohort study with 2-week telephone follow-up was carried out March 1 to May 4, 2020, in a primary healthcare center in Barcelona. Individuals aged ≥15 years with symptoms of SARS-CoV-2 were included. Outpatients with non-severe disease were called on days 2, 4, 7, 10 and 14 after diagnosis; patients with risk factors for pneumonia received daily calls through day 5 and then the regularly scheduled calls. Patients hospitalized due to pneumonia received calls on days 1, 3, 7 and 14 post-discharge. Of the 453 included patients, 435 (96%) were first attended to at a primary healthcare center. The 14-day follow-up was completed in 430 patients (99%), with 1798 calls performed. Of the 99 cases of pneumonia detected (incidence rate 20.8%), one-third appeared 7 to 10 days after onset of SARS-CoV-2 symptoms. Ten deaths due to pneumonia were recorded. Telephone follow-up by a primary healthcare center was effective to detect SARS-CoV-2 pneumonias and to monitor related complications. Thus, telephone appointments between a patient and their health care practitioner benefit both health outcomes and convenience.