Welcome, everyone. Myself, Dr. Vikas Bansal, currently working as a research associate at Mayo Clinic Rochester in Department of Nephrology and Critical Care Medicine. So my title for project is the assessment of readmission risk factors after index hospital discharge for COVID-19 patient results from the International SCCM Discovery Virus COVID-19 Registry. And this is a little bit biography about me. I am an experienced research physician, and my research focus is mainly in critical care medicine, evidence-based medicine, and ARDS. There is no financial disclosure associated with this registry, but however, the virus COVID-19 registry, SCCM virus COVID-19 registry was supported in part by the Gordon and Betty Moore Foundation and Jensen Research Development to create the virus registry. There is no, they don't have any role in data gathering, analysis, interpretation, and no others have any conflict of interest. So little bit more background about this study. It is well known that the hospital readmission are linked to increased healthcare resource utilization and unfavorable patient outcomes outside the COVID-19 as well as in COVID-19 also. And during the COVID-19 pandemic, the immense burden of healthcare system associated with the reinfection readmission patterns to be increased. In this study, we evaluated the readmission rates and risk factors associated with the patient presenting with the COVID-19. This research, as I mentioned in the background section, this research was conducted as an ancillary study using the data gathered as part of the Society of Critical Care Medicine Discovery Viral Infection and Universal Study. The virus COVID-19 registry is an international database of hospitalized patient. This slide shows current status of virus registry when we download the data for this registry in July, 2022. We have more than 85,000 adult patient from 300 sites and 28 countries globally. This is a schematic representation of how we include the patient in this study. So we included all the adult patients admitted due to laboratory-confirmed COVID-19 and we excluded all the patient with the no demographic available. And then we divided in the group of patient who has readmitted, that is 1,195 patient, 95.9% and the patient who did not have any readmission within 30 days of hospital discharge and that is around 94.1%. So I already and we use the multivariate logistic regression to measure the risk factor association with 30-day readmission. This slide represents the baseline demographic of our study and we have median age of 66, median age of 66, median age and 42.5% is females and more patient with white caucasian 60.2% and followed by black that is 25.9%. The most common comorbidity in the readmission group were hypertension that is in 62.9% followed by obesity that is 46%, then diabetes 39.7% and dyslipidemia 27.0%. And even in the readmission group that patient who has, we have age group is more than 63.4% and the readmission group has more age more than 60% 60 years. Patients who were readmitted had significant higher rates of baseline comorbidity compared. May I go back? So patient who were readmitted had slightly higher rates of baseline comorbidity as shown in this table. So when we compare the diabetes mellitus that is readmitted patient have 39.7% versus 33.2% in the non readmitted. Similarly hypertension also is more in readmitted patient that is 62.9% compared to 51.8% in non readmitted group. So all of this in comorbidities are significantly statistically significant in the readmission group. When we look for the acute condition of COVID-19 experienced during the index hospitalization which was summarized in this table, we can see that 50.5% of acute respiratory failure overall and 5.5% have acute respiratory distress syndrome. Patient who were readmitted were less likely than those who were not have been diagnosed with acute respiratory failure or ARDS during the initial hospitalization. That is 45.7% versus 50.8%. And extra pulmonary manifestation of COVID-19 were also common during the first hospitalization among patient who were readmitted versus not readmitted including acute kidney injury. That is 12.4% in readmitted group and 10.5% in non readmitted group. And congestive heart failure was more common in readmitted group that is 3.6% compared to 2.2% in the non readmitted group. Patient who were readmitted had significantly shorter. So when we compare the patients who is readmitted have significant shorter length of stay that is six days versus seven days were less likely to require ICU admission in readmission group that is 29.8% versus 41.19%. Less require mechanical ventilation in readmitted group that is 13% versus 17.5% and need for high flow nasal cannula that is 17.4% versus 21.5%. These are comparison, they are all statistically significant. So overall median IQR length of hospitalization was seven and most patients were discharged home without home assistance that is 59.8% while 17.6% discharged home with home health and 10.5% at long term skilled care facility, 5.5% transferred to subacute rehabilitation. Overall 41.2% of patient required ICU readmission and 21.3% high flow nasal cannula and 17.2% mechanical ventilation. When we look for univariate analysis, when we look for the risk factor associated with the readmission we did the univariate and multivariate analysis and all multivariate is the risk factor associated was displayed here and age ratio per one increase 1.01 in multivariate model and is statistically significant and patient with the 41 to 60 years group has compared to patient with the 80 to 40 years group has more increase, all groups are significant when we compare with the 80 to 40 years or compare more than 60 versus 41, 60 versus group and females who are more females, there is no statistically significant for sex but white Caucasian was readmitted more than other racial groups including the black African-American and coronary artery disease, congestive heart failure and chronic kidney disease are associated with the readmission risk factor in multivariate analysis. And when we look for the ICU admission, if patient required index ICU admission, that index ICU admission was significantly associated with the readmission risk factor if patient was not admitted in ICU in index hospitalization, they have more chances to readmitted when they compared to the non-readmitted group. Similarly, when we look for the hospital length of stay, when patient was discharged before seven days, they have more chances to readmitted and patient who were discharged between less than seven days and we compare with the readmission rate after the 14 days, it is still statistically significant. So overall, in our SCCM discovery virus registry, there was readmission percentage among COVID-19 patient was very less, that is 5.9%, available literature says it's around 6.8% after 30 days of index hospital discharge. These patients were more likely to be more than 60 years old and more likely to have associated with cardiac, renal, pulmonary and metabolic comorbidities. And readmitted patients were more likely to have had an initial hospitalization course shorter than seven days, less likely to require ICU admission and have any pulmonary complication in the index hospitalization, including ARDS. Our strength of this study is we have large sample size with the, we have extensive multi-center registry which is not, and data in this registry was collected in a systemic manner with the standard definition across the participating centers, which reduce the heterogeneity in our result. We also explored other multiple factors beside the main outcome of the hospital readmission, which will give the more clear picture about an underlying causes. So we also have various limitation. So first limitation we have that in hospital discharge criteria, variation we did not have any, at participating site we did not adjust for that variation, that what is the criteria they use during the COVID-19 pandemic. Even the, we did not have any data for long COVID and hospital readmission. So we, and even our registry was not built as a, to capture the long COVID patients. So that is, we are not fully captured. And we also did not account for the heterogeneity in management practices and between the hospitals and clinician throughout the pandemic as the pandemic start in March, 2020. And when it progressed the multiple times that treatment modality changes. So how, what is the effect of that change in that clinical practice? We did not account for that one also. This study also could not capture the outcome of patients who were transferred to other hospital, healthcare facility, or hospice center, which, because as we did not capture the data from those centers, we also excluded the patient who did not, this, we also excluded the patient who died in the index hospitalization. So there is a survivor bias. So that is also there. So our finding reveal an overall readmission percentages in COVID-19 patient, and we readmitted have, readmitted patient have higher burden of comorbidity than non readmitted patient and future research should explore our, these risk factors in wider population. I am happy to take any question answer.

readmission risk factors

COVID-19 patients

comorbidities

hospital stays

ICU admission rates