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SUMMARY OBJECTIVE: The aim of this study was to assess the rate of bacterial infections in COVID-19-hospitalized patients and to analyze the most prevalent germs, sources, risk factors, and its impact on in-hospital mortality. METHODS: This observational retrospective study was conducted on 672 patients hospitalized between April and August 2020 in Nossa Senhora da Conceição Hospital, a public hospital located in Porto Alegre, Brazil. The inclusion criterion was adult patients hospitalized with confirmed COVID-19. Data were collected through chart review. Risk factors for bacterial infection and mortality were analyzed using both univariate and multivariate robust Poisson regression models. RESULTS: Bacterial coinfection was observed in 22.2% of patients. Risk factors for bacterial infections were dementia (RR=2.06 (1.18–3.60); p=0.011), cerebrovascular disease (RR=1.75 (1.15–2.67); p=0.009), active cancer (RR=1.52 (1.082–2.15); p=0.01), need for noninvasive ventilation (RR=2.320 (1.740–3.094); p<0.01), invasive mechanical ventilation (RR=4.63 (2.24–9.56); p<0.01), and renal replacement therapy (RR=1.68 (1.26–2.25); p<0.01). In the adjusted model, bacterial infections were not associated with mortality (0.96 (0.75–1.24); p=0.79). The most common source of infection was due to respiratory, blood, and central venous catheters, with 69 (29.36%), 61 (25.96%), and 59 (25.11%) positive cultures, respectively. CONCLUSION: We observed a high rate of bacterial infections in COVID-19-hospitalized patients, most commonly of respiratory source. Neurologic and oncologic morbidities and need for ventilation and renal replacement therapy was associated with risk factors for bacterial infections. Nevertheless, an association between bacterial infections and hospital mortality was not established. OBJECTIVE COVID19hospitalized COVIDhospitalized COVID 19 germs sources inhospital METHODS 67 202 Hospital Alegre Brazil COVID19. COVID19 19. COVID-19 review models RESULTS 222 22 2 22.2 RR=2.06 RR206 RR 06 (RR=2.0 1.18–3.60 118360 1 18 3 60 (1.18–3.60) p=0.011, p0011 p p=0.011 , 0 011 p=0.011) RR=1.75 RR175 75 (RR=1.7 1.15–2.67 115267 15 (1.15–2.67) p=0.009, p0009 p=0.009 009 p=0.009) RR=1.52 RR152 52 (RR=1.5 1.082–2.15 1082215 082 (1.082–2.15) p=0.01, p001 p=0.01 01 p=0.01) RR=2.320 RR2320 320 (RR=2.32 1.740–3.094 17403094 740 094 (1.740–3.094) p<0.01, p<0.01 p<0.01) RR=4.63 RR463 4 63 (RR=4.6 2.24–9.56 224956 24 9 56 (2.24–9.56) RR=1.68 RR168 68 (RR=1.6 1.26–2.25 126225 26 25 (1.26–2.25) p<0.01. . model 0.96 096 96 (0.9 0.75–1.24 075124 (0.75–1.24) p=0.79. p079 p=0.79 79 p=0.79) blood catheters 6 29.36%, 2936 29.36% 29 36 (29.36%) 25.96%, 2596 25.96% (25.96%) 5 25.11% 2511 11 (25.11% cultures respectively CONCLUSION Nevertheless established 20 COVID1 COVID-1 22. RR=2.0 RR20 (RR=2. 1.18–3.6 11836 (1.18–3.60 RR=1.7 RR17 7 (RR=1. 1.15–2.6 11526 (1.15–2.67 p000 p=0.00 00 RR=1.5 RR15 1.082–2.1 108221 08 (1.082–2.15 p00 p=0.0 RR=2.32 RR232 32 (RR=2.3 1.740–3.09 1740309 74 09 (1.740–3.094 p<0.0 RR=4.6 RR46 (RR=4. 2.24–9.5 22495 (2.24–9.56 RR=1.6 RR16 1.26–2.2 12622 (1.26–2.25 0.9 (0. 0.75–1.2 07512 (0.75–1.24 p07 p=0.7 293 29.36 (29.36% 259 25.96 (25.96% 25.11 251 (25.11 COVID- RR=2. RR2 (RR=2 1.18–3. 1183 (1.18–3.6 RR=1. RR1 (RR=1 1.15–2. 1152 (1.15–2.6 1.082–2. 10822 (1.082–2.1 p0 p=0. RR=2.3 RR23 1.740–3.0 174030 (1.740–3.09 p<0. RR=4. RR4 (RR=4 2.24–9. 2249 (2.24–9.5 1.26–2. 1262 (1.26–2.2 0. (0 0.75–1. 0751 (0.75–1.2 29.3 (29.36 25.9 (25.96 25.1 (25.1 RR=2 (RR= 1.18–3 118 (1.18–3. RR=1 1.15–2 115 (1.15–2. 1.082–2 1082 (1.082–2. p=0 1.740–3. 17403 (1.740–3.0 p<0 RR=4 2.24–9 224 (2.24–9. 1.26–2 126 (1.26–2. ( 0.75–1 075 (0.75–1. 29. (29.3 25. (25.9 (25. RR= (RR 1.18– (1.18–3 1.15– (1.15–2 1.082– 108 (1.082–2 p= 1.740–3 1740 (1.740–3. p< 2.24– (2.24–9 1.26– 12 (1.26–2 0.75– 07 (0.75–1 (29. (25 1.18 (1.18– 1.15 (1.15– 1.082 10 (1.082– 1.740– 174 (1.740–3 2.24 (2.24– 1.26 (1.26– 0.75 (0.75– (29 (2 1.1 (1.18 (1.15 1.08 (1.082 1.740 17 (1.740– 2.2 (2.24 1.2 (1.26 0.7 (0.75 1. (1.1 1.0 (1.08 1.74 (1.740 2. (2.2 (1.2 (0.7 (1. (1.0 1.7 (1.74 (2. (1 (1.7