COVID-19 Hospital Admissions Research Articles

Extremely missing numerical data in Electronic Health Records for machine learning can be managed through simple imputation methods considering informative missingness: A comparative of solutions in a COVID-19 mortality case study

Background and objectiveReusing Electronic Health Records (EHRs) for Machine Learning (ML) leads on many occasions to extremely incomplete and sparse tabular datasets, which can hinder the model development processes and limit their performance and generalization. In this study, we aimed to characterize the most effective data imputation techniques and ML models for dealing with highly missing numerical data in EHRs, in the case where only a very limited number of data are complete, as opposed to the usual case of having a reduced number of missing values. MethodsWe used a case study including full blood count laboratory data, demographic and survival data in the context of COVID-19 hospital admissions and evaluated 30 processing pipelines combining imputation methods with ML classifiers. The imputation methods included missing mask, translation and encoding, mean imputation, k-nearest neighbors’ imputation, Bayesian ridge regression imputation and generative adversarial imputation networks. The classifiers included k-nearest neighbors, logistic regression, random forest, gradient boosting and deep multilayer perceptron. ResultsOur results suggest that in the presence of highly missing data, combining translation and encoding imputation—which considers informative missingness—with tree ensemble classifiers—random forest and gradient boosting—is a sensible choice when aiming to maximize performance, in terms of area under curve. ConclusionsBased on our findings, we recommend the consideration of this imputer-classifier configuration when constructing models in the presence of extremely incomplete numerical data in EHR.

Analysis of an Individualised Stepwise Approach to Non-Pharmacological Therapy in COVID-19

Background: Early intubation versus use of conventional or high-flow nasal cannula oxygen therapy (COT/HFNC), continuous positive airway pressure (CPAP), and non-invasive ventilation (NIV) has been debated throughout the COVID-19 pandemic. Our centre followed a stepwise approach, in concordance with German national guidelines, escalating non-invasive modalities prior to invasive mechanical ventilation (IMV) or extracorporeal membrane oxygenation (ECMO), rather than early or late intubation. Objectives: The aims of the study were to investigate the real-life usage of these modalities and analyse patient characteristics and survival. Method: A retrospective monocentric observation was conducted of all consecutive COVID-19 hospital admissions between March 2020 and December 2021 at a university-affiliated pulmonary centre in Germany. Anthropometric data, therapy, and survival status were descriptively analysed. Results: From 1,052 COVID-19-related admissions, 835 patients were included (54% male, median 58 years). Maximum therapy was as follows: 34% (n = 284) no therapy, 40% (n = 337) COT, 3% (n = 22) HFNC, 9% (n = 73) CPAP, 7% (n = 56) NIV, 4% (n = 34) IMV, and 3% (n = 29) ECMO. Of 551 patients treated with at least COT, 12.3% required intubation. Overall, 183 patients required intensive unit care, and 106 (13%) died. Of the 68 patients who received IMV/ECMO, 48 died (74%). The strategy for non-pharmacological therapy was individual but remained consistent throughout the studied period. Conclusions: This study provides valuable insight into COVID-19 care in Germany and shows how the majority of patients could be treated with the maximum treatment required according to disease severity following the national algorithm. Escalation of therapy modality is interlinked with disease severity and thus associated with mortality.

General Hospitalization and Intensive Care Unit-Related Factors of COVID-19 Patients in Northeastern Colombia: Baseline Characteristics of a Cohort Study.

Objective This study aims to describe demographic and clinical characteristics and the factors associated with the risk of COVID-19 general hospitalization and intensive care unit (ICU) care of patients who consulted in a third-level hospital in Santander, Colombia. Methods We used baseline data from an ambidirectional cohort study. We included all patients with positive real-time polymerase chain reaction (PCR) tests for COVID-19 who came to the emergency room (ER) for respiratory symptoms related to COVID-19. Information regarding patients' baseline characteristics and symptoms was collected through telephone interviews and review of medical records. Vital signs were extracted from medical records as well. Results We enrolled 3,030 patients, predominantly men, with a median age of 60(interquartile range (IQR): 44-73). Symptoms of the acute phase varied between men and women. Men presented with more respiratory symptoms, and women had general symptoms. Hypertension, obesity, and diabetes were common risk factors for hospital admission. Antibiotic consumption may also play a role in hospital admission. Conclusions Male sex, older age,hypertension, obesity, prior thrombotic events, and self-medicated antibiotics were associated with general hospitalization. Hypertension, obesity, diabetes, and cancer were associated with ICU admission. The Charlson comorbidity index (CCI) is a powerful tool for evaluate the impact of pre-existing health conditions on COVID-19 hospital admission. We highlight the importance of these findings as possible predictors in our region.

A proportional incidence rate model for aggregated data to study the vaccine effectiveness against COVID-19 hospital and ICU admissions.

We develop a proportional incidence model that estimates vaccine effectiveness (VE) at the population level using conditional likelihood for aggregated data. Our model assumes that the population counts of clinical outcomes for an infectious disease arise from a superposition of Poisson processes with different vaccination statuses. The intensity function in the model is calculated as the product of per capita incidence rate and the at-risk population size, both of which are time-dependent. We formulate a log-linear regression model with respect to the relative risk, defined as the ratio between the per capita incidence rates of vaccinated and unvaccinated individuals. In the regression analysis, we treat the baseline incidence rate as a nuisance parameter, similar to the Cox proportional hazard model in survival analysis. We then apply the proposed models and methods to age-stratified weekly counts of COVID-19-related hospital and ICU admissions among adults in Ontario, Canada. The data spanned from 2021 to February 2022, encompassing the Omicron era and the rollout of booster vaccine doses. We also discuss the limitations and confounding effects while advocating for the necessity of more comprehensive and up-to-date individual-level data that document the clinical outcomes and measure potentialconfounders.

Long term risk of death and readmission after hospital admission with covid-19 among older adults: retrospective cohort study

ObjectivesTo characterize the long term risk of death and hospital readmission after an index admission with covid-19 among Medicare fee-for-service beneficiaries, and to compare these outcomes with historical control patients...

Associations between self-reported healthcare disruption due to covid-19 and avoidable hospital admission: evidence from seven linked longitudinal studies for England

ObjectivesTo examine whether there is an association between people who experienced disrupted access to healthcare during the covid-19 pandemic and risk of an avoidable hospital admission.DesignObservational analysis using evidence from...

Long-term neurological outcome after COVID-19 using all SARS-CoV-2 test results and hospitalisations in Denmark with 22-month follow-up

Hospitalisation with COVID-19 is associated with an increased risk of neurological sequelae; however, representative nationwide studies comparing to other infections with similar severity and also including milder SARS-CoV-2 infections have been lacking. Using the nationwide Danish registers including all SARS-CoV-2 PCR test results and hospitalisations between March 1, 2020, and December 31, 2021, we estimate the risk of any first neurological disorder diagnosed in inpatient, outpatient, or emergency room settings. We show that positive tests increase the rate of neurological disorders by a hazard ratio of 1.96 (95% confidence interval: 1.88–2.05) compared to individuals not tested and by a hazard ratio of 1.11 (95% confidence interval: 1.07-1.16) compared to individuals with negative tests only. However, there is no evidence that the risk of neurological disorders is higher for individuals who test positive compared to non-COVID-19 infections treated with anti-infective medication. The risk of neurological disorders is increased after COVID-19-hospitalisation compared to no COVID-19 hospital admission; however, these risks are comparable to hospitalisation with other respiratory infections (P value 0.328). In conclusion, COVID-19 is associated with an increased risk of neurological disorders, but no more than that observed after other infections of similar severity.

T-cell immunity against SARS-CoV-2 measured by an interferon-γ release assay is strongly associated with patient outcomes in vaccinated persons hospitalized with Delta or Omicron variants.

We measured T-cell and antibody responses to SARS-CoV-2 in vaccinated patients hospitalized for COVID-19, and explored their potential value to predict outcomes. Prospective, longitudinal study including vaccinated patients hospitalized with Delta and Omicron SARS-CoV-2 variants. TrimericS-IgG antibodies and SARS-CoV-2 T-cell response were measured using a specific quantitative interferon-γ release assay (IGRA). Primary outcome was all-cause 28-day mortality or need for ICU admission. Cox models were used to assess associations with outcomes. 158 (87.3%) of 181 individuals had detectable SARS-CoV-2 antibodies, 92 (50.8%) showed SARS-CoV-2 specific T-cell responses, and 87 (48.1%) had both responses. Patients who died within 28 days or needed ICU were less likely to have both unspecific and specific T-cell responses in IGRA. In adjusted analyses, for the entire cohort, having both T-cell and antibody responses at admission (aHR:0.16; 95%CI, 0.05-0.58) and Omicron variant (aHR:0.38; 95%CI, 0.17-0.87) reduced the hazard of 28-day mortality or ICU, whereas higher Charlson comorbidity index (aHR:1.27; 95%CI, 1.07-1.51) and lower SpO2/FIO2 (aHR:2.36; 95%CI, 1.51-3.67) increased the risk. Pre-existing immunity against SARS-CoV-2 is strongly associated with patient outcomes in vaccinated individuals requiring hospital admission for COVID-19. Persons showing both T-cell and antibody responses have the lowest risk of severe outcomes.

The Impact of SARS-CoV-2 Lineages (Variants) and COVID-19 Vaccination on the COVID-19 Epidemic in South Africa: Regression Study.

Emerging SARS-CoV-2 variants have been attributed to the occurrence of secondary, tertiary, quaternary, and quinary COVID-19 epidemic waves threatening vaccine efforts owing to their immune invasiveness. Since the importation of SARS-CoV-2 in South Africa, with the first reported COVID-19 case on March 5, 2020, South Africa has observed 5 consecutive COVID-19 epidemic waves. The evolution of SARS-CoV-2 has played a major role in the resurgence of COVID-19 epidemic waves in South Africa and across the globe. We aimed to conduct descriptive and inferential statistical analysis on South African COVID-19 epidemiological data to investigate the impact of SARS-CoV-2 lineages and COVID-19 vaccinations in South African COVID-19 epidemiology. The general methodology involved the collation and stratification, covariance, regression analysis, normalization, and comparative inferential statistical analysis through null hypothesis testing (paired 2-tailed t tests) of South African COVID-19 epidemiological data. The mean daily positive COVID-19 tests in South Africa's first, second, third, fourth, and fifth COVID-19 epidemic wave periods were 11.5% (SD 8.58%), 11.5% (SD 8.45%), 13.3% (SD 9.72%), 13.1% (SD 9.91%), and 14.3% (SD 8.49%), respectively. The COVID-19 transmission rate in the first and second COVID-19 epidemic waves in South Africa was similar, while the COVID-19 transmission rate was higher in the third, fourth, and fifth COVID-19 epidemic waves than in the aforementioned waves. Most COVID-19 hospitalized cases in South Africa were in the general ward (60%-79.1%). Patients with COVID-19 on oxygen were the second-largest admission status (11.2%-16.8%), followed by patients with COVID-19 in the intensive care unit (8.07%-16.7%). Most patients hospitalized owing to COVID-19 in South Africa's first, second, third, and fourth COVID-19 epidemic waves were aged between 40 and 49 years (16.8%-20.4%) and 50 and 59 years (19.8%-25.3%). Patients admitted to the hospital owing to COVID-19 in the age groups of 0 to 19 years were relatively low (1.98%-4.59%). In general, COVID-19 hospital admissions in South Africa for the age groups between 0 and 29 years increased after each consecutive COVID-19 epidemic wave, while for age groups between 30 and 79 years, hospital admissions decreased. Most COVID-19 hospitalization deaths in South Africa in the first, second, third, fourth, and fifth COVID-19 epidemic waves were in the ages of 50 to 59 years (15.8%-24.8%), 60 to 69 years (15.9%-29.5%), and 70 to 79 years (16.6%-20.7%). The relaxation of COVID-19 nonpharmaceutical intervention health policies in South Africa and the evolution of SARS-CoV-2 were associated with increased COVID-19 transmission and severity in the South African population. COVID-19 vaccination in South Africa was strongly associated with a decrease in COVID-19 hospitalization and severity in South Africa.

Patient emergency health-care use before hospital admission for COVID-19 and long-term outcomes in Scotland: a national cohort study

It is unclear what effect the pattern of health-care use before admission to hospital with COVID-19 (index admission) has on the long-term outcomes for patients. We sought to describe mortality and emergency readmission to hospital after discharge following the index admission (index discharge), and to assess associations between these outcomes and patterns of health-care use before such admissions. We did a national, retrospective, complete cohort study by extracting data from several national databases and linking the databases for all adult patients admitted to hospital in Scotland with COVID-19. We used latent class trajectory modelling to identify distinct clusters of patients on the basis of their emergency admissions to hospital in the 2 years before the index admission. The primary outcomes were mortality and emergency readmission up to 1 year after index admission. We used multivariable regression models to explore associations between these outcomes and patient demographics, vaccination status, level of care received in hospital, and previous emergency hospital use. Between March 1, 2020, and Oct 25, 2021, 33 580 patients were admitted to hospital with COVID-19 in Scotland. Overall, the Kaplan-Meier estimate of mortality within 1 year of index admission was 29·6% (95% CI 29·1-30·2). The cumulative incidence of emergency hospital readmission within 30 days of index discharge was 14·4% (95% CI 14·0-14·8), with the number increasing to 35·6% (34·9-36·3) patients at 1 year. Among the 33 580 patients, we identified four distinct patterns of previous emergency hospital use: no admissions (n=18 772 [55·9%]); minimal admissions (n=12 057 [35·9%]); recently high admissions (n=1931 [5·8%]), and persistently high admissions (n=820 [2·4%]). Patients with recently or persistently high admissions were older, more multimorbid, and more likely to have hospital-acquired COVID-19 than patients with no or minimal admissions. People in the minimal, recently high, and persistently high admissions groups had an increased risk of mortality and hospital readmission compared with those in the no admissions group. Compared with the no admissions group, mortality was highest in the recently high admissions group (post-hospital mortality HR 2·70 [95% CI 2·35-2·81]; p<0·0001) and the risk of readmission was highest in the persistently high admissions group (3·23 [2·89-3·61]; p<0·0001). Long-term mortality and readmission rates for patients hospitalised with COVID-19 were high; within 1 year, one in three patients had died and a third had been readmitted as an emergency. Patterns of hospital use before index admission were strongly predictive of mortality and readmission risk, independent of age, pre-existing comorbidities, and COVID-19 vaccination status. This increasingly precise identification of individuals at high risk of poor outcomes from COVID-19 will enable targeted support. Chief Scientist Office Scotland, UK National Institute for Health Research, and UK Research and Innovation.

Risk prediction of covid-19 related death or hospital admission in adults testing positive for SARS-CoV-2 infection during the omicron wave in England (QCOVID4): cohort study

ObjectivesTo derive and validate risk prediction algorithms (QCOVID4) to estimate the risk of covid-19 related death and hospital admission in people with a positive SARS-CoV-2 test result during the period...

Safety of COVID-19 booster dose: is the juice worth the squeeze?

Safety of COVID-19 booster dose: is the juice worth the squeeze?

#3356 COVID-19 PANDEMIC EFFECT ON HOSPITALIZATION OF HEMODIALYSIS PATIENTS

Abstract Background and Aims During the COVID-19 pandemic many facets of our lives were affected, including the healthcare systems worldwide. We aimed to assess the hospitalization rates of hemodialysis (HD) patients with confirmed COVID-19 infection (COVID-19+) versus other patients (i.e., patients with no documented COVID-19 infection in the European Clinical Database (EuCliD®)) in 2020 and their relation to HD patients from the pre-pandemic era in 2019. Method We included 63,216 HD patients treated in 2019-2020 in NephroCare centers from 23 countries from EuCliD® in the Europe, Middle East, and Africa (EMEA) region. Monthly hospitalization rates (hospital admission per 10,000 patients) were estimated separately for COVID-19+ and other HD patients in 2020, and HD patients treated in 2019, with COVID-19+ status as a time-varying variable. The number of COVID-19 cases and deaths were extracted from data released by the European Commission Joint Research Centre (ECJRC)[1]. The correlation between the monthly hospitalization rates and numbers of COVID-19 cases and deaths in general population (GP) were evaluated. Results Characteristics of the approximately 42,000-43,000 monthly treated HD patients were comparable between 2019 and 2020 (Table 1). The hospitalization rates were much higher for COVID-19+ HD patients throughout 2020 compared to other HD patients in 2020 and HD patients in 2019. The hospitalization rates were highest in the spring of 2020 for COVID-19+ HD patients. However, in 2020 other HD patients’ hospitalization rates were lower than the 2019 HD patients’ hospitalization rates. Interestingly the hospitalization rates of other HD patients in 2020 dipped even more below hospitalizations for HD patients in 2019 following the GP pandemic waves (Figure 1). Hospital admission for both COVID-19+ and other HD patients were inversely correlation with the pandemic conditions in GP (Figure 2; spearman correlation coefficients [ρ] with COVID-19 cases/deaths in GP, -0.77/-0.22 and -0.29/-0.64, respectively, for COVID-19+ and other HD patients). Conclusion Our study indicated the COVID-19 pandemic had an impact on hospitalization rates of both HD patients with a COVID-19 diagnosis and those patients without a documented COVID-19 infection. Potential reasons of decreased hospitalization rates in patients without a documented COVID-19 diagnosis could be healthcare resource constraints or regarding patient concerns about exposure to COVID-19 in hospital settings [2].

#5056 REGIONAL VARIATION IN COVID-19 HOSPITALISATION AND OUTCOMES IN ADULTS IN ENGLAND

Abstract Background and Aims The possibility that population demographic characteristics, changes in viral strain and therapeutic advances in management of COVID-19 might influence clinical outcomes is of great interest given potential regional variations. The aim of our study was to describe regional variation in COVID-19 hospitalisation rates in England and factors affecting in-hospital mortality as well as acute kidney injury (AKI). Method In this retrospective cohort study using hospital episode statistics, we collected data from all adult hospitalised patients with COVID-19 infection (diagnostic code U07.1 in any of the 20 diagnoses codes) between 1st March 2020 and 31st March 2021, to end of discharge period. We also extracted all available secondary diagnoses and procedure codes. Patients with codes for chronic dialysis were excluded. We divided the observation period as per the dominant SARS CoV-2 variant and further, in relation to publication of the RECOVERY trial. SARS CoV-2 “Other” strain was prevalent between 1st March 2020 and 21st December 2020 and “Alfa” between 22nd December 2020 to 17th May 2021. The end date of each phase was based on more than 50% decline in each variant. Results We extracted 3,24,748 finished consultant episodes (FCE) for all patients with U071 code in any of the 20 diagnoses codes and admitted during the study period. After exclusion of multiple FCEs within same spell, chronic RRT and patients not residing in England, there were 749,844 unique admission spells with ICD10 code of U071 in one of the diagnoses codes in 337,029 patients. London had the highest number of COVID-19 admissions at 131,338 (18%) followed by North-west region with 122,683 admissions (16%). Population incidence of COVID-19 hospital admissions was highest in North-west at 21,167 per million population (pmp) and lowest in South-west at 9,292 admissions pmp. Patients with COVID-19 were younger (67.0 ± 17.7 years) in London as compared to patients in East of England (72.2 ± 16.8 years). Length of stay was lowest in North-east (12.2 ± 14.9 days) and highest in North-west (15.2 ± 17.9 days). As compared to London, all eight regions had higher odds of death, ranging from OR of 1.04 (95% CI 1.00, 1.07) in South-west to OR 1.24 (95% CI 1.21, 1.28) in North-west. Odds of death were lower in patients with COVID-19 in post-RECOVERY period, both with “Other” (OR 0.72, 95% CI 0.71, 0.74) and Alfa strain of SARS CoV-2 (OR 0.75, 95% CI 0.74, 0.76). Overall, AKI incidence was 30.3%. All eight regions in England had lower odds of developing AKI as compared to London. Post-RECOVERY periods with the “Other” variant (OR 0.87, 95% CI 0.85, 0.88) and “Alfa variant (OR 0.87, 95% CI 0.86, 0.88) both had lower odds of developing AKI. Conclusion This large national study of COVID-19 found a higher hospital admission rate and AKI incidence but lower odds of death in London compared with other regions in England. The incidence of AKI and mortality due to any cause were lower in the post-RECOVERY period irrespective of the prevalent SARS CoV-2 strain.

Impact of prior SARS-COV-2 infection and vaccination on COVID-19 hospital admission and mortality amongst nursing home residents.

Nursing home residents (NHRs) have experienced disproportionately high risk of severe outcomes due to COVID-19 infection. We investigated the impact of COVID-19 vaccinations and previous SARS-CoV-2 episodes in preventing hospitalization and mortality in NHRs. Retrospective study of a cohort of all NHRs in our area who were alive at the start of the vaccination campaign. The first three doses of SARS-CoV-2 vaccine and prior COVID-19 infections were registered. The main outcomes were hospital admission and mortality during each follow up. Random effects time-varying Cox models adjusted for age, sex, and comorbidities were fitted to estimate hazard ratios (HRs) according to vaccination status. COVID-19 hospitalization and death rates for unvaccinated NHRs were respectively 2.39 and 1.42 per 10,000 person-days, falling after administration of the second dose (0.37 and 0.34) and rising with the third dose (1.08 and 0.8). Rates were much lower amongst people who had previously had COVID-19. Adjusted HRs indicated a significant decrease in hospital admission amongst those with a two- and three-dose status; those who had had a previous COVID-19 infection had even lower hospital admission rates. Death rates decreased as NHRs received two and three doses, and the probability of death was much lower among those who had previously had the infection. The effectiveness of current vaccines against severe COVID-19 disease in NHRs remains high and SARS-CoV-2 episodes prior to vaccination entail a major reduction in hospitalization and mortality rates. The protection conferred by vaccines appears to decline in the following months. ClinicalTrials.gov Identifier: NCT04463706.

Differential in-hospital mortality and intensive care treatment over time: Informing hospital pathways for modelling COVID-19 in South Africa.

There are limited published data within sub-Saharan Africa describing hospital pathways of COVID-19 patients hospitalized. These data are crucial for the parameterisation of epidemiological and cost models, and for planning purposes for the region. We evaluated COVID-19 hospital admissions from the South African national hospital surveillance system (DATCOV) during the first three COVID-19 waves between May 2020 and August 2021. We describe probabilities and admission into intensive care units (ICU), mechanical ventilation, death, and lengths of stay (LOS) in non-ICU and ICU care in public and private sectors. A log-binomial model was used to quantify mortality risk, ICU treatment and mechanical ventilation between time periods, adjusting for age, sex, comorbidity, health sector and province. There were 342,700 COVID-19-related hospital admissions during the study period. Risk of ICU admission was 16% lower during wave periods (adjusted risk ratio (aRR) 0.84 [0.82-0.86]) compared to between-wave periods. Mechanical ventilation was more likely during a wave overall (aRR 1.18 [1.13-1.23]), but patterns between waves were inconsistent, while mortality risk in non-ICU and ICU were 39% (aRR 1.39 [1.35-1.43]) and 31% (aRR 1.31 [1.27-1.36]) higher during a wave, compared to between-wave periods, respectively. If patients had had the same probability of death during waves vs between-wave periods, we estimated approximately 24% [19%-30%] of deaths (19,600 [15,200-24,000]) would not have occurred over the study period. LOS differed by age (older patients stayed longer), ward type (ICU stays were longer than non-ICU) and death/recovery outcome (time to death was shorter in non-ICU); however, LOS remained similar between time periods. Healthcare capacity constraints as inferred by wave period have a large impact on in-hospital mortality. It is crucial for modelling health systems strain and budgets to consider how input parameters related to hospitalisation change during and between waves, especially in settings with severely constrained resources.

Incidence of idiopathic nephrotic syndrome during the Covid-19 pandemic in the Paris area (France) and in the Netherlands

BackgroundThe aetiology of idiopathic nephrotic syndrome (INS) remains partially unknown. Viral infections have been associated with INS onset. Since we observed fewer first onset INS cases during the Covid-19 pandemic, we hypothesised that lower INS incidence was the result of lockdown measures. Therefore, the aim of this study was to evaluate the incidence of childhood INS before and during the COVID-19 pandemic in two independent European INS cohorts.MethodsChildren with new INS in the Netherlands (2018–2021) and Paris area (2018–2021) were included. We estimated incidences using census data for each region. Incidences were compared using two proportion Z-tests.ResultsA total of 128 and 324 cases of first onset INS were reported in the Netherlands and Paris area, respectively, corresponding to an annual incidence of 1.21 and 2.58 per 100,000 children/year. Boys and young children (< 7 years) were more frequently affected. Incidence before and during the pandemic did not differ. When schools were closed, incidence was lower in both regions: 0.53 vs. 1.31 (p = 0.017) in the Netherlands and 0.94 vs. 2.63 (p = 0.049) in the Paris area. During peaks of hospital admissions for Covid-19, no cases were reported in the Netherlands or Paris area.ConclusionsIncidence of INS before and during the Covid-19 pandemic was not different, but when schools were closed during lockdown, incidence was significantly lower. Interestingly, incidences of other respiratory viral infections were also reduced as was air pollution. Together, these results argue for a link between INS onset and viral infections and/or environmental factors.Graphical abstractA higher resolution version of the Graphical abstract is available as Supplementary information

Correlations and Timeliness of COVID-19 Surveillance Data Sources and Indicators - United States, October 1, 2020-March 22, 2023.

When the U.S. COVID-19 public health emergency declaration expires on May 11, 2023, national reporting of certain categories of COVID-19 public health surveillance data will be transitioned to other data sources or will be discontinued; COVID-19 hospitalization data will be the only data source available at the county level (1). In anticipation of the transition, national COVID-19 surveillance data sources and indicators were evaluated for purposes of ongoing monitoring. The timeliness and correlations among surveillance indicators were analyzed to assess the usefulness of COVID-19-associated hospital admission rates as a primary indicator for monitoring COVID-19 trends, as well as the suitability of other replacement data sources. During April 2022-March 2023, COVID-19 hospital admission rates from the National Healthcare Safety Network (NHSN)† lagged 1 day behind case rates and 4 days behind percentages of positive test results and COVID-19 emergency department (ED) visits from the National Syndromic Surveillance Program (NSSP). In the same analysis, National Vital Statistics System (NVSS) trends in the percentage of deaths that were COVID-19-associated, which is tracked by date of death rather than by report date, were observable 13 days earlier than those from aggregate death count data, which will be discontinued (1). During October 2020-March 2023, strong correlations were observed between NVSS and aggregate death data (0.78) and between the percentage of positive SARS-CoV-2 test results from the National Respiratory and Enteric Viruses Surveillance System (NREVSS) and COVID-19 electronic laboratory reporting (CELR) (0.79), which will also be discontinued (1). Weekly COVID-19 Community Levels (CCLs) will be replaced with levels of COVID-19 hospital admission rates (low, medium, or high) which demonstrated >99% concordance by county during February 2022-March 2023. COVID-19-associated hospital admission levels are a suitable primary metric for monitoring COVID-19 trends, the percentage of COVID-19 deaths is a timely disease severity indicator, and the percentages of positive SARS-CoV-2 test results from NREVSS and ED visits serve as early indicators for COVID-19 monitoring. Collectively, these surveillance data sources and indicators can support monitoring of the impact of COVID-19 and related prevention and control strategies as ongoing public health priorities.

PO-02-173 TRENDS IN COVID-19 AND ATRIAL FIBRILLATION: A NATIONAL INPATIENT SAMPLE TREND FROM 2020

Atrial fibrillation (AF) is the most common arrhythmia in the United States. Concomitant Covid-19 infection and the outcomes of AF are unknown. The study's goals were to analyze the outcomes of AF during the Covid-19 pandemic. We conducted a retrospective cohort study based on the 2020 National Inpatient Sample (NIS) of Adults (>18 years) hospitalized for AF as the primary admitting diagnosis based on the ICD-10 codes and stratified these groups into concomitant covid-19 infection vs. non-covid-19 infection. All-cause mortality was our primary outcome, while the rate of ICU admission, length of stay, hospital charges were our secondary outcomes. Temporal trends were assessed using logistic regression. In 2020, there were 1,994,985 admissions for atrial fibrillation, out of whom 104,495 (5.3%) had concomitant Covid-19. In the 104,495 AF admissions with covid-19, the mean age was 75y and 56.8% were males. Our results, image 1, showed AF with and without concomitant Covid-19 had similar rates of comorbid conditions including HTN, DM, OSA, CAD. HFrEF, and ESRD. AF patients with Covid-19 infection had a lower prevalence of smoking (31.83% vs. 39.4%, p<.001) and alcohol use (2% vs. 4.2%, p<.001). AF patients from both groups had similar rates of stroke (1.6% vs. 1.0%, p<.001). New AF patients with concomitant Covid-19 had worsening in-hospital outcomes such as shock (12.8% vs. 3.7%, p<.001), admission to the ICU (18.1% vs. 6.4%, p<0.001), higher all-cause mortality (21.8% vs. 3.9%, p<0.001), a longer length of stay (9.96 days vs 6.08 days, p<.001), and total hospital costs ($114,387 vs. $85,830, p<.0001). The incidence of AF catheter ablation on initial hospital admission for AF Covid-19 was lower compared to the AF non-covid-19 patients (.08% vs. 1.39%, p<.001). In 2020, Covid-19 infection was an independent predictor of higher all-cause mortality, length of stay, and costs in patients admitted for atrial fibrillation. In addition, these patients were less likely to get catheter ablation on hospital admission.

Effects of sleep disturbance on dyspnoea and impaired lung function following hospital admission due to COVID-19 in the UK: a prospective multicentre cohort study

Sleep disturbance is common following hospital admission both for COVID-19 and other causes. The clinical associations of this for recovery after hospital admission are poorly understood despite sleep disturbance contributing to morbidity in other scenarios. We aimed to investigate the prevalence and nature of sleep disturbance after discharge following hospital admission for COVID-19 and to assess whether this was associated with dyspnoea. CircCOVID was a prospective multicentre cohort substudy designed to investigate the effects of circadian disruption and sleep disturbance on recovery after COVID-19 in a cohort of participants aged 18 years or older, admitted to hospital for COVID-19 in the UK, and discharged between March, 2020, and October, 2021. Participants were recruited from the Post-hospitalisation COVID-19 study (PHOSP-COVID). Follow-up data were collected at two timepoints: an early time point 2-7 months after hospital discharge and a later time point 10-14 months after hospital discharge. Sleep quality was assessed subjectively using the Pittsburgh Sleep Quality Index questionnaire and a numerical rating scale. Sleep quality was also assessed with an accelerometer worn on the wrist (actigraphy) for 14 days. Participants were also clinically phenotyped, including assessment of symptoms (ie, anxiety [Generalised Anxiety Disorder 7-item scale questionnaire], muscle function [SARC-F questionnaire], dyspnoea [Dyspnoea-12 questionnaire] and measurement of lung function), at the early timepoint after discharge. Actigraphy results were also compared to a matched UK Biobank cohort (non-hospitalised individuals and recently hospitalised individuals). Multivariable linear regression was used to define associations of sleep disturbance with the primary outcome of breathlessness and the other clinical symptoms. PHOSP-COVID is registered on the ISRCTN Registry (ISRCTN10980107). 2320 of 2468 participants in the PHOSP-COVID study attended an early timepoint research visit a median of 5 months (IQR 4-6) following discharge from 83 hospitals in the UK. Data for sleep quality were assessed by subjective measures (the Pittsburgh Sleep Quality Index questionnaire and the numerical rating scale) for 638 participants at the early time point. Sleep quality was also assessed using device-based measures (actigraphy) a median of 7 months (IQR 5-8 months) after discharge from hospital for 729 participants. After discharge from hospital, the majority (396 [62%] of 638) of participants who had been admitted to hospital for COVID-19 reported poor sleep quality in response to the Pittsburgh Sleep Quality Index questionnaire. A comparable proportion (338 [53%] of 638) of participants felt their sleep quality had deteriorated following discharge after COVID-19 admission, as assessed by the numerical rating scale. Device-based measurements were compared to an age-matched, sex-matched, BMI-matched, and time from discharge-matched UK Biobank cohort who had recently been admitted to hospital. Compared to the recently hospitalised matched UK Biobank cohort, participants in our study slept on average 65 min (95% CI 59 to 71) longer, had a lower sleep regularity index (-19%; 95% CI -20 to -16), and a lower sleep efficiency (3·83 percentage points; 95% CI 3·40 to 4·26). Similar results were obtained when comparisons were made with the non-hospitalised UK Biobank cohort. Overall sleep quality (unadjusted effect estimate 3·94; 95% CI 2·78 to 5·10), deterioration in sleep quality following hospital admission (3·00; 1·82 to 4·28), and sleep regularity (4·38; 2·10 to 6·65) were associated with higher dyspnoea scores. Poor sleep quality, deterioration in sleep quality, and sleep regularity were also associated with impaired lung function, as assessed by forced vital capacity. Depending on the sleep metric, anxiety mediated 18-39% of the effect of sleep disturbance on dyspnoea, while muscle weakness mediated 27-41% of this effect. Sleep disturbance following hospital admission for COVID-19 is associated with dyspnoea, anxiety, and muscle weakness. Due to the association with multiple symptoms, targeting sleep disturbance might be beneficial in treating the post-COVID-19 condition. UK Research and Innovation, National Institute for Health Research, and Engineering and Physical Sciences Research Council.