Coronavirus Disease 2019 Outcomes, Patient Vaccination Status, and Cancer-Related Delays During the Omicron Wave: A Brief Report From the TERAVOLT Analysis.

Introduction: The Thoracic Centers International coronavirus disease 2019 (COVID-19) Collaboration (TERAVOLT) registry found approximately 30% mortality in patients with thoracic malignancies during the initial COVID-19 surges. Data from South Africa suggested a decrease in severity and mortality with the Omicron wave. Our objective was to assess mortality of patients with thoracic malignancies with the Omicron-predominant wave and evaluate efficacy of vaccination. Methods: A prospective, multicenter observational study was conducted. A total of 28 institutions contributed data from January 14, 2022, to February 4, 2022. Inclusion criteria were any thoracic cancer and a COVID-19 diagnosis on or after November 1, 2021. End points included mortality, hospitalization, symptomatic COVID-19 infection, asymptomatic COVID-19 infection, and delay in cancer therapy. Analysis was done through contingency tables and a multivariable logistic model. Results: We enrolled a total of 346 patients. Median age was 65 years, 52.3% were female, 74.2% were current or former smokers, 86% had NSCLC, 72% had stage IV at time of COVID-19 diagnosis, and 66% were receiving cancer therapy. Variant was unknown for 70%; for those known, Omicron represented 82%. Overall mortality was 3.2%. Using multivariate analysis, COVID-19 vaccination with booster compared with no vaccination had a protective effect on hospitalization or death (OR = 0.30, confidence interval: 0.15-0.57, p = 0.0003), whereas vaccination without booster did not (OR = 0.64, confidence interval: 0.33-1.24, p = 0.1864). Cancer care was delayed in 56.4% of the patients. Conclusions: TERAVOLT found reduced patient mortality with the most recent COVID-19 surge. COVID-19 vaccination with booster improved outcomes of hospitalization or death. Delays in cancer therapy remain an issue, which has the potential to worsen cancer-related mortality.

A Definitive Prognostication System for Patients With Thoracic Malignancies Diagnosed With Coronavirus Disease 2019: An Update From the TERAVOLT Registry.

INTRODUCTION: Patients with thoracic malignancies are at increased risk for mortality from coronavirus disease 2019 (COVID-19), and a large number of intertwined prognostic variables have been identified so far. METHODS: Capitalizing data from the Thoracic Cancers International COVID-19 Collaboration (TERAVOLT) registry, a global study created with the aim of describing the impact of COVID-19 in patients with thoracic malignancies, we used a clustering approach, a fast-backward step-down selection procedure, and a tree-based model to screen and optimize a broad panel of demographics and clinical COVID-19 and cancer characteristics. RESULTS: As of April 15, 2021, a total of 1491 consecutive eligible patients from 18 countries were included in the analysis. With a mean observation period of 42 days, 361 events were reported with an all-cause case fatality rate of 24.2%. The clustering procedure screened 73 covariates in 13 clusters. A further multivariable logistic regression for the association between clusters and death was performed, resulting in five clusters significantly associated with the outcome. The fast-backward step-down selection procedure then identified the following seven major determinants of death: Eastern Cooperative Oncology Group-performance status (ECOG-PS) (OR = 2.47, 1.87-3.26), neutrophil count (OR = 2.46, 1.76-3.44), serum procalcitonin (OR = 2.37, 1.64-3.43), development of pneumonia (OR = 1.95, 1.48-2.58), C-reactive protein (OR = 1.90, 1.43-2.51), tumor stage at COVID-19 diagnosis (OR = 1.97, 1.46-2.66), and age (OR = 1.71, 1.29-2.26). The receiver operating characteristic analysis for death of the selected model confirmed its diagnostic ability (area under the receiver operating curve = 0.78, 95% confidence interval: 0.75-0.81). The nomogram was able to classify the COVID-19 mortality in an interval ranging from 8% to 90%, and the tree-based model recognized ECOG-PS, neutrophil count, and c-reactive protein as the major determinants of prognosis. CONCLUSIONS: From 73 variables analyzed, seven major determinants of death have been identified. Poor ECOG-PS was found to have the strongest association with poor outcome from COVID-19. With our analysis, we provide clinicians with a definitive prognostication system to help determine the risk of mortality for patients with thoracic malignancies and COVID-19.

Case-finding for alpha1-antitrypsin deficiency in Kazakh patients with COPD.

BACKGROUND: Alpha-1-antitrypsin deficiency (AATD) is an under-diagnosed condition in patients with chronic obstructive pulmonary disease (COPD). The aim of this study was to screen for AATD in Kazakh patients with COPD using dried blood spot specimens. METHODS: The alpha1-antitrypsin (AAT) concentration was determined by nephelometry, PCR was used to detect PiS and PiZ alleles; and isoelectric focusing was used to confirm questionable genotype results and detect rare AAT variants. RESULTS: To this aim, 187 Kazakh subjects with COPD were recruited. Blood samples were collected as dried blood spot. Genotyping of 187 samples revealed 3 (1.6%) PI*MZ and 1 (0.53%) PI*MS, Phenotyping identified also two sample (1.1%) with phenotype PiMI. Allelic frequencies of pathological mutations Z, S and I resulted 0.8%, 0.3%, 0.5%, respectively, in COPD Kazakh population. CONCLUSION: This study proved that AATD is present in the Kazakh population. These results support the general concept of targeted screening for AAT deficiency in countries like Kazakhstan, with a large population of COPD patients and low awareness among care-givers about this genetic condition.