AI Insight
Researchers developed a decision tree model to predict one-year survival in 200 ambulatory patients with advanced cancer using three objective clinical variables: chemotherapy response, C-reactive protein to albumin ratio, and lactate dehydrogenase levels. The model demonstrated good predictive accuracy with an area under the curve of 0.749 and performed well in internal validation using bootstrap resampling. This tool addresses a gap in long-term prognostication, as most existing models focus on short-term survival of weeks to months.
Why it matters
One-year survival prediction is clinically important for palliative care referral decisions and end-of-life planning in advanced cancer patients. This simple model using only three readily available laboratory and clinical parameters could help clinicians make more informed decisions about appropriate timing for palliative care interventions, though external validation in different patient populations is needed before widespread clinical implementation.
Understand the Science
by Yusuke Hiratsuka, Seok-Joon Yoon, Sang-Yeon Suh, Yu Jung Kim
Background
An accurate prognostication is crucial for end-of-life decision-making in advanced cancer care. While existing prognostic tools focus on short-term survival (weeks/months), there is a paucity of studies that have examined the long-term prediction at one year. A one-year timeframe is regarded as a general indicator of palliative care referral; however, there are many uncertain issues. This study aimed to develop a one-year survival prediction model using objective parameters for patients with advanced cancer.
Methods
This was a secondary analysis of data from a Korean prospective cohort study. Participants, with clinician-predicted survival of ≤1 year, were assessed using clinical data, performance status, laboratory data and chemotherapy response. Recursive partitioning analyses (RPA) were used to identify the prognostic factors and build a prediction model.
Results
Of the 200 advanced cancer patients (mean age 64.4, 36% female; 33.5% lung cancer), the median survival was 228 days. Using three variables (chemotherapy response, C reactive protein -Albumin Ratio, and lactate dehydrogenase level), we developed a 4-node survival tree. The model demonstrated an optimism-corrected area under the curve of 0.749 (95% confidence interval: 0.696–0.800) at one year, after 200 bootstrap resampling. The Brier score was 0.161, and the calibration slope was 0.99, indicating high predictive accuracy.
Conclusions
We developed an RPA model to facilitate one-year survival prediction in patients with advanced cancer. The 4-leaf model incorporated only three readily available variables. Following external validation, this model may prove valuable in assisting clinicians with one-year survival prognostication.
Source: Decision tree model to predict one-year survival in ambulatory patients with advanced cancer