Background Most patients with COVID-19 receive antibiotics despite the fact that bacterial co-infections are rare. This can lead to increased complications, including antibacterial resistance. We aim to analyze risk factors for inappropriate antibiotic prescription in these patients and describe possible complications arising from their use. Methods The SEMI-COVID-19 Registry is a multicenter, retrospective patient cohort. Patients with antibiotic were divided into two groups according to appropriate or inappropriate prescription, depending on whether the patient fulfill any criteria for its use. Comparison was made by means of multilevel logistic regression analysis. Possible complications of antibiotic use were also identified. Results Out of 13,932 patients, 3047 (21.6%) were prescribed no antibiotics, 6116 (43.9%) were appropriately prescribed antibiotics, and 4769 (34.2%) were inappropriately prescribed antibiotics. The following were independent factors of inappropriate prescription: February-March 2020 admission (OR 1.54, 95%CI 1.18–2.00), age (OR 0.98, 95%CI 0.97–0.99), absence of comorbidity (OR 1.43, 95%CI 1.05–1.94), dry cough (OR 2.51, 95%CI 1.94–3.26), fever (OR 1.33, 95%CI 1.13–1.56), dyspnea (OR 1.31, 95%CI 1.04–1.69), flu-like symptoms (OR 2.70, 95%CI 1.75–4.17), and elevated C-reactive protein levels (OR 1.01 for each mg/L increase, 95% CI 1.00–1.01). Adverse drug reactions were more frequent in patients who received ANTIBIOTIC (4.9% vs 2.7%, p < .001). Conclusion The inappropriate use of antibiotics was very frequent in COVID-19 patients and entailed an increased risk of adverse reactions. It is crucial to define criteria for their use in these patients. Knowledge of the factors associated with inappropriate prescribing can be helpful.

Background Since December 2019, the COVID-19 pandemic has changed the concept of medicine. This work aims to analyze the use of antibiotics in patients admitted to the hospital due to SARS-CoV-2 infection. Methods This work analyzes the use and effectiveness of antibiotics in hospitalized patients with COVID-19 based on data from the SEMI-COVID-19 registry, an initiative to generate knowledge about this disease using data from electronic medical records. Our primary endpoint was all-cause in-hospital mortality according to antibiotic use. The secondary endpoint was the effect of macrolides on mortality. Results Of 13,932 patients, antibiotics were used in 12,238. The overall death rate was 20.7% and higher among those taking antibiotics (87.8%). Higher mortality was observed with use of all antibiotics (OR 1.40, 95% CI 1.21–1.62; p < .001) except macrolides, which had a higher survival rate (OR 0.70, 95% CI 0.64–0.76; p < .001). The decision to start antibiotics was influenced by presence of increased inflammatory markers and any kind of infiltrate on an x-ray. Patients receiving antibiotics required respiratory support and were transferred to intensive care units more often. Conclusions Bacterial co-infection was uncommon among COVID-19 patients, yet use of antibiotics was high. There is insufficient evidence to support widespread use of empiric antibiotics in these patients. Most may not require empiric treatment and if they do, there is promising evidence regarding azithromycin as a potential COVID-19 treatment.

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Background: Since December 2019, the COVID-19 pandemic has changed the concept of medicine. This work aims to analyze the use of antibiotics in patients admitted to the hospital due to SARS-CoV-2 infection. Methods: This work analyzes the use and effectiveness of antibiotics in hospitalized patients with COVID-19 based on data from the SEMI-COVID-19 registry, an initiative to generate knowledge about this disease using data from electronic medical records. Our primary endpoint was all-cause in-hospital mortality according to antibiotic use. The secondary endpoint was the effect of macrolides on mortality. Results: Of 13,932 patients, antibiotics were used in 12,238. The overall death rate was 20.7% and higher among those taking antibiotics (87.8%). Higher mortality was observed with use of all antibiotics (OR 1.40, 95%CI 1.21-1.62; p<.001) except macrolides, which had a higher survival rate (OR 0.70, 95%CI 0.64-0.76; p<.001). The decision to start antibiotics was influenced by presence of increased inflammatory markers and any kind of infiltrate on an x-ray. Patients receiving antibiotics required respiratory support and were transferred to intensive care units more often. Conclusions: Bacterial co-infection was uncommon among COVID-19 patients, yet use of antibiotics was high. There is insufficient evidence to support widespread use of empiric antibiotics in these patients. Most may not require empiric treatment and if they do, there is promising evidence regarding azithromycin as a potential COVID-19 treatment.

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Some patients with COVID-19 pneumonia develop an associated cytokine storm syndrome that aggravates the pulmonary disease. These patients may benefit of anti-inflammatory treatment. The role of colchicine in hospitalized patients with COVID-19 pneumonia and established hyperinflammation remains unexplored. In a prospective, randomized controlled, observer-blinded endpoint, investigator-initiated trial, 240 hospitalized patients with COVID-19 pneumonia and established hyperinflammation were randomly allocated to receive oral colchicine or not. The primary efficacy outcome measure was a composite of non-invasive mechanical ventilation (CPAP or BiPAP), admission to the intensive care unit, invasive mechanical ventilation requirement or death. The composite primary outcome occurred in 19.3% of the total study population. The composite primary outcome was similar in the two arms (17% in colchicine group vs. 20.8% in the control group; p = 0.533) and the same applied to each of its individual components. Most patients received steroids (98%) and heparin (99%), with similar doses in both groups. In this trial, including adult patients with COVID-19 pneumonia and associated hyperinflammation, no clinical benefit was observed with short-course colchicine treatment beyond standard care regarding the combined outcome measurement of CPAP/BiPAP use, ICU admission, invasive mechanical ventilation or death (Funded by the Community of Madrid, EudraCT Number: 2020-001841-38; 26/04/2020).

The significant impact of COVID-19 worldwide has made it necessary to develop tools to identify patients at high risk of severe disease and death. This work aims to validate the RIM Score-COVID in the SEMI-COVID-19 Registry. The RIM Score-COVID is a simple nomogram with high predictive capacity for in-hospital death due to COVID-19 designed using clinical and analytical parameters of patients diagnosed in the first wave of the pandemic. The nomogram uses five variables measured on arrival to the emergency department (ED): age, sex, oxygen saturation, C-reactive protein level, and neutrophil-to-platelet ratio. Validation was performed in the Spanish SEMI-COVID-19 Registry, which included consecutive patients hospitalized with confirmed COVID-19 in Spain. The cohort was divided into three time periods: T1 from February 1 to June 10, 2020 (first wave), T2 from June 11 to December 31, 2020 (second wave, pre-vaccination period), and T3 from January 1 to December 5, 2021 (vaccination period). The model’s accuracy in predicting in-hospital COVID-19 mortality was assessed using the area under the receiver operating characteristics curve (AUROC). Clinical and laboratory data from 22,566 patients were analyzed: 15,976 (70.7%) from T1, 4,233 (18.7%) from T2, and 2,357 from T3 (10.4%). AUROC of the RIM Score-COVID in the entire SEMI-COVID-19 Registry was 0.823 (95%CI 0.819–0.827) and was 0.834 (95%CI 0.830–0.839) in T1, 0.792 (95%CI 0.781–0.803) in T2, and 0.799 (95%CI 0.785–0.813) in T3. The RIM Score-COVID is a simple, easy-to-use method for predicting in-hospital COVID-19 mortality that uses parameters measured in most EDs. This tool showed good predictive ability in successive disease waves. Supplementary Information The online version contains supplementary material available at 10.1007/s11739-023-03200-3.