Early predictors of in-hospital mortality in patients with COVID-19 in a large American cohort

Clinical characteristics and predictors of complications and mortality in hospitalized octogenarian patients with COVID-19: an ambispective study

OBJECTIVE

This study aimed to describe the clinical presentation of COVID-19 in hospitalized patients aged 80 or above and to identify predictors for death and complications throughout the epidemic waves of the disease.

METHODS

This was an observational, multicenter, ambispective study conducted between March 2020 and August 2021 using data collected in five centers from southern metropolitan area of Barcelona (COVID-MetroSud cohort). Patients were grouped based on the pandemic waves of inclusion in the registry. We conducted a descriptive analysis, followed by bivariate and multivariate analyses (binary logistic regression) to identify predictors of risk for death or complications.

RESULTS

A total of 1192 patients (mean [SD] age 85.7 [4.22] years and 46.8% female) were included. The most frequently reported symptoms in all waves were fever (63.1%), cough (56.5%), dyspnea (48.2%), and asthenia (27.5%). Laboratory and radiological findings consistently showed abnormal bilateral chest X-ray results (72.5% of patients) and elevated inflammatory markers such as lactate dehydrogenase (mean [SD] 335 [188] U/L), C-reactive protein (CRP) (mean [SD] 110 [88.4] U/L), and ferritin (mean [SD] 842 [1561] U/L). Acute respiratory distress syndrome (43.7%), renal failure (19.2%), and delirium (17.5%) were the most frequent complications. The overall mortality rate was 41.4% and declined across the epidemic waves. Age, diabetes mellitus, heart failure, dyspnea, and higher baseline levels of creatinine were identified as risk factors for complications, while a higher Barthel index and presence of cough were found to be protective. Age, dyspnea, abnormal bilateral chest X-ray, CRP, and sodium were identified as risk factors for death.

CONCLUSIONS

This study demonstrates the clinical presentation of COVID-19 (fever, cough, dyspnea, and asthenia) and the different risk factors for mortality and complications in octogenarian hospitalized patients throughout the pandemic. These findings could be highly valuable for managing future virus pandemics.

Accuracy of routine laboratory tests to predict mortality and deterioration to severe or critical COVID-19 in people with SARS-CoV-2

BACKGROUND

Identifying patients with COVID-19 disease who will deteriorate can be useful to assess whether they should receive intensive care, or whether they can be treated in a less intensive way or through outpatient care. In clinical care, routine laboratory markers, such as C-reactive protein, are used to assess a person's health status.

OBJECTIVES

To assess the accuracy of routine blood-based laboratory tests to predict mortality and deterioration to severe or critical (from mild or moderate) COVID-19 in people with SARS-CoV-2.

SEARCH METHODS

On 25 August 2022, we searched the Cochrane COVID-19 Study Register, encompassing searches of various databases such as MEDLINE via PubMed, CENTRAL, Embase, medRxiv, and ClinicalTrials.gov. We did not apply any language restrictions.

SELECTION CRITERIA

We included studies of all designs that produced estimates of prognostic accuracy in participants who presented to outpatient services, or were admitted to general hospital wards with confirmed SARS-CoV-2 infection, and studies that were based on serum banks of samples from people. All routine blood-based laboratory tests performed during the first encounter were included. We included any reference standard used to define deterioration to severe or critical disease that was provided by the authors.

DATA COLLECTION AND ANALYSIS

Two review authors independently extracted data from each included study, and independently assessed the methodological quality using the Quality Assessment of Prognostic Accuracy Studies tool. As studies reported different thresholds for the same test, we used the Hierarchical Summary Receiver Operator Curve model for meta-analyses to estimate summary curves in SAS 9.4. We estimated the sensitivity at points on the SROC curves that corresponded to the median and interquartile range boundaries of specificities in the included studies. Direct and indirect comparisons were exclusively conducted for biomarkers with an estimated sensitivity and 95% CI of ≥ 50% at a specificity of ≥ 50%. The relative diagnostic odds ratio was calculated as a summary of the relative accuracy of these biomarkers.

MAIN RESULTS

We identified a total of 64 studies, including 71,170 participants, of which 8169 participants died, and 4031 participants deteriorated to severe/critical condition. The studies assessed 53 different laboratory tests. For some tests, both increases and decreases relative to the normal range were included. There was important heterogeneity between tests and their cut-off values. None of the included studies had a low risk of bias or low concern for applicability for all domains. None of the tests included in this review demonstrated high sensitivity or specificity, or both. The five tests with summary sensitivity and specificity above 50% were: C-reactive protein increase, neutrophil-to-lymphocyte ratio increase, lymphocyte count decrease, d-dimer increase, and lactate dehydrogenase increase. Inflammation For mortality, summary sensitivity of a C-reactive protein increase was 76% (95% CI 73% to 79%) at median specificity, 59% (low-certainty evidence). For deterioration, summary sensitivity was 78% (95% CI 67% to 86%) at median specificity, 72% (very low-certainty evidence). For the combined outcome of mortality or deterioration, or both, summary sensitivity was 70% (95% CI 49% to 85%) at median specificity, 60% (very low-certainty evidence). For mortality, summary sensitivity of an increase in neutrophil-to-lymphocyte ratio was 69% (95% CI 66% to 72%) at median specificity, 63% (very low-certainty evidence). For deterioration, summary sensitivity was 75% (95% CI 59% to 87%) at median specificity, 71% (very low-certainty evidence). For mortality, summary sensitivity of a decrease in lymphocyte count was 67% (95% CI 56% to 77%) at median specificity, 61% (very low-certainty evidence). For deterioration, summary sensitivity of a decrease in lymphocyte count was 69% (95% CI 60% to 76%) at median specificity, 67% (very low-certainty evidence). For the combined outcome, summary sensitivity was 83% (95% CI 67% to 92%) at median specificity, 29% (very low-certainty evidence). For mortality, summary sensitivity of a lactate dehydrogenase increase was 82% (95% CI 66% to 91%) at median specificity, 60% (very low-certainty evidence). For deterioration, summary sensitivity of a lactate dehydrogenase increase was 79% (95% CI 76% to 82%) at median specificity, 66% (low-certainty evidence). For the combined outcome, summary sensitivity was 69% (95% CI 51% to 82%) at median specificity, 62% (very low-certainty evidence). Hypercoagulability For mortality, summary sensitivity of a d-dimer increase was 70% (95% CI 64% to 76%) at median specificity of 56% (very low-certainty evidence). For deterioration, summary sensitivity was 65% (95% CI 56% to 74%) at median specificity of 63% (very low-certainty evidence). For the combined outcome, summary sensitivity was 65% (95% CI 52% to 76%) at median specificity of 54% (very low-certainty evidence). To predict mortality, neutrophil-to-lymphocyte ratio increase had higher accuracy compared to d-dimer increase (RDOR (diagnostic Odds Ratio) 2.05, 95% CI 1.30 to 3.24), C-reactive protein increase (RDOR 2.64, 95% CI 2.09 to 3.33), and lymphocyte count decrease (RDOR 2.63, 95% CI 1.55 to 4.46). D-dimer increase had higher accuracy compared to lymphocyte count decrease (RDOR 1.49, 95% CI 1.23 to 1.80), C-reactive protein increase (RDOR 1.31, 95% CI 1.03 to 1.65), and lactate dehydrogenase increase (RDOR 1.42, 95% CI 1.05 to 1.90). Additionally, lactate dehydrogenase increase had higher accuracy compared to lymphocyte count decrease (RDOR 1.30, 95% CI 1.13 to 1.49). To predict deterioration to severe disease, C-reactive protein increase had higher accuracy compared to d-dimer increase (RDOR 1.76, 95% CI 1.25 to 2.50). The neutrophil-to-lymphocyte ratio increase had higher accuracy compared to d-dimer increase (RDOR 2.77, 95% CI 1.58 to 4.84). Lastly, lymphocyte count decrease had higher accuracy compared to d-dimer increase (RDOR 2.10, 95% CI 1.44 to 3.07) and lactate dehydrogenase increase (RDOR 2.22, 95% CI 1.52 to 3.26).

AUTHORS' CONCLUSIONS

Laboratory tests, associated with hypercoagulability and hyperinflammatory response, were better at predicting severe disease and mortality in patients with SARS-CoV-2 compared to other laboratory tests. However, to safely rule out severe disease, tests should have high sensitivity (> 90%), and none of the identified laboratory tests met this criterion. In clinical practice, a more comprehensive assessment of a patient's health status is usually required by, for example, incorporating these laboratory tests into clinical prediction rules together with clinical symptoms, radiological findings, and patient's characteristics.

Pre-Existing Atrial Fibrillation in Hospitalized Patients with COVID-19: Insights from the CARDIO COVID 19-20 Registry

Pre-existing (chronic) atrial fibrillation (AF) has been identified as a risk factor for cardiovascular complications and mortality in patients with COVID-19; however, evidence in Latin America (LATAM) is scarce. This prospective and multicenter study from the CARDIO COVID 19-20 database includes hospitalized adults with COVID-19 from 14 countries in LATAM. A parsimonious logistic regression model was used to identify the main factors associated with mortality in a simulated case-control setting comparing patients with a history of AF to those without. In total, 3260 patients were included, of which 115 had AF. The AF group was older, had a higher prevalence of comorbidities, and had greater use of cardiovascular medications. In the model, AF, chronic kidney disease, and a respiratory rate > 25 at admission were associated with higher in-hospital mortality. The use of corticosteroids did not reach statistical significance; however, an effect was seen through the confidence interval. Thus, pre-existing AF increases mortality risk irrespective of other concomitant factors. Chronic kidney disease and a high respiratory rate at admission are also key factors for in-hospital mortality. These findings highlight the importance of comorbidities and regional characteristics in COVID-19 outcomes, in this instance, enhancing the evidence for patients from LATAM.

Outcomes of a home telemonitoring program for SARS-CoV-2 viral infection at a large academic medical center

INTRODUCTION

Telemedicine serves as a viable option during the COVID-19 pandemic to provide in-home care, maintain home isolation precautions, reduce unnecessary healthcare exposures, and de-burden hospitals.

METHODS

We created a novel telemedicine program to closely monitor patients infected with SARS-CoV-2 (COVID-19) at home. Adult patients with COVID-19 were enrolled in the program at the time of documented infection. Patients were followed by a team of providers via telephone or video visits at frequent intervals until resolution of their acute illness. Additionally, patients were stratified into high-risk and low-risk categories based on demographics and underlying comorbidities. The primary outcome was hospitalization after enrollment in the home monitoring program, including 30 days after discharge from the program.

RESULTS

Over a 3.5-month period, 1128 patients met criteria for enrollment in the home monitoring program. 30.7% were risk stratified as high risk for poor outcomes based on their comorbidities and age. Of the 1128 patients, 6.2% required hospitalization and 1.2% required ICU admission during the outcome period. Hospitalization was more frequent in patients identified as high risk (14.2% vs 2.7%, P < 0.001).

DISCUSSION

Enrollment in a home monitoring program appears to be an effective and sustainable modality for the ambulatory management of COVID-19.

Updated Bivalent COVID-19 Vaccines Reduce Risk of Hospitalization and Severe Outcomes in Adults: An Observational Cohort Study

Background

This study evaluates the real-world effectiveness of updated bivalent coronavirus disease 2019 (COVID-19) vaccines in adults, as the virus evolves and the need for new vaccinations increases.

Methods

In this observational, retrospective, multi-center, cohort analysis, we examined emergency care encounters with COVID-19 in metro Detroit, Michigan, from January 1, 2022, to March 9, 2023. Patients were categorized by vaccination status: unvaccinated, fully vaccinated, fully vaccinated and boosted (FV&B), or fully vaccinated and bivalent boosted (FV&BB). The primary outcome was to assess the impact of bivalent COVID-19 vaccinations on the risk of composite severe outcomes (intensive care unit (ICU) admission, mechanical ventilation, or death) among patients presenting to a hospital with a primary diagnosis of COVID-19.

Results

A total of 21,439 encounters met inclusion criteria: 9,630 (44.9%) unvaccinated, 9,223 (43.0%) vaccinated, 2,180 (10.2%) FV&B, and 406 (1.9%) FV&BB. The average age was 48.8, with 59.6% female; 61.1% were White, 32.8% Black, and 6.0% other races. Severe disease affected 5.5% overall: 5.0% unvaccinated, 5.7% vaccinated, 7.0% FV&B, and 4.7% FV&BB (P = 0.001). Severe disease rates among admitted patients were 13.3% unvaccinated, 11.9% vaccinated, 12.2% boosted, and 8.1% FV&BB (P = 0.052). The FV&BB group showed a 4.0% (P = 0.0369) lower risk of severe disease compared to FV&B and a 5.1% (P = 0.0203) lower probability of hospitalization.

Conclusions

As the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to mutate and evolve, updated vaccines are necessary to better combat COVID-19. In a real-world hospital-based population, this investigation demonstrates the incremental benefit of the bivalent booster vaccine in reducing the risk of hospitalization and severe outcomes in those diagnosed with COVID-19 compared to all other forms of vaccination.

Impact and evolution of risk factors associated with hospitalization and mortality due to COVID-19 during the six epidemic waves in Mexico

Objectives

This study aims to analyze and compare the main risk factors for hospitalization and deaths due to COVID-19 during the six epidemic waves from February 2020 to June 2023 in Mexico.

Methods

First, a descriptive analysis of the risk factors that led to hospitalization and mortality due to COVID-19 was performed. Next, the degree of relationship of each risk factor with hospitalization and death was determined using Cramer's V coefficient. Finally, logistic regression models were applied to estimate the odds ratios of the most statistically significant risk factors for hospitalization and mortality.

Results

A direct relationship between age and the possibility of hospitalization and death due to COVID-19 was found. Moreover, the comorbidities most likely to lead to hospitalization and death were pneumonia, hypertension, diabetes, obesity and CKD. It is also remarkable that the second factor of death is endotracheal intubation.

Conclusion

The COVID-19 pandemic in Mexico revealed the reality of an epidemiological scenario where infectious diseases and chronic degenerative diseases coexist and interrelate.

Evaluation of hematological changes and immune response biomarkers as a prognostic factor in critical patients with COVID-19

COVID-19 disease has been a challenge for health systems worldwide due to its high transmissibility, morbidity, and mortality. Severe COVID-19 is associated with an imbalance in the immune response, resulting in a cytokine storm and a hyperinflammation state. While hematological parameters correlate with prognosis in COVID patients, their predictive value has not been evaluated specifically among those severely ill. Therefore, we aim to evaluate the role of hematological and immune response biomarkers as a prognostic factor in critically ill patients with COVID-19 admitted to the intensive care unit. From May 2020 to July 2021, a retrospective cohort study was conducted in a reference hospital in Manaus, which belongs to the Brazilian public health system. This study was carried out as single-center research. Clinical and laboratory parameters were analyzed to evaluate the association with mortality. We also evaluated the role of neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), and C-reactive protein-to-lymphocyte ratio (CLR). We gathered information from medical records, as well as from prescriptions and forms authorizing the use of antimicrobial medications. During the study period, 177 patients were included, with a mean age of 62.58 ± 14.39 years. The overall mortality rate was 61.6%. Age, mechanical ventilation (MV) requirement, leukocytosis, neutrophilia, high c-reactive protein level, NLR, and CLR showed a statistically significant association with mortality in the univariate analysis. In the multivariate logistic regression analysis, only MV (OR 35.687, 95% CI: 11.084-114.898, p< 0.001) and NLR (OR 1.026, 95% CI: 1.003-1.050, p = 0.028) remained statistically associated with the outcome of death (AUC = 0.8096). While the need for mechanical ventilation is a parameter observed throughout the hospital stay, the initial NLR can be a primary risk stratification tool to establish priorities and timely clinical intervention in patients with severe COVID-19 admitted to the ICU.

Deaths induced by compassionate use of hydroxychloroquine during the first COVID-19 wave: an estimate

BACKGROUND

During the first wave of COVID-19, hydroxychloroquine (HCQ) was used off-label despite the absence of evidence documenting its clinical benefits. Since then, a meta-analysis of randomised trials showed that HCQ use was associated with an 11% increase in the mortality rate. We aimed to estimate the number of HCQ-related deaths worldwide.

METHODS AND FINDINGS

We estimated the worldwide in-hospital mortality attributable to HCQ use by combining the mortality rate, HCQ exposure, number of hospitalised patients, and the increased relative risk of death with HCQ. The mortality rate in hospitalised patients for each country was calculated using pooled prevalence estimated by a meta-analysis of published cohorts. The HCQ exposure was estimated using median and extreme estimates from the same systematic review. The number of hospitalised patients during the first wave was extracted from dedicated databases. The systematic review included 44 cohort studies (Belgium: k = 1, France: k = 2, Italy: k = 12, Spain: k = 6, Turkey: k = 3, USA: k = 20). HCQ prescription rates varied greatly from one country to another (range 16-84%). Overall, using median estimates of HCQ use in each country, we estimated that 16,990 HCQ-related in-hospital deaths (range 6267-19256) occurred in the countries with available data. The median number of HCQ-related deaths in Belgium, Turkey, France, Italy, Spain, and the USA was 240 (range not estimable), 95 (range 92-128), 199 (range not estimable), 1822 (range 1170-2063), 1895 (range 1475-2094) and 12739 (3244- 15570), respectively.

CONCLUSIONS

Although our estimates are limited by their imprecision, these findings illustrate the hazard of drug repurposing with low-level evidence.

Demographic characteristics, clinical presentation and in-hospital outcome among patients with Covid-19 in a Nigerian tertiary hospital

Background

We described the demographic/clinical characteristics and in-hospital outcome of patients with COVID-19 at the University of Nigeria Teaching Hospital (UNTH) during the first wave to inform evidence-based responses during subsequent waves in Africa.

Methodology

We conducted retrospective cohort analyses of adult patients ≥18 years with PCR or GeneXpert-confirmed SARS-CoV-2 infection. Data was extracted from patients' medical records from 1st May to 30th September 2020. Based on disease severity, patients were either hospitalized (82) or managed at home (90). Logistic regression and cox-proportional hazard models were used to determine predictors of severe COVID-19 disease and in-hospital mortality, respectively.

Results

Of 172 cases, 113 (65.7%) were males, and the mean age was 45 ± 19 years. The majority were urban dwellers (72.1%), 19.8% had a positive history of contact with a confirmed/suspected case, 15.7% were healthcare workers while 68 (39.5%) had co-morbidities. Symptomatic patients comprised 73.3% of cases. Fever (p=0.02) and breathlessness (p=0.03) were commoner in males while diarrhoea (p<0.01) was predominant in females. On multivariate analysis, severe COVID-19 was predicted by the presence of co-morbidity (AOR= 14.44, 95% C.I= 4.79- 43.58, p <0.001)and prior antibiotic/antimalarial use (AOR= 6.35, 95% C.I= 2.24- 18.05, p =0.001) while being a non-healthcare worker (AOR= 0.18, 95% C.I= 0.04-0.78, p=0.02) was protective. However, none of the variables assessed predicted in-hospital mortality.

Conclusion

Our findings underscore the contributions of demographic variables in COVID-19 transmission and gender differences in clinical presentation. Underlying comorbidity likewise prior antimicrobial use increased the likelihood of severe COVID-19. The absence of mortality predictors in our study may be related to the relatively small number of deaths. Further studies are recommended to unravel the predominance of severe disease in healthcare workers.

Clinical and pharmacological factors associated with mortality in patients with COVID-19 in a high complexity hospital in Manaus: A retrospective study

COVID-19 is a contagious infection caused by the SARS-CoV-2 virus, responsible for more than 5 million deaths worldwide, and has been a significant challenge for healthcare systems worldwide. Characterized by multiple manifestations, the most common symptoms are fever, cough, anosmia, ageusia, and myalgia. However, several organs can be affected in more severe cases, causing encephalitis, myocarditis, respiratory distress, hypercoagulable state, pulmonary embolism, and stroke. Despite efforts to identify appropriate clinical protocols for its management, there are still no fully effective therapies to prevent patient death. The objective of this study was to describe the demographic, clinical, and pharmacotherapeutic management characteristics employed in patients hospitalized for diagnosis of COVID-19, in addition to identifying predictive factors for mortality. This is a single-center, retrospective cohort study carried out in a reference hospital belonging to the Brazilian public health system, in Manaus, from March 2020 to July 2021. Data were obtained from analyzing medical records, physical and electronic forms, medical prescriptions, and antimicrobial use authorization forms. During the study period, 530 patients were included, 51.70% male, with a mean age of 58.74 ± 15.91 years. The overall mortality rate was 23.58%. The variables age, number of comorbidities, admission to the ICU, length of stay, oxygen saturation, serum aspartate transaminase, and use of mechanical ventilation showed a positive correlation with the mortality rate. Regarding pharmacological management, 88.49% of patients used corticosteroids, 86.79% used antimicrobials, 94.15% used anticoagulant therapy, and 3.77% used immunotherapy. Interestingly, two specific classes of antibiotics showed a positive correlation with the mortality rate: penicillins and glycopeptides. After multivariate logistic regression analysis, age, number of comorbidities, need for mechanical ventilation, length of hospital stay, and penicillin or glycopeptide antibiotics use were associated with mortality (AUC = 0.958).

Active Smokers Are at Higher Risk of COVID-19 Death: A Systematic Review and Meta-analysis

INTRODUCTION

Current evidence indicates that smoking worsens COVID-19 outcomes. However, when studies restricted their analyses to current smokers, the risks for COVID-19 severity and death are inconsistent.

AIMS AND METHODS

This meta-analysis explored the association between current smoking and the risk for mortality based on the studies that reported all three categories of smoking (current, former, and never smokers) to overcome the limitation of the previous meta-analyses which former smokers might have been classified as nonsmokers. We searched PubMed and Embase up to January 1, 2021. We included studies reporting all three categories of smoking behaviors of COVID-19 patients and mortality outcomes. A random-effects meta-analysis and meta-regression were used to examine relationships in the data.

RESULTS

A total of 34 articles with 35 193 COVID-19 patients was included. The meta-analysis confirmed the association between current smoking (odds ratio [OR] 1.26, 95% confidence interval [CI]: 1.01-1.58) and former smoking (OR 1.76, 95% CI: 1.53-2.03) with COVID-19 mortality. We also found that the risk for COVID-19 death in current smokers does not vary by age, but significantly drops by age in former smokers. Moreover, current smokers in non-high-income countries have higher risks of COVID-19 death compared with high-income countries (OR 3.11, 95% CI: 2.04-4.72 vs. OR 1.14, 95% CI: 0.91-1.43; p = .015).

CONCLUSIONS

Current and former smokers are at higher risk of dying from COVID-19. Tobacco control should be strengthened to encourage current smokers to quit and prevent the initiation of smoking. Public health professionals should take the COVID-19 pandemic as an opportunity to promote smoking prevention and cession.

IMPLICATIONS

This study makes an important contribution to the existing literature by distinguishing between current and former smoking and their separate effects on COVID-19 mortality. We also explore the effects by age of patients and country income level. Findings from this study provide empirical evidence against misinformation about the relationship between smoking and COVID-19 mortality.

A novel scoring system for early assessment of the risk of the COVID-19-associated mortality in hospitalized patients: COVID-19 BURDEN

BACKGROUND

Corona Virus Disease 2019 (COVID-19) presentations range from those similar to the common flu to severe pneumonia resulting in hospitalization with significant morbidity and/or mortality. In this study, we made an attempt to develop a predictive scoring model to improve the early detection of high risk COVID-19 patients by analyzing the clinical features and laboratory data available on admission.

METHODS

We retrospectively included 480 consecutive adult patients, aged 21-95, who were admitted to Faghihi Teaching Hospital. Clinical and laboratory features were collected from the medical records and analyzed using multiple logistic regression analysis. The final data analysis was utilized to develop a simple scoring model for the early prediction of mortality in COVID-19 patients. The score given to each associated factor was based on the coefficients of the regression analyses.

RESULTS

A novel mortality risk score (COVID-19 BURDEN) was derived, incorporating risk factors identified in this cohort. CRP (> 73.1 mg/L), O2 saturation variation (greater than 90%, 84-90%, and less than 84%), increased PT (> 16.2 s), diastolic blood pressure (≤ 75 mmHg), BUN (> 23 mg/dL), and raised LDH (> 731 U/L) were the features constituting the scoring system. The patients are triaged to the groups of low- (score < 4) and high-risk (score ≥ 4) groups. The area under the curve, sensitivity, and specificity for predicting mortality in patients with a score of ≥ 4 were 0.831, 78.12%, and 70.95%, respectively.

CONCLUSIONS

Using this scoring system in COVID-19 patients, the patients with a higher risk of mortality can be identified which will help to reduce hospital care costs and improve its quality and outcome.

Prediction of low pulse oxygen saturation in COVID-19 using remote monitoring post hospital discharge

BACKGROUND

Monitoring systems have been developed during the COVID-19 pandemic enabling clinicians to remotely monitor physiological measures including pulse oxygen saturation (SpO₂), heart rate (HR), and breathlessness in patients after discharge from hospital. These data may be leveraged to understand how symptoms vary over time in COVID-19 patients. There is also potential to use remote monitoring systems to predict clinical deterioration allowing early identification of patients in need of intervention.

METHODS

A remote monitoring system was used to monitor 209 patients diagnosed with COVID-19 in the period following hospital discharge. This system consisted of a patient-facing app paired with a Bluetooth-enabled pulse oximeter (measuring SpO₂ and HR) linked to a secure portal where data were available for clinical review. Breathlessness score was entered manually to the app. Clinical teams were alerted automatically when SpO₂ < 94 %. In this study, data recorded during the initial ten days of monitoring were retrospectively examined, and a random forest model was developed to predict SpO₂ < 94 % on a given day using SpO₂ and HR data from the two previous days and day of discharge.

RESULTS

Over the 10-day monitoring period, mean SpO₂ and HR increased significantly, while breathlessness decreased. The coefficient of variation in SpO₂, HR and breathlessness also decreased over the monitoring period. The model predicted SpO₂ alerts (SpO₂ < 94 %) with a mean cross-validated. sensitivity of 66 ± 18.57 %, specificity of 88.31 ± 10.97 % and area under the receiver operating characteristic of 0.80 ± 0.11. Patient age and sex were not significantly associated with the occurrence of asymptomatic SpO₂ alerts.

CONCLUSION

Results indicate that SpO₂ alerts (SpO₂ < 94 %) on a given day can be predicted using SpO₂ and heart rate data captured on the two preceding days via remote monitoring. The methods presented may help early identification of patients with COVID-19 at risk of clinical deterioration using remote monitoring.

Hospitalised COVID-19 outcomes are predicted by hypoxaemia and pneumonia phenotype irrespective of the timing of their emergence

Despite the known clinical importance of hypoxemia and pneumonia, there is a paucity of evidence for these variables with respect to risk of mortality and short-term outcomes among those hospitalised with COVID-19.

OBJECTIVE

Describe the prevalence and clinical course of patients hospitalised with COVID-19 based on oxygenation and pneumonia status at presentation and determine the incidence of emergent hypoxaemia or radiographic pneumonia during admission.

METHODS

A retrospective study was conducted using a Canadian regional registry. Patients were stratified according to hypoxaemia/pneumonia phenotype and prevalence. Clinical parameters were compared between phenotypes using χ² and one-way Analysis of variance (ANOVA). Cox analysis estimated adjusted Hazard Ratios (HR) for associations between disease outcomes and phenotypes.

RESULTS

At emergency department (ED) admission, the prevalence of pneumonia and hypoxaemia was 43% and 50%, respectively, and when stratified to phenotypes: 28.2% hypoxaemia⁺/pneumonia⁺, 22.2% hypoxaemia⁺/pneumonia^-, 14.5% hypoxaemia^-/pneumonia⁺ and 35.1% hypoxaemia^-/pneumonia^-. Mortality was 31.1% in the hypoxaemia⁺/pneumonia^- group and 26.3% in the hypoxaemia⁺/pneumonia⁺ group. Hypoxaemia with pneumonia and without pneumonia predicted higher probability of death. Hypoxaemia either <24 hours or ≥24 hours after hospitalisation predicted higher mortality and need for home oxygen compared with those without hypoxaemia. Patients with early hypoxaemia had higher probability of Intensive care unit (ICU) admission compared with those with late hypoxaemia.

CONCLUSION

Mortality in COVID-19 infection is predicted by hypoxaemia with or without pneumonia and was greatest in patients who initially presented with hypoxaemia. The emergence of hypoxaemia was predicted by radiographic pneumonia. Patients with early and emergent hypoxaemia had similar mortality but were less likely to be admitted to ICU. There may be delayed identification of hypoxaemia, which prevents timely escalation of care.

Comparison Between Physiological Scores SIPF, CURB-65, and APACHE II as Predictors of Prognosis and Mortality in Hospitalized Patients with COVID-19 Pneumonia: A Multicenter Study, Saudi Arabia

Background

A coronavirus pandemic (COVID-19) is associated with catastrophic effects on the world with high morbidity and mortality. We aimed to evaluate the accuracy of physiological shock index (SIPF) (shock index and hypoxemia), CURB -65, acute physiology, and chronic health assessment II (APACHE II) as predictors of prognosis and in-hospital mortality in patients with COVID-19 pneumonia.

Methods

In Saudi Arabia, a multicenter retrospective study was conducted on hospitalized adult patients confirmed to have COVID-19 pneumonia. Information needed to calculate SIPF, CURB-65, and APACHE II scores were obtained from medical records within 24 hours of admission.

Results

The study included 1131 COVID-19 patients who met the inclusion criteria. They were divided into two groups: (A) the ICU group (n=340; 30.1%) and (B) the ward group (n=791; 69.9%). The most common concomitant diseases of patients at initial ICU admission were hypertension (71.5%) and diabetes (62.4%), and most of them were men (63.8%). The overall mortality was 18.7%, and the mortality rate was higher in the ICU group than in the ward group (39.4% vs 9.6%; p < 0.001). The SIPF score showed a significantly higher ability to predict both ICU admission and mortality in patients with COVID-19 pneumonia compared with APACHE II and CURB -65; (AUC 0.89 vs 0.87; p < 0.001) and (AUC 0.89 vs 0.84; p < 0.001) for ICU admission and (AUC 0.90 vs 0.65; p < 0.001) and (AUC 0.90 vs 0.80; p < 0.001) for mortality, respectively.

Conclusion

The ability of the SIPF score to predict ICU admission and mortality in COVID-19 pneumonia is higher than that of APACHE II and CURB-65. The overall mortality was 18.7%, and the mortality rate was higher in the ICU group than in the ward group (39.4% vs 9.6%; p < 0.001).

Effect of alcohol consumption on relapse outcomes among tuberculosis patients: A systematic review and meta-analysis

Introduction

Tuberculosis (TB) is one of the major public health issues in every country. Alcohol consumption is one of the reasons associated with the severity of symptoms and death among TB patients. The impact of alcohol use on TB relapse outcomes is still debatable. This study aimed to conduct a systematic review and meta-analysis (SR/MA) to find the link between alcohol use and TB relapse outcomes.

Methods

Data collection was performed from December 2021 to March 2022; and was obtained from electronic databases including CINAHL, PubMed, and Scopus. The researcher carefully searched and reviewed all the relevant research concerning drinking alcohol and relapse outcomes among TB patients. A set of inclusion and exclusion criteria was used to assess research publications. The methodological quality of eligible publications was assessed using the Newcastle-Ottawa Scale. Random meta-analysis was used to determine odds ratios (ORs) with a 95% confidence interval (CIs). The funnel plot, Begg's test, and Egger's test were employed to investigate publication bias.

Results

There were a total of 2,113 studies found and reviewed, and eight publications were chosen for the analysis. It was found that among TB patients with a moderate appearance of heterogeneity, drinking alcohol increases the probability of relapse (OR = 3.64; 95% CI: 2.26-5.88, p < 0.001) and mortality (OR = 1.72; 95% CI: 1.40-2.12, p < 0.001). The funnel plot, Begg's test, and Egger's test all revealed that there was no indication of publication bias.

Conclusions

Relapses and mortality among tuberculosis patients are considerably increased by alcohol drinking. More research into the causality of this link between the degree of alcohol use and the underlying processes is required.

Systematic review registration

PROSPERO [CRD 42022295865].

Rapid systematic review of respiratory rate as a vital sign of clinical deterioration in COVID-19

OBJECTIVES

This meta-analysis aimed to establish a clinical evidence base for respiratory rate (RR) as a single predictor of early-onset COVID-19. The review also looked to determine the practical implementation of mobile respiratory rate measuring devices where information was available.

METHODS

We focused on domestic settings with older adults. Relevant studies were identified through MEDLINE, Embase, and CENTRAL databases. A snowballing method was also used. Articles published from the beginning of the COVID-19 pandemic (2019) until Feb 2022 were selected. Databases were searched for terms related to COVID-19 and respiratory rate measurements in domestic patients.

RESULTS

A total of 2,889 articles were screened for relevant content, of which 60 full-text publications were included. We compared the Odds Ratios and statistically significant results of both vital signs.

CONCLUSION

Multinational studies across dozens of countries have shown respiratory rate to have predictive accuracy in detecting COVID-19 deterioration. However, considerable variability is present in the data, making it harder to be sure about the effects. There is no meaningful difference in data quality in terms of variability (95% CI intervals) between vital signs as predictors of decline in COVID-19 patients. Contextual and economic factors will likely determine the choice of measurement used.

The experiences of care home staff during the COVID-19 pandemic: A systematic review

Objectives: To examine experiences of care home staff to better understand how to support them during the ongoing pandemic and in the future.Method: A systematic review examining experiences of care staff over the last year (March 2020-2021).Results: Fourteen papers related to experiences of staff and one was an intervention study. Quantitatively there was evidence of anxiety, PTSD and depression amongst the staff. Qualitatively, seven themes were identified: Poor working conditions; Lack of skills and knowledge; Psychological/Mental health concerns; Feeling undervalued and abandoned; Fears of contagion; Support and the positive impacts of COVID. The intervention study recommended employing needs-based approaches, including educational and wellbeing components.Conclusion: Recommendations are made in terms of how to work with staff, both practically and clinically. There are also suggestions about how to deal with similar situations if they were to reoccur. It is evident that lessons need to be learned because errors were made. Indeed, from a UK perspective, discharging thousands back to care homes, without testing, cost lives. This may have been done to protect the NHS, but it unwittingly 'lockdown' the virus within the care sector.

Migrant Health and COVID-19 Pandemic: A Cross-sectional Study of Characteristics, Clinical Features, and Health Outcome from Iran

BACKGROUND

As the pandemic unfolds, major concerns remain with those in disadvantaged positions who may be disproportionately affected. This paper aimed to present the characteristics of COVID-19 immigrant patients and investigate whether they were disproportionately affected by COVID-19 pandemic.

METHODS

A cross-sectional study was performed using data on 589,146 patients diagnosed with COVID-19 in Iran. Descriptive analyses were used to summarize the study population's characteristics. Chi-squared test and logistic regression model were applied.

RESULTS

After accounting for possible confounding covariates, being an immigrant was significantly associated with increased risk of death due to COVID-19 (OR 1.64, CI 1.568-1.727). When compared to Iranian-born patients, the prevalence of low blood oxygen levels on admission was higher among immigrant patients (53.9% versus 47.7%, P value < 0.001). Moreover, greater proportions of immigrants who were diagnosed with COVID-19 were admitted to an ICU (17% versus 15.8%, P value < 0.001). Patients aged 65 and above were the largest age category in both populations. However, there was a significant difference between the age profiles of patients, with children under the age of eighteen presenting 16% of immigrant patients vs 6.6% of Iranian-born patients (P value < 0.001). In both groups, more men were affected by COVID-19 than women, yet the sex bias was more prominent for migrant patients (P value < 0.001).

CONCLUSION

The evidence from this study revealed that immigrant patients infected with COVID-19 were more likely to suffer from severe health outcome of the disease compared to Iranian-born patients.

Procalcitonin as a predictive marker in COVID-19: A systematic review and meta-analysis

BACKGROUND

Coronavirus disease 2019 has emerged as a global pandemic causing millions of critical cases and deaths. Early identification of at-risk patients is crucial for planning triage and treatment strategies.

METHODS AND FINDINGS

We performed this systematic review and meta-analysis to determine the pooled prognostic significance of procalcitonin in predicting mortality and severity in patients with COVID-19 using a robust methodology and clear clinical implications.

DESIGN

We used Preferred Reporting Items for Systematic Reviews and Meta-Analyses and Cochrane Handbook for Systematic Reviews of Interventions guidelines. We included thirty-two prospective and retrospective cohort studies involving 13,154 patients.

RESULTS

The diagnostic odds ratio of procalcitonin for predicting mortality were estimated to be 11 (95% CI: 7 to 17) with sensitivity, specificity, and summary area under the curveof 0.83 (95% CI: 0.70 to 0.91), 0.69 (95% CI: 0.58 to 0.79), and 0.83 (95% CI: 0.79 to 0.86) respectively. While for identifying severe cases of COVID-19, the odds ratio was 8.0 (95% CI 5.0 to 12.0) with sensitivity, specificity, and summary area under the curve of 0.73 (95% CI 0.67 to 0.78), 0.74 (0.66 to 0.81), and 0.78 (95% CI 0.74 to 0.82) respectively.

CONCLUSION

Procalcitonin has good discriminatory power for predicting mortality and disease severity in COVID-19 patients. Therefore, procalcitonin measurement may help identify potentially severe cases and thus decrease mortality by offering early aggressive treatment.

Preexisting coronary artery disease among coronavirus disease 2019 patients: a systematic review and meta-analysis

AIMS

The prevalence and prognostic implications of coronary artery disease (CAD) in patients infected by the novel coronavirus 2019 (COVID-19) disease remain unclear.

METHODS

We conducted a systematic review and meta-analysis to investigate the prevalence and mortality risk in COVID-19 patients with preexisting CAD. We searched Medline and Scopus to locate all articles published up to December 8, 2021, reporting data of COVID-19 survivors and nonsurvivors with preexisting CAD. Data were pooled using the Mantel-Haenszel random effects models with odds ratio (OR) as the effect measure with the related 95% confidence interval (CI).

RESULTS

Thirty-eight studies including 27 435 patients (mean age 61.5 and 70.9 years) were analysed. The pooled prevalence of preexisting CAD was 12.6% (95% CI: 11.2-16.5%, I2 : 95.6%), and resulted as higher in intensive care unit patients (17.5%, 95% CI: 11.9-25.1, I2 : 88.4%) and in European cohorts (13.1%, 95% CI: 7.8-21.6%, P  < 0.001, I2 : 98.4%). COVID-19 patients with preexisting CAD had a two-fold risk of short-term mortality (OR 2.61, 95% CI 2.10-3.24, P  < 0.001, I2  = 73.6%); this risk was higher among Asian cohorts (OR: 2.66, 95% CI: 1.79-3.90, P  < 0.001, I2 : 77.3%) compared with European (OR: 2.44, 95% CI: 1.90-3.14, P  < 0.001, I2 : 56.9%) and American (OR: 1.86, 95% CI: 1.41-2.44, P  < 0.001, I2 : 0%) populations. The association between CAD and poor short-term prognosis was influenced by age, prevalence of hypertension (HT), DM and CKD.

CONCLUSIONS

Preexisting CAD is present in approximately 1 in 10 patients hospitalized for COVID-19 and significantly associated with an increased risk of short-term mortality, which is influenced by age, HT, DM and CKD.

Impact of cardiometabolic multimorbidity and ethnicity on cardiovascular/renal complications in patients with COVID-19

OBJECTIVE

Using a large national database of people hospitalised with COVID-19, we investigated the contribution of cardio-metabolic conditions, multi-morbidity and ethnicity on the risk of in-hospital cardiovascular complications and death.

METHODS

A multicentre, prospective cohort study in 302 UK healthcare facilities of adults hospitalised with COVID-19 between 6 February 2020 and 16 March 2021. Logistic models were used to explore associations between baseline patient ethnicity, cardiometabolic conditions and multimorbidity (0, 1, 2, >2 conditions), and in-hospital cardiovascular complications (heart failure, arrhythmia, cardiac ischaemia, cardiac arrest, coagulation complications, stroke), renal injury and death.

RESULTS

Of 65 624 patients hospitalised with COVID-19, 44 598 (68.0%) reported at least one cardiometabolic condition on admission. Cardiovascular/renal complications or death occurred in 24 609 (38.0%) patients. Baseline cardiometabolic conditions were independently associated with increased odds of in-hospital complications and this risk increased in the presence of cardiometabolic multimorbidity. For example, compared with having no cardiometabolic conditions, 1, 2 or ≥3 conditions was associated with 1.46 (95% CI 1.39 to 1.54), 2.04 (95% CI 1.93 to 2.15) and 3.10 (95% CI 2.92 to 3.29) times higher odds of any cardiovascular/renal complication, respectively. A similar pattern was observed for all-cause death. Compared with the white group, the South Asian (OR 1.19, 95% CI 1.10 to 1.29) and black (OR 1.53 to 95% CI 1.37 to 1.72) ethnic groups had higher risk of any cardiovascular/renal complication.

CONCLUSIONS

In hospitalised patients with COVID-19, cardiovascular complications or death impacts just under half of all patients, with the highest risk in those of South Asian or Black ethnicity and in patients with cardiometabolic multimorbidity.

Admission Blood Glucose Level and Its Association With Cardiovascular and Renal Complications in Patients Hospitalized With COVID-19

OBJECTIVE

To investigate the association between admission blood glucose levels and risk of in-hospital cardiovascular and renal complications.

RESEARCH DESIGN AND METHODS

In this multicenter prospective study of 36,269 adults hospitalized with COVID-19 between 6 February 2020 and 16 March 2021 (N = 143,266), logistic regression models were used to explore associations between admission glucose level (mmol/L and mg/dL) and odds of in-hospital complications, including heart failure, arrhythmia, cardiac ischemia, cardiac arrest, coagulation complications, stroke, and renal injury. Nonlinearity was investigated using restricted cubic splines. Interaction models explored whether associations between glucose levels and complications were modified by clinically relevant factors.

RESULTS

Cardiovascular and renal complications occurred in 10,421 (28.7%) patients; median admission glucose level was 6.7 mmol/L (interquartile range 5.8-8.7) (120.6 mg/dL [104.4-156.6]). While accounting for confounders, for all complications except cardiac ischemia and stroke, there was a nonlinear association between glucose and cardiovascular and renal complications. For example, odds of heart failure, arrhythmia, coagulation complications, and renal injury decreased to a nadir at 6.4 mmol/L (115 mg/dL), 4.9 mmol/L (88.2 mg/dL), 4.7 mmol/L (84.6 mg/dL), and 5.8 mmol/L (104.4 mg/dL), respectively, and increased thereafter until 26.0 mmol/L (468 mg/dL), 50.0 mmol/L (900 mg/dL), 8.5 mmol/L (153 mg/dL), and 32.4 mmol/L (583.2 mg/dL). Compared with 5 mmol/L (90 mg/dL), odds ratios at these glucose levels were 1.28 (95% CI 0.96, 1.69) for heart failure, 2.23 (1.03, 4.81) for arrhythmia, 1.59 (1.36, 1.86) for coagulation complications, and 2.42 (2.01, 2.92) for renal injury. For most complications, a modifying effect of age was observed, with higher odds of complications at higher glucose levels for patients age <69 years. Preexisting diabetes status had a similar modifying effect on odds of complications, but evidence was strongest for renal injury, cardiac ischemia, and any cardiovascular/renal complication.

CONCLUSIONS

Increased odds of cardiovascular or renal complications were observed for admission glucose levels indicative of both hypo- and hyperglycemia. Admission glucose could be used as a marker for risk stratification of high-risk patients. Further research should evaluate interventions to optimize admission glucose on improving COVID-19 outcomes.

Determinants of Mortality in Patients with Acute Kidney Injury Caused by Coronavirus Disease 2019 Infection in a Tertiary Care Hospital of South India

Acute kidney injury (AKI) is common in coronavirus disease 2019 (COVID-19), and its severity is linked to the adverse outcomes of multiorgan involvement. We attempted to assess the clinical outcomes and determinants of mortality in patients admitted to our COVID-19 care center from May to November 2020 who developed AKI through a retrospective examination of their case records. The data of those who developed AKI were analyzed for their clinical parameters, clinical presentation, history, laboratory parameters, urine output measurements, requirements for dialysis, treatment, and treatment outcomes (discharge or death). These outcomes were correlated to the comorbidities, laboratory parameters, the Kidney Disease Improving Global Outcomes (KDIGO) AKI stage, sequential organ failure assessment score, and need for dialysis or renal replacement therapy. Of 1890 patients admitted, 132 (6.98%) patients developed AKI. Of 450 deaths from all causes, 67 (14%) were in AKI patients. There were 100 (75.8%) males and 32 (24.8%) females. The mean age was 62.05 ± 11.48 years. The median duration of stay was 7 days (interquartile range: 4--11.75 days). The number of patients in AKI Stages 1, 2, and 3 was 1 (0.75%), 64 (48.48%), and 67 (50.75%), respectively. The most common symptoms were breathlessness (85.6%), fever (84.5%), myalgia (81.19%), and weakness (76.5%). Multivariate analysis showed that the determinants of mortality were a partial pressure of oxygen saturation (pO2) of <75% and of 76%-85% compared with a pO2 of >85%, not receiving remdesivir, and KDIGO Stage >2.

Predicting the necessity of oxygen therapy in the early stage of COVID-19 using machine learning

Medical oxygen is a critical element in the treatment process of COVID-19 patients which its shortage impacts the treatment process adversely. This study aims to apply machine learning (ML) to predict the requirement for oxygen-based treatment for hospitalized COVID-19 patients. In the first phase, demographic information, symptoms, and patient's background were extracted from the databases of two local hospitals in Iran, and preprocessing actions were applied. In the second step, the related features were selected. Lastly, five ML models including logistic regression (LR), random forest (RF), XGBoost, C5.0, and neural networks (NNs) were implemented and compared based on their accuracy and capability. Among the variables related to the patient's background, consuming opium due to the high rate of opium users in Iran was considered in the models. Of the 398 patients included in the study, 112 (28.14%) received oxygen-based treatment. Shortness of breath (71.42%), fever (62.5%), and cough (59.82%) had the highest frequency in patients with oxygen requirements. The most important variables for prediction were shortness of breath, cough, age, and fever. For opioid-addicted patients, in addition to the high mortality rate (23.07%), the rate of oxygen-based treatment was twice as high as non-addicted patients. XGBoost and LR obtained the highest area under the curve with values of 88.7% and 88.3%, respectively. For accuracy, LR and NNs achieved the best and same accuracy (86.42%). This approach provides a tool that accurately predicts the need for oxygen in the treatment process of COVID-19 patients and helps hospital resource management.

Predictors and microbiology of respiratory and bloodstream bacterial infection in patients with COVID-19: living rapid review update and meta-regression

BACKGROUND

The prevalence of bacterial infection in patients with COVID-19 is low, however, empiric antibiotic use is high. Risk stratification may be needed to minimize unnecessary empiric antibiotic use.

OBJECTIVE

To identify risk factors and microbiology associated with respiratory and bloodstream bacterial infection in patients with COVID-19.

DATA SOURCES

We searched MEDLINE, OVID Epub and EMBASE for published literature up to 5 February 2021.

STUDY ELIGIBILITY CRITERIA

Studies including at least 50 patients with COVID-19 in any healthcare setting.

METHODS

We used a validated ten-item risk of bias tool for disease prevalence. The main outcome of interest was the proportion of COVID-19 patients with bloodstream and/or respiratory bacterial co-infection and secondary infection. We performed meta-regression to identify study population factors associated with bacterial infection including healthcare setting, age, comorbidities and COVID-19 medication.

RESULTS

Out of 33 345 studies screened, 171 were included in the final analysis. Bacterial infection data were available from 171 262 patients. The prevalence of co-infection was 5.1% (95% CI 3.6-7.1%) and secondary infection was 13.1% (95% CI 9.8-17.2%). There was a higher odds of bacterial infection in studies with a higher proportion of patients in the intensive care unit (ICU) (adjusted OR 18.8, 95% CI 6.5-54.8). Female sex was associated with a lower odds of secondary infection (adjusted OR 0.73, 95% CI 0.55-0.97) but not co-infection (adjusted OR 1.05, 95% CI 0.80-1.37). The most common organisms isolated included Staphylococcus aureus, coagulase-negative staphylococci and Klebsiella species.

CONCLUSIONS

While the odds of respiratory and bloodstream bacterial infection are low in patients with COVID-19, meta-regression revealed potential risk factors for infection, including ICU setting and mechanical ventilation. The risk for secondary infection is substantially greater than the risk for co-infection in patients with COVID-19. Understanding predictors of co-infection and secondary infection may help to support improved antibiotic stewardship in patients with COVID-19.

Mortality and associated risk factors in patients hospitalized due to COVID-19 in a Peruvian reference hospital

OBJECTIVES

To determine the risk factors for in-hospital mortality in patients with COVID-19 from a Peruvian national hospital.

METHODS

Retrospective cohort study of medical records of patients with COVID-19 hospitalized at Hospital Nacional Hipólito Unanue (HNHU) during the months of April to August 2020. The dependent variable was in-hospital mortality. Independent variables included sociodemographic and clinical characteristics, physical examination findings, oxygen saturation (SaO2) at admission, treatment received during hospitalization and laboratory results at admission. A Cox regression model was used to evaluate the crude and adjusted hazard ratios for associated factors.

RESULTS

We included 1418 patients. Median age was 58 years (IQR 47-68 years) and 944 (66.6%) were male. The median length of hospitalization was 7 (4-13) days, and the mortality rate was 46%. The most frequent comorbidities were type 2 diabetes mellitus, hypertension, and obesity. In the adjusted analysis, mortality was associated with age (HR 1.02; 95%CI 1.02-1.03), history of surgery (HR 1.89; 95%CI 1.31-2.74), lower oxygen saturation at admission (HR 4.08; CI95% 2.72-8.05 for SaO2<70% compared to SaO2>94%), the presence of poor general condition (HR 1.81; 95% CI 1.29-2.53), altered state of consciousness (HR 1.58; 95%CI 1.18-2.11) and leukocyte levels (HR 1.01; 95%CI 1.00-1. 02). Treatment with ivermectin (HR 1.44; 95%CI 1.18-1.76) and azithromycin (HR 1.25; 95%CI 1.03-1.52) were associated with higher mortality. Treatment with corticosteroids at low to moderate doses was associated with lower mortality (HR 0.56 95%CI 0. 37-0. 86) in comparison to no steroid use.

CONCLUSION

A high mortality was found in our cohort. Low oxygen saturation at admission, age, and the presence of hematological and biochemical alterations were associated with higher mortality. The use of hydroxychloroquine, ivermectin or azithromycin was not useful and was probably associated with unfavorable outcomes. The use of corticosteroids at moderate doses was associated with lower mortality.

Radiologist-supervised Transfer Learning: Improving Radiographic Localization of Pneumonia and Prognostication of Patients With COVID-19

PURPOSE

To assess the potential of a transfer learning strategy leveraging radiologist supervision to enhance convolutional neural network-based (CNN) localization of pneumonia on radiographs and to further assess the prognostic value of CNN severity quantification on patients evaluated for COVID-19 pneumonia, for whom severity on the presenting radiograph is a known predictor of mortality and intubation.

MATERIALS AND METHODS

We obtained an initial CNN previously trained to localize pneumonia along with 25,684 radiographs used for its training. We additionally curated 1466 radiographs from patients who had a computed tomography (CT) performed on the same day. Regional likelihoods of pneumonia were then annotated by cardiothoracic radiologists, referencing these CTs. Combining data, a preexisting CNN was fine-tuned using transfer learning. Whole-image and regional performance of the updated CNN was assessed using receiver-operating characteristic area under the curve and Dice. Finally, the value of CNN measurements was assessed with survival analysis on 203 patients with COVID-19 and compared against modified radiographic assessment of lung edema (mRALE) score.

RESULTS

Pneumonia detection area under the curve improved on both internal (0.756 to 0.841) and external (0.864 to 0.876) validation data. Dice overlap also improved, particularly in the lung bases (R: 0.121 to 0.433, L: 0.111 to 0.486). There was strong correlation between radiologist mRALE score and CNN fractional area of involvement (ρ=0.85). Survival analysis showed similar, strong prognostic ability of the CNN and mRALE for mortality, likelihood of intubation, and duration of hospitalization among patients with COVID-19.

CONCLUSIONS

Radiologist-supervised transfer learning can enhance the ability of CNNs to localize and quantify the severity of disease. Closed-loop systems incorporating radiologists may be beneficial for continued improvement of artificial intelligence algorithms.

Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine

Coronavirus disease-2019 (COVID-19) has made the world more cautious about widespread viruses, and a tragic pandemic that was caused by a novel coronavirus has harmed human beings in recent years. The new coronavirus pneumonia outbreak is spreading rapidly worldwide. We collect arterial blood samples from 51 patients with a COVID-19 diagnosis. Blood gas analysis is performed using a Siemens RAPID Point 500 blood gas analyzer. To accurately determine the factors that play a decisive role in the early recognition and discrimination of COVID-19 severity, a prediction framework that is based on an improved binary Harris hawk optimization (HHO) algorithm in combination with a kernel extreme learning machine is proposed in this paper. This method uses specular reflection learning to improve the original HHO algorithm and is referred to as HHOSRL. The experimental results show that the selected indicators, such as age, partial pressure of oxygen, oxygen saturation, sodium ion concentration, and lactic acid, are essential for the early accurate assessment of COVID-19 severity by the proposed feature selection method. The simulation results show that the established methodlogy can achieve promising performance. We believe that our proposed model provides an effective strategy for accurate early assessment of COVID-19 and distinguishing disease severity. The codes of HHO will be updated in https://aliasgharheidari.com/HHO.html.

Cardiometabolic Morbidity and Other Prognostic Factors for Mortality in Adult Hospitalized COVID-19 Patients in North Jakarta, Indonesia

Background

Although there have been several studies investigating prognostic factors for mortality in COVID-19, there have been lack of studies in low- and middle-income countries, including Indonesia. To date, the country has the highest mortality rate among Asian countries.

Objective

We sought to identify the prognostic factors of mortality in hospitalized patients with COVID-19 in Jakarta.

Methods

In this retrospective cohort study, we included all adult inpatients (≥18 years old) with confirmed COVID-19 from Koja General Hospital (North Jakarta, Indonesia) who had been hospitalized between March 20th and July 31st, 2020. Demographic, clinical, laboratory, and radiology data were extracted from the medical records and compared between survivors and non-survivors. Univariate and multivariate logistic regression analysis were used to explore the prognostic factors associated with in-hospital death.

Results

Two hundred forty-three patients were included in the study, of whom 32 died. Comorbid of hypertension (OR 3.59; 95% CI 1.12-11.48; p = 0.031), obesity (OR 6.34; 95% CI 1.68-23.98; p = 0.007), immediate need of HFNC and/or IMV (OR 64.93; 95% CI 11.08-380.61; p < 0.001), abnormal RDW (OR 3.68; 95% CI 1.09-12.34; p = 0.035), ALC < 1,000/µL (OR 3.51; 95% CI 1.08-11.44; p = 0.038), D-dimer > 500 ng/mL (OR 9.36; 95% CI 1.53-57.12; p = 0.015) on admission, as well as chloroquine treatment (OR 3.61; 95% CI 1.09-11.99; p = 0.036) were associated with greater risk of overall mortality in COVID-19 patients. The likelihood of mortality increased with increasing number of prognostic factors.

Conclusion

The potential prognostic factors of hypertension, obesity, immediate need of HFNC and/or IMV, abnormal RDW, ALC < 1,000/µL, D-dimer > 500 ng/mL, and chloroquine treatment could help clinicians to identify COVID-19 patients with poor prognosis at an early stage.

Prediction models for severe manifestations and mortality due to COVID-19: A systematic review

BACKGROUND

Throughout 2020, the coronavirus disease 2019 (COVID-19) has become a threat to public health on national and global level. There has been an immediate need for research to understand the clinical signs and symptoms of COVID-19 that can help predict deterioration including mechanical ventilation, organ support, and death. Studies thus far have addressed the epidemiology of the disease, common presentations, and susceptibility to acquisition and transmission of the virus; however, an accurate prognostic model for severe manifestations of COVID-19 is still needed because of the limited healthcare resources available.

OBJECTIVE

This systematic review aims to evaluate published reports of prediction models for severe illnesses caused COVID-19.

METHODS

Searches were developed by the primary author and a medical librarian using an iterative process of gathering and evaluating terms. Comprehensive strategies, including both index and keyword methods, were devised for PubMed and EMBASE. The data of confirmed COVID-19 patients from randomized control studies, cohort studies, and case-control studies published between January 2020 and May 2021 were retrieved. Studies were independently assessed for risk of bias and applicability using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). We collected study type, setting, sample size, type of validation, and outcome including intubation, ventilation, any other type of organ support, or death. The combination of the prediction model, scoring system, performance of predictive models, and geographic locations were summarized.

RESULTS

A primary review found 445 articles relevant based on title and abstract. After further review, 366 were excluded based on the defined inclusion and exclusion criteria. Seventy-nine articles were included in the qualitative analysis. Inter observer agreement on inclusion 0.84 (95%CI 0.78-0.89). When the PROBAST tool was applied, 70 of the 79 articles were identified to have high or unclear risk of bias, or high or unclear concern for applicability. Nine studies reported prediction models that were rated as low risk of bias and low concerns for applicability.

CONCLUSION

Several prognostic models for COVID-19 were identified, with varying clinical score performance. Nine studies that had a low risk of bias and low concern for applicability, one from a general public population and hospital setting. The most promising and well-validated scores include Clift et al.,¹⁵ and Knight et al.,¹⁸ which seem to have accurate prediction models that clinicians can use in the public health and emergency department setting.

CHANGE LEADERSHIP AND CRISIS MANAGEMENT: CURRICULUM TRANSFORMATION BEFORE, DURING, AND AFTER A PANDEMIC

Washington University School of Medicine has not undergone substantive curricular change for 25 years. In 2018, we embarked on a curriculum transformation project informed by the change leadership principles of James Kotter and William Bridges. This included developing a structured process to create a sense of urgency for why we needed to change, building coalitions to support change, and developing a compelling vision across multiple stakeholder groups. Implementation work began in 2019 with a planned curriculum start of July 2020. The COVID-19 pandemic disrupted these plans. Employing the Cynefin framework for decision making and guided by our core principles, we were able to launch the new curriculum in September 2020. In this paper, I will describe the principles underpinning our curriculum transformation, how the Cynefin framework allowed us to move forward productively during the COVID-19 crisis, and the impact COVID-19 had, both positively and negatively, on the curriculum transformation process.

Identification of Risk Factors for COVID-19-related Death using Machine Learning Methods

Background

Unknown cases of pneumonia appeared in late 2019 in Wuhan, China. Following the worldwide spread of the disease, the World Health Organization declared it a pandemic on March 11, 2020. The total number of infected people worldwide as of December 16, 2020, was more than 74 million, more than one million and six hundred thousand of whom died from Coronavirus Disease 2019 (COVID-19). This study aimed to identify the risk factors for the mortality of COVID-19 in Hamadan, west of Iran.

Materials and Methods

This cross-sectional study used the information of all patients with COVID-19 admitted to Shahid Beheshti and Sina hospitals in Hamadan during January 2020-November 2020. Logistic regression model, decision tree, and random forest were used to assess risk factors for death due to COVID-19.

Results

This study was conducted on 1853 people with COVID-19. Blood urea nitrogen change, SPO₂ at admission, the duration of hospitalization, age, neutrophil count, lymphocyte count, number of breaths, complete blood count, systolic blood pressure, hemoglobin, and sodium were effective predictors in both methods of decision tree and random forest.

Conclusion

The risk factors identified in the present study may serve as surrogate indicators to identify the risk of death due to COVID-19. The proper model to predict COVID-19-related mortality is random forest based on sensitivity.

Clinical progression and outcomes of patients hospitalized with COVID-19 in humanitarian settings: A prospective cohort study in South Sudan and Eastern Democratic Republic of the Congo

Little information is available on COVID-19 in Africa and virtually none is from humanitarian and more resource-constrained settings. This study characterizes hospitalized patients in the African humanitarian contexts of Juba, South Sudan and North and South Kivu in Eastern Democratic Republic of the Congo. This observational cohort was conducted between December 2020 and June 2021. Patients presenting for care at five facilities or referred from home-based care by mobile medical teams were eligible for enrollment and followed until death or recovery. Disease progression was characterized for hospitalized patients using survival analysis and mixed effects regression model to estimate survival odds for patient characteristics and treatments received. 144 COVID-19 cases enrolled as hospitalized patients were followed to recovery/death. The observed mortality proportion among hospitalized patients was 16.7% (CI: 11.2-23.3%); mortality was three times higher in South Sudan, where patients presented later after symptom onset and in worse conditions. Age and diabetes history were the only patient characteristics associated with decreased survival; clinical status indicators associated with decreased survival included fever, low oxygen level, elevated respiratory and pulse rates. The only therapy associated with survival was non-invasive oxygen; invasive oxygen therapies and other specialized treatments were rarely received. Improving availability of oxygen monitoring and proven COVID-19 therapies in humanitarian and resource-poor settings is critical for health equity. Customizing training to reflect availability of specific medications, therapies and operational constraints is particularly important given the range of challenges faced by providers in these settings.

Impact of comorbidities on mortality in hospitalized patients with COVID-19: An experience from Iran

BACKGROUND

COVID-19 pandemic has spread all over the world. However, information regarding clinical characteristics and prognostic factors is scarce. The aim of this study was to explore the impact of preexistent chronic comorbid conditions and multimorbidity on risk of mortality in patients with COVID-19.

MATERIALS AND METHODS

We designed a retrospective, cross-sectional, observational, single-center study. Data were analyzed from all consecutive patients diagnosed with COVID-19 who admitted in a pandemic hospital affiliated with Tabriz University of Medical Sciences, Tabriz, Iran, from February 20, 2020, to September 25, 2020. The independent effects of preexistent conditions were evaluated using multivariate logistic regression model.

RESULTS

A total of 2597 hospitalized patients with COVID-19 were included. At least one preexistent condition was observed in 36.5% of study population. Multivariate logistic regression analysis showed that older age, male sex, diabetes, cardiovascular disease, hypertension, cancer, chronic kidney diseases, liver diseases, and chronic obstructive pulmonary disease were associated with increased risk of mortality. In addition, the number of comorbidities was significantly associated with increased odds of mortality compared to no-comorbidity.

CONCLUSION

The results of this study suggest that patients with comorbidities have an increased risk of in-hospital mortality following COVID-19 infection.

Impact of asthma on COVID-19 mortality in the United States: Evidence based on a meta-analysis

OBJECTIVE

The aim of this study was to investigate the impact of asthma on the risk for mortality among coronavirus disease 2019 (COVID-19) patients in the United States by a quantitative meta-analysis.

METHODS

A random-effects model was used to estimate the pooled odds ratio (OR) with corresponding 95% confidence interval (CI). I² statistic, sensitivity analysis, Begg's test, meta-regression and subgroup analyses were also performed.

RESULTS

The data based on 56 studies with 426,261 COVID-19 patients showed that there was a statistically significant association between pre-existing asthma and the reduced risk for COVID-19 mortality in the United States (OR: 0.82, 95% CI: 0.74-0.91). Subgroup analyses by age, male proportion, sample size, study design and setting demonstrated that pre-existing asthma was associated with a significantly reduced risk for COVID-19 mortality among studies with age ≥ 60 years old (OR: 0.79, 95% CI: 0.72-0.87), male proportion ≥ 55% (OR: 0.79, 95% CI: 0.72-0.87), male proportion ＜ 55% (OR: 0.81, 95% CI: 0.69-0.95), sample sizes ≥ 700 cases (OR: 0.80, 95% CI: 0.71-0.91), retrospective study/case series (OR: 0.82, 95% CI: 0.75-0.89), prospective study (OR: 0.83, 95% CI: 0.70-0.98) and hospitalized patients (OR: 0.82, 95% CI: 0.74-0.91). Meta-regression did reveal none of factors mentioned above were possible reasons of heterogeneity. Sensitivity analysis indicated the robustness of our findings. No publication bias was detected in Begg's test (P = 0.4538).

CONCLUSION

Our findings demonstrated pre-existing asthma was significantly associated with a reduced risk for COVID-19 mortality in the United States.

Assessment of 28-Day In-Hospital Mortality in Mechanically Ventilated Patients With Coronavirus Disease 2019: An International Cohort Study

Supplemental Digital Content is available in the text.

Factors associated with mortality in coronavirus disease 2019 patients on invasive mechanical ventilation are still not fully elucidated.

A hitchhiker's guide through the COVID-19 galaxy

Numerous reviews have summarized the epidemiology, pathophysiology and the various therapeutic aspects of Coronavirus disease 2019 (COVID-19), but a practical guide on "how to treat whom with what and when" based on an understanding of the immunological background of the disease stages remains missing. This review attempts to combine the current knowledge about the immunopathology of COVID-19 with published evidence of available and emerging treatment options. We recognize that the information about COVID-19 and its treatment is rapidly changing, but hope that this guide offers those on the frontline of this pandemic an understanding of the host response in COVID-19 patients and supports their ongoing efforts to select the best treatments tailored to their patient's clinical status.

Associations Between Structural and Social Determinants of Health With COVID Infection Rates at a Safety Net Hospital

Background: The reported disproportionate impact of COVID-19 infections on minority populations may be due to living in disinvested communities with a high level of poverty, pollution, inadequate unsafe employment, and overcrowded housing.

Objective: To determine the association of county, city, and individual risk factors with COVID-19 infection rates.

Methods: Retrospective chart review on COVID-19 tests performed from March through July 2020 at Arrowhead Regional Medical Center (ARMC), Colton, California.

Results: A total of 7104 tests were performed with 69% in the drive-through testing center. The mean duration of test-to-results time was 2.36 (+0.02) days. COVID-19 positive tests occurred in 1095 (15.4%). At least one symptom occurred in 414 (33%) with a sensitivity of 37.8, specificity of 86.02, a positive predictive value of 33.01, and a negative predictive value of 72.76. Individual factors significantly associated with testing positive for COVID-19 were diabetes, Hispanic ethnicity, and male gender. Younger age was significantly associated with testing COVID positive with the highest risk in children <10 years.

COVID-19 positive persons significantly resided in cities with higher population density, household members, poverty, non-English speaking homes, disability, lower median household income, lack of health insurance and decreased access to a computer and Wi-Fi services. County health rankings showed a significant positive association between testing positive for COVID-19 with increased smoking, air pollution, violent crimes, physical inactivity, decreased education, and access to exercise. 

Conclusion: Adverse county health rankings, socially and economically disadvantaged cities are associated with an increased risk of testing positive for COVD-19. This information can be used in strategic planning and invention mitigation.

Chronic Diseases as a Predictor for Severity and Mortality of COVID-19: A Systematic Review With Cumulative Meta-Analysis

Introduction: Given the ongoing coronavirus disease 2019 (COVID-19) pandemic and the consequent global healthcare crisis, there is an urgent need to better understand risk factors for symptom deterioration and mortality among patients with COVID-19. This systematic review aimed to meet the need by determining the predictive value of chronic diseases for COVID-19 severity and mortality.

Methods: We searched PubMed, Embase, Web of Science, and Cumulative Index to Nursing and Allied Health Complete to identify studies published between December 1, 2019, and December 31, 2020. Two hundred and seventeen observational studies from 26 countries involving 624,986 patients were included. We assessed the risk of bias of the included studies and performed a cumulative meta-analysis.

Results: We found that among COVID-19 patients, hypertension was a very common condition and was associated with higher severity, intensive care unit (ICU) admission, acute respiratory distress syndrome, and mortality. Chronic obstructive pulmonary disease was the strongest predictor for COVID-19 severity, admission to ICU, and mortality, while asthma was associated with a reduced risk of COVID-19 mortality. Patients with obesity were at a higher risk of experiencing severe symptoms of COVID-19 rather than mortality. Patients with cerebrovascular disease, chronic liver disease, chronic renal disease, or cancer were more likely to become severe COVID-19 cases and had a greater probability of mortality.

Conclusions: COVID-19 patients with chronic diseases were more likely to experience severe symptoms and ICU admission and faced a higher risk of mortality. Aggressive strategies to combat the COVID-19 pandemic should target patients with chronic diseases as a priority.

Simple demographic characteristics and laboratory findings on admission may predict in-hospital mortality in patients with SARS-CoV-2 infection: development and validation of the covid-19 score

Background

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) constitutes a major health burden worldwide due to high mortality rates and hospital bed shortages. SARS-CoV-2 infection is associated with several laboratory abnormalities. We aimed to develop and validate a risk score based on simple demographic and laboratory data that could be used on admission in patients with SARS-CoV-2 infection to predict in-hospital mortality.

Methods

Three cohorts of patients from different hospitals were studied consecutively (developing, validation, and prospective cohorts). The following demographic and laboratory data were obtained from medical records: sex, age, hemoglobin, mean corpuscular volume (MCV), platelets, leukocytes, sodium, potassium, creatinine, and C-reactive protein (CRP). For each variable, classification and regression tree analysis were used to establish the cut-off point(s) associated with in-hospital mortality outcome based on data from developing cohort and before they were used for analysis in the validation and prospective cohort. The covid-19 score was calculated as a sum of cut-off points associated with mortality outcome.

Results

The developing, validation, and prospective cohorts included 129, 239, and 497 patients, respectively (median age, 71, 67, and 70 years, respectively). The following cut of points associated with in-hospital mortality: age > 56 years, male sex, hemoglobin < 10.55 g/dL, MCV > 92.9 fL, leukocyte count > 9.635 or < 2.64 10³/µL, platelet count, < 81.49 or > 315.5 10³/µL, CRP > 51.14 mg/dL, creatinine > 1.115 mg/dL, sodium < 134.7 or > 145.4 mEq/L, and potassium < 3.65 or > 6.255 mEq/L. The AUC of the covid-19 score for predicting in-hospital mortality was 0.89 (0.84–0.95), 0.850 (0.75–0.88), and 0.773 (0.731–0.816) in the developing, validation, and prospective cohorts, respectively (P < 0.001The mortality of the prospective cohort stratified on the basis of the covid-19 score was as follows: 0–2 points,4.2%; 3 points, 15%; 4 points, 29%; 5 points, 38.2%; 6 and more points, 60%.

Conclusion

The covid-19 score based on simple demographic and laboratory parameters may become an easy-to-use, widely accessible, and objective tool for predicting mortality in hospitalized patients with SARS-CoV-2 infection.

Vaccination reduces need for emergency care in breakthrough COVID-19 infections: A multicenter cohort study

Background

While recent literature has shown the efficacy of the SARS-CoV-2 vaccine in preventing infection, it's impact on need for emergency care/hospitalization in breakthrough infections remain unclear, particularly in regions with a high rate of variant viral strains. We aimed to determine if vaccination reduces hospital visits in breakthrough COVID-19.

Methods

This observational cohort analysis compared unvaccinated (UV), partially vaccinated (PV), and fully vaccinated (FV) adult patients with SARS-CoV-2 infection requiring emergency care(EC)/hospitalization within an eight-hospital system in Michigan. Demographic and clinical variables were obtained from the electronic record. Vaccination data was obtained from the Michigan Care Improvement Registry and Centers for Disease Control vaccine tracker. Primary endpoint was rate of emergency care/hospitalization encounters among patients diagnosed with COVID-19. Secondary outcome was severe disease-composite outcome (ICU, mechanical ventilation, or in-hospital death).

Findings

Between December 15,2020 and April 30,2021, 11,834 EC encounters were included:10,880 (91.9%) UV, 825 (7%) PV, 129 (1.1%) FV. Average age was 53.0 ± 18.2 and 52.8% were female. Accounting for the SARS-CoV-2 vaccination population groups in Michigan, the ED encounters/hospitalizations rate relevant to COVID-19 was 96% lower in FV versus UV (multiplicative effect:0.04, 95% CI 0.03 to 0.06, p < 0.001) in negative binomial regression. COVID-19 EC visits rate peaked at 22.61, 12.88, and 1.29 visits per 100000 for the UV, PV, and FV groups, respectively. In the propensity-score matching weights analysis, FV had a lower risk of composite disease compared to UV but statistically insignificant (HR 0.84, 95% CI 0.52 to 1.38).

Interpretation

The need for emergency care/hospitalization due to breakthrough COVID-19 is an exceedingly rare event in fully vaccinated patients. As vaccination has increased regionally, EC visits amongst fully vaccinated individuals have remained low and occur much less frequently than unvaccinated individuals. If hospital-based treatment is required, elderly patients with significant comorbidities are at high-risk for severe outcomes regardless of vaccination status.

Timing of corticosteroids impacts mortality in hospitalized COVID-19 patients

The optimal timing of initiating corticosteroid treatment in hospitalized patients is unknown. We aimed to assess the relationship between timing of initial corticosteroid treatment and in-hospital mortality in COVID-19 patients. In this observational study through medical record analysis, we quantified the mortality benefit of corticosteroids in two equally matched groups of hospitalized COVID-19 patients. We subsequently evaluated the timing of initiating corticosteroids and its effect on mortality in all patients receiving corticosteroids. Demographic, clinical, and laboratory variables were collected and employed for multivariable regression analyses. 1461 hospitalized patients with confirmed COVID-19 were analyzed. Of these, 760 were also matched into two equal groups based on having received corticosteroid therapy. Patients receiving corticosteroids had a lower risk of death than those who did not (HR 0.67, 95% CI 0.67-0.90; p = 0.01). Timing of corticosteroids was assessed for all 615 patients receiving corticosteroids during admission. Patients receiving first dose of corticosteroids > 72 h into hospitalization had a lower risk of death compared to patients with first dose at earlier time intervals (HR 0.56, 95% CI 0.38-0.82; p = 0.003). There was a mortality benefit in patients with > 7 days of symptom onset to initiation of corticosteroids (HR 0.56, 95% CI 0.33-0.95; p = 0.03). In patients receiving oxygen therapy, corticosteroids reduced risk of death in mechanically ventilated patients (HR 0.38, 95% CI 0.24-0.60; p < 0.001) but not in patients on high-flow or other oxygen therapy (HR 0.46, 95% CI 0.20-1.07; p = 0.07) and (HR 0.84, 95% CI 0.35-2.00; p = 0.69), respectively. Timing of corticosteroids initiation was related to in-hospital mortality for COVID-19 patients. Time from symptom onset > 7 days should trigger initiation of corticosteroids. In the absence of invasive mechanical ventilation, corticosteroids should be initiated if the patient remains hospitalized at 72 h. Hypoxia requiring supplemental oxygen therapy should not be a trigger for initiation of corticosteroids unless the timing is appropriate.

Epidemiological, Clinical, and Laboratory Predictors of In-Hospital Mortality Among COVID-19 Patients Admitted in a Tertiary COVID Dedicated Hospital, Northern India: A Retrospective Observational Study

Introduction

COVID-19 pandemic still poses a serious challenge to health system worldwide. This study was planned to determine exposure characteristics, in-hospital mortality, and predictors of in hospital mortality among COVID-19 patients.

Material and methods

We retrospectively investigated epidemiological, clinical, and laboratory profile of confirmed COVID-19 patients admitted from 25th March to 31st August 2020. COVID-19 patient profiles were collected from Medical Record Section of the hospital.

Results

In hospital mortality occurred in 159 (11%) cases. Increasing respiratory rate, higher temperature, higher total leukocyte count, and high blood urea levels were found to be independent risk factors for in hospital mortality whereas higher hemoglobin and higher oxygen saturation at the time of hospital admission were found to be protective against in hospital mortality.

Conclusion

In hospital mortality among COVID-19 patients is almost 1 in 10 in tertiary care hospital. Patients with advancing age (AOR: 1.048; 95% CI: 1.021-1.076), higher respiratory rate (AOR: 1.248; 95% CI: 1.047-1.489), higher temperature (AOR: 1.758; 95% CI: 1.025-3.016), higher leukocyte count (AOR: 1.147; 95% CI: 1.035-1.270), and higher urea levels (AOR: 1.034; 95% CI: 1.005-1.064) at the time of admission are important predictors of COVID-19 in-hospital mortality.

A meta-analysis on the risk factors adjusted association between cardiovascular disease and COVID-19 severity

BACKGROUND

Cardiovascular disease (CVD), one of the most common comorbidities of coronavirus disease 2019 (COVID-19), has been suspected to be associated with adverse outcomes in COVID-19 patients, but their correlation remains controversial.

METHOD

This is a quantitative meta-analysis on the basis of adjusted effect estimates. PubMed, Web of Science, MedRxiv, Scopus, Elsevier ScienceDirect, Cochrane Library and EMBASE were searched comprehensively to obtain a complete data source up to January 7, 2021. Pooled effects (hazard ratio (HR), odds ratio (OR)) and the 95% confidence intervals (CIs) were estimated to evaluate the risk of the adverse outcomes in COVID-19 patients with CVD. Heterogeneity was assessed by Cochran's Q-statistic, I2test, and meta-regression. In addition, we also provided the prediction interval, which was helpful for assessing whether the variation across studies was clinically significant. The robustness of the results was evaluated by sensitivity analysis. Publication bias was assessed by Begg's test, Egger's test, and trim-and-fill method.

RESULT

Our results revealed that COVID-19 patients with pre-existing CVD tended more to adverse outcomes on the basis of 203 eligible studies with 24,032,712 cases (pooled ORs = 1.41, 95% CIs: 1.32-1.51, prediction interval: 0.84-2.39; pooled HRs = 1.34, 95% CIs: 1.23-1.46, prediction interval: 0.82-2.21). Further subgroup analyses stratified by age, the proportion of males, study design, disease types, sample size, region and disease outcomes also showed that pre-existing CVD was significantly associated with adverse outcomes among COVID-19 patients.

CONCLUSION

Our findings demonstrated that pre-existing CVD was an independent risk factor associated with adverse outcomes among COVID-19 patients.

2020 Year in Review: Mechanical Ventilation During the First Year of the COVID-19 Pandemic

Coronavirus disease 2019 (COVID-19) represents the greatest medical crisis encountered in the young history of critical care and respiratory care. During the early months of the pandemic, when little was known about the virus, the acute hypoxemic respiratory failure it caused did not appear to fit conveniently or consistently into our classification of ARDS. This not only re-ignited a half-century's long simmering debate over taxonomy, but also fueled similar debates over how PEEP and lung-protective ventilation should be titrated, as well as the appropriate role of noninvasive ventilation in ARDS. COVID-19 ignited other debates on emerging concepts such as ARDS phenotypes and patient self-inflicted lung injury from vigorous spontaneous breathing. Over a year later, these early perplexities have receded into the background without having been reviewed or resolved. With a full year of evidence having been published, this narrative review systematically analyzes whether COVID-19-associated respiratory failure is essentially ARDS, with perhaps a somewhat different course of presentation. This includes a review of the severity of hypoxemia and derangements in pulmonary mechanics, PEEP requirements, recruitment potential, ability to achieve lung-protective ventilation goals, duration of mechanical ventilation, associated mortality, and response to noninvasive ventilation. This paper also reviews the concepts of ARDS phenotypes and patient self-inflicted lung injury as these are crucial to understanding the contentious debate over the nature and management of COVID-19.

Comparison of hospitalized patients with COVID-19 who did and did not live in residential care facilities in Montréal: a retrospective case series

BACKGROUND

As in other jurisdictions, the demographics of people infected with SARS-CoV-2 changed in Quebec over the course of the first COVID-19 pandemic wave, and affected those living in residential care facilities (RCFs) disproportionately. We evaluated the association between clinical characteristics and outcomes of hospitalized patients with COVID-19, comparing those did or did not live in RCFs.

METHODS

We conducted a retrospective case series of all consecutive adults (≥ 18 yr) admitted to the Jewish General Hospital in Montréal with laboratory-confirmed SARS-CoV-2 infection from Mar. 4 to June 30, 2020, with in-hospital follow-up until Aug. 6, 2020. We collected patient demographics, comorbidities and outcomes (i.e., admission to the intensive care unit, mechanical ventilation and death) from medical and laboratory records and compared patients who did or did not live in public and private RCFs. We evaluated factors associated with the risk of in-hospital death with a Cox proportional hazard model.

RESULTS

In total, 656 patients were hospitalized between March and June 2020, including 303 patients who lived in RCFs and 353 patients who did not. The mean age was 72.9 (standard deviation 18.3) years (range 21 to 106 yr); 349 (53.2%) were female and 118 (18.0%) were admitted to the intensive care unit. The overall mortality rate was 23.8% (156/656), but was higher among patients living in RCFs (36.6% [111/303]) compared with those not living in RCFs (12.7% [45/353]). Increased risk of death was associated with age 80 years and older (hazard ratio [HR] 2.39, 95% confidence interval [CI] 1.35-4.24), male sex (HR 1.74, 95% CI 1.25-2.41), the presence of 4 or more comorbidities (HR 2.01, 95% CI 1.18-3.42) and living in an RCF (HR 1.62, 95% CI 1.09-2.39).

INTERPRETATION

During the first wave of the COVID-19 epidemic in Montréal, more than one-third of RCF residents hospitalized with SARS-CoV-2 infection died during hospitalization. Policies and practices that prevent future outbreaks of SARS-CoV-2 infection in this setting must be implemented to prevent high mortality in this vulnerable population.

The predictors of COVID-19 mortality in a nationwide cohort of Turkish patients

The COVID-19-related death rate varies between countries and is affected by various risk factors. This multicenter registry study was designed to evaluate the mortality rate and the related risk factors in Turkey. We retrospectively evaluated 1500 adults with COVID-19 from 26 centers who were hospitalized between March 11 and July 31, 2020. In the study group, 1041 and 459 cases were diagnosed as definite and highly probable cases, respectively. There were 993 PCR-positive cases (66.2%). Among all cases, 1144 (76.3%) were diagnosed with non-severe pneumonia, whereas 212 (14.1%) had severe pneumonia. Death occurred in 67 patients, corresponding to a mortality rate of 4.5% (95% CI:3.5-5.6). The univariate analysis demonstrated that various factors, including male sex, age ≥65 years and the presence of dyspnea or confusion, malignity, chronic obstructive lung disease, interstitial lung disease, immunosuppressive conditions, severe pneumonia, multiorgan dysfunction, and sepsis, were positively associated with mortality. Favipiravir, hydroxychloroquine and azithromycin were not associated with survival. Following multivariate analysis, male sex, severe pneumonia, multiorgan dysfunction, malignancy, sepsis and interstitial lung diseases were found to be independent risk factors for mortality. Among the biomarkers, procalcitonin levels on the 3rd-5th days of admission showed the strongest associations with mortality (OR: 6.18; 1.6-23.93). This study demonstrated that the mortality rate in hospitalized patients in the early phase of the COVID-19 pandemic was a serious threat and that those patients with male sex, severe pneumonia, multiorgan dysfunction, malignancy, sepsis and interstitial lung diseases were at increased risk of mortality; therefore, such patients should be closely monitored.

Racial disparities in COVID-19 pandemic cases, hospitalisations, and deaths: A systematic review and meta-analysis

Background

People from racial minority groups in western countries experience disproportionate socioeconomic and structural determinants of health disadvantages. These disadvantages have led to inequalities and inequities in health care access and poorer health outcomes. We report disproportionate disparities in prevalence, hospitalisation, and deaths from COVID-19 by racial minority populations.

Methods

We conducted a systematic literature search of relevant databases to identify studies reporting on prevalence, hospitalisations, and deaths from COVID-19 by race groups between 01 January 2020 – 15 April 2021. We grouped race categories into Blacks, Hispanics, Whites and Others. Random effects model using the method of DerSimonian and Laird were fitted, and forest plot with respective ratio estimates and 95% confidence interval (CI) for each race category, and subgroup meta-regression analyses and the overall pooled ratio estimates for prevalence, hospitalisation and mortality rate were presented.

Results

Blacks experienced significantly higher burden of COVID-19: prevalence ratio 1.79 (95% confidence interval (CI) = 1.59-1.99), hospitalisation ratio 1.87 (95% CI = 1.69-2.04), mortality ratio 1.68 (95% CI = 1.52-1.83), compared to Whites: prevalence ratio 0.70 (95% CI = 0.0.64-0.77), hospitalisation ratio 0.74 (95% CI = 0.65-0.82), mortality ratio 0.82 (95% CI = 0.78-0.87). Also, Hispanics experienced a higher burden: prevalence ratio 1.78 (95% CI = 1.63-1.94), hospitalisation ratio 1.32 (95% CI = 1.08-1.55), mortality ratio 0.94 (95% CI = 0.84-1.04) compared to Whites. A higher burden was also observed for Other race groups: prevalence ratio 1.43 (95% CI = 1.19-1.67), hospitalisation ratio 1.12 (95% CI = 0.89-1.35), mortality ratio 1.06 (95% CI = 0.89-1.23) compared to Whites. The disproportionate burden among Blacks and Hispanics remained following correction for publication bias.

Conclusions

Blacks and Hispanics have been disproportionately affected by COVID-19. This is deeply concerning and highlights the systemically entrenched disadvantages (social, economic, and political) experienced by racial minorities in western countries; and this study underscores the need to address inequities in these communities to improve overall health outcomes.

Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID)

In Coronavirus disease 2019 (COVID-19), early identification of patients with a high risk of mortality can significantly improve triage, bed allocation, timely management, and possibly, outcome. The study objective is to develop and validate individualized mortality risk scores based on the anonymized clinical and laboratory data at admission and determine the probability of Deaths at 7 and 28 days. Data of 1393 admitted patients (Expired-8.54%) was collected from six Apollo Hospital centers (from April to July 2020) using a standardized template and electronic medical records. 63 Clinical and Laboratory parameters were studied based on the patient's initial clinical state at admission and laboratory parameters within the first 24 h. The Machine Learning (ML) modelling was performed using eXtreme Gradient Boosting (XGB) Algorithm. 'Time to event' using Cox Proportional Hazard Model was used and combined with XGB Algorithm. The prospective validation cohort was selected of 977 patients (Expired-8.3%) from six centers from July to October 2020. The Clinical API for the Algorithm is  http://20.44.39.47/covid19v2/page1.php being used prospectively. Out of the 63 clinical and laboratory parameters, Age [adjusted hazard ratio (HR) 2.31; 95% CI 1.52-3.53], Male Gender (HR 1.72, 95% CI 1.06-2.85), Respiratory Distress (HR 1.79, 95% CI 1.32-2.53), Diabetes Mellitus (HR 1.21, 95% CI 0.83-1.77), Chronic Kidney Disease (HR 3.04, 95% CI 1.72-5.38), Coronary Artery Disease (HR 1.56, 95% CI - 0.91 to 2.69), respiratory rate > 24/min (HR 1.54, 95% CI 1.03-2.3), oxygen saturation below 90% (HR 2.84, 95% CI 1.87-4.3), Lymphocyte% in DLC (HR 1.99, 95% CI 1.23-2.32), INR (HR 1.71, 95% CI 1.31-2.13), LDH (HR 4.02, 95% CI 2.66-6.07) and Ferritin (HR 2.48, 95% CI 1.32-4.74) were found to be significant. The performance parameters of the current model is at AUC ROC Score of 0.8685 and Accuracy Score of 96.89. The validation cohort had the AUC of 0.782 and Accuracy of 0.93. The model for Mortality Risk Prediction provides insight into the COVID Clinical and Laboratory Parameters at admission. It is one of the early studies, reflecting on 'time to event' at the admission, accurately predicting patient outcomes.