Preoperative serum albumin has been considered to be closely correlated with the prognosis of various cancers, including urothelial carcinoma (UC). However, to date, this conclusion remains controversial. The aim of this meta-analysis is to investigate the prognostic significance of preoperative serum albumin in UC. A literature search was performed in PubMed, Web of Science, Embase, and Cochrane Library up to 4 July 2017. Herein, a total of 15506 patients from 23 studies were enrolled in our meta-analysis. Decreased preoperative serum albumin level predicted poor overall survival (OS) (HR = 1.88, 95% CI: 1.44–2.45, P<0.0001), cancer-specific survival (CSS) (HR = 2.03, 95% CI: 1.42–2.90, P=0.0001), recurrence-free survival (HR = 1.85, 95% CI: 1.15–2.97, P=0.01), 30-day complications (30dCs) after surgery (odds ratio (OR) = 1.93, 95% CI: 1.16–3.20, P=0.01), and 90-day mortality after surgery (OR = 4.24, 95% CI: 2.20–8.16, P<0.001). The subgroup analyses indicated that low preoperative serum albumin level is still positively associated with a worse prognosis of UC based on ethnicity, cut-off value, tumor type, analyses type, and sample size. Our meta-analysis indicated that reduced preoperative serum albumin level was a predictor of poor prognosis of UC.

Introduction

Urothelial carcinoma (UC), the major histologic type of bladder cancer (BC), is one of the most common and fatal types of genitourinary tract malignancies [1], while upper tract UC (UTUC) makes up only 5–10% of UC with a poor prognosis [2–4]. The standard treatments for UC are radical resection including radical cystectomy (RC) with pelvic lymph node (LN) dissection for BC and radical nephroureterectomy (RNU) coupled with excision of a bladder cuff for UTUC [2,5]. However, radical surgery correlates with a high incidence of early postoperative complications [6,7] and mortality [6,8]. Even worse, tumor recurrence occurs in more than 20% of patients within 10 years of the operation [7,9,10]. Therefore, it is imperative to establish an effective prognostic model to stratify patients and then make a plan for an optimal preoperative management. Presently, postoperative TNM stage and grade are the factors that are most widely used to stratify UC patients, but their accuracy may be unsatisfactory. Additional predictive factors should be explored to solve the intractable clinical problem.

Serum albumin, the main serum protein [11], can be tested to estimate visceral protein function. The normal level of serum albumin for an adult varies from 3.5 to 5.0 g/dl, and the definition of hypoalbuminemia is <3.5 g/dl [12,13]. It has been demonstrated that albumin synthesis can be inhibited by malnutrition and inflammation during the later stages of disease [14,15]. Moreover, inflammation is a key step in the development and progression of cancers [16,17]. In recent years, many studies have indicated that preoperative serum albumin level can serve as an indicator of inflammation [18,19] and is closely related to the prognosis of various types of cancers [20–22]. In particular, the link between the preoperative serum albumin level and the prognosis of UC patients has been widely investigated in many studies [23–27]. Nevertheless, the prognostic significance of preoperative serum albumin level in UC patient remains controversial. For instance, some studies reported that preoperative serum albumin can act as a predictor of the prognosis of UC patients [24,28,29], but the conclusion was different in the other studies [27,30,31]. Hence, we performed a systematic review and meta-analysis to assess the prognostic significance of preoperative serum albumin in UC patients.

Materials and methods

Publication search strategy

A comprehensive literature search was conducted in PubMed, Embase, Web of Science, and Cochrane library up to 4 July 2017. The following terms were used to perform the search: ‘urothelial carcinoma or urothelial cancer or bladder cancer or upper tract urothelial carcinoma or radical cystectomy or radical nephroureterectomy’ and ‘albumin or serum albumin or hypoalbuminemia’ and ‘prognosis or survival or outcome or prognostic’. The search was limited to articles published in English.

Inclusion criteria

The enrolled studies were required to meet the following criteria: (i) the diagnosis of UC patients was histopathologically validated; (ii) preoperative serum albumin was measured and the correlation with prognosis of UC patients was analyzed; and (iii) the full text of publications should be available in order to access the data. The exclusion criteria were as follows: (i) reviews, case reports, animal research, letters, and meeting abstracts; and (ii) the same institution or authors between articles, as this may result in duplicate data.

Data extraction

The eligible relevant data were extracted from the included articles by two independent investigators. Any disagreements encountered during data extraction were resolved through a consensus. The extracted data included the author, year, number of cases, sex, the use of neoadjuvant chemotherapy, follow-up duration, cut-off value, and the end points. We were interested in the overall survival (OS), cancer-specific survival (CSS), recurrence-free survival (RFS), 30-day complication (30dC), and 90-day mortality (90dM).

If data from multivariable and univariate analyses were both available in the publications, the former was chosen. Survival data were extracted by applying Engauge Digitizer (version 4.1) if studies only included Kaplan–Meier curves. The HRs and their 95% CIs for prognosis were estimated according to the Tierney et al. [32] methods.

Quality assessment

The quality of included articles was evaluated independently by two investigators using the Newcastle–Ottawa Scale (NOS), in which the scores ranged from 0 to 9 [33]. In present meta-analysis, a study was regarded as high quality if it obtained 6 or more points.

Statistical analysis

This meta-analysis was carried out using Review sManager 5.0 (Cochrane Collaboration, Oxford, U.K.) and Stata SE12.0 (StataCorp, College Station, TX). The association between preoperative serum albumin and survival outcomes of UC patients was described by HRs with 95% CIs. In addition, odds ratios (ORs) with 95% CIs were used for the description of the relationship between early postoperative outcomes and preoperative serum albumin. Chi-square-based Q and I2 tests were used to estimate the heterogeneity amongst the studies. In the present study, I2 > 50% and P<0.05 indicate that a significant statistical heterogeneity exists. A random effects model was applied to pool the data if heterogeneity was significant. On the contrary, a fixed effects model was used when there is no significant heterogeneity.

Subgroup analysis, meta-regression, and sensitivity analysis were conducted to explore the possible source of heterogeneity, while also determining whether our pooled analyses were robust. The subgroup analysis and meta-regression were performed according to tumor type, ethnicity, analysis type, cut-off value, and sample size, and the sensitivity analysis was performed by omitting a single study in each step.

Publication bias

Begg’s and the Egger’s tests [34] were used to test the publication bias for the outcomes mentioned in the least included studies. Significant publication bias was considered to exist if the funnel plot was asymmetric and the P-values in Egger’s or Begg’s test are less than 0.05.

Results

Search results and characteristics of eligible articles

The publication search found 835 potentially relevant articles, but only 23 eligible studies with 15506 patients were finally included in this meta-analysis. The study selection process is shown in Figure 1.

The study flow of study selection process

Figure 1
The study flow of study selection process
Figure 1
The study flow of study selection process

The detailed information on the eligible articles is presented in Table 1. The main characteristics that we were interested in included country, tumor type, case number, age, sex, the use of neoadjuvant chemotherapy, follow-up duration, and cut-off value. The 23 studies used for the meta-analysis included 4 from Japan, 4 from China, 2 from Korea, 9 from the U.S.A., 1 from Britain, 1 from France, 1 from Turkey, 1 from Egypt, and 1 from Canada. Amongst the studies, 18 analyzed the prognostic value of preoperative serum albumin in participants with BC, 5 with UTUC, and 1 with both BC and UTUC. Meanwhile, the relationship between preoperative serum albumin and OS was assessed in 12 studies; CSS was analyzed in 9 studies; RFS was reported in 6 studies; 30dCs after surgery were evaluated in 7 studies; and 90dM after surgery was reported in 6 studies (Table 2). HRs were directly presented in 15 studies, and there were 2 articles only providing Kaplan–Meier curves, which could be used to estimate HRs. In addition, the quality of the included studies was assessed according to the NOS, and the scores ranged from 5 to 7, with a mean of 6.5, indicating that the included studies had moderate to high quality (Table 3).

Table 1
Main characteristics of the included studies
Study Country Tumor type Case number (LSA/HSA) Age (years) Sex (M/F) Neoadjuvant chemotherapy (LSA/HSA) Surgical treatment Follow-up (months) Survival analysis Cut-off value 
Caras et al. (2017) [23U.S.A. BC and UTUC 1292/4282 65 4228/1340 NA RC or TURBT NA 3.5 g/dl 
Chan et al. (2013) [35China BC 62/55 68 ± 10 99/18 NA RC 31 ± 29 CSS, OCS, OS 3.9 g/l 
Cho et al. (2014) [30Korea UTUC 35/112 70 40/106 NA RNU 33 RFS 3.5 g/dl 
Cui et al. (2017) [24China UTUC 54/36 65.66 107/62 NA RNU 53.7 OS, CSS 4.37 g/l 
Djaladat et al. (2014) [28U.S.A. BC 197/1274 67 1154/317 15/92 RC 148.8 OS, RFS 3.5 g/dl 
Fujita et al. (2015) [36Japan UTUC NA 70 221/85 NA RNU 41 RFS, CSS NA 
Garg et al. (2014) [37U.S.A. BC 150/947 68 831/266 19/109 RC 25.2 NA 4.0 g/dl 
Hinata et al. (2015) [31Japan BC NA 68.6 575/155 NA RC 52 OS, RFS 3.5 g/l 
Huang et al. (2017) [25China UTUC 17/408 67 279/146 NA RUN 38.5 OS, CSS 3.5 g/l 
Johnson et al. (2015) [38U.S.A. BC 102/587 73 530/159 NA RC NA 3.5 g/dl 
Kluth et al. (2014) [39U.S.A. UTUC NA 70 175/67 NA RNU CSS 3.7 g/dl 
Krane et al. (2013) [29U.S.A. BC NA 67.4 55/13 NA RC 25 OS, CSS 3.5 g/dl 
Ku et al. (2015) [40Korea BC NA 65.1 362 /57 NA RC 37.7 OS, CSS 3.5 g/dl 
Lambert et al. (2013) [41Britain BC 31/156 67.4 153/34 29/6 RC 26.2 OS, CSS 3.5 g/dl 
Laurent et al. (2017) [26France BC 95/98 75.2 164/29 NA RC 9.1 OS 3.5 g/dl 
Lavallee et al. (2014) [42Canada BC 341/1090 70 1819/484 NA RC NA NA 3.7 g/dl 
Liu et al. (2016) [43China BC 129/167 61.71 250/45 NA RC 72 RFS, CSS 4.0 g/dl 
Morgan et al. (2011) [44U.S.A. BC 30/139 78.8 122/47 NA RC NA 3.7 g/dl 
Mursi et al. (2013) [45Egypt BC 24/7 58.4 22/9 NA RC NA 3.5 g/dl 
Nakagawa et al. (2017) [27Japan BC NA 69 248/58 NA RC 6.8 OS 3.5 g/dl 
Sharma et al. (2016) [46U.S.A. BC NA 70.1 209/65 NA RC NA NA 4.0 g/dl 
Sheth et al. (2016) [47U.S.A. UTUC NA 71 77/24 NA RNU or partial ureterectomy 18.5 RFS, OS 4.0 g/dl 
Taguchi et al. (2016) [48Japan UC NA 68 160/40 NA RNU and RC 12 OS 3.5 g/dl 
Study Country Tumor type Case number (LSA/HSA) Age (years) Sex (M/F) Neoadjuvant chemotherapy (LSA/HSA) Surgical treatment Follow-up (months) Survival analysis Cut-off value 
Caras et al. (2017) [23U.S.A. BC and UTUC 1292/4282 65 4228/1340 NA RC or TURBT NA 3.5 g/dl 
Chan et al. (2013) [35China BC 62/55 68 ± 10 99/18 NA RC 31 ± 29 CSS, OCS, OS 3.9 g/l 
Cho et al. (2014) [30Korea UTUC 35/112 70 40/106 NA RNU 33 RFS 3.5 g/dl 
Cui et al. (2017) [24China UTUC 54/36 65.66 107/62 NA RNU 53.7 OS, CSS 4.37 g/l 
Djaladat et al. (2014) [28U.S.A. BC 197/1274 67 1154/317 15/92 RC 148.8 OS, RFS 3.5 g/dl 
Fujita et al. (2015) [36Japan UTUC NA 70 221/85 NA RNU 41 RFS, CSS NA 
Garg et al. (2014) [37U.S.A. BC 150/947 68 831/266 19/109 RC 25.2 NA 4.0 g/dl 
Hinata et al. (2015) [31Japan BC NA 68.6 575/155 NA RC 52 OS, RFS 3.5 g/l 
Huang et al. (2017) [25China UTUC 17/408 67 279/146 NA RUN 38.5 OS, CSS 3.5 g/l 
Johnson et al. (2015) [38U.S.A. BC 102/587 73 530/159 NA RC NA 3.5 g/dl 
Kluth et al. (2014) [39U.S.A. UTUC NA 70 175/67 NA RNU CSS 3.7 g/dl 
Krane et al. (2013) [29U.S.A. BC NA 67.4 55/13 NA RC 25 OS, CSS 3.5 g/dl 
Ku et al. (2015) [40Korea BC NA 65.1 362 /57 NA RC 37.7 OS, CSS 3.5 g/dl 
Lambert et al. (2013) [41Britain BC 31/156 67.4 153/34 29/6 RC 26.2 OS, CSS 3.5 g/dl 
Laurent et al. (2017) [26France BC 95/98 75.2 164/29 NA RC 9.1 OS 3.5 g/dl 
Lavallee et al. (2014) [42Canada BC 341/1090 70 1819/484 NA RC NA NA 3.7 g/dl 
Liu et al. (2016) [43China BC 129/167 61.71 250/45 NA RC 72 RFS, CSS 4.0 g/dl 
Morgan et al. (2011) [44U.S.A. BC 30/139 78.8 122/47 NA RC NA 3.7 g/dl 
Mursi et al. (2013) [45Egypt BC 24/7 58.4 22/9 NA RC NA 3.5 g/dl 
Nakagawa et al. (2017) [27Japan BC NA 69 248/58 NA RC 6.8 OS 3.5 g/dl 
Sharma et al. (2016) [46U.S.A. BC NA 70.1 209/65 NA RC NA NA 4.0 g/dl 
Sheth et al. (2016) [47U.S.A. UTUC NA 71 77/24 NA RNU or partial ureterectomy 18.5 RFS, OS 4.0 g/dl 
Taguchi et al. (2016) [48Japan UC NA 68 160/40 NA RNU and RC 12 OS 3.5 g/dl 

Abbreviations: HSA, high serum albumin; LSA, low serum albumin; NA, not available; TURBT, transurethral resection of bladder tumor.

Table 2
The interest outcomes extracted from included studies
Study HR HR HR OR OR 
 (95% CI) for OS (95% CI) for CSS (95% CI) for RFS (95% CI) for 30dC (95% CI) for 90dM 
Caras et al. (2017) NA NA NA 3.14 (2.86, 3.45) (overall morbidity)* 7.66 (5.80, 10.12) (overall morbidity)* 
    1.85 (1.41, 2.44) (RC)* 1.71 (0.85, 3.45) (RC)* 
    4.32 (3.47, 5.39) (TURBT)* 9.89 (6.05, 16.16)(TURBT)* 
Chan et al. (2013) NA 1.79 (0.78, 4.08)* NA NA NA 
Cho et al. (2014) 0.60 (0.26–1.39) NA 2.88 (1.80–4.62) NA NA 
Cui et al. (2017) 5.509 (2.144–14.158) 5.521 (2.074–14.697) NA NA NA 
Djaladat et al. (2014) 1.93 (1.43–2.63) NA 1.68 (1.16–2.43) 1.41 (0.98–2.02) 2.42 (1.31, 4.45)* 
Fujita et al. (2015)  2.63 (1.149, 6.02) 2.63 (5.882, 1.149)   
Garg et al. (2014) NA   1.68 (1.17, 2.41) 3.03 (7.143, 1.33) 
Hinata et al. (2015) 1.062 (0.643–1.703) NA 1.077 (0.654–1.718) NA NA 
Huang et al. (2017) 1.96 (0.96–4.01) 2.51 (1.22–5.18) NA NA NA 
Johnson et al. (2015) NA NA NA 1.79 (1.06, 3.03) NA 
Kluth et al. (2014) NA 1.754 (1.2987, 2.326) NA NA NA 
Krane et al. (2013) 4.96 (2.18–11.67) 8.10 (2.63–27.59) NA NA NA 
Ku et al. (2015) 1.670 (1.007–2.767) 1.794 (1.010–3.187) NA NA NA 
Lambert et al. (2013) 1.76 (1.03, 2.12)* 1.57 (1.24, 1.90)* NA 3.9 (1.3–12.2) 22.96 (2.47, 213.36)* 
Laurent et al. (2017) 3.06 (1.81–5.17) NA NA NA NA 
Lavallee et al. (2014) NA NA NA 1.16 (1.06–1.26) NA 
Liu et al. (2016) NA 0.979 (0.880–1.089) 0.998 (0.908–1.096) NA NA 
Morgan et al. (2011) NA NA NA NA 2.50 (1.40–4.45) 
Mursi et al. (2013) NA NA NA NA 9.20 (0.69, 122.38)* 
Nakagawa et al. (2017) 1.51 (0.95–2.41) NA NA NA NA 
Sharma et al. (2016) NA NA NA 2.27 (5.56, 0.94) NA 
Sheth et al. (2016) 3.37 (1.43–7.92) NA 4.4 (2.04–9.30) NA NA 
Taguchi et al. (2016) 1.345 (0.969–1.855) NA NA NA NA 
Study HR HR HR OR OR 
 (95% CI) for OS (95% CI) for CSS (95% CI) for RFS (95% CI) for 30dC (95% CI) for 90dM 
Caras et al. (2017) NA NA NA 3.14 (2.86, 3.45) (overall morbidity)* 7.66 (5.80, 10.12) (overall morbidity)* 
    1.85 (1.41, 2.44) (RC)* 1.71 (0.85, 3.45) (RC)* 
    4.32 (3.47, 5.39) (TURBT)* 9.89 (6.05, 16.16)(TURBT)* 
Chan et al. (2013) NA 1.79 (0.78, 4.08)* NA NA NA 
Cho et al. (2014) 0.60 (0.26–1.39) NA 2.88 (1.80–4.62) NA NA 
Cui et al. (2017) 5.509 (2.144–14.158) 5.521 (2.074–14.697) NA NA NA 
Djaladat et al. (2014) 1.93 (1.43–2.63) NA 1.68 (1.16–2.43) 1.41 (0.98–2.02) 2.42 (1.31, 4.45)* 
Fujita et al. (2015)  2.63 (1.149, 6.02) 2.63 (5.882, 1.149)   
Garg et al. (2014) NA   1.68 (1.17, 2.41) 3.03 (7.143, 1.33) 
Hinata et al. (2015) 1.062 (0.643–1.703) NA 1.077 (0.654–1.718) NA NA 
Huang et al. (2017) 1.96 (0.96–4.01) 2.51 (1.22–5.18) NA NA NA 
Johnson et al. (2015) NA NA NA 1.79 (1.06, 3.03) NA 
Kluth et al. (2014) NA 1.754 (1.2987, 2.326) NA NA NA 
Krane et al. (2013) 4.96 (2.18–11.67) 8.10 (2.63–27.59) NA NA NA 
Ku et al. (2015) 1.670 (1.007–2.767) 1.794 (1.010–3.187) NA NA NA 
Lambert et al. (2013) 1.76 (1.03, 2.12)* 1.57 (1.24, 1.90)* NA 3.9 (1.3–12.2) 22.96 (2.47, 213.36)* 
Laurent et al. (2017) 3.06 (1.81–5.17) NA NA NA NA 
Lavallee et al. (2014) NA NA NA 1.16 (1.06–1.26) NA 
Liu et al. (2016) NA 0.979 (0.880–1.089) 0.998 (0.908–1.096) NA NA 
Morgan et al. (2011) NA NA NA NA 2.50 (1.40–4.45) 
Mursi et al. (2013) NA NA NA NA 9.20 (0.69, 122.38)* 
Nakagawa et al. (2017) 1.51 (0.95–2.41) NA NA NA NA 
Sharma et al. (2016) NA NA NA 2.27 (5.56, 0.94) NA 
Sheth et al. (2016) 3.37 (1.43–7.92) NA 4.4 (2.04–9.30) NA NA 
Taguchi et al. (2016) 1.345 (0.969–1.855) NA NA NA NA 

Abbreviation: NA, not available; TURBT, transurethral resection of bladder tumor.

*Data extracted indirectly.

Table 3
The NOS quality assessment of the included studies
Study ID Selection Comparability Outcome Total 
 Representativeness of the exposed cohort Selection of the non-exposed cohort Ascertainment of exposure Demonstration that outcome of interest was not present at the start of the study Comparability of cohorts on the basis of the design or analysis Assessment of outcome Was follow-up long enough for outcomes to occur Adequacy of follow-up of cohorts  
Caras et al. (2017) ★ ★ ★ ★ ★☆ ☆ ★ ★ 
Chan et al. (2013) ☆ ★ ★ ★ ★☆ ★ ★ ☆ 
Cho et al. (2014) ★ ★ ★ ★ ★☆ ★ ★ ☆ 
Cui et al. (2017) ★ ★ ★ ★ ★☆ ★ ★ ☆ 
Djaladat et al. (2014) ★ ★ ☆ ★ ★☆ ★ ★ ★ 
Fujita et al. (2015) ★ ★ ☆ ★ ★☆ ★ ★ ☆ 
Garg et al. (2014) ★ ★ ☆ ★ ★☆ ★ ★ ★ 
Hinata et al. (2015) ★ ☆ ★ ★ ★☆ ★ ★ ★ 
Huang et al. (2017) ☆ ★ ★ ★ ★☆ ★ ★ ★ 
Johnson et al. (2015) ★ ☆ ★ ★ ★☆ ★ ★ ★ 
Kluth et al. (2014) ★ ★ ★ ★ ★☆ ★ ★ ☆ 
Krane et al. (2013) ☆ ☆ ★ ★ ★☆ ★ ★ ★ 
Ku et al. (2015) ★ ★ ★ ★ ★☆ ★ ★ ☆ 
Lambert et al. (2013) ☆ ★ ★ ★ ★☆ ★ ★ ★ 
Laurent et al. (2017) ★ ★ ★ ★ ★☆ ★ ☆ ★ 
Lavallee et al. (2014) ★ ★ ☆ ★ ★☆ ★ ★ ★ 
Liu et al. (2016) ☆ ★ ★ ★ ★☆ ★ ☆ ★ 
Morgan et al. (2011) ☆ ★ ★ ★ ★☆ ★ ★ ★ 
Mursi et al. (2013) ☆ ★ ☆ ★ ★☆ ★ ☆ ★ 
Nakagawa et al. (2017) ★ ★ ★ ★ ★☆ ★ ★ ☆ 
Sharma et al. (2016) ☆ ★ ☆ ★ ★☆ ★ ★ ★ 
Sheth et al. (2016) ☆ ★ ★ ★ ★☆ ★ ★ ★ 
Taguchi et al. (2016) ★ ★ ☆ ★ ★☆ ★ ★ ★ 
Study ID Selection Comparability Outcome Total 
 Representativeness of the exposed cohort Selection of the non-exposed cohort Ascertainment of exposure Demonstration that outcome of interest was not present at the start of the study Comparability of cohorts on the basis of the design or analysis Assessment of outcome Was follow-up long enough for outcomes to occur Adequacy of follow-up of cohorts  
Caras et al. (2017) ★ ★ ★ ★ ★☆ ☆ ★ ★ 
Chan et al. (2013) ☆ ★ ★ ★ ★☆ ★ ★ ☆ 
Cho et al. (2014) ★ ★ ★ ★ ★☆ ★ ★ ☆ 
Cui et al. (2017) ★ ★ ★ ★ ★☆ ★ ★ ☆ 
Djaladat et al. (2014) ★ ★ ☆ ★ ★☆ ★ ★ ★ 
Fujita et al. (2015) ★ ★ ☆ ★ ★☆ ★ ★ ☆ 
Garg et al. (2014) ★ ★ ☆ ★ ★☆ ★ ★ ★ 
Hinata et al. (2015) ★ ☆ ★ ★ ★☆ ★ ★ ★ 
Huang et al. (2017) ☆ ★ ★ ★ ★☆ ★ ★ ★ 
Johnson et al. (2015) ★ ☆ ★ ★ ★☆ ★ ★ ★ 
Kluth et al. (2014) ★ ★ ★ ★ ★☆ ★ ★ ☆ 
Krane et al. (2013) ☆ ☆ ★ ★ ★☆ ★ ★ ★ 
Ku et al. (2015) ★ ★ ★ ★ ★☆ ★ ★ ☆ 
Lambert et al. (2013) ☆ ★ ★ ★ ★☆ ★ ★ ★ 
Laurent et al. (2017) ★ ★ ★ ★ ★☆ ★ ☆ ★ 
Lavallee et al. (2014) ★ ★ ☆ ★ ★☆ ★ ★ ★ 
Liu et al. (2016) ☆ ★ ★ ★ ★☆ ★ ☆ ★ 
Morgan et al. (2011) ☆ ★ ★ ★ ★☆ ★ ★ ★ 
Mursi et al. (2013) ☆ ★ ☆ ★ ★☆ ★ ☆ ★ 
Nakagawa et al. (2017) ★ ★ ★ ★ ★☆ ★ ★ ☆ 
Sharma et al. (2016) ☆ ★ ☆ ★ ★☆ ★ ★ ★ 
Sheth et al. (2016) ☆ ★ ★ ★ ★☆ ★ ★ ★ 
Taguchi et al. (2016) ★ ★ ☆ ★ ★☆ ★ ★ ★ 

★ indicates that a score (1) was assigned; ☆ indicates a score of zero.

Preoperative serum albumin level and the survival of UC patients

The UC patients with decreased preoperative serum albumin level suffered from significantly worse OS (random-effects model; HR = 1.88, 95% CI: 1.44–2.45, P<0.00001; I2 = 66%, P=0.0008) (Figure 2A), CSS (random-effects model; HR = 2.03, 95% CI: 1.42–2.90, P=0.0001; I2 = 86%, P<0.00001) (Figure 2B), and RFS (random-effects model; HR = 1.85, 95% CI: 1.15–2.97, P=0.01; I2 = 88%, P<0.00001) (Figure 2C). With respect to short-term outcomes after surgery, low preoperative serum albumin level was significantly related to a lower 90dM after surgery (random-effects model; OR = 4.24, 95% CI: 2.20–8.16, P<0.0001; I2 = 78%, P=0.0003) (Figure 3A), and a lower rate of 30dCs after surgery (random-effects model; OR = 1.93, 95% CI: 1.16–3.20, P=0.01; I2 = 97%, PP<0.00001) (Figure 3B).

Meta-analysis of preoperative serum albumin level and OS (A), CSS (B), and RFS (C) in UC patients

Figure 2
Meta-analysis of preoperative serum albumin level and OS (A), CSS (B), and RFS (C) in UC patients

Abbreviations: LSA, low level of preoperative serum albumin; HSA, high level of preoperative serum albumin.

Figure 2
Meta-analysis of preoperative serum albumin level and OS (A), CSS (B), and RFS (C) in UC patients

Abbreviations: LSA, low level of preoperative serum albumin; HSA, high level of preoperative serum albumin.

Meta-analysis of preoperative serum albumin level at 30dC (A) and 90dM (B) in UC patients

Figure 3
Meta-analysis of preoperative serum albumin level at 30dC (A) and 90dM (B) in UC patients
Figure 3
Meta-analysis of preoperative serum albumin level at 30dC (A) and 90dM (B) in UC patients

The analysis of potential sources of heterogeneity

To explore the potential sources of heterogeneity amongst the studies, the subgroup analysis, sensitivity analysis, and meta-regression analysis were performed in our meta-analysis. The subgroup analyses were conducted to evaluate the prognostic values of preoperative serum albumin according to ethnicity, cut-off value, tumor type, analysis type, and sample size. Our results showed that the significant heterogeneity still existed in all the subgroups, indicating that these factors might not be the sources of heterogeneity in our meta-analysis. In addition, except for RFS, the pooled results of the other outcomes in subgroup analyses did not fluctuate significantly, demonstrating the robustness of our pooled analyses. The detailed results of all the subgroup analyses are presented in Table 4. The sensitivity analyses were conducted by omitting a single study step by step and the results showed that no single study exerted significant influence on the pooled results of the outcomes, also indicating that our findings were stable (Figure 4). Next, we performed the meta-regression analysis to further explore the potential sources of heterogeneity based on five covariates, including tumor type (UTUC compared with BC), ethnicity (Asian compared with non-Asian), analysis type (univariate compared with multivariate), cut-off value (3.5 g/dl compared with others), and sample size (>600 compared with ≤600). The results indicated that tumor type accounted for most heterogeneity of the pooled HR or OR of RFS (Coef. = 0.27, 95% CI: 0.18–0.79, P=0.02), 30dC (Coef. = 0.48, 95% CI: 0.25–0.92, P=0.03), and 90dM (Coef. = 0.36, 95% CI: 0.18–0.74, P=0.02), and explained 84.61, 69.5, and 100% between-study variance in RFS, 30dC, and 90dM, respectively. The other covariates could not clarify the heterogeneity, and the details of meta-regression were presented in Table 5.

Sensitivity analyses for OS (A), CSS (B), RFS (C), 30dC (D), 90dM (E), and the Begg’s and Egger’s test results for UC patients’ OS (F)

Figure 4
Sensitivity analyses for OS (A), CSS (B), RFS (C), 30dC (D), 90dM (E), and the Begg’s and Egger’s test results for UC patients’ OS (F)
Figure 4
Sensitivity analyses for OS (A), CSS (B), RFS (C), 30dC (D), 90dM (E), and the Begg’s and Egger’s test results for UC patients’ OS (F)
Table 4
Subgroup analysis of preoperative serum albumin and the prognosis of UC patients
Variables Outcome Studies Patients HR (95% CI) P-value Model Heterogeneity 
       I2 (%) P-value 
Tumor type         
UTUC OS 841 2.03 (1.34, 3.07) <0.01 Random 78 <0.01 
 CSS 900 1.69 (1.16, 2.46) <0.01 Random 87 <0.01 
 RFS 553 3.11 (2.17, 4.46) <0.01 Fixed 0.59 
 30dC 5735 3.14 (2.86, 3.45) 
 90dM 2669 7.66 (5.80, 10.12) 
BC OS 3288 1.80 (1.38, 2.33) <0.01 Random 60 0.01 
 CSS 1328 1.69 (1.16, 2.46) <0.01 Random 87 <0.01 
 RFS 2210 1.19 (0.85, 1.68) 0.32 Random 72 0.03 
 30dC 5568 1.55 (1.19, 2.02) <0.01 Random 62 0.02 
 90dM 5568 2.87 (1.89, 4.36) <0.01 Fixed 14 0.33 
Ethnicity         
Asian OS 1665 1.59 (1.09, 2.32) 0.02 Random 62 0.02 
 CSS 1731 2.03 (1.17, 3.54) 0.01 Random 82 <0.01 
 RFS 1477 1.60 (0.90, 2.86) 0.11 Random 87 <0.01 
Non-Asian OS 2271 2.20 (1.50, 3.23) <0.01 Random 67 0.01 
 CSS 497 2.02 (1.28, 3.19) <0.01 Random 75 0.02 
 RFS 1286 2.56 (1.00, 6.52) 0.05 Random 80 0.03 
Analysis type         
Univariate OS 10 3859 1.70 (1.31, 2.20) <0.01 Random 62 <0.01 
 CSS 953 2.25 (1.46, 3.47) <0.01 Fixed 43 0.15 
 RFS 247 1.64 (0.23, 11.53) 0.62 Random 92 <0.01 
 30dC 6939 2.35 (1.43, 3.87) <0.01 Random 82 <0.01 
 90dM 7140 4.64 (2.15, 10.03) <0.01 Random 82 <0.01 
Multivariate OS 270 4.21 (2.23, 7.94) <0.01 Fixed 0.45 
 CSS 1275 1.81 (1.14, 2.87) 0.01 Random 88 <0.01 
 RFS 2516 1.33 (0.91, 1.94) 0.13 Random 75 <0.01 
 30dC 4364 1.46 (1.06, 2.00) 0.02 Random 62 0.05 
 90dM 1097 3.03 (1.33, 6.90) 
Cut-off value         
=3.5 g/dl OS 10 3859 1.70 (1.31, 2.20) <0.01 Random 62 <0.01 
 CSS 1216 2.11 (1.40, 3.17) <0.01 Random 55 0.06 
 RFS 2061 1.74 (1.04, 2.91) 0.03 Random 75 0.02 
 30dC 8726 2.09 (1.35, 3.25) <0.01 Random 87 <0.01 
 90dM 8068 4.92 (2.35, 10.31) <0.01 Random 74 <0.01 
Others OS 270 4.21 (2.23, 7.94) <0.01 Fixed 0.45 
 CSS 1012 1.92 (1.06, 3.47) 0.03 Random 89 <0.01 
 RFS 702 2.14 (0.77, 5.97) 0.15 Random 90 <0.01 
 30dC 2577 1.40 (0.78, 2.52) 0.27 Random 55 0.14 
 90dM 169 2.50 (1.40, 4.46) 
Sample size         
n>600 OS 1915 1.48 (0.83, 2.65) 0.18 Random 75 0.05 
 RFS 1915 2.30 (1.02, 5.18) 0.13 Random 49 0.16 
 30dC 10842 1.73 (0.98, 3.08) 0.06 Random 98 <0.01 
 90dM 387 4.01 (1.72, 9.32) <0.01 Random 86 <0.01 
n<600 OS 10 2214 2.02 (1.45, 2.81) <0.01 Random 67 <0.01 
 RFS 848 1.39 (0.90, 2.14) 0.05 Random 92 <0.01 
 30dC 461 2.81 (1.41, 5.58) <0.01 Fixed 0.45 
 90dM 7850 5.68 (1.32, 24.41) 0.02 Random 53 0.12 
Variables Outcome Studies Patients HR (95% CI) P-value Model Heterogeneity 
       I2 (%) P-value 
Tumor type         
UTUC OS 841 2.03 (1.34, 3.07) <0.01 Random 78 <0.01 
 CSS 900 1.69 (1.16, 2.46) <0.01 Random 87 <0.01 
 RFS 553 3.11 (2.17, 4.46) <0.01 Fixed 0.59 
 30dC 5735 3.14 (2.86, 3.45) 
 90dM 2669 7.66 (5.80, 10.12) 
BC OS 3288 1.80 (1.38, 2.33) <0.01 Random 60 0.01 
 CSS 1328 1.69 (1.16, 2.46) <0.01 Random 87 <0.01 
 RFS 2210 1.19 (0.85, 1.68) 0.32 Random 72 0.03 
 30dC 5568 1.55 (1.19, 2.02) <0.01 Random 62 0.02 
 90dM 5568 2.87 (1.89, 4.36) <0.01 Fixed 14 0.33 
Ethnicity         
Asian OS 1665 1.59 (1.09, 2.32) 0.02 Random 62 0.02 
 CSS 1731 2.03 (1.17, 3.54) 0.01 Random 82 <0.01 
 RFS 1477 1.60 (0.90, 2.86) 0.11 Random 87 <0.01 
Non-Asian OS 2271 2.20 (1.50, 3.23) <0.01 Random 67 0.01 
 CSS 497 2.02 (1.28, 3.19) <0.01 Random 75 0.02 
 RFS 1286 2.56 (1.00, 6.52) 0.05 Random 80 0.03 
Analysis type         
Univariate OS 10 3859 1.70 (1.31, 2.20) <0.01 Random 62 <0.01 
 CSS 953 2.25 (1.46, 3.47) <0.01 Fixed 43 0.15 
 RFS 247 1.64 (0.23, 11.53) 0.62 Random 92 <0.01 
 30dC 6939 2.35 (1.43, 3.87) <0.01 Random 82 <0.01 
 90dM 7140 4.64 (2.15, 10.03) <0.01 Random 82 <0.01 
Multivariate OS 270 4.21 (2.23, 7.94) <0.01 Fixed 0.45 
 CSS 1275 1.81 (1.14, 2.87) 0.01 Random 88 <0.01 
 RFS 2516 1.33 (0.91, 1.94) 0.13 Random 75 <0.01 
 30dC 4364 1.46 (1.06, 2.00) 0.02 Random 62 0.05 
 90dM 1097 3.03 (1.33, 6.90) 
Cut-off value         
=3.5 g/dl OS 10 3859 1.70 (1.31, 2.20) <0.01 Random 62 <0.01 
 CSS 1216 2.11 (1.40, 3.17) <0.01 Random 55 0.06 
 RFS 2061 1.74 (1.04, 2.91) 0.03 Random 75 0.02 
 30dC 8726 2.09 (1.35, 3.25) <0.01 Random 87 <0.01 
 90dM 8068 4.92 (2.35, 10.31) <0.01 Random 74 <0.01 
Others OS 270 4.21 (2.23, 7.94) <0.01 Fixed 0.45 
 CSS 1012 1.92 (1.06, 3.47) 0.03 Random 89 <0.01 
 RFS 702 2.14 (0.77, 5.97) 0.15 Random 90 <0.01 
 30dC 2577 1.40 (0.78, 2.52) 0.27 Random 55 0.14 
 90dM 169 2.50 (1.40, 4.46) 
Sample size         
n>600 OS 1915 1.48 (0.83, 2.65) 0.18 Random 75 0.05 
 RFS 1915 2.30 (1.02, 5.18) 0.13 Random 49 0.16 
 30dC 10842 1.73 (0.98, 3.08) 0.06 Random 98 <0.01 
 90dM 387 4.01 (1.72, 9.32) <0.01 Random 86 <0.01 
n<600 OS 10 2214 2.02 (1.45, 2.81) <0.01 Random 67 <0.01 
 RFS 848 1.39 (0.90, 2.14) 0.05 Random 92 <0.01 
 30dC 461 2.81 (1.41, 5.58) <0.01 Fixed 0.45 
 90dM 7850 5.68 (1.32, 24.41) 0.02 Random 53 0.12 
Table 5
Assessment of potential sources of heterogeneity amongst studies by meta-regression
Covariates Outcomes P-value Regression coefficient (95% CI) 
Tumor type (UTUC compared with BC)    
 OS 0.82 0.92 (0.36–2.37) 
 CSS 0.15 0.52 (0.20–1.35) 
 RFS 0.02 0.27 (0.18–0.79) 
 30dC 0.03 0.48 (0.25–0.92) 
 90dM 0.02 0.36 (0.18–0.74) 
Ethnicity (Asian compared with non-Asian)    
 OS 0.10 1.68 (0.90–3.14) 
 CSS 0.81 1.11 (0.39–3.16) 
 RFS 0.43 1.58 (0.37–6.80) 
Analysis type (univariate compared with multivariate)    
 OS 0.74 0.88 (0.36–2.10) 
 CSS 0.54 0.77 (0.29–2.04) 
 RFS 0.05 0.39 (0.19–1.02) 
 30dC 0.16 0.62 (0.30–1.30) 
 90dM 0.66 0.66 (0.06–7.13) 
Cut-off value (3.5 g/dl compared with others)    
 OS 0.08 2.50 (0.89–7.01) 
 CSS 0.72 0.86 (0.32–2.30) 
 RFS 0.78 1.18 (0.27–5.22) 
 30dC 0.32 0.64 (0.21–1.92) 
 90dM 0.42 0.52 (0.07–4.04) 
Sample size (>600 compared with ≤600)    
 OS 0.46 1.38 (0.54–3.49) 
 RFS 0.40 1.63 (0.38–6.97) 
 30dC 0.35 1.64 (0.47–5.72) 
 90dM 0.82 1.20 (0.16–9.20) 
Covariates Outcomes P-value Regression coefficient (95% CI) 
Tumor type (UTUC compared with BC)    
 OS 0.82 0.92 (0.36–2.37) 
 CSS 0.15 0.52 (0.20–1.35) 
 RFS 0.02 0.27 (0.18–0.79) 
 30dC 0.03 0.48 (0.25–0.92) 
 90dM 0.02 0.36 (0.18–0.74) 
Ethnicity (Asian compared with non-Asian)    
 OS 0.10 1.68 (0.90–3.14) 
 CSS 0.81 1.11 (0.39–3.16) 
 RFS 0.43 1.58 (0.37–6.80) 
Analysis type (univariate compared with multivariate)    
 OS 0.74 0.88 (0.36–2.10) 
 CSS 0.54 0.77 (0.29–2.04) 
 RFS 0.05 0.39 (0.19–1.02) 
 30dC 0.16 0.62 (0.30–1.30) 
 90dM 0.66 0.66 (0.06–7.13) 
Cut-off value (3.5 g/dl compared with others)    
 OS 0.08 2.50 (0.89–7.01) 
 CSS 0.72 0.86 (0.32–2.30) 
 RFS 0.78 1.18 (0.27–5.22) 
 30dC 0.32 0.64 (0.21–1.92) 
 90dM 0.42 0.52 (0.07–4.04) 
Sample size (>600 compared with ≤600)    
 OS 0.46 1.38 (0.54–3.49) 
 RFS 0.40 1.63 (0.38–6.97) 
 30dC 0.35 1.64 (0.47–5.72) 
 90dM 0.82 1.20 (0.16–9.20) 

Publication bias

As Figure 4F shows, the funnel plots for OS were almost symmetrical, and the P-values of Begg’s and Egger’s tests were 0.170 and 0.266, respectively, which suggested that there was no significant publication bias in our meta-analysis.

Discussion

Numerous articles have reported that pretreatment serum albumin is correlated with the prognosis of UC patients, but the conclusions amongst studies remain inconsistent [23–31,35–48]. Therefore, we combined 23 studies with 15506 patients to perform this meta-analysis to evaluate the prognostic role of preoperative serum albumin in UC patients [23–31,35–48]. Our results demonstrated that low levels of preoperative serum albumin are significantly associated with worse OS, CSS, RFS, complications, and early mortality. In spite of significant heterogeneity, subgroup analyses conducted according to ethnicity, tumor type, analysis type, cut-off value, and sample size showed that our pooled results did not alter significantly, which indicated the robustness of the pooled results. Generally, all these findings suggested that preoperative serum albumin level played an important prognostic role in UC patients.

Although it has been demonstrated that preoperative serum albumin is closely correlated with the prognosis of UC and other cancers [20–22,24,29], the latent mechanisms remain complex and unclear. However, it is widely recognized that malnutrition and inflammation may be partly responsible for the mechanisms [49,50]. Malnutrition, partly mirrored by hypoalbuminemia, is a severe problem in cancer patients, due to a variety of mechanisms, which involve anticancer therapies and the host response to the tumor [51]. Furthermore, many unfavorable clinical consequences are related to malnutrition, including a deteriorated quality of life, an enhanced risk of chemotherapy-induced toxicity, and poor long-term survival [52]. In addition, inflammation, which is a critical step in cancer initiation and progression [16,53], can alter the levels of serum albumin [15]. Under inflammatory conditions, immune cells and tumor cells release various inflammatory mediators, including interleukin-1β, interleukin-6, and tumor necrosis factor (TNF), which can suppress albumin synthesis in liver cells [53–55]. Moreover, albumin might directly be lost from the circulatory system, since TNF can increase the permeability of capillaries [55]. In addition, previous studies have also demonstrated that serum albumin is associated with several anticancer mechanisms, including its antioxidant function [56]. Therefore, preoperative serum albumin could serve as a good predictor of the prognosis of cancers.

Serum albumin is a low cost and easily accessible predictor for UC patients, but it still does have some limitations for clinical implications. For instance, under overhydrated conditions or other disease processes, hypoalbuminemia may not indicate malnutrition [57,58], so its prognostic value in UC patients will be reduced. In addition, diet and other non-tumor-related factors can also affect the levels of serum albumin. To some degree, those factors mentioned above may explain the significant heterogeneities in our meta-analysis. Actually, to predict the prognosis of UC patients more precisely, several predictive factors involving serum albumin have been studied for clinical practice. For instance, Liu et al. [43] indicated that the albumin/globulin ratio calculated from preoperative serum albumin and globulin levels can act as an independent predictor of long-term RFS and CSS in bladder UC. Additionally, Cui et al. [24] recently reported that a predictive model based on preoperative plasma fibrinogen and serum albumin level, also known as an FA score, can be used to predict OS and CSS in UTUC. Regardless, our meta-analysis demonstrated that preoperative serum albumin plays a significant prognostic role in UC patients.

To the best of our knowledge, the present study is the first to systematically analyze the predictive value of preoperative serum albumin for prognosis of UC patients. However, the results of our meta-analysis may be challenged by some limitations. First, most of the included studies were retrospective and thus may have bias in patient selection and data analysis. Second, the cutoffs of low preoperative serum albumin were not consistent amongst the included studies. Other inconsistencies amongst the various studies included the follow-up period and the base characteristics of patients, which may cause significant heterogeneities and thus affect the robustness of the pooled analysis. In summary, heterogeneity may be the biggest limitation of our meta-analysis. Therefore, the value of preoperative serum albumin as a prognostic predictor in UC patients still requires further investigation in the future.

Conclusion

In conclusion, this meta-analysis indicated that preoperative serum albumin is a useful predictor for the prognosis of patients with UC. The patients with decreased preoperative serum albumin have more unfavorable long-term survival and short-term outcomes. Considering the limitations in present meta-analyses, further homogeneous prospective studies are needed to confirm our findings.

We thank American Journal Experts (AJE) to help us edit our manuscript.

Competing interests

The authors declare that there are no competing interests associated with the manuscript.

Funding

This work was supported by the Youth science and technology foundation of Gansu Province (1606RJYA286),the National Natural Science Foundation (31360508), the Health industry Research Project of Gansu Province (GSWSKY2018-04), and the Health industry Research Project of Gansu Province (GSWSKY2017-16).

Author contribution

Jing Liu and Fang Wang extracted data and writed this paper. Shaohong Li and Wenhui Huang searched for the eligible studies. Yanjuan Jia performed the statistical analysis. Chaojun Wei designed this work.

Abbreviations

     
  • BC

    bladder cancer

  •  
  • CI

    confidence interval

  •  
  • CSS

    cancer-specific survival

  •  
  • HR

    hazard ratio

  •  
  • NOS

    Newcastle–Ottawa scale

  •  
  • OR

    odds ratio

  •  
  • OS

    overall survival

  •  
  • RC

    radical cystectomy

  •  
  • RFS

    recurrence-free survival

  •  
  • RNU

    radical nephroureterectomy

  •  
  • TNF

    tumor necrosis factor

  •  
  • UC

    urothelial carcinoma

  •  
  • UTUC

    upper tract UC

  •  
  • 30dC

    30-day complication

  •  
  • 90dM

    90-day mortality

References

References
1
Van Batavia
J.
et al
(
2014
)
Bladder cancers arise from distinct urothelial sub-populations
.
Nat. Cell Biol.
16
,
982
991, 1-5
[PubMed]
2
Roupret
M.
et al
(
2015
)
European association of urology guidelines on upper urinary tract urothelial cell carcinoma: 2015 update
.
Eur. Urol.
68
,
868
879
[PubMed]
3
Siegel
R.L.
and
Miller
K.D.
Jemal
A.
(
2016
)
Cancer statistics, 2016
.
CA Cancer J. Clin.
66
,
7
30
[PubMed]
4
Lughezzani
G.
et al
(
2012
)
Prognostic factors in upper urinary tract urothelial carcinomas: a comprehensive review of the current literature
.
Eur. Urol.
62
,
100
114
[PubMed]
5
Babjuk
M.
et al
(
2017
)
EAU Guidelines on non-muscle-invasive urothelial carcinoma of the bladder: update 2016
.
Eur. Urol.
71
,
447
461
[PubMed]
6
Shabsigh
A.
et al
(
2009
)
Defining early morbidity of radical cystectomy for patients with bladder cancer using a standardized reporting methodology
.
Eur. Urol.
55
,
164
174
[PubMed]
7
Stein
J.P.
et al
(
2001
)
Radical cystectomy in the treatment of invasive bladder cancer: long-term results in 1,054 patients
.
J. Clin. Oncol.
19
,
666
675
[PubMed]
8
Chahal
R.
et al
(
2003
)
A study of the morbidity, mortality and long-term survival following radical cystectomy and radical radiotherapy in the treatment of invasive bladder cancer in Yorkshire
.
Eur. Urol.
43
,
246
257
[PubMed]
9
Yin
M.
et al
(
2016
)
Neoadjuvant chemotherapy for muscle-invasive bladder cancer: a systematic review and two-step meta-analysis
.
Oncologist
21
,
708
715
[PubMed]
10
Shariat
S.F.
et al
(
2010
)
Statistical consideration for clinical biomarker research in bladder cancer
.
Urol. Oncol.
28
,
389
400
[PubMed]
11
Margarson
M.P.
and
Soni
N.
(
1998
)
Serum albumin: touchstone or totem?
Anaesthesia
53
,
789
803
[PubMed]
12
Di Fiore
F.
et al
(
2007
)
Baseline nutritional status is predictive of response to treatment and survival in patients treated by definitive chemoradiotherapy for a locally advanced esophageal cancer
.
Am. J. Gastroenterol.
102
,
2557
2563
[PubMed]
13
Ishizuka
M.
et al
(
2007
)
Inflammation-based prognostic score is a novel predictor of postoperative outcome in patients with colorectal cancer
.
Ann. Surg.
246
,
1047
1051
[PubMed]
14
Yeun
J.Y.
and
Kaysen
G.A.
(
1998
)
Factors influencing serum albumin in dialysis patients
.
Am. J. Kidney Dis.
32
(
6 Suppl. 4
),
S118
S125
[PubMed]
15
Ballmer
P.E.
and
Ochsenbein
A.F.
Schutz-Hofmann
S.
(
1994
)
Transcapillary escape rate of albumin positively correlates with plasma albumin concentration in acute but not in chronic inflammatory disease
.
Metabolism
43
,
697
705
[PubMed]
16
Coussens
L.M.
and
Werb
Z.
(
2002
)
Inflammation and cancer
.
Nature
420
,
860
867
[PubMed]
17
Del Prete
A.
et al
(
2011
)
Molecular pathways in cancer-related inflammation
.
Biochem. Med. (Zagreb)
21
,
264
275
[PubMed]
18
Gabay
C.
and
Kushner
I.
(
1999
)
Acute-phase proteins and other systemic responses to inflammation
.
N. Engl. J. Med.
340
,
448
454
[PubMed]
19
McMillan
D.C.
(
2008
)
An inflammation-based prognostic score and its role in the nutrition-based management of patients with cancer
.
Proc. Nutr. Soc.
67
,
257
262
[PubMed]
20
Lai
C.C.
et al
(
2011
)
Low preoperative serum albumin in colon cancer: a risk factor for poor outcome
.
Int. J. Colorectal Dis.
26
,
473
481
[PubMed]
21
Seebacher
V.
et al
(
2013
)
Does thyroid-stimulating hormone influence the prognosis of patients with endometrial cancer? A multicentre trial.
Br. J. Cancer
109
,
215
218
[PubMed]
22
Tanriverdi
O.
et al
(
2015
)
Pretreatment serum albumin level is an independent prognostic factor in patients with stage IIIB non-small cell lung cancer: a study of the Turkish descriptive oncological researches group
.
Asian Pac. J. Cancer Prev.
16
,
5971
5976
[PubMed]
23
Caras
R.J.
et al
(
2017
)
Preoperative albumin is predictive of early postoperative morbidity and mortality in common urologic oncologic surgeries
.
Clin. Genitourin. Cancer
15
,
e255
e262
[PubMed]
24
Cui
J.
et al
(
2017
)
Prognostic scores based on the preoperative plasma fibrinogen and serum albumin level as a prognostic factor in patients with upper urinary tract urothelial carcinoma
.
Oncotarget
8
,
68964
68973
25
Huang
J.
et al
(
2017
)
Preoperative serum pre-albumin as an independent prognostic indicator in patients with localized upper tract urothelial carcinoma after radical nephroureterectomy
.
Oncotarget
8
,
36772
36779
[PubMed]
26
Laurent
M.
et al
(
2017
)
Early chemotherapy discontinuation and mortality in older patients with metastatic bladder cancer: the AGEVIM multicenter cohort study
.
Urol. Oncol.
35
,
34e9
34e16
27
Nakagawa
T.
et al
(
2017
)
Nomogram for predicting survival of postcystectomy recurrent urothelial carcinoma of the bladder
.
Urol. Oncol.
35
,
457.e15
457.e21
28
Djaladat
H.
et al
(
2014
)
The association of preoperative serum albumin level and American Society of Anesthesiologists (ASA) score on early complications and survival of patients undergoing radical cystectomy for urothelial bladder cancer
.
BJU Int.
113
,
887
893
[PubMed]
29
Krane
L.S.
et al
(
2013
)
Preoperative neutrophil/lymphocyte ratio predicts overall survival and extravesical disease in patients undergoing radical cystectomy
.
J. Endourol.
27
,
1046
1050
[PubMed]
30
Cho
Y.H.
et al
(
2014
)
Predictors of intravesical recurrence after radical nephroureterectomy for upper urinary tract urothelial carcinoma: an inflammation-based prognostic score
.
Korean J. Urol.
55
,
453
459
[PubMed]
31
Hinata
N.
et al
(
2015
)
Performance status as a significant prognostic predictor in patients with urothelial carcinoma of the bladder who underwent radical cystectomy
.
Int. J. Urol.
22
,
742
746
[PubMed]
32
Tierney
J.F.
et al
(
2007
)
Practical methods for incorporating summary time-to-event data into meta-analysis
.
Trials
8
,
16
[PubMed]
33
Stang
A.
(
2010
)
Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses
.
Eur. J. Epidemiol.
25
,
603
605
[PubMed]
34
Egger
M.
et al
(
1997
)
Bias in meta-analysis detected by a simple, graphical test
.
BMJ
315
,
629
634
[PubMed]
35
Chan
E.S.
et al
(
2013
)
Age, tumour stage, and preoperative serum albumin level are independent predictors of mortality after radical cystectomy for treatment of bladder cancer in Hong Kong Chinese
.
Hong Kong Med. J.
19
,
400
406
[PubMed]
36
Fujita
K.
et al
(
2015
)
Preoperative risk stratification for cancer-specific survival of patients with upper urinary tract urothelial carcinoma treated by nephroureterectomy
.
Int. J. Clin. Oncol.
20
,
156
163
[PubMed]
37
Garg
T.
et al
(
2014
)
Preoperative serum albumin is associated with mortality and complications after radical cystectomy
.
BJU Int.
113
,
918
923
[PubMed]
38
Johnson
D.C.
et al
(
2015
)
Nutritional predictors of complications following radical cystectomy
.
World J. Urol.
33
,
1129
1137
[PubMed]
39
Kluth
L.A.
et al
(
2014
)
Predictors of survival in patients with disease recurrence after radical nephroureterectomy
.
BJU Int.
113
,
911
917
[PubMed]
40
Ku
J.H.
et al
(
2015
)
The prognostic value of pretreatment of systemic inflammatory responses in patients with urothelial carcinoma undergoing radical cystectomy
.
Br. J. Cancer
112
,
461
467
[PubMed]
41
Lambert
J.W.
et al
(
2013
)
Using preoperative albumin levels as a surrogate marker for outcomes after radical cystectomy for bladder cancer
.
Urology
81
,
587
592
[PubMed]
42
Lavallee
L.T.
et al
(
2014
)
Peri-operative morbidity associated with radical cystectomy in a multicenter database of community and academic hospitals
.
PLoS ONE
9
,
e111281
[PubMed]
43
Liu
J.
et al
(
2016
)
The prognostic role of preoperative serum albumin/globulin ratio in patients with bladder urothelial carcinoma undergoing radical cystectomy
.
Urol. Oncol.
34
,
484.e1
484.e8
44
Morgan
T.M.
et al
(
2011
)
Predicting the probability of 90-day survival of elderly patients with bladder cancer treated with radical cystectomy
.
J. Urol.
186
,
829
834
[PubMed]
45
Mursi
K.
et al
(
2013
)
The effect of preoperative clinical variables on the 30- and 90-day morbidity and mortality after radical cystectomy: a single-centre study
.
Arab J. Urol.
11
,
152
158
[PubMed]
46
Sharma
P.
et al
(
2016
)
Preoperative patient reported mental health is associated with high grade complications after radical cystectomy
.
J. Urol.
195
,
47
52
[PubMed]
47
Sheth
K.R.
et al
(
2016
)
Prognostic serum markers in patients with high-grade upper tract urothelial carcinoma
.
Urol. Oncol.
34
,
418.e9
418.e16
48
Taguchi
S.
et al
(
2016
)
Validation of major prognostic models for metastatic urothelial carcinoma using a multi-institutional cohort of the real world
.
World J. Urol.
34
,
163
171
[PubMed]
49
Lien
Y.C.
et al
(
2004
)
Preoperative serum albumin level is a prognostic indicator for adenocarcinoma of the gastric cardia
.
J. Gastrointest. Surg.
8
,
1041
1048
[PubMed]
50
Wu
N.
et al
(
2015
)
Low pretherapeutic serum albumin as a risk factor for poor outcome in esophageal squamous cell carcinomas
.
Nutr. Cancer
67
,
481
485
[PubMed]
51
von Meyenfeldt
M.
(
2005
)
Cancer-associated malnutrition: an introduction
.
Eur. J. Oncol. Nurs.
9
,
S35
S38
[PubMed]
52
Dewys
W.D.
et al
(
1980
)
Prognostic effect of weight loss prior to chemotherapy in cancer patients. Eastern Cooperative Oncology Group
.
Am. J. Med.
69
,
491
497
[PubMed]
53
Mantovani
A.
et al
(
2008
)
Cancer-related inflammation
.
Nature
454
,
436
444
[PubMed]
54
Elinav
E.
et al
(
2013
)
Inflammation-induced cancer: crosstalk between tumours, immune cells and microorganisms
.
Nat. Rev. Cancer
13
,
759
771
[PubMed]
55
Gupta
D.
and
Lis
C.G.
(
2010
)
Pretreatment serum albumin as a predictor of cancer survival: a systematic review of the epidemiological literature
.
Nutr. J.
9
,
69
[PubMed]
56
Namiesnik
J.
et al
(
2014
)
In vitro studies on the relationship between the antioxidant activities of some berry extracts and their binding properties to serum albumin
.
Appl. Biochem. Biotechnol.
172
,
2849
2865
[PubMed]
57
Detsky
A.S.
et al
(
1984
)
Evaluating the accuracy of nutritional assessment techniques applied to hospitalized patients: methodology and comparisons
.
JPEN J. Parenter. Enteral Nutr.
8
,
153
159
[PubMed]
58
Mahdavi
A.M.
and
Ostadrahimi
A.
Safaiyan
A.
(
2010
)
Subjective global assessment of nutritional status in children
.
Matern. Child Nutr.
6
,
374
381
[PubMed]
This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).