Clusters of inflammation in COVID-19: descriptive analysis and prognosis on more than 15,000 patients from the Spanish SEMI-COVID-19 Registry.

Uncontrolled inflammation following COVID-19 infection is an important characteristic of the most seriously ill patients. The present study aims to describe the clusters of inflammation in COVID-19 and to analyze their prognostic role. This is a retrospective observational study including 15,691 patients with a high degree of inflammation. They were included in the Spanish SEMI-COVID-19 registry from March 1, 2020 to May 1, 2021. The primary outcome was in-hospital mortality. Hierarchical cluster analysis identified 7 clusters. C1 is characterized by lymphopenia, C2 by elevated ferritin, and C3 by elevated LDH. C4 is characterized by lymphopenia plus elevated CRP and LDH and frequently also ferritin. C5 is defined by elevated CRP, and C6 by elevated ferritin and D-dimer, and frequently also elevated CRP and LDH. Finally, C7 is characterized by an elevated D-dimer. The clusters with the highest in-hospital mortality were C4, C6, and C7 (17.4% vs. 18% vs. 15.6% vs. 36.8% vs. 17.5% vs. 39.3% vs. 26.4%). Inflammation clusters were found as independent factors for in-hospital mortality. In detail and, having cluster C1 as reference, the model revealed a worse prognosis for all other clusters: C2 (OR = 1.30, p = 0.001), C3 (OR = 1.14, p = 0.178), C4 (OR = 2.28, p < 0.001), C5 (OR = 1.07, p = 0.479), C6 (OR = 2.29, p < 0.001), and C7 (OR = 1.28, p = 0.001). We identified 7 groups based on the presence of lymphopenia, elevated CRP, LDH, ferritin, and D-dimer at the time of hospital admission for COVID-19. Clusters C4 (lymphopenia + LDH + CRP), C6 (ferritin + D-dimer), and C7 (D-dimer) had the worst prognosis in terms of in-hospital mortality.

Date of Admission during COVID-19 Pandemic Impacted Patient Outcomes in Addition to the Higher Efficacy of Tocilizumab Plus High-Dose Corticosteroid Therapy Compared to Tocilizumab Alone.

BACKGROUND: The evidence for the efficacy of glucocorticoids combined with tocilizumab (TCZ) in COVID-19 comes from observational studies or subgroup analysis. Our aim was to compare outcomes between hospitalized COVID-19 patients who received high-dose corticosteroid pulse therapy and TCZ and those who received TCZ. METHODS: A retrospective single-center study was performed on consecutive hospitalized patients with severe COVID-19 between 1 March and 23 April 2020. Patients treated with either TCZ (400-600 mg, one to two doses) and methylprednisolone pulses (MPD-TCZ group) or TCZ alone were analyzed for the occurrence of a combined endpoint of death and need for invasive mechanical ventilation during admission. The independence of both treatment groups was tested using machine learning classifiers, and relevant variables that were potentially different between the groups were measured through a mean decrease accuracy algorithm. RESULTS: An earlier date of admission was significantly associated with worse outcomes regardless of treatment type. Twenty patients died (27.0%) in the TCZ group, and 33 (44.6%) died or required intubation (n = 74), whereas in the MPD-TCZ group, 15 (11.0%) patients died and 29 (21.3%) patients reached the combined endpoint (n = 136; p = 0.006 and p < 0.001, respectively). Machine learning methodology using a random forest classifier confirmed significant differences between the treatment groups. CONCLUSIONS: MPD and TCZ improved outcomes (death and invasive mechanical ventilation) among hospitalized COVID-19 patients, but confounding variables such as the date of admission during the COVID-19 pandemic should be considered in observational studies.

Low molecular weight heparin is useful in adult COVID-19 inpatients. Experience during the first Spanish wave: observational study.

BACKGROUND: The intensity of the thromboprophylaxis needed as a potential factor for preventing inpatient mortality due to coronavirus disease-19 (COVID-19) remains unclear. OBJECTIVE: To explore the association between anticoagulation intensity and COVID-19 survival. DESIGN AND SETTING: Retrospective observational study in a tertiary-level hospital in Spain. METHODS: Low-molecular-weight heparin (LMWH) status was ascertained based on prescription at admission. To control for immortal time bias, anticoagulant use was analyzed as a time-dependent variable. RESULTS: 690 patients were included (median age, 72 years). LMWH was administered to 615 patients, starting from hospital admission (89.1%). 410 (66.7%) received prophylactic-dose LMWH; 120 (19.5%), therapeutic-dose LMWH; and another 85 (13.8%) who presented respiratory failure, high D-dimer levels (> 3 mg/l) and non-worsening of inflammation markers received prophylaxis of intermediate-dose LMWH. The overall inpatient-mortality rate was 38.5%. The anticoagulant nonuser group presented higher mortality risk than each of the following groups: any LMWH users (HR 2.1; 95% CI: 1.40-3.15); the prophylactic-dose heparin group (HR 2.39; 95% CI, 1.57-3.64); and the users of heparin dose according to biomarkers (HR 6.52; 95% CI, 2.95-14.41). 3.4% of the patients experienced major hemorrhage. 2.8% of the patients developed an episode of thromboembolism. CONCLUSIONS: This observational study showed that LMWH administered at the time of admission was associated with lower mortality among unselected adult COVID-19 inpatients. The magnitude of the benefit may have been greatest for the intermediate-dose subgroup. Randomized controlled trials to assess the benefit of heparin within different therapeutic regimes for COVID-19 patients are required.

Seroprevalence of SARS-CoV-2 Antibodies and Factors Associated with Seropositivity at the University of Salamanca: The DIANCUSAL Study.

BACKGROUND: Systematic screening for antibodies against SARS-CoV-2 is a crucial tool for surveillance of the COVID-19 pandemic. The University of Salamanca (USAL) in Spain designed a project called "DIANCUSAL" (Diagnosis of New Coronavirus, COVID-19, in University of Salamanca) to measure antibodies against SARS-CoV-2 among its ~34,000 students and academic staff, as the influence of the university community in the spread of the SARS-CoV-2 pandemic in the city of Salamanca and neighboring towns hosting USAL campuses could be substantial. OBJECTIVE: The aim of this study was to estimate the prevalence of SARS-CoV-2 antibodies among USAL students, professors and staff and to evaluate the demographic, academic, clinical and lifestyle and behavioral factors related to seropositivity. METHODOLOGY: The DIANCUSAL study is an ongoing university population-based cross-sectional study, with the work described herein conducted from July-October 2020. All USAL students, professors and staff were invited to complete an anonymized questionnaire. Seroprevalence of anti-SARS-CoV-2 antibodies was detected and quantified by using chemiluminescent assays for IgG and IgM. PRINCIPAL FINDINGS: A total of 8197 (24.71%) participants were included. The mean age was 31.4 (14.5 SD) years, and 66.0% of the participants were female. The seroprevalence was 8.25% overall and was highest for students from the education campus (12.5%) and professors from the biomedical campus (12.6%), with significant differences among faculties (p = 0.006). Based on the questionnaire, loss of smell and fever were the symptoms most strongly associated with seropositivity, and 22.6% of seropositive participants were asymptomatic. Social distancing was the most effective hygiene measure (p = 0.0007). There were significant differences in seroprevalence between participants with and without household exposure to SARS-CoV-2 (p = 0.0000), but not between students who lived in private homes and those who lived in dormitories. IgG antibodies decreased over time in the participants with confirmed self-reported COVID-19 diagnoses. CONCLUSIONS: The analysis revealed an overall 8.25% seroprevalence at the end of October 2020, with a higher seroprevalence in students than in staff. Thus, there is no need for tailored measures for the USAL community as the official average seroprevalence in the area was similar (7.8% at 22 June and 12.4 at 15 November of 2020). Instead, USAL members should comply with public health measures.

Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients.

BACKGROUND: Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management. METHODS: We trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity. RESULTS: A total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression. CONCLUSIONS: This machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.

Corticosteroid Pulses for Hospitalized Patients with COVID-19: Effects on Mortality.

OBJECTIVES: To assess the influence of corticosteroid pulses on 60-day mortality in hospitalized patients with severe COVID-19. METHODS: We designed a multicenter retrospective cohort study in three teaching hospitals of Castilla y León, Spain (865,096 people). We selected patients with confirmed COVID-19 and lung involvement with a pO2/FiO2<300, excluding those exposed to immunosuppressors before or during hospitalization, patients terminally ill at admission, or those who died in the first 24 hours. We performed a propensity score matching (PSM) adjusting covariates that modify the probability of being treated. Then, we used a Cox regression model in the PSM group to consider factors affecting mortality. RESULTS: From 2933 patients, 257 fulfilled the inclusion and exclusion criteria. 124 patients were on corticosteroid pulses (250 mg of methylprednisolone for three days), and 133 were not. 30.3% (37/122) of patients died in the corticosteroid pulse group and 42.9% (57/133) in the nonexposed cohort. These differences (12.6%, 95% CI [8·54-16.65]) were statically significant (log-rank 4.72, p = 0, 03). We performed PSM using the exact method. Mortality differences remained in the PSM group (log-rank 5.31, p = 0.021) and were still significant after a Cox regression model (HR for corticosteroid pulses 0.561; p = 0.039). CONCLUSIONS: This study provides evidence about treatment with corticosteroid pulses in severe COVID-19 that might significantly reduce mortality. Strict inclusion and exclusion criteria with that selection process set a reliable frame to compare mortality in both the exposed and nonexposed groups.

Predicting Clinical Outcome with Phenotypic Clusters in COVID-19 Pneumonia: An Analysis of 12,066 Hospitalized Patients from the Spanish Registry SEMI-COVID-19.

(1) Background: Different clinical presentations in COVID-19 are described to date, from mild to severe cases. This study aims to identify different clinical phenotypes in COVID-19 pneumonia using cluster analysis and to assess the prognostic impact among identified clusters in such patients. (2) Methods: Cluster analysis including 11 phenotypic variables was performed in a large cohort of 12,066 COVID-19 patients, collected and followed-up from 1 March to 31 July 2020, from the nationwide Spanish Society of Internal Medicine (SEMI)-COVID-19 Registry. (3) Results: Of the total of 12,066 patients included in the study, most were males (7052, 58.5%) and Caucasian (10,635, 89.5%), with a mean age at diagnosis of 67 years (standard deviation (SD) 16). The main pre-admission comorbidities were arterial hypertension (6030, 50%), hyperlipidemia (4741, 39.4%) and diabetes mellitus (2309, 19.2%). The average number of days from COVID-19 symptom onset to hospital admission was 6.7 (SD 7). The triad of fever, cough, and dyspnea was present almost uniformly in all 4 clinical phenotypes identified by clustering. Cluster C1 (8737 patients, 72.4%) was the largest, and comprised patients with the triad alone. Cluster C2 (1196 patients, 9.9%) also presented with ageusia and anosmia; cluster C3 (880 patients, 7.3%) also had arthromyalgia, headache, and sore throat; and cluster C4 (1253 patients, 10.4%) also manifested with diarrhea, vomiting, and abdominal pain. Compared to each other, cluster C1 presented the highest in-hospital mortality (24.1% vs. 4.3% vs. 14.7% vs. 18.6%; p < 0.001). The multivariate study identified age, gender (male), body mass index (BMI), arterial hypertension, chronic obstructive pulmonary disease (COPD), ischemic cardiopathy, chronic heart failure, chronic hepatopathy, Charlson's index, heart rate and respiratory rate upon admission >20 bpm, lower PaO2/FiO2 at admission, higher levels of C-reactive protein (CRP) and lactate dehydrogenase (LDH), and the phenotypic cluster as independent factors for in-hospital death. (4) Conclusions: The present study identified 4 phenotypic clusters in patients with COVID-19 pneumonia, which predicted the in-hospital prognosis of clinical outcomes.