Mortality in hospitalized COVID-19 patients was associated with the COVID-19 admission rate during the first year of the pandemic in Sweden

Severity outcomes of SARS-CoV-2 infection in the Omicron and pre-Omicron periods, in unvaccinated first-time test positive adults less than 65 years old without comorbidity, in Sweden

BACKGROUND

The COVID-19 pandemic has had several phases with varying characteristics. We aimed to compare severity outcomes in different phases in a population with limited bias from risk factors.

METHODS

In a nationwide observational study of all unvaccinated first-time COVID-19 test positive individuals in Sweden aged 18-64 years without comorbidity, from week 45 of 2020 to week 5 of 2022, variant periods with certain variants constituting ≥ 92 % of all whole genome-sequenced cases nationwide, were compared regarding hospitalisation (with main discharge code of COVID-19), severe illness (use of high-flow nasal oxygen or admission to intensive care unit), and death due to COVID-19. Logistic regression was used to estimate odds ratios (ORs) for comparison of these outcomes between variant periods, using adjustments for variant period, age, sex, country of birth, place of residence, income, and education.

FINDINGS

The study included 789,133 individuals, including 15,145 hospitalised individuals. Among all individuals, the adjusted ORs for hospitalisation were 1.7 for the Alpha period vs the Pre-variant period (week 45-52 2020), 1.8 for the Delta period vs the Alpha period, and 0.1 for the Omicron period vs the Delta period (all comparisons significant). Among hospitalised individuals, the adjusted ORs for severe illness were 1.4 for the Alpha period vs the Pre-variant period, 1.7 for the Delta period vs the Alpha period, and 0.5 for the Omicron period vs the Delta period (all comparisons significant), and the adjusted ORs for death were 1.1 for the Alpha period vs the Pre-variant period (non-significant), 1.8 for the Delta period vs the Alpha period (significant), and 0.1 for the Omicron period vs the Delta period (non-significant).

INTERPRETATION

In this population with limited bias from risk factors, vaccination, and previous infection, disease severity increased from the pre-variant to the Delta period and then decreased with the Omicron period, among all individuals and among hospitalised individuals. These severity outcome differences should be considered when the pandemic is evaluated.

Survival, mortality and epidemic risk status of COVID-19: a population-based Study in Golestan province, Iran

BACKGROUND

Appreciating the various dimensions of the coronavirus disease 2019 (COVID-19) pandemic can improve health systems and prepare them to deal better with future pandemics and public health events. This study was conducted to investigate the association between the survival of hospitalized patients with COVID-19 and the epidemic risk stratification of the disease in Golestan province, Iran.

METHODS

In this study, all patients with COVID-19 who were hospitalized in the hospitals of Golestan province of Iran from February 20, 2020, to December 19, 2022, and were registered in the Medical Care Monitoring Center (MCMC) system (85,885 individuals) were examined.The community's epidemic risk status (ERS) was determined based on the daily incidence statistics of COVID-19. The survival distribution and compare Survival in different subgroups was investigated using Kaplan-Meier and log-rank test and association between the survival and ERS by multiple Cox regression modeling.

RESULTS

Out of 68,983 individuals whose data were correctly recorded, the mean age was 49 (SD = 23.98) years, and 52.8% were women. In total, 11.1% eventually died. The length of hospital stay was varying significantly with age, gender, ERS, underlying diseases, and COVID-19 severity (P < 0.001 for all). The adjusted hazard ratio of death for the ERS at medium, high, and very high-risk status compared to the low-risk status increased by 19%, 26%, and 56%, respectively (P < 0.001 for all).

CONCLUSIONS

Enhancing preparedness, facilitating rapid rises in hospital capacities, and developing backup healthcare capacities can prevent excessive hospital referrals during health crises and further deaths.

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.

Comparative analysis of mortality in patients admitted with an infection with influenza A/B virus, respiratory syncytial virus, rhinovirus, metapneumovirus or SARS-CoV-2

Background

While influenza virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are recognised as a cause of severe illness and mortality, clinical interest for respiratory syncytial virus (RSV), rhinovirus and human metapneumovirus (hMPV) infections is still limited.

Methods

We conducted a retrospective database study comparing baseline characteristics and 30-day mortality in a large cohort of adult patients admitted for an overnight stay or longer with an influenza virus (A/B), rhinovirus, hMPV, RSV or SARS-CoV-2 infection. For non-SARS-CoV-2 viruses, data were included for the period July 2017-February 2020. For SARS-CoV-2, data between March 2020 and March 2022 were included.

Results

Covariate-adjusted 30-day mortality following RSV, hMPV or rhinovirus infections was substantial (crude mortality 8-10%) and comparable with mortality following hospitalisation with an influenza A virus infection. Mortality following a SARS-CoV-2 infection was consistently higher than for any other respiratory virus, at any point in time (crude mortality 14-25%). Odds of mortality for SARS-CoV-2 compared with influenza A declined from 4.9 to 1.7 over the course of the pandemic. Patients with SARS-CoV-2 infection had less comorbidity than patients with other respiratory virus infections and were more often male. In this cohort, age was related to mortality following hospitalisation, while an association with comorbidity was not apparent.

Conclusions

With the exception of SARS-CoV-2 infections, we find the clinical outcome of common respiratory virus infections requiring hospitalisation more similar than often assumed. The observed mortality from SARS-CoV-2 was significantly higher, but the difference with other respiratory viruses became less distinct over time.

Flattening the Curve after the Initial Outbreak of Coronavirus Disease 2019: A Data-Driven Modeling Analysis for the Omicron Pandemic in China

China is relaxing COVID-19 measures from the "dynamic zero tolerance" (DZT) level. The "flatten-the-curve" (FTC) strategy, which decreases and maintains the low rate of infection to avoid overwhelming the healthcare system by adopting relaxed nonpharmaceutical interventions (NPIs) after the outbreak, has been perceived as the most appropriate and effective method in preventing the spread of the Omicron variant. Hence, we established an improved data-driven model of Omicron transmission based on the age-structured stochastic compartmental susceptible-latent-infectious-removed-susceptible model constructed by Cai to deduce the overall prevention effect throughout China. At the current level of immunity without the application of any NPIs, more than 1.27 billion (including asymptomatic individuals) were infected within 90 days. Moreover, the Omicron outbreak would result in 1.49 million deaths within 180 days. The application of FTC could decrease the number of deaths by 36.91% within 360 days. The strict implementation of FTC policy combined with completed vaccination and drug use, which only resulted in 0.19 million deaths in an age-stratified model, will help end the pandemic within about 240 days. The pandemic would be successfully controlled within a shorter period of time without a high fatality rate; therefore, the FTC policy could be strictly implemented through enhancement of immunity and drug use.

Clinical predictors of Covid-19 mortality in a tertiary hospital in Lagos, Nigeria: A retrospective cohort study

Background

The predictors of mortality among patients presenting with severe to critical disease in Nigeria are presently unknown.

Aim

The aim of this study was to identify the predictors of mortality among patients with COVID-19 presenting for admission in a tertiary referral hospital in Lagos, Nigeria.

Patients and Methods

The study was a retrospective study. Patients' sociodemographics, clinical characteristics, comorbidities, complications, treatment outcomes, and hospital duration were documented. Pearson's Chi-square, Fischer's Exact test, or Student's t-test were used to assess the relationship between the variables and mortality. To compare the survival experience across medical comorbidities, Kaplan Meir plots and life tables were used. Univariable and multivariable Cox-proportional hazard analyses were conducted.

Results

A total of 734 patients were recruited. Participants' age ranged from five months to 92 years, with a mean ± SD of 47.4 ± 17.2 years, and a male preponderance (58.5% vs. 41.5%). The mortality rate was 9.07 per thousand person-days. About 73.9% (n = 51/69) of the deceased had one or more co-morbidities, compared to 41.6% (252/606) of those discharged. Patients who were older than 50 years, with diabetes mellitus, hypertension, chronic renal illness, and cancer had a statistically significant relationship with mortality.

Conclusion

These findings call for a more comprehensive approach to the control of non-communicable diseases, the allocation of sufficient resources for ICU care during outbreaks, an improvement in the quality of health care available to Nigerians, and further research into the relationship between obesity and COVID-19 in Nigerians.

Rapid response nursing triage outcomes for COVID-19: factors associated with patient's participation in triage recommendations

BACKGROUND

COVID-19 is an ongoing global health crisis with prevention and treatment recommendations rapidly changing. Rapid response telephone triage and advice services are critical in providing timely care during pandemics. Understanding patient participation with triage recommendations and factors associated with patient participation can assist in developing sensitive and timely interventions for receiving the treatment to prevent adverse health effects of COVID-19.

METHODS

This cohort study aimed to assess patient participation (percentage of patients who followed nursing triage suggestions from the COVID hotline) and identify factors associated with patient participation in four quarterly electronic health records from March 2020 to March 2021 (Phase 1: 14 March 2020-6 June 2020; Phase 2: 17 June 2020-16 September 2020; Phase 3: 17 September 2020-16 December 2020; Phase 4: 17 December 2020-16 March 2021). All callers who provided their symptoms (including asymptomatic with exposure to COVID) and received nursing triage were included in the study. Factors associated with patient participation were identified using multivariable logistic regression analyses, including demographic variables, comorbidity variables, health behaviors, and COVID-19-related symptoms.

RESULTS

The aggregated data included 9849 encounters/calls from 9021 unique participants. Results indicated: (1) 72.5% of patient participation rate; (2) participants advised to seek emergency department care had the lowest patient participation rate (43.4%); (3) patient participation was associated with older age, a lower comorbidity index, a lack of unexplained muscle aches, and respiratory symptoms. The absence of respiratory symptoms was the only factor significantly associated with patient participation in all four phases (OR = 0.75, 0.60, 0.64, 0.52, respectively). Older age was associated with higher patient participation in three out of four phases (OR = 1.01-1.02), and a lower Charlson comorbidity index was associated with higher patient participation in phase 3 and phase 4 (OR = 0.83, 0.88).

CONCLUSION

Public participation in nursing triage during the COVID pandemic requires attention. This study supports using a nurse-led telehealth intervention and reveals crucial factors associated with patient participation. It highlighted the importance of timely follow-up in high-risk groups and the benefit of a telehealth intervention led by nurses serving as healthcare navigators during the COVID-19 pandemic.

Evaluating methodological approaches to assess the severity of infection with SARS-CoV-2 variants: scoping review and applications on Belgian COVID-19 data

Background

Differences in the genetic material of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants may result in altered virulence characteristics. Assessing the disease severity caused by newly emerging variants is essential to estimate their impact on public health. However, causally inferring the intrinsic severity of infection with variants using observational data is a challenging process on which guidance is still limited. We describe potential limitations and biases that researchers are confronted with and evaluate different methodological approaches to study the severity of infection with SARS-CoV-2 variants.

Methods

We reviewed the literature to identify limitations and potential biases in methods used to study the severity of infection with a particular variant. The impact of different methodological choices is illustrated by using real-world data of Belgian hospitalized COVID-19 patients.

Results

We observed different ways of defining coronavirus disease 2019 (COVID-19) disease severity (e.g., admission to the hospital or intensive care unit versus the occurrence of severe complications or death) and exposure to a variant (e.g., linkage of the sequencing or genotyping result with the patient data through a unique identifier versus categorization of patients based on time periods). Different potential selection biases (e.g., overcontrol bias, endogenous selection bias, sample truncation bias) and factors fluctuating over time (e.g., medical expertise and therapeutic strategies, vaccination coverage and natural immunity, pressure on the healthcare system, affected population groups) according to the successive waves of COVID-19, dominated by different variants, were identified. Using data of Belgian hospitalized COVID-19 patients, we were able to document (i) the robustness of the analyses when using different variant exposure ascertainment methods, (ii) indications of the presence of selection bias and (iii) how important confounding variables are fluctuating over time.

Conclusions

When estimating the unbiased marginal effect of SARS-CoV-2 variants on the severity of infection, different strategies can be used and different assumptions can be made, potentially leading to different conclusions. We propose four best practices to identify and reduce potential bias introduced by the study design, the data analysis approach, and the features of the underlying surveillance strategies and data infrastructure.

Developing and validating a machine learning prognostic model for alerting to imminent deterioration of hospitalized patients with COVID-19

Our study was aimed at developing and validating a new approach, embodied in a machine learning-based model, for sequentially monitoring hospitalized COVID-19 patients and directing professional attention to patients whose deterioration is imminent. Model development employed real-world patient data (598 prediction events for 210 patients), internal validation (315 prediction events for 97 patients), and external validation (1373 prediction events for 307 patients). Results show significant divergence in longitudinal values of eight routinely collected blood parameters appearing several days before deterioration. Our model uses these signals to predict the personal likelihood of transition from non-severe to severe status within well-specified short time windows. Internal validation of the model's prediction accuracy showed ROC AUC of 0.8 and 0.79 for prediction scopes of 48 or 96 h, respectively; external validation showed ROC AUC of 0.7 and 0.73 for the same prediction scopes. Results indicate the feasibility of predicting the forthcoming deterioration of non-severe COVID-19 patients by eight routinely collected blood parameters, including neutrophil, lymphocyte, monocyte, and platelets counts, neutrophil-to-lymphocyte ratio, CRP, LDH, and D-dimer. A prospective clinical study and an impact assessment will allow implementation of this model in the clinic to improve care, streamline resources and ease hospital burden by timely focusing the medical attention on potentially deteriorating patients.

Managing Hospital Capacity: Achievements and Lessons from the COVID-19 Pandemic

INTRODUCTION

The coronavirus disease 2019 (COVID-19) pandemic challenged health care systems in an unprecedented way. Due to the enormous amount of hospital ward and intensive care unit (ICU) admissions, regular care came to a standstill, thereby overcrowding ICUs and endangering (regular and COVID-19-related) critical care. Acute care coordination centers were set up to safely manage the influx of COVID-19 patients. Furthermore, treatments requiring ICU surveillance were postponed leading to increased waiting lists.

HYPOTHESIS

A coordination center organizing patient transfers and admissions could reduce overcrowding and optimize in-hospital capacity.

METHODS

The acute lack of hospital capacity urged the region West-Netherlands to form a new regional system for patient triage and transfer: the Regional Capacity and Patient Transfer Service (RCPS). By combining hospital capacity data and a new method of triage and transfer, the RCPS was able to effectively select patients for transfer to other hospitals within the region or, in close collaboration with the National Capacity and Patient Transfer Service (LCPS), transfer patients to hospitals in other regions within the Netherlands.

RESULTS

From March 2020 through December 2021 (22 months), the RCPS West-Netherlands was requested to transfer 2,434 COVID-19 patients. After adequate triage, 1,720 patients with a mean age of 62 (SD = 13) years were transferred with the help of the RCPS West-Netherlands. This concerned 1,166 ward patients (68%) and 554 ICU patients (32%). Overcrowded hospitals were relieved by transferring these patients to hospitals with higher capacity.

CONCLUSION

The health care system in the region West-Netherlands benefitted from the RCPS for both ward and ICU occupation. Due to the coordination by the RCPS, regional ICU occupation never exceeded the maximal ICU capacity, and therefore patients in need for acute direct care could always be admitted at the ICU. The presented method can be useful in reducing the waiting lists caused by the delayed care and for coordination and transfer of patients with new variants or other infectious diseases in the future.

COVID-19 pandemic in Southern Brazil: Hospitalizations, intensive care unit admissions, lethality rates, and length of stay between March 2020 and April 2022

Coronavirus disease 2019 (COVID‐19) pandemic spread rapidly with more than 515 million cases and 6.2 million deaths. Epidemiological factors are important for understanding the state of the pandemic. This study aims to evaluate the hospitalizations, intensive care unit (ICU) admissions, and lethality from March 2020 to April 2022. Data were collected from a hospital in Porto Alegre city, southern Brazil. The Mann–Whitney, analysis of variance, and Kruskal–Wallis tests were used to compare quantitative variables. Categorical variables were compared by Pearson's χ² test. p values <0.05 for all tests were considered significant. Were observed 3784 hospitalizations. Males were 51.4% and the age was 60.4± 20.3. Intensive care unit (ICU) patients were 31.2%, the median length of stay (LOS) was 9.0 and lethality was 13.3%. ICU lethality was 34.5% versus 4.6% in other inpatients (p < 0.01). The LOS of ICU patients was 22.0 versus 7.0 in other inpatients (p < 0.01). The first peak (July–Novemebr 2020) showed ICU occupancy of 79.1%. The second peak (December 2020–June 2021) with 91.6% occupancy. The third peak January–March 2022 with 81.0% occupancy (p < 0.01). Lethality rates were 10.3% in 2020, 14.9% in 2021 and 15.4% in 2022 (p < 0.01). In conclusion, the ICU occupancy rate was higher in 2021 and the lethality rates of ICU patients were high during pandemic years (10.3% in 2020, 14.9% in 2021, and 15.2% in 2022). The lethality of these patients ranged from 25.0% in March to 21.8% in December 2020, from 20.9% in January 22.2% in Decemebr 2021, and 35.7% in January 2022 to 21.4% in April 2022. These data demonstrate that COVID‐19 is a critical illness, even in a private hospital setting.

Age-Dependent Biomarkers for Prediction of In-Hospital Mortality in COVID-19 Patients

Background: Several biomarkers and models have been proposed to predict in-hospital mortality among COVID-19 patients. However, these studies have not examined the association in sub-populations. The present study aimed to identify the association between the two most common inflammatory biomarkers in the emergency department and in-hospital mortality in subgroups of patients. Methods: A historical cohort study of adult patients who were admitted to acute-care hospital between March and December 2020 and had a diagnosis of COVID-19 infection. Data on age, sex, Charlson comorbidity index, white blood cell (WBC) count, C-reactive protein (CRP), and in-hospital mortality were collected. Discrimination ability of each biomarker was observed and the CHAID method was used to identify the association in subgroups of patients. Results: Overall, 762 patients (median age 70.9 years, 59.7% males) were included in the study. Of them, 25.1% died during hospitalization. In-hospital mortality was associated with higher CRP (median 138 mg/L vs. 85 mg/L, p < 0.001), higher WBC count (median 8.5 vs. 6.6 K/µL, p < 0.001), and higher neutrophil-to-lymphocyte ratio (NLR) (median 9.2 vs. 5.4, p < 0.001). The area under the ROC curve was similar among all biomarkers (WBC 0.643, NLR 0.677, CRP 0.646, p > 0.1 for all comparisons). The CHAID method revealed that WBC count was associated with in-hospital mortality in patients aged 43.1−66.0 years (<11 K/µL: 10.1% vs. 11+ K/µL: 27.9%), NLR in patients aged 66.1−80 years (≤8: 15.7%, >8: 43.3%), and CRP in patients aged 80.1+ years (≤47 mg/L: 18.8%, 47.1−149 mg/L: 43.1%, and 149.1+: 71.7% mortality). Conclusions: WBC, NLR, and CRP present similar discrimination abilities. However, each biomarker should be considered as a predictor for in-hospital mortality in different age groups.

Convalescent plasma treatment in severely immunosuppressed patients hospitalized with COVID-19: an observational study of 28 cases

BACKGROUND

Immunosuppressed patients are particularly vulnerable to severe infection from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), risking prolonged viremia and symptom duration. In this study we describe clinical and virological treatment outcomes in a heterogeneous group of patients with severe immunosuppression due to various causes suffering from COVID-19 infection, who were all treated with convalescent plasma (CCP) along with standard treatment.

METHODS

We performed an observational, retrospective case series between May 2020 to March 2021 at three sites in Skåne, Sweden, with a population of nearly 1.4 million people. All patients hospitalized for COVID-19 who received CCP with the indication severe immunosuppression as defined by the treating physician were included in the study (n = 28).

RESULTS

In total, 28 severely immunocompromised patients, half of which previously had been treated with rituximab, who had received in-hospital convalescent plasma treatment of COVID-19 were identified. One week after CCP treatment, 13 of 28 (46%) patients had improved clinically defined as a decrease of at least one point at the WHO-scale. Three patients had increased score points of whom two had died. For 12 patients, the WHO-scale was unchanged.

CONCLUSION

As one of only few studies on CCP treatment of COVID-19 in hospitalized patients with severe immunosuppression, this study adds descriptive data. The study design prohibits conclusions on safety and efficacy, and the results should be interpreted with caution. Prospective, randomized trials are needed to investigate this further.