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

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 greater than or equal to50% and physical well-being <25; 292 days for patients with KPS >50%, physical well-being greater than or equal to25, and physical symptom distress score >0.6 and 610 days for patients with KPS >50%, physical well-being greater than or equal to25, and physical symptom distress score less than or equal to0.6. The Cox model found, in addition to KPS (p <.000l) 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.