Predicting 1-year post-COVID-19 mortality based on chest computed tomography scan

Prognostication and integration of bedside lung ultrasound and computed tomography imaging findings with clinical features to Predict COVID-19 In-hospital mortality and ICU admission

INTRODUCTION

Bedside lung ultrasound (LUS) and computed tomography (CT) imaging are valuable modalities in screening and diagnosis of pulmonary diseases. This study aims to investigate the prognostic value of integrating LUS and CT imaging findings with clinical features to predict poor outcomes upon ER admission in COVID-19.

METHODS

Patients visiting the study center with clinical presentation and laboratory findings compatible with COVID-19 between April 2020 to January 2022 were considered for this study. Several imaging findings (ground glass opacity, consolidation, atelectatic bands, mosaic attenuation, ARDS pattern, crazy paving, pleural thickening in CT and A-line, comet-tail artifact, confluent B-Line in BLUS, pleural thickening and Consolidation in both modalities) were evaluated, alongside clinical assessments upon admission, to assess their prognostic value. The top radiological, LUS findings, and clinical signs were integrated in a nomogram for predicting mortality.

RESULTS

A total of 1230 patients were included in the analyses. Among the findings, consolidation in BLUS and CT imaging, and absence of A-lines were associated with mortality. In addition to these findings, ground-glass opacities, atelectatic band, mosaic attenuation, crazy paving, and confluent B-line were also associated with ICU hospitalization. Although, the prognostic value of individual markers was poor and comparable (AUC < 0.65), the combined use of top clinical and imaging findings in the associated nomogram led to a high accuracy in predicting mortality (Area under curve: 87.3%).

CONCLUSIONS

BLUS and CT imaging findings alone provide limited utility in stratifying patients for higher mortality and ICU admission risk and should not be used for risk stratification alone outside the context of each patient and their clinical presentations in suspected COVID-19 patients.

One-year mortality and associated factors in older hospitalized COVID-19 survivors: a Nationwide Cohort Study in Korea

This study aimed to evaluate the 1-year mortality rate among older patients with COVID-19 discharged from hospital and to identify risk factors associated with this outcome. Using a COVID-19 dataset from the Korean National Health Insurance System, this study's evaluation period spanned from October 8, 2020, through December 31, 2021. The primary outcome was the 1-year mortality rate following hospital discharge. A logistic regression model was employed for multivariable analysis to estimate the odds ratios for the outcomes, and the Kaplan-Meier method was used to analyze differences in 1-year survival rates. Among the 66,810 COVID-19 patients aged 60 years or older who were hospitalized during the study period, the in-hospital mortality rate was 4.8% (n = 3219). Among the survivors (n = 63,369), the 1-year mortality rate was 4.9% (n = 3093). Non-survivors, compared to survivors, were significantly older (79.2 ± 9.5 vs. 68.9 ± 7.8, P < 0.001) and exhibited a lower rate of COVID-19 vaccination (63.0% vs. 91.7%, P < 0.001). Additionally, non-survivors experienced a higher incidence of organ dysfunction, along with a greater proportion of required mechanical ventilation (14.6% vs. 1.0%, P < 0.001) and extracorporeal membrane oxygenation (4.0% vs. 0.1%, P < 0.001). Multivariable logistic regression analysis identified older age, male sex, cardiovascular disease, immunosuppression, organ dysfunction, illness severity, and corticosteroid use during hospitalization as factors associated with death within 1 year after hospital discharge. However, vaccination was found to have a long-term protective effect against death among COVID-19 survivors. The 1-year mortality rate after hospital discharge for older COVID-19 patients was comparable to the in-hospital mortality rate for these patients in Korea. The long-term mortality rate among hospitalized older COVID-19 patients was influenced by demographic factors and the severity of illness experienced during hospitalization.

1-year survival rate of SARS-CoV-2 infected patients with acute respiratory distress syndrome based on ventilator types: a multi-center study

The aim of this study was to evaluate the association between types of ventilator and the one-year survival rate of patients with acute respiratory distress syndrome (ARDS) due to SARS‑CoV-2 infection. This multi-center, retrospective observational study was conducted on 1078 adult patients admitted to five university-affiliated hospitals in Iran who underwent mechanical ventilator (MV) due to ARDS. Of the 1078 patients, 781 (72.4%) were managed with ICU ventilators and 297 (27.6%) with transport ventilators. Overall mortality was significantly higher in patients supported with transport ventilator compared to patients supported with ICU ventilator (16.5% vs. 9.3% P = 0.001). Regression analysis revealed that the expected hazard overall increased with age (HR: 1.525, 95% CI 1.112-1.938, P = 0.001), opacity score (HR: 1.448, 95% CI 1.122-2.074, P = 0.001) and transport ventilator versus ICU ventilator (HR: 1.511, 95% CI 1.143-2.187, P = 0.029). The Kaplan-Meier curves of survival analysis showed that patients supported with ICU ventilator had a significantly higher 1-year survival rate (P = 0.001). In MV patients with ARDS due to COVID-19, management with non-ICU sophisticated ventilators was associated with a higher mortality rate compared to standard ICU ventilators. However, more studies are needed to determine the exact effect of ventilator types on the outcome of critically ill patients.

Watershed infarction in COVID-19: The necessity of neuroimaging in patients with subtle neurological symptoms

Background: Cerebrovascular diseases comprise a significant portion of neurological disorders related to coronavirus disease 2019 (COVID-19). We evaluated the clinical and imaging characteristics of a cohort of COVID-19 patients with stroke and also identified patients with watershed infarcts. Methods: In this cross-sectional study, seventy-three COVID-19 patients with ischemic stroke were included between October 2020 and January 2021. Patients were evaluated based on the following clinical and imaging features: severity of COVID-19 (critical/ non-critical), stroke type, presence/absence of clinical suspicion of stroke, medical risk factors, Fazekas scale, atherothrombosis, small vessel disease, cardiac pathology, other causes, and dissection (ASCOD) criteria classification, and presence or absence of watershed infarction. Clinical outcomes were assessed based on Modified Rankin Scale (MRS) and mortality. Results: Most cases of ischemic stroke were due to undetermined etiology (52.1%) and cardioembolism (32.9%). In terms of imaging pattern, 17 (23.0%) patients had watershed infarction. Watershed infarction was associated with the clinically non-suspicious category [odds ratio (OR) = 4.67, P = 0.007] and death after discharge (OR = 7.1, P = 0.003). Patients with watershed infarction had a higher odds of having high Fazekas score (OR = 5.17, P = 0.007) which was also shown by the logistic regression model (adjusted OR = 6.87, P = 0.030). Thirty-one (42%) patients were clinically non-suspected for ischemic stroke. Critical COVID-19 was more common among patients with watershed infarct and clinically non-suspicious patients (P = 0.020 and P = 0.005, respectively). Patients with chronic kidney disease (CKD) were more prone to having stroke with watershed pattern (P = 0.020). Conclusion: Watershed infarct is one of the most common patterns of ischemic stroke in patients with COVID-19, for which clinicians should maintain a high index of suspicion in patients with critical COVID-19 without obvious clinical symptoms of stroke.

Risk Factors for One-Year Mortality in Hospitalized Adults with Severe COVID-19

As the body's immunity declines with age, elderly-hospitalized patients due to COVID-19 might be at higher mortality risk. Therefore, the aim of this prospective study was to examine the possible risk factors (demographic, social or comorbidities) most associated with mortality one-year after diagnosis of COVID-19. Routine data were collected from a cohort of hospitalized adults with severe COVID-19. The primary endpoint was mortality at one-year after diagnosis of COVID-19. We used a Cox proportional hazard model to estimate the hazard ratios (HRs) for both all-cause and specific cardiorespiratory mortality. A fully adjusted model included sex, socioeconomic status, institutionalization status, disability, smoking habit, and comorbidities as confounders. A total of 368 severe cases hospitalized on average 67.3 ± 15.9 years old were included. Participants aged ≥ 71 years had significantly higher HRs for all-cause mortality (adjusted HRs = 2.86, 95%CI: 2.01-4.07) and cardiorespiratory mortality (adjusted HRs = 2.86, 95%CI: 1.99-4.12). The association between age and mortality after diagnosis of COVID-19 due to both all-causes and cardiorespiratory mortality showed a consistent dose-response fashion. Institutionalization, disability, and socioeconomic status also showed a significant association with mortality. In conclusion, aging itself was the most important risk factor associated with mortality one year after diagnosis of COVID-19. People with disabilities, institutionalized or low socioeconomic status are significantly more likely to die after COVID-19.

Hospital readmissions and post-discharge all-cause mortality in COVID-19 recovered patients; A systematic review and meta-analysis

OBJECTIVE

The present study aimed to perform a systematic review and meta-analysis on the prevalence of one-year hospital readmissions and post-discharge all-cause mortality in recovered COVID-19 patients. Moreover, the country-level prevalence of the outcomes was investigated.

METHODS

An extensive search was performed in Medline (PubMed), Embase, Scopus, and Web of Science databases until the end of August 3rd, 2021. A manual search was also performed in Google and Google Scholar search engines. Cohort and cross-sectional studies were included. Two independent reviewers screened the papers, collected data, and assessed the risk of bias and level of evidence. Any disagreement was resolved through discussion.

RESULTS

91 articles were included. 48 studies examined hospital readmissions; nine studies assessed post-discharge all-cause mortality, and 34 studies examined both outcomes. Analyses showed that the prevalence of hospital readmissions during the first 30 days, 90 days, and one-year post-discharge were 8.97% (95% CI: 7.44, 10.50), 9.79% (95% CI: 8.37, 11.24), and 10.34% (95% CI: 8.92, 11.77), respectively. The prevalence of post-discharge all-cause mortality during the 30 days, 90 days and one-year post-discharge was 7.87% (95% CI: 2.78, 12.96), 7.63% (95% CI: 4.73, 10.53) and 7.51% (95% CI, 5.30, 9.72), respectively. 30-day hospital readmissions and post-discharge mortality were 8.97% and 7.87%, respectively. The highest prevalence of hospital readmissions was observed in Germany (15.5%), Greece (15.5%), UK (13.5%), Netherlands (11.7%), China (10.8%), USA (10.0%) and Sweden (9.9%). In addition, the highest prevalence of post-discharge all-cause mortality belonged to Italy (12.7%), the UK (11.8%), and Iran (9.2%). Sensitivity analysis showed that the prevalence of one-year hospital readmissions and post-discharge all-cause mortality in high-quality studies were 10.38% and 4.00%, respectively.

CONCLUSION

10.34% of recovered COVID-19 patients required hospital readmissions after discharge. Most cases of hospital readmissions and mortality appear to occur within 30 days after discharge. The one-year post-discharge all-cause mortality rate of COVID-19 patients is 7.87%, and the majority of patients' readmission and mortality happens within the first 30 days post-discharge. Therefore, a 30-day follow-up program and patient tracking system for discharged COVID-19 patients seems necessary.