Prognostic value of quality of life scores in terminal cancer patients

How accurate are physicians' clinical predictions of survival and the available prognostic tools in estimating survival times in terminally ill cancer patients? A systematic review

The purpose of this review was to examine the accuracy of physicians' clinical predictions of survival and the available prognostic tools in estimating survival times in terminally ill cancer patients. A MEDLINE search for English language articles published between 1966 and March 2000 was performed using the following keywords: forecasting/clinical prediction, prognosis/prognostic factors, survival and neoplasm metastasis. Searches in CancerLit, EMBASE, PubMed, the Cochrane Library and reference sections of articles were performed. Studies were included if they concerned adult patients with various cancer histological diagnoses and employed clinical prediction and the readily available clinical parameters. Biochemical and molecular markers were excluded. Grading of the evidence and recommendations was performed. Twelve articles on clinical prediction and 19 on prognostic factors met the inclusion criteria. Clinical prediction tends to be incorrect in the optimistic direction but improves with repeated measurements. Performance status has been found to be most strongly correlated with the duration of survival, followed by the 'terminal syndrome', which includes anorexia, weight loss and dysphagia. Cognitive failure and confusion have also been associated with a shorter life span. Performance status combined with clinical symptoms and the clinician's estimate helps to guide an accurate prediction, as reviewed in an Italian series. There is fair evidence to support using performance status, and clinical and biochemical parameters, in addition to clinicians' judgement to aid survival prediction. However, there is weak evidence to support that clinicians' estimates alone could be specifically employed for survival prediction.

A typology of fatigue in children with cancer

Fatigue in adults with cancer has received considerable attention as a troublesome symptom that requires nursing intervention. Fatigue in children with cancer, however, has received considerably less focus. The first phase of the present study used qualitative methods to generate a detailed description of fatigue in children with cancer. Thirteen children (ages 5 to 15) and 12 parents from the oncology service in two regional children's hospitals participated in the initial interviews; a validation sample comprised another 7 children and 6 parents from a third site. Transcribed interviews were subjected to grounded theory analysis. Energy, as an overriding phenomenon, was a core concept in the descriptions of fatigue. Findings suggest that children with cancer may experience three subjectively distinct types of fatigue that represent different levels of energy: typical tiredness, treatment fatigue, and shutdown fatigue. Children managed their dwindling energy and minimized further energy loss through strategies of replenishing, conserving, and preserving. Children's use of these strategies was influenced by temperament, lifestyle, environmental factors, and treatment modalities. Knowledge of the specific types of fatigue in children can offer direction for optimal intervention and for further research.

A simple stratification factor prognostic for survival in advanced cancer: the good/bad/uncertain index

PURPOSE

This article summarizes the third step of a research program to identify variables that supplement the predictive power of the the Eastern Cooperative Oncology Group (ECOG) performance status (PS) for survival. The objective was to produce a simple, practical, stratification factor for phase III oncology clinical trials involving patients with advanced malignant disease.

PATIENTS AND METHODS

A questionnaire was administered to 729 patients with metastatic colorectal or lung cancers. Patients provided a Karnofsky index and appetite rating while physicians provided a survival estimate and the ECOG-PS. Scores for each item were categorized as having a positive, neutral, or negative indication for survival. A patient was classified as having a relatively good prognosis if three or more of the four items showed a positive indication, a bad prognosis if three or more items were negative, and an uncertain prognosis otherwise (Good/Bad/Uncertain [GBU] index).

RESULTS

The GBU index improved on the prognostic power of a Cox model quartile index and PS alone and increased the accuracy of survival classification estimates by 5% to 10% more than ECOG-PS alone. For patients with PS of 0 or 1, significant survival patterns exist between GBU groups (P=.002 and.0001, respectively).

CONCLUSION

The GBU index may be recommended as a supplementary stratification factor for certain future phase III trials in metastatic lung or colorectal cancer where patient heterogeneity is a particular concern. The GBU represents a relatively modest increase to the cost and patient burden of a clinical trial given the additional control that is achieved over the potentially confounding concomitant to the treatment variable.

Quality of life assessment and outcome of palliative care

Quality of life (QoL) assessment is crucial for the evaluation of palliative care outcome. In this paper, our methodological approach was based on the creation of summary measures. Fifty-eight Palliative Care Units (PCUs) in Italy participated in the study. Each PCU randomly selected patients to be 'evaluated' among the consecutively 'registered' patients. At baseline (first visit) and each week the patient was asked to fill in a QoL questionnaire, the Therapy Impact Questionnaire (TIQ). Short-survivors (<7 days) were not included in the QoL study. The random sample of patients (n = 601) was highly representative of the general patient population cared for by the PCUs in Italy. The median survival was 37.9 days. We collected 3546 TIQ, 71.4 % completed by the patients. A Summary Measure Outcome score was calculated for 409 patients (81% of the patients included in the QoL study). The results of this national study showed that cooperative clinical research in palliative care is possible and QoL measures can be used to assess the outcome.

Quality-of-life scores predict outcome in metastatic but not early breast cancer. International Breast Cancer Study Group

PURPOSE

We compared the prognostic value of quality-of-life (QL) scores in the adjuvant setting and after relapse in two randomized trials of the International Breast Cancer Study Group.

PATIENTS AND METHODS

More than 2,000 premenopausal and postmenopausal patients with node-positive breast cancer who were participating in randomized trials that compared adjuvant therapies completed QL assessments for physical well-being, mood, appetite, and coping at study entry and at months 3 and 18 if they remained relapse-free and, in case of relapse, at 1 month and at 6 months after relapse. Cox regression models were used to test the relationship between QL scores and disease-free survival (DFS), in the adjuvant setting, or overall survival, in the case of postrelapse QL measurement. All models were stratified by language/country group and included other factors related to QL and/or outcome.

RESULTS

DFS was not significantly predicted by QL scores at baseline or month 18, or by changes in QL score between baseline and months 3 or 18. In contrast, after relapse, QL scores were predictive for subsequent overall survival. One month after relapse, better mood (P =.04) in premenopausal patients and better appetite (P =.005) in postmenopausal patients were associated with longer survival. Six months after relapse, better physical well-being (P =.03) and appetite (P =.03) in premenopausal patients and better physical well-being (P <.0001), mood (P =.002), appetite (P =.0001), and coping (P =.0001) in postmenopausal patients predicted longer survival.

CONCLUSION

Any prognostic significance of QL scores in the adjuvant setting is minimal or obscured by chemotherapy effects, but there is strong prognostic significance of QL scores after disease relapse. The contrast suggests that patient perception of the severity of underlying illness may determine reported QL scores.

Survival prediction in terminal cancer patients: a systematic review of the medical literature

The clinical significance of studies on survival predictors in terminal cancer patients is hindered by both methodological limitations and the difficulty of finding common predictors for all final events in cancer related deaths. To evaluate the published medical literature concerned with the survival of patients with terminal cancer and identify potential prognostic factors, major electronic databases including MEDLINE (1966-), CANCERLIT (1983-) and EMBASE (1988-) were searched up to September 1999. Studies were included in our review if published in English, were cohort studies, addressed the identification of clinical prognostic factors for survival and looked at samples with median survival of < or = 3 months. Data extracted from selected papers included: sample size, median survival, type of study, sampling frame, cohort type, type of statistical analysis (univariate or multivariate), choice of models and underlying assumptions, predictors examined and their reported level of statistical significance. A total of 24 studies were found and reviewed. On the basis of these studies, performance status and the presence of cognitive failure, weight loss, dysphagia, anorexia and dyspnoea appear to be independent survival predictors in this population. Clinical estimation of survival by the treating physician appeared independently associated with survival but the magnitude of the association generally appeared small. Clinical predictions should be considered as one of many criteria, rather than as a unique criterion by which to choose therapeutic interventions or health care programmes for terminally ill cancer patients. The use of convenient samples as opposed to more representative inception cohorts, the inclusion of different variables in the statistical analyses and inappropriate statistical methods appear to be major limitations of the reviewed literature. Methodological improvements in the design and conduction of future studies may reduce the prognostic uncertainty in this population.

Identification and interpretation of clinical and quality of life prognostic factors for survival and response to treatment in first-line chemotherapy in advanced breast cancer

The aim of the project was to identify clinical and quality of life (QL) factors that together predict survival and response to chemotherapy in advanced breast cancer. Potential prognostic factors were studied in 187 women with baseline QL data from a trial of paclitaxel versus doxorubicin as first-line chemotherapy. Demographic and clinical factors studied were age, performance status, dominant site of disease and preceding disease-free interval (DFI). Factors from the EORTC QLQ-C30 were all function scales, fatigue, nausea/vomiting, pain, dyspnoea, insomnia, loss of appetite and global QL. The proportional hazards regression model with stratification for treatment, and the logistic regression model adjusting for treatment arm were used for univariate and multivariate analyses of survival and response to treatment, respectively. For survival, multiple sites of visceral disease, pain, global QL and fatigue were significant prognostic factors in the univariate analysis. The final multivariate model predicted poor survival with multiple sites of visceral disease (P=0.003), DFI </=2 years (P=0.026) and pain (P=0.003). For response, age, dyspnoea, fatigue and global QL were significant predictive factors in the univariate analysis. The final multivariate model for response selected DFI (P=0.009), multiple sites of visceral disease (P=0.037) and dyspnoea (P=<0.001) using forward selection, but model instability was indicated by the inclusion of fatigue and emotional function in the final model when backward selection was used. In addition to known clinical factors, patient-assessed QL variables appear to be prognostic for survival and response to chemotherapy in women with advanced breast cancer. However, identification of prognostic factors from responses to questionnaires may be unstable, and their reliability and clinical utility should be tested prospectively.

Beyond Reliability and Validity: Analysis of Selected Quality-of-Life Instruments for Use in Palliative Care

The purpose of this study was to review quality-of-life instruments for their potential usefulness in the palliative care setting. Conceptualizations of quality of life throughout history, and contemporary conceptualizations of quality of life were briefly discussed. The specific conceptualizations of six quality-of-life measurement tools (the Medical Outcomes Study Short-Form Health Survey [SF-36], the European Organization for Research and Treatment of Cancer [EORTC] QLQ-C30, the Quality of Life Index [QLI], the Hospice Quality of Life Index [HQLI], the McGill Quality of Life Questionnaire [MQOL], and the Missoula-VITAS Quality of Life Index [MVQOLI]) were evaluated. The origins, target populations, acceptability of individual items, completion time, number of questions, type of response format, and type of scoring of each instrument were discussed, and evidence of the instruments' reliability, validity, and responsiveness were reviewed. The researcher or clinician should consider all of these factors when choosing the quality-of-life instrument that best fits the purpose.

A new palliative prognostic score: a first step for the staging of terminally ill cancer patients. Italian Multicenter and Study Group on Palliative Care

In recent years, extensive research has been performed to identify prognostic factors that predict survival in terminally ill cancer patients. This study describes the construction of a simple prognostic score based on factors identified in a prospective multicenter study of 519 patients with a median survival of 32 days. An exponential multiple regression model was adopted to evaluate the joint effect of some clinico-biological variables on survival. From an initial model containing 36 variables, a final parsimonious model was obtained by means of a backward selection procedure. The Palliative Prognostic Score (PaP Score) is based on the final model and includes the following variables: Clinical Prediction of Survival (CPS), Karnofsky Performance Status (KPS), anorexia, dyspnea, total white blood count (WBC) and lymphocyte percentage. A numerical score was given to each variable, based on the relative weight of the independent prognostic significance shown by each single category in the multivariate analysis. The sum of the single scores gives the overall PaP Score for each patient and was used to subdivide the study population into three groups, each with a different probability of survival at 30 days: (1) group A: probability of survival at 30 days > 70%, with patient score < or = 5.5; (2) group B: probability of survival at 30 days 30-70%, with patient score 5.6-11.0; and (3) group C: probability of survival at 30 days < 30%, with patient score > 11.0. Using this method, 178/519 (34.3%) patients were classified in risk group A, 205 (39.5%) patients were in risk group B, and 136 (26.2%) patients were in risk group C. The patients classified in the three risk groups had a very different survival experience (logrank = 294.8, P < 0.001), with a median survival of 64 days for group A, 32 days for group B, and 11 days for group C. The PaP Score based on simple clinical and biohumoral variables proved to be statistically significant in a multivariate analysis. The score is valid in this population (training set). An independent validation on another patient series (testing set) is required and is the object of a companion paper.

Quality-of-life assessment in health care research and practice

This article reviews the different ways in which quality-of-life assessment has been applied to and has affected health care research and practice. A schema that describes the steps involved in the ongoing challenge of improving health outcomes is used to structure the review. The role of quality-of-life assessment is addressed with regard to: the identification of health problems, the evaluation of new treatments, the formulation of treatment guidelines and health policies, the delivery of optimal care in practice, and the assessment of outcomes in the wider community. The benefit of quality-of-life assessment has been demonstrated in a number of these areas (e.g., in identifying problems and evaluating treatments). Its role in other applications (e.g., in clinical practice to assess patients' needs) shows great promise and requires additional evaluation.

Symptom distress in adult patients with cancer

Symptom distress in the adult population with cancer is of concern to clinicians who care for these patients. Increased research has been directed toward the development and refinement of symptom distress scales, the identification of determinants of symptom distress, the investigation of symptom distress as a predictor, and the examination of the relationship between quality of life and symptom distress. Findings from this research have increased our understanding of symptom distress in adult patients with cancer. However, a major limitation of work to date has been a lack of consensus related to the definition and measurement of the symptom distress construct. The purpose of this article is to address existing conceptual and methodological challenges inherent in the study of symptom distress, and to make recommendations for further research in this area.