J Thorac Oncol: A definitive predictive system for patients with thoracic malignancies diagnosed with COVID-19

Background: Coronavirus disease 2019 (COVID-19), a respiratory infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has already had an impact on healthcare that will be felt for decades to come to this effect
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The rapid global spread of this unpredictable virus led the World Health Organization to declare a pandemic in March 2020, with more than 219 million confirmed cases and 4.

It has been established early on that certain groups of people, including older adults and those with underlying comorbidities, are more likely to develop severe forms of COVID-19 and experience harmful outcomes than the general population
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Initial reports from a single institution reporting conflicting data among patients with cancer led the oncology community to create registries and determine the true impact of COVID-19 on this vulnerable patient population

 It has been established early on that certain groups of people, including older adults and those with underlying comorbidities, are more likely to develop severe forms of COVID-19 and experience harmful outcomes than the general population

RESULTS: As of April 15, 2021, 1491 consecutive evaluable patients from 18 countries were included in the analysis
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The mean observation period was 42 days, with 361 incidents reported, with an all-cause fatality rate of 24.

Table 1 Final multivariate logistic model of death associations
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Fast backward degradation variable selection with total remaining AIC as stopping criterion

Table 1 Final multivariate logistic model of death associations

Figure 1.
Prognostic nomogram including major determinants of death: occurrence of pneumonia (YES vs NO), age (≤65 vs >65 years), neutrophil count (> vs ≤ upper limit of normal), procalcitonin (> vs≤ULN), C-reactive protein (>vs≤ULN), EcoG-PS (≥2 vs 0~1), COVID-19 stage (stage IV vs I~III)
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The nomogram was able to classify the risk of death from COVID-19 within the range of 8% to 90%

Figure 1.

Figure 3: Sankey diagram provides a visual representation of shopping cart analysis with hierarchical classification of variables
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The first nodule was isolated according to EcoG-PS (0-1:1120 patients vs ≥2:371 patients)

Figure 3: Sankey diagram provides a visual representation of shopping cart analysis with hierarchical classification of variables

From the 73-variable analysis, seven major determinants of death were identified
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Poor EcoG-PS showed the strongest correlation with poor prognosis in COVID-19
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Through our analysis, we provide clinicians with a clear prediction system to help determine mortality risk in patients with thoracic malignancies and COVID-19
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Original source: Whisenant JG, Baena J, Cortellini A, et al.
A definitive prognostication system for patients with thoracic malignancies diagnosed with COVID-19 : an update from the TERAVOLT registry .
J Thorac Oncol 2022 Jan 24

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