Advances in the work of multidisciplinary teams for the care of patients with severe uncontrolled asthma. A post-COVID vision (TEAM 2.0 project). / Avances en el trabajo de equipos multidisciplinares para la atención al paciente con asma grave no controlada. Una visión post-COVID (Proyecto TEAM 2.0).

Asthma is a chronic respiratory disease with a high health, social and economic impact, particularly in the case of Severe Uncontrolled Asthma (SUA). For this reason, new strategies are especially necessary to improve its approach, with a personalized approach to each patient and from a multidisciplinary perspective, in addition to integrating the new telemedicine and telepharmacy practices promoted as a result of the COVID-19 pandemic. In this context, the TEAM 2.0 project ("Work in Multidisciplinary Asthma Teams") has been developed, following the TEAM project carried out in 2019, with the aim of updating and prioritizing good multidisciplinary work practices in SUA in a post pandemic context and analyze the progress made. A coordinating group, made up of eight multidisciplinary teams of hospital pharmacists, pulmonologists, and allergists, carried out an updated bibliographic review, sharing of good multidisciplinary practices, and analysis of advances. Through five regional meetings with other experts with experience in SUA, the good practices identified were shared and subjected to debate, evaluation and prioritization. In total, 23 good multidisciplinary work practices in SUA, grouped into five work areas: 1) Organization of work in multidisciplinary teams, 2) Patient education, self-management and adherence, 3) Health results, data monitoring and persistence, 4) Telepharmacy and experiences implemented during the COVID-19 pandemic and 5) Training and research, were evaluated and prioritized by 57 professionals from the field of Hospital Pharmacy, Pulmonology, Allergology and Nursing. This work has made it possible to update the roadmap of priority actions to continue advancing in optimal models of care for patients with AGNC in a post-COVID-19 context.

Machine-learning-derived predictive score for early estimation of COVID-19 mortality risk in hospitalized patients.

The clinical course of COVID-19 is highly variable. It is therefore essential to predict as early and accurately as possible the severity level of the disease in a COVID-19 patient who is admitted to the hospital. This means identifying the contributing factors of mortality and developing an easy-to-use score that could enable a fast assessment of the mortality risk using only information recorded at the hospitalization. A large database of adult patients with a confirmed diagnosis of COVID-19 (n = 15,628; with 2,846 deceased) admitted to Spanish hospitals between December 2019 and July 2020 was analyzed. By means of multiple machine learning algorithms, we developed models that could accurately predict their mortality. We used the information about classifiers' performance metrics and about importance and coherence among the predictors to define a mortality score that can be easily calculated using a minimal number of mortality predictors and yielded accurate estimates of the patient severity status. The optimal predictive model encompassed five predictors (age, oxygen saturation, platelets, lactate dehydrogenase, and creatinine) and yielded a satisfactory classification of survived and deceased patients (area under the curve: 0.8454 with validation set). These five predictors were additionally used to define a mortality score for COVID-19 patients at their hospitalization. This score is not only easy to calculate but also to interpret since it ranges from zero to eight, along with a linear increase in the mortality risk from 0% to 80%. A simple risk score based on five commonly available clinical variables of adult COVID-19 patients admitted to hospital is able to accurately discriminate their mortality probability, and its interpretation is straightforward and useful.

Impact of intermediate to high doses of methylprednisolone on mortality rate in patients with COVID-19 pneumonia-induced severe systemic inflammation.

INTRODUCTION: In addition to respiratory support needs, patients' characteristics to guide indication or timing of corticosteroid treatment in COVID-19 patients are not completely established. This study aimed to evaluate the impact of methylprednisolone on mortality rate in patients with COVID-19 pneumonia-induced severe systemic inflammation (PI-SSI). METHODS: Between 9 March and 5 May 2020 (final follow-up on 2 July 2020), a retrospective cohort study was conducted in hospitalised patients with COVID-19 PI-SSI (≥2 inflammatory biomarkers [IBs]: temperature ≥38℃, lymphocyte ≤800 cell/µL, C-reactive protein ≥100 mg/L, lactate dehydrogenase ≥300 units/L, ferritin ≥1000 mcg/L, D-dimer ≥500 ng/mL). Patients received 0.5-1.0 mg/kg of methylprednisolone for 5-10 days or standard of care. The primary outcome was 28-day all-cause mortality. Secondary outcomes included ≥2 points improvement on a 7-item WHO-scale (Day 14), transfer to intensive care unit (ICU) (Day 28) and adverse effects. Kaplan-Meier method and Cox proportional hazard regression were implemented to analyse the time to event outcomes. RESULTS: A total of 142 patients (corticosteroid group n = 72, control group n = 70) were included. A significant reduction in 28-day all-cause mortality was shown with methylprednisolone in patients with respiratory support (HR: 0.15; 95% CI 0.03-0.71), with ≥3 (HR: 0.17; 95% CI 0.05-0.61) or ≥4 altered IB (HR: 0.15; 95% CI 0.04-0.54) and in patients with both respiratory support and ≥3 (HR: 0.11; 95% CI 0.02-0.53] or ≥4 altered IB (HR: 0.14; 95% CI 0.04-0.51). No significant differences were found in secondary outcomes. CONCLUSION: Intermediate to high doses of methylprednisolone, initiated between 5 and 12 days after symptom onset, was associated with a significant reduction in 28-day all-cause mortality in patients with COVID-19 pneumonia and ≥3 o ≥ 4 altered IB, independently of the need of respiratory support.