Clinical characteristics, systemic complications, and in-hospital outcomes for patients with COVID-19 in Latin America. LIVEN-Covid-19 study: A prospective, multicenter, multinational, cohort study

Risk factors for mortality in hospitalized COVID-19 patients across five waves in Pakistan

This retrospective cohort study aims to describe the clinical characteristics and outcomes and assess risk factors for mortality across the epidemic waves in hospitalized COVID-19 patients in a major tertiary-care center in Pakistan. A total of 5368 patients with COVID-19, hospitalized between March 2020 and April 2022 were included. The median age was 58 years (IQR: 44-69), 41% were females, and the overall mortality was 12%. Comparative analysis of COVID-19 waves showed that the proportion of patients aged ≥ 60 years was highest during the post-wave 4 period (61.4%) and Wave 4 (Delta) (50%) (p < 0.001). Male predominance decreased from 65.2% in Wave 2 to 44.2% in Wave 5 (Omicron) (p < 0.001). Mortality rate was lowest at 9.4% in wave 5 and highest at 21.6% in the post-wave 4 period (p = 0.041). In multivariable analysis for risk factors of mortality, acute respiratory distress syndrome (ARDS) was most strongly associated with mortality (aOR 22.98, 95% CI 15.28-34.55, p < 0.001), followed by need for mechanical ventilation (aOR 6.81, 95% CI 5.13-9.05, p < 0.001). Other significant risk factors included acute kidney injury (aOR 3.05, 95% CI 2.38-3.91, p < 0.001), stroke (aOR 2.40, 95% CI 1.26-4.60, p = 0.008), pulmonary embolism (OR 2.07, 95% CI 1.28-3.35, p = 0.003), and age ≥ 60 years (aOR 2.45, 95% CI 1.95-3.09, p < 0.001). Enoxaparin use was associated with lower mortality odds (aOR 0.45, 95% CI 0.35-0.60, p < 0.001. Patients hospitalized during Wave 4 (aOR 2.22, 95% CI 1.39-3.56, p < 0.001) and the post-wave 4 period (aOR 2.82, 95% CI 1.37-5.80, p = 0.005) had higher mortality odds compared to other waves. The study identifies higher mortality risk in patients admitted in Delta wave and post-wave, aged ≥ 60 years, and with respiratory and renal complications, and lower risk with anticoagulation during COVID-19 waves.

Risk factors for COVID-19 and their association with mortality in Ecuadorian patients admitted to the ICU: A retrospective cohort multicentric study

Several risk factors were associated with mortality in patients with coronavirus disease 2019 (COVID-19) infection in intensive care units (ICU). We assessed the effect of risk factors related to the characteristics and clinical history of the population, laboratory test results, drug management, and type of ventilation on the probability of survival/discharge from the ICU. A retrospective cohort multicentric study of adults with COVID-19 admitted to the ICU between March 2020 and December 2021. Data were collected from 6 hospitals in 5 cities in Ecuador. The primary outcome was ICU survival/discharge. Survival analysis was conducted using semi-parametric Cox proportional hazards models. Of those admitted to the ICU with COVID-19, (n = 991), mean age was 56.76 ± 13.14, and 65.9% were male. Regarding the primary outcome, 51.1% (n = 506) died and 48.9% (n = 485) survived. Of the group that died, their mean age was higher than the survivors (60.7 vs 52.60 years, respectively), and they had a higher prevalence of comorbidities such as arterial hypertension (37.2% vs 20.4%, respectively) and diabetes mellitus (26.9% vs 15.7%, respectively), with P < .001. In ventilatory management, 32.7% of patients used noninvasive ventilation and high-flow nasal cannula, and 67.3% required invasive ventilatory support. After adjusting for confounders, Cox regression analysis showed that patients were less likely to be discharged alive from the ICU if they met the following conditions: arterial hypertension (hazard ratio [HR] = 0.83 95% CI 0.723-0.964), diabetes mellitus (HR = 0.80 95% CI 0.696-0.938), older than 62 years (HR = 0.86 95% CI 0.790-0.956), obese (body mass index ≥ 30) (HR = 0.78 95% CI 0.697-0.887), 1 unit increase in SOFA score (HR = 0.94 95% CI 0.937-0.961), PaO2/FiO2 ratio <100 mm Hg (HR = 0.84 95% CI 0.786-0.914), and the use of invasive mechanical ventilation (HR = 0.68 95% CI 0.614-0.769). Risk factors associated with increased mortality were older age, obesity, arterial hypertension, and diabetes. Factors such as male gender, chronic obstructive pulmonary disease, acute kidney injury, and cancer reported in other investigations did not have the same effect on mortality in our study.

Effects of Glycemic Variability in Critically Ill Patients with Coronavirus Disease 2019: A Retrospective Observational Study

Aim and background

Hyperglycemia is considered an adaptive metabolic manifestation of stress and is associated with poor outcomes. Herein, we analyzed the association between glycemic variability (GV) and hospital mortality in patients with coronavirus disease 2019 (COVID-19) admitted to the intensive care unit (ICU), and the association between GV and mechanical ventilation (MV), ICU stay, length of hospital stays, renal replacement therapy (RRT), hypoglycemia, nosocomial infections, insulin use, and corticosteroid class.

Materials and methods

In this retrospective observational study, we collected information on blood glucose levels during the first 10 days of hospitalization in a cohort of ICU patients with COVID-19 and its association with outcomes.

Results

In 239 patients, an association was observed between GV and hospital mortality between the first and last quartiles among patients without diabetes [odds ratio (OR), 3.78; confidence interval, 1.24-11.5]. A higher GV was associated with a greater need for RRT (p = 0.002), regular insulin (p < 0.001), and episodes of hypoglycemia (p < 0.001). Nosocomial infections were associated with intermediate GV quartiles (p = 0.02). The corticosteroid class had no association with GV (p = 0.21).

Conclusion

Glycemic variability was associated with high mortality in patients with COVID-19 and observed in the subgroup of patients without diabetes.

Clinical significance

Glycemic control in critically ill patients remains controversial and hyperglycemia is associated with worse outcomes. Diabetes mellitus (DM) is one of the most prevalent comorbidities in patients with COVID-19. In addition, they require corticosteroids due to pulmonary involvement, representing a challenge and an opportunity to better understand how glycemic changes can influence the outcome of these patients.

How to cite this article

Boschi E, Friedman G, Moraes RB. Effects of Glycemic Variability in Critically Ill Patients with Coronavirus Disease 2019: A Retrospective Observational Study. Indian J Crit Care Med 2024;28(4):381-386.

Clinical characteristics and outcomes of patients admitted with COVID-19 at a public-sector hospital over the first two waves of SARS-CoV-2 infection in Harare, Zimbabwe: A prospective cohort study

BACKGROUND

We investigated the clinical profile, complications, and outcomes of inpatients with COVID-19 at Parirenyatwa Hospital, Harare, across the first two waves of SARS-CoV-2 infection, and factors associated with mortality.

METHODS

We conducted a prospective cohort study on all patients admitted to the COVID-19 unit. Data were extracted from medical records and negative binomial regression with robust standard errors was used to assess the association between sociodemographic and clinical characteristics and mortality. Cox Regression was used for sensitivity analysis.

RESULTS

Of 563 people admitted with COVID-19 between 2 July 2020 and 19 March 2021, 214 (38.0%) died, 340 were discharged and 9 transferred. The median age was 56 (IQR 44-68) years and 53.8% were male. Overall, 38.8% experienced a complication, the most common being acute kidney injury (17.9%) and hyperglycaemia (13.1%). The most common comorbidity was hypertension (41.3%) followed by diabetes (28.6%), HIV (12.1%), cardiovascular disease (10.9%) and chronic kidney disease (7.8%). Among participants who stayed in the ward for more than 1 night, mortality was higher in patients with comorbidity compared to those without any comorbidity (38.7% vs 25.5%, risk ratio (RR) = 1.52 (95% CI 1.11, 2.07), p = 0.008). After adjusting for oxygen saturation, comorbidities, sex and pregnancy, mortality was higher in the second wave than in the first (adjusted RR 1.23, 95% CI 1.00-1.51, p = 0.05). In the second wave 57/161 (35.4%) deaths were attributed to lack of resources, mainly human resources.

CONCLUSION

The mortality rate was high and clinical COVID-19 care needs to pay careful attention to patient monitoring for complications and management of comorbidities. This will require addressing the critical health workforce shortage issues. Prevention of COVID-19 including vaccination particularly among individuals with comorbidities remains a high priority.

The effect of limited healthcare access on poor outcomes among hospitalized COVID-19 patients in Honduras: A single center cohort study

Background

The COVID-19 pandemic has had a severe impact on the Latin American subcontinent, particularly in areas with limited hospital resources and a restricted Intensive Care Unit (ICU) capacity. This study aimed to provide a comprehensive description of the clinical characteristics, outcomes, and factors associated with survival of COVID-19 hospitalized patients in Honduras.

Research question

What were the characteristics and outcomes of COVID-19 patients in a large referral center in Honduras?

Study design and methods

This study employed a retrospective cohort design conducted in a single center in San Pedro Sula, Honduras, between October 2020 to March 2021. All hospitalized cases of confirmed COVID-19 during this timeframe were included in the analysis. Univariable and multivariable survival analysis were performed using Kaplan-Meier curves and Cox proportional hazards model aiming to identify factors associated with decreased 30 day in-hospital survival, using a priori-selected factors.

Results

A total of 929 confirmed cases were identified in this cohort, with males accounting for 55.4 % of cases. The case fatality rate among the hospitalized patients was found to be 50.1 % corresponding to 466 deaths. Patients with comorbidities such as hypertension, diabetes, obesity, chronic kidney disease, chronic obstructive pulmonary disease and cardiovascular disease had a higher likelihood of mortality. Additionally, non-survivors had a significantly longer time from illness onset to hospital admission compared to survivors (8.2 days vs 4.7 days). Among the cohort, 306 patients (32.9 %) met criteria for ICU admission. However, due to limited capacity, only 60 patients (19·6 %) were admitted to the ICU. Importantly, patients that were unable to receive level-appropriate care had lower likelihood of survival compared to those who received level-appropriate care (hazard ratio: 1.84).

Interpretation

This study represents, the largest investigation of in-hospital COVID-19 cases in Honduras and Central America. The findings highlight a substantial case fatality rate among hospitalized patients. In this study, patients who couldn't receive level-appropriate care (ICU admission) had a significantly lower likelihood of survival when compared to those who did. These results underscore the significant impact of healthcare access during the pandemic, particularly in low- and middle-income countries.

Impact of vaccination and risk factors on COVID-19 mortality amid delta wave in Libya: A single center cohort study

INTRODUCTION

The Delta variant has led to a surge in COVID-19 cases in Libya, making it crucial to investigate the impact of vaccination on mortality rates among hospitalized patients and the critically ill. This study aimed to explore the risk factors for COVID-19 mortality and the mortality rates among unvaccinated and vaccinated adults during the Delta wave who were admitted to a single COVID-19 care center in Tripoli, Libya.

METHODS

The study involved two independent cohorts (n = 341). One cohort was collected retrospectively from May 2021-August 2021 and the second cohort was prospectively collected from August 2021-October 2021. Most of the patients in the study became ill during the Delta wave. The two cohorts were merged and analysed as one group.

RESULTS

Most patients were male (60.5%) and 53.3% were >60 years old. The vast majority of patients did not have a previous COVID-19 infection (98.9%) and were unvaccinated (90.3%). Among vaccinated patients, 30 had received one dose of vaccine and only 3 had received two doses. Among patients who received one dose, 58.1% (18/31) died and 41.9% (13/31) survived. Most patients (72.2%) had a pre-existing medical condition. A multivariable prediction model showed that age >60 years was significantly associated with death (odds ratio = 2.328, CI 1.5-3.7, p-value = <0.0001).

CONCLUSION

Our results indicate that previous infection or full vaccination against COVID-19 significantly reduces hospitalization and death. However, a single vaccine dose may not be adequate, especially for older individuals and those with underlying medical conditions. High-risk older patients with comorbidities should be fully vaccinated and offered up to date bivalent COVID-19 booster doses.

Risk factors for developing ventilator-associated lower respiratory tract infection in patients with severe COVID-19: a multinational, multicentre study, prospective, observational study

Around one-third of patients diagnosed with COVID-19 develop a severe illness that requires admission to the Intensive Care Unit (ICU). In clinical practice, clinicians have learned that patients admitted to the ICU due to severe COVID-19 frequently develop ventilator-associated lower respiratory tract infections (VA-LRTI). This study aims to describe the clinical characteristics, the factors associated with VA-LRTI, and its impact on clinical outcomes in patients with severe COVID-19. This was a multicentre, observational cohort study conducted in ten countries in Latin America and Europe. We included patients with confirmed rtPCR for SARS-CoV-2 requiring ICU admission and endotracheal intubation. Only patients with a microbiological and clinical diagnosis of VA-LRTI were included. Multivariate Logistic regression analyses and Random Forest were conducted to determine the risk factors for VA-LRTI and its clinical impact in patients with severe COVID-19. In our study cohort of 3287 patients, VA-LRTI was diagnosed in 28.8% [948/3287]. The cumulative incidence of ventilator-associated pneumonia (VAP) was 18.6% [610/3287], followed by ventilator-associated tracheobronchitis (VAT) 10.3% [338/3287]. A total of 1252 bacteria species were isolated. The most frequently isolated pathogens were Pseudomonas aeruginosa (21.2% [266/1252]), followed by Klebsiella pneumoniae (19.1% [239/1252]) and Staphylococcus aureus (15.5% [194/1,252]). The factors independently associated with the development of VA-LRTI were prolonged stay under invasive mechanical ventilation, AKI during ICU stay, and the number of comorbidities. Regarding the clinical impact of VA-LRTI, patients with VAP had an increased risk of hospital mortality (OR [95% CI] of 1.81 [1.40-2.34]), while VAT was not associated with increased hospital mortality (OR [95% CI] of 1.34 [0.98-1.83]). VA-LRTI, often with difficult-to-treat bacteria, is frequent in patients admitted to the ICU due to severe COVID-19 and is associated with worse clinical outcomes, including higher mortality. Identifying risk factors for VA-LRTI might allow the early patient diagnosis to improve clinical outcomes.Trial registration: This is a prospective observational study; therefore, no health care interventions were applied to participants, and trial registration is not applicable.

Risk factors for persistent tomographic abnormalities at 6 months of follow-up in a cohort of hospitalized patients with moderate and severe COVID-19 living at high altitude

Introduction

After COVID-19, functional and tomographic lung alterations may occur, but there are no studies at high altitude where, due to lower barometric pressure, there are lower levels of arterial oxygen pressure and saturation in both normal subjects and patients with respiratory disease. In this study, we evaluated the computed tomographic (CT), clinical, and functional involvement at 3 and 6 months post-hospitalization in survivors with moderate-severe COVID-19, as well the risk factors associated with abnormal lung computed tomography (ALCT) at 6 months of follow-up.

Materials and methods

Prospective cohort, after hospitalization for COVID-19, of patients older than 18 years residing at high altitude. Follow-up at 3 and 6 months with lung CT, spirometry, diffusing capacity of the lung for carbon monoxide (DLCO), six-minute walk test (6MWT), and oxygen saturation (SpO₂). Comparisons between ALCT and normal lung computed tomography (NLCT) groups with X² and Mann-Whitney U test, and paired test for changes between 3 and 6 months. A multivariate analysis was performed to evaluate the variables associated with ALCT at 6-month follow-up.

Results

We included 158 patients, 22.2% hospitalized in intensive care unit (ICU), 92.4% with typical COVID CT scan (peripheral, bilateral, or multifocal ground glass, with or without consolidation or findings of organizing pneumonia), and median hospitalization of 7 days. At 6 months, 53 patients (33.5%) had ALCT. There were no differences between ALCT and NLCT groups in symptoms or comorbidities on admission. ALCT patients were older and more frequently men, smokers and hospitalized in ICU. At 3 months, ALCT patients had more frequently a reduced forced vital capacity (< 80%), and lower meters walked (6MWT) and SpO₂. At 6 months, all patients improved lung function with no differences between groups, but there were more dyspnea and lower exercise SpO₂ in ALCT group. The variables associated with ALCT at 6 months were age, sex, ICU stay, and typical CT scan.

Conclusion

At 6-month follow-up, 33.5% of patients with moderate and severe COVID had ALCT. These patients had more dyspnea and lower SpO₂ in exercise. Regardless of the persistence of tomographic abnormalities, lung function and 6MWT improved. We identified the variables associated with ALCT.

Respiratory support in patients with severe COVID-19 in the International Severe Acute Respiratory and Emerging Infection (ISARIC) COVID-19 study: a prospective, multinational, observational study

BACKGROUND

Up to 30% of hospitalised patients with COVID-19 require advanced respiratory support, including high-flow nasal cannulas (HFNC), non-invasive mechanical ventilation (NIV), or invasive mechanical ventilation (IMV). We aimed to describe the clinical characteristics, outcomes and risk factors for failing non-invasive respiratory support in patients treated with severe COVID-19 during the first two years of the pandemic in high-income countries (HICs) and low middle-income countries (LMICs).

METHODS

This is a multinational, multicentre, prospective cohort study embedded in the ISARIC-WHO COVID-19 Clinical Characterisation Protocol. Patients with laboratory-confirmed SARS-CoV-2 infection who required hospital admission were recruited prospectively. Patients treated with HFNC, NIV, or IMV within the first 24 h of hospital admission were included in this study. Descriptive statistics, random forest, and logistic regression analyses were used to describe clinical characteristics and compare clinical outcomes among patients treated with the different types of advanced respiratory support.

RESULTS

A total of 66,565 patients were included in this study. Overall, 82.6% of patients were treated in HIC, and 40.6% were admitted to the hospital during the first pandemic wave. During the first 24 h after hospital admission, patients in HICs were more frequently treated with HFNC (48.0%), followed by NIV (38.6%) and IMV (13.4%). In contrast, patients admitted in lower- and middle-income countries (LMICs) were less frequently treated with HFNC (16.1%) and the majority received IMV (59.1%). The failure rate of non-invasive respiratory support (i.e. HFNC or NIV) was 15.5%, of which 71.2% were from HIC and 28.8% from LMIC. The variables most strongly associated with non-invasive ventilation failure, defined as progression to IMV, were high leukocyte counts at hospital admission (OR [95%CI]; 5.86 [4.83-7.10]), treatment in an LMIC (OR [95%CI]; 2.04 [1.97-2.11]), and tachypnoea at hospital admission (OR [95%CI]; 1.16 [1.14-1.18]). Patients who failed HFNC/NIV had a higher 28-day fatality ratio (OR [95%CI]; 1.27 [1.25-1.30]).

CONCLUSIONS

In the present international cohort, the most frequently used advanced respiratory support was the HFNC. However, IMV was used more often in LMIC. Higher leucocyte count, tachypnoea, and treatment in LMIC were risk factors for HFNC/NIV failure. HFNC/NIV failure was related to worse clinical outcomes, such as 28-day mortality. Trial registration This is a prospective observational study; therefore, no health care interventions were applied to participants, and trial registration is not applicable.

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use.

The Effects of Biological Sex and Cardiovascular Disease on COVID-19 Mortality

Cardiovascular disease (CVD) is a common comorbidity observed in patients with COVID-19 that is associated with increased severity and mortality. However, the effects of biological sex on CVD associated mortality in COVID-19 patients is poorly established particularly among Hispanic/Latin Americans. We examined the association of preexisting CVD with COVID-19 mortality in hospitalized Latin American men and women. This multicenter study included hospitalized Mexican patients with a positive diagnosis of COVID-19. The main outcome was in-hospital mortality. Multivariable regression analyses were used to calculate the adjusted odd ratios with 95% confidence interval for mortality in women and men. Of 81,400 patients with a positive diagnosis for SARS-CoV-2 infection, 28,929 (35.54%) hospitalized patients were evaluated. Of these, the 35.41% (10,243) were women. In-hospital death was higher in men than in women. In relation to CVD between the sexes, women had a higher incidence of CVD than men (4.69% vs 3.93%. P=0.0023). The adjusted logistic regression analyses showed that CVD was significantly associated with COVID-19 mortality in women but not men. We then stratified by sex according to age <52 and ≥52 years old. Similar significant association was also found in pre-specified analysis in women ≥52 years old but not in men of similar age. We conclude that CVD's effect on mortality among COVID-19 hospitalized patients is dependent on biological sex and age in this Latin American cohort. These results suggest that therapeutic strategies for Latin American women with CVD and COVID-19 should include particular attention to their cardiovascular health.

Predicting Intensive Care Unit Admission for COVID-19 Patients from Laboratory Results

Trends in routine laboratory tests, such as high white blood cell and low platelet counts, correlate with COVID-19-related intensive care unit (ICU) admissions. Other related biomarkers include elevated troponin, alanine aminotransferase, and aspartate transaminase levels (liver function tests). To this end, the aim of this study was to investigate the effect of changes in laboratory test parameters on ward-based and ICU COVID-19 patients. A total of 280 COVID-19 patients were included in the study and were divided based on admission status into ICU (37) or ward (243) patients. ICU admission correlated significantly with higher levels of several tested parameters, including lactate dehydrogenase, creatinine, D-dimer, creatine kinase, white blood cell count, and neutrophil count. In conclusion, routine laboratory tests offer an indication of which COVID-19 patients are most likely to be admitted to the ICU. These associations can assist healthcare providers in addressing the needs of patients who are at risk of COVID-19 complications.

A novel model to predict severe COVID-19 and mortality using an artificial intelligence algorithm to interpret chest X-Rays and clinical variables

Background

Patients with coronavirus disease 2019 (COVID-19) could develop severe disease requiring admission to the intensive care unit (ICU). This article presents a novel method that predicts whether a patient will need admission to the ICU and assesses the risk of in-hospital mortality by training a deep-learning model that combines a set of clinical variables and features in chest radiographs.

Methods

This was a prospective diagnostic test study. Patients with confirmed severe acute respiratory syndrome coronavirus 2 infection between March 2020 and January 2021 were included. This study was designed to build predictive models obtained by training convolutional neural networks for chest radiograph images using an artificial intelligence (AI) tool and a random forest analysis to identify critical clinical variables. Then, both architectures were connected and fine-tuned to provide combined models.

Results

2552 patients were included in the clinical cohort. The variables independently associated with ICU admission were age, fraction of inspired oxygen (F_iO2) on admission, dyspnoea on admission and obesity. Moreover, the variables associated with hospital mortality were age, F_iO2 on admission and dyspnoea. When implementing the AI model to interpret the chest radiographs and the clinical variables identified by random forest, we developed a model that accurately predicts ICU admission (area under the curve (AUC) 0.92±0.04) and hospital mortality (AUC 0.81±0.06) in patients with confirmed COVID-19.

Conclusions

This automated chest radiograph interpretation algorithm, along with clinical variables, is a reliable alternative to identify patients at risk of developing severe COVID-19 who might require admission to the ICU.

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