Factors Associated With Readmission in the United States Following Hospitalization With Coronavirus Disease 2019

Remote Patient Monitoring at Home in Patients With COVID-19: Narrative Review

BACKGROUND

During the pandemic, health care providers implemented remote patient monitoring (RPM) for patients experiencing COVID-19. RPM is an interaction between health care professionals and patients who are in different locations, in which certain patient functioning parameters are assessed and followed up for a certain duration of time. The implementation of RPM in these patients aimed to reduce the strain on hospitals and primary care.

OBJECTIVE

With this literature review, we aim to describe the characteristics of RPM interventions, report on patients with COVID-19 receiving RPM, and provide an overview of outcome variables such as length of stay (LOS), hospital readmission, and mortality.

METHODS

A combination of different searches in several database types (traditional databases, trial registers, daily [Google] searches, and daily PubMed alerts) was run daily from March 2020 to December 2021. A search update for randomized controlled trials (RCTs) was performed in April 2022.

RESULTS

The initial search yielded more than 4448 articles (not including daily searches). After deduplication and assessment for eligibility, 241 articles were retained describing 164 telemonitoring studies from 160 centers. None of the 164 studies covering 248,431 patients reported on the presence of a randomized control group. Studies described a "prehosp" group (96 studies) with patients who had a suspected or confirmed COVID-19 diagnosis and who were not hospitalized but closely monitored at home or a "posthosp" group (32 studies) with patients who were monitored at home after hospitalization for COVID-19. Moreover, 34 studies described both groups, and in 2 studies, the description was unclear. In the prehosp and posthosp groups, there were large variations in the number of emergency department (ED) visits (0%-36% and 0%-16%, respectively) and no convincing evidence that RPM leads to less or more ED visits or hospital readmissions (0%-30% and 0%-22%, respectively). Mortality was generally low, and there was weak to no evidence that RPM is associated with lower mortality. Moreover, there was no evidence that RPM shortens previous LOS. A literature update identified 3 small-scale RCTs, which could not demonstrate statistically significant differences in these outcomes. Most papers claimed savings; however, the scientific base for these claims was doubtful. The overall patient experiences with RPM were positive, as patients felt more reassured, although many patients declined RPM for several reasons (eg, technological embarrassment, digital literacy).

CONCLUSIONS

Based on these results, there is no convincing evidence that RPM in COVID-19 patients avoids ED visits or hospital readmissions and shortens LOS or reduces mortality. On the other hand, there is no evidence that RPM has adverse outcomes. Further research should focus on developing, implementing, and evaluating an RPM framework.

Predictors of readmission and mortality in adults with diabetes or stress hyperglycemia after initial hospitalization for COVID-19

INTRODUCTION

We previously reported predictors of mortality in 1786 adults with diabetes or stress hyperglycemia (glucose>180 mg/dL twice in 24 hours) admitted with COVID-19 from March 2020 to February 2021 to five university hospitals. Here, we examine predictors of readmission.

RESEARCH DESIGN AND METHODS

Data were collected locally through retrospective reviews of electronic medical records from 1786 adults with diabetes or stress hyperglycemia who had a hemoglobin A1c (HbA1c) test on initial admission with COVID-19 infection or within 3 months prior to initial admission. Data were entered into a Research Electronic Data Capture (REDCap) web-based repository, and de-identified. Descriptive data are shown as mean±SD, per cent (%) or median (IQR). Student's t-test was used for comparing continuous variables with normal distribution and Mann-Whitney U test was used for data not normally distributed. X2 test was used for categorical variable.

RESULTS

Of 1502 patients who were alive after initial hospitalization, 19.4% were readmitted; 90.3% within 30 days (median (IQR) 4 (0-14) days). Older age, lower estimated glomerular filtration rate (eGFR), comorbidities, intensive care unit (ICU) admission, mechanical ventilation, diabetic ketoacidosis (DKA), and longer length of stay (LOS) during the initial hospitalization were associated with readmission. Higher HbA1c, glycemic gap, or body mass index (BMI) were not associated with readmission. Mortality during readmission was 8.0% (n=23). Those who died were older than those who survived (74.9±9.5 vs 65.2±14.4 years, p=0.002) and more likely had DKA during the first hospitalization (p<0.001). Shorter LOS during the initial admission was associated with ICU stay during readmission, suggesting that a subset of patients may have been initially discharged prematurely.

CONCLUSIONS

Understanding predictors of readmission after initial hospitalization for COVID-19, including older age, lower eGFR, comorbidities, ICU admission, mechanical ventilation, statin use and DKA but not HbA1c, glycemic gap or BMI, can help guide treatment approaches and future research in adults with diabetes.

Rural-urban Differences in Long-term Mortality and Readmission Following COVID-19 Hospitalization, 2020 to 2023

Background

We compared long-term mortality and readmission rates after COVID-19 hospitalization based on rural-urban status and assessed the impact of COVID-19 vaccination introduction on clinical outcomes by rurality.

Methods

The study comprised adults hospitalized for COVID-19 at 17 hospitals in 4 US states between March 2020 and July 2022, followed until May 2023. The main analysis included all patients, whereas a sensitivity analysis focused on residents from 4 states containing 17 hospitals. Additional analyses compared the pre- and postvaccination periods.

Results

The main analysis involved 9325 COVID-19 hospitalized patients: 31% were from 187 rural counties in 31 states; 69% from 234 urban counties in 44 states; the mean age was 65 years (rural, 66 years; urban, 64 years); 3894 women (rural, 41%; urban, 42%); 8007 Whites (rural, 87%; urban, 83%); 1738 deaths (rural, 21%; urban, 17%); and 2729 readmissions (rural, 30%; urban, 29%). During a median follow-up of 602 days, rural residence was associated with a 22% higher all-cause mortality (log-rank, P < .001; hazard ratio, 1.22; 95% confidence interval, 1.10-1.34, P < .001), and a trend toward a higher readmission rate (log-rank, P = .038; hazard ratio, 1.06; 95% confidence interval, .98-1.15; P = .130). The results remained consistent in the sensitivity analysis and in both pre- and postvaccination time periods.

Conclusions and Relevance

Patients from rural counties experienced higher mortality and tended to be readmitted more frequently following COVID-19 hospitalization over the long term compared with those from urban counties, a difference that remained even after the introduction of COVID-19 vaccines.

Analysis of 30-day hospital readmissions and related risk factors for COVID-19 patients with myocarditis hospitalized in the United States during 2020

Background

Despite extensive research on COVID-19 and its association with myocarditis, limited data are available on readmission rates for this subset of patients. Thirty-day hospital readmission rate is an established quality metric that is associated with increased mortality and cost.

Methods

This retrospective analysis utilized the Nationwide Readmission Database for the year 2020 to evaluate 30-day hospital readmission rates, risk factors, and clinical outcomes among COVID-19 patients who presented with myocarditis at their index hospitalization.

Results

Our analysis revealed that 1) the 30-day all-cause hospital readmission rate for patients initially hospitalized with COVID-19 and myocarditis was 11.7%; 2) after multivariate adjustment, the primary predictor of readmission for COVID-19 patients with myocarditis was discharge against medical advice; 3) COVID-19 patients with myocarditis who required readmission had a higher proportion of older patients and Medicare beneficiaries; 4) the most common diagnoses at readmission were COVID-19, sepsis, congestive heart failure, acute myocardial infarction, and pneumonia; and 5) readmitted patients were more likely to require renal replacement therapy during their index hospitalization.

Conclusion

This study underscores the importance of optimizing discharge plans, preventing irregular discharges through shared decision-making, and ensuring robust post-hospital follow-up for patients with COVID-19 and myocarditis at index admission.

[COVID-19 readmissions during the first three epidemic periods in Orihuela, Spain: incidence, risk factors and letality]

OBJECTIVE

Readmission for COVID-19 is associated with high mortality, saturation of health services, and high costs. This study aimed to assess the incidence and risk factors of readmissions in COVID-19 patients in a regional hospital of Spain from February 2020 to March 2021.

METHODS

A retrospective cohort study describing the characteristics of adult patients readmitted within thirty days of discharge after being infected with SARS-CoV-2 was carried out. Readmission associated risk factors were analysed using a binary logistic regression model.

RESULTS

Of the 967 patients who survived their first COVID-19 admission, 70 (7.2%) were readmitted within thirty days. Of these, 34.3% presented pneumonia progression, 15.7% functional deterioration, and 12.9% other infections. The mortality rate during readmission was 28.6%. There were no statistically significant differences in the cumulative incidence of readmissions between the epidemic periods (p=0.241). Factors independently associated with readmission were: diabetes mellitus (aOR 1.96, 95%CI 1.07-3.57, p=0.030); acute kidney failure (aOR 2.69, 95%CI 1.43-5.07, p=0.002); not being a candidate for intensive care (aOR 7.68, 95% CI 4.28-13.80, p<0.001); and not being prescribed corticosteroids at discharge (aOR 2.15, 95% CI 1.04-4.44; p=0.039).

CONCLUSIONS

A substantial proportion of patients admitted due to COVID-19 are readmitted, and they carry a high letality. Diabetes mellitus, acute kidney failure, not being a candidate for ICU admission, and not being prescribed corticosteroids on discharge are independently associated with an increased risk of readmission.

Comorbidities associated with 30-day readmission following index coronavirus disease 2019 (COVID-19) hospitalization: A retrospective cohort study of 331,136 patients in the United States

OBJECTIVE

Hospital readmission is unsettling to patients and caregivers, costly to the healthcare system, and may leave patients at additional risk for hospital-acquired infections and other complications. We evaluated the association between comorbidities present during index coronavirus disease 2019 (COVID-19) hospitalization and the risk of 30-day readmission.

DESIGN, SETTING, AND PARTICIPANTS

We used the Premier Healthcare database to perform a retrospective cohort study of COVID-19 hospitalized patients discharged between April 2020 and March 2021 who were followed for 30 days after discharge to capture readmission to the same hospital.

RESULTS

Among the 331,136 unique patients in the index cohort, 36,827 (11.1%) had at least 1 all-cause readmission within 30 days. Of the readmitted patients, 11,382 (3.4%) were readmitted with COVID-19 as the primary diagnosis. In the multivariable model adjusted for demographics, hospital characteristics, coexisting comorbidities, and COVID-19 severity, each additional comorbidity category was associated with an 18% increase in the odds of all-cause readmission (adjusted odds ratio [aOR], 1.18; 95% confidence interval [CI], 1.17-1.19) and a 10% increase in the odds of readmission with COVID-19 as the primary readmission diagnosis (aOR, 1.10; 95% CI, 1.09-1.11). Lymphoma (aOR, 1.86; 95% CI, 1.58-2.19), renal failure (aOR, 1.32; 95% CI, 1.25-1.40), and chronic lung disease (aOR, 1.29; 95% CI, 1.24-1.34) were most associated with readmission for COVID-19.

CONCLUSIONS

Readmission within 30 days was common among COVID-19 survivors. A better understanding of comorbidities associated with readmission will aid hospital care teams in improving postdischarge care. Additionally, it will assist hospital epidemiologists and quality administrators in planning resources, allocating staff, and managing bed-flow issues to improve patient care and safety.

Early Outcomes of SARS-CoV-2 Infection in a Multisite Prospective Cohort of Inpatient Veterans

Background

Over 870 000 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections have occurred among Veterans Health Administration users, and 24 000 have resulted in death. We examined early outcomes of SARS-CoV-2 infection in hospitalized veterans.

Methods

In an ongoing, prospective cohort study, we enrolled veterans age ≥18 tested for SARS-CoV-2 and hospitalized at 15 Department of Veterans Affairs medical centers between February 2021 and June 2022. We estimated adjusted odds ratios (aORs), adjusted incidence rate ratios (aIRRs), and adjusted hazard ratios (aHRs) for maximum illness severity within 30 days of study entry (defined using the 4-category VA Severity Index for coronavirus disease 2019 [COVID-19]), as well as length of hospitalization and rehospitalization within 60 days, in relationship with demographic characteristics, Charlson comorbidity index (CCI), COVID-19 vaccination, and calendar period of enrollment.

Results

The 542 participants included 329 (61%) who completed a primary vaccine series (with or without booster; "vaccinated"), 292 (54%) enrolled as SARS-CoV-2-positive, and 503 (93%) men, with a mean age of 64.4 years. High CCI scores (≥5) occurred in 61 (44%) vaccinated and 29 (19%) unvaccinated SARS-CoV-2-positive participants. Severe illness or death occurred in 29 (21%; 6% died) vaccinated and 31 (20%; 2% died) unvaccinated SARS-CoV-2-positive participants. SARS-CoV-2-positive inpatients per unit increase in CCI had greater multivariable-adjusted odds of severe illness (aOR, 1.21; 95% CI, 1.01-1.45), more hospitalization days (aIRR, 1.06; 95% CI, 1.03-1.10), and rehospitalization (aHR, 1.07; 95% CI, 1.01-1.12).

Conclusions

In a cohort of hospitalized US veterans with SARS-CoV-2 infection, those with a higher CCI had more severe COVID-19 illness, more hospital days, and rehospitalization, after adjusting for vaccination status, age, sex, and calendar period.

Effectiveness of Remdesivir Treatment Protocols Among Patients Hospitalized with COVID-19: A Target Trial Emulation

BACKGROUND

Remdesivir is recommended for certain hospitalized patients with COVID-19. However, these recommendations are based on evidence from small randomized trials, early observational studies, or expert opinion. Further investigation is needed to better inform treatment guidelines with regard to the effectiveness of remdesivir among these patients.

METHODS

We emulated a randomized target trial using chargemaster data from 333 US hospitals from 1 May 2020 to 31 December 2021. We compared three treatment protocols: remdesivir within 2 days of hospital admission, no remdesivir within the first 2 days of admission, and no remdesivir ever. We used baseline comorbidities recorded from encounters up to 12 months before admission and identified the use of in-hospital medications, procedures, and oxygen supplementation from charges. We estimated the cumulative incidence of mortality or mechanical ventilation/extracorporeal membrane oxygenation with an inverse probability of censoring weighted estimator. We conducted analyses in the total population as well as in subgroups stratified by level of oxygen supplementation.

RESULTS

A total of 274,319 adult patients met the eligibility criteria for the study. Thirty-day in-hospital mortality risk differences for patients adhering to the early remdesivir protocol were -3.1% (95% confidence interval = -3.5%, -2.7%) compared to no early remdesivir and -3.7% (95% confidence interval -4.2%, -3.2%) compared to never remdesivir, with the strongest effect in patients needing high-flow oxygen. For mechanical ventilation/extracorporeal membrane oxygenation, risk differences were minimal.

CONCLUSIONS

We estimate that, among hospitalized patients with COVID-19, remdesivir treatment within 2 days of admission reduced 30-day in-hospital mortality, particularly for patients receiving supplemental oxygen on the day of admission.

Disease-Specific Factors Associated with Readmissions or Mortality After Hospital Discharge in COVID-19 Patients: a Retrospective Cohort Study

BACKGROUND

Understanding the implications of disease-specific factors beyond baseline patient characteristics for coronavirus disease 2019 (COVID-19) may allow for identification of indicators for safe hospital discharge.

OBJECTIVE

Assess whether disease-specific factors are associated with adverse events post-discharge using a data-driven approach.

DESIGN

Retrospective cohort study.

SETTING

Fifteen medical centers within Kaiser Permanente Southern California.

PARTICIPANTS

Adult patients (n=3508) discharged alive following hospitalization for COVID-19 between 05/01/2020 and 09/30/2020.

INTERVENTIONS

None.

MAIN MEASURES

Adverse events defined as all-cause readmission or mortality within 14 days of discharge. Least absolute shrinkage and selection operator (LASSO) was used for variable selection and logistic regression was performed to estimate odds ratio (OR) and 95% confidence interval (CI).

KEY RESULTS

Four variables including age, Elixhauser index, treatment with remdesivir, and symptom duration at discharge were selected by LASSO. Treatment with remdesivir was inversely associated with adverse events (OR: 0.46 [95%CI: 0.36-0.61]), while symptom duration ≤ 10 days was associated with adverse events (OR: 2.27 [95%CI: 1.79-2.87]) in addition to age (OR: 1.02 [95%CI: 1.01-1.03]) and Elixhauser index (OR: 1.15 [95%CI: 1.11-1.20]). A significant interaction between remdesivir and symptom duration was further observed (p=0.01). The association of remdesivir was stronger among those with symptom duration ≤10 days vs >10 days at discharge (OR: 0.30 [95%CI: 0.19-0.47] vs 0.62 [95%CI: 0.44-0.87]), while the association of symptom duration ≤ 10 days at discharge was weaker among those treated with remdesivir vs those not treated (OR: 1.31 [95%CI: 0.79-2.17] vs 2.71 [95%CI 2.05-3.59]).

CONCLUSIONS

Disease-specific factors including treatment with remdesivir, symptom duration, and their interplay may help guide clinical decision making at time of discharge.

Readmission Risk after COVID-19 Hospitalization: A Moderation Analysis by Vital Signs

Hospital admissions for coronavirus disease 2019 (COVID-19) are a common occurrence during periods of widespread viral transmission as are 30-day readmissions after COVID-19 hospitalization. This article provides an analysis of risk factors for readmission after COVID-19 hospitalization, in particular, vital signs upon discharge and their predictive value. In addition, this article evaluates whether the stabilization of vital signs within 24 hours of discharge can reduce readmission risk attributable to less modifiable primary factors such as underlying pulmonary and cardiac disease. The authors aim to aid the practicing clinician by providing an approach to safely risk stratify hospitalized COVID-19 patients who may be ready for discharge from the hospital.

Objective

Readmission to the hospital after hospitalization with coronavirus disease 2019 (COVID-19) is associated with significant morbidity and mortality. Hospital clinicians may identify the presence of a patient’s comorbid conditions, overall severity of illness, and clinical status at discharge as risk factors for readmission. Objective data are lacking to support reliance on these factors for discharge decision making. The objective of our study was to examine risk factors for readmission to the hospital after COVID-19 hospitalization and the impact of vital sign abnormalities, within 24 hours of discharge, on readmission rates.

Methods

In total, 2557 COVID-19-related hospital admissions within the Lifespan Health System, a large multicenter health system (Rhode Island), of 2230 unique patients aged 18 years and older, occurring from April 1, 2020 to December 31, 2020 were analyzed. Risk factors associated with readmission within 30 days were identified and analyzed using Cox regression. A moderation analysis by vital signs at discharge on the risk of readmission was performed.

Results

Clinical factors associated with readmissions included existing cardiovascular conditions (risk ratio 2.32, 95% confidence interval [CI] 1.10–4.90) and pulmonary disease (risk ratio 3.25, 95% CI 1.62–6.52). The absence of abnormal vital signs within 24 hours of discharge was associated with decreased 30-day readmission rates (risk ratio 0.70, 95% CI 0.52–0.94). Elevated C-reactive protein and d-dimer values and in-hospital complications including stroke, myocardial infarction, acute renal failure, and gastrointestinal bleeding were not associated with an increased risk of readmission. In moderation analysis, the presence of normal vital signs within 24 hours of discharge was associated with decreased readmission risk in patients who had primary risk factors for readmission including pulmonary disease (risk ratio 0.80, 95% CI 0.65–0.99), psychiatric disorders, and substance use (risk ratio 0.70, 95% CI 0.52–0.94).

Conclusions

Comorbid conditions, including pulmonary and cardiovascular disease, are associated with readmission risk after COVID-19 hospitalization. The normalization of vital signs within 24 hours of discharge during COVID-19 hospitalization may be an indicator of readiness for discharge and may mitigate some readmission risk conferred by comorbid conditions.

Systematic Review on COVID-19 Readmission and Risk Factors: Future of Machine Learning in COVID-19 Readmission Studies

In this review, current studies on hospital readmission due to infection of COVID-19 were discussed, compared, and further evaluated in order to understand the current trends and progress in mitigation of hospital readmissions due to COVID-19. Boolean expression of (“COVID-19” OR “covid19” OR “covid” OR “coronavirus” OR “Sars-CoV-2”) AND (“readmission” OR “re-admission” OR “rehospitalization” OR “rehospitalization”) were used in five databases, namely Web of Science, Medline, Science Direct, Google Scholar and Scopus. From the search, a total of 253 articles were screened down to 26 articles. In overall, most of the research focus on readmission rates than mortality rate. On the readmission rate, the lowest is 4.2% by Ramos-Martínez et al. from Spain, and the highest is 19.9% by Donnelly et al. from the United States. Most of the research (n = 13) uses an inferential statistical approach in their studies, while only one uses a machine learning approach. The data size ranges from 79 to 126,137. However, there is no specific guide to set the most suitable data size for one research, and all results cannot be compared in terms of accuracy, as all research is regional studies and do not involve data from the multi region. The logistic regression is prevalent in the research on risk factors of readmission post-COVID-19 admission, despite each of the research coming out with different outcomes. From the word cloud, age is the most dominant risk factor of readmission, followed by diabetes, high length of stay, COPD, CKD, liver disease, metastatic disease, and CAD. A few future research directions has been proposed, including the utilization of machine learning in statistical analysis, investigation on dominant risk factors, experimental design on interventions to curb dominant risk factors and increase the scale of data collection from single centered to multi centered.

Predictive modeling for COVID-19 readmission risk using machine learning algorithms

Introduction

The COVID-19 pandemic overwhelmed healthcare systems with severe shortages in hospital resources such as ICU beds, specialized doctors, and respiratory ventilators. In this situation, reducing COVID-19 readmissions could potentially maintain hospital capacity. By employing machine learning (ML), we can predict the likelihood of COVID-19 readmission risk, which can assist in the optimal allocation of restricted resources to seriously ill patients.

Methods

In this retrospective single-center study, the data of 1225 COVID-19 patients discharged between January 9, 2020, and October 20, 2021 were analyzed. First, the most important predictors were selected using the horse herd optimization algorithms. Then, three classical ML algorithms, including decision tree, support vector machine, and k-nearest neighbors, and a hybrid algorithm, namely water wave optimization (WWO) as a precise metaheuristic evolutionary algorithm combined with a neural network were used to construct predictive models for COVID-19 readmission. Finally, the performance of prediction models was measured, and the best-performing one was identified.

Results

The ML algorithms were trained using 17 validated features. Among the four selected ML algorithms, the WWO had the best average performance in tenfold cross-validation (accuracy: 0.9705, precision: 0.9729, recall: 0.9869, specificity: 0.9259, F-measure: 0.9795).

Conclusions

Our findings show that the WWO algorithm predicts the risk of readmission of COVID-19 patients more accurately than other ML algorithms. The models developed herein can inform frontline clinicians and healthcare policymakers to manage and optimally allocate limited hospital resources to seriously ill COVID-19 patients.

Coronavirus Disease 2019 and Hospital Readmissions: Patient Characteristics and Socioeconomic Factors Associated With Readmissions in an Urban Safety-Net Hospital System

BACKGROUND

It is not yet known whether socioeconomic factors (ie, social determinants of health) are associated with readmission following hospitalization for coronavirus disease 2019 (COVID-19).

METHODS

We conducted a retrospective cohort study of 6191 adult patients hospitalized with COVID-19 in a large New York City safety-net hospital system between March 1 and June 1, 2020. Associations between 30-day readmission and selected demographic characteristics, socioeconomic factors, prior health care utilization, and relevant features of the index hospitalization were analyzed using a multivariable generalized estimating equation model.

RESULTS

The readmission rate was 7.3%, with a median of 7 days between discharge and readmission. The following were risk factors for readmission: age 65 and older [adjusted odds ratio (aOR): 1.32; 95% confidence interval (CI): 1.13-1.55], history of homelessness, (aOR: 2.03 95% CI: 1.49-2.77), baseline coronary artery disease (aOR: 1.68; 95% CI: 1.34-2.10), congestive heart failure (aOR: 1.34; 95% CI: 1.20-1.49), cancer (aOR: 1.68; 95% CI: 1.26-2.24), chronic kidney disease (aOR: 1.74; 95% CI: 1.46-2.07). Patients' sex, race/ethnicity, insurance, and presence of obesity were not associated with increased odds of readmission. A longer length of stay (aOR: 0.98; 95% CI: 0.97-1.00) and use of noninvasive supplemental oxygen (aOR: 0.68; 95% CI: 0.56-0.83) was associated with lower odds of readmission. Upon readmission, 18.4% of patients required intensive care, and 13.7% expired.

CONCLUSION

We have found some factors associated with increased odds of readmission among patients hospitalized with COVID-19. Awareness of these risk factors, including patients' social determinants of health, may ultimately help to reduce readmission rates.

Impact of tozinameran (BNT162b2) mRNA vaccine on kidney transplant and chronic dialysis patients: 3-5 months follow-up

BACKGROUND

Determining the humoral immunogenicity of tozinameran (BNT162b2) in patients requiring chronic renal replacement therapy, and its impact on COVID-19 morbidity several months after vaccination, may guide risk assessment and changes in vaccination policy.

METHODS

In a prospective post-vaccination cohort study with up to 5 months follow-up we studied outpatient dialysis and kidney transplant patients and respective healthcare teams. Outcomes were anti S1/S2 antibody responses to vaccine or infection, and infection rate during follow-up.

RESULTS

One hundred seventy-five dialysis patients (40% women, 65 ± 15 years), 252 kidney transplant patients (33% women, 54 ± 14 years) and 71 controls (65% women, 44 ± 14 years) were followed. Three months or longer after vaccination we detected anti S1/S2 IgG antibodies in 79% of dialysis patients, 42% of transplant recipients and 100% of controls, whereas respective rates after infection were 94%, 69% and 100%. Predictors of non-response were older age, diabetes, history of cancer, lower lymphocyte count and lower vitamin-D levels. Factors associated with lower antibody levels in dialysis patients were modality (hemodialysis vs peritoneal) and high serum ferritin levels. In transplant patients, hypertension and higher calcineurin or mTOR inhibitor drug levels were linked with lower antibody response. Vaccination was associated with fewer subsequent infections (HR 0.23, p < 0.05). Moreover, higher antibody levels (particularly above 59 AU/ml) were associated with fewer events, with a HR 0.41 for each unit increased in log10titer (p < 0.05).

CONCLUSIONS

Dialysis patients, and more strikingly kidney transplant recipients, mounted reduced antibody response to COVID-19 mRNA vaccination. Lesser humoral response was associated with more infections. Measures to identify and protect non-responsive patients are required.

Hospital readmissions and post-discharge all-cause mortality in COVID-19 recovered patients; A systematic review and meta-analysis

OBJECTIVE

The present study aimed to perform a systematic review and meta-analysis on the prevalence of one-year hospital readmissions and post-discharge all-cause mortality in recovered COVID-19 patients. Moreover, the country-level prevalence of the outcomes was investigated.

METHODS

An extensive search was performed in Medline (PubMed), Embase, Scopus, and Web of Science databases until the end of August 3rd, 2021. A manual search was also performed in Google and Google Scholar search engines. Cohort and cross-sectional studies were included. Two independent reviewers screened the papers, collected data, and assessed the risk of bias and level of evidence. Any disagreement was resolved through discussion.

RESULTS

91 articles were included. 48 studies examined hospital readmissions; nine studies assessed post-discharge all-cause mortality, and 34 studies examined both outcomes. Analyses showed that the prevalence of hospital readmissions during the first 30 days, 90 days, and one-year post-discharge were 8.97% (95% CI: 7.44, 10.50), 9.79% (95% CI: 8.37, 11.24), and 10.34% (95% CI: 8.92, 11.77), respectively. The prevalence of post-discharge all-cause mortality during the 30 days, 90 days and one-year post-discharge was 7.87% (95% CI: 2.78, 12.96), 7.63% (95% CI: 4.73, 10.53) and 7.51% (95% CI, 5.30, 9.72), respectively. 30-day hospital readmissions and post-discharge mortality were 8.97% and 7.87%, respectively. The highest prevalence of hospital readmissions was observed in Germany (15.5%), Greece (15.5%), UK (13.5%), Netherlands (11.7%), China (10.8%), USA (10.0%) and Sweden (9.9%). In addition, the highest prevalence of post-discharge all-cause mortality belonged to Italy (12.7%), the UK (11.8%), and Iran (9.2%). Sensitivity analysis showed that the prevalence of one-year hospital readmissions and post-discharge all-cause mortality in high-quality studies were 10.38% and 4.00%, respectively.

CONCLUSION

10.34% of recovered COVID-19 patients required hospital readmissions after discharge. Most cases of hospital readmissions and mortality appear to occur within 30 days after discharge. The one-year post-discharge all-cause mortality rate of COVID-19 patients is 7.87%, and the majority of patients' readmission and mortality happens within the first 30 days post-discharge. Therefore, a 30-day follow-up program and patient tracking system for discharged COVID-19 patients seems necessary.

Factors predicting readmission in patients with COVID-19

OBJECTIVE

COVID-19 has been introduced by the World Health Organization as a health emergency worldwide. Up to 9% of the patients with COVID-19 may be readmitted by 2 months after discharge. This study aimed to estimate the readmission rate and identify main risk factors for readmission in these patients. In this prospective study, 416 discharged COVID patients followed up with a minimum 1 month and the readmission rate was recorded. Evaluated characteristics included time of readmission, age and sex, main symptoms of disease, result of computed tomography scan, reverse transcription polymerase chain reaction test and treatment modalities.

RESULTS

Regarding readmission, 51 patients of 416 discharged patients, was readmitted during the study period. The rate of readmission for 30 and 60 days after discharge was 7.6% and 8.1%, respectively. The median age of the readmitted patients was 67 years (IQR: 53-78). About 65% of readmitted patients had underlying disease. The most significant factor in readmission rate was related to the site of lung involvement (OR > 4). Age over 60 years, underlying disease especially diabetes (OR = 3.43), high creatinine level (≥ to 1.2 mg/dl) (OR = 2.15) were the most important predictors of readmission.