Baricitinib reduces 30-day mortality in older adults with moderate-to-severe COVID-19 pneumonia.

BACKGROUND: Older adults are at the highest risk of severe disease and death due to COVID-19. Randomized data have shown that baricitinib improves outcomes in these patients, but focused stratified analyses of geriatric cohorts are lacking. Our objective was to analyze the efficacy of baricitinib in older adults with COVID-19 moderate-to-severe pneumonia. METHODS: This is a propensity score [PS]-matched retrospective cohort study. Patients from the COVID-AGE and Alba-Score cohorts, hospitalized for moderate-to-severe COVID-19 pneumonia, were categorized in two age brackets of age <70 years old (86 with baricitinib and 86 PS-matched controls) or ≥70 years old (78 on baricitinib and 78 PS-matched controls). Thirty-day mortality rates were analyzed with Kaplan-Meier and Cox proportional hazard models. RESULTS: Mean age was 79.1 for those ≥70 years and 58.9 for those <70. Exactly 29.6% were female. Treatment with baricitinib resulted in a significant reduction in death from any cause by 48% in patients aged 70 or older, an 18.5% reduction in 30-day absolute mortality risk (n/N: 16/78 [20.5%] baricitinib, 30/78 [38.5%] in PS-matched controls, p < 0.001) and a lower 30-day adjusted fatality rate (HR 0.21; 95% CI 0.09-0.47; p < 0.001). Beneficial effects on mortality were also observed in the age group <70 (8.1% reduction in 30-day absolute mortality risk; HR 0.14; 95% CI 0.03-0.64; p = 0.011). CONCLUSIONS: Baricitinib is associated with an absolute mortality risk reduction of 18.5% in adults older than 70 years hospitalized with COVID-19 pneumonia.

Generation and validation of in-hospital mortality prediction score in COVID-19 patients: Alba-score.

BACKGROUND: COVID-19 has a wide range of symptoms reported, which may vary from very mild cases (even asymptomatic) to deadly infections. Identifying high mortality risk individuals infected with the SARS-CoV-2 virus through a prediction instrument that uses simple clinical and analytical parameters at admission can help clinicians to focus on treatment efforts in this group of patients. METHODS: Data was obtained retrospectively from the electronic medical record of all COVID-19 patients hospitalized in the Albacete University Hospital Complex until July 2020. Patients were split into two: a generating and a validating cohort. Clinical, demographical and laboratory variables were included. A multivariate logistic regression model was used to select variables associated with in-hospital mortality in the generating cohort. A numerical and subsequently a categorical score according to mortality were constructed (A: mortality from 0% to 5%; B: from 5% to 15%; C: from 15% to 30%; D: from 30% to 50%; E: greater than 50%). These scores were validated with the validation cohort. RESULTS: Variables independently related to mortality during hospitalization were age, diabetes mellitus, confusion, SaFiO2, heart rate and lactate dehydrogenase (LDH) at admission. The numerical score defined ranges from 0 to 13 points. Scores included are: age ≥71 years (3 points), diabetes mellitus (1 point), confusion (2 points), onco-hematologic disease (1 point), SaFiO2 ≤ 419 (3 points), heart rate ≥ 100 bpm (1 point) and LDH ≥ 390 IU/L (2 points). The area under the curve (AUC) for the numerical and categorical scores from the generating cohort were 0.8625 and 0.848, respectively. In the validating cohort, AUCs were 0.8505 for the numerical score and 0.8313 for the categorical score. CONCLUSIONS: Data analysis found a correlation between clinical admission parameters and in-hospital mortality for COVID-19 patients. This correlation is used to develop a model to assist physicians in the emergency department in the COVID-19 treatment decision-making process.