ArticleCancer research and treatment2022
The Value of the Illness-Death Model for Predicting Outcomes in Patients with Non-Small Cell Lung Cancer.
Article in Cancer research and treatment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Lung cancer mortality and associated predictors: systematic review using 32 scientific research findings.Frontiers in oncology · 2023Pooled it
- A population-based projection of psoriatic arthritis in Germany until 2050: analysis of national statutory health insurance data of 65 million German population.Rheumatology international · 2023Article
- Difference of serum tumor markers in different clinical stages of elderly patients with non-small cell lung cancer and evaluation of diagnostic value.Journal of medical biochemistry · 2023Article
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4 authors.
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Abstract
purposeThe illness-death model (IDM) is a comprehensive approach to evaluate the relationship between relapse and death. This study aimed to illustrate the value of the IDM for identifying risk factors and evaluating predictive probabilities for relapse and death in patients with non-small cell lung cancer (NSCLC) in comparison with the disease-free survival (DFS) model. MATERIALS AND
methodsWe retrospectively analyzed 612 NSCLC patients who underwent a curative operation. Using the IDM, the risk factors and predictive probabilities for relapse, death without relapse, and death after relapse were simultaneously evaluated and compared to those obtained from a DFS model.
resultsThe IDM provided more detailed risk factors according to the patient's disease course, including relapse, death without relapse, and death after relapse, in patients with resected lung cancer. In the IDM, history of malignancy (other than lung cancer) was related to relapse and smoking history was associated with death without relapse; both were indistinguishable in the DFS model. In addition, the IDM was able to evaluate the predictive probability and risk factors for death after relapse; this information could not be obtained from the DFS model.
conclusionCompared to the DFS model, we found that the IDM provides more comprehensive information on transitions between states and disease stages and provides deeper insights with respect to understanding the disease process among lung cancer patients.
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