alexa Prediction of survival for advanced cancer patients by recursive partitioning analysis: role of Karnofsky performance status, quality of life, and symptom distress.
Oncology

Oncology

Journal of Carcinogenesis & Mutagenesis

Author(s): Hwang SS, Scott CB, Chang VT, Cogswell J, Srinivas S,

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Abstract We performed an exploratory recursive partitioning analysis (RPA) in 429 metastatic cancer patients who had completed a Functional Assessment of Cancer Therapy-General (FACT-G) and a Memorial Symptom Assessment Scale-Short Form (MSAS-SF) to define survival prognostic groups. The Cox model analysis also was performed. Both RPA and Cox models included Karnofsky performance status (KPS), age, FACT-G subscales, and MSAS-SF subscales as survival predictors. Of 429 patients, 348 patients (81.1\%) had expired at time of analysis. The median age was 67 years (27-89), with median length of survival of 147 days. The RPA identified four distinct survival groups (p < .0001) with three variables: KPS, physical well-being, and physical symptom distress. The most significant split was KPS of 50\%, followed by physical well-being score of 25 and physical symptom distress score of 0.6. The median survival time was 29 days for patients with KPS < 50\%; 146 days for patients with KPS > or = 50\% and physical well-being < 25; 292 days for patients with KPS > 50\%, physical well-being > or = 25, and physical symptom distress score > 0.6; and 610 days for patients with KPS > or = 50\%, physical well-being > or = 25, and physical symptom distress score < or = 0.6. The Cox model found, in addition to KPS (p < .0001) and physical well-being (p = .08), different predictors: psychological symptom distress (p = .0007), global distress index (p = .02), and age (p < .0001). We concluded that the KPS, quality of life, and symptom distress scores can be combined to define prognostic groups. Such models may be helpful for clinical decision making.
This article was published in Cancer Invest and referenced in Journal of Carcinogenesis & Mutagenesis

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