Elevated effluent lactate independently predicts the adverse outcomes of peritoneal dialysis associated peritonitis

Abstract

Peritonitis is the primary complication associated with peritoneal dialysis (PD). The acute inflammatory response is promoted by glycolysis, thereby enhancing lactate production. Lactate level has been identified as a prognostic factor of many acute inflammatory disease, however with no observation in peritoneal dialysis associated peritonitis (PDAP). Therefore, the need to monitor lactate levels of PDAP patients should be emphasized. Retrospective data on effluent and serum lactate levels and other clinical and laboratory characteristics for PDAP (2022-Nov to 2024-Jul) were analyzed. No-cure was defined as adverse outcomes like recurrence, relapse, catheter removal, hemodialysis transfer ≥ 30 days or death. We used logistic regressions and receiver operating characteristic curve (ROC) analysis to assess factors linked to outcomes and the lactate level predictive accuracy for PDAP adverse outcomes, respectively. The total number of PDAP episodes enrolled was 118, involving 100 PD patients. Regarding the clinical outcomes of the PDAP, 84 episodes were cured, while 34 episodes were no-cure. Catheter removal and transfer to hemodialysis were the leading adverse outcomes (n = 22, 64.7%), followed by relapse and recurrence. Compared with the cure group, no-cure group showed significantly lower serum albumin levels, higher effluent white cell count (WCC) on day 5, and higher percentage of gram-negative bacteria infections. The effluent lactate levels in no-cure group were statistically higher than those of cure group (8.50 [5.48, 13.1] versus 5.4 [3.18, 8.25] mmol/L, p < 0.001). Patients with higher effluent lactate levels (odds ratio [OR] = 1.419, p < 0.001) and gram-negative peritonitis (OR = 6.444, p = 0.009) had a higher risk of adverse outcomes. A moderate discriminative capacity for adverse outcomes was observed in the effluent lactate levels through ROC analyses (area under curve [AUC] = 0.752, p < 0.001), and with a threshold values of 10.2 mmol/L yielding the highest Youden’s index. Elevated levels of lactate in the effluent may independently indicate adverse outcomes in PDAP.

Introduction

Peritoneal dialysis (PD) is a highly effective treatment for patients with end-stage kidney disease (ESKD) due to its simplicity of operation and the advantages of preserving residual renal function. Peritoneal dialysis associated peritonitis (PDAP) represents a prevalent and severe complication of PD, it accounting for more than 15% of patient deaths due to PD as the primary or significant contributing factor¹. This acute inflammatory condition initiates a series of metabolic alterations and disrupts normal peritoneal ultrafiltration functions. As for the lactate levels in PD patients, previous studies have reported an abnormal blood lactate level is often seen in PD patients presenting to the emergency department, and have postulated that there is a transient disruption in the metabolism of lactate absorbed from the PD fluid in the setting of an acute intercurrent illness². However, no studies have observed the relationship between lactate level and PDAP.

During the acute inflammatory response, the metabolic transition from oxidative phosphorylation (OXPHOS) to aerobic glycolysis expedites energy production by converting pyruvate to lactate^3, thereby enhancing lactate production⁴. Hence, lactate accumulation in the tissue microenvironment is characteristic of inflammatory diseases. Traditionally, lactate has been considered a metabolic ‘waste product’ of glycolysis. However, recent research has shown that lactate supports cell proliferation, cytokine production, and regulates inflammatory responses⁵. More recently, the discovery of lactylation further revealed the role of lactate in regulating inflammatory processes^5,6.

Due to the important role of lactate in the occurrence and development of inflammation, lactate levels have significant clinical implications in monitoring the progression of acute inflammatory diseases, such as acute pancreatitis, sepsis, and septic shock⁶. There is a curvilinear relationship between lactate concentrations and mortality in emergency department patients with suspected infection^7,8. Clinical trials have confirmed that serum lactate concentration can be used to guide the medication of clinical patients, as prognosis forecast⁹ and predict the prognosis^10,11 in sepsis patients. However, no reports were available in PDAP. Our research aimed to explore the relationship between lactate levels and the clinical outcomes of PDAP.

Results

Characteristics of patients

During the research period, there were a total of 506 patients undergoing PD, with a cumulative treatment duration of 8181 months. As shown in Figs. 1 and 121 PDAP episodes were documented in 103 PD patients. The incidence of peritonitis was 0.18 occurrences per patient-year. After excluding three episodes, 118 PDAP episodes from 100 patients were enrolled in the study (Fig. 1). Table 1 presents the baseline characteristics of these 100 PD patients. Their mean age was 55.9 ± 13.6 years, with 65(65%) being male. The median Charlson Comorbidity Index (CCI) score was 4 points. Their median estimated glomerular filtration rate (eGFR) at the initiation of PD treatment was 5 ml/min/1.73 m². The primary cause of ESKD was chronic glomerulonephritis. The predominant baseline peritoneal membrane transport (PET) types were low average and high average (Table 1).

All episodes were treated with glucose-based PD solution (PD-2 Dianeal or PD-4 Dianeal, Baxter Healthcare, Mississauga, ON, USA, Lactate 35–40 [mmol/L]). Among the 118 peritonitis episodes, 92.4% occurred in continuous ambulatory peritoneal dialysis (CAPD) patients. The median PD duration was 43 months, with a median PD exchange volume of 56 L/week. The most common clinical manifestation was cloudy dialysis effluent (n = 118, 100%), followed by abdominal pain (n = 117, 99.2%). The leading microbiologic cause was gram-positive cocci infection (n = 76, 64.4%), which mainly consisted of 45 coagulase-negative staphylococci, 13 Staphylococcus aureus, and 4 Enterococcus. A total of 20 episodes involved gram-negative bacteria. The percentage of gram-negative bacterial infections was significantly higher in the no-cure group (35.3% versus 9.5%, p = 0.001) (Table 2), and all of 4 Pseudomonas aeruginosa peritonitis were reported in no-cure group. No significant differences were observed in the type of organism with respect to either effluent or serum lactate levels (p = 0.412 versus p = 0.216). The median exit-site score was 1.0 (range 1.0, 2.0), with a total of 5 episodes of catheter-related infections (4 exit-site infections and 1 tunnel infection).

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Fig. 1

Flow chart of the study. PDAP, peritoneal dialysis associated peritonitis. Cure was described as the complete resolution of peritonitis together with none of the following complications: relapse or recurrent peritonitis, catheter removal, transfer to hemodialysis for ≥ 30 days or death. No-cure was defined as adverse outcomes of PDAP including recurrence, relapse, catheter removal, transfer to hemodialysis for ≥ 30 days, or death associated with PDAP.

Table 1. Demographic and clinical features of all patients at baseline.

Characteristics	Total population (n = 100)
Male gender [n (%)]	65 (65)
Age (years)	55.9 ± 13.6
BMI (kg/m2)	22.8 ± 3.3
MAP (mmHg)	105(96, 113)
CCI	4 (2, 5)
ESKD cause [n (%)]
Chronic glomerulonephritis	58 (58)
Diabetic nephropathy	26 (26)
Hypertensive nephrosclerosis	11 (11)
Others	5 (5)
Hemoglobin (g/L)	80.0 (69.3, 90.3)
Serum albumin (g/L)	34.3 ± 5.26
Urine protein (g/24 h)	1.61 (0.55, 2.99)
eGFR(ml/min/1.73m2)	5.0 (4.0, 7.0)
D/P (creatinine) at 4 h	0.64 ± 0.12
Peritoneal membrane transport type, n = 79 [n (%)]
Low and Low Average transport	43 (54.4)
High and High Average transport	36 (45.6)
Residual kidney Kt/V urea (/week)	0.11 (0.00,0.59)
Peritoneal Kt/V urea (/week)	1.50 ± 0.48

BMI, body mass index; ESKD, end-stage kidney disease; MAP, mean arterial pressure; eGFR, estimated glomerular filtration rate(calculated using by the 2021 CKD–EPI formula); CCI, Charlson Comorbidity Index [The CCI assigns 1 point for history of myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease (including transient ischemic attack or cerebrovascular accident with minor or no residua), dementia, chronic pulmonary disease, connective tissue disorder, peptic ulcer disease, mild liver disease, and diabetes without end-organ damage; 2 points are assigned for hemiplegia, moderate to severe renal disease, diabetes with end-organ damage, tumor without metastases, leukemia, lymphoma, and myeloma; 3 points are assigned for moderate or severe liver disease; and 6 points are assigned for metastatic solid tumor or acquired immunodeficiency syndrome (AIDS). For every decade over 40 years of age, 1 point is added to the score]; D/P (creatinine) at 4 h, dialysate creatinine divided by plasma creatinine at 4 hours. Kt/V urea: urea clearance index.

Correlation between lactate levels and clinical outcomes

In terms of the treatment outcomes of PDAP, 84 episodes were cured (cure group), whereas 34 episodes experienced adverse outcomes (no-cure group). Catheter removal and transfer to hemodialysis were the leading adverse outcomes (n = 22, 64.7%), which included 10 relapsing peritonitis, 8 refractory peritonitis, 3 recurrent peritonitis, 1 Pseudomonas peritonitis with a concomitant tunnel infection. No deaths related to peritonitis were reported.

There were no statistical significant differences between the cure group and the no-cure group in terms of age at peritonitis, gender, CCI, PD duration or PD characteristics (including PD modality, daily exchange volume, and PD fluid types) (Table 2). Compared with cure group, the no-cure group showed significantly lower serum albumin levels ([26.8 ± 6.1] versus [29.2 ± 6.0] g/L, p = 0.051). Although effluent white cell count (WCC) was comparable on day 1, a significantly higher effluent WCC was observed on day 5 in the no-cure group (p < 0.001), with a median count of 182/µL. All systemic inflammation markers including blood WCC, blood C-reactive protein (CRP), and serum IL-6 showed no significant differences between the two groups (Table 2).

Table 2. Comparison of the clinical features associated with peritonitis in the two groups of patients.

Characteristics	Cure(N = 84)	No-cure(N = 34)	p-value
Male gender [n (%)]	53 (63.1)	20 (68.8)	0.665
Age at peritonitis (years)	56.7 ± 14.2	54.6 ± 11.9	0.449
CCI	4.0(2.0, 5.8)	3.0(2.0, 5.3)	0.446
PD duration (months)	43.5 (13.3,7 1.5)	49.0 (12.8, 85.3)	0.662
PD modality (CAPD/APD)	78/6	33/1	0.382
Exchange volume (L/week)	56 (56, 70)	56 (52, 70)	0.516
D/P (creatinine) at 4 h	0.61 (0.54, 0.72)	0.63 (0.56, 0.77)	0.619
Total Kt/V urea (/week)	1.90 ± 0.35	1.75 ± 0.34	0.946
Clinical features [n (%)]
Abdominal pain	83 (98.9)	34 (100)	0.523
Cloudy dialysis effluent	84 (100)	34 (100)	1.000
Fever	24 (28.6)	9 (26.5)	0.818
Blood WCC(×109/L)	8.34 (6.04, 13.71)	7.18 (5.75, 12.39)	0.262
Haemoglobin (g/L)	104.8 ± 22.2	101.3 ± 16.7	0.407
Blood C-reactive protein(mg/L)	73.1 (26.8, 125.7)	88.6 (42.2, 171.1)	0.269
Procalcitonin(ng/mL)	1.08 (0.47, 4.77)	1.50 (0.56, 6.65)	0.297
Interleukin-6(pg/mL)	40.9 (19.0, 161.5)	40.1 (14.5, 188.8)	0.864
Serum albumin (g/L)	29.2 ± 6.0	26.8 ± 6.1	0.051
Effluent Ca-125(U/mL)	19.9 (8.8, 46.4)	13.0 (4.6, 30.3)	0.358
Day 1 Effluent WCC (/µL)	1737 (648, 6108)	2431 (739, 5058)	0.585
Day 5 Effluent WCC(/µL)	35 (11, 104)	182 (50, 810)	< 0.001
Blood lactate(mmol/L)	2.40 (1.83, 3.28)	2.70 (2.20, 3.40)	0.138
Effluent lactate(mmol/L)	5.4 (3.18, 8.25)	8.5 (5.48, 13.1)	< 0.001
Microbiologic cause [n (%)]
Coagulase-negative staphylococci	35 (41.7)	10 (29.4)	0.215
Staphylococcus aureus	7 (8.3)	6 (17.6)	0.143
Enterococcus	4 (4.8)	0 (0)	0.195
Gram-negative bacterial	8 (9.5)	12 (35.3)	0.001
Polymicrobial	5 (6.0)	0 (0)	0.146
Culture-negative	14 (16.7)	3 (8.8)	0.272
Catheter-related infection (either exit-site or tunnel) [n (%)]	5 (6.0)	0 (0)	0.146
Exit-site score	1.0 (1.0, 2.0)	2.0 (1.0, 2.0)	0.147

CCI, Charlson Comorbidity Index; PD, peritoneal dialysis; CAPD, continuous ambulatory peritoneal dialysis; APD, automated peritonea dialysis; Kt/V urea, urea clearance index; WCC: white cell count.

The effluent lactate levels were statistically higher in the no-cure group compared to the cure group (8.50 [5.48, 13.1] versus 5.4 [3.18, 8.25] mmol/L, p < 0.001). However, a similar trend was not observed in serum lactate levels (Table 2). The predictive role of effluent lactate levels in adverse outcomes was examined using logistic regression models (Fig. 2). Results from both univariable and multivariable logistic regression analyses confirmed that gender, age at peritonitis, PD duration, and CCI were not predictive factors for adverse outcomes of PDAP. In contrast, effluent lactate level (adjusted per mmol/L) was a significant predictor for adverse outcomes of PDAP, after adjusting for factors thought to be related to treatment outcomes including gender, age at peritonitis, PD duration, CCI, gram-negative bacterial infection, serum albumin, and effluent WCC on day 5. An increase in effluent lactate levels was associated with 41.9% relative increase in risk of adverse outcomes of peritonitis (95% confidence interval [CI] 1.191 to 1.691). Meanwhile, gram-negative peritonitis was also independently associated with a higher risk of adverse outcomes, with an odds ratio (OR) of 6.444 (95% CI 1.605 to 25.869) (Fig. 2).

The ROC analysis of effluent lactate levels demonstrated a moderate discriminative capacity for adverse outcomes. The area under the curve (AUC) was 0.752 (95% CI [0.654–0.850], p < 0.001) (Fig. 3). The optimal threshold value, determined by the highest Youden’s index, was 10.2 mmol/L, resulting in a sensitivity of 48.1% and a specificity of 91.9%. The calibration of ROC curve of effluent lactate concentration in diagnosing adverse outcomes of PDAP was 0.568. This was determined by the Hosmer-Lemeshow goodness-of-fit test, which yielded a chi-square value of 6.708 (Fig. 4). We also incorporated effluent WCC on day 5 and effluent lactate into a multi-index ROC curve, which showed a larger AUC than that of effluent WCC on day 5 alone (Fig. 3), indicating a better ability to predict prognosis.

Discussion

This is a retrospective, single-center observational study to investigate the correlation between effluent lactate levels and clinical outcomes of PDAP. Our study revealed that effluent lactate levels, rather than serum lactate levels were significantly higher in peritonitis episodes that associated with adverse outcomes including relapse, recurrence, catheter removal, transfer to hemodialysis, and death. Elevated effluent lactate levels were found to be independent predictor of the adverse outcomes in PDAP. ROC analyses indicated that the diagnostic threshold value of effluent lactate for adverse outcomes was 10.2 mmol/L.

We found that effluent lactate levels rather than serum lactate levels could independently predict the adverse outcome in PDAP. The lactate concentration in PD fluid is 35–40 mmol/L, which is significantly lower than the lactate production rate in healthy people (15–30 mmol/kg/day)¹², and no reports have revealed elevated serum lactate levels in patients undergoing PD¹³. Patients with elevated lactate levels (> 2 mmol/L) were more likely to present with infectious events, but these elevated levels were not always predictive of disease severity². Similarly, our PD patients experiencing peritonitis often had elevated blood lactate levels, but these levels were not significantly predictive of adverse outcomes. Acute inflammatory diseases are characterized by lactate accumulation in the tissue microenvironment⁵ which may be mainly prompted by local metabolic transition to aerobic glycolysis. Since peritonitis is an acute inflammatory process originating from abdomen cavity infection, and our detection of lactate was arranged on the first day of peritonitis presentation, local accumulation of lactate is thought to be more prominent than systemic accumulation. No difference was found in other systemic inflammatory markers including serum IL-6 and blood CRP between the two groups might support our speculation. Moreover, systemic lactate levels are influenced by other various factors such as respiration, catecholamines, β-agonist use, alcohol, ketoacidosis, hypoxia, and perfusion insufficiency^6. We could not preclude unrecognized factors that confounded the results due to the observational nature of the study design.

Recent studies have confirmed that lactate has an inhibitory effect on acute inflammation⁶ which may weaken host defense and contribute to adverse outcomes. Acute inflammation is generally considered a host defense mechanism, during which the immune cells are recruited and release a burst of proinflammatory factors to fight pathogenic microorganisms and cope with bacterial infection⁶. Lactate has been shown to significantly reduce the production of TNF-α and IL-6, as well as the activation and nuclear accumulation of NF-κB and yes-associated protein (YAP), through the GPR81 pathway in LPS-stimulated macrophages¹⁴. As lactate accumulates, histone lysine lactylation is significantly increased, and acts as the ‘lactate clock’ to promote the switch of macrophage from an inflammatory phenotype (M1-like) to a steady-state phenotype (M2-like) in macrophages¹⁵. Increased lactate levels lead to mitochondrial dysfunction and respiratory function impaired¹⁶ and the PD-1/PD-L1 pathway activation¹⁷, which reduces the activity and promotes the apoptosis of lymphocytes, inducing immunosuppression in sepsis and resulting in the progress of the multiple organ dysfunction syndromes (MODS)¹⁸. Inhibition of the lactate transporter decreased peritoneal lactate levels to reestablish T-cell migration away from inflammatory sites and blocked the production of proinflammatory cytokines in mice with glycan-induced peritonitis¹⁹. Further mechanistic studies should be conducted to confirm the underlying mechanisms of this phenomenon.

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Fig. 2

Risk factors for the adverse outcome (no-cure) of peritonitis by logistic regression analysis. Variables at the time of peritonitis were used in the models. Factors thought to be related to the clinical outcomes of peritonitis were entered in the multivariable logistic regression model. CI, confidence interval; WCC, white cell count; CCI, Charlson Comorbidity Index; PD, peritoneal dialysis.

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Fig. 3

Receiver operating charecteristic curve (ROC) curve of effluent lactate levels in diagnosing adverse outcomes of peritoneal dialysis associated peritonitis(PDAP). The predictive value of effluent lactate levels (Model A) ^a, day 5 effluent white cell count (WCC) on day 5 (Model B), and multi-index ROC curve for effluent lactate levels and day 5 effluent WCC (Model C) for adverse outcomes of PDAP were showed. ^a The threshold value with the highest Youden’s index was 10.2 mmol/L (sensitivity = 48.1%, specificity = 91.9%). AUC, area under the curve; CI, confidence interval; WCC, white cell count; PDAP, peritoneal dialysis associated peritonitis.

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Fig. 4

Calibration plots for the receiver operating charecteristic curve (ROC) curve of effluent lactate concentration in diagnosing adverse outcomes of peritoneal dialysis associated peritonitis (PDAP). Hosmer-Lemeshow goodness-of-fit test, χ² = 6.708, p = 0.568.

Excepting effluent lactate levels which were chosen independently according to our study design, all other variables were chosen based on previously established criteria. Our logistic regression models also confirmed the predictive value of gram-negative bacterial infection on adverse outcomes in PDAP. Previous retrospective and prospective cohort studies have shown that gram-negative peritonitis had higher risks of catheter loss and death than gram-positive episodes^20,21. It is known that the SPICE organisms (Serratia, Pseudomonas, indole-positive organisms such as Proteus and Providentia, Citrobacter, and Enterobacter), which are a series of gram-negative bacteria, have amp-C beta-lactamases, and it could inactivate cephalosporins, and subsequently attribute to a high risk of relapse²². Meanwhile, all four Pseudomonas aeruginosa peritonitis reported in the no-cure group were identified as refractory peritonitis, with one case diagnosed as catheter-related peritonitis with concomitant Pseudomonas aeruginosa tunnel infection. These are in consistent with a generally acknowledged fact that Pseudomonas peritonitis is generally severe with less than 50% complete cure rate^{23, 24–25} and is often associated with a high rate of catheter removal and permanent hemodialysis transfer^24,26 as well as PD catheter infection²². Such phenomenon can be partly explained by biofilm formation, which often leads to antibiotic therapy resistance and relapse²³.

We attempted to identify additional factors associated with peritonitis treatment outcomes. Compared with the effluent WCC on day 3, the effluent WCC on day 5 better reflects the response to treatment²⁷. The no-cure group showed a significantly higher effluent WCC on day 5, and it was confirmed as a prognostic factor for adverse outcomes of PDAP through univariable logistic regression model. Similarly, the work from Hong²⁸ and Berke²⁹ have showed that the high fifth-day dialysate effluent WCC predicts peritonitis outcomes. Although its predictive value was adjusted in our multivariable logistic regression models, it deserves further investigation in larger cohorts³⁰. Besides, serum albumin levels were significantly lower in no-cure group, and an increase in serum albumin was associated with a 6.7% relative reduction in adverse outcomes of peritonitis. However, the protective value was adjusted in multivariable model. Recently, serum albumin has been involved in a nomogram for predicting refractory PDAP^31, and hypoalbuminemia has been concluded as an independent risk factors for withdrawal from PD due to PDAP by another single center retrospective cohort study^32, whereas without direct explanation of the potential reasons. Meanwhile, increased age³³ and long PD duration have been reported as independent risk factor for poor prognosis in PDAP patients. However, we failed to verify them in our study, possibly due to the limited size of our cohort.

In addition, the combination of markers for prognosis prediction has been increasingly attractive³⁴. Studies from Thailand³⁵ and China³⁶ tried to developed prediction tools for PDAP treatment failure with a combination of predictors weighted to calculate the risk score. In our study, the combination of effluent WCC on day 5 and effluent lactate levels was a superior independent prognostic predictor, which showed better performance. We presume such kind of combination of old and new markers need external validation studies.

The study possessed certain limitations. First, a single-center examination with a relatively small sample size meant that the study was open to both type I and type II statistical errors, and the power of the study was not strong. Second, due to the retrospective observational nature of the study design, we were unable to measure and incorporate the analysis of the “trend of effluent lactate level” in this study, although lactate clearance and serial lactate measures may be more useful prognostic markers than initial lactate alone³⁷. And we were unable to differentiate between L-lactate and D-lactate, which may be interesting. Additionally, we failed to find any difference in the sub-type of organism with effluent lactate levels, which deserved further investigation due to the potential diversity in lactate metabolism.Third, we were unable to generalize the findings to different populations, and the single-center retrospective nature of the study limits the relevance of the result. To validate our results, future large-scale prospective studies involving multiple centers are needed. Nevertheless, this clinical cohort study did provide some preliminary data from a real-world cohort and was the first to assess the relationship between effluent lactate level and peritonitis outcomes in PD patients.

Conclusions

In this observational study examining the relationship between lactate levels and adverse outcomes of peritonitis, we found that high effluent lactate levels rather than serum lactate levels could independently predict adverse outcome in PDAP. The predictive value for elevated effluent lactate levels of adverse outcomes of peritonitis supports lactate monitoring as standard of follow-up procedure for PDAP patients.

Materials and methods

Experimental design and data collection

This investigation constituted a single-center retrospective observational study that recruited individuals who encountered peritonitis while receiving CAPD or automated peritoneal dialysis (APD) for ESKD at the First Affiliated Hospital of Xi’an Jiaotong University from 1 November 2022 to 31 July 2024. Peritonitis was diagnosed based on the presence of at least two of the following criteria: (1) clinical symptoms such as abdominal pain and/or cloudy dialysis effluent; (2) an increase in WCC in the dialysate, with more than 100/µL and over 50% of the cells being polymorphonuclear cells; and (3) a positive culture of the dialysis effluent, as recommended by the 2022 International Society for Peritoneal Dialysis (ISPD) recommendations³⁸. The inclusion criteria stipulated those episodes with complete baseline data and no less than three months follow-up. Episodes from patients under 18 years of age or with fungal infections were excluded from the analysis.

Demographic and clinical data encompassing age, gender, CCI³⁹ body mass index (BMI), blood pressure, etiology of ESKD, and extensive laboratory examinations of both urine and blood samples, as well as PET type and dialysis adequacy assessment urea clearance index (Kt/V urea per week) were retrospectively collected. In the context of our program, PET was conducted every 6 to 12 months, and Kt/V urea was conducted every 3 to 6 months. For the PDAP episodes, both serum and effluent lactate levels were measured on the onset of peritonitis (recorded as day 1). Other peritonitis-related data including age at peritonitis, duration of PD, PD modality types (CAPD or APD), and exchange volume were recorded. The crucial data regarding PET type and Kt/V urea were extracted from the assessments that conducted closest in time to the occurrence of peritonitis. Clinical features of peritonitis, including manifestations and laboratory examinations such as blood WCC, blood CRP, serum Interleukin-6 (IL-6), effluent WCC, and microbiologic causes, were collected.

According to the recommendations of the ISPD guidelines³⁸ we immediately started empirical antibiotic treatment in patients diagnosed with peritonitis. Patients in the outpatient program received intraperitoneal administration of gentamicin at a dosage of 0.6 mg/kg per day, with a maximum of 40 mg per PD exchange, in combination with intraperitoneal vancomycin at a dose of 15 mg/kg of body weight, rounded to the nearest 500 mg, as previously described⁴⁰. The antibiotic treatment was modified either after 5 days or upon receiving the culture results, taking into account the patient’s reaction to the treatment. For instances of culture-negative peritonitis, the administration of vancomycin was extended for 2 weeks. Gentamicin treatment was stopped if the peritonitis was completely resolved, indicated by the normalization of body temperature, relief of abdominal pain, as well as clearance of dialysate including effluent WCC below 100/µl and a neutrophil percentage below 50%. If the condition of peritonitis continued, the antibiotic gentamicin was replaced by either ceftazidime or meropenem, depending on the patient’s reaction to therapy and the severity of their clinical condition. The recommended way of administering antibiotics was intraperitoneal, using ceftazidime at a daily dosage of 1000 mg and meropenem at a daily dosage of 1000 mg unless the patient developed systemic sepsis. The intended duration of antibiotic therapy followed ISPD guidelines. In the case of Microbacterium spp., vancomycin and meropenem were continued for a duration of 3 weeks as we previously reported⁴¹.

The follow-up survey was carried out until October 31, 2024, with the identification and categorization of outcomes for all episodes as cure, recurrence, relapse, refractory, catheter removal, hemodialysis transfer, and peritonitis associated death according to ISPD guidelines³⁸. Relapse episodes were not considered separate occurrences. Specifically, cure was described as the complete resolution of peritonitis (as mentioned above) together with none of the following complications: relapse or recurrent peritonitis, catheter removal, transfer to hemodialysis for ≥ 30 days or death. Cure episodes were recorded into cure group. Removal of PD catheters was recommended if fungal species were detected in peritoneal dialysate specimens at any point during follow-up. It was also advised for refractory peritonitis unless the PD effluent WCC was decreasing towards normal. Moreover, the decision to eliminate these catheters was impacted by clinical seriousness of peritonitis and patient desires. Adverse outcomes encompass recurrence, relapse, catheter removal and transfer to hemodialysis for ≥ 30 days, and death associated with peritonitis. Those episodes with adverse outcomes were classified into the no-cure group.

Lactate collection and measurement technique

When peritonitis was suspected in our center, a 10 mL effluent sample was retained from PD dialysate effluent after 4 to 6 h intraperitoneal duration, and was sent for testing immediately for lactate level. The VITROS Chemistry Products LAC Slides (colorimetric method by Quidel Ortho VITROS 5600 Integrated System &Analyzer) was used to detect PD effluent lactate and blood lactate levels. The Bland-Altman analysis demonstrated good agreement the colorimetric method and the standard verified method (Supplementary Fig. 1). The detection methods are detailed in the Supplementary Methods.

Statistical analysis

Data analysis was performed using SPSS for Windows (version 20.0; IBM Corporation, Armonk, New York, USA). Descriptive statistics were presented as follows: frequencies and percentages for categorical variables, mean ± standard deviation for normally distributed continuous variables, and median values with interquartile range (IQR) from the 25th to 75th percentile for non-normally distributed continuous variables. The chi-square test, Wilcoxon rank-sum test, or Student’s t-test were implemented to compare the variances of the groups, as appropriate. Logistic regression analyses were implemented to assess the influence of clinical factors on PDAP treatment outcomes. Lactate levels and variables reportedly associated with peritonitis outcomes were incorporated into a multivariable logistic regression model. We used ROC curve analysis to evaluate the predictive accuracy of effluent lactate levels for adverse outcomes of PD. The Youden index was employed to determine the optimal cut-off point. Statistical significance of all probabilities was assessed at p-value of less than 0.05, with a two-tailed distribution.

Author contributions

XYmade substantial contributions to the design of the work and to the acquisition, analysis and interpretation of data for the work and drafted the work. JW made substantial contributions to the conception of the work and to the acquisition of the work. CL and ZL made substantial contributions to the acquisition of data for this study. WL and JL critically revised the draft for important intellectual content. YM made substantial contributions to the conception and design of the work, revised the draft critically, and agreed to be accountable for all aspects of the work. All authors approved the final version of the manuscript.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by Key Research and Development Program of Shaanxi Province, China (2024JC-YBQN-0960) for publication and research.

Data availability

The datasets analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethics statement

The studies involving human participants were approved by the Ethics Committee of the First Affiliated Hospital of Xi’an Jiaotong University, the approval number was XJTU1AF2024LSYY-260. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

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References

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