Validation of a Predictive Model for Survival in Patients With Advanced Cancer: Secondary Analysis of RTOG 9714.

BackgroundThe objective of this study was to validate a simple predictive model for survival of patients with advanced cancer.MethodsPrevious studies with training and validation datasets developed a model predicting survival of patients referred for palliative radiotherapy using three readily available factors: primary cancer site, site of metastases and Karnofsky performance score (KPS). This predictive model was used in the current study, where each factor was assigned a value proportional to its prognostic weight and the sum of the weighted scores for each patient was survival prediction score (SPS). Patients were also classified according to their number of risk factors (NRF). Three risk groups were established. The Radiation Therapy and Oncology Group (RTOG) 9714 data was used to provide an additional external validation set comprised of patients treated among multiple institutions with appropriate statistical tests.ResultsThe RTOG external validation set comprised of 908 patients treated at 66 different radiation facilities from 1998 to 2002. The SPS method classified all patients into the low-risk group. Based on the NRF, two distinct risk groups with significantly different survival estimates were identified. The ability to predict survival was similar to that of the training and previous validation datasets for both the SPS and NRF methods.ConclusionsThe three variable NRF model is preferred because of its relative simplicity

Validation of a Predictive Model for Survival in Patients With Advanced Cancer: Secondary Analysis of RTOG 9714.

BackgroundThe objective of this study was to validate a simple predictive model for survival of patients with advanced cancer.MethodsPrevious studies with training and validation datasets developed a model predicting survival of patients referred for palliative radiotherapy using three readily available factors: primary cancer site, site of metastases and Karnofsky performance score (KPS). This predictive model was used in the current study, where each factor was assigned a value proportional to its prognostic weight and the sum of the weighted scores for each patient was survival prediction score (SPS). Patients were also classified according to their number of risk factors (NRF). Three risk groups were established. The Radiation Therapy and Oncology Group (RTOG) 9714 data was used to provide an additional external validation set comprised of patients treated among multiple institutions with appropriate statistical tests.ResultsThe RTOG external validation set comprised of 908 patients treated at 66 different radiation facilities from 1998 to 2002. The SPS method classified all patients into the low-risk group. Based on the NRF, two distinct risk groups with significantly different survival estimates were identified. The ability to predict survival was similar to that of the training and previous validation datasets for both the SPS and NRF methods.ConclusionsThe three variable NRF model is preferred because of its relative simplicity

The Complementary Nature of Patient-Reported Outcomes and Adverse Event Reporting in Cooperative Group Oncology Clinical Trials: A Pooled Analysis (NCCTG N0591)

CONTEXT: Clinical trials use clinician-graded adverse events (AEs) and patient-reported outcomes (PROs) to describe symptoms. OBJECTIVES: The aim of the study was to examine the agreement between PROs and AEs in the clinical trial setting. METHODS: Patient-level data were pooled from seven North Central Cancer Treatment Group, two Southwest Oncology Group, and three Radiation Therapy Oncology Group lung studies that included both PROs and AE data. Ten-point changes (on a 0-100 scale) in PRO scores were considered clinically significant differences (CSDs). PRO score changes were compared to AE grade (Gr) categories (2+ yes vs. no and 3+ yes vs. no) using Wilcoxon rank-sum or two-sample t-tests between Gr categories. Incidence rates and concordance of CSD in PRO scores and AE Gr categories were compiled. Spearman correlations were computed between PRO scores and AE severity. RESULTS: PROs completed by patients (n = 1013) were the Uniscale, Lung Cancer Symptom Scale (LCSS), Functional Assessment of Cancer Therapy-Lung (FACT-L), Symptom Distress Scale, and/or Functional Living Index-Cancer. Significantly worse PRO score changes were found for the FACT-L in patients with Gr 2+ AEs. Worse scores were seen for the Uniscale for patients with Gr 2+ AEs (P = 0.07) and LCSS for patients with Gr 3+ AEs (P = 0.09). Agreement between incidence of any Gr 2+ (Gr 3+) AE and a CSD in PROs ranged from 27% to 67% (36%-61%). Correlations between PRO scores and AE severity were low: -0.06 Uniscale, -0.03 LCSS, 0.10 FACT-L, -0.11 Symptom Distress Scale, and -0.51 Functional Living Index-Cancer. CONCLUSION: These results support previous work and an a priori hypothesis that AEs and PROs measure differing aspects of the disease experience and are complementary

A literature synthesis of symptom prevalence and severity in persons receiving active cancer treatment

PURPOSE: Patients with cancer experience acute and chronic symptoms caused by their underlying disease or by the treatment. While numerous studies have examined the impact of various treatments on symptoms experienced by cancer patients, there are inconsistencies regarding the symptoms measured and reported in treatment trials. This article presents a systematic review of the research literature of the prevalence and severity of symptoms in patients undergoing cancer treatment. METHODS: A systematic search for studies of persons receiving active cancer treatment was performed with the search terms of “multiple symptoms” and “cancer” for studies involving patients over the age of 18 years and published in English during the years 2001 to 2011. Search outputs were reviewed independently by seven authors, resulting in the synthesis of 21 studies meeting criteria for generation of an Evidence Table reporting symptom prevalence and severity ratings. RESULTS: Data were extracted from 21 multi-national studies to develop a pooled sample of 4067 cancer patients in whom the prevalence and severity of individual symptoms was reported. In total, the pooled sample across the 21 studies was comprised of 62% female, with a mean age of 58 years (range: 18 to 97 years). A majority (62%) of these studies assessed symptoms in homogeneous samples with respect to tumor site (predominantly breast and lung cancer), while 38% of the included studies utilized samples with mixed diagnoses and treatment regimens. Eighteen instruments and structured interviews were including those measuring single symptoms, multi-symptom inventories, and single symptom items drawn from HRQOL or health status measures. The MD Anderson Symptom Inventory (MDASI) was the most commonly used instrument in the studies analyzed (n=9 studies; 43%), while the Functional Assessment of Cancer Therapy (FACT-G), Hospital Anxiety and Depression Subscale (HADS-D), Medical Outcomes Survey Short Form-36 (SF-36), and Symptom Distress Scale (SDS) were each employed in two studies. Forty-seven symptoms were identified across the 21 studies which were then categorized into 17 logical groupings. Symptom prevalence and severity were calculated across the entire cohort and also based upon sample sizes in which the symptoms were measured providing the ability to rank symptoms. CONCLUSIONS: Symptoms are prevalent and severe among patients with cancer. Therefore, any clinical study seeking to evaluate the impact of treatment on patients should consider including measurement of symptoms. This study demonstrates that a discrete set of symptoms is common across cancer types. This set may serve as the basis for defining a “core” set of symptoms to be recommended for elicitation across cancer clinical trials, particularly among patients with advanced disease