Literature DB >> 30367618

Prognostic value of inflammation-based prognostic scores on outcome in patients undergoing continuous ambulatory peritoneal dialysis.

Lu Cai1,2, Jianwen Yu1,2, Jing Yu1,2, Yuan Peng1,2, Habib Ullah1,2, Chunyan Yi1,2, Jianxiong Lin1,2, Xiao Yang1,2, Xueqing Yu3,4,5.   

Abstract

BACKGROUND: Inflammation-based prognostic scores have been used as outcome predictors in patients with cancer or on hemodialysis. However, their role in patients on continuous ambulatory peritoneal dialysis (CAPD) remains unclear. This study aimed to examine the prognostic value of inflammation-based composite scores for mortality in CAPD patients.
METHODS: This study was conducted in CAPD patients enrolled from January 1, 2006 to December 31, 2014 and followed until December 31, 2016. Three inflammation-based prognostic scores, including Glasgow prognostic score (GPS), prognostic nutritional index (PNI), and prognostic index (PI), were conducted in this study. The associations between these scores and all-cause or cardiovascular mortality were evaluated by Kaplan-Meier method and Cox proportional hazards models. The areas under the curve (AUC) of receiver-operating characteristic (ROC) analysis were used to determine the predictive values of mortality.
RESULTS: A total of 1501 patients were included. During a median follow-up of 38.7 (range, 21.6-62.3) months, 346 (23.1%) patients died, of which 168 (48.6%) were due to cardiovascular diseases (CVD). After adjustment for confounders, the results showed that elevated GPS, PNI, and PI scores were all independently associated with all-cause [GPS: Score 1: hazard ratio(HR) 3.94, 95% confidence interval(CI) 2.90-5.35; Score 2: HR 7.56, 95% CI 5.35-10.67; PNI: HR 1.82, 95% CI 1.36-2.43; PI: Score 1: HR 2.08, 95% CI 1.63-2.65; Score 2: HR 3.03, 95% CI 2.00-4.60)] and CVD mortality(GPS: Score 1: HR 4.41, 95% CI 2.76-7.03; Score 2: HR 9.64, 95% CI 5.72-16.26; PNI: HR 1.63, 95% CI 1.06-2.51; PI: Score 1: HR 2.57, 95% CI 1.81-3.66, Score 2: HR 3.85, 95% CI 1.99-7.46).The AUC values of GPS score were 0.798 (95% CI0.770-0.826) for all-cause mortality and 0.781 (95% CI 0.744-0.817) for CVD mortality, both of which significantly higher than those of PNI and PI scores (P < 0.001, respectively).
CONCLUSIONS: All elevated GPS, PNI, and PI scores were independently associated with all-cause and CVD mortality. The GPS score showed better predictive value than PNI and PI scores in CAPD patients.

Entities:  

Keywords:  All-cause mortality; Cardiovascular mortality; Continuous ambulatory peritoneal dialysis; Inflammation-based prognostic scores

Mesh:

Year:  2018        PMID: 30367618      PMCID: PMC6204053          DOI: 10.1186/s12882-018-1092-1

Source DB:  PubMed          Journal:  BMC Nephrol        ISSN: 1471-2369            Impact factor:   2.388


Background

Peritoneal dialysis (PD) has been established as a successful treatment modality of renal replacement therapy over decades [1]. However, the mortality of PD patients remains much higher compared to general population, nearly half of which are caused by cardiovascular disease (CVD) [2, 3]. Numerous risk factors have been identified to be associated with CVD [4-7]. Among them, systemic inflammation is well recognized for its close relationship to cardiovascular morbidity and mortality [8]. We and others found that elevated C-reactive protein (CRP) levels, especially its elevated trend over time, could be independently predictive of mortality in PD population [9-11]. Importantly, inflammation drives the development of malnutrition, which may in turn amplify systemic inflammation responses, leading to a vicious cycle [12, 13]. Recently, International Society for Peritoneal Dialysis (ISPD) cardiovascular and metabolic guidelines suggest that PD patients with persistently elevated CRP should be investigated for any treatable cause of inflammation and nutritional status should be assessed within 6–8 weeks after commencement of PD for reducing the risk of CVD mortality [2]. Therefore, comprehensive assessment of inflammatory and nutritional status will help to identify patients at high risk and are crucial in the management of PD cohorts. However, standardized methods or systems available for this purpose remain to be explored. Inflammation-based prognostic scores have been developed since last decade and successfully used to monitor patients’ status and predict outcomes in cancer management [14-20]. The Glasgow prognostic score (GPS), composed of serum CRP and albumin, has been reported as a powerful predictor for mortality in many cancer patients [14-16]. The prognostic nutritional index (PNI), which was originally developed to monitor nutritional status of perioperative patients, can predict long-term outcomes in patients with a variety of malignancy [17-19]. The prognostic index (PI), based on CRP and white blood cell (WBC) count, has also been shown to be associated with survival in advanced lung cancer patients [20]. However, few studies have investigated the association of these composite scores with outcomes in continuous ambulatory peritoneal dialysis (CAPD) patients. Therefore, the purpose of this study was to evaluate the prognostic values of these scores in CAPD patients.

Methods

Study participants

Patients were enrolled from PD center of The First Affiliated Hospital of Sun Yat-sen University from January 1, 2006 to December 31, 2014. Patients who had received CAPD for more than 3 months were included. Patients who were younger than 18 years old, undergone CAPD for less than 3 months, transferred from hemodialysis (HD), with a history of renal transplantation or malignancy before PD, or without data of serum CRP, albumin, or WBC count, were excluded from this study. The study was approved by the Ethics Committee of The First Affiliated Hospital of Sun Yat-sen University. All participants provided their written informed consent for this study.

Data collection and laboratory measurements

This work was a retrospective cohort study. Baseline demographic and clinical data, including age, gender, a history of smoke, diabetes, hypertension, cardiovascular disease, were collected at the start of CAPD treatment. Diabetes and hypertension were recorded as previously defined [21]. Baseline biochemical parameters were collected 1–3 months after the initiation of PD therapy, including blood pressure (BP), hemoglobin, WBC count, serum CRP, albumin, total triglycerides, total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), uric acid, and creatinine. Residual renal function, in ml/min/1.73m2, was estimated from mean values of creatinine clearance and urea clearance and adjusted for body surface area calculated with the Gehan and George equation [6]. All measurements of biochemical parameters were performed in the biochemical laboratory of The First Affiliated Hospital of Sun Yat-sen University. The constituents of three inflammation-based prognostic scores (GPS, PNI and PI) were listed in Table 1.
Table 1

Inflammation-based prognostic scores

Scoring systemsScore
GPS
 CRP ≤ 10 mg/L and ALB ≥ 35 g/L0
 CRP > 10 mg/L or ALB < 35 g/L1
 CRP > 10 mg/L and ALB < 35 g/L2
PNI
 10 × serum albumin value (g/dl) + 0.005 × peripheral lymphocyte count (/ul) ≥ 450
 10 × serum albumin value (g/dl) + 0.005 × peripheral lymphocyte count (/ul) < 451
PI
 CRP ≤ 10 mg/L and WBC ≤ 11 × 109/L0
 CRP ≤10 mg/L and WBC > 11 × 109/L1
 CRP > 10 mg/L and WBC ≤ 11 × 109/L1
 CRP > 10 mg/L and WBC > 11 × 109/L2

Abbreviations: GPS Glasgow Prognostic Score, CRP C-reactive protein, ALB albumin, PNI prognostic nutritional index, PI prognostic index, WBC white blood cell

Inflammation-based prognostic scores Abbreviations: GPS Glasgow Prognostic Score, CRP C-reactive protein, ALB albumin, PNI prognostic nutritional index, PI prognostic index, WBC white blood cell

Outcomes

The primary endpoint of this study was all-cause mortality, and the second endpoint was CVD mortality. CVD mortality was defined as death caused by events including acute myocardial infarction, cardiac arrhythmia, congestive heart failure, atherosclerotic heart disease, cardiomyopathy, cardiac arrest, intracranial hemorrhage, cerebral infarction and peripheral vascular disease [22]. All participants were followed up until death, cessation of PD, or December 31, 2016.

Statistical analysis

The data were presented as mean ± standard deviation for normally distributed continuous variables, median (interquartile range) for skewed continuous variables, and number (proportion) for categorical variables. The Kaplan-Meier curve was used to calculate survival rate followed by log-rank test to compare differences among groups. Univariate and multivariate Cox proportional hazards models were used to analyze the associations between prognostic scores and all-cause and CVD mortality. The multivariate Cox regression model was constructed by adjusting covariates using a backward stepwise selection procedure with a stay criterion of 0.10 (the selection cut-off value was from default in SPSS software system as well as the importance of clinical concern). Receiver-operating characteristic (ROC) analysis was performed and the area under the curve (AUC) was calculated to determine the predictive power of prognostic scores for mortality. Comparison of AUC values among groups was determined using MedCalc software version 15.0 (Broekstraat, Mariakerke, Belgium) [23]. All other statistical analyses were performed using SPSS version 22.0 for Windows (SPSS, Chicago, IL, USA). P < 0.05 was considered statistically significant using two-tailed tests.

Results

Baseline demographic and clinical characteristics

Baseline demographic and clinical characteristics of the cohort study are given in Table 2. A total of 1501 eligible CAPD patients were included in this study. The mean age was 46.4 ± 15.1 years, 59.1% were male, 21.7% had a history of diabetes mellitus. The leading cause of ESRD was primary glomerulonephritis (928, 61.8%), followed by diabetic nephropathy (292, 19.5%), hypertension (135, 9.0%) and others (146, 9.7%). The median vintage of PD was 38.7 (range, 21.6–62.3) months.
Table 2

Baseline characteristics of 1501 CAPD patients

CharacteristicsValues
Age (years)46.4 ± 15.1
Gender (Male)887 (59.1%)
Smoke253 (16.9%)
Body mass index (kg/m2)21.5 ± 3.7
Systolic BP (mmHg)136.2 ± 20.6
Diastolic BP (mmHg)84.9 ± 14.4
Hypertension605 (40.3%)
Diabetes mellitus326 (21.7%)
Cardiovascular disease249 (16.6%)
Serum albumin (g/L)36.4 ± 5.0
Calcium (mmol/L)2.2 ± 0.3
Phosphorus (mmol/L)1.7 ± 0.6
iPTH (pg/mL)289.0 (144.4–455.5)
CRP (mg/L)1.6 (0.8–5.5)
WBC (× 109/L)6.9 ± 2.4
Lymphocyte (× 109//L)1.4 ± 0.6
Hemoglobin (g/L)89.8 ± 22.8
Total cholesterol (mmol/L)5.1 ± 1.4
Total triglycerides (mmol/L)1.6 ± 1.1
HDL-C (mmol/L)1.2 ± 0.4
LDL-C (mmol/L)3.0 ± 1.1
Plasma uric acid (μmol/L)430.3 ± 101.0
Plasma creatinine (μmol/L)766.7 ± 277.5
RRF (ml/min/1.73m2)3.7 ± 3.0

Abbreviations: CAPD continuous ambulatory peritoneal dialysis, BP blood pressure, iPTH intact parathyroid hormone, CRP C-reactive protein, WBC white blood cell, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, RRF residual renal function

Baseline characteristics of 1501 CAPD patients Abbreviations: CAPD continuous ambulatory peritoneal dialysis, BP blood pressure, iPTH intact parathyroid hormone, CRP C-reactive protein, WBC white blood cell, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, RRF residual renal function During the follow-up period, 318 (21.2%) patients underwent renal transplantation, 185 (12.3%) were transferred to HD, 59 (3.9%) were transferred to other centers, 36 (2.4%) were lost to follow-up, and finally, 903 (60.2%) were followed up until the end of the study.

Inflammation-based prognostic scores

According to the GPS scoring system, 909 (60.6%) of the 1501 patients showed a score of 0, while 456 (30.4%) and 136 (9.1%) patients had a score of 1 and 2, respectively. PNI classification revealed that 897 (59.8%) patients had a score of 1. With regard to PI, there were 253 (16.9%) and 35 (2.3%) patients who displayed a score of 1 and 2, respectively. In mortality population, larger proportions of patients were categorized into higher score groups (Table 3). Compared with non-diabetic patients, diabetic patients presented with higher scores (Table 4).
Table 3

Distribution of inflammation-based prognostic scores among groups

Prognostic scoreAll patients (n = 1501)Survival patients (n = 1155)All-cause mortality (n = 346)CVD mortality (n = 168)
GPS
 0909 (60.6%)845 (73.2%)64 (18.5%)26 (15.5%)
 1456 (30.4%)272 (23.5%)184 (53.2%)89 (53.0%)
 2136 (9.1%)38 (3.3%)98 (28.3%)53 (31.5%)
PNI
 0604 (40.2%)537 (46.5%)67 (19.4%)29 (17.3%)
 1897 (59.8%)618 (53.5%)279 (80.6%)139 (82.7%)
PI
 01213 (80.8%)1016 (88.0%)197 (56.9%)89 (53.0%)
 1253 (16.9%)130 (11.3%)123 (35.6%)68 (40.5%)
 235 (2.3%)9 (0.8%)26 (7.5%)11 (6.5%)

Abbreviations: GPS Glasgow Prognostic Score, PNI prognostic nutritional index, PI prognostic index, CVD cardiovascular disease

Table 4

Comparison of inflammation-based prognostic scores between diabetic and non-diabetic patients

Prognostic scoreAll patients (n = 1501)Diabetic patients (n = 326)Non-diabetic Patients (n = 1175)P value
GPS
 0909 (60.6%)104 (31.9%)805 (68.5%)< 0.001
 1456 (30.4%)175 (53.7%)281 (23.9%)
 2136 (9.1%)47 (14.4%)89 (7.6%)
PNI
 0604 (40.2%)75 (23.0%)529 (45.0%)< 0.001
 1897 (59.8%)251 (71.0%)646 (55.0%)
PI
 01213 (80.8%)233 (71.5%)980 (83.4%)< 0.001
 1253 (16.9%)79 (24.2%)174 (14.8%)
 235 (2.3%)14 (4.3%)21 (1.8%)

Abbreviations: GPS Glasgow Prognostic Score, PNI prognostic nutritional index, PI prognostic index, CVD cardiovascular disease

Distribution of inflammation-based prognostic scores among groups Abbreviations: GPS Glasgow Prognostic Score, PNI prognostic nutritional index, PI prognostic index, CVD cardiovascular disease Comparison of inflammation-based prognostic scores between diabetic and non-diabetic patients Abbreviations: GPS Glasgow Prognostic Score, PNI prognostic nutritional index, PI prognostic index, CVD cardiovascular disease

Patient survival

A total of 346 deaths (23.1%) occurred, of which 168 (48.6%) were attributed to CVD (Fig. 1). Kaplan-Meier analyses indicated that the cumulative overall survival rates of patients with a GPS score of 0, 1, 2, were 93.0%, 59.6%, 27.9%, respectively (log-rank test, P < 0.001); the CVD survival rates were also significantly lower in patients with higher scores (score 1: 80.5%; score 2: 61.0%) than those with a score of 0 (97.1%) (log-rank test, P < 0.001). Elevated PNI and PI scores were also shown to be associated with reduced all-cause and CVD survival rates (Fig. 2).
Fig. 1

Flowchart of the patient selection process. Abbreviations: CRP, C-reactive protein; WBC, white blood cell; PD, peritoneal dialysis; HD, hemodialysis; CVD, cardiovascular disease

Fig. 2

Kaplan–Meier estimates of cumulative overall (a, c, e) and CVD-free (b, d, f) survival rate according to different prognostic scores. Abbreviations: CVD, cardiovascular disease; GPS, Glasgow Prognostic Score; PNI, prognostic nutritional index; PI, prognostic index

Flowchart of the patient selection process. Abbreviations: CRP, C-reactive protein; WBC, white blood cell; PD, peritoneal dialysis; HD, hemodialysis; CVD, cardiovascular disease Kaplan–Meier estimates of cumulative overall (a, c, e) and CVD-free (b, d, f) survival rate according to different prognostic scores. Abbreviations: CVD, cardiovascular disease; GPS, Glasgow Prognostic Score; PNI, prognostic nutritional index; PI, prognostic index Univariate cox hazards analysis revealed that increased GPS, PNI and PI scores were all significantly related to all-cause and CVD mortality (Table 5). After adjusting for covariates including age, BP, diabetes, hypertension, cardiovascular disease, infection, hemoglobin, total triglycerides, total cholesterol, LDL-C, HDL-C, uric acid, and creatinine, the patients with increased GPS scores still had a significant increased risk for overall [Score 1: hazard ratio(HR) 3.94, 95% confidence interval(CI) 2.90–5.35, P < 0.001; Score 2: HR 7.56, 95% CI 5.35–10.67, P < 0.001] and CVD mortality (Score 1: HR 4.41, 95% CI 2.76–7.03,P < 0.001; Score 2: HR 9.64, 95% CI 5.72–16.26, P < 0.001). Increased PNI and PI values were also independently predictive of all-cause and CVD mortality (Table 6).
Table 5

Univariate cox proportional analysis for all-cause and CVD mortality

Univariate (All-cause mortality)Univariate (CVD mortality)
HR (95%CI)P valueHR (95%CI)P value
Age1.06 (1.05–1.07)< 0.0011.07 (1.06–1.08)< 0.001
Gender (Male)0.92 (0.74–1.13)0.4290.96 (0.71–1.30)0.790
Smoke1.16 (0.88–1.52)0.3001.18 (0.80–1.73)0.401
Body mass index(kg/m2)1.06 (1.03–1.09)< 0.0011.07 (1.03–1.12)0.002
Systolic BP (mmHg)1.01 (1.00–1.01)0.0131.01 (1.00–1.02)0.003
Diastolic BP (mmHg)0.98 (0.97–0.98)< 0.0010.98 (0.97–0.99)< 0.001
Hypertension2.86 (2.29–3.56)< 0.0013.92 (2.81–5.48)< 0.001
Diabetes mellitus3.35 (2.71–4.14)< 0.0014.45 (3.29–6.03)< 0.001
Cardiovascular disease3.54 (2.84–4.43)< 0.0014.96 (3.65–6.73)< 0.001
Infection3.46 (2.43–4.94)< 0.0012.64 (1.50–4.66)0.001
Residual renal function0.97 (0.92–1.01)0.120.94 (0.88–1.01)0.09
Calcium (mmol/L)0.31 (0.21–0.45)< 0.0010.28 (0.16–0.49)< 0.001
Phosphorus (mmol/L)1.01 (0.85–1.20)0.9020.94 (0.73–1.20)0.610
iPTH (pg/mL)1.00 (1.00–1.00)0.2291.00 (1.00–1.00)0.626
Hemoglobin (g/L)0.99 (0.99–1.00)0.0010.99 (0.98–0.99)< 0.001
Total cholesterol (mmol/L)1.04 (0.97–1.13)0.2571.12 (1.02–1.23)0.022
Total triglycerides (mmol/L)1.12 (1.03–1.21)0.0081.16 (1.04–1.29)0.007
HDL-C (mmol/L)0.58 (0.44–0.77)< 0.0010.62 (0.42–0.93)0.021
LDL-C (mmol/L)1.05 (0.95–1.16)0.3411.12 (0.99–1.28)0.070
Serum uric acid (μmol/L)1.00 (1.00–1.00)0.0691.00 (1.00–1.00)0.007
Serum creatinine (μmol/L)1.00 (1.00–1.00)< 0.0011.00 (1.00–1.00)< 0.001
GPS
 0referenceReference
 16.37 (4.79–8.48)< 0.0017.46 (4.81–11.56)< 0.001
 214.66 (10.68–20.13)< 0.00119.09 (11.91–30.57)< 0.001
PNI
 0referenceReference
 12.84 (2.17–3.70)< 0.0013.27 (2.19–4.88)< 0.001
PI
 0referenceReference
 13.48 (2.78–4.37)< 0.0014.26 (3.10–5.85)< 0.001
 25.29 (3.51–7.98)< 0.0015.08 (2.71–9.53)< 0.001

Abbreviations: HR hazard ratio, CI confidence interval, CAPD continuous ambulatory peritoneal dialysis, BP blood pressure, iPTH intact parathyroid hormone, CRP C-reactive protein, WBC white blood cell, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, RRF residual renal function, GPS Glasgow Prognostic Score, PNI prognostic nutritional index, PI prognostic index, CVD cardiovascular disease

Table 6

Multivariate cox proportional analysis for all-cause and CVD mortality

Multivariate (All-cause mortality)Multivariate (CVD mortality)
HR (95%CI)P valueHR (95%CI)P value
GPS
 0referenceReference
 13.94 (2.90–5.35)< 0.0014.41 (2.76–7.03)< 0.001
 27.56 (5.35–10.67)< 0.0019.64 (5.72–16.26)< 0.001
PNI
 0Referencereference
 11.82 (1.36–2.43)< 0.0011.63 (1.06–2.51)0.027
PI
 0referencereference
 12.08 (1.63–2.65)< 0.0012.57 (1.81–3.66)< 0.001
 23.03 (2.00–4.60)< 0.0013.85 (1.99–7.46)< 0.001

Adjustments were made for variables from the predictor variables of Table 5 using a backward stepwise cox proportional hazards model with a stay criterion of 0.10

Abbreviations: GPS Glasgow Prognostic Score, PNI prognostic nutritional index, PI prognostic index, CVD cardiovascular disease

Univariate cox proportional analysis for all-cause and CVD mortality Abbreviations: HR hazard ratio, CI confidence interval, CAPD continuous ambulatory peritoneal dialysis, BP blood pressure, iPTH intact parathyroid hormone, CRP C-reactive protein, WBC white blood cell, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, RRF residual renal function, GPS Glasgow Prognostic Score, PNI prognostic nutritional index, PI prognostic index, CVD cardiovascular disease Multivariate cox proportional analysis for all-cause and CVD mortality Adjustments were made for variables from the predictor variables of Table 5 using a backward stepwise cox proportional hazards model with a stay criterion of 0.10 Abbreviations: GPS Glasgow Prognostic Score, PNI prognostic nutritional index, PI prognostic index, CVD cardiovascular disease

Comparison of prognostic values of inflammation-based scores

When all-cause mortality was used as an endpoint, the area under the curve (AUC) was 0.798 (95% CI 0.770–0.826, P < 0.001) for GPS, 0.636 (95% CI 0.604–0.667, P < 0.001) for PNI, 0.658 (95% CI 0.622–0.694, P < 0.001) for PI. The AUC values for CVD mortality were 0.781 (95% CI 0.744–0.817, P < 0.001) for GPS, 0.629 (95% CI 0.589–0.670, P < 0.001) for PNI and 0.658 (95% CI 0.609–0.706, P < 0.001) for PI. By comparison of AUC values among groups, the GPS score showed a better distinguishing power for predicting all-cause and CVD mortality compared with PNI and PI (P < 0.001, respectively) (Fig. 3 & Table 7).
Fig. 3

ROC curve of prognostic scores for mortality

Table 7

Area under the ROC curve of prognostic scores for all-cause and CVD mortality

Prognostic scoreArea under the ROC curve95% CIP value (vs. GPS)
All-cause mortality
 GPS0.7980.770–0.826
 PNI0.6360.604–0.667< 0.001
 PI0.6580.622–0.694< 0.001
CVD mortality
 GPS0.7810.744–0.817
 PNI0.6290.589–0.670< 0.001
 PI0.6580.609–0.706< 0.001

Abbreviations: ROC receiver-operating characteristic analysis, HR hazard ratio, CI confidence interval, CAPD continuous ambulatory peritoneal dialysis, BP blood pressure, iPTH intact parathyroid hormone, CRP C-reactive protein, WBC white blood cell, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, RRF residual renal function, GPS Glasgow Prognostic Score, PNI prognostic nutritional index, PI prognostic index, CVD cardiovascular disease

ROC curve of prognostic scores for mortality Area under the ROC curve of prognostic scores for all-cause and CVD mortality Abbreviations: ROC receiver-operating characteristic analysis, HR hazard ratio, CI confidence interval, CAPD continuous ambulatory peritoneal dialysis, BP blood pressure, iPTH intact parathyroid hormone, CRP C-reactive protein, WBC white blood cell, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, RRF residual renal function, GPS Glasgow Prognostic Score, PNI prognostic nutritional index, PI prognostic index, CVD cardiovascular disease

Discussion

In this retrospective cohort study of 1501 CAPD patients with a median follow-up of 38.7 months, we demonstrated that increased GPS, PNI, and PI scores were all significantly related to all-cause and CVD mortality after adjustment for confounders. ROC analysis indicated that GPS had the best predictive value among these three scores system for CAPD patients. Inflammation is prevalent in PD patients [8]. Besides acute episodes of peritonitis, micro-inflammation also constitutes an important component of systemic inflammation responses [8, 9, 12]. Micro-inflammation in PD patients may be attributed to accumulation of uremic toxins, catheter implantation, bioincompatible dialysis solution, and so on [8]. Infections in the occult areas may also play a role, such as periodontal problems [24]. Systemic inflammation status is closely related to malnutrition and atherosclerosis. These three factors interrelate with each other and form a vicious cycle, eventually leading to increased cardiovascular morbidity and mortality [9, 12, 13]. In our study, although a minor population (62/1501) had active infection during data collection period, most patients did not present obvious signs of infection. The median level of CRP of the whole cohort was in the normal range, which may support the importance of micro-inflammation. GPS, comprising CRP and serum albumin, is a concise prognostic score that may reflect presence of both the systemic inflammatory response and deteriorating nutritional status. Inamoto et al. found the GPS was an independent prognostic factor for cancer-specific survival and overall survival after surgery with curative intent for localized upper tract urothelial carcinoma [10]. A study based on regular HD patients showed that elevated GPS was independently predictive of all-cause mortality and hospitalization during 42-month follow-up [25]. Consistent to these reports, our results showed that raised GPS values consistently related to both overall and CVD mortality in CAPD patients. The strong power for outcome prediction of this score may be attributed to the combined effects of its components. Both markers, CRP and serum albumin, have been demonstrated to be strongly associated with all-cause and CVD mortality in patients on PD [9–11, 26, 27]. However, ROC analysis revealed that GPS had the higher value than hypoalbuminemia or increased CRP alone (data not shown), which may indicate a reciprocal interaction between these two factors. The PNI score, which is based on serum albumin and total lymphocyte count, has been developed mainly to assess the nutritional status of patients [17-19]. In this study we found that elevated PNI score was independently associated with increased risk for overall and CVD mortality in CAPD patients. To our knowledge, another 2 studies have explored the predictive effect of PNI in PD cohorts [28, 29]. One study was limited to Korean subjects and showed that the PNI score was significantly related to all-cause mortality in PD patients, which is in agreement with our result [28]. The other study reported that PNI was associated with increased risk for CVD mortality but not all-cause mortality in 345 Chinese PD patients, which is partly conflicting with our findings [29]. The discrepancy may be due to differences in sample size, definition of PNI thresholds, or confounders chosen for adjustment. The PI score is composed of CRP and WBC count and has been validated as a useful predictive factor in lung and colon cancer [20, 30]. It is also suggested that PI was related to all-cause mortality in patients on regular HD [24]. Our study added new evidence that elevated PI scores were also independently predictive of overall and CVD mortality in a large population of CAPD patients. The prognostic values of these prognostic scores in CAPD patients were compared in our study. Results indicated that the GPS consistently exhibited a higher AUC value compared with PNI and PI scores and showed an excellent discriminatory performance for the CAPD patients. These findings were consistent with Akihiko’s report [24], in which the GPS score had the best predictive power for prognosis of HD patients. The GPS score was a combination of suitable markers for inflammation and malnutrition, while the other two were inclined to isolated aspects. These comparisons thus imply that a comprehensive monitor of both inflammatory and nutritional status may help better improve outcomes in dialysis patients. In addition, these inflammation-based prognostic scores consist of components which are routinely available with low cost. There are some limitations in the present study. Firstly, this was a retrospective study conducted in one single center and may thus have potential selection bias. Secondly, a large number of patients without certain blood test results were excluded, making those enrolled may not be well representative for the PD population. Thirdly, we calculated the values of these scoring systems at baseline, while a time-averaged score may be better for outcome prediction. Last but not the least, a minor population of patients with active infection were included in our cohort. Although our results showed the existence of infection did not affect the prognostic significance of scoring systems, we could not exclude the possibility of other confounding effects that deranged CRP or albumin levels during infection may produce.

Conclusions

In conclusion, the present study demonstrated that three well-standardized prognostic scores, GPS, PNI, and PI, are all independently associated with all-cause and CVD mortality in CAPD patients. In particularly, the GPS score shows the better predictive power for mortality compared to the other two scores. The GPS score may thus represent a simple and feasible tool for outcome prediction in CAPD patients.
  30 in total

Review 1.  US Renal Data System 2013 Annual Data Report.

Authors:  Allan J Collins; Robert N Foley; Blanche Chavers; David Gilbertson; Charles Herzog; Areef Ishani; Kirsten Johansen; Bertram L Kasiske; Nancy Kutner; Jiannong Liu; Wendy St Peter; Haifeng Guo; Yan Hu; Allyson Kats; Shuling Li; Suying Li; Julia Maloney; Tricia Roberts; Melissa Skeans; Jon Snyder; Craig Solid; Bryn Thompson; Eric Weinhandl; Hui Xiong; Akeem Yusuf; David Zaun; Cheryl Arko; Shu-Cheng Chen; Frank Daniels; James Ebben; Eric Frazier; Roger Johnson; Daniel Sheets; Xinyue Wang; Beth Forrest; Delaney Berrini; Edward Constantini; Susan Everson; Paul Eggers; Lawrence Agodoa
Journal:  Am J Kidney Dis       Date:  2014-01       Impact factor: 8.860

2.  Relationship between serum uric acid and all-cause and cardiovascular mortality in patients treated with peritoneal dialysis.

Authors:  Xi Xia; Feng He; Xianfeng Wu; Fenfen Peng; Fengxian Huang; Xueqing Yu
Journal:  Am J Kidney Dis       Date:  2013-10-28       Impact factor: 8.860

3.  Platelet index levels and cardiovascular mortality in incident peritoneal dialysis patients: a cohort study.

Authors:  Fenfen Peng; Zhijian Li; Chunyan Yi; Qunying Guo; Rui Yang; Haibo Long; Fengxian Huang; Xueqing Yu; Xiao Yang
Journal:  Platelets       Date:  2016-11-25       Impact factor: 3.862

4.  Low prognostic nutritional index associated with cardiovascular disease mortality in incident peritoneal dialysis patients.

Authors:  Fenfen Peng; Wenjing Chen; Weidong Zhou; Peilin Li; Hongxin Niu; Yihua Chen; Yan Zhu; Haibo Long
Journal:  Int Urol Nephrol       Date:  2017-02-10       Impact factor: 2.370

Review 5.  The systemic inflammation-based Glasgow Prognostic Score: a decade of experience in patients with cancer.

Authors:  Donald C McMillan
Journal:  Cancer Treat Rev       Date:  2012-09-17       Impact factor: 12.111

6.  Significance of the Glasgow Prognostic Score for patients with colorectal liver metastasis.

Authors:  Sho Okimoto; Tsuyoshi Kobayashi; Hirotaka Tashiro; Shintaro Kuroda; Kohei Ishiyama; Kentaro Ide; Tomoyuki Abe; Masakazu Hashimoto; Hiroshi Iwako; Michinori Hamaoka; Naruhiko Honmyo; Megumi Yamaguchi; Hideki Ohdan
Journal:  Int J Surg       Date:  2017-05-05       Impact factor: 6.071

7.  The predictive value of pre-treatment inflammatory markers in advanced non-small-cell lung cancer.

Authors:  G Kasymjanova; N MacDonald; J S Agulnik; V Cohen; C Pepe; H Kreisman; R Sharma; D Small
Journal:  Curr Oncol       Date:  2010-08       Impact factor: 3.677

8.  C-reactive protein and cardiovascular disease in peritoneal dialysis patients.

Authors:  Didier Ducloux; Catherine Bresson-Vautrin; Marc Kribs; Aboubakr Abdelfatah; Jean-Marc Chalopin
Journal:  Kidney Int       Date:  2002-10       Impact factor: 10.612

9.  A comparison of systemic inflammation-based prognostic scores in patients on regular hemodialysis.

Authors:  Akihiko Kato; Takayuki Tsuji; Yukitoshi Sakao; Naro Ohashi; Hideo Yasuda; Taiki Fujimoto; Takako Takita; Mitsuyoshi Furuhashi; Hiromichi Kumagai
Journal:  Nephron Extra       Date:  2013-10-11

10.  The diagnostic accuracy of ultrasonography versus endoscopy for primary nasopharyngeal carcinoma.

Authors:  Yong Gao; Jun-Jie Liu; Shang-Yong Zhu; Xiang Yi
Journal:  PLoS One       Date:  2014-06-02       Impact factor: 3.240

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  5 in total

1.  Prognostic nutritional index is a predictor of mortality in elderly patients with chronic kidney disease.

Authors:  Dilek Barutcu Atas; Murat Tugcu; Ebru Asicioglu; Arzu Velioglu; Hakki Arikan; Mehmet Koc; Serhan Tuglular
Journal:  Int Urol Nephrol       Date:  2021-09-25       Impact factor: 2.370

2.  Association between fibrinogen/albumin ratio and severity of coronary artery calcification in patients with chronic kidney disease: a retrospective study.

Authors:  Yuyu Zhu; Shuman Tao; Danfeng Zhang; Jianping Xiao; Xuerong Wang; Liang Yuan; Haifeng Pan; Deguang Wang
Journal:  PeerJ       Date:  2022-06-06       Impact factor: 3.061

3.  Sexual Effect of Platelet-to-Lymphocyte Ratio in Predicting Cardiovascular Mortality of Peritoneal Dialysis Patients.

Authors:  Hui Sheng; Yagui Qiu; Xi Xia; Chunyan Yi; Jianxiong Lin; Xiao Yang; Fengxian Huang
Journal:  Mediators Inflamm       Date:  2022-01-04       Impact factor: 4.711

4.  Risk factors for mortality in patients undergoing peritoneal dialysis: a systematic review and meta-analysis.

Authors:  Jialing Zhang; Xiangxue Lu; Han Li; Shixiang Wang
Journal:  Ren Fail       Date:  2021-12       Impact factor: 2.606

5.  Serum growth differentiation factor-15 analysis as a malnutrition marker in hemodialysis patients

Authors:  Didem Turgut; Deniz Ilhan Topcu; Cemile Cansu Alperen; Esra Baskın
Journal:  Turk J Med Sci       Date:  2021-08-30       Impact factor: 0.973

  5 in total

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