Mortality trends and lengths of stay among hospitalized COVID-19 patients in Ontario and Québec (Canada): a population-based cohort study of the first three epidemic waves

Mortality in orthopedic patients: a retrospective review of 333 medical records

BACKGROUND

The burden of orthopedic admissions has notably increased in recent years. Managing orthopedic conditions is challenging in clinical settings. Orthopedic complaints often necessitate urgent medical intervention to prevent complications and mortality. Despite advancements in medical care, some patients still experience severe complications, extended hospital stays, and death following orthopedic admission. In this study, we aimed to explore the distribution of potential risk factors and common patterns in orthopedic patients who died during their hospitalization.

MATERIALS AND METHODS

All the patients who were admitted to three tertiary trauma centers with orthopedic complaints from 2010 to 2023 and died during hospitalization were enrolled in this study. Demographic, injury-related, laboratory-related, intervention-related, complication-related, and healthcare-related data were extracted using the patient's medical records. Descriptive analysis of the collected data was performed using the SPSS version 27 software.

RESULTS

333 patients who died in the hospital with orthopedic complaints were included in the study and examined. The mean age of patients in this study was 67.89 years, comprising 68% males and 32% females. Trauma was patients' most common clinical cause of admission (63.7%). The prevalence of death before surgery, death during the first 24 h after surgery, and death after 24 h postoperatively were 26.4%, 18.6%, and 55%, respectively.

CONCLUSIONS

Our findings suggest a high prevalence of trauma as a clinical complaint leading to death among patients, emphasizing the importance of developing an integrated protocol for trauma preventive strategies.

COVID-19-specific prefectural hospital bed utilization rate and in-hospital mortality among COVID-19 patients throughout the first three years of the pandemic in Japan

BACKGROUND

We examined the association between the COVID-19-specific prefectural bed utilization rate and in-hospital mortality during the first three years of the pandemic in Japan.

METHODS

This nationwide study included 58,175 COVID-19 patients from the COVID-19 Registry Japan, hospitalized between May 1, 2020 and November 30, 2022. Based on the weekly COVID-19-specific bed utilization rate in each prefecture at diagnosis, patients were categorized into four groups (< 25%, 25% to < 50%, 50% to < 75%, and ≥ 75%). Odds ratios (ORs) were estimated by fitting a generalized linear mixed model with prefecture as a random intercept and adjusting for covariates (age, gender, body mass index, smoking and drinking status, and comorbidities). Additional analyses according to age group, gender, and wave of the pandemic were conducted.

RESULTS

We observed 2312 (4.0%) all-cause in-hospital deaths. All-cause in-hospital mortality increased with higher COVID-19 bed utilization rates at diagnosis (OR for multivariable model 1.35, 95% confidence interval [CI] 1.19-1.54 for 25% to <50%; 1.89, 1.66-2.16 for 50 to <75%; 2.16, 1.80-2.58 for ≥75%; P for trend<0.0001). Stronger associations were noted among the younger population (aged <70 years, OR: 3.18, 1.96-5.19) and during the fourth (March 1-June 30, 2021, OR: 3.81, 2.13-6.80) and sixth pandemic waves (January 1-Jun 30, 2022, OR: 2.67, 1.68-4.23).

CONCLUSIONS

Our results emphasize that preventing hospital bed shortages during outbreaks is an important public health strategy to reduce the associated mortality, particularly when new strains emerge and in younger people.

COVID-19 in the years 2020 to 2022 in Germany: effects of comorbidities and co-medications based on a large-scale database analysis

BACKGROUND

The SARS-CoV-2 pandemic was a challenge for health care systems worldwide. People with pre-existing chronic diseases have been identified as vulnerable patient groups. Furthermore, some of the drugs used for these chronic diseases such as antihypertensive drugs have been discussed as possible influencing factors on the progression of COVID-19. This study examines the effect of medication- and morbidity-associated risk factors suspected to moderate the disease course and progression of COVID-19.

METHODS

The study is based on claims data of the Techniker Krankenkasse, Germany's largest statutory health insurance. The data cover the years 2020 to 2022 and include insured persons with COVID-19 diagnosis from both the outpatient and inpatient sectors and a control of insured persons without COVID-19 diagnosis. We conducted a matched case-control study and matched each patient with an inpatient diagnosis of COVID-19 to (a) 10 control patients and (b) one patient with an outpatient diagnosis of COVID-19 to form two study cohorts. We performed a descriptive analysis to describe the proportion of patients in the two cohorts who were diagnosed with comorbidities or medication use known to influence the risk of COVID-19 progression. Multiple logistic regression models were used to identify risk factors for disease progression.

RESULTS

In the first study period the first study cohort comprised a total of 150,018 patients (13,638 cases hospitalised with COVID-19 and 136,380 control patients without a COVID-19 infection). Study cohort 2 included 27,238 patients (13,619 patients hospitalised with COVID-19 and 13,619 control patients with an outpatient COVID-19 diagnosis). Immunodeficiencies and use of immunosuppressives were strongest risk modifying factors for hospitalization in both study populations. Other comorbidities associated with hospitalization were diabetes, hypertension, and depression.

CONCLUSION

We have shown that hospitalisation with COVID-19 is associated with past medical history and medication use. Furthermore, we have demonstrated the ability of claims data as a timely available data source to identify risk factors for COVID-19 severity based on large numbers of patients. Given our results, claims data have the potential to be useful as part of a surveillance protocol allowing early-stage access to epidemiological data in future pandemics.

Evaluation of In-Hospital and Community-Based Healthcare Utilization and Costs During the Coronavirus 2019 (COVID-19) Pandemic in Alberta, Canada: A Population-Based Descriptive Study

Background

Assessing the financial burden of COVID-19 is important for planning health services and resource allocation to inform future pandemic response.

Objectives

This study examines the changing dynamics in healthcare utilization patterns and costs from a public healthcare perspective during the COVID-19 pandemic in Alberta, Canada.

Design

Population-based descriptive study.

Methods

All adult patients over the age of 18 years who had a laboratory-confirmed COVID-19 diagnosis in Alberta, Canada from March 1, 2020 to December 15, 2021. We described demographic information and community- and hospital-based healthcare utilization and costs. We compared changes in each outcome throughout the first four waves of the pandemic.

Results

Among 255,037 patients, hospitalization incurred significantly higher costs (N = 20,603; aRR = 755.51; marginal cost: $21,738.17 CAD; P < .01). Wave 2 recorded the highest cost for Emergency Department (ED) visits (aRR = 1.10; marginal cost: $79.19 CAD; P < .01). Compared to Wave 1, Waves 2-4 all recorded significantly lower costs for out-patient visits. Wave 2's in-patient cost for patients that required ICU admission was significantly lower than Wave 1 (aRR = 0.75; marginal cost: -$24,142.47 CAD; P = .02).

Conclusion

COVID-19 exerted a heavy toll on healthcare services, and the dynamics of this continue to evolve. Utilization of ED and in-patient services were particularly high. Severe infections requiring hospitalization and ICU admission are more expensive than non-hospitalized and non-ICU hospital admits. Future studies should clarify specific factors, such as sociodemographic determinants, that contribute to evolving patterns of health services consumption and changing trends in cost to holistically inform responses to future pandemics.

Frailty as a Predictor of COVID-19 Mortality in the South Indian Population: An Observational Study

Background Frailty is a clinical syndrome characterized by diminished strength, endurance, and physiological function that significantly increases vulnerability to adverse health outcomes, including infections. In the context of COVID-19, frailty has emerged as a critical risk factor for severe disease, complications, and mortality, particularly in older adults. The severity and fatality rates among the geriatric group were notably high, as the virus's pathogenesis, marked by prolonged inflammation, contributed to increased morbidity and mortality in this age group. The study was conducted to explore the role of frailty in influencing mortality among the elderly affected by COVID-19. Objective The objective of this study was to identify the association between frailty and mortality in COVID-19-affected elderly patients.  Methods We conducted a prospective observational study among elderly patients who tested positive for COVID-19 and received treatment in a tertiary care hospital. Data were collected from 250 patients from March 2021 to December 2021. Lab parameters, the necessity for mechanical ventilation, the need for oxygen use, and the number of days of hospital stay were recorded. The Clinical Frailty Score (CFS) was used to evaluate frailty. The chi-square test with Fisher's exact test was used to assess the association between frailty and mortality in the data set. Multivariate binary logistic regression was employed to identify the most significant predictors of mortality. Results Among the 250 patients, 159 (63.6%) survived and were discharged, while 91 (36.4%) succumbed to the illness. Fifty-eight patients were not identified as frail, and there were no deaths in the group. On the contrary, among the 192 COVID-positive patients who were identified as frail, 91 (47.4%) patients died, and 101 (52.6%) patients were alive. This depicted the association between frailty and mortality in COVID-19 geriatric patients. While assessing comorbidities, malignancy (53.3%, p-value = 0.009) and chronic kidney disease (CKD) (43.3%) had a significant association with mortality. Symptoms like fever (43.6%), dyspnea (68.6%), myalgia (20%), and altered sensorium (84%) showed a strong correlation with mortality (p<0.001). Frailty was a significant predictor of mortality, with 47.4% of frail patients not surviving (p<0.001). Biochemical markers including leukocytosis (64.8%), neutrophilia (65.3%), eosinopenia (66.9%), anemia (57.8%), hypoalbuminemia (63.5%), hypoproteinemia (70.1%), elevated alanine aminotransferase (ALT) (66%), aspartate aminotransferase (AST) (65.2%), alkaline phosphatase (ALP) (67.5%), elevated creatinine (68.9%), hypernatremia (100%), hyperkalemia (80%), and elevated D-dimer (44.7%) were all significantly linked to mortality. Additionally, patients requiring oxygen (65%), ventilation (96.8%), or bilevel positive airway pressure (BiPAP) (77.8%) had higher mortality rates. A shorter length of hospital stay was also associated with increased mortality (24%). Conclusion Frailty, combined with certain comorbidities such as cancer and CKD, along with various clinical and biochemical markers, played a significant role in predicting mortality among geriatric COVID-19 patients. Incorporating frailty assessments into routine evaluations for elderly COVID-19 survivors could be beneficial. Early detection and focused management of these high-risk factors are essential for improving outcomes in frail patients within tertiary care settings.

The prevalence of postacute sequelae of coronavirus disease 2019 in solid organ transplant recipients: Evaluation of risk in the National COVID Cohort Collaborative

Postacute sequelae after the coronavirus disease (COVID) of 2019 (PASC) is increasingly recognized, although data on solid organ transplant (SOT) recipients (SOTRs) are limited. Using the National COVID Cohort Collaborative, we performed 1:1 propensity score matching (PSM) of all adult SOTR and nonimmunosuppressed/immunocompromised (ISC) patients with acute COVID infection (August 1, 2021 to January 13, 2023) for a subsequent PASC diagnosis using International Classification of Diseases, 10th Revision, Clinical Modification codes. Multivariable logistic regression was used to examine not only the association of SOT status with PASC, but also other patient factors after stratifying by SOT status. Prior to PSM, there were 8769 SOT and 1 576 769 non-ISC patients with acute COVID infection. After PSM, 8756 SOTR and 8756 non-ISC patients were included; 2.2% of SOTR (n = 192) and 1.4% (n = 122) of non-ISC patients developed PASC (P value < .001). In the overall matched cohort, SOT was independently associated with PASC (adjusted odds ratio [aOR], 1.48; 95% confidence interval [CI], 1.09-2.01). Among SOTR, COVID infection severity (aOR, 11.6; 95% CI, 3.93-30.0 for severe vs mild disease), older age (aOR, 1.02; 95% CI, 1.01-1.03 per year), and mycophenolate mofetil use (aOR, 2.04; 95% CI, 1.38-3.05) were each independently associated with PASC. In non-ISC patients, only depression (aOR, 1.96; 95% CI, 1.24-3.07) and COVID infection severity were. In conclusion, PASC occurs more commonly in SOTR than in non-ISC patients, with differences in risk profiles based on SOT status.

Examining the effects of voluntary avoidance behaviour and policy-mediated behaviour change on the dynamics of SARS-CoV-2: A mathematical model

Background

Throughout the SARS-CoV-2 pandemic, policymakers have had to navigate between recommending voluntary behaviour change and policy-driven behaviour change to mitigate the impact of the virus. While individuals will voluntarily engage in self-protective behaviour when there is an increasing infectious disease risk, the extent to which this occurs and its impact on an epidemic is not known.

Methods

This paper describes a deterministic disease transmission model exploring the impact of individual avoidance behaviour and policy-mediated avoidance behaviour on epidemic outcomes during the second wave of SARS-CoV-2 infections in Ontario, Canada (September 1, 2020 to February 28, 2021). The model incorporates an information feedback function based on empirically derived behaviour data describing the degree to which avoidance behaviour changed in response to the number of new daily cases COVID-19.

Results

Voluntary avoidance behaviour alone was estimated to reduce the final attack rate by 23.1%, the total number of hospitalizations by 26.2%, and cumulative deaths by 27.5% over 6 months compared to a counterfactual scenario in which there were no interventions or avoidance behaviour. A provincial shutdown order issued on December 26, 2020 was estimated to reduce the final attack rate by 66.7%, the total number of hospitalizations by 66.8%, and the total number of deaths by 67.2% compared to the counterfactual scenario.

Conclusion

Given the dynamics of SARS-CoV-2 in a pre-vaccine era, individual avoidance behaviour in the absence of government action would have resulted in a moderate reduction in disease however, it would not have been sufficient to entirely mitigate transmission and the associated risk to the population in Ontario. Government action during the second wave of the COVID-19 pandemic in Ontario reduced infections, protected hospital capacity, and saved lives.

Canada's provincial COVID-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses

SETTING

Mathematical modelling played an important role in the public health response to COVID-19 in Canada. Variability in epidemic trajectories, modelling approaches, and data infrastructure across provinces provides a unique opportunity to understand the factors that shaped modelling strategies.

INTERVENTION

Provinces implemented stringent pandemic interventions to mitigate SARS-CoV-2 transmission, considering evidence from epidemic models. This study aimed to summarize provincial COVID-19 modelling efforts. We identified modelling teams working with provincial decision-makers, through referrals and membership in Canadian modelling networks. Information on models, data sources, and knowledge translation were abstracted using standardized instruments.

OUTCOMES

We obtained information from six provinces. For provinces with sustained community transmission, initial modelling efforts focused on projecting epidemic trajectories and healthcare demands, and evaluating impacts of proposed interventions. In provinces with low community transmission, models emphasized quantifying importation risks. Most of the models were compartmental and deterministic, with projection horizons of a few weeks. Models were updated regularly or replaced by new ones, adapting to changing local epidemic dynamics, pathogen characteristics, vaccines, and requests from public health. Surveillance datasets for cases, hospitalizations and deaths, and serological studies were the main data sources for model calibration. Access to data for modelling and the structure for knowledge translation differed markedly between provinces.

IMPLICATION

Provincial modelling efforts during the COVID-19 pandemic were tailored to local contexts and modulated by available resources. Strengthening Canadian modelling capacity, developing and sustaining collaborations between modellers and governments, and ensuring earlier access to linked and timely surveillance data could help improve pandemic preparedness.

Defining the correlates of lymphopenia and independent predictors of poor clinical outcome in adults hospitalized with COVID-19 in Australia

Lymphopenia is a common feature of acute COVID-19 and is associated with increased disease severity and 30-day mortality. Here we aim to define the demographic and clinical characteristics that correlate with lymphopenia in COVID-19 and determine if lymphopenia is an independent predictor of poor clinical outcome. We analysed the ENTER-COVID (Epidemiology of hospitalized in-patient admissions following planned introduction of Epidemic SARS-CoV-2 to highly vaccinated COVID-19 naïve population) dataset of adults (N = 811) admitted for COVID-19 treatment in South Australia in a retrospective registry study, categorizing them as (a) lymphopenic (lymphocyte count < 1 × 109/L) or (b) non-lymphopenic at hospital admission. Comorbidities and laboratory parameters were compared between groups. Multiple regression analysis was performed using a linear or logistic model. Intensive care unit (ICU) patients and non-survivors exhibited lower median lymphocyte counts than non-ICU patients and survivors respectively. Univariate analysis revealed that low lymphocyte counts associated with hypertension and correlated with haemoglobin, platelet count and negatively correlated with urea, creatinine, bilirubin, and aspartate aminotransferase (AST). Multivariate analysis identified age, male, haemoglobin, platelet count, diabetes, creatinine, bilirubin, alanine transaminase, c-reactive protein (CRP) and lactate dehydrogenase (LDH) as independent predictors of poor clinical outcome in COVID-19, while lymphopenia did not emerge as a significant predictor.

Evolution of illness severity in hospital admissions due to COVID-19, Québec, Canada, January to April 2022

Background

The coronavirus disease 2019 (COVID-19) severity is influenced by multiple factors, such as age, underlying medical conditions, individual immunity, infecting variant, and clinical practice. The highly transmissible Omicron variants resulted in decreased COVID-19 screening capacity, which limited disease severity surveillance.

Objective

To report on the temporal evolution of disease severity among patients admitted to Québec hospitals due to COVID-19 between January 2, 2022, and April 23, 2022, which corresponded to the peak period of hospitalizations due to Omicron.

Methods

Retrospective population-based cohort study of all hospital admissions due to COVID-19 in Québec, between January 2, 2022, and April 23, 2022. Study period was divided into four-week periods, corresponding roughly to January, February, March and April. Regression using Cox and Poisson generalized estimating equations (GEEs) was used to quantify temporal variations in length of stay and risk of complications (intensive care admission or in-hospital death) through time, using the Omicron peak (January 2022) as reference. Measures were adjusted for age, sex, vaccination status, presence of chronic diseases, and clustering by hospital.

Results

During the study period, 9,178 of all 18,272 (50.2%) patients hospitalized with a COVID-19 diagnosis were admitted due to COVID-19. Of these, 1,026 (11.2%) were admitted to intensive care and 1,523 (16.6%) died. Compared to January, the risk of intensive care admission was 25% and 31% lower in March and April respectively, while in-hospital fatality continuously decreased by 45% lower in April. The average length of stay was temporarily lower in March (9%).

Conclusion

Severity of admissions due to COVID-19 decreased in the first months of 2022, when predominant circulating variants were considered to be of similar severity. Monitoring hospital admissions due to COVID-19 can contribute to disease severity surveillance.

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.

Tracheostomy in Critically Ill COVID-19 Patients on Extracorporeal Membrane Oxygenation: A Single-Center Experience

OBJECTIVES

Novel coronavirus-19 (COVID-19) has led to over 6 million fatalities globally. An estimated 75% of COVID-19 patients who require critical care admission develop acute respiratory distress syndrome (ARDS) needing invasive mechanical ventilation (IMV) and/or extracorporeal membrane oxygenation (ECMO). Due to prolonged ventilation requirements, these patients often also require tracheostomy. We performed a review of clinical outcomes in COVID-19 patients on ECMO at a high-volume tertiary care center in Hamilton, Ontario, Canada.

METHODOLOGY

We performed a retrospective case series, including 24 adult patients diagnosed with COVID-19 who required IMV, veno-venous (ECMO), and tracheostomy. All patients were included from April to December 2021. We extracted demographic and clinical variables pertaining to the tracheostomy procedure and ECMO therapy. We performed descriptive statistical analyses. This study was approved by the Hamilton Integrated Research Ethics Board (14217-C).

RESULTS

We included 24 consecutive patients with COVID-19 who required tracheostomy while undergoing ECMO therapy. The mean age was 49.4 years [standard deviation (SD): 7.33], the majority of patients were male (75%), with mean body mass index of 32 (SD: 8.81). Overall mortality rate was 33.3%. Percutaneous tracheostomy was performed most frequently (83.3%) and, similar to open tracheostomy, was associated with a low rate of perioperative bleeding complications. Within surviving patients, the mean time to IMV weaning and decannulation was 60.2 (SD: 24.6) and 49.4 days (SD: 21.8), respectively.

CONCLUSION

Percutaneous tracheostomy appears to be safe in COVID-19 patients on ECMO and holding anticoagulation 24 hours prior to and after tracheostomy may limit bleeding events in these patients.

Impact of a vaccine passport on first-dose SARS-CoV-2 vaccine coverage by age and area-level social determinants of health in the Canadian provinces of Quebec and Ontario: an interrupted time series analysis

BACKGROUND

In Canada, all provinces implemented vaccine passports in 2021 to reduce SARS-CoV-2 transmission in non-essential indoor spaces and increase vaccine uptake (policies active September 2021-March 2022 in Quebec and Ontario). We sought to evaluate the impact of vaccine passport policies on first-dose SARS-CoV-2 vaccination coverage by age, and area-level income and proportion of racialized residents.

METHODS

We performed interrupted time series analyses using data from Quebec's and Ontario's vaccine registries linked to census information (population of 20.5 million people aged ≥ 12 yr; unit of analysis: dissemination area). We fit negative binomial regressions to first-dose vaccinations, using natural splines adjusting for baseline vaccination coverage (start: July 2021; end: October 2021 for Quebec, November 2021 for Ontario). We obtained counterfactual vaccination rates and coverage, and estimated the absolute and relative impacts of vaccine passports.

RESULTS

In both provinces, first-dose vaccination coverage before the announcement of vaccine passports was 82% (age ≥ 12 yr). The announcement resulted in estimated increases in coverage of 0.9 percentage points (95% confidence interval [CI] 0.4-1.2) in Quebec and 0.7 percentage points (95% CI 0.5-0.8) in Ontario. This corresponds to 23% (95% CI 10%-36%) and 19% (95% CI 15%-22%) more vaccinations over 11 weeks. The impact was larger among people aged 12-39 years. Despite lower coverage in lower-income and more-racialized areas, there was little variability in the absolute impact by area-level income or proportion racialized in either province.

INTERPRETATION

In the context of high vaccine coverage across 2 provinces, the announcement of vaccine passports had a small impact on first-dose coverage, with little impact on reducing economic and racial inequities in vaccine coverage. Findings suggest that other policies are needed to improve vaccination coverage among lower-income and racialized neighbourhoods and communities.

Epidemiological Characteristics and Outcomes Predictors for Intensive Care Unit COVID-19 Patients in Al-Madinah, Saudi Arabia. Retrospective Cohort Study

Introduction

The global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) increased the demand for intensive care unit (ICU) services. Mortality and morbidity rates among ICU COVID-19 patients are affected by several factors, such as severity, comorbidities, and coinfections. In this study, we describe the demographic characteristics of COVID-19 patients admitted to an ICU in Saudi Arabia, and we determined the predictors for mortality and prolonged ICU length of stay. Additionally, we determined the prevalence of bacterial coinfection and its effect on the outcomes for ICU COVID-19 patients.

Methods

We retrospectively studied the medical records of 142 COVID-19 patients admitted to the ICU at a tertiary hospital in Madinah, Saudi Arabia. Data on demographics, medical history, mortality, length of stay, and presence of coinfection were collected for each patient.

Results

Neutrophil-to-Lymphocyte ratio (NLR) and intubation were reliable predictors of mortality and ICU length of stay among these ICU COVID-19 patients. Moreover, bacterial coinfections were detected in 23.2% of the patients and significantly (p < 0.001) prolonged their ICU length of stay, explaining the 10% increase in the length of stay for these patients. Furthermore, mortality reached 70% among the coinfected patients, and 60.8% of the isolated coinfecting pathogens were multidrug-resistant (MDR) strains of Klebsiella pneumoniae, Acinetobacter baumannii, and Staphylococcus aureus.

Conclusion

Increased NLR and intubation are predictors of mortality and prolonged length of stay in COVID-19 patients admitted to the ICU. Coinfection with MDR bacterial strains potentially results in complications and is a high-risk factor for prolonged ICU length of stay.

A Machine Learning-Based Model to Predict In-Hospital Mortality of Lung Cancer Patients: A Population-Based Study of 523,959 Cases

Background: Stratify new lung cancer patients based on the risk of in-hospital mortality rate after diagnosis. Methods: 522,941 lung cancer cases with available data on the Surveillance, Epidemiology, and End Results (SEER) were analyzed for the predicted probability based on six fundamental variables including age, gender, tumor size, T, N, and AJCC stages. The patients were randomly assigned to the training (n = 115,145) and validation datasets (n = 13,017). The remaining cohort with missing values (n = 394,779) was then combined with the primary lung tumour datasets (n = 1018) from The Cancer Genome Atlas, Lung Adenocarcinoma and Lung Squamous Cell Carcinoma projects (TCGA-LUAD & TCGA-LUSC) for external validation and sensitivity analysis. Results: Receiver Operating Characteristic (ROC) analyses showed high discriminatory power in the training and internal validation cohorts (Area under the curve [AUC] of 0.78 (95%CI = 0.78-0.79) and 0.78 (95%CI = 0.77-0.79), respectively), whereas that of the model on external validation data was 0.759 (95%CI = 0.757-0.761). We developed a static nomogram, a web app, and a risk table based on a logistic regression model using algorithm-selected variables. Conclusions: Our model can stratify lung cancer patients into high- and low-risk of in-hospital mortality to assist clinical further planning.

Differential Patterns by Area-Level Social Determinants of Health in Coronavirus Disease 2019 (COVID-19)-Related Mortality and Non-COVID-19 Mortality: A Population-Based Study of 11.8 Million People in Ontario, Canada

BACKGROUND

Social determinants of health (SDOH) have been associated with coronavirus disease 2019 (COVID-19) outcomes. We examined patterns in COVID-19-related mortality by SDOH and compared these patterns to those for non-COVID-19 mortality.

METHODS

Residents of Ontario, Canada, aged ≥20 years were followed from 1 March 2020 to 2 March 2021. COVID-19-related death was defined as death within 30 days following or 7 days prior to a positive COVID-19 test. Area-level SDOH from the 2016 census included median household income; proportion with diploma or higher educational attainment; proportion essential workers, racially minoritized groups, recent immigrants, apartment buildings, and high-density housing; and average household size. We examined associations between SDOH and COVID-19-related mortality, and non-COVID-19 mortality using cause-specific hazard models.

RESULTS

Of 11 810 255 individuals, we observed 3880 COVID-19-related deaths and 88 107 non-COVID-19 deaths. After accounting for demographics, baseline health, and other area-level SDOH, the following were associated with increased hazards of COVID-19-related death (hazard ratio [95% confidence interval]: lower income (1.30 [1.04-1.62]), lower educational attainment (1.27 [1.07-1.52]), higher proportions essential workers (1.28 [1.05-1.57]), racially minoritized groups (1.42 [1.08-1.87]), apartment buildings (1.25 [1.07-1.46]), and large vs medium household size (1.30 [1.12-1.50]). Areas with higher proportion racially minoritized groups were associated with a lower hazard of non-COVID-19 mortality (0.88 [0.84-0.92]).

CONCLUSIONS

Area-level SDOH are associated with COVID-19-related mortality after accounting for demographic and clinical factors. COVID-19 has reversed patterns of lower non-COVID-19 mortality among racially minoritized groups. Pandemic responses should include strategies to address disproportionate risks and inequitable coverage of preventive interventions associated with SDOH.

Long-Term Survival among Patients Hospitalized for COVID-19 during the First Three Epidemic Waves: An Observational Study in a Northern Italy Hospital

The mortality rate of hospitalized COVID-19 patients differed strongly between the first three pandemic waves. Nevertheless, their long-term survival has been poorly assessed. The aim of this study was to compare the clinical characteristics and mortality rates of 825 patients with coronavirus disease 2019 (COVID-19) infection who were hospitalized at the Alessandria hub hospital, in Northern Italy, during the first fifty days of the first three pandemic waves. Each subject was followed in terms of vital status for six months from the date of hospital admission or until deceased. Patients admitted during the three waves differed in age (p = 0.03), disease severity (p < 0.0001), Charlson comorbidity index (p = 0.0002), oxygen therapy (p = 0.002), and invasive mechanical ventilation (p < 0.0001). By the end of follow-up, 309 deaths (38.7%) were observed, of which 186 occurred during hub hospitalization (22.5%). Deaths were distributed differently among the waves (p < 0.0001), resulting in being higher amongst those subjects admitted during the first wave. The COVID-19 infection was reported as the main cause of death and patients with a higher mortality risk were those aged ≥65 years [adjusted HR = 3.40 (95% CI 2.20-5.24)], with a higher disease severity [adjusted HR = 1.87 (95%CI 1.43-2.45)], and those requiring oxygen therapy [adjusted HR = 2.30 (95%CI 1.61-3.30)]. In conclusion, COVID-19 patients admitted to our hub hospital during the second and the third waves had a lower risk of long-term mortality than those admitted during the first. Older age, more severe disease, and the need for oxygen therapy were among the strongest risk factors for poor prognosis.

Changing trends of patient characteristics and treatment pathways during the COVID-19 pandemic: A cross-sectional analysis of 72,459 inpatient cases from the German Helios database

Background

This study compared patient profiles and clinical courses of SARS-CoV-2 infected inpatients over different pandemic periods.

Methods

In a retrospective cross-sectional analysis, we examined administrative data of German Helios hospitals using ICD-10-codes at discharge. Inpatient cases with SARS-CoV-2 infection admitted between 03/04/2020 and 07/19/2022 were included irrespective of the reason for hospitalization. All endpoints were timely assigned to admission date for trend analysis. The first pandemic wave was defined by change points in time-series of incident daily infections and compared with different later pandemic phases according to virus type predominance.

Results

We included 72,459 inpatient cases. Patients hospitalized during the first pandemic wave (03/04/2020-05/05/2020; n = 1,803) were older (68.5 ± 17.2 vs. 64.4 ± 22.6 years, p < 0.01) and severe acute respiratory infections were more prevalent (85.2 vs. 53.3%, p < 0.01). No differences were observed with respect to distribution of sex, but comorbidity burden was higher in the first pandemic wave. The risk of receiving intensive care therapy was reduced in all later pandemic phases as was in-hospital mortality when compared to the first pandemic wave. Trend analysis revealed declines of mean age and Elixhauser comorbidity index over time as well as a decline of the utilization of intensive care therapy, mechanical ventilation and in-hospital mortality.

Conclusion

Characteristics and outcomes of inpatients with SARS-CoV-2 infection changed throughout the observational period. An ongoing evaluation of trends and care pathways will allow for the assessment of future demands.