Prediction of Survival Time in Advanced Cancer: A Prognostic Scale for Chinese Patients

Article Outline

• Abstract
• Introduction
• Methods
  • Patients
  • Data Collection
  • Statistical Analysis
• Results
• Discussion
• Acknowledgments
• References
• Copyright

Abstract 

This study reports the development of a simple Chinese Prognostic Scale (ChPS) for predicting survival in advanced cancer patients. Data relating to 1,019 advanced cancer patients referred to a palliative home care service were retrospectively analyzed. The records were divided into two sets using stratified random sampling: 80% as a “training set” for developing the scale and 20% as a “testing set” for validating it. Demographic data, symptoms/signs, Karnofsky Performance Status (KPS), quality of life (QOL), and survival time were statistically analyzed to create the scale. In the training set, a total of 10 prognostic factors were determined: weight loss, nausea, dysphagia, dyspnea, edema, cachexia, dehydration, gender, KPS, and QOL. The ChPS score was calculated for each case by summing the partial scores of prognostic factors, ranging from 0 (no altered variables) to 124 (maximal altered variables). The score for a cutoff point of three months' survival was 28 (95% confidence interval: 26.6, 28.9). When scores were more than 28, survival appeared to be usually less than three months. The accuracy rate was 69.4% in the training set and 65.4% in the testing set. In conclusion, it is possible with this prognostic scale to guide physicians in predicting more accurately the likely survival time of Chinese cancer patients, and to help policy makers in establishing appropriate referral for hospice care.

Key Words: Prediction, prognosis of survival, advanced cancer, palliative care

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Introduction 

Accurate prediction of survival time is useful in oncology and palliative care for a number of reasons. First, it influences the physician's and patient's decision making about treatment. Second, it can be an important component of hospice referral criteria. Third, it can be used by policy makers for appropriate allocation of resources. Fourth, perhaps most importantly, patients and families desire more accurate prognostic information to plan and make decisions toward the end of life. Previous studies have shown that clinicians do not accurately estimate survival time.¹, ² A meta-analysis showed that clinicians' survival predictions overestimated actual survival of terminal cancer patients by at least four weeks in 27% of cases.³ However, it was also found that clinicians could improve their prediction accuracy with repeated measurement over time and with the application of prognostic tools and indicators in the prediction process.⁴

Some studies have reported to have identified prognostic factors in terminally ill patients, and developed prognostic tools for all life-threatening diseases, for example, the Palliative Performance Scale⁵, ⁶, ⁷ and the Palliative Prognostic Score,⁸, ⁹ but few have been developed into cancer-specific prognostic tools. Morita et al.¹⁰ developed a scoring system, the Palliative Prognostic Index (PPI), to predict three- and six-week survival, with a sensitivity and specificity of more than 70%. Chuang et al.¹¹ constructed a prognostic scale, the Cancer Prognostic Scale (CPS), to predict short-term survival of one to two weeks. Bozcuk et al.¹² developed a prognostic tool, the Intrahospital Cancer Mortality Risk Model, to predict, with a complex formula, the probability of intrahospital death at the time of hospitalization. Schonwetter et al.¹³ produced a lung cancer-specific prognostic tool, the Lung Cancer Prognostic Model, to predict 50% and 90% mortality in days after admission to a hospice.

Palliative care is a relatively new and developing specialty in China. Health care is dominated by a therapeutic imperative toward life-prolonging interventions,¹⁴ perhaps because of a lack of related policy and professional practice standards or guidelines, low numbers of professional staff, minimal palliative care training, and others.¹⁵, ¹⁶, ¹⁷ In our opinion, an important initial step in developing palliative care across China is to establish the referral criteria for hospice care. In doing so, the prediction of survival time, such as three or six months, is crucial. More accurate prediction could guide physicians about when to recommend hospice care to patients. To our knowledge, no formal studies of survival prediction have been performed in advanced cancer patients in mainland China. The prevalence of cancer is high in China, with more than 3 million cancer patients, and it is also the top cause of death in urban residents. The number of new cancer cases is 1.6–2 million every year.¹⁸ There were 0.17 million cancer patients in Shanghai in 2006, with a prevalence rate of 1.31%.¹⁹ This epidemiology indicates that cancer patients would be major service users of palliative care in China. In light of the aforementioned, the aim of our study was to attempt to identify prognostic factors and integrate them into a predictive scale for Chinese advanced cancer patients.

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Methods 

Patients 

A total of 1,019 advanced cancer patients referred and accepted into the palliative home care service of Shanghai Xinhua Hospice Center between September 2003 and August 2007 were included in this retrospective study. The Shanghai Xinhua Hospice Center, founded in June 2001 and supported by the Li Ka Shing Foundation, is the only center which provides home care for advanced cancer patients from all around Shanghai. The main criterion for admission to the service is that the patient must be in the terminal stage of cancer, which has been formally diagnosed in a “high-level hospital” but who prefers to die at home. Palliative care services cannot be provided to patients with cognitive impairment because of the low workforce of the team and the likelihood that, in China, such patients are admitted to hospital anyway. The team members, consisting of two physicians, two nurses, one social worker, and one driver, attempt to restore the patient's best possible functional status, liaise with the acute hospitals, and follow up until the patient's death. They plan the patient's care and visit or telephone the patient, according to his or her condition. All patients are offered free analgesics.

Data Collection 

A retrospective case analysis approach was used in this study. Members of the research group extracted data from the case records and entered them into a computerized database, using Microsoft Office Access. The data from all those whose survival time was less than six months from admission to the service were selected for this study. The data were divided into two sets by stratified random sampling: 80% of the cases for developing the scale and 20% for validating it. As in Morita et al.'s study,¹⁰ we used the terms “training set” and “testing set,” respectively, for these sets of cases. The clinical team recorded information about individual patients, including patient characteristics, tumor location, Karnofsky Performance Scale (KPS) score, quality-of-life (QOL) score, medication details, and clinical symptoms, on a series of structured data collection sheets on admission and after every follow-up visit.

The QOL scale (Chinese version) used in the study was developed by Sun Yan in the 1990s by applying widely used international scales to the context of the Chinese culture.²⁰, ²¹ There are 12 items on this scale: appetite, energy, sleep, fatigue, pain, family relationships, work relationships, perception of cancer, attitude toward treatment, activities of daily life, side effects of treatment, and facial expression. Each item is evaluated from worst to best, with scores of 0–5. For example, the relationship of family is scored from no support (0) to complete support (5). Facial expression is evaluated using the pain scale with five different facial expressions, from happy (5) to crying (0). Patients rate the QOL scale themselves. If they cannot read or write, carers or health providers may help but cannot impose their judgment on the score. The total score for this scale is 60, divided into five levels: best (51–60), good (41–50), fair (31–40), bad (21–30), and worst (≤20). It is now widely used in China and was used to establish the QOL score in this study. Patient performance status was assessed according to the KPS, which has been translated into Chinese.

The symptoms investigated were pain, nausea/vomiting, dyspnea, constipation, diarrhea, abdominal swelling, ascites, hydrothorax, edema, dehydration, insomnia, delirium, anorexia, xerostomia, oral ulcer, wound, bleeding, fever, dysphagia, dizziness, numbness, weight loss, pallor, fatigue, cachexia, urinary retention, palpitation, cough, and incontinence. Patients were asked about the presence or absence of each symptom, including pain, nausea/vomiting, dyspnea, constipation, diarrhea, abdominal swelling, insomnia, anorexia, xerostomia, oral ulcer, dysphagia, dizziness, numbness, fatigue, urinary retention, palpitation, cough, and incontinence. The other objective symptoms were rated by physicians based on direct observations during each visit as “present” or “absent.”

Statistical Analysis 

The data from case records of patients who survived less than six months were analyzed in this study. The survival time was calculated from the date of admission to the date of death. This information was applied to the training set of cases to identify prognostic factors and construct a prognostic indicator, whereas the testing set was used to validate the indicators.

First, to create a convenient scoring system, different KPS scores from 0 to 100 were combined by comparing the difference in survival curve of every level of KPS by the log-rank test. KPS was categorized as 50 or lesser, 51–60, 61–70, and 70 or more (Table 1). The QOL was similarly combined to establish three levels, that is, 0–30, 31–40, and 40 or more, by comparing the difference in survival curve of every level of QOL using the log-rank test (Table 2). Second, to identify prognostic factors, the survival curve of every variable in the data of the training set was compared by log-rank test. Then, variables with P≤0.05 were considered putative prognostic factors and included in the multivariate analysis. The Cox regression model was applied in the multivariate analysis. Third, the partial score value was defined as the nearest integer of the quotient obtained by dividing each regression coefficient of a significant prognostic factor by the smallest one. The total score of this prognostic scale was calculated for each case by summing the partial scores. Fourth, the cutoff point for survival prediction was determined based on the training set by linear regression between scores and survival time. Fifth, the accuracy of this scale was evaluated by counting the number of concordance cases between the predictive survival time and actual survival time. It was tested both in the training set and the testing set. All these statistical analyses were performed using SAS version 9.1.3 (SAS Institute Inc., Cary, NC), and used a P value of 0.05 or less as the significant standard.

Table 1. Log-Rank Test Results of Different KPS Levels

Table 2. Log-Rank Test Results of Different QOL Levels

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Results 

Table 3 summarizes the patients' characteristics for the training and testing data sets. There was no significant difference in patients' sex, age, marital status, survival time, KPS score, and QOL score between the two sets. A total of 10 prognostic factors were determined by means of the univariate log-rank test (Table 4): weight loss, nausea, dysphagia, dyspnea, edema, cachexia, dehydration, gender, KPS, and QOL. These were included in the Cox regression model. Table 5 shows the partial score value for each category. The Chinese Prognostic Scale (ChPS) score was calculated for each case by summing the partial scores, ranging from 0 (no altered variables) to 124 (maximal altered variables).

Table 3. Patient Characteristics

Characteristics	Training Set	Testing Set	χ2 Value	P-value of Log-Rank Test
Number of patients	814	205
Median/mean survival (days)	41/52.78	35/45.33	3.18	0.0745
Age (in years)			0.89	0.3466
≤60	285	79
>60	529	126
Gender			0.57	0.4501
Male	464	111
Female	349	94
Marital status			0.03	0.8597
Unmarried	12	4
Married	802	201
KPS score
≤50	225	57	0.00	0.9778
60	335	84
70	210	53
>70	44	11
QOL score
0–30	291	84	0.36	0.5510
31–40	400	89
>40	84	25

Table 4. Univariate Analysis Results of Factors and Survival Time by Log-Rank Test

Table 5. Multivariate Analysis Results of Factors and Survival Time by Cox Regression Model

Factors	Regression Coefficient	Standard Error	Partial Score
Weight loss
No	0		0
Yes	0.03264	0.09159	1
Nausea
No	0		0
Yes	0.04312	0.08590	1
Dysphagia
No	0		0
Yes	0.11268	0.17221	3
Dyspnea
No	0		0
Yes	0.14526	0.08498	4
Edema
No	0		0
Yes	0.20071	0.11510	6
Gender
Male	0		0
Female	−0.20259	0.07586	−6
Cachexia
No	0		0
Yes	0.27104	0.10657	8
Dehydration
No	0		0
Yes	0.64714	0.30127	20
QOL
>40	0		0
31–40	0.19111	0.12829	6
0–30	0.45951	0.14719	14
KPS
>70	0		0
70	0.30278	0.17757	9
60	0.55809	0.17982	17
≤50	0.93371	0.19104	29

A cutoff point for predicting whether patients would live less than three months was explored using data from the training set. The score for a cutoff point of three months' survival was 28 (95% confidence interval: 26.6, 28.9). When the prognostic score of a patient is more than 28, the prediction of survival appears to be less than three months. The accuracy of this scale was evaluated by counting the number of concordance cases between the predictive survival time and actual survival time. Table 6 shows the accuracy rates in both the training and testing sets. When the score of 28 was used as a cutoff point, a less than three-month survival was predicted, with 69.4% accuracy in the training set and 65.4% in the testing set. Figure 1 shows the survival curves of two groups identified by the ChPS: Group A (score≤28; prediction of survival: three to six months) and Group B (score>28; prediction of survival: less than three months). The log-rank test results were: χ²=25.16, P<0.0001. There is a significant difference in survival time between Group A and Group B.

Table 6. Accuracy Rate of Prediction in the Training Set and Testing Set

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• Fig. 1 

  Survival curves of two groups identified by the Chinese Prognostic Scale.

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Discussion 

There are numerous potential prognostic factors that may be used to improve the prediction of survival. These factors can be categorized into tumor-related, patient-related, and environment-related factors.²² Previous studies suggested performance status together with some clinical symptoms and mental status could be recommended to guide the physician in predicting the survival of terminal cancer patients.⁴, ²³, ²⁴ Some symptoms, such as anorexia, weight loss, cachexia, xerostomia, dysphagia, dyspnea, cognitive failure, and delirium, have been consistently revealed as useful prognostic factors in patients with advanced cancer, and others, including nausea, constipation, dizziness, fever, pain, and diarrhea, have been indicated but not confirmed as prognostic factors.⁴, ²², ²⁵, ²⁶, ²⁷

Glare, in his systematic review, reported that there appeared to be important differences in prognostic factors between those relevant to advanced cancer (predicted survival in the 3- to 12-month range) vs. far advanced or terminal disease (predicted survival of less than three months).²² In this study, we developed a ChPS to predict survival of either more or less than three months. Ten prognostic factors were determined in this study: weight loss, nausea, dysphagia, dyspnea, edema, cachexia, dehydration, gender, KPS, and QOL. This is consistent with previous studies. The symptoms determined in this study, such as weight loss, dysphagia, dyspnea, and cachexia, have been revealed as “terminal symptoms.” Gender and nausea have also been indicated but not confirmed as prognostic factors.

Performance status has been found to be most strongly correlated with the duration of survival.⁴, ²⁸ This parallels our findings. A KPS score of 50 or lesser results in a partial score of 29 on the ChPS. Considering that we have already established that a score of 28 represents the cutoff point for a three-month survival, patients with a KPS score of 50 or lesser should be assumed to have a likely survival time of less than three months. However, we should note that the KPS categories relating to survival in this study (Table 5) are different from the categories of 10–20, 30–40, and 50 or higher used in other studies.⁸, ⁹, ²² One reason may be that, in this study, the data were collected from cancer patients in home care, most of whom are likely to be in good condition on admission to the service. The KPS score distribution that we found in this study supports this: KPS<50, 4.5%; KPS=50, 23.3%; KPS=60, 41.2%; KPS=70, 25.3%; and KPS>70, 5.8%. Another reason might be that the KPS scores may have been overestimated by the physicians in the team who have little experience in the assessment of KPS. This needs to be explored in future studies.

Studies of QOL in patients with advanced cancer have shown different degrees of correlation between the QOL instruments and survival time.²⁹, ³⁰, ³¹ The findings in this study are in accordance with the literature. The QOL (China vision) scale was included in the prognostic scale; its impact increased with decreasing scores. This also indicates the feasibility of using this QOL instrument in palliative care. This QOL instrument includes some symptoms that have been evaluated separately. In our opinion, on the one hand, it should provide a comprehensive meaning of QOL assessment. The scores represent the QOL status of patients, the meaning of which may be different from that of symptoms evaluated separately. On the other hand, the symptoms (weight loss, nausea, dysphagia, dyspnea, edema, cachexia, dehydration) included in our final scale do not contain some of the symptoms in QOL (appetite, energy, sleep, fatigue, pain). It may be that the QOL score represents the prognostic function of these symptoms. However, the QOL scale has not been included in any other prognostic scale developed in other studies. This needs further evaluation.

Unlike the other symptoms, edema and dehydration have been less frequently identified as prognostic factors. The CPS, developed by Chuang et al.,¹¹ included edema in the scale. Their explanation was that the edema was often iatrogenic and was caused by excessive artificial fluid supplementation. This also was supported by the findings of Morita et al.,¹⁰ who included edema in the PPI. In our opinion, edema and dehydration may represent the controversial question between dehydration and artificial rehydration, as discussed in previous studies.³², ³³, ³⁴ Both may be implicated in electrolyte imbalance, which may be an important factor in timing of death in terminal cancer patients. The research in this area needs to be considered.

Developing a cancer-specific prognostic scale that can predict long-term survival is important in establishing the referral criteria for hospice care. The dying trajectory of cancer is one of the most predictable trajectories.³⁵, ³⁶ Cancer decedents were highly functional early in their final year but markedly more disabled three months before death (the turning point). Hospice care is beneficial for cancer patients, especially as their functional decline begins. Moreover, it is estimated that at least 90% of cancer patients have a terminal care period of an average duration of approximately three months.³⁷, ³⁸ There is some evidence that appropriate provision of palliative care can positively alter the end-of-life experiences of terminal patients and their caregivers.³⁹ Mintzer and Zagrabbe reported that the proliferation of newer, available antineoplastic agents could lead to delays in referral to hospice.⁴⁰ This could lead to patients and families being denied potentially beneficial palliative and hospice care until closer to death. Globally, an estimated prognosis of less than six months is used as a criterion for hospice referrals. However, this has to be considered in the light of the culture and conditions in China. There are less medical resources devoted to this area of care, a lower degree of awareness and acceptance of hospice care among the public, and insufficient numbers of professional workers. Hence, it is hard to develop hospice care for the earlier stages of advanced cancer at present. Considering this fact, the prognosis of survival time less than three months could be pragmatically recommended as the criterion for hospice referral in China, at least for now. In this study, we developed and tested the ChPS with a prediction of three-month survival, which could be recommended for use as a tool to assess patients for referral to hospice care. Obviously, the ideal aspiration would be to enable hospice care to become more widely available to all those who need it.

In conclusion, the ChPS in our study has an acceptable accuracy rate both in a training set and a testing set. This scale consists of patient-rated measures of subjective sensation and symptoms like QOL, nausea, dysphagia, and dyspnea, and objectively rated measures and symptoms, such as weight loss, edema, cachexia, dehydration, and KPS. It does not require any invasive examinations, such as blood sampling, making it suitable for use in the home care setting. The potential use of the ChPS should now be tested prospectively in the clinical setting.

This study has several limitations. First, the sample in this study, from one hospice center in Shanghai, may not represent the general population of patients with advanced cancer, either in other parts of China or in other countries. With the number of hospice centers in China increasing, a multicenter study would allow more robust testing, and further development, of this prognostic scale. Second, the accuracy of this scale, although quite high, could be further improved. This may be partly because of the retrospective nature of this study and the lack of our ability to capture the degree of severity of symptoms in the scale. Data collection was restricted to a binary choice: absence or presence of each symptom. On the other hand, the simple nature of this scale made it easy to be used, especially for the young and less experienced physicians. In addition, there was only one cutoff point in this scale, maybe because of the data collected only on admission. The research to develop a scale with more survival intervals by the analysis of data collected on every follow-up visit maybe clinically helpful and should be considered. Finally, we used the case records of patients who died within six months. This may have the possibility of subject bias that could make the accuracy appear better than it really is. The sensitivity and specificity of this scale need to be validated in a prospective study.

Prognostication is a complex process because of the wide range of factors, including individuals, care settings, diseases, and environment. Any prognostic tool is only effective as a guide to help clinicians increase the accuracy of prediction. Clinicians and patients need to remember that such tools are not intended to be used slavishly or in a black-and-white manner. It is important to remember that every patient is unique, and we can only observe and not decide their final time.⁴¹ Despite that, we think it is possible to use the ChPS to guide physicians in predicting more accurately the likely survival time of cancer patients and to help policy makers to establish appropriate referral criteria for hospice care. A prospective study is required to test this more robustly.

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Acknowledgments 

The authors are indebted to the Shanghai Xinhua Hospice Center for their assistance in the use of case data. The collaboration of Prof. Chen Qiang and Prof. Shen Wei was very valuable. The authors are also grateful to Ms. Song Lijuan and Ms. Guo Xiangli for their help in data input.

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