Severe COVID-19 is associated with a systemic hyperinflammatory response leading to acute respiratory distress syndrome (ARDS), multi-organ failure, and death. Galectin-3 is a ß-galactoside binding lectin known to drive neutrophil infiltration and the release of pro-inflammatory cytokines contributing to airway inflammation. Thus, we aimed to investigate the potential of galectin-3 as a biomarker of severe COVID-19 outcomes. We prospectively included 156 patients with RT-PCR confirmed COVID-19. A severe outcome was defined as the requirement of invasive mechanical ventilation (IMV) and/or in-hospital death. A non-severe outcome was defined as discharge without IMV requirement. We used receiver operating characteristic (ROC) and multivariable logistic regression analysis to determine the prognostic ability of serum galectin-3 for a severe outcome. Galectin-3 levels discriminated well between severe and non-severe outcomes and correlated with markers of COVID-19 severity, (CRP, NLR, D-dimer, and neutrophil count). Using a forward-stepwise logistic regression analysis we identified galectin-3 [odds ratio (OR) 3.68 (95% CI 1.47–9.20), p < 0.01] to be an independent predictor of severe outcome. Furthermore, galectin-3 in combination with CRP, albumin and CT pulmonary affection > 50%, had significantly improved ability to predict severe outcomes [AUC 0.85 (95% CI 0.79–0.91, p < 0.0001)]. Based on the evidence presented here, we recommend clinicians measure galectin-3 levels upon admission to facilitate allocation of appropriate resources in a timely manner to COVID-19 patients at highest risk of severe outcome.

Most COVID-19 mortality scores were developed at the beginning of the pandemic and clinicians now have more experience and evidence-based interventions. Therefore, we hypothesized that the predictive performance of COVID-19 mortality scores is now lower than originally reported. We aimed to prospectively evaluate the current predictive accuracy of six COVID-19 scores and compared it with the accuracy of clinical gestalt predictions. 200 patients with COVID-19 were enrolled in a tertiary hospital in Mexico City between September and December 2020. The area under the curve (AUC) of the LOW-HARM, qSOFA, MSL-COVID-19, NUTRI-CoV, and NEWS2 scores and the AUC of clinical gestalt predictions of death (as a percentage) were determined. In total, 166 patients (106 men and 60 women aged 56±9 years) with confirmed COVID-19 were included in the analysis. The AUC of all scores was significantly lower than originally reported: LOW-HARM 0.76 (95% CI 0.69 to 0.84) vs 0.96 (95% CI 0.94 to 0.98), qSOFA 0.61 (95% CI 0.53 to 0.69) vs 0.74 (95% CI 0.65 to 0.81), MSL-COVID-19 0.64 (95% CI 0.55 to 0.73) vs 0.72 (95% CI 0.69 to 0.75), NUTRI-CoV 0.60 (95% CI 0.51 to 0.69) vs 0.79 (95% CI 0.76 to 0.82), NEWS2 0.65 (95% CI 0.56 to 0.75) vs 0.84 (95% CI 0.79 to 0.90), and neutrophil to lymphocyte ratio 0.65 (95% CI 0.57 to 0.73) vs 0.74 (95% CI 0.62 to 0.85). Clinical gestalt predictions were non-inferior to mortality scores, with an AUC of 0.68 (95% CI 0.59 to 0.77). Adjusting scores with locally derived likelihood ratios did not improve their performance; however, some scores outperformed clinical gestalt predictions when clinicians’ confidence of prediction was <80%. Despite its subjective nature, clinical gestalt has relevant advantages in predicting COVID-19 clinical outcomes. The need and performance of most COVID-19 mortality scores need to be evaluated regularly.

BACKGROUND: Prognostic biomarkers are needed to identify patients at high-risk for severe COVID-19. Galectin-3 is known to drive neutrophil infiltration and release of pro-inflammatory cytokines contributing to airway inflammation. METHODS: In this prospective cohort, we assessed galectin-3 levels in 156 hospitalized patients with confirmed COVID-19. COVID-19 patients were diagnosed as either critical (>50% lung damage) or moderate (<50% of lung damage) based on computerized tomography. Patients who required invasive mechanical ventilation (IMV) and/or died during hospitalization were categorized as having a severe outcome, and non-severe outcome if they were discharged and none of the former occurred. RESULTS: Elevated serum galectin-3 was significantly higher in critical patients compared to moderate ones (35.91 ± 19.37 ng/mL vs. 25 ± 14.85 ng/mL, p<0.0001). Patients who progressed to a severe outcome including IMV and/or in-hospital death, presented higher galectin-3 levels (41.17 ng/mL [IQR 29.71 - 52.25] vs. 23.76 ng/mL [IQR 15.78 - 33.97] compared to those of a non-severe outcome, p<0.0001). Galectin-3 discriminated well between those with severe and non-severe outcome, with an AUC of 0.75 (95% CI 0.67 - 0.84, p<0.0001)and was found to be an independent predictor of severe outcome regardless of the percentage of lung involvement. Additionally, the combination of galectin-3, CRP and albumin, significantly improved its individual predicting ability with an AUC 0.84 (95% CI 0.77 - 0.91, p<0.0001). CONCLUSION: Circulating galectin-3 levels can be used to predict severe outcomes in COVID-19 patients, including the requirement of mechanical ventilation and/or death, regardless of the initial severity of the disease.

Background: A notable pandemic arisen during the COVID-19 pandemic has developed globally in intensive care units, with patients developing pressure ulcers (PUs) after being ventilated mechanically in the prone position. Objectives: The aim was to identify risk factors independently predictive of the development of PUs in adult patient populations treated with prone positioning and to evaluate a possible epidemiological association between the prevalence of PUs and specific clinical characteristics so as to develop clinical indicators for the prevention of PUs. Finally, the aim was to examine our study participants against the incidence of PUs with respect to the length of their stay. Methods: : This retrospective study enrolled patients hospitalized during the period of May 2020 through January 2021. Data was collected from 299 patients hospitalized and having required prone positioning ventilatory therapy in critical care areas (short-stay units, emergency units, and intensive care units), all of which had developed Pus of at least grade two according to the classification system proposed by the NPUAP/EPUAP. Results: : Patients who had developed PUs had a longer hospitalization stay overall and were more prone to die during hospitalization. Patients who developed Pus were more frequently males, with higher initial levels of CPK and ferritin. Conclusions: The study reveals valuable information on the most important risk factors in the development of PUs due to prone positioning. We have described how the total number of days of hospitalization is significantly related to the development of PUs. Even a PU is not a life-threatening lesion, the implementation of improved positioning protocols may enhance results in critical patient care. We believe that this is a current, globally underestimated problem as the incidence of COVID-19 patients requiring prone positioning—and, therefore, at risk for PUs—is increasing daily.

Background: Most COVID-19 mortality scores were developed in the early months of the pandemic and now available evidence-based interventions have helped reduce its lethality. It has not been evaluated if the original predictive performance of these scores holds true nor compared it against Clinical Gestalt predictions. We tested the current predictive accuracy of six COVID-19 scores and compared it with Clinical Gestalt predictions. Methods: 200 COVID-19 patients were enrolled in a tertiary hospital in Mexico City between September and December 2020. Clinical Gestalt predictions of death (as a percentage) and LOW-HARM, qSOFA, MSL-COVID-19, NUTRI-CoV and NEWS2 were obtained at admission. We calculated the AUC of each score and compared it against Clinical Gestalt predictions and against their respective originally reported value. Results: 106 men and 60 women aged 56+/-9 and with confirmed COVID-19 were included in the analysis. The observed AUC of all scores was significantly lower than originally reported; LOW-HARM 0.96 (0.94-0.98) vs 0.76 (0.69-0.84), qSOFA 0.74 (0.65-0.81) vs 0.61 (0.53-0.69), MSL-COVID-19 0.72 (0.69-0.75) vs 0.64 (0.55-0.73) NUTRI-CoV 0.79 (0.76-0.82) vs 0.60 (0.51-0.69), NEWS2 0.84 (0.79-0.90) vs 0.65 (0.56-0.75), Neutrophil-Lymphocyte ratio 0.74 (0.62-0.85) vs 0.65 (0.57-0.73). Clinical Gestalt predictions were non-inferior to mortality scores (AUC=0.68 (0.59-0.77)). Adjusting the LOW-HARM score with locally derived likelihood ratios did not improve its performance. However, some scores performed better than Clinical Gestalt predictions when clinician's confidence of prediction was <80%. Conclusion: No score was significantly better than Clinical Gestalt predictions. Despite its subjective nature, Clinical Gestalt has relevant advantages for predicting COVID-19 clinical outcomes.