Real-World Evidence of the Impact of the COVID-19 Pandemic on Lung Cancer Survival: Canadian Perspective
Bibliographic record
Abstract
Background: The effect of COVID-19 on treatment outcomes in the literature remains limited and is mostly reported either as predictive survival using prioritization and modeling techniques. We aimed to quantify the effect of COVID-19 on lung cancer survival using real-world data collected at the Jewish General Hospital, Montreal. Methods: This is a retrospective chart review study of patients diagnosed between March 2019 and March 2022. We compared three cohorts: pre-COVID-19, and 1st and 2nd year of the pandemic. Results: 417 patients were diagnosed and treated with lung cancer at our centre: 130 in 2019, 103 in 2020 and 184 in 2021. Although the proportion of advanced/metastatic-stage lung cancer remained the same, there was a significant increase in the late-stage presentation during the pandemic. The proportion of M1c (multiple extrathoracic sites) cases in 2020 and 2021 was 57% and 51%, respectively, compared to 31% in 2019 (p < 0.05). Median survival for early stages of lung cancer was similar in the three cohorts. However, patients diagnosed in the M1c stage had a significantly increased risk of death. The 6-month mortality rate was 53% in 2021 compared to 47% in 2020 and 29% in 2019 (p = 0.004). The median survival in this subgroup of patients decreased significantly from 13 months in 2019 to 6 months in 2020 and 5 months in 2021 (p < 0.001). Conclusions: This study is, to our knowledge, the largest single-institution study in Canada looking at lung cancer survival during the COVID-19 pandemic. Our study looks at overall survival in the advanced/metastatic setting of NSCLC during the COVID-19 pandemic. We have previously reported on treatment pattern changes and increased wait times for NSCLC patients during the pandemic. In this study, we report that the advanced/metastatic subgroup had both an increase in the 6-month mortality rate and worsening overall survival during this same time period. Although there was no statistical difference in the proportion of patients with advanced disease, there was a concerning trend of increased M1c disease in cohorts 2 and 3. The higher M1c disease during the COVID-19 pandemic (cohorts 2 and 3) likely played a crucial role in increasing the 6-month mortality rate and leading to a reduced overall survival of lung cancer patients during the pandemic. These findings are more likely to be better identified with longer follow-up.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".