Use of fractional exhaled nitric oxide as a potential predictor of bronchodilator response.

Nitric oxide (NO) is an important product of eosinophilic metabolism, and its increase is associated with bronchial remodeling and airway hyperresponsiveness. Fractional exhaled NO (FENO) in the expired air of patients with suspected or diagnosed asthma has been used as a marker for eosinophilic inflammation. This cohort study included asthmatic patients classified under steps 3, 4, or 5 of the global strategy for asthma management and prevention. In the morning of the same day, all patients underwent blood collection for eosinophil counts, followed by FENO measurement and spirometry. We considered 2 groups based on the bronchodilation (BD) response on spirometry (>10% of FVC or FEV1): positive (BD+) and negative (BD-). Differences between the 2 groups were analyzed for demographic features, FENO values, and predictive correlations between FENO and BD. Both groups of patients showed an increase in the eosinophil count (BD+, P = .03; BD-, P = .04) and FENO values (P = .015 for both) with an increase in the asthma severity from step 3 to step 5 of the global strategy for asthma management and prevention. The correlations of FENO and eosinophils as well as FENO values and BD + were 0.127 (95% confidence interval,-0.269 to -0.486) and 0.696 (95% confidence interval, 0.246-0.899; P = .007), respectively. Measuring FENO levels may be useful for identifying patients with BD+.

Small airway dysfunction on impulse oscillometry and pathological signs on lung ultrasound are frequent in post-COVID-19 patients with persistent respiratory symptoms.

BACKGROUND: Thousands of people worldwide are suffering the consequences of coronavirus disease-2019 (COVID-19), and impulse oscillometry (IOS) and lung ultrasound (LUS) might be important tools for the follow-up of this population. Our objective was to prospectively evaluate abnormalities detected using these two methods in a cohort of COVID-19 survivors with respiratory symptoms. METHODS: In this follow-up study, 59 patients underwent clinical evaluations, spirometry, IOS and LUS in the 2nd (M1) and 5th (M2) months after diagnostic confirmation of COVID-19 by real-time reverse transcriptase-polymerase chain reaction. Aeration scores were obtained from the LUS exams based on the following findings: B-lines >2, coalescent B-lines, and subpleural consolidations. RESULTS: Fifty-nine (100%) participants had cough and/or dyspnea at M1, which decreased to 38 (64.4%) at M2 (p = 0.0001). Spirometry was abnormal in 26 (44.1%) and 20 (33.9%) participants at M1 and M2, respectively, although without statistical significance (p = 0.10). Normal examination, restrictive patterns, and obstructive patterns were observed in 33 (55.9%), 18 (30.5%), and 8 (13.6%) participants, respectively, at M1 and in 39 (66.1%), 13 (22%), and 7 (11.9%) participants at M2 (p = 0.14). Regarding IOS, considering changes in resistive and reactive parameters, abnormal exams were detected in 52 (88.1%) and 42 (71.2%) participants at M1 and M2, respectively (p = 0.002). Heterogeneity of resistance between 4 and 20 Hz >20% was observed in 38 (64.4%) and 33 (55.9%) participants at M1 and M2, respectively (p = 0.30). Abnormal LUS was observed in 46 (78%) and 36 (61%) participants at M1 and M2, respectively (p = 0.002), with a reduction in aeration scores between M1 and M2 [5 (2-8) vs. 3 (0-6) points, p<0.0001]. CONCLUSIONS: IOS and LUS abnormalities are frequent in the first 5 months post-COVID-19 infection; however, when prospectively evaluated, significant improvement is evident in the parameters measured by these two methods.

The impact of healthcare-associated infections on COVID-19 mortality: a cohort study from a Brazilian public hospital.

OBJECTIVE: This study aims to analyze the risk factors for in-hospital mortality in a cohort of patients admitted to a newly adapted intensive care unit in a public hospital in Rio de Janeiro. METHODS: This was an observational, retrospective, and descriptive study. Data were obtained from electronic medical records. Coronavirus disease 2019 (COVID-19) was diagnosed by detecting viral ribonucleic acid using reverse transcription polymerase chain reaction. Factors associated with the risk/protection from death were determined using the odds ratio and adjusted odds ratio. RESULTS: Fifty-one patients were admitted to the hospital. The median age of the patients was 63 years, 60% were male patients, and 54% were white patients. Sixty-seven percent of the patients were diagnosed with COVID-19. Sepsis at admission increased the chance of in-hospital death by 21 times (adjusted odds ratio=21.06 [0.79-555.2]; p=0.06). The strongest risk factor for death was the development of septic shock during hospitalization (adjusted odds ratio=98.56 [2.75-352.5]; p=0.01), and one in four patients had multidrug-resistant bacteria. Mechanical ventilation, vasopressors, neuromuscular blockers, and sedatives were also the risk factors for in-hospital mortality. The in-hospital mortality rate was 41%, and the mortality rate of patients on mechanical ventilation was 60%. The diagnosis of COVID-19 was not statistically related to the adverse outcomes. CONCLUSIONS: In this cohort, the strongest risk factor for in-hospital death was the development of nosocomial septic shock. Healthcare-associated infections have a significant impact on mortality rates. Therefore, to have a better outcome, it is important to consider not only the availability of beds but also the way healthcare is delivered.

The impact of healthcare-associated infections on COVID-19 mortality: a cohort study from a Brazilian public hospital

SUMMARY OBJECTIVE: This study aims to analyze the risk factors for in-hospital mortality in a cohort of patients admitted to a newly adapted intensive care unit in a public hospital in Rio de Janeiro. METHODS: This was an observational, retrospective, and descriptive study. Data were obtained from electronic medical records. Coronavirus disease 2019 (COVID-19) was diagnosed by detecting viral ribonucleic acid using reverse transcription polymerase chain reaction. Factors associated with the risk/protection from death were determined using the odds ratio and adjusted odds ratio. RESULTS: Fifty-one patients were admitted to the hospital. The median age of the patients was 63 years, 60% were male patients, and 54% were white patients. Sixty-seven percent of the patients were diagnosed with COVID-19. Sepsis at admission increased the chance of in-hospital death by 21 times (adjusted odds ratio=21.06 [0.79-555.2]; p=0.06). The strongest risk factor for death was the development of septic shock during hospitalization (adjusted odds ratio=98.56 [2.75-352.5]; p=0.01), and one in four patients had multidrug-resistant bacteria. Mechanical ventilation, vasopressors, neuromuscular blockers, and sedatives were also the risk factors for in-hospital mortality. The in-hospital mortality rate was 41%, and the mortality rate of patients on mechanical ventilation was 60%. The diagnosis of COVID-19 was not statistically related to the adverse outcomes. CONCLUSIONS: In this cohort, the strongest risk factor for in-hospital death was the development of nosocomial septic shock. Healthcare-associated infections have a significant impact on mortality rates. Therefore, to have a better outcome, it is important to consider not only the availability of beds but also the way healthcare is delivered.