ABSTRACT
Objective: To characterize the frequency, causes, and predictors of readmissions of COVID-19 patients after discharge from heath facilities or emergency departments, interventions used to reduce readmissions, and outcomes of COVID-19 patients discharged from such settings. Methods: We performed a systematic review for case series and observational studies published between January 2020 and April 2021 in PubMed, Embase, LILACS, and MedRxiv, reporting the frequency, causes, or risk factors for readmission of COVID-19 survivors/patients. We conducted a narrative synthesis and assessed the methodological quality using the JBI critical appraisal checklist. Results: We identified 44 studies including data from 10 countries. The overall 30-day median readmission rate was 7.1%. Readmissions varied with the length of follow-up, occurring <10.5%, <14.5%, <21.5%, and <30%, respectively, for 10, 30, 60, and 253 days following discharge. Among those followed up for 30 and 60 days, the median time from discharge to readmission was 3 days and 8-11 days, respectively. The significant risk factor associated with readmission was having shorter length of stay, and the important causes included respiratory or thromboembolic events and chronic illnesses. Emergency department re-presentation was >20% in four studies. Risk factors associated with mortality were male gender, advanced age, and comorbidities. Conclusions: Readmission of COVID-19 survivors is frequent, and post-discharge mortality is significant in specific populations. There is an urgent need to further examine underlying reasons for early readmission and to prevent additional readmissions and adverse outcomes in COVID-19 survivors.
ABSTRACT
Objective. To characterize the frequency, causes, and predictors of readmissions of COVID-19 patients after discharge from heath facilities or emergency departments, interventions used to reduce readmissions, and outcomes of COVID-19 patients discharged from such settings. Methods. We performed a systematic review for case series and observational studies published between January 2020 and April 2021 in PubMed, Embase, LILACS, and MedRxiv, reporting the frequency, causes, or risk factors for readmission of COVID-19 survivors/patients. We conducted a narrative synthesis and assessed the methodological quality using the JBI critical appraisal checklist. Results. We identified 44 studies including data from 10 countries. The overall 30-day median readmission rate was 7.1%. Readmissions varied with the length of follow-up, occurring <10.5%, <14.5%, <21.5%, and <30%, respectively, for 10, 30, 60, and 253 days following discharge. Among those followed up for 30 and 60 days, the median time from discharge to readmission was 3 days and 8–11 days, respectively. The significant risk factor associated with readmission was having shorter length of stay, and the important causes included respiratory or thromboembolic events and chronic illnesses. Emergency department re-presentation was >20% in four studies. Risk factors associated with mortality were male gender, advanced age, and comorbidities. Conclusions. Readmission of COVID-19 survivors is frequent, and post-discharge mortality is significant in specific populations. There is an urgent need to further examine underlying reasons for early readmission and to prevent additional readmissions and adverse outcomes in COVID-19 survivors.
ABSTRACT
BACKGROUND: Approximately 5% of COVID-19 patients develop respiratory failure and need ventilatory support, yet little is known about the impact of mechanical ventilation strategy in COVID-19. Our objective was to describe baseline characteristics, ventilatory parameters, and outcomes of critically ill patients in the largest referral center for COVID-19 in Sao Paulo, Brazil, during the first surge of the pandemic. METHODS: This cohort included COVID-19 patients admitted to the intensive care units (ICUs) of an academic hospital with 94 ICU beds, a number expanded to 300 during the pandemic as part of a state preparedness plan. Data included demographics, advanced life support therapies, and ventilator parameters. The main outcome was 28-day survival. We used a multivariate Cox model to test the association between protective ventilation and survival, adjusting for PF ratio, pH, compliance, and PEEP. RESULTS: We included 1503 patients from March 30 to June 30, 2020. The mean age was 60 ± 15 years, and 59% were male. During 28-day follow-up, 1180 (79%) patients needed invasive ventilation and 666 (44%) died. For the 984 patients who were receiving mechanical ventilation in the first 24 h of ICU stay, mean tidal volume was 6.5 ± 1.3 mL/kg of ideal body weight, plateau pressure was 24 ± 5 cmH2O, respiratory system compliance was 31.9 (24.4-40.9) mL/cmH2O, and 82% of patients were ventilated with protective ventilation. Noninvasive ventilation was used in 21% of patients, and prone, in 36%. Compliance was associated with survival and did not show a bimodal pattern that would support the presence of two phenotypes. In the multivariable model, protective ventilation (aHR 0.73 [95%CI 0.57-0.94]), adjusted for PF ratio, compliance, PEEP, and arterial pH, was independently associated with survival. CONCLUSIONS: During the peak of the epidemic in Sao Paulo, critically ill patients with COVID-19 often required mechanical ventilation and mortality was high. Our findings revealed an association between mechanical ventilation strategy and mortality, highlighting the importance of protective ventilation for patients with COVID-19.
ABSTRACT
OBJECTIVES: We designed a cohort study to describe characteristics and outcomes of patients with coronavirus disease (COVID-19) admitted to the intensive care unit (ICU) in the largest public hospital in Sao Paulo, Brazil, as Latin America becomes the epicenter of the pandemic. METHODS: This is the protocol for a study being conducted at an academic hospital in Brazil with 300 adult ICU beds dedicated to COVID-19 patients. We will include adult patients admitted to the ICU with suspected or confirmed COVID-19 during the study period. The main outcome is ICU survival at 28 days. Data will be collected prospectively and retrospectively by trained investigators from the hospital's electronic medical records, using an electronic data capture tool. We will collect data on demographics, comorbidities, severity of disease, and laboratorial test results at admission. Information on the need for advanced life support and ventilator parameters will be collected during ICU stay. Patients will be followed up for 28 days in the ICU and 60 days in the hospital. We will plot Kaplan-Meier curves to estimate ICU and hospital survival and perform survival analysis using the Cox proportional hazards model to identify the main risk factors for mortality. ClinicalTrials.gov: NCT04378582. RESULTS: We expect to include a large sample of patients with COVID-19 admitted to the ICU and to be able to provide data on admission characteristics, use of advanced life support, ICU survival at 28 days, and hospital survival at 60 days. CONCLUSIONS: This study will provide epidemiological data about critically ill patients with COVID-19 in Brazil, which could inform health policy and resource allocation in low- and middle-income countries.
ABSTRACT
OBJECTIVES: We designed a cohort study to describe characteristics and outcomes of patients with coronavirus disease (COVID-19) admitted to the intensive care unit (ICU) in the largest public hospital in Sao Paulo, Brazil, as Latin America becomes the epicenter of the pandemic. METHODS: This is the protocol for a study being conducted at an academic hospital in Brazil with 300 adult ICU beds dedicated to COVID-19 patients. We will include adult patients admitted to the ICU with suspected or confirmed COVID-19 during the study period. The main outcome is ICU survival at 28 days. Data will be collected prospectively and retrospectively by trained investigators from the hospital's electronic medical records, using an electronic data capture tool. We will collect data on demographics, comorbidities, severity of disease, and laboratorial test results at admission. Information on the need for advanced life support and ventilator parameters will be collected during ICU stay. Patients will be followed up for 28 days in the ICU and 60 days in the hospital. We will plot Kaplan-Meier curves to estimate ICU and hospital survival and perform survival analysis using the Cox proportional hazards model to identify the main risk factors for mortality. ClinicalTrials.gov: NCT04378582. RESULTS: We expect to include a large sample of patients with COVID-19 admitted to the ICU and to be able to provide data on admission characteristics, use of advanced life support, ICU survival at 28 days, and hospital survival at 60 days. CONCLUSIONS: This study will provide epidemiological data about critically ill patients with COVID-19 in Brazil, which could inform health policy and resource allocation in low- and middle-income countries.