Calculated inflammatory markers derived from complete blood count results, along with routine laboratory and clinical data, predict treatment failure of acute peritonitis in chronic peritoneal dialysis patients (2024)

2.1. Study design and participants

It was a single-center, retrospective observational study of PDAP patients at the First Affiliated Hospital of Dalian Medical University, China. The data regarding all episodes of PDAP patients from 01 January 2012 to 31 December 2021 were collected by reviewing case records. The inclusion criteria included: (1) age ≥ 18 years, (2) PD vintage ≥1 month, and (3) meeting the diagnostic criteria of PDAP [15]. The exclusion criteria included: (1) without CBC results and peritoneal effluent test results; (2) episodes in the perioperative period; (3) a current history of hemodialysis (HD) or renal transplantation; (4) diagnosis of systemic inflammatory diseases (chronic autoimmune diseases, acute infection, and surgical peritonitis) within the preceding 1 month; (5) the episode of acute cardiovascular and cerebrovascular events within the preceding 1 month; (6) diagnosis of malignant tumors; (7) treatment with glucocorticoid or immunosuppressant within the preceding 1 year; (8) treatment with aspirin, clopidogrel, low molecular weight heparin (affecting CBC); and (9) those receiving antibiotics prior to the CBC tube collection. All the patients received continuous ambulatory peritoneal dialysis (CAPD) using glucose-based and lactate-buffered PD solutions through double cuff Silastic PD catheters. Our study was consistent with the ethical principles of the revised Declaration of Helsinki. Relevant information about personal identifiers was wiped off. The study was performed under a project license (NO. PJ-KY-2019-166) granted by Ethics Committee of The First Affiliated Hospital of Dalian Medical University.

2.2. Basic demographic, laboratory, and clinical data

Basic demographic data, including gender, age, duration of PD, etiology of end-stage renal disease (ESRD), and bacterial culture results, were obtained from the record system of our hospital. Blood for CBC and calculation of inflammatory markers was collected before empirical antibiotic therapy was initiated. The inflammatory markers were calculated, consisting of NLR, PLR, MLR, PMR, HPR, HLR, SII, and dNLR. SII was calculated as SII = neutrophil count × platelet count/lymphocyte count [16], and dNLR was calculated as dNLR = neutrophil count/(white blood cell count – neutrophil count) [17].

2.3. Laboratory measurements

Dialysate effluent was collected into blood culture bottles, and sterile tubes under aseptic operation for cell counts, Gram stain, microbial culture, and drug sensitivity tests when peritonitis of PD patients was suspected [15]. Meantime, blood samples were collected in anticoagulated tubes for CBC results before empirical antibiotic therapy.

2.4. Diagnosis and treatment of peritonitis

Peritonitis was diagnosed according to the 2022 ISPD guidelines [15] as the presence of at least two conditions below: (1) clinical features consistent with peritonitis, that is, abdominal pain and/or cloudy dialysis effluent; (2) dialysis effluent white cell count > 100/μL or > 0.1 × 10⁹/L (after a dwell time of at least 2 h), with > 50% polymorphonuclear leukocytes; (3) positive dialysis effluent culture.

According to the guidelines, initial empiric antibiotic therapy for peritonitis covered Gram-positive (a first-generation cephalosporin or vancomycin) and Gram-negative (a third-generation cephalosporin or an aminoglycoside) organisms. The medication regimen was adjusted once the culture results and drug sensitivity test became available. According to the ISPD guide, recurrent peritonitis was defined as an episode that occurred within 4 weeks of completion of therapy of a prior episode but with a different organism, and it was recorded as one episode. Relapsing peritonitis was defined as an episode that occurred within 4 weeks of completion of therapy of a prior episode with the same organism or one sterile (culture negative) episode. However, it was recorded as one episode with the prior episode.

2.5. Clinical Outcomes of peritonitis

Our primary clinical outcome was treatment failure, defined as unresolved peritonitis symptoms (abdominal pain and/or cloudy dialysis effluent) and dialysis effluent white cell count (>100/μL), resulting in catheter removal, switching to hemodialysis, and death (because of peritonitis). Treatment success was defined as the disappearance of clinical features (abdominal pain and/or cloudy dialysis effluent) and normal dialysis effluent white cell count (<100/μL).

2.6. Statistical analysis

Normality was tested by using the Shapiro-Wilk test. As for continuous variables in the study, normally distributed data were expressed as mean ± standard deviation (SD), and skewed data were presented as median with interquartile range (IQR). Categorical variables were statistically described as numbers (n) and percentages (%). The statistical differences between treatment success and failure groups were conducted by applying Student’s t-test for continuous normally distributed data, Wilcoxon-Mann-Whitney test for continuous skewed variables, and Pearson’s Chi-square test or Fisher’s exact test for categorical variables.

The prediction ability of all relevant variables was determined using the area under the receiver operating characteristics curve (AUROC) analysis. The optimal cutoff values, sensitivity, and specificity, were determined by maximizing the Youden’s index (sensitivity plus specificity minus 1). The univariate logistic regression model was performed to assess the relevant markers and determine the independent markers of treatment failure. Odds ratios (ORs) and their 95% confidence intervals (CI) were calculated to determine the relationship between related factors and treatment outcomes.

The variables with a p value of <.2 in the univariate logistic regression analysis were selected for the multivariate logistic regression model to explore the independent protective and risk factors of treatment failure. In the multivariate logistic regression model, the backward stepwise regression procedure played a vital role in isolating the predictors. The difference between the two groups was considered statistically significant at p < .05. All analyses were performed using Stata Edition 15.1 (Stata Corp, College Station, TX, USA).

3.1. Patient characteristics and episodes

During the 10-year study period, 244 episodes of PDAP in 171 PD patients were involved in the initial cohort in the present study (Figure 1). One hundred and six episodes were excluded for the following reasons: 70 episodes were without CBC results, 14 were in the perioperative period, 11 were without effluent test results, 7 were malignant tumor patients, 2 were patients with combination PD and HD, 1 was under 18 years, and 1 was in oral aspirin therapy. One hundred thirty-eight episodes (in 116 PD patients) were eventually eligible and divided into treatment success and failure groups. Ninety-seven (70.29%) episodes (in 77 PD patients) were identified as treatment success; 41 (29.71%) episodes (in 41 PD patients) were identified as treatment failure, including 30 (21.73%) transfers to hemodialysis and 11 (7.97%) deaths (Figure 1).

Baseline characteristics are presented in Table 1. The median age was 58.44 (IQR, 42.39–69.11) years, and 57 patients (58.76%) were males in the success group. The median age was 57.71 (IQR, 49.24–71.09) years, and 24 patients (58.54%) were males in the failure group. No significant differences were observed in age and gender between the two groups. However, there was a significant difference between the success and failure groups in the PD vintage (23.10 months, IQR, 8.80–46.47 vs. 30.57 months, IQR, 14.30–56.57, p = .016). Regarding the etiology of ESRD, we analyzed diabetic nephropathy, chronic glomerulonephritis, benign arteriolar nephrosclerosis, and other diseases. No significant differences were found between the two groups. According to culture results, there were 45 (32.61%) episodes with Gram-positive organisms, 31 (22.46%) episodes with Gram-negative organisms, 7 (5.07%) episodes with fungus, and 55 (39.86%) episodes with negative cultures. There were no significant differences between the two groups except for the culture-negative peritonitis (45, 46.39% vs. 10, 24.39%, p = .022). All the episodes with different strains have also been analyzed, including Staphylococcus epidermidis, Staphylococcus aureus, Staphylococcus haemolyticus, Staphylococcus hominis, Staphylococcus auricularis, Escherichia coli, and Pseudomonas aeruginosa. No significant statistical difference was found between the success and failure groups except for Staphylococcus aureus (4, 11.76% vs. 5, 45.45%, p = .028).

3.2. Association between CBCs-derived inflammatory markers with treatment outcome

Analysis results of peritoneal effluent results and CBCs and CBCs-derived inflammatory markers in the patients were also presented in Table 1. No significant differences were observed in white blood cells, neutrophils, monocyte, HLR, PMR, and HPR between the two groups. The proportion of effluent lymphocytes, CBC lymphocytes, and hemoglobin was significantly higher in the success group than in the failure group. However, effluent WBC count, platelet, NLR, PLR, MLR, SII, dNLR were significantly higher in the failure group than in the success group. The violin charts also demonstrated the distribution of inflammation ratios in PDAP patients (Supplementary Figure 1).

3.3. The predictors in PDAP patients

To determine whether the variables were correlated with treatment failure, they were enrolled in the analysis to perform the odds ratios. As shown in Table 2, 5 variables were significant risk factors for treatment failure of PDAP, including Pseudomonas aeruginosa (OR 13.333; 95%CI, 1.506–118.060; p = .020), NLR (OR, 1.101; 95%CI, 1.036–1.171; p = .002), PD vintage (OR, 1.015; 95%CI, 1.001–1.029; p = .033), platelet (OR, 1.005; 95%CI, 1.001–1.010; p = .018), and PLR (OR, 1.005; 95%CI, 1.002–1.007; p < .001). Nevertheless, culture-negative (OR, 0.373; 95%CI, 0.165–0.844; p = .018) and hemoglobin (OR, 0.979; 95%CI, 0.960–0.999; p = .042) suggested good prognosis in PDAP patients.

The backward stepwise multivariate logistic regression model was further performed to investigate the risk factors of treatment outcomes (Table 3), and the results demonstrated that Staphylococcus aureus [Standard β value, 3.073; adjusted OR (aOR), 21.614; 95%CI, 2.829–165.144; p = .003) was a highly significant risk factor for treatment failure, followed by NLR (Standard β value, 0.319; aOR, 1.376; 95%CI, 1.105–1.713; p = .004), age (Standard β value, 0.042; aOR, 1.043; 95%CI, 1.007–1.079; p = .018), PD vintage (Standard β value, 0.021; aOR, 1.021; 95%CI, 1.003–1.040; p = .025) and PLR (Standard β value, 0.010; aOR, 1.010; 95%CI, 1.004–1.017; p = .002). Meanwhile, Gram-positive peritonitis (Standard β value, −2.606; aOR, 0.074; 95%CI, 0.019–0.290; p < .001) was a significant protective factor for the prognosis of PDAP, followed by culture-negative peritonitis (Standard β value,-1.795; aOR, 0.166; 95%CI, 0.054–0.508; p = .002), HLR (Standard β value, −0.023; aOR, 0.977; 95%CI, 0.963–0.991; p = .001), and SII (Standard β value, −0.001; aOR, 0.999; 95%CI, 0.998–1.000; p = .040).

In order to determine whether the combination of the 9 variables (age, PD vintage, Gram-positive peritonitis, Staphylococcus aureus, culture-negative, NLR, PLR, HLR, and SII) would improve their prognostic performance, ROC curves were plotted, and presented in Figure 2. Meantime, two ROC curves of 5 risk factors (Staphylococcus aureus, NLR, age, PD vintage, PLR) and 4 protective factors (Gram-positive peritonitis, culture-negative peritonitis, HLR, SII) were also plotted, and presented in Figure 2 according to the multivariate logistic regression analysis about risk factors and protective factors (Supplementary Table 1 and 2). The area under this ROC curve (9 factors) was 0.85, higher than any of the independent indicators (Supplementary Table 3), 5 risk factors (0.74), and 4 protective factors (0.75) (Figure 2), suggesting that the combination had a better predictor of prognosis for PADP patients.

Supplemental Material:

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