Objective: Research has been conducted worldwide from the perspective of data detection in the hope of understanding the factors influencing the survival of stage IV cancer patients. However, the majority of existing research has only focused on single influencing variables. We collected the basic information, cancer type, sites of metastasis, performance status, pain, comorbidities, and relevant laboratory test data of inpatients with stage IV cancer to conduct a comprehensive assessment of the factors influencing survival period.

Methods: A cohort study design was adopted, in which data were collected from the medical records of stage IV cancer patients undergoing cancer treatment at a teaching hospital in Northern Taiwan from January 2018 to December 2022.

Results: Data were collected from the medical records of 739 stage IV cancer patients, among whom 488 patients passed away. The mortality risks of patients who had gastric or esophageal cancer and those who had hepatic cancer, cholangiocarcinoma, or pancreatic cancer were higher than those who had head and neck cancer. Furthermore, the mortality risks were higher for those with a body mass index (BMI) < 25 kg/m², liver metastases, Eastern Cooperative Oncology Group performance status scores of 2 or higher, and peptic ulcers. Regarding blood test values, the mortality risks of stage IV cancer patients with albumin < 3.5 g/dL, hemoglobin (Hgb) < 10.0 gm/dL, and C-reactive protein (CRP) ≥ 10.0 gm/dL were also higher.

Conclusions: Cancer stage and metastasis conditions are beyond their control; however, effort can be devoted to maintaining the BMI and performance status of stage IV cancer patients, treating their peptic ulcers, and monitoring their albumin, Hgb, and CRP levels. These variables can serve as a reference for medical personnel in care decisions regarding stage IV cancer patients.

1. Introduction

Stage IV is the most severe stage of cancer where the cancer has metastasized to different parts of the body. Though it has the highest risk of mortality, it is not necessarily terminal [1]. With advancements in medical technology, new treatment methods introduced in recent years, such as novel chemotherapies as well as targeted drugs and immunotherapy, can effectively lengthen the survival period of stage IV cancer patients and enable them to live with their cancer [2]. At present, the medical community is divided on what factors affect the survival of stage IV cancer patients [3]; however, the most frequently asked question by patients and their family members is regarding the length of survival periods [1].

A number of factors may influence the predicted life expectancy of stage IV cancer patients. Demographic characteristics, such as gender, age, or body mass index (BMI), may be associated with survival. For instance, among patients with stage IV non-small-cell lung cancer, prognoses are much better for female patients than for male patients [4]; older cancer patients tend to have shorter survival periods [5, 6], and Tu et al. [7] noted that although being overweight (BMI 25–29.9 kg/m²) or obese (BMI ≥ 30 kg/m²) increases the general risk of contracting cancer, it could in fact lengthen the survival period of patients.

Clinical care teams believe that the survival rate of stage IV cancer patients is linked to cancer type. For example, the five-year survival rate is 30% for breast cancer patients with distant metastasis but only 3% for small-cell lung cancer patients [8]. Some researchers have reported that the site to which the cancer has metastasized is the most crucial factor impacting cancer patient survival [9]. Other studies have indicated that the survival period of patients with aggressive cancer can only be accurately predicted based on the performance status or pain of the patients [10, 11]. It is common for cancer patients to simultaneously suffer from chronic diseases, and some studies have shown that these comorbidities are connected to the mortality of stage IV cancer patients [12, 13].

In another aspect, correlations also exist between clinical laboratory test data and the survival of stage IV cancer patients. For instance, a study conducted by Ju and Ma [14] on 1655 aggressive cancer patients undergoing treatment revealed that high C-reactive protein (CRP) to albumin ratios could be used as a predictor of their survival prognoses within 30 days. Anemia is also fairly common among stage IV cancer patients and has a significant and negative impact on survival [15, 16].

The majority of the existing research on the survival periods of stage IV cancer patients has focused on single influencing variables. We therefore collected the basic information, cancer type, sites of metastasis, performance status, pain, comorbidities, and relevant laboratory test data of inpatients with stage IV cancer to conduct a comprehensive assessment of the factors that influence the survival period. Furthermore, this should provide a valid basis for medical teams to assess the survival periods of stage IV cancer patients. We also compiled relevant literature and established the framework of this study (Figure 1).

Details are in the caption following the image — **Figure 1**
Open in figure viewer PowerPoint

Factors influencing overall survival of stage IV cancer patients. Abbreviations: BMI: body mass index; ECOG: Eastern Cooperative Oncology Group; NRS: Numerical Rating Scale; Hgb: hemoglobin; CRP: C-reactive protein.

2. Methods

2.1. Study Design and Samples

We adopted a cohort study design and collected data from the medical records at a large-scale teaching hospital in Northern Taiwan during a five-year period from January 2018 to December 2022. Our targets were conscious hospitalized stage IV cancer patients over the age of 18 undergoing cancer treatment (their main admission diagnosis was confirmed as stage IV cancer). The follow-up duration (in months) began from the day of their admission. Patients who had nonsolid malignant tumors, were diagnosed with more than one type of primary malignant tumor, or were lost to follow-up due to discharge or transfer (and whose exact time of death was unknown) were excluded from our analysis. Thus, 739 stage IV cancer patients were tracked. The period from the day of their admission to the date of their last outpatient visit or death served as a reference for the calculation of survival period.

2.2. Measures

We collected the basic information, clinical features, and laboratory test results up to 24 h after the hospitalization of the stage IV cancer patients. The data collected from their medical records included age, gender, educational background, marital status, current state of employment, religion, BMI, type of cancer, sites of metastasis, Eastern Cooperative Oncology Group (ECOG) performance status assessment score, pain scores according to the Numerical Rating Scale (NRS), and comorbidities (including hypertension, diabetes, hypertrophic cardiomyopathy/cardiac dysrhythmia/heart failure, stroke, Parkinson’s disease or dementia, chronic obstructive pulmonary disease or asthma, peptic ulcer, hepatitis B or C, cirrhosis, and chronic kidney disease). The laboratory test data included blood test results for albumin, hemoglobin (Hgb), and CRP. Grouping for each variable was as follows:

1.
Cancer-related research defines patients who are 65 years old or above as elderly [17, 18]. Accordingly, we divided the patients into two age groups for analysis: < 65 years old and ≥ 65 years old.
2.
BMI is a person’s weight (kg) divided by the square of their height (m). Based on cancer-related research, we divided the patients into two BMI groups for analysis: < 25 kg/m² and ≥ 25 kg/m² [7, 19].
3.
We used ECOG scores, which range from 0 to 5, to evaluate the performance status of the patients (0 means physically normal and able to engage in predisease work activities without restriction; 1 means restricted in physically strenuous activity but able to carry out light work activities; 2 means ambulatory, capable of self-care, and not confined to a bed or chair for more than 50% of waking hours, but unable to carry out any work activities; 3 means capable of only some self-care and confined to a bed or chair for more than 50% of waking hours; 4 means completely disabled and confined to a bed or chair and incapable of self-care; and 5 means deceased). Patients with ECOG ≥ 3 are considered to have poor performance status, and those with ECOG ≤ 2 are considered to have good performance status. Clinically, this standard is widely used to assess the physical capacity of cancer patients and determine whether they can continue receiving treatment [20, 21]. We therefore divided the patients into three ECOG score groups for analysis: 0-1, 2, and 3 or higher.
4.
Pain was rated using the NRS, which is a 10-cm horizontal line with 11 points marked 0 on the very left, which indicates no pain, and 10 on the very right, indicating severe pain. The higher the number, the greater the intensity of pain. As suggested in cancer-related research, we divided the patients into three NRS score groups for comparison: no/very mild pain (NRS scores 0–3), moderate pain (NRS scores 4–6), and extreme/very severe pain (NRS scores 7–10) [22].
5.
The blood test items included albumin, Hgb, and CRP. The normal reference range for albumin is 3.5–5.5 g/dL. As suggested in another study, we used 3.5 g/dL as the cutoff point for albumin [23]. The normal reference values for Hgb differ slightly depending on gender: male adults, 13.0–18.0 g/dL; and female adults, 11.0–16.0 g/dL. The World Health Organization and the National Cancer Institute of the US define anemia using Hgb [24]. Hgb levels of 10 g/dL are the lower limit of the normal range and indicate mild anemia; Hgb levels of 8.0–9.9 g/dL indicate moderate anemia; and Hgb levels below 8 g/dL indicate severe and life-threatening anemia [25]. We used 10 g/dL as the cutoff point for our statistics. Furthermore, although CRP is not a marker specific to cancer, it may rise in cancer patients and is clinically relevant in determining prognoses [26]. As suggested by studies regarding cancer and the guidelines of the US Food and Drug Administration on care for inflammatory diseases, we used 10 μg/mL as the cutoff point for CRP [27, 28].

2.3. Ethical Considerations

This study was approved by the Taipei Medical University-Joint Institutional Review Board (TMU-Joint IRB) before data collection began. It was ensured that the personnel participating in this study would never disclose any personal or private information. The patients whose medical records that we reviewed were all cases that received routine treatments. All treatments had been completed before this study was proposed; therefore, consent by the subjects was waived. All medical records and test data that were copied during this study were also de-identified to protect the rights of the patients and uphold research ethics.

2.4. Statistical Analyses

Analyses were performed using SPSS for Windows 21.0 (SPSS, Chicago, IL, USA). We first used descriptive statistics to explain the distributions of the samples. All comparisons of the survivors and the deceased involved categorical variables and were thus analyzed using chi-squared tests. We then used the Kaplan–Meier survival analysis and the Cox proportional hazard model to analyze survival and follow-up durations with personal attributes (including age, gender, and BMI), cancer-related variables (including cancer type, site of metastasis, ECOG score, and NRS score), comorbidities (hypertension, diabetes, hypertrophic cardiomyopathy/cardiac dysrhythmia/heart failure, stroke, Parkinson’s disease or dementia, chronic obstructive pulmonary disease or asthma, peptic ulcer, hepatitis B or C, cirrhosis, and chronic kidney disease), and blood test data (including albumin, Hgb, and CRP) as independent variables. To identify influence factors, the multivariate Cox proportional hazard model first selects variables that are significantly correlated with survival and follow-up duration using univariate analysis and subsequently analyzes the influence of these variables on survival and follow-up duration using a multivariate model. Those with p values less than 0.05 were deemed statistically significant.

3. Results

3.1. Attributes and Basic Information of Stage IV Cancer Patients

The median follow-up duration of the stage IV cancer patients in this study was 6.1 months (25%–75% IQR = 2.4–13.3). During the study period, a total of 488 patients passed away (overall survival 66.0%). The mean age of the patients was 62.45 ± 11.89 years old. Male patients, of whom there were 405, accounted for over half of the patients (54.8%). The majority of the patients had a senior high school degree or lower (80.8%). Most of the patients were married (75.4%). Among the patients, 68.5% were unemployed or retired, and 52.8% were religious. Their mean BMI was 22.72 ± 4.45 kg/m². The most common cancer type among the patients was colorectal cancer, which 193 of the patients (26.1%) had. Head and neck cancers were the second most common cancers (18.5%). In terms of sites of metastasis, the most common sites were the pleura, pericardium, mesentery, or neck lymph nodes (55.2%), followed by the lungs (35.2%) and the liver (34.4%). Over half of the patients, i.e., 411 patients (55.6%), had ECOG scores of 0-1. The NRS score of the majority of the patients (547 patients, 74.0%) was 0–3. The most common comorbidities were hypertension (36.5%) and diabetes (24.5%). The mean albumin, Hgb, and CRP levels were 3.4 ± 0.65 g/dL, 10.8 ± 1.98 gm/dL, and 7.1 ± 7.61 μg/mL, respectively (Table 1).

Table 1. Characteristics of sample and distributions of parameters (N = 739).

Feature	Population
Age, years (mean ± SD)	62.45 ± 11.89

Gender, n (%)
Female	334 (45.2)
Male	405 (54.8)

Educational background
Senior high school or below	597 (80.8)
Junior college or above	142 (19.2)

Marital status
Married	557 (75.4)
Single, divorced, or widowed	182 (24.6)

Current state of employment
Employed	233 (31.5)
Unemployed or retired	506 (68.5)

Religious
Yes	390 (52.8)
No	349 (47.2)
BMI, kg/m² (mean ± SD)	22.72 ± 4.45

Cancer type, n (%)
Head and neck cancer	137 (18.5)
Colorectal cancer	193 (26.1)
Breast cancer	120 (16.2)
Genitourinary cancer	55 (7.4)
Gastric or esophageal cancer	97 (13.1)
Cholangiocarcinoma, hepatic or pancreatic cancer	79 (10.7)
Lung cancer	58 (7.8)

Site of metastasis, n (%)
Bones	225 (30.4)
Lungs	260 (35.2)
Liver	254 (34.4)
Brain	67 (9.1)
Other (pleura, pericardium, mesentery, or neck lymph nodes)	408 (55.2)

ECOG score, n (%)
0-1	411 (55.6)
2	218 (29.5)
3 or higher	110 (14.9)

NRS score, n (%)
0–3	547 (74.0)
4–6	178 (24.1)
7–10	14 (1.9)

Comorbidity, n (%)
Hypertension	270 (36.5)
Diabetes	181 (24.5)
Cardiomyopathy, arrhythmia, or heart failure	95 (12.9)
Stroke	40 (5.4)
Parkinson’s disease or dementia	15 (2.0)
Chronic obstructive pulmonary disease or asthma	18 (2.4)
Peptic ulcer	64 (8.7)
Hepatitis B or C	91 (12.3)
Cirrhosis	25 (3.4)
Chronic kidney disease	45 (6.1)

Blood test values
Albumin, g/dL (mean ± SD)	3.4 ± 0.65
Hgb, gm/dL (mean ± SD)	10.8 ± 1.98
CRP, μg/mL (mean ± SD)	7.1 ± 7.61
Overall survival^a (months), median (25%–75% IQR)	6.1 (2.4–13.3)

Note: Hgb, hemoglobin; IQR, interquartile range.
Abbreviations: BMI, body mass index; CRP, C-reactive protein; ECOG, Eastern Cooperative Oncology Group; NRS, Numerical Rating Scale; SD, standard deviation.
^aOverall survival: The period between the day of patient admission and the date of their last outpatient visit or death served as a reference for the calculation of survival period.

3.2. Attributes and Clinical Basic Information of Survivors and Deceased Among Stage IV Cancer Patients

A comparison of the stage IV cancer patients divided into a survivor group and a deceased group revealed significant differences in age (p = 0.032), BMI (p = 0.033), liver metastases (p < 0.001), ECOG score (p < 0.001), and NRS score (p < 0.001) as well as comorbidities peptic ulcer (p < 0.001) and chronic kidney disease (p = 0.041). They also presented differences in albumin (p < 0.001), Hgb (p < 0.001), and CRP (p < 0.001) levels as well as in the overall survival of the patients (p < 0.001) (Table 2).

Table 2. Comparison of attributes and clinical data of surviving and deceased stage IV cancer patients (N = 739).

Feature	Survivors n = 251	Deceased n = 488	p^a
Feature	n (%)	n (%)	p^a
Age			0.032
≤ 65 years old	163 (64.9)	277 (56.8)
> 65 years old	88 (35.1)	211 (43.2)
Gender			0.386
Female	119 (47.4)	215 (44.1)
Male	132 (52.6)	273 (55.9)
BMI, kg/m²			0.033
< 25 kg/m²	177 (70.5)	379 (77.7)
≥ 25 kg/m²	74 (29.5)	109 (22.3)
Cancer type			0.268
Head and neck cancer	61 (24.3)	76 (15.6)
Colorectal cancer	64 (25.5)	129 (26.4)
Breast cancer	52 (20.7)	68 (13.9)
Genitourinary cancer	10 (4.0)	45 (9.2)
Gastric or esophageal cancer	24 (9.6)	73 (15.0)
Cholangiocarcinoma, hepatic or pancreatic cancer	16 (6.4)	63 (12.9)
Lung cancer	24 (9.6)	34 (7.0)
Site of metastasis
Bones	68 (27.1)	157 (32.2)	0.155
Lungs	77 (30.7)	183 (37.5)	0.066
Liver	61 (24.3)	193 (39.5)	< 0.001
Brain	20 (8.0)	47 (9.6)	0.456
Other (pleura, pericardium, mesentery, or neck lymph nodes)	134 (53.4)	274 (56.1)	0.475
ECOG score			< 0.001
0-1	194 (77.3)	217 (44.5)
2	45 (17.9)	173 (35.5)
3 or higher	12 (4.8)	98 (20.1)
NRS score			< 0.001
0–3	229 (91.2)	318 (65.2)
4–6	21 (8.4)	157 (32.2)
7–10	1 (0.4)	13 (2.7)
Comorbidity
Hypertension	86 (34.3)	184 (37.7)	0.357
Diabetes	55 (21.9)	126 (25.8)	0.242
Cardiomyopathy, arrhythmia, or heart failure	30 (12.0)	65 (13.3)	0.599
Stroke	9 (3.6)	31 (6.4)	0.115
Parkinson’s disease or dementia	2 (0.8)	13 (2.7)	0.088
Chronic obstructive pulmonary disease or asthma	7 (2.8)	11 (2.3)	0.655
Peptic ulcer	7 (2.8)	57 (11.7)	< 0.001
Hepatitis B or C	32 (12.7)	59 (12.1)	0.796
Cirrhosis	7 (2.8)	18 (3.7)	0.522
Chronic kidney disease	9 (3.6)	36 (7.4)	0.041
Blood test values
Albumin			< 0.001
< 3.5 g/dL	48 (20.8)	317 (66.9)
≥ 3.5 g/dL	183 (79.2)	157 (33.1)
Hgb			< 0.001
< 10.0 gm/dL	33 (13.1)	238 (48.8)
≥ 10.0 gm/dL	218 (86.9)	250 (51.2)
CRP			< 0.001
< 10.0 μg/mL	149 (87.6)	285 (68.0)
≥ 10.0 μg/mL	21 (12.4)	134 (32.0)
Overall survival^a (months), median (25%–75% IQR)	14.6 (8.4–24.0)	3.6 (1.6–7.3)	< 0.001

Note: Hgb, hemoglobin; IQR, interquartile range.
Abbreviations: BMI, body mass index; CRP, C-reactive protein; ECOG, Eastern Cooperative Oncology Group; NRS, Numerical Rating Scale.
^aCategorical variables were analyzed using chi-squared tests.

3.3. Risk Analysis of Survival Period of Stage IV Cancer Patients Using Univariate Factors

The univariate Cox regression analysis in Table 3 shows that the regression coefficients of age > 65 years old (HR = 1.37, p = 0.001), BMI < 25 kg/m2(HR = 1.37, p = 0.004), and cancer type being colorectal cancer (HR = 1.49, p = 0.006), genitourinary cancer (HR = 2.13, p < 0.001), gastric or esophageal cancer (HR = 1.96, p < 0.001), hepatic cancer, cholangiocarcinoma, or pancreatic cancer (HR = 2.48, p < 0.001) were significant. The regression coefficients of lung metastases (HR = 1.28, p = 0.008) and liver metastases (HR = 1.53, p < 0.001) were also significant. For ECOG scores, the regression coefficients of ECOG scores of 2 (HR = 2.24, p < 0.001) and 3 or higher (HR = 3.65, p < 0.001) were significant. Regarding NRS scores, the regression coefficients of NRS scores 4–6 (HR = 2.10, p < 0.001) and 7–10 (HR = 2.87, p < 0.001) were significant. The regression coefficients of the comorbidities stroke (HR = 1.48, p = 0.036) and peptic ulcer (HR = 1.96, p < 0.001) and those of the blood test values for albumin < 3.5 g/dL (HR = 3.66, p < 0.001), Hgb < 10.0 gm/dL (HR = 2.77, p < 0.001), and CRP≥ 10.0 μg/mL (HR = 2.04, p < 0.001) were also significant, thereby indicating clear associations between these variables and survival period. Thus, these variables were included in the multivariate model analysis.

Table 3. Cox regression model of variables influencing survival of stage IV cancer patients (N = 739).

Variable	Univariate		Multivariate
Variable	HR (95% CI)	p	HR (95% CI)	p
Age > 65 years old (reference group: ≤ 65 years old)	1.37 (1.15–1.64)	0.001	1.00 (0.81–1.23)	0.983
Female (reference group: male)	1.07 (0.90–1.28)	0.438
BMI < 25 kg/m² (reference group: ≥ 25 kg/m²)	1.37 (1.11–1.70)	0.004	1.32 (1.04–1.69)	0.023

Cancer type (reference group: head and neck cancer)
Colorectal cancer	1.49 (1.12–1.99)	0.006	1.20 (0.84–1.71)	0.317
Breast cancer	1.09 (0.78–1.51)	0.609	1.27 (0.87–1.86)	0.213
Genitourinary cancer	2.13 (1.47–3.09)	< 0.001	1.40 (0.93–2.10)	0.109
Gastric or esophageal cancer	1.96 (1.42–2.71)	< 0.001	1.66 (1.15–2.39)	0.007
Cholangiocarcinoma, hepatic or pancreatic cancer	2.48 (1.77–3.49)	< 0.001	1.57 (1.05–2.35)	0.030
Lung cancer	1.15 (0.77–1.73)	0.498	1.10 (0.70–1.75)	0.672

Site of metastasis
Bones	1.14 (0.94–1.38)	0.180
Lungs	1.28 (1.07–1.54)	0.008	1.07 (0.86–1.32)	0.558
Liver	1.53 (1.27–1.83)	< 0.001	1.56 (1.25–1.95)	< 0.001
Brain	1.05 (0.78–1.43)	0.729
Other	0.93 (0.78–1.11)	0.435

ECOG score (reference group: 0-1)
2	2.24 (1.83–2.74)	< 0.001	1.95 (1.55–2.47)	< 0.001
3 or higher	3.65 (2.86–4.65)	< 0.001	2.68 (2.01–3.58)	< 0.001

NRS score (reference group: 0–3)
4–6	2.10 (1.73–2.55)	< 0.001	1.22 (0.98–1.51)	0.079
7–10	2.87 (1.64–5.01)	< 0.001	1.36 (0.74–2.49)	0.326

Comorbidity
Hypertension	1.17 (0.97–1.40)	0.097
Diabetes	1.20 (0.98–1.48)	0.072
Cardiomyopathy, arrhythmia, or heart failure	1.15 (0.89–1.49)	0.297
Stroke	1.48 (1.03–2.13)	0.036	0.99 (0.65–1.53)	0.978
Parkinson’s disease or dementia	1.59 (0.92–2.77)	0.097
Chronic obstructive pulmonary disease or asthma	0.81 (0.45–1.47)	0.492
Peptic ulcer	1.96 (1.49–2.59)	< 0.001	1.59 (1.16–2.17)	0.004
Hepatitis B or C	0.97 (0.74–1.27)	0.800
Cirrhosis	1.37 (0.85–2.19)	0.195
Chronic kidney disease	1.11 (0.79–1.56)	0.551

Blood test values
Albumin < 3.5 g/dL	3.66 (3.01–4.45)	< 0.001	1.99 (1.57–2.53)	< 0.001
Hgb < 10.0 gm/dL	2.77 (2.31–3.32)	< 0.001	1.99 (1.61–2.46)	< 0.001
CRP ≥ 10.0 μg/mL	2.04 (1.66–2.50)	< 0.001	1.28 (1.02–1.62)	034

Note: Hgb, hemoglobin; 95% CL, confidence limits at 95%.
Abbreviations: BMI, body mass index; CRP, C-reactive protein; ECOG, Eastern Cooperative Oncology Group; HR, hazard ratio; NRS, Numerical Rating Scale.

3.4. Predictors of Survival Period of Stage IV Cancer Patients

The results of the multivariate Cox regression analysis showed that the regression coefficient of BMI < 25 kg/m² (HR = 1.32, p = 0.023) was significant. A Kaplan–Meier survival analysis also indicated that the survival periods of patients with BMI ≥ 25 kg/m² were longer than those of patients with BMI < 25 kg/m² (log rank: 8.62, p = 0.003). In cancer type, the regression coefficients of gastric or esophageal cancer (HR = 1.66, p = 0.007) as well as hepatic cancer, cholangiocarcinoma, or pancreatic cancer (HR = 1.57, p = 0.030) were significant. For the site of metastasis, the regression coefficient of liver metastases (HR = 1.56, p < 0.001) was significant. A Kaplan–Meier survival analysis also indicated that the survival periods of patients with no liver metastases were longer than those of patients with liver metastases (log rank: 20.96, p < 0.001). Regarding ECOG scores, the regression coefficients of ECOG scores of 2 (HR = 1.95, p < 0.001) and 3 or higher (HR = 2.68, p < 0.001) were significant. A Kaplan–Meier survival analysis also showed that the survival periods of patients with ECOG scores of 0-1 were longer than those of patients with ECOG scores of 2 and those of patients with ECOG scores of 3 or higher (log rank: 139.56, p < 0.001). In comorbidities, the regression coefficient of peptic ulcer (HR = 1.59, p = 0.004) was significant, and a Kaplan–Meier survival analysis also revealed that the survival periods of patients with no peptic ulcers were longer than those of patients with peptic ulcers (log rank: 23.82, p < 0.001). For blood test values, the regression coefficient of albumin < 3.5 g/dL (HR = 1.99, p < 0.001) was significant, and a Kaplan–Meier survival analysis indicated that the survival periods of patients with albumin ≥ 3.5 g/dL were longer than those of patients with albumin < 3.5 g/dL (log rank: 193.55, p < 0.001); the regression coefficient of Hgb < 10.0 gm/dL (HR = 1.99, p < 0.001) was significant, and a Kaplan–Meier survival analysis indicated that the survival periods of patients with Hgb ≥ 10.0 gm/dL were longer than those of patients with Hgb < 10.0 gm/dL (log rank: 132.31, p < 0.001); the regression coefficient of CRP ≥ 10.0 μg/mL (HR = 1.28, p = 0.034) was significant, and a Kaplan–Meier survival analysis indicated that the survival periods of patients with CRP < 10.0 μg/mL were longer than those of patients with CRP ≥ 10.0 μg/mL (log rank: 47.80, p < 0.001) (Table 3 and Figure 2).

4. Discussion

The results demonstrate that the survival periods of stage IV cancer patients with BMI ≥ 25 kg/m² were significantly longer than those of stage IV cancer patients with BMI < 25 kg/m². In cancer type, the mortality risks of patients who had stage IV gastric or esophageal cancer and those who had stage IV hepatic cancer, cholangiocarcinoma, or pancreatic cancer were higher than those who had stage IV head and neck cancer. Moreover, the mortality risk of patients with liver metastases was higher, and the mortality risks of patients with ECOG scores of 2 or higher were higher than those of patients with ECOG scores of 0-1. Furthermore, the mortality risks of stage IV cancer patients who had peptic ulcers were even higher. Regarding blood test values, the mortality risk of stage IV cancer patients with albumin < 3.5 g/dL was higher than that of stage IV cancer patients with albumin ≥ 3.5 g/dL; the mortality risk of stage IV cancer patients with Hgb < 10.0 gm/dL was higher than that of stage IV cancer patients with Hgb ≥ 10.0 gm/dL, and the mortality risk of stage IV cancer patients with CRP ≥ 10.0 gm/dL was higher than that of stage IV cancer patients with CRP < 10.0 gm/dL.

This study employed the Cox proportional hazard model to assess the influence of different cancer types on the survival of stage IV cancer patients. In this model, we regarded cancer type as an independent variable in consideration of the heterogeneity among different cancers [29]. Owing to our separate hazard analyses of colorectal cancer, breast cancer, genitourinary cancer, gastric or esophageal cancer, cholangiocarcinoma, hepatic or pancreatic cancer, and lung cancer, we discovered significant differences in the mortality risks of different cancers, indicating that the personal attributes of patients with different cancer types are already reflected in the cancer type variable. This analysis method enables the recognition of the heterogeneity among different cancers and furthers our understanding of the impact of cancer characteristics on patient survival.

Collet et al. [19] analyzed 272 patients with aggressive cancer. A Kaplan–Meier survival analysis that they conducted indicated that the survival periods of patients with BMI ≥ 25 kg/m² were significantly longer than those of patients with BMI < 25 kg/m² (p = 0.015). A meta-analysis performed by Greenlee et al. [30] on 22 clinical trials also presented similar results; the authors believed that male patients with aggressive cancer and BMI < 25 kg/m² have poorer prognoses and higher mortality risks. In support, the results of our study were also consistent: the mortality risk of stage IV cancer patients with BMI < 25 kg/m² was 1.32 times higher than that of stage IV cancer patients with BMI ≥ 25 kg/m². Indeed, evidence in existing research supports the notion that obesity caused by overeating can lead to antitumor effects by reducing the proportion of immature CD8+ T cells and increasing the size of bone marrow cells [31].

This study also found that the mortality risk of patients with stage IV gastric or esophageal cancer was higher than that of patients with stage IV head and neck cancer. Verstegen et al. [32] reported that over half of gastric or esophageal cancers had already metastasized by the time of diagnosis. Due to the high incidence of local recurrence and distant metastasis, the prognoses of gastric or esophageal cancer patients are generally poor. Similarly, the prognoses of liver cancer patients are also poor, and cholangiocarcinoma and pancreatic cancer are even bigger medical challenges at present because these cancers are more difficult to diagnose and treat than other cancers [33–35]. Pancreatic cancer, in particular, is widely recognized as one of the most lethal types of cancer; the 5-year survival rate of stage IV pancreatic cancer is only 2.9% [36]. In contrast, a large-scale database study indicated that although the mortality risk of patients with head and neck cancer is 7.72 times higher than that of individuals of the same age and gender with no cancer, the mortality risk of patients with other types of cancer is 8.87 times higher than that of individuals of the same age and gender with no cancer, thereby proving that the mortality risk of patients with head and neck cancer is lower than those of patients with other types of cancer [37].

We found in our study that patients with liver metastases had a higher mortality risk. Other studies have also demonstrated that cancer metastasis to the liver increases the risk of mortality compared to metastasis to other locations. A large-scale database cohort study conducted by Wang et al. [38] found that among 1.6 million cancer patients, liver metastases were present in 6.46% of these patients, and the median survival period of these patients was only 4 months. Similarly, Li et al. [39] investigated the prognoses of lung cancer patients whose cancer had metastasized to different sites and discovered that among all patients with distant metastasis, those with liver metastases had the shortest survival periods.

This study considered and included pre-existing conditions in the multivariate analysis model. For instance, peptic ulcers were listed as a risk factor, and our analysis demonstrated that they were significantly correlated with survival. Although this study did not examine previous cancer treatments (chemotherapy, radiotherapy, or immunotherapy) in detail, it emphasizes the impact of physical condition (ECOG score) and physiological indices (albumin, Hgb, and CRP), which play key roles in clinical decisions. The ECOG score is generally used to assess the physical capacity of patients; higher scores indicate poorer physical conditions and are associated with poorer prognoses [40]. Albumin, Hgb, and CRP provide information on the nutritional, inflammatory, and anemic conditions of patients, which are also closely associated with patient survival [14, 41] due to their direct reflection of a patient’s overall health and tolerance to treatment. These clinical indices can not only help medical teams to evaluate the health status of terminal cancer patients but also serve as decision aids for the selection and adjustment of palliative care plans.

An early research study by Walsh et al. [42] indicated a connection between the performance status and survival period of aggressive cancer patients; they found that for each one-point increase in the ECOG score, the mortality risk of the patient increases by 1.4 times. Recent research has identified similar findings, making the ECOG score a crucial factor in predicting the survival period of patients with aggressive cancer [43]. We analyzed the performance status of stage IV cancer patients and similarly found that their mortality risk is higher in patients who have an ECOG score of 2 or 3 or higher compared to patients with an ECOG score of 0-1.

We further discovered that stage IV cancer patients with peptic ulcers as a comorbidity had a 1.59 times higher mortality risk compared to patients without peptic ulcers. Furthermore, cancer patients may also succumb to noncancer causes. For instance, radiotherapy and chemotherapy are routine treatments for patients with unresectable cancer. However, these treatments are also toxic and can damage the gastrointestinal tract, making peptic ulcers a common problem. Peptic ulcers are a chronic progressive disease resulting from damaged gastrointestinal mucosa in which the protective effect is diminished. Complications such as erosion, bleeding, and perforation can lead to patient death [44]. Yang et al. [45] investigated the mortality rate of cancer patients with peptic ulcers and found that 4698 out of 8.4 million cancer patients died of peptic ulcers and that their mortality risk was 1.78 times higher than that of peptic ulcer patients with no cancer. The mortality risk of cancer patients with distant metastasis as well as peptic ulcers was even higher: 3.40 times higher compared to peptic ulcer patients without cancer.

In another aspect, researchers have presented strong evidence that albumin is an acute phase reactant of inflammation. Overly low levels of albumin in patients are a strong indication of cachexia [46, 47]; on the other hand, increases in albumin can hinder the progression of cancer cachexia and inflammation. We analyzed the albumin levels in stage IV cancer patients and found that cancer patients with albumin levels below 3.5 g/dL had higher mortality risks than those with albumin levels of 3.5 g/dL or higher. Viganò et al. [48] discovered that albumin levels below 3.5 g/dL are a crucial predictor of whether the patients could survive past the median survival time of 15.3 weeks.

In this study, the mortality risk of stage IV cancer patients with Hgb levels below 10.0 gm/dL was higher than that of stage IV cancer patients with Hgb levels at 10.0 gm/dL or higher. A systematic review based on two hundred papers conducted by Caro et al. [49] similarly indicated that anemia increases the relative mortality risk of cancer patients: anemia increases mortality risk by 19% in lung cancer patients, by 75% in head and neck cancer patients, by 47% in prostate cancer patients, and by 67% in lymphoma patients. Overall, anemia increases the risk of mortality by 65% in cancer patients.

When stimulated by interleukin-6 and tumor necrosis factor-α, liver cells synthesize increasing levels of CRP. Past research has shown that CRP is an independent factor that can be used to predict the survival time of late-stage cancer patients [50]. A study conducted by Stone et al. [51] found that increases in CRP are a significant predictor of whether patients can survive past the median of 14 days. Hart et al. [27] pointed out that CRP has unique properties in the development of prometastatic tumor microenvironments. In recent years, researchers have demonstrated that the CRP/albumin ratio can be used to effectively predict the prognoses of late-stage cancer patients [52, 53]. Recent studies have also indicated that ECOG scores and CRP/albumin ratios can both independently predict the survival period of late-stage cancer patients and that together, unfavorable values have a synergistic effect that accelerates the death of late-stage cancer patients [54]. This is consistent with the results of this study, in which a higher mortality risk was found in cancer patients with CRP levels of 10.0 gm/dL or higher.

4.1. Implications for Practice

For medical teams, cancer stage and metastasis conditions are out of their control; however, effort should be devoted to maintaining a healthy BMI and performance status of stage IV cancer patients, treating their peptic ulcers, and monitoring their albumin, Hgb, and CRP levels. For instance, medical teams must pay even closer attention to improving the nutritional status and physical conditions of stage IV cancer patients with a BMI below 25 and more proactively control the ulcer conditions of stage IV cancer patients with peptic ulcers in order to reduce their mortality risk. Clinically, additional nutritional support, treatments to improve anemia such as blood transfusions, or assessments of the source of inflammation and subsequent active measures should, respectively, be considered for stage IV cancer patients with albumin lower than 3.5 g/dL, Hgb lower than 10.0 gm/dL, or CRP higher than 10.0 μg/mL to improve their survival and quality of life.

4.2. Conclusion

This study examined 739 stage IV cancer patients for 6.1 months. The results showed that the overall mortality rate was 66.0% and that the mortality risks of gastric or esophageal cancer as well as hepatic cancer, cholangiocarcinoma, or pancreatic cancer were higher than that of head and neck cancer. Patients with BMI < 25 kg/m², liver metastases, poor performance status, and peptic ulcers as a comorbidity also had a greater mortality risk. With regard to blood test values, we found a higher mortality risk in stage IV cancer patients with albumin < 3.5 g/dL, Hgb < 10.0 gm/dL, and CRP ≥ 10.0 gm/dL.

This study is unique in its comprehensive consideration of multiple variables that impact the survival of stage IV cancer patients, simultaneously taking into account demographic characteristics, cancer type and site of metastasis, physical conditions, pain conditions, comorbidities, and laboratory blood test data. We employed the Kaplan–Meier survival analysis and Cox proportional hazard model to comprehensively assess the influence of different factors on survival, and these methods are more extensive than single-variable studies. Furthermore, this study demonstrated the predictive value of blood test data (albumin, Hgb, and CRP) for the survival of stage IV cancer patients, which has been underreported in other studies.

Conflicts of Interest

The authors declare no conflicts of interest.

Funding

No funding was obtained for this study.

Acknowledgments

The authors have nothing to report.

Open Research

Data Availability Statement

The datasets used or analyzed during the current study are available from the corresponding author upon reasonable request.

References

1 Brand D. A., The Stage IV Shuffle: Elusiveness of Straight Talk About Advanced Cancer, Journal of General Internal Medicine. (2019) 34, no. 11, 2637–2642, https://doi.org/10.1007/s11606-019-05158-5, 2-s2.0-85070274259.
10.1007/s11606-019-05158-5
PubMed Web of Science® Google Scholar
2 Raskin W., Harle I., Hopman W. M., and Booth C. M., Prognosis, Treatment Benefit and Goals of Care: What Do Oncologists Discuss With Patients Who Have Incurable Cancer?, Clinical Oncology. (2016) 28, no. 3, 209–214, https://doi.org/10.1016/j.clon.2015.11.011, 2-s2.0-84949818076.
10.1016/j.clon.2015.11.011
CAS Google Scholar
3 Sebastian A. M. and Peter D., Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions, Life (Basel, Switzerland). (2022) 12, no. 12, https://doi.org/10.3390/life12121991.
10.3390/life12121991
Web of Science® Google Scholar
4 Barquín M., Calvo V., García-García F. et al., Sex Is a Strong Prognostic Factor in Stage IV Non-Small-Cell Lung Cancer Patients and Should Be Considered in Survival Rate Estimation, Cancer Epidemiology. (2020) 67, https://doi.org/10.1016/j.canep.2020.101737.
10.1016/j.canep.2020.101737
PubMed Web of Science® Google Scholar
5 de Glas N. A., Bastiaannet E., de Craen A. J. et al., Survival of Older Patients With Metastasised Breast Cancer Lags behind Despite Evolving Treatment Strategies—A Population-Based Study, European Journal of Cancer. (2015) 51, no. 3, 310–316, https://doi.org/10.1016/j.ejca.2014.11.021, 2-s2.0-84922604304.
10.1016/j.ejca.2014.11.021
CAS PubMed Web of Science® Google Scholar
6 Van Herck Y., Feyaerts A., Alibhai S. et al., Is Cancer Biology Different in Older Patients?, The Lancet Healthy Longevity. (2021) 2, no. 10, e663–e677, https://doi.org/10.1016/S2666-7568(21)00179-3.
10.1016/S2666-7568(21)00179-3
PubMed Google Scholar
7 Tu H., McQuade J. L., Davies M. A. et al., Body Mass Index and Survival After Cancer Diagnosis: A Pan-Cancer Cohort Study of 114430 Patients With Cancer, Innovation. (2022) 3, no. 6, https://doi.org/10.1016/j.xinn.2022.100344.
10.1016/j.xinn.2022.100344
Google Scholar
8 American Society of Clinical Oncology, Cancer Facts & Figures 2023, National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) Program. (2023) https://www.cancer.net/cancer-types/breast-cancer/statistics.
Google Scholar
9 Wang J., Li S., Liu Y., Zhang C., Li H., and Lai B., Metastatic Patterns and Survival Outcomes in Patients With Stage IV Colon Cancer: A Population-Based Analysis, Cancer Medicine. (2020) 9, no. 1, 361–373, https://doi.org/10.1002/cam4.2673.
10.1002/cam4.2673
PubMed Web of Science® Google Scholar
10 Kao J., Gold K. D., Zarrili G. et al., Clinical Predictors of Survival for Patients With Stage IV Cancer Referred to Radiation Oncology, PLoS One. (2015) 10, no. 4, https://doi.org/10.1371/journal.pone.0124329, 2-s2.0-84929497860.
10.1371/journal.pone.0132748
Web of Science® Google Scholar
11 Zheng J., He J., Wang W. et al., The Impact of Pain and Opioids Use on Survival in Cancer Patients: Results From a Population-Based Cohort Study and a Meta-Analysis, Medicine. (2020) 99, no. 9, https://doi.org/10.1097/MD.0000000000019306.
10.1097/MD.0000000000019306
Web of Science® Google Scholar
12 Fowler H., Belot A., Ellis L. et al., Comorbidity Prevalence Among Cancer Patients: A Population-Based Cohort Study of Four Cancers, BMC Cancer. (2020) 20, no. 1, https://doi.org/10.1186/s12885-019-6472-9.
10.1186/s12885-019-6472-9
PubMed Web of Science® Google Scholar
13 Wu A. H., Kurian A. W., Kwan M. L. et al., Diabetes and Other Comorbidities in Breast Cancer Survival by Race/Ethnicity: The California Breast Cancer Survivorship Consortium (CBCSC), Cancer Epidemiology, Biomarkers & Prevention. (2015) 24, no. 2, 361–368, https://doi.org/10.1158/1055-9965.EPI-14-1140, 2-s2.0-84922749733.
10.1158/1055-9965.EPI-14-1140
CAS PubMed Web of Science® Google Scholar
14 Ju S. Y. and Ma S. J., High C-Reactive Protein to Albumin Ratio and the Short-Term Survival Prognosis Within 30 Days in Terminal Cancer Patients Receiving Palliative Care in a Hospital Setting: A Retrospective Analysis, Medicine. (2020) 99, no. 9, https://doi.org/10.1097/MD.0000000000019350.
10.1097/MD.0000000000019350
Web of Science® Google Scholar
15 Busti F., Marchi G., Ugolini S., Castagna A., and Girelli D., Anemia and Iron Deficiency in Cancer Patients: Role of Iron Replacement Therapy, Pharmaceuticals. (2018) 11, no. 4, https://doi.org/10.3390/ph11040094, 2-s2.0-85055752234.
10.3390/ph11040094
Web of Science® Google Scholar
16 Wei J., Xiang J., Hao Y. et al., Baseline Anemia Predicts a Poor Prognosis in Patients With Non-Small Cell Lung Cancer With Epidermal Growth Factor Receptor Mutations: A Retrospective Study, BMC Pulmonary Medicine. (2022) 22, no. 1, https://doi.org/10.1186/s12890-022-02158-w.
10.1186/s12890-022-02158-w
Web of Science® Google Scholar
17 An L., Ju W., Zheng R. et al., Trends in Survival for Cancer Patients Aged 65 Years or Over From 1995 to 2014 in the United States: A Population-Based Study, Cancer Medicine. (2023) 12, no. 5, 6283–6293, https://doi.org/10.1002/cam4.5398.
10.1002/cam4.5398
CAS PubMed Web of Science® Google Scholar
18 Liang Y., Zhao L., Chen H. et al., Correction to: Survival Analysis of Elderly Patients over 65 Years Old With Stage II/III Gastric Cancer Treated With Adjuvant Chemotherapy After Laparoscopic D2 Gastrectomy: A Retrospective Cohort Study, BMC Cancer. (2021) 21, no. 1, https://doi.org/10.1186/s12885-021-08037-7.
10.1186/s12885-021-08037-7
Google Scholar
19 Collet L., Delrieu L., Bouhamama A. et al., Association Between Body Mass Index and Survival Outcome in Metastatic Cancer Patients Treated by Immunotherapy: Analysis of a French Retrospective Cohort, Cancers. (2021) 13, no. 9, https://doi.org/10.3390/cancers13092200.
10.3390/cancers13092200
Web of Science® Google Scholar
20 Hess L. M., Smith D., Cui Z. L. et al., The Relationship Between Eastern Cooperative Oncology Group Performance Status and Healthcare Resource Utilization Among Patients With Advanced or Metastatic Colorectal, Lung or Gastric Cancer, Journal of Drug Assessment. (2020) 10, no. 1, 10–17, https://doi.org/10.1080/21556660.2020.1851504.
10.1080/21556660.2020.1851504
PubMed Google Scholar
21 Yang F., Markovic S. N., Molina J. R. et al., Association of Sex, Age, and Eastern Cooperative Oncology Group Performance Status With Survival Benefit of Cancer Immunotherapy in Randomized Clinical Trials: A Systematic Review and Meta-Analysis, JAMA Network Open. (2020) 3, no. 8, https://doi.org/10.1001/jamanetworkopen.2020.12534.
10.1001/jamanetworkopen.2020.12534
Web of Science® Google Scholar
22 Kim H. J. and Jung S. O., Comparative Evaluations of Single-Item Pain-Intensity Measures in Cancer Patients: Numeric Rating Scale vs. Verbal Rating Scale, Journal of Clinical Nursing. (2020) 29, no. 15-16, 2945–2952, https://doi.org/10.1111/jocn.15341.
10.1111/jocn.15341
PubMed Web of Science® Google Scholar
23 Guven D. C., Sahin T. K., Erul E. et al., The Association Between Albumin Levels and Survival in Patients Treated With Immune Checkpoint Inhibitors: A Systematic Review and Meta-Analysis, Frontiers in Molecular Biosciences. (2022) 9, https://doi.org/10.3389/fmolb.2022.1039121.
10.3389/fmolb.2022.1039121
PubMed Web of Science® Google Scholar
24 Aapro M., Osterwalder B., Scherhag A., and Burger H. U., Epoetin-Beta Treatment in Patients With Cancer Chemotherapy-Induced Anaemia: The Impact of Initial Haemoglobin and Target Haemoglobin Levels on Survival, Tumour Progression and Thromboembolic Events, British Journal of Cancer. (2009) 101, no. 12, 1961–1971, https://doi.org/10.1038/sj.bjc.6605255, 2-s2.0-71649091673.
10.1038/sj.bjc.6605255
CAS PubMed Web of Science® Google Scholar
25 Abella V., Scotece M., Conde J. et al., Leptin in the Interplay of Inflammation, Metabolism and Immune System Disorders, Nature Reviews Rheumatology. (2017) 13, no. 2, 100–109, https://doi.org/10.1038/nrrheum.2016.209, 2-s2.0-85008312137.
10.1038/nrrheum.2016.209
CAS PubMed Web of Science® Google Scholar
26 Edimiris-Herrmann A., Kolberg-Liedtke C., Bittner A. K. et al., The Role of C-Reactive Protein as a Prognostic Biomarker in Patients With Early Breast Cancer Treated With Neoadjuvant Chemotherapy, Breast Care. (2022) 17, no. 4, 371–376, https://doi.org/10.1159/000522606.
10.1159/000522606
PubMed Web of Science® Google Scholar
27 Hart P. C., Rajab I. M., Alebraheem M., and Potempa L. A., C-Reactive Protein and Cancer-Diagnostic and Therapeutic Insights, Frontiers in Immunology. (2020) 11, https://doi.org/10.3389/fimmu.2020.595835.
10.3389/fimmu.2020.595835
Web of Science® Google Scholar
28 Potempa L. A., Rajab I. M., Olson M. E., and Hart P. C., C-Reactive Protein and Cancer: Interpreting the Differential Bioactivities of Its Pentameric and Monomeric, Modified Isoforms, Frontiers in Immunology. (2021) 12, https://doi.org/10.3389/fimmu.2021.744129.
10.3389/fimmu.2021.744129
Web of Science® Google Scholar
29 Balan T. A. and Putter H., Nonproportional Hazards and Unobserved Heterogeneity in Clustered Survival Data: When Can We Tell the Difference?, Statistics in Medicine. (2019) 38, no. 18, 3405–3420, https://doi.org/10.1002/sim.8171, 2-s2.0-85065317302.
10.1002/sim.8171
PubMed Google Scholar
30 Greenlee H., Unger J. M., LeBlanc M., Ramsey S., and Hershman D. L., Association Between Body Mass Index and Cancer Survival in a Pooled Analysis of 22 Clinical Trials, Cancer Epidemiology, Biomarkers & Prevention. (2017) 26, no. 1, 21–29, https://doi.org/10.1158/1055-9965.EPI-15-1336, 2-s2.0-85009067836.
10.1158/1055-9965.EPI-15-1336
PubMed Web of Science® Google Scholar
31 Ringel A. E., Drijvers J. M., Baker G. J. et al., Obesity Shapes Metabolism in the Tumor Microenvironment to Suppress Anti-Tumor Immunity, Cell. (2020) 183, no. 7, 1848–1866.e26, https://doi.org/10.1016/j.cell.2020.11.009.
10.1016/j.cell.2020.11.009
CAS PubMed Web of Science® Google Scholar
32 Verstegen M. H., Harker M., Water C. v. d. et al., Metastatic Pattern in Esophageal and Gastric Cancer: Influenced by Site and Histology, World Journal of Gastroenterology. (2020) 26, no. 39, 6037–6046, https://doi.org/10.3748/wjg.v26.i39.6037.
10.3748/wjg.v26.i39.6037
PubMed Web of Science® Google Scholar
33 Bazeed A. Y., Day C. M., and Garg S., Pancreatic Cancer: Challenges and Opportunities in Locoregional Therapies, Cancers. (2022) 14, no. 17, https://doi.org/10.3390/cancers14174257.
10.3390/cancers14174257
PubMed Web of Science® Google Scholar
34 Chen S. C., Challenges in Translating Clinical Guidelines into Real-Life Practice for Management of Hepatocellular Carcinoma in Taiwan, Clinical and Molecular Hepatology. (2023) 29, no. 2, 352–354, https://doi.org/10.3350/cmh.2023.0101.
10.3350/cmh.2023.0101
PubMed Web of Science® Google Scholar
35 Mauro E., Ferrer-Fàbrega J., Sauri T. et al., New Challenges in the Management of Cholangiocarcinoma: The Role of Liver Transplantation, Locoregional Therapies, and Systemic Therapy, Cancers. (2023) 15, no. 4, https://doi.org/10.3390/cancers15041244.
10.3390/cancers15041244
Web of Science® Google Scholar
36 United States Department of Health and Human Services, Stage 4 Pancreatic Cancer Survival Rates, National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) Program. (2023) https://seer.cancer.gov/statfacts/html/pancreas.html.
Google Scholar
37 Liu F. H., Huang J. Y., Lin C., and Kuo T. J., Suicide Risk After Head and Neck Cancer Diagnosis in Taiwan: A Retrospective Cohort Study, Journal of Affective Disorders. (2023) 320, 610–615, https://doi.org/10.1016/j.jad.2022.09.151.
10.1016/j.jad.2022.09.151
PubMed Web of Science® Google Scholar
38 Wang S., Feng Y., Swinnen J., Oyen R., Li Y., and Ni Y., Incidence and Prognosis of Liver Metastasis at Diagnosis: A Pan-Cancer Population-Based Study, American Journal of Cancer Research. (2020) 10, no. 5, 1477–1517.
PubMed Web of Science® Google Scholar
39 Li J., Zhu H., Sun L., Xu W., and Wang X., Prognostic Value of Site-Specific Metastases in Lung Cancer: A Population Based Study, Journal of Cancer. (2019) 10, no. 14, 3079–3086, https://doi.org/10.7150/jca.30463, 2-s2.0-85070562398.
10.7150/jca.30463
PubMed Web of Science® Google Scholar
40 Simcock R. and Wright J., Beyond Performance Status, Clinical Oncology (Royal College of Radiologists (Great Britain)). (2020) 32, no. 9, 553–561, https://doi.org/10.1016/j.clon.2020.06.016.
10.1016/j.clon.2020.06.016
CAS PubMed Web of Science® Google Scholar
41 Duzkopru Y., Kocanoglu A., Dogan O., Sahinli H., Cilbir E., and Altinbas M., Hemoglobin, Albumin, Lymphocyte, and Platelet Score as a Predictor of Prognosis in Metastatic Gastric Cancer, World Journal of Gastrointestinal Oncology. (2023) 15, no. 9, 1626–1635, https://doi.org/10.4251/wjgo.v15.i9.1626.
10.4251/wjgo.v15.i9.1626
PubMed Web of Science® Google Scholar
42 Walsh D., Rybicki L., Nelson K. A., and Donnelly S., Symptoms and Prognosis in Advanced Cancer, Supportive Care in Cancer. (2002) 10, no. 5, 385–388, https://doi.org/10.1007/s00520-001-0318-z, 2-s2.0-0036942014.
10.1007/s00520-001-0318-z
PubMed Web of Science® Google Scholar
43 Cunha M. T., de Souza Borges A. P., Carvalho Jardim V., Fujita A., and de CastroG.Jr, Predicting Survival in Metastatic Non-Small Cell Lung Cancer Patients With Poor ECOG-PS: A Single-Arm Prospective Study, Cancer Medicine. (2023) 12, no. 4, 5099–5109, https://doi.org/10.1002/cam4.5254.
10.1002/cam4.5254
CAS PubMed Web of Science® Google Scholar
44 Vora P., Pietila A., Peltonen M., Brobert G., and Salomaa V., Thirty-Year Incidence and Mortality Trends in Upper and Lower Gastrointestinal Bleeding in Finland, JAMA Network Open. (2020) 3, no. 10, https://doi.org/10.1001/jamanetworkopen.2020.20172.
10.1001/jamanetworkopen.2020.20172
Web of Science® Google Scholar
45 Yang P., Zheng Y., Zhang L., and Hou X., Incidence and Characteristics of Death From Peptic Ulcer Among Cancer Patients in the United States, Scientific Reports. (2021) 11, no. 1, https://doi.org/10.1038/s41598-021-00602-1.
10.1038/s41598-021-00602-1
Web of Science® Google Scholar
46 Guo Y., Wei L., Patel S. H. et al., Serum Albumin: Early Prognostic Marker of Benefit for Immune Checkpoint Inhibitor Monotherapy but Not Chemoimmunotherapy, Clinical Lung Cancer. (2022) 23, no. 4, 345–355, https://doi.org/10.1016/j.cllc.2021.12.010.
10.1016/j.cllc.2021.12.010
CAS PubMed Web of Science® Google Scholar
47 Soeters P. B., Wolfe R. R., and Shenkin A., Hypoalbuminemia: Pathogenesis and Clinical Significance, Journal of Parenteral and Enteral Nutrition. (2019) 43, no. 2, 181–193, https://doi.org/10.1002/jpen.1451, 2-s2.0-85054545778.
10.1002/jpen.1451
CAS PubMed Web of Science® Google Scholar
48 Viganò A., Dorgan M., Buckingham J., Bruera E., and Suarez-Almazor M. E., Survival Prediction in Terminal Cancer Patients: A Systematic Review of the Medical Literature, Palliative Medicine. (2000) 14, no. 5, 363–374, https://doi.org/10.1191/026921600701536192, 2-s2.0-0033823960.
10.1191/026921600701536192
CAS PubMed Web of Science® Google Scholar
49 Caro J. J., Salas M., Ward A., and Goss G., Anemia as an Independent Prognostic Factor for Survival in Patients With Cancer: A Systemic, Quantitative Review, Cancer. (2001) 91, no. 12, 2214–2221, https://doi.org/10.1002/1097-0142(20010615)91:12%3A2214::aid-cncr1251%3E3.3.co;2-g.
10.1002/1097-0142(20010615)91:12<2214::AID-CNCR1251>3.0.CO;2-P
CAS PubMed Google Scholar
50 Elahi M. M., McMillan D. C., McArdle C. S., Angerson W. J., and Sattar N., Score Based on Hypoalbuminemia and Elevated C-Reactive Protein Predicts Survival in Patients With Advanced Gastrointestinal Cancer, Nutrition and Cancer. (2004) 48, no. 2, 171–173, https://doi.org/10.1207/s15327914nc4802_6, 2-s2.0-3843053571.
10.1207/s15327914nc4802_6
CAS PubMed Web of Science® Google Scholar
51 Stone P., Kelly L., Head R., and White S., Development and Validation of a Prognostic Scale for Use in Patients With Advanced Cancer, Palliative Medicine. (2008) 22, no. 6, 711–717, https://doi.org/10.1177/0269216308095200, 2-s2.0-61549128930.
10.1177/0269216308095200
CAS PubMed Web of Science® Google Scholar
52 Alkurt E. G., Durak D., Turhan V. B., and Sahiner I. T., Effect of C-Reactive Protein-To-Albumin Ratio on Prognosis in Gastric Cancer Patients, Cureus. (2022) 14, no. 4, https://doi.org/10.7759/cureus.23972.
10.7759/cureus.23972
Google Scholar
53 Shirakawa T., Makiyama A., Shimokawa M. et al., C-Reactive Protein/Albumin Ratio Is the Most Significant Inflammatory Marker in Unresectable Pancreatic Cancer Treated With FOLFIRINOX or Gemcitabine Plus Nab-Paclitaxel, Scientific Reports. (2023) 13, no. 1, https://doi.org/10.1038/s41598-023-34962-7.
10.1038/s41598-023-34962-7
Web of Science® Google Scholar
54 Stone P., Buckle P., Dolan R. et al., Prognostic Evaluation in Patients With Advanced Cancer in the Last Months of Life: ESMO Clinical Practice Guideline, ESMO Open. (2023) 8, no. 2, https://doi.org/10.1016/j.esmoop.2023.101195.
10.1016/j.esmoop.2023.101195
PubMed Web of Science® Google Scholar

All articles

Factors Influencing Overall Survival Period of Stage IV Cancer Patients

Abstract

1. Introduction