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The primary objective of this analysis was to identify a simple scoring system based on geriatric parameters to predict overall survival (OS). The secondary objectives included the impact evaluation of the frailty scoring system on treatment-related toxicity and progression-free survival (PFS).
This analysis showed that a frailty score that combines age, functional status, and comorbidities can predict survival and toxicity, and is useful to determine the feasibility of a treatment regimen. The frailty profile was associated with an increased risk of death, progression, nonhematologic AEs, and treatment discontinuation, regardless of ISS stage, chromosome abnormalities, and type of treatment.
Although this analysis is based on patients enrolled in clinical trials, the less strict inclusion/exclusion criteria allowed 30% of frail patients to be treated. In our analysis, the 3-year OS rate was 84% in fit, 76% in intermediate-fitness, and 57% in frail patients. The OS for fit patients compares favorably with the standard treatments3,4 ; similarly, the survival of frail patients is comparable to that of the community-based population previously reported.36 A significantly higher cumulative incidence of nonhematologic toxicities and drug discontinuation was reported in frail compared with fit patients, and severe nonhematologic AEs and drug discontinuation induced a shorter survival.12 Unexpectedly, the performance status did not affect OS, whereas the frailty status increased the risk of death by approximately threefold, thus confirming the need for a more sophisticated evaluation of elderly patients before starting therapy. Our findings suggest that the cutoff age of 80 years instead of 75 years should be used for the definition of frail conditions. Indeed, the risk of death is only slightly increased in patients 75 to 80 years of age, whereas it is 2.4 times higher in patients >80 years. Besides age, the most common reasons for an increase in frailty were losing independence in self-care activities, household management, and transferring/transportation.
PURPOSE: We developed a drug-disease simulation model to predict antitumor response and overall survival in phase III studies from longitudinal tumor size data in phase II trials. METHODS: We developed a longitudinal exposure-response tumor-growth inhibition (TGI) model of drug effect (and resistance) using phase II data of capecitabine (n = 34) and historical phase III data of fluorouracil (FU; n = 252) in colorectal cancer (CRC); and we developed a parametric survival model that related change in tumor size and patient characteristics to survival time using historical phase III data (n = 245). The models were validated in simulation of antitumor response and survival in an independent phase III study (n = 1,000 replicates) of capecitabine versus FU in CRC. RESULTS: The TGI model provided a good fit of longitudinal tumor size data. A lognormal distribution best described the survival time, and baseline tumor size and change in tumor size from baseline at week 7 were predictors (P < .00001). Predicted change of tumor size and survival time distributions in the phase III study for both capecitabine and FU were consistent with observed values, for example, 431 days (90% prediction interval, 362 to 514 days) versus 401 days observed for survival in the capecitabine arm. A modest survival improvement of 39 days (90% prediction interval, -21 to 110 days) versus 35 days observed was predicted for capecitabine. CONCLUSION: The modeling framework successfully predicted survival in a phase III trial on the basis of capecitabine phase II data in CRC. It is a useful tool to support end-of-phase II decisions and design of phase III studies. 2b1af7f3a8