COVID-19 hospital prevalence as a risk factor for mortality: an observational study of a multistate cohort of 62 hospitals

Measures and Impact of Caseload Surge During the COVID-19 Pandemic: A Systematic Review

OBJECTIVES

COVID-19 pandemic surges strained hospitals globally. We performed a systematic review to examine measures of pandemic caseload surge and its impact on mortality of hospitalized patients.

DATA SOURCES

PubMed, Embase, and Web of Science.

STUDY SELECTION

English-language studies published between December 1, 2019, and November 22, 2023, which reported the association between pandemic "surge"-related measures and mortality in hospitalized patients.

DATA EXTRACTION

Three authors independently screened studies, extracted data, and assessed individual study risk of bias. We assessed measures of surge qualitatively across included studies. Given multidomain heterogeneity, we semiquantitatively aggregated surge-mortality associations.

DATA SYNTHESIS

Of 17,831 citations, we included 39 studies, 17 of which specifically described surge effects in ICU settings. The majority of studies were from high-income countries ( n = 35 studies) and included patients with COVID-19 ( n = 31). There were 37 different surge metrics which were mapped into four broad themes, incorporating caseloads either directly as unadjusted counts ( n = 11), nested in occupancy ( n = 14), including additional factors (e.g., resource needs, speed of occupancy; n = 10), or using indirect proxies (e.g., altered staffing ratios, alternative care settings; n = 4). Notwithstanding metric heterogeneity, 32 of 39 studies (82%) reported detrimental adjusted odds/hazard ratio for caseload surge-mortality outcomes, reporting point estimates of up to four-fold increased risk of mortality. This signal persisted among study subgroups categorized by publication year, patient types, clinical settings, and country income status.

CONCLUSIONS

Pandemic caseload surge was associated with lower survival across most studies regardless of jurisdiction, timing, and population. Markedly variable surge strain measures precluded meta-analysis and findings have uncertain generalizability to lower-middle-income countries (LMICs). These findings underscore the need for establishing a consensus surge metric that is sensitive to capturing harms in everyday fluctuations and future pandemics and is scalable to LMICs.

COVID-19 Admission Rates and Changes in Care Quality in US Hospitals

Importance

Unprecedented increases in hospital occupancy rates during COVID-19 surges in 2020 caused concern over hospital care quality for patients without COVID-19.

Objective

To examine changes in hospital nonsurgical care quality for patients without COVID-19 during periods of high and low COVID-19 admissions.

Design, Setting, and Participants

This cross-sectional study used data from the 2019 and 2020 Agency for Healthcare Research and Quality's Healthcare Cost and Utilization Project State Inpatient Databases. Data were obtained for all nonfederal, acute care hospitals in 36 states with admissions in 2019 and 2020, and patients without a diagnosis of COVID-19 or pneumonia who were at risk for selected quality indicators were included. The data analysis was performed between January 1, 2023, and March 15, 2024.

Exposure

Each hospital and week in 2020 was categorized based on the number of COVID-19 admissions per 100 beds: less than 1.0, 1.0 to 4.9, 5.0 to 9.9, 10.0 to 14.9, and 15.0 or greater.

Main Outcomes and Measures

The main outcomes were rates of adverse outcomes for selected quality indicators, including pressure ulcers and in-hospital mortality for acute myocardial infarction, heart failure, acute stroke, gastrointestinal hemorrhage, hip fracture, and percutaneous coronary intervention. Changes in 2020 compared with 2019 were calculated for each level of the weekly COVID-19 admission rate, adjusting for case-mix and hospital-month fixed effects. Changes during weeks with high COVID-19 admissions (≥15 per 100 beds) were compared with changes during weeks with low COVID-19 admissions (<1 per 100 beds).

Results

The analysis included 19 111 629 discharges (50.3% female; mean [SD] age, 63.0 [18.0] years) from 3283 hospitals in 36 states. In weeks 18 to 48 of 2020, 35 851 hospital-weeks (36.7%) had low COVID-19 admission rates, and 8094 (8.3%) had high rates. Quality indicators for patients without COVID-19 significantly worsened in 2020 during weeks with high vs low COVID-19 admissions. Pressure ulcer rates increased by 0.09 per 1000 admissions (95% CI, 0.01-0.17 per 1000 admissions; relative change, 24.3%), heart failure mortality increased by 0.40 per 100 admissions (95% CI, 0.18-0.63 per 100 admissions; relative change, 21.1%), hip fracture mortality increased by 0.40 per 100 admissions (95% CI, 0.04-0.77 per 100 admissions; relative change, 29.4%), and a weighted mean of mortality for the selected indicators increased by 0.30 per 100 admissions (95% CI, 0.14-0.45 per 100 admissions; relative change, 10.6%).

Conclusions and Relevance

In this cross-sectional study, COVID-19 surges were associated with declines in hospital quality, highlighting the importance of identifying and implementing strategies to maintain care quality during periods of high hospital use.

COVID-19 inpatient mortality in Brazil from 2020 to 2022: a cross-sectional overview study based on secondary data

BACKGROUND

In Brazil, the COVID-19 pandemic found the universal and public Unified Health System (SUS) with problems accumulated over time, due, among other reasons, to low investments, and disparities in resource distribution. The preparedness and response of the healthcare system, involving the SUS and a private sector, was affected by large socioeconomic and healthcare access inequities. This work was aimed at offering an overview of COVID-19 inpatient mortality during the pandemic in Brazil, exploring factors associated with its variations and, specifically, differences across public, private (for-profit) and philanthropic (private non-profit) inpatient healthcare units, providers, and non-providers of services to the SUS.

METHODS

This cross-sectional study used public secondary data. The main data source was the SIVEP-Gripe, which comprises data on severe acute respiratory illness records prospectively collected. We also employed the National Record of Health Establishments, the SUS' Hospitalization Information System and municipalities' data from IBGE. We considered adult COVID-19 hospitalizations registered in SIVEP-Gripe from February 2020 to December 2022 in inpatient healthcare units with a minimum of 100 cases in the period. Data analyses explored the occurrence of inpatient mortality, employing general linear mixed models to identify the effects of patients', health care processes', healthcare units' and municipalities' characteristics on it.

RESULTS

About 70% of the COVID-19 hospitalizations in Brazil were covered by the SUS, which attended the more vulnerable population groups and had worse inpatient mortality. In general, non-SUS private and philanthropic hospitals, mostly reimbursed by healthcare insurance plans accessible for more privileged socioeconomic classes, presented the best outcomes. Southern Brazil had the best performance among the macro-regions. Black and indigenous individuals, residents of lower HDI municipalities, and those hospitalized out of their residence city presented higher odds of inpatient mortality. Moreover, adjusted inpatient mortality rates were higher in the pandemic peak moments and were significantly reduced after COVID-19 vaccination reaching a reasonable coverage, from July 2021.

CONCLUSIONS

COVID-19 exposed socioeconomic and healthcare inequalities and the importance and weaknesses of SUS in Brazil. This work indicates the need to revert the disinvestment in the universal public system, a fundamental policy for reduction of inequities in the country.

Clinical characteristics and predictors for in-hospital mortality in adult COVID-19 patients: A retrospective single center cohort study in Vilnius, Lithuania

BACKGROUND

The COVID-19 infection had spread worldwide causing many deaths. Mortality rates and patients' characteristics varied within and between countries, making it important to understand the peculiarities of different populations. The aim of this study was to identify the main predictors associated with in-hospital mortality due to COVID-19 in Vilnius, Lithuania.

MATERIALS AND METHODS

This was a retrospective observational cohort study conducted at Vilnius University Hospital Santaros Clinics, Lithuania. The study included SARS-CoV-2 positive patients aged over 18 years and hospitalized between March 2020 and May 2021. Depersonalized data were retrieved from electronic medical records. The predictive values of laboratory parameters were evaluated using ROC analysis. Multivariable binary logistic regression was performed to reveal predictors of in-hospital mortality due to COVID-19.

RESULTS

Among 2794 patients, 54.4% were male, the age median was 59 years (IQR 48-70), 47.4% had at least one comorbidity. The most common comorbidities were arterial hypertension (36.9%) and diabetes mellitus (13.7%). Overall, 12.7% of patients died. Multivariable regression revealed that age (OR 1.04, 95%CI 1.02-1.06), congestive heart failure (OR 3.06, 95%CI 1.96-4.77), obesity (OR 3.90, 95%CI 2.12-7.16), COPD (OR 2.92, 95%CI 1.12-7.60), previous stroke (OR 5.80, 95%CI 2.07-16.21), urea >7.01 mmol/l (OR 2.32, 95%CI 1.47-3.67), AST/ALT >1.49 (OR 1.54, 95%CI 1.08-2.21), LDH >452.5 U/l (OR 2.60, 95%CI 1.74-3.88), CRP >92.68 mg/l (OR 1.58, 95%CI 1.06-2.35), IL-6 >69.55 ng/l (OR 1.62, 95%CI 1.10-2.40), and troponin I >18.95 ng/l (OR 2.04, 95%CI 1.38-3.02), were associated with increased risk for in-hospital mortality in COVID-19 patients.

CONCLUSIONS

Age, congestive heart failure, obesity, COPD, prior stroke, and increased concentration of urea, LDH, CRP, IL-6, troponin I, ALT to AST ratio were identified to be the predictors for in-hospital mortality of COVID-19 patients.

Experience of the United Arab Emirates in the use of monoclonal antibody drug sotrovimab in high-risk vaccinated and unvaccinated patients with COVID-19: an observational cohort study

OBJECTIVES

Monoclonal antibodies can slow COVID-19 progression. This study describes the experience of using sotrovimab in patients with COVID-19 at high risk for disease progression and hospitalisation within the United Arab Emirates (UAE).

DESIGN

Observational cohort study.

SETTING

A tertiary hospital in the Emirate of Sharjah, UAE.

PARTICIPANTS

Patients with mild or moderate COVID-19 at high risk for disease progression.

INTERVENTIONS

Infusion with a single 500 mg dose of the monoclonal antibody drug sotrovimab.

PRIMARY AND SECONDARY OUTCOME MEASURES

Any adverse effect within 24 hours, disease progression within 5 days, emergency department visit within 10 days, hospital admission within 10 days or mortality within 28 days of infusion.

RESULTS

3227 high-risk COVID-19 patients were infused with sotrovimab during the mild (n=3107, 96.3%) or moderate (n=120, 3.7%) disease stages. The incidence of at least one outcome was recorded in 196 (6.1%) of the patients (60.7 per 1000 patients). The most common outcome was disease progression within 5 days of infusion in 129 patients (4.0%), followed by emergency department visits by 90 patients (2.8%) within 10 days. Twenty-nine (0.9%) patients were hospitalised within 10 days of infusion with only two deaths (0.1%). Patients infused with sotrovimab during the moderate disease stage had 11 times greater odds of developing at least one outcome compared with patients infused during the mild stage (adjusted OR, aOR 10.86, 95% CI 7.14 to 16.54). SARS-CoV-2 vaccinated (aOR 12.8, 95% CI 7.3 to 20.5) and unvaccinated (aOR 7.2, 95% CI 3.4 to 15.3) patients infused with sotrovimab during the moderate disease stage had similar odds of at least one outcome compared with patients infused during the mild stage.

CONCLUSIONS

Among high-risk sotrovimab-infused COVID-19 patients, there were relatively low incidences of disease progression and hospitalisation. Regardless of vaccination history, monoclonal antibody intervention during the early stages of COVID-19 results in better outcomes.

Fear of making a mistake: a prominent cause of stress for COVID-19 ICU staff-a mixed-methods study

INTRODUCTION

The COVID-19 pandemic has had a profound effect on many domains of healthcare. Even in high-income countries such as Sweden, the number of patients has vastly outnumbered the resources in affected areas, in particular during the first wave. Staff caring for patients with COVID-19 in intensive care units (ICUs) faced a very challenging situation that continued for months. This study aimed to describe burnout, safety climate and causes of stress among staff working in COVID-19 ICUs.

METHOD

A survey was distributed to all staff working in ICUs treating patients with COVID-19 in five Swedish hospitals during 2020 and 2021. The numbers of respondents were 104 and 603, respectively. Prepandemic data including 172 respondents from 2018 served as baseline.

RESULTS

Staff exhaustion increased during the pandemic, but disengagement decreased compared with prepandemic levels (p<0.001). Background factors such as profession and work experience had no significant impact, but women scored higher in exhaustion. Total workload and working during both the first and second waves correlated positively to exhaustion, as did being regular ICU staff compared with temporary staff. Teamwork and safety climate remained unchanged compared with prepandemic levels.Respondents reported 'making a mistake' as the most stressful of the predefined stressors. Qualitative analysis of open-ended questions identified 'lack of knowledge and large responsibility', 'workload and work environment', 'uncertainty', 'ethical stress' and 'organization and teamwork' as major causes of stress.

CONCLUSION

Despite large workloads, disengagement at work was low in our sample, even compared with prepandemic levels. High levels of exhaustion were reported by the ICU staff who carried the largest workload. Multiple significant causes of stress were identified, with fear of making a mistake the most significant stressor.

d-Alanine as a biomarker and a therapeutic option for severe influenza virus infection and COVID-19

Since the outbreak of coronavirus disease 2019 (COVID-19), biomarkers for evaluating severity, as well as supportive care to improve clinical course, remain insufficient. We explored the potential of d-amino acids, rare enantiomers of amino acids, as biomarkers for assessing disease severity and as protective nutrients against severe viral infections. In mice infected with influenza A virus (IAV) and in patients with severe COVID-19 requiring artificial ventilation or extracorporeal membrane oxygenation, blood levels of d-amino acids, including d-alanine, were reduced significantly compared with those of uninfected mice or healthy controls. In mice models of IAV infection or COVID-19, supplementation with d-alanine alleviated severity of clinical course, and mice with sustained blood levels of d-alanine showed favorable prognoses. In severe viral infections, blood levels of d-amino acids, including d-alanine, decrease, and supplementation with d-alanine improves prognosis. d-Alanine has great potentials as a biomarker and a therapeutic option for severe viral infections.

Effect of COVID-19 on the implementation of a multifaceted intervention to improve teamwork and quality for hospitalized patients: a qualitative interview study

Background

Healthcare organizations made major adjustments to deliver care during the COVID pandemic, yet little is known about how these adjustments shaped ongoing quality and safety improvement efforts. We aimed to understand how COVID affected four U.S. hospitals’ prospective implementation efforts in an ongoing quality improvement initiative, the REdesigning SystEms to Improve Teamwork and Quality for Hospitalized Patients (RESET) project, which implemented complementary interventions to redesign systems of care for medical patients.

Methods

We conducted individual semi-structured interviews with 40 healthcare professionals to determine how COVID influenced RESET implementation. We used conventional qualitative content analysis to inductively code transcripts and identify themes in MAXQDA 2020.

Results

We identified three overarching themes and nine sub-themes. The three themes were (1) COVID exacerbated existing problems and created new ones. (2) RESET and other quality improvement efforts were not the priority during the pandemic. (3) Fidelity of RESET implementation regressed.

Conclusion

COVID had a profound impact on the implementation of a multifaceted intervention to improve quality and teamwork in four hospitals. Notably, COVID led to a diversion of attention and effort away from quality improvement efforts, like RESET, and sites varied in their ability to renew efforts over time. Our findings help explain how COVID adversely affected hospitals’ quality improvement efforts throughout the pandemic and support the need for research to identify elements important for fostering hospital resilience.

The Disproportionate Impact of Coronavirus Disease 2019 (COVID-19) Pandemic on Healthcare-Associated Infections in Community Hospitals: Need for Expanding the Infectious Disease Workforce

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic had a considerable impact on US healthcare systems, straining hospital resources, staff, and operations. However, a comprehensive assessment of the impact on healthcare-associated infections (HAIs) across different hospitals with varying level of infectious disease (ID) physician expertise, resources, and infrastructure is lacking.

METHODS

This retrospective longitudinal multicenter cohort study included central-line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), Clostridioides difficile infections (CDIs), and ventilator-associated events (VAEs) from 53 hospitals (academic and community) in Southeastern United States from 1 January 2018 to 31 March 2021. Segmented negative binomial regression generalized estimating equations models estimated changes in monthly incidence rates in the baseline (01/2018-02/2020) compared to the pandemic period (03/2020-03/2021, further divided into three pandemic phases).

RESULTS

CLABSIs and VAEs increased by 24% and 34%, respectively, during the pandemic period. VAEs increased in all phases of the pandemic, while CLABSIs increased in later phases of the pandemic. CDI trend increased by 4.2% per month in the pandemic period. On stratifying the analysis by hospital characteristics, the impact of the pandemic on healthcare-associated infections was more significant in smaller sized and community hospitals. CAUTIs did not change significantly during the pandemic across all hospital types.

CONCLUSIONS

CLABSIs, VAEs, and CDIs increased significantly during the pandemic, especially in smaller community hospitals, most of which lack ID physician expertise. Future efforts should focus on better understanding challenges faced by community hospitals, strengthening the infection prevention infrastructure, and expanding the ID workforce, particularly to community hospitals.

Impact of COVID-19 pandemic on hospital onset bloodstream infections (HOBSI) at a large health system

Background

The COVID-19 pandemic has had a considerable impact leading to increases in health care-associated infections, particularly bloodstream infections (BSI).

Methods

We evaluated the impact of COVID-19 in 69 US hospitals on BSIs before and during the pandemic. Events associated with 5 pathogens (Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Candida sp.) were stratified by community onset (CO) if ≤ 3 days from admission or hospital onset (HO) if > 3 days after admission. We compared pre-pandemic CO and HO rates with pandemic periods and the rates of BSI for those with and without COVID-19.

Results

COVID-19 patients were less likely to be admitted with COBSI compared to others (10.85 vs 22.35 per 10,000 patient days; P < .0001). There was a significant increase between pre-pandemic and pandemic HOBSI rates (2.78 vs 3.56 per 10,000 patient days; P < .0001). Also, COVID-19 infected patients were 3.5 times more likely to develop HOBSI compared to those without COVID-19 infection (9.64 vs 2.74 per 10,000 patient-days; P < .0001).

Conclusions

The COVID-19 pandemic period was associated with substantial increases in HOBSI and largely attributed to COVID-19 infected patients. Future research should evaluate whether such measures would be beneficial to incorporate in evaluating infection prevention trends.

A model of infection and immune response to low dose radiation

PURPOSE

Low dose radiation therapy (LDRT) using doses in the range of 30-150 cGy has been proposed as a means of mitigating the pneumonia associated with COVID-19. However, preliminary results from ongoing clinical trials have been mixed. The aim of this work is to develop a mathematical model of the viral infection and associated systemic inflammation in a patient based on the time evolution of the viral load. The model further proposes an immunomodulatory response to LDRT based on available data. Inflammation kinetics are then explored and compared to clinical results.

METHODS

The time evolution of a viral infection, inflammatory signaling factors, and inflammatory response are modeled by a set of coupled differential equations. Adjustable parameters are taken from the literature where available and otherwise iteratively adjusted to fit relevant data. Simple functions modeling both the suppression of pro-inflammatory signal factors and the enhancement of anti-inflammatory factors in response to low doses of radiation are developed. The inflammation response is benchmarked against C-reactive protein (CRP) levels measured for cohorts of patients with severe COVID-19.

RESULTS

The model fit the time-evolution of viral load data, cytokine data, and inflammation (CRP) data. When LDRT was applied early, the model predicted a reduction in peak inflammation consistent with the difference between the non-surviving and surviving cohorts. This reduction of peak inflammation diminished as the application of LDRT was delayed.

CONCLUSION

The model tracks the available data on viral load, cytokine levels, and inflammatory biomarkers well. An LDRT effect is large enough in principle to provide a life-saving immunomodulatory effect, though patients treated with LDRT already near the peak of their inflammation trajectory are unlikely to see drastic reductions in that peak. This result potentially explains some discrepancies in the preliminary clinical trial data.