Telemonitoring and home hospitalization in patients with chronic obstructive pulmonary disease: study TELEPOC.

Background: Chronic obstructive pulmonary disease (COPD) is a major consumer of healthcare resources, with most costs related to disease exacerbations. Telemonitoring of patients with COPD may help to reduce the number of exacerbations and/or the related costs. On the other hand, home hospitalization is a cost-saving alternative to inpatient hospitalization associated with increased comfort 15 for patients. The results are reported regarding using telemonitoring and home hospitalization for the management of patients with COPD. Methods: Twenty-eight patients monitored their health parameters at home for six months. A nurse remotely revised the collected parameters and followed the patients as programmed. A home care unit was dispatched to the patients’ home if an alarm signal was detected. The outcomes were compared to 20 historical data from the same patients. Results: The number of COPD exacerbations during the study period did not reduce but the number of hospital admissions decreased by 60% and the number of emergency room visits by 38%. On average, costs related to utilization of healthcare resources were reduced by €1,860.80 per patient per year. Conclusions: Telemonitoring of patients with COPD combined with home hospitalization may allow for AQ425 a reduction in healthcare costs, although its usefulness in preventing exacerbations is still unclearpre-print664 K

COVID-19 in hospitalized HIV-positive and HIV-negative patients : A matched study

CatedresObjectives: We compared the characteristics and clinical outcomes of hospitalized individuals with COVID-19 with [people with HIV (PWH)] and without (non-PWH) HIV co-infection in Spain during the first wave of the pandemic. Methods: This was a retrospective matched cohort study. People with HIV were identified by reviewing clinical records and laboratory registries of 10 922 patients in active-follow-up within the Spanish HIV Research Network (CoRIS) up to 30 June 2020. Each hospitalized PWH was matched with five non-PWH of the same age and sex randomly selected from COVID-19@Spain, a multicentre cohort of 4035 patients hospitalized with confirmed COVID-19. The main outcome was all-cause in-hospital mortality. Results: Forty-five PWH with PCR-confirmed COVID-19 were identified in CoRIS, 21 of whom were hospitalized. A total of 105 age/sex-matched controls were selected from the COVID-19@Spain cohort. The median age in both groups was 53 (Q1-Q3, 46-56) years, and 90.5% were men. In PWH, 19.1% were injecting drug users, 95.2% were on antiretroviral therapy, 94.4% had HIV-RNA < 50 copies/mL, and the median (Q1-Q3) CD4 count was 595 (349-798) cells/μL. No statistically significant differences were found between PWH and non-PWH in number of comorbidities, presenting signs and symptoms, laboratory parameters, radiology findings and severity scores on admission. Corticosteroids were administered to 33.3% and 27.4% of PWH and non-PWH, respectively (P = 0.580). Deaths during admission were documented in two (9.5%) PWH and 12 (11.4%) non-PWH (P = 0.800). Conclusions: Our findings suggest that well-controlled HIV infection does not modify the clinical presentation or worsen clinical outcomes of COVID-19 hospitalization

Discovering HIV related information by means of association rules and machine learning

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts