Clinical Study on the Impact of Long-term Survival Quality in 204 Postoperative Patients with Breast Cancer by Cox Proportional Hazard Models

doi:10.4236/ojpm.2011.11002

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Open Journal of Preventive Medicine, 2011, 1, 8-12

doi:10.4236/ojpm.2011.11002 Published Online May 2011 (http://www.SciRP.org/journal/ojpm/

OJPM

Published Online May 2011 in SciRes. http://www.scirp.org/journal/OJPM

Clinical study on the impact of long-term survival quality

in 204 postoperative patients with breast cancer by cox

proportional hazard models

Bei Liu1, Qiong Dai1, Yukai Du1, Xueqing Jiang2, Gujun Zhou2

1Department of Maternal and Children Health Care and Adolescent Hygiene, School of Public Health, Tongji Medical College,

Hua Zhong University of Science and Technical, Wuhan, 430030, China

2Thyroid and Breast Surgery, Wuhan Central Hospital

Email: [email protected]; [email protected]

Received 19 April 2011; revised 10 May 2011; accepted 12 May 2011.

ABSTRACT

The aim of study was to evaluate clinical characteristics,

social support and the association with the prognosis of

breast cancer patients. A total of 204 participants were

followed from 2003 until the end of 2008. Information

about patients with breast cancer was submitted by

investigators. Data were analyzed by Cox’s propor-

tional hazard model. The clinical staging of breast can-

cer we used was the TNM cl assification. A “T” score is

based upon the size and/or extent of invasion. The “N”

score indicates the extent of lymph node involvement.

Age at diagnose was associated with protective factors

(HR = 0.972; 95%CI (0.834 - 1.130)), T staging (HR =

2.075; 95%CI (1.424 - 3.022)), N staging (HR = 1.513;

95%CI (1.066 - 2.148)), were associated with risk factor.

Two survival graphs of nodes with negative effects by

histology and nodes with positive effects by histology

was analyzed by log-rank test, there was statistically

significant relationship between two survival graphs (x2

= 136.8467, p < 0.0001). Age at diagnoses, Clinical stage

tumor and node could contribute to the development of

breast cancer and disease free survival in Chinese

women.

Keywords: Survival quality; Breast Cancer; Postopera-

tive; Cox Proportional Hazard Models

1. INTRODUCTION

Several well-established factors have been associated with

the prognosis of breast cancer such as size of tumour,

lymph node involvement, histological type, oestrogen and

progesterone receptor status, and so on. With modern

medical model transforming from biomedical model to

biology-psychology-community medical model, the ther-

apy no longer simply emphasize elimination of tumor and

prolongation of life span, at the same time, the improve-

ment of the quality of life is emphasized as well [1]. Ow-

ing to the fact that success of treatment in prolonging life is

a mixed blessing–it is not enough to survive, patients also

want to live [2]. Quality of life (QoL) is currently an im-

portant factor in oncological research [3]. QoL and its

components and determinants have received growing in-

terest [4-8], and physical, mental and social well-being,

with varying levels of emphasis and in various combina-

tions, have been included in the concept [2,4,9,10]. As a

whole, women who remain free of breast cancer seem to

have levels of functioning and QoL that are comparable to

those of the general female population, although those who

receive systemic adjuvant chemotherapy may do less well

[11]. As a result, study for patients’ QoL is being empha-

sized. At the same time, we also hypothesized that women

with greater social-emotional support would also survive

longer when compared with women with less or no sup-

port. Therefore, we explored other potential barriers to

patients. By assessing its associations with demographic

and clinical characteristics and social support (given retro-

spective evidence of its positive relationship with partici-

pation). The aim of our study was to evaluate clinical

characteristics, social support and the association with the

prognosis of breast cancer patients. Our study pulled 21

factors about clinical pathology and lifestyle into Cox

model which may influence postoperative patients with

breast cancer to make clinical synthetic evaluation and

analysis in order to improve their QoL and get long-term

survival.

2. METHODS

2.1. Participants

Women aged 23-82 years, diagnosed with a first pathol-

B. Liu et al. / Open Journal of Preventive Medicine 1 (2011) 8-12

ogically confirmed breast cancer between January 2000

and February 2001, were identified through three hospi-

tals, including Tongji Hospital, Xiehe Hospital and Wu-

han Central Hospital in Wuhan city of China. These

hospitals were requested to provide complete informa-

tion for all known cases of female breast cancer. All pa-

tients who entered the study in May 2003 have been

received follow-up visit till June, 2008. All of documents

included 163 completed data cases and 41 censored

cases, which had 25 visit loss cases, 1 death of other

diseases case and 15 survival cases.

2.2. Data Collection

On the basis of all sources of information, we recon-

structed a detailed medical history for each patient. In

compiling all sources of information, we also ascertained

breast cancer stage, histology, estrogen receptor (ER)

status, methods of treatment, age at diagnosis, gestagenic

history, and married status [12] through their abstraction

of pathology reports and medical records relating to

breast cancer diagnosis. For women who had two or

more primary cancers diagnosed within the follow-up

time, we took the earliest diagnosis, or if both tumors

were diagnosed on the same date, the tumor characteris-

tics are those associated with the larger tumor.

Although we were able to confirm most exposure his-

tories of patients through interview of medical records,

we were unable to confirm other information. Thus, the

exposure information, such as educational level, occupa-

tion, emotional function, social function and economical

status correlated with health, were obtained by trained

interviewers that asked patients, their husband or first-

degree relatives. All data were collected with a stan-

dardized questionnaire using a telephone interview.

Variables included demographics, emotional function,

social function and economical status. If patients die,

interviewers must obtain their age at death. Due to the

nature of the data collected (medical records and tele-

phone interview), complete information was impossible

for some of the variable assessed. In some instances (e.g.

the history of other chronic disease, emotional function,

social function and economical status) over 35% of the

data were missing, therefore these variables were ex-

cluded from the analysis. In the end, complete informa-

tion was available for 204 cases from the initial study

population of 300 cases.

2.3. Statistical Methods

Twenty-one features of patients, clinical pathologic fac-

tors and lifestyle have been selected as the indexes of

analysis and been quantified, which came from clinical

records and may influence prognosis of patients with

breast cancer. Patients’ live time were calculated by

month, which means the time span from the operation

day to death or termination of follow-up visit, and we

put corresponding data of every patient into computer on

the basis of clinical records and results of follow-up visit,

and data was dealt by SAS9.0 for WINDOWS software

and the survival rate was calculated by life table method.

All indexes of survival rate difference were analyzed by

multiple factor Cox proportional hazard model (using

gradually backward progressive method, two-tailed α =

0.05).

3. RESULTS

The 17 categorical variables were summarized in Table

1. For the model selection there were records with miss-

ing variables. Previous analysis on these datasets sug-

gested that missing variables might be informative.

Therefore, any missing values in the 17 categories were

coded as a separate attribute.

The distributions of various demographic, reproduc-

tive and medical characteristics of the cohort were pro-

vided in Table 2. Among the 204 women included in this

analysis, 48.04% had at least a college degree, 13.24%

had a first-degree family history of breast cancer and

83.33% had biopsy for benign breast cancer. The major-

ity was later-stage cancers, and 190 (93.14%) had infil-

trating type.

Multivariate Cox proportional hazard model analysis

was showed in Table 3. On the level of α = 0. 05, it was

indicated by analytic results that outstanding factors in-

cluding age at diagnosis, T staging, N staging, emotional

function, the level of the hospital, may influence survival

time statistically. Age at diagnosis was protective factors

(coefficient of regression was negative), the rest were all

risk factor (coefficient of regression was upright).

Survival analysis of clinical stage nodes was showed

in Figure 1. Two survival graphs of nodes found nega-

tive by histology and nodes found positive by histology

were analyzed by log-rank test, there was statistically

significant relationship between two survival graphs (x2

= 136.8467, p < 0.0001).

4. DISCUSSION

Age at diagnosis is one of the most definitive risk factors

on breast cancer. Sixty-five percent of all breast cancers

occur in women aged 55 and older. Our study found that

age at diagnosis was one of prognostic factors. The

younger patients at diagnosis showed lower death hazard

(Hazard Ratio: 0.972). But none of the prior studies

showed that age was one of the prognostic factors, and it

is difficult to hypothesize why age at diagnoses would

be more closely related to survival time. We presume

that the younger patients at diagnose may have fewer

hance of developing other chronic diseases and be more c

B. Liu et al. / Open Journal of Preventive Medicine 1 (2011) 8-12

Table 1. 17 items of survival analysis index and quantification.

Variable index quantification

X1 Age at diagnosis Years of age

X2 Years of education High school or less = 1 high school graduate = 2

college graduate or higher = 3

X3 Married status Married = 0 single = 1

X4 Occupation Mental labor = 0 manual labor = 1

X5 Gestation during breast cancer No = 0 yes = 1

X6 Menopausal status Post-menopausal = 0 pre-menopausal = 1

X7 Age at menopause ≤40 = 1 41-45 = 2 46-50 = 3 ≥51 = 4

X8 Family history of breast cancer in first-

degree relative No = 0 yes = 1

X9 Previous biopsy for benign breast cancer No = 0 yes = 1

X10 Other chronic disease No = 0 yes = 1

X11 Histology In situ = 0 infiltrating type = 1

X12 Clinical stage tumor T1 = 0 T2 = 1 T3 = 2 T4 = 3

X13 Clinical stage nodes N0 = 0 N1 = 1 N2 = 2 N3 = 3

X14 Clinical stage metastasis M0 = 0 M1 = 1

X15 Pathology differentiation Well-differentiated = 1 moderately differentiated = 2

poorly differentiated = 3

X16 Methods of treatment Surgical = 1 surgical+chemotherapy = 2; Surgical + chemother-

apy+radiotherapy = 3; no accept any treatment = 4

X17 Hormonal dependent (ER, PR detection result) Entirely masculine = 1 partly masculine = 2 entirely negative = 3

Table 2. Distributions of demographic, reproductive and medical factors (n = 204).

Characteristic N (%)

Age at diagnosis

≤40 years 36 (17.65%)

41 - 50 years 36 (17.65%)

≥51 years 132 (64.70%)

Years of education

High school or less 65 (31.86%)

High school graduate 41 (20.10%)

College graduate or higher 98 (48.04%)

Married status

Married 141 ( 69.12%)

Single 63 (30.88%)

Occupation

Mental labor 105 (51.47%)

Manual labor 99 (48.53%)

Menopausal status

Post-menopausal 107 (52.45%)

Pre-menopausal 97 (47.55%)

Age at menopause

≤40 years 15 (7.35%)

41 - 45 years 58 (28.43%)

46 - 50 years 99 (48.53%)

≥ 51 years 32 (15.69%)

Family history of breast cancer in first-degree relative

No 177 (86.76%)

Yes 27 (13.24%)

Previous biopsy for benign breast cancer

No 34 (16.67%)

Yes 170 (83.33%)

Histology

In situ 14 (6.86%)

Infiltrating type 190 (93.14%)

Hormonal dependent (ER, PR detection result)

Entirely masculine 99 (48.53%)

Partly masculine 48 (23.53%)

Entirely negative 57 (27.94%)

B. Liu et al. / Open Journal of Preventive Medicine 1 (2011) 8-12

Table 3. Multivariate Cox proportional hazard model analysis.

Variable Parameter EstimateSE HR (95%Cl) x2 P

Age at diagnose –0.02829 0.00770 0.972 (0.834 - 1.130) 13.506 0.0002

T staging 0.72984 0.19196 2.075 (1.424 - 3.022) 14.456 0.0001

N staging 0.41402 0.17879 1.513 (1.066 - 2.148) 5.362 0.0206

Figure 1. Survival analysis of clinical stage nodes.

healthy than elder patients so that they can survive

longer. Further studies are needed to confirm these find-

ings, given that this is the first study to report these as-

sociations of breast cancer patients.

Tumor size and clinical stage nodes are two of the

most important prognostic factors, although tumor grade

may modify this risk assessment. Most commonly used

indexes of clinic to evaluate its prognosis referred to

TMN staging system and degree of tumor pathology [13],

which reflects pathological anatomic scope and histo-

logical transformation affecting prognosis. In general,

women who have a tumor that measures less than 1 cm

with negative axillary lymph nodes have a greater than

95% chance of a 10-year disease-free survival. As the

tumor size approaches 2 cm, the chance of being disease

free within 10 years drops to about 70% [13]. Previous

studies had indicated the relationship between clinical

stage tumor, clinical stage nodes and prognosis. Our

study also found that HR of T4 was 2.075-fold higher

than T1 and Hazard Ratio of N3 was 1.513-fold higher

than N0. Moreover, we analyzed two survival graph in

different clinical stage nodes because clinical stage

nodes as Variable was entered firstly into Cox propor-

tional hazard model, and the analysis result of log-rank

test of survival graph showed that there was statistically

significant relationship between two survival graphs (x2

= 136.8467, p < 0.0001). Thus, we conclude that clinical

stage tumor and clinical stage nodes are two of the most

important prognostic factors and the patients of nodes

removed indicated lower survival than no nodes found

clinically or node negative by histology.

In summary, our results suggest that age at diagnoses,

Clinical stage tumor and node could contribute to the

development of breast cancer and disease free survival in

Chinese women.

4. LIMITATIONS

After interpreting the results of this study, it is important

to acknowledge its limitations. A limitation of this study

was the small, cross-sectional sample related to reported

frequencies of symptoms, yet the size of the sample is

consistent with qualitative inquiry. Another limitation is

participants’ recall of their symptoms and information

and support needs during the 5 years following therapy.

The primary exposures of interest include early-life

events, and given that some women in participants are

older than 50 years, recall of exact events may have been

poor for some women resulting in exposure misclassifi-

cation. The resultant bias would be non-differential,

given that a cohort design was used and, thus would lead

to underestimations of the true relative risks. Finally, we

were only able to include 68% of the potentially eligible

women in this study because 32% confounder data were

missing. Given the prospective nature of this study these

exclusions are unlikely to bias our results. Some vari-

ables, such as Hormonal dependent (ER, PR detection

result), Family history of breast cancer in first-degree

relative, Menopausal status are not internalized into the

COX function, but they could be further researched on

the basis of expanded sample in future.

B. Liu et al. / Open Journal of Preventive Medicine 1 (2011) 8-12

5. ACKNOWLEDGMENTS

This work was supported by grants from Tongji Hospital, Xiehe Hos-

pital and the Wuhan Central Hospital. We thanks the help of Professor

Ping Yin in data analysis and Assistant Professor Ping Wang in recti-

fying the manuscript.

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