PloS one · 2026
Decision tree model to predict one-year survival in ambulatory patients with advanced cancer.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Hiratsuka Yusuke, Yoon Seok-Joon, Suh Sang-Yeon, Kim Yu Jung
Аннотация
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.
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.