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Original Investigation |

Clinical Survival Predictors in Patients With Advanced Cancer FREE

Antonio Viganó, MD, MSc; Eduardo Bruera, MD; Gian S. Jhangri, MSc; Stephen C. Newman, MD, MSc; Anthony L. Fields, MD; Maria E. Suarez-Almazor, MD, PhD
[+] Author Affiliations

From the Division of Palliative Care Medicine (Drs Viganó and Bruera), the Department of Public Health Sciences (Mr Jhangri and Drs Newman and Suarez-Almazor), and the Department of Oncology (Dr Fields), University of Alberta, Edmonton. Dr Bruera is now with the Department of Symptom Control and Palliative Care, University of Texas, Houston. Dr Suarez-Almazor is now with the Department of Medicine Health Services Research, Baylor College of Medicine, Veterans Affairs Medical Center, Houston.


Arch Intern Med. 2000;160(6):861-868. doi:10.1001/archinte.160.6.861.
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Published online

Background  The clinical and epidemiological relevance of different prognostic factors for survival in patients with advanced or terminal cancer remains controversial.

Purposes  To establish the survival of patients with cancer after diagnosis of terminal disease and to determine the predictors of survival.

Methods  An inception cohort of 227 consecutive patients aged 18 years or older with terminal cancer of the lung, breast, and gastrointestinal tract were observed from July 1, 1996, through December 31, 1998. Tumor- and treatment-specific, clinical, laboratory, demographic, and socioeconomic variables were recorded at baseline. The relationships between these characteristics and survival time were examined using univariate Kaplan-Meier and multivariate Cox regression analyses.

Results  At the time of data analysis, 208 patients (91.6%) had died; the overall median survival for the sample was 15.3 weeks. Shorter survival was independently associated (P≤.05) with a primary tumor of the lung (vs breast and gastrointestinal tract combined), liver metastases, moderate-to-severe comorbidity levels (vs absent-to-mild levels), weight loss of greater than 8.1 kg in the previous 6 months, serum albumin levels of less than 35 g/L, lymphocyte counts of less than 1 × 109/L, serum lactate dehydrogenase levels of greater than 618 U/L, and clinical estimation of survival by the treating physician of less than 2 months (vs 2-6 and >6 months). Performance status, symptoms other than nausea and vomiting, tumor burden, and socioeconomic characteristics such as social support and education and income levels did not appear to be independently associated with survival after adjusting for the effect of prognostic factors.

Conclusions  Simple clinical and laboratory assessments are useful aids in the prediction of survival in patients with solid malignant neoplasms at the onset of terminal stages. Methodological improvements in the design and implementation of survival studies may reduce prognostic uncertainty and ultimately provide better care for the terminally ill patients and their families.

AT PRESENT, cancer will be diagnosed in about one third of the population in developed countries during their lifetime.1 In approximately 50% of patients with a diagnosis of cancer, a stage is reached when active treatment will not prolong life.2 Most authors have defined the period extending from this time to the patient's death as the terminal cancer phase.37 The terminal cancer phase may last from days to months, but there are no validated criteria to enable adequate predictions of its length.814 This prognostic uncertainty makes clinical decisions difficult for caregivers, patients, and families15,16 and may lead to inappropriate resource expenditure or denial of potentially beneficial therapy for the terminally ill.17,18 In the United States19 and Canada,20 admission criteria for government-funded hospices or certain regional palliative care programs20 require physicians to identify those patients with life expectancies of 6 months or less. In the United States, a 1993 report from the National Hospice Organization showed that more than 50% of patients with terminal cancer were not given access to hospice services21 or were referred too late in the course of their illness to take full advantage of the support provided by hospice programs.22 Overly optimistic survival predictions made by different health care providers have affected patient referrals to US hospice programs adversely.19 On the other hand, premature referral to hospices or palliative care programs may create organizational, financial, clinical, and emotional problems for administrators, health care providers, and patients.23 Several studies have been conducted to elucidate the role of prognostic factors on survival of patients with advanced or terminal cancer, including simple, noninvasive, and clinically based assessments. In studies focusing on prognostic factors of survival, length of survival has been associated with the following factors: clinical estimate of survival by the treating physicians,2429 performance status,3044 some physical symptoms,4,17,18,35,37,38,40,41,4550 some biological markers (eg, albumin and lactate dehydrogenase [LDH] levels and white blood cell counts),32,36,39,42,5160 some psychological6171 and socioeconomic variables,7274 and tumor type and stage.18,43,49,70

Methodological limitations in earlier research diminished the predictive value of putative prognostic factors, ie, difficulties in sampling of populations with terminal cancer, failure to use inception cohorts,75 use of nonstandardized measures,76 variation in the predictors across studies,77 failure to use time-adjusted analyses,75 and estimation of survival at particular times instead of considering the entire survival curve.77 Added to these problems is the inherent difficulty in predicting survival in patients with terminal cancer because of the many causes of death in those patients.78,79

Our study was designed to overcome these limitations and to identify survival predictors in terminally ill patients with common solid malignant neoplasms. To our knowledge, no previous attempts have been made to evaluate the independent value of prognostic factors for survival in a population-based, prospectively accrued inception cohort of patients with terminal cancer.

Patients were recruited at the Cross Cancer Institute (CCI), Edmonton, Alberta, from July 1, 1996, through December 31, 1998. The CCI represents the only referral center for oncological treatment in northern Alberta and has a catchment population of approximately 1.5 million people. Patients were eligible if aged 18 years or older with a diagnosis of terminal cancer of the lung, breast, or gastrointestinal tract. These tumors were chosen because they rank among the top 4 types for incidence and death rates in developed countries.1

According to information derived from the Physician Data Query statements for health professionals80 and a consensus of oncologists at the CCI, specific criteria were elaborated to define when patients with solid malignant neoplasms were considered to be in a terminal phase. These criteria, which relied on histological findings, disease stage, and treatments received, were used to identify patients to whom no further life-prolonging treatments could be offered. Breast cancer was considered terminal if disease was progressive after the failure of second-line chemotherapy and/or hormonotherapy given for metastatic or recurrent disease. An alternative criterion was a recent diagnosis of brain metastases. Patients with gastrointestinal tract cancers were considered to have entered the terminal phase if they presented with inoperable primary tumors, recurrences, and/or unresectable metastatic lesions. Patients with inoperable non–small cell lung cancer or recurrent small cell lung cancer were considered to be in a terminal stage regardless of oncological treatment. These criteria could be overridden if, according to the clinical judgment of the treating oncologists, patients had particularly aggressive diseases, patients were considered unsuitable for any specific treatment at first diagnosis of cancer, or there were coexisting medical conditions that precluded any therapeutic attempts to prolong life.

It was not possible to identify all potential subjects for the study at precisely the time that they entered the terminal phase. Enrollment in the study was considered for patients who, according to our criteria, entered the terminal phase no more than 30 days before the time that baseline assessments could be conducted. Eligible patients were identified and underwent screening by the principal investigator (A.V.) through a daily review of medical records of patients who were scheduled for certain outpatient clinics or were admitted at the CCI. Patient accrual was consecutive within each tumor group. The study was approved by the Ethics and Scientific Committee of the Alberta Cancer Board, and all patients provided written, informed consent before participation.

Of the 249 patients who were asked to enroll in the study, 227 (91.2%) agreed to participate. Survival was recorded from the date when patients were accrued into the study. All patients were followed up until December 31, 1998, or death, thus providing a minimum follow-up period of approximately 20 months.

Patients underwent an initial, in-person assessment and monthly follow-ups throughout the course of their disease until death occurred. The following data were recorded at baseline:

  • Demographic data, including age, sex, race, individual and family income, and education level. The level of social support was measured using the Older Americans' Resources and Services Multidimensional Functional Assessment Questionnaire.81,82 Social support was measured in extent of contact with others, family satisfaction with contact, and availability of help.

  • Primary and secondary tumor sites.

  • Last and concurrent treatments (none, surgery, chemotherapy, radiotherapy, or hormonotherapy).

  • Tumor burden expressed as the total number of cancerous lesions.83

  • Performance status according to the Karnofsky Performance Status (KPS),84 the Eastern Co-operative Oncology Group (ECOG),85 and the Edmonton Functional Assessment Tool.86 The Edmonton Functional Assessment Tool assesses communication, pain, mental status, dyspnea, sitting or standing balance, mobility, walking or wheelchair locomotion, activities of daily living, fatigue, motivation, and judgment of functional performance.

  • Physical indicators of nutritional status, including weight loss in the previous 6 months and triceps skinfold thickness as measured using a caliper (Baseline Skinfold Caliper; Fabrication Enterprise Incorporated, New York, NY).

  • Type and intensity of symptoms experienced at the time of patient enrollment as measured by the Edmonton Symptom Assessment Scale (ESAS).87 The ESAS consists of 9 visual analog scales for measuring pain, shortness of breath, nausea, depression, activity, anxiety, well-being, drowsiness, and appetite. For each patient, the overall mean intensity of all the symptoms recorded using the ESAS was calculated to determine a distress score.

  • Concurrent diseases, as measured using the Charlson comorbidity score.88 This score ranges from 0 to a maximum of 33 and is based on the presence of certain diseases with assigned values or weights. We developed an adjusted Charlson score, which excluded the diagnosis of cancer, since our intention was to measure conditions other than the patient's principal diagnosis.

  • Cognitive status, as measured using the Mini-Mental State Examination.89 The Mini-Mental State Examination measures orientation to time and place, immediate recall, short-term memory, calculation, language, and construct ability. The maximum score is 30, with a score of 23 or less generally accepted as indicating the presence of cognitive impairment.

  • Serum and hematologic variables, including levels of albumin, sodium, calcium, alkaline phosphatase, LDH, and hemoglobin, and blood cell and differential counts.

  • Clinical estimation of survival (CES) by the treating oncologist (number of months, weeks, or days).

These variables were selected because they have been found to be of prognostic significance in patients with terminal cancer90,91 and were believed to be measurable and reproducible even in seriously ill patients.

To account for the heterogeneity of cancer treatments in the 3 primary sites, the following 3-category classification proposed by McCusker was adopted6: patients who entered the terminal phase without ever receiving any tumor-directed therapy (eg, owing to poor medical conditions or too advanced stages of diseases), patients for whom cancer treatments were discontinued (eg, owing to disease progression or recurrence), and patients for whom cancer therapies were started or continued for symptom palliation (Table 1).

Table Graphic Jump LocationTable 1. Characteristics of the Sample and Summary of Univariate Survival Analyses*

The literature does not offer specific indications for categorization of the intensity of symptoms, and patients similar to our population may present with comorbidities and mild symptoms unrelated to their cancer. Therefore, for comorbidity and symptom levels, cutoff points of absent to mild and moderate to severe were used.

The CES was divided into the following 3 categories: less than 2 months, from 2 to 6 months, and longer than 6 months. These prognostic intervals are generally used to determine the eligibility of patients for government-funded hospices or some regional palliative care programs in Canada and the United States.19,20 Functioning levels as measured by the ECOG and KPS scales were recoded in 3 comparable categories, according to the simple conversion table recently proposed by Buccheri and colleagues.44

Variables were examined in the continuous and categorical form. The cutoff points for the latter were chosen according to reference intervals for all laboratory variables, description in other studies, distribution of cases, clinical meaningfulness, and biological plausibility. Other cutoff points were based on mean values (eg, personal and family incomes), median values (eg, distress score, symptom number, and weight loss), and median for the healthy population (eg, triceps skinfold measurements).

STATISTICAL ANALYSIS

Kaplan-Meier survival curves were constructed for each categorical variable.92 The statistical significance of differences among survival curves was determined using 2-tailed log-rank test.93 The Cox regression method94 was also used to examine variables as single main-effect associations with survival for all variables. A stepwise forward regression procedure based on the partial likelihood ratio was applied to select factors of prognostic importance in a multivariate Cox regression model. P≤.06 and P>.10 were set, respectively, as limits for variable inclusion and exclusion.

The proportionality of hazards associated with all independent predictors of survival was checked by visual inspection of the log-minus-log survival plots. For levels of performance status and serum albumin, the difference between the hazards was found to steadily decrease over time. For these variables, Cox regression with time-dependent covariates was used.95 Interaction terms that were biologically meaningful were also investigated. Regression diagnostics included detection of outliers from Martingale residuals96 and identification of influential observations from plots of DfBeta.97

SAMPLE SIZE

Power estimates were performed a priori, using the method of Schoenfeld98 and the EGRET Size software program.99 In both methods, albumin serum levels were considered as the main exposure. This variable was dichotomized as high-normal (ie, ≥35 g/L) and low (<35 g/L). According to previous reports, a sampling fraction of 46% of patients51 and conservative hazards ratios for the risk for dying ranging from 2 to 356 were assigned to the low serum albumin level group. Both methods indicated a sample of approximately 80 patients would have a power of at least 80% to detect a hazards ratio of 2.0 at the 5% significance level. The SPSS 6.0 statistical software package100 was used for all other statistical analyses.

At the closing date of the study (December 31, 1998), 227 patients were accrued, of whom 208 patients (91.6%) had died, and no patient was unavailable for follow-up. Mean age for the sample was 62 years (range, 29-92 years). The median and mean survival times of the overall group were 15.3 and 25.0 weeks, respectively. The Kaplan-Meier estimates of the 2-, 4-, and 6-month survival rates were 69.0%, 48.8%, and 34.3%, respectively.

As can be seen from Table 1, most of the patients were white (91.6%), presented with high tumor burden (67.0%) with a prevalence of visceral metastasis (85.9%), and received cancer treatments in the terminal phase (64.8%). They also presented with triceps skinfolds in the lower range (67.0%) and experienced moderate-to-severe fatigue (68.7%), anorexia (62.1%) and impairment of well-being (68.3%).

Since the results of survival analyses using continuous variables were substantially the same as when the variables were categorized, and since the latter are more easily described and clinically interpreted, findings are presented in terms of categorical variables (Table 2). Variables more discriminant for worse survival in the univariate analysis (P<.01) were lung cancer; liver metastasis; more than 5 cancerous lesions; moderate-to-severe comorbidity; cognitive impairment; weight loss above the 50th percentile of the sample; triceps skinfold measurements less than the 50th percentile for a standard population of North American men and women of the same mean age as our sample101; lower performance status; above-average number of symptoms; serum levels of sodium, albumin, LDH, and alkaline phosphatase beyond reference ranges; and granulocyte and lymphocyte absolute counts beyond reference ranges. Patients with CES of 2 to 6 months and longer than 6 months had significantly better survivals than patients with poorer prognostications.

Table Graphic Jump LocationTable 2. Final Cox Regression Models Based on Clinical Variables (Model 1) and Clinical and Laboratory Variables (Model 2)*

Characteristics that had shown some degree of correlation with survival in our data set and/or were previously found to be important prognostic factors were screened using multivariate analysis. These included age; sex; marital status; education level; personal yearly income; tumor type; brain and liver metastases; tumor burden; comorbidity level; antineoplastic treatments (never received or discontinued before study accrual vs continued or initiated after study accrual); asthenia; depression; anorexia; nausea; anxiety; dyspnea; pain; well-being; weight loss; cognitive status; CES; serum levels of sodium, albumin, and LDH; and granulocyte and lymphocyte absolute counts.

There were virtually no missing data for domains that considered patient, disease, and symptom characteristics. In 165 patients, blood work was requested and results were obtained for study purposes only. In the remaining patients who refused or were too unwell physically or psychologically to undergo blood work at the time of assessment, we used data from any blood work performed within 2 weeks from the study accrual. Nevertheless, 24.7% of the patients could not be included in the multivariate analyses because of missing data in the laboratory assessments. To reflect both clinical scenarios, we fitted models that considered patient, disease, and symptom characteristics but not laboratory data, and models with laboratory data were included for patients with complete data (n = 171). The final Cox regression models for these analyses will be referred to as models 1 and 2, respectively. In model 1, the most significant hazard ratios were associated with CES, disease-related characteristics, and performance status (Table 2). Patients who were predicted to live from 2 to 6 months or longer than 6 months were 2.0 and 3.3 times, respectively, less likely to die within 24 months than patients who were predicted to die within 2 months. Colinearity was found for variables that corresponded to the KPS and ECOG scales. We chose the ECOG scale because it showed a stronger association with survival than the KPS scale and appeared to differentiate ambulatory (ECOG status, 0-1) from bed-ridden patients (ECOG status, 3-4) better in terms of survival.

Performance status along with tumor burden appear to lose prognostic significance after adjustment for laboratory values in model 2. Besides the time-by-performance status and time-by-serum albumin interactions, other interactions were lung cancer by weight loss (models 1 and 2), serum albumin level by weight loss (model 2), and serum albumin level by lymphocyte counts (model 2). As can be seen from Table 3 and Table 4, when there is interaction between a predictor and another variable, an estimate of the hazard ratio for the predictor depends on the value of the variable that is interacting with it.

Table Graphic Jump LocationTable 3. Hazard Ratios for Interacting Covariates in Model 1*
Table Graphic Jump LocationTable 4. Hazard Ratios for Interacting Covariates in Model 2*

The associations of performance status and serum albumin level with survival significantly decreased over time. The hazard ratio for low performance status decreased at an average rate of 2% per week, whereas the same estimates for low albumin level had an average drop of 4% per week (Table 2). The negative effect on survival of having a lung primary tumor is clinically and statistically different according to the amount of weight loss reported for these patients and clearly increases in patients who experienced greater weight loss (Table 3). However, high weight loss and low lymphocyte counts are in themselves important poor survival indicators only in patients with serum albumin levels of at least 35 g/L. In patients with lower serum albumin levels, the hazard ratios for low lymphocyte counts and high weight loss become almost insignificant.

Examination of the outliers did not show particular trends (ie, outlier observation was not typical of patients with specific lengths of survival). Some data points were found to be more influential with respect to some of the estimated coefficients. The removal of these observations from the database did not modify substantially these coefficients, and because they were correctly recorded, they were included in the final models.

A major difficulty in this type of study arises from the lack of clinical criteria to define the onset of the terminal phase in these patients.6 We established simple criteria to define the onset of the terminal stage in patients with breast, lung, and gastrointestinal tract cancers. These criteria present certain limitations. They rely on specific therapeutic schemes (eg, those undergoing treatment of advanced breast cancer may not contemplate chemotherapy sequential trials); they may change according to the state of the art in the management of neoplastic diseases; and they are influenced by the time patients seek cancer care (eg, disease progression may be discovered earlier through a 3-month instead of 6-month follow-up). However, these criteria provide benchmarks by which to enroll patients at common points in the course of their terminal disease that would otherwise be difficult to define. Furthermore, the palliative nature of the tumor-directed treatments administered after the study accrual was confirmed by the nonsignificant differences in survival between patients in whom these therapies were discontinued, continued, or initiated in the terminal phase. Our sample seems to comply with most theoretical definitions of patients with terminal cancer.37

The median survival in our sample was 15.3 weeks, which is longer than that observed in studies of patients with end-stage disease,90 but shorter than that reported for patients with advanced cancer.91 However, in contrast to most studies dealing with survival in patients with advanced or terminal cancer, our study was population based and not hospice based, and our patients were not accrued in clinical trials. All patients were examined while seeking regular cancer care in the referral center for oncological treatment in northern Alberta.

PERFORMANCE STATUS was no longer a significant predictor of survival in the presence of laboratory variables such as serum albumin level. This is in agreement with the work of Cohen et al.51 Performance status is well recognized as an important prognostic factor for survival in patients with end-stage and advanced cancer.90,91 However, several studies, including ours, have shown that the strength of the association between performance status and survival may vary with length of follow-up.18,43 In addition, performance status is a subjective rating that may be markedly influenced by acute but self-limited events. An ECOG performance status of 0 or 1 in an ambulatory and relatively asymptomatic patient may temporarily drop to an ECOG performance status of 3 or 4 resulting from the occurrence of acute infectious illnesses or a bone pathologic fracture.

Also, the influence of tumor burden on survival was superseded by the influence of laboratory variables such as LDH level. This has been correlated with the disease extent of different malignant neoplasms102,103 and may represent a more accurate measure of the tumor burden than the clinical assessment of the number of tumor lesions.

The independent prognostic values of weight loss, low lymphocyte counts, and low serum albumin levels confirm the detrimental role of malnutrition in survival of patients with terminal cancer.104 The hazard ratios found for low lymphocyte counts and weight loss among patients with low serum albumin levels show that the association between malnutrition and survival is probably better measured by serum albumin level than by lymphocyte counts or the amount of weight loss. However, the correlation between low serum albumin levels and survival seems to decrease in magnitude over time, whereas the association of low lymphocyte counts and weight loss with survival, although smaller in magnitude, appear to be constant over time. These findings suggest that survival in patients with shorter prognoses (<2 months) is associated with the decrease in serum albumin level. For terminally ill patients with cancer who survive longer than 2 months, the prognosis appears to be more correlated with other consequences of malnutrition such as the impairment in the immune system and the decrease in body weight.105

Several studies have advocated the inclusion of CES in multivariate models for the survival prediction of patients with advanced and terminal cancer.14,106 In our study, CES remained independently and strongly associated with survival.

The independent prognostic role of tumor-related characteristics (presence of malignant neoplasms of the lung and liver metastases) contradict the theory of the terminal cancer syndrome. Although patients appear to present with similar symptomatic features in the terminal phase,35,45 their individual survival is highly variable and appears to be correlated with disease-specific features. The association between lung cancer and worse prognosis is explained partly by the positive interaction between primary tumors of the lung and weight loss found in our study.

Nausea was the only symptom that remained independently correlated with survival in our final model. In contrast to previous studies,14,34,37 the prognostic importance of anorexia and dyspnea was not significant. Although the pathogenesis of nausea remains multifactorial in patients with terminal cancer,107 this symptom frequently reflects dysfunctions in the autonomic nervous system of this population.108 Our data may confirm an early and independent prognostic role of autonomic dysfunctions in the terminal cancer phase that has been suggested in patients with advanced37 or end-stage cancer.41

An independent prognostic role for the presence of moderate to severe comorbidity in patients with terminal cancer is suggested by our data. To our knowledge, this is the first study that shows such a finding. Two previous studies did not find any significant association between comorbidity and survival in patients with advanced gastrointestinal tract cancer.109,110 Further studies are needed to better determine the prognostic value of comorbidity in these patients.

Our study had some limitations. The sample sizes used in the multiple regression models were affected by missing data in the laboratory assessments. However, sample sizes were adequate in most cases to guarantee enough power for the estimated hazard ratios according to sample size calculations that we performed a priori. Furthermore, the magnitude of the confidence intervals calculated for our estimates were found to be relatively small. These results would need to be validated in an independent data set gathered on similar patients. It was believed that the relatively small sample sizes obtained for our models would not allow meaningful split-sample or cross-validation techniques.111

Prognostic uncertainty in terminal cancer will always be a reality for health care providers, patients, and families. Our results, however, indicate that primary lung cancer, presence of liver metastases, amount of weight loss, levels of LDH and serum albumin, and lymphocyte count are important factors to reduce this uncertainty.

Other prognostic factors of secondary importance appear to be nausea intensity and the level of comorbidity experienced at the onset of the terminal phase. No other symptoms (eg, dyspnea or anorexia) or socioeconomic characteristics, such as social support or education and income levels, appeared as independent survival predictors when adjusted for the above prognostic factors. The major role of malnutrition in the survival of these patients is suggested by the prognostic predominance of serum albumin level, lymphocyte counts, and weight loss found in our study.

Our data indicate that simple and objective clinical assessments may be useful aids to determine patient survival at the onset of their terminal stages. Certain routine laboratory measurements appear to be complementary to other clinical information, but a limited availability of the former should be taken into account in palliative care settings.

Accepted for publication August 12, 1999.

This study was supported in part by a Clinical Research Fellowship from the Alberta Heritage Foundation for Medical Research, Edmonton (Dr Viganó).

We are particularly grateful to Nora Donaldson, PhD, for having reviewed this manuscript. The indispensable support for clinical advice and patient accrual of John Mackey, MD; Jean-Marc Nabholtz, MD; Peter Venner, MD; Raul Urtasun, MD; and Sharon Watanabe, MD, at the Cross Cancer Institute, Edmonton, is also acknowledged.

Corresponding author: Antonio Viganó, MD, MSc, Division of Palliative Care Medicine, Room 4324, Grey Nuns Community Hospital, 1100 Youville Dr W, Edmonton, Alberta, Canada T6L 5X8 (e-mail: avigano@cha.ab.ca).

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Schonwetter  RSRobinson  BERamirez  G Prognostic factors for survival in terminal lung cancer patients. J Gen Intern Med. 1994;9366- 371
Link to Article
Pesmans  MSculier  JPLibert  P  et al.  Prognostic factors for survival in advanced non–small-cell lung cancer: univariate and multivariate analysis including recursive partitioning and amalgamation algorithms in 1052 patients. J Clin Oncol. 1995;131221- 1230
Allard  PDionne  APotvin  D Factors associated with length of survival among 1081 terminally ill cancer patients. J Palliat Care. 1995;1120- 24
Buccheri  GFerrigno  DTamburini  M Karnofsky and ECOG performance status scoring in lung cancer: a prospective, longitudinal study of 536 patients from a single institution. Eur J Cancer. 1996;32A1135- 1141
Link to Article
Wachtel  TMasterson  SAReuben  DGoldberg  RMor  V The end stage cancer patient: terminal common pathway. Hospice J. 1988;443- 80
Kaasa  SMastekaasa  ALund  E Prognostic factors for patients with inoperable non–small cell lung cancer, limited disease: the importance of patients' subjective experience of disease and psychosocial well-being. Radiother Oncol. 1989;15235- 242
Link to Article
Ventafridda  VRipamonti  CTamburini  MCassileth  RBDe Conno  F Unendurable symptom as prognostic indicators of impending death in terminal cancer patients. Eur J Cancer. 1990;261000- 1001
Link to Article
Krech  RLWalsh  D Symptoms of pancreatic cancer. J Pain Symptom Manage. 1991;6360- 367
Link to Article
Hardy  RJTurner  RSauders  MA'Hern  R Prediction of survival in a hospital based continuing care unit. Eur J Cancer. 1994;30A284- 288
Link to Article
Degner  LFSloan  JA Symptom distress in newly diagnosed ambulatory cancer patients as a predictor of survival in lung cancer. J Pain Symptom Manage. 1995;10423- 431
Link to Article
Cohen  MHMakuch  RJohnston-Early  A  et al.  Laboratory parameters as an alternative to performance status in prognostic stratification of patients with small cell lung cancer. Cancer Treat Rep. 1981;65187- 195
Shoenfeld  YTal  ABerliner  SPinkhas  J Leukocytosis in non haematological malignancies: a possible tumor-associated marker. J Cancer Res Clin Oncol. 1986;11154- 58
Link to Article
Ventafridda  VDe Conno  FSaita  LRipamonti  CBaronzio  GF Leucocyte-lymphocytes ratio as prognostic indicator of survival in cachectic cancer patients [letter]. Ann Oncol. 1991;2196
Maltoni  MPirovano  MNanni  O  et al.  Biological indices predictive of survival in 519 terminally ill cancer patients. J Pain Symptom Manage. 1997;131- 9
Link to Article
Fulop  THerrmann  FRapin  CH Prognostic role of serum albumin and prealbumin in elderly patients at admission to a geriatric hospital. Arch Gerontol Geriatr. 1991;1231- 39
Link to Article
Herrmann  FRSafran  CLevkoff  SFKenneth  I Serum albumin level on admission as a predictor of death, length of stay, and readmission. Arch Intern Med. 1992;152125- 130
Link to Article
Constans  TBruyere  AGrab  BRapin  CH PINI as a mortality index in hospitalized elderly patients [Research Note]. Int J Vitam Nutr Res. 1992;62191
Salamagne  MEVinant-Binam  P Valeur pronostique de parametres biologiques de denutrition chez des patients hospitalises en unité de soins palliatifs. InfoKara. 1996;4421- 32
Ralston  SHGallacher  SJPatel  UCampbell  JBoyle  IT Cancer-associated hypercalcemia: morbidity and mortality. Ann Intern Med. 1990;112499- 504
Link to Article
Schwartz  MK Enzymes as prognostic markers and therapeutic indicators in patients with cancer. Clin Chim Acta. 1992;20677- 82
Link to Article
Cassileth  BRLusk  EJMiller  DSBrowne  LLMiller  C Psychosocial correlates of survival in advanced malignant disease? N Engl J Med. 1985;3121551- 1555
Link to Article
Cassileth  BRWalsh  WLusk  EJ Psychosocial correlates of cancer survival: a subsequent report 3 to 8 years after cancer diagnosis. J Clin Oncol. 1988;61753- 1759
Coates  A Prognostic implications of quality of life. Cancer Treat Rev. 1993;19(suppl A)53- 57
Link to Article
Morris  JNSuissa  SSherwood  SWright  SMGreer  D Last days: a study of the quality of life of terminally ill cancer patients. J Chronic Dis. 1986;3947- 62
Link to Article
Morris  JNSherwood  S Quality of life of cancer patients at different stages in the disease trajectory. J Chronic Dis. 1987;40545- 553
Link to Article
Coates  AGebsky  VBishop  JF  et al.  Improving the quality of life during chemotherapy for advanced breast cancer: a comparison of intermittent and continuous treatment. N Engl J Med. 1987;3171490- 1495
Link to Article
Coates  AGebsky  VSignorini  D  et al.  Prognostic value of quality of life score during chemotherapy for advanced breast cancer. J Clin Oncol. 1992;101833- 1838
Coates  AThompson  DMcLeod  GRM  et al.  Prognostic value of quality of life scores in a trial of chemotherapy with or without interferon in patients with metastatic malignant melanoma. Eur J Cancer. 1993;29A1731- 1734
Link to Article
Earlam  SGlover  CFordy  CBurke  DAllen-Mersh  TG Relation between tumor size, quality of life, and survival in patients with colorectal liver metastases. J Clin Oncol. 1996;14171- 175
Tamburini  MBrunelli  CRosso  SVentafridda  V Prognostic value of quality of life scores in terminal cancer patients. J Pain Symptom Manage. 1996;1132- 41
Link to Article
Ganz  PLee  JSiau  J Quality of life assessment. Cancer. 1991;673131- 3135
Link to Article
Monnet  EBoutton  MCFaivre  JMilan  C Influence of socioeconomic status on prognosis of colorectal cancer: a population-based study in Cote d'Or, France. Cancer. 1993;721165- 1170
Link to Article
Cella  DOrav  JKornblith  AB  et al.  Socioeconomic status and cancer survival. J Clin Oncol. 1991;91500- 1509
Goodwin  JSSamet  JMHunt  WC Determinants of survival in older cancer patients. J Natl Cancer Inst. 1996;881031- 1038
Link to Article
Christakis  NA Timing of referral of terminally ill patients to an outpatient hospice. J Gen Intern Med. 1994;9314- 320
Link to Article
Jamison  RNBurish  TGWallston  KA Psychogenic factors in predicting survival of breast cancer patients. J Clin Oncol. 1987;5768- 772
Buccheri  GFerrigno  D Prognostic factors in lung cancer: tables and comments. Eur Respir J. 1994;71350- 1364
Link to Article
Klastersky  JDaneau  DVerhest  A Causes of death in patients with cancer. Eur J Cancer. 1972;8149- 154
Link to Article
Inagaki  JRodriguez  VBodey  G Causes of death in cancer patients. Cancer. 1973;2568- 573
Not Available, CANCERNET-PDQ [database online].  Bethesda, Md National Cancer Institute1996;Updated July 1996.
Fillenbaum  GGSmyer  MA The development, validity, and reliability of the OARS Multidimensional Functional Assessment Questionnaire. J Gerontol. 1981;36428- 434
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Kaasa  TLoomis  JGillis  KBruera  EHanson  J The Edmonton Functional Assessment Tool: preliminary development and evaluation for use in palliative care. J Pain Symptom Manage. 1997;1310- 19
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Bruera  EKuehn  NMiller  MJSelmser  PMacmillan  K The Edmonton Symptom Assessment System (ESAS): a simple method for the assessment of palliative care patients. J Palliat Care. 1991;76- 9
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Viganó  A Epidemiological, statistical and clinical aspects of research on prognostic factors in advanced/terminal cancer patients: reason for controversies.  Paper presented at: Seventh Canadian National Palliative Care Conference September 29, 1997 Saskatoon, Saskatchewan.
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Statistics and Epidemiological Research Corporation, EGRET.  Seattle, Wash SERC1989;
SPSS Inc, SPSS for Windows: Base System User's Guide, Release 6.0.  Chicago, Ill SPSS Inc1993;350
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Arsenau  JCCarnellos  GPBanks  PM American Burkitt's lymphoma: clinicopathologic study of 30 cases. Am J Med. 1975;58314- 321
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Breton  HDSimon  RPomeroy  TC Pretreatment lactate dehydrogenase predicting metastatic spread in Ewing's sarcoma. Ann Intern Med. 1975;83352- 354
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Fatzinger  PDeMeester  TRDarakjian  H  et al.  The use of serum albumin for further classification of Stage III non–oat cell lung cancer and its therapeutic implications. Ann Thorac Surg. 1984;37115- 122
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Bozzetti  FMigliavacca  SScotti  A  et al.  Impact of cancer type and treatment on nutritional status of patients. Ann Surg. 1982;196170- 179
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Muers  MFShelvin  PBrown  Jparticipating members of the Thoracic Group of the Yorkshire Cancer Organization, Prognosis in lung cancer: physicians' opinions compared with outcome and a predictive model. Thorax. 1996;51894- 902
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Perreira  JBruera  E Chronic nausea. Bruera  EHigginson  ICachexia-Anorexia in Cancer Patients. New York, NY Oxford University Press1996;
Bruera  EChadwick  SMacDonald  N  et al.  Study of cardiovascular autonomic insufficiency in advanced cancer patients. Cancer Treat Rep. 1986;701383- 1387
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De Brauw  LMVan De Velde  CJBouwhuis-Hoogerwerf  MLZwaveling  A Diagnostic evaluation and survival analysis of colorectal cancer patients with liver metastases. J Surg Oncol. 1987;3481- 86
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Schlichting  PChristensen  EAndersen  PK  et al.  Prognostic factors in cirrhosis identified by the Cox's regression model. Hepatology. 1983;3889- 895
Link to Article

Figures

Tables

Table Graphic Jump LocationTable 1. Characteristics of the Sample and Summary of Univariate Survival Analyses*
Table Graphic Jump LocationTable 2. Final Cox Regression Models Based on Clinical Variables (Model 1) and Clinical and Laboratory Variables (Model 2)*
Table Graphic Jump LocationTable 3. Hazard Ratios for Interacting Covariates in Model 1*
Table Graphic Jump LocationTable 4. Hazard Ratios for Interacting Covariates in Model 2*

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Cassileth  BRWalsh  WLusk  EJ Psychosocial correlates of cancer survival: a subsequent report 3 to 8 years after cancer diagnosis. J Clin Oncol. 1988;61753- 1759
Reuben  DBMor  VHiris  J Clinical symptoms and length of survival in patients with terminal cancer. Arch Intern Med. 1988;1481586- 1591
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Schonwetter  RSTeasdale  TAStorey  P The terminal cancer syndrome. Arch Intern Med. 1989;149965- 966
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Sorensen  JBBadsberg  JHOlsen  J The prognostic factors in inoperable adenocarcinoma of the lung: a multivariate regression analysis in 259 patients. Cancer Res. 1989;495748- 5754
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Bruera  EMiller  MJKuehn  NMacEachern  THanson  J Estimated survival of patients admitted to a palliative care unit: a prospective study. J Pain Symptom Manage. 1992;782- 86
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Rosenthal  MAGebski  VJKefford  RStuart Harris  RC Prediction of life-expectancy in hospice patients: identification of novel prognostic factors. Palliat Med. 1993;7199- 204
Link to Article
Loprinzi  CLLaurie  JAWieand  HS  et al.  Prospective evaluation of prognostic variables from patient-completed questionnaires. J Clin Oncol. 1994;12601- 607
Schonwetter  RSRobinson  BERamirez  G Prognostic factors for survival in terminal lung cancer patients. J Gen Intern Med. 1994;9366- 371
Link to Article
Pesmans  MSculier  JPLibert  P  et al.  Prognostic factors for survival in advanced non–small-cell lung cancer: univariate and multivariate analysis including recursive partitioning and amalgamation algorithms in 1052 patients. J Clin Oncol. 1995;131221- 1230
Allard  PDionne  APotvin  D Factors associated with length of survival among 1081 terminally ill cancer patients. J Palliat Care. 1995;1120- 24
Buccheri  GFerrigno  DTamburini  M Karnofsky and ECOG performance status scoring in lung cancer: a prospective, longitudinal study of 536 patients from a single institution. Eur J Cancer. 1996;32A1135- 1141
Link to Article
Wachtel  TMasterson  SAReuben  DGoldberg  RMor  V The end stage cancer patient: terminal common pathway. Hospice J. 1988;443- 80
Kaasa  SMastekaasa  ALund  E Prognostic factors for patients with inoperable non–small cell lung cancer, limited disease: the importance of patients' subjective experience of disease and psychosocial well-being. Radiother Oncol. 1989;15235- 242
Link to Article
Ventafridda  VRipamonti  CTamburini  MCassileth  RBDe Conno  F Unendurable symptom as prognostic indicators of impending death in terminal cancer patients. Eur J Cancer. 1990;261000- 1001
Link to Article
Krech  RLWalsh  D Symptoms of pancreatic cancer. J Pain Symptom Manage. 1991;6360- 367
Link to Article
Hardy  RJTurner  RSauders  MA'Hern  R Prediction of survival in a hospital based continuing care unit. Eur J Cancer. 1994;30A284- 288
Link to Article
Degner  LFSloan  JA Symptom distress in newly diagnosed ambulatory cancer patients as a predictor of survival in lung cancer. J Pain Symptom Manage. 1995;10423- 431
Link to Article
Cohen  MHMakuch  RJohnston-Early  A  et al.  Laboratory parameters as an alternative to performance status in prognostic stratification of patients with small cell lung cancer. Cancer Treat Rep. 1981;65187- 195
Shoenfeld  YTal  ABerliner  SPinkhas  J Leukocytosis in non haematological malignancies: a possible tumor-associated marker. J Cancer Res Clin Oncol. 1986;11154- 58
Link to Article
Ventafridda  VDe Conno  FSaita  LRipamonti  CBaronzio  GF Leucocyte-lymphocytes ratio as prognostic indicator of survival in cachectic cancer patients [letter]. Ann Oncol. 1991;2196
Maltoni  MPirovano  MNanni  O  et al.  Biological indices predictive of survival in 519 terminally ill cancer patients. J Pain Symptom Manage. 1997;131- 9
Link to Article
Fulop  THerrmann  FRapin  CH Prognostic role of serum albumin and prealbumin in elderly patients at admission to a geriatric hospital. Arch Gerontol Geriatr. 1991;1231- 39
Link to Article
Herrmann  FRSafran  CLevkoff  SFKenneth  I Serum albumin level on admission as a predictor of death, length of stay, and readmission. Arch Intern Med. 1992;152125- 130
Link to Article
Constans  TBruyere  AGrab  BRapin  CH PINI as a mortality index in hospitalized elderly patients [Research Note]. Int J Vitam Nutr Res. 1992;62191
Salamagne  MEVinant-Binam  P Valeur pronostique de parametres biologiques de denutrition chez des patients hospitalises en unité de soins palliatifs. InfoKara. 1996;4421- 32
Ralston  SHGallacher  SJPatel  UCampbell  JBoyle  IT Cancer-associated hypercalcemia: morbidity and mortality. Ann Intern Med. 1990;112499- 504
Link to Article
Schwartz  MK Enzymes as prognostic markers and therapeutic indicators in patients with cancer. Clin Chim Acta. 1992;20677- 82
Link to Article
Cassileth  BRLusk  EJMiller  DSBrowne  LLMiller  C Psychosocial correlates of survival in advanced malignant disease? N Engl J Med. 1985;3121551- 1555
Link to Article
Cassileth  BRWalsh  WLusk  EJ Psychosocial correlates of cancer survival: a subsequent report 3 to 8 years after cancer diagnosis. J Clin Oncol. 1988;61753- 1759
Coates  A Prognostic implications of quality of life. Cancer Treat Rev. 1993;19(suppl A)53- 57
Link to Article
Morris  JNSuissa  SSherwood  SWright  SMGreer  D Last days: a study of the quality of life of terminally ill cancer patients. J Chronic Dis. 1986;3947- 62
Link to Article
Morris  JNSherwood  S Quality of life of cancer patients at different stages in the disease trajectory. J Chronic Dis. 1987;40545- 553
Link to Article
Coates  AGebsky  VBishop  JF  et al.  Improving the quality of life during chemotherapy for advanced breast cancer: a comparison of intermittent and continuous treatment. N Engl J Med. 1987;3171490- 1495
Link to Article
Coates  AGebsky  VSignorini  D  et al.  Prognostic value of quality of life score during chemotherapy for advanced breast cancer. J Clin Oncol. 1992;101833- 1838
Coates  AThompson  DMcLeod  GRM  et al.  Prognostic value of quality of life scores in a trial of chemotherapy with or without interferon in patients with metastatic malignant melanoma. Eur J Cancer. 1993;29A1731- 1734
Link to Article
Earlam  SGlover  CFordy  CBurke  DAllen-Mersh  TG Relation between tumor size, quality of life, and survival in patients with colorectal liver metastases. J Clin Oncol. 1996;14171- 175
Tamburini  MBrunelli  CRosso  SVentafridda  V Prognostic value of quality of life scores in terminal cancer patients. J Pain Symptom Manage. 1996;1132- 41
Link to Article
Ganz  PLee  JSiau  J Quality of life assessment. Cancer. 1991;673131- 3135
Link to Article
Monnet  EBoutton  MCFaivre  JMilan  C Influence of socioeconomic status on prognosis of colorectal cancer: a population-based study in Cote d'Or, France. Cancer. 1993;721165- 1170
Link to Article
Cella  DOrav  JKornblith  AB  et al.  Socioeconomic status and cancer survival. J Clin Oncol. 1991;91500- 1509
Goodwin  JSSamet  JMHunt  WC Determinants of survival in older cancer patients. J Natl Cancer Inst. 1996;881031- 1038
Link to Article
Christakis  NA Timing of referral of terminally ill patients to an outpatient hospice. J Gen Intern Med. 1994;9314- 320
Link to Article
Jamison  RNBurish  TGWallston  KA Psychogenic factors in predicting survival of breast cancer patients. J Clin Oncol. 1987;5768- 772
Buccheri  GFerrigno  D Prognostic factors in lung cancer: tables and comments. Eur Respir J. 1994;71350- 1364
Link to Article
Klastersky  JDaneau  DVerhest  A Causes of death in patients with cancer. Eur J Cancer. 1972;8149- 154
Link to Article
Inagaki  JRodriguez  VBodey  G Causes of death in cancer patients. Cancer. 1973;2568- 573
Not Available, CANCERNET-PDQ [database online].  Bethesda, Md National Cancer Institute1996;Updated July 1996.
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