Instant Diagnosis of Peritoneal Dialysis- Associated Peritonitis: Validation of a Simple Bedside Microscopy Approach

Instant Diagnosis of Peritoneal Dialysis- Associated Peritonitis: Validation of a Simple Bedside Microscopy Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Instant Diagnosis of Peritoneal Dialysis- Associated Peritonitis: Validation of a Simple Bedside Microscopy Approach Luca Nardelli, Antonio Scalamogna, Giuseppe Garigali, Anna Sikharulidze, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8821797/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Apr, 2026 Read the published version in International Urology and Nephrology → Version 1 posted You are reading this latest preprint version Abstract Background: Peritoneal dialysis (PD)–related peritonitis remains a major cause of technique failure, morbidity, and mortality. Timely diagnosis is crucial, yet conventional white blood cell (WBC) quantification by flow cytometry (FCC) requires centralized laboratory processing, delaying treatment. This study aimed to validate a direct manual microscopic (DMC) method for bedside quantification of WBCs in peritoneal effluent as a rapid, low-cost diagnostic alternative. Methods: In this single-center study, 40 PD patients underwent 250 paired WBC assessments between January 2024 and June 2025. Peritoneal effluent samples were analyzed by both DMC and FCC at peritonitis onset, during treatment follow-up, and in asymptomatic controls. Diagnostic performance was evaluated using Spearman’s correlation, receiver operating characteristic (ROC) analysis, and the Youden index, employing FCC as the reference standard (>100 cells/μL). Results: Across all evaluations, DMC and FCC showed a strong correlation (ρ = 0.91, p < 0.0001). In patients evaluated for suspected peritonitis (n = 36), DMC achieved a sensitivity of 97.0% and specificity of 100% at a cut-off of 20 cells/20 HPFs (AUC = 0.99). During follow-up (n = 187), correlation remained high (ρ = 0.89, p < 0.0001), with sensitivity 97.2% and specificity 83.4% at 12 cells/20 HPFs (AUC = 0.97). Conclusion: DMC provides a reliable, rapid, and quantitative alternative to automated cytometry for diagnosing and monitoring PD-related peritonitis. Its simplicity, affordability, and bedside applicability make it particularly suitable for integration with home turbidity monitoring systems and for use in low-resource settings to improve timely peritonitis management. peritoneal dialysis peritonitis white blood cells peritoneal effluent microscopy flow cytometry point of care bedside Figures Figure 1 Figure 2 Figure 3 SUMMARY AT GLANCE This study validates a direct manual microscopic method for bedside white blood cell quantification in peritoneal dialysis effluent. The method showed excellent agreement with flow cytometry and high diagnostic accuracy for peritonitis onset and treatment monitoring. Its simplicity and rapid availability support timely, point-of-care peritonitis management. 1. INTRODUCTION Peritoneal dialysis (PD)–related peritonitis remains the leading cause of PD technique failure, morbidity, and mortality 1 – 3 . Despite substantial advances in catheter design, connection systems, and patient education, the global incidence of peritonitis continues to limit PD utilization and confidence in home-based therapy. The clinical management of PD-related peritonitis typically involves a sequence of steps: symptom recognition and visual assessment of effluent cloudiness by the patient, presentation to the PD clinic for evaluation and laboratory testing, and subsequent initiation of antibiotic therapy 4 . The appearance of cloudy effluent is an early indicator of PD–related peritonitis and reflects the presence of leukocytes in the dialysate. However, this sign is not universally present—some patients experience abdominal pain without effluent cloudiness, whereas others have cloudy effluent in the absence of symptoms. Once peritonitis is suspected, prompt initiation of antibiotic therapy is crucial 5 , as each hour of delay from the onset of symptoms increases the risk of PD failure or death by approximately 6.8% 6 . Additional delays may occur when effluent samples are sent to centralized laboratories for leukocyte quantification, further extending the time to diagnosis and treatment. According to the International Society for Peritoneal Dialysis guidelines, PD-related peritonitis is diagnosed when at least two of the following three criteria are met 4 : (1) clinical features consistent with peritonitis, such as abdominal pain and/or cloudy effluent; (2) a dialysis effluent white blood cell (WBC) count greater than 100/µL (after a dwell time of at least two hours), with more than 50% polymorphonuclear cells; and (3) a positive dialysate culture. Among these, the WBC count in the peritoneal effluent plays a pivotal role, as microbiological culture results require several days to become available. Currently, WBC quantification is primarily performed using automated instruments based on flow cytometry. Although highly accurate, this method requires trained personnel, specialized equipment, and considerable processing time. Because samples must be transported to centralized laboratories, delays in transport, analysis, and reporting are frequent. Consequently, this approach is unsuitable for bedside or point-of-care use, particularly in outpatient or home-based PD management. Given that most PD-related peritonitis cases are managed outside the hospital, rapid bedside diagnosis is essential to ensure timely antibiotic initiation and optimize patient outcomes. However, a widely available and validated point-of-care method for effluent WBC quantification remains lacking. In this context, direct manual microscopy (DMC) of peritoneal effluent has been proposed as a practical alternative to automated flow cytometry (FCC) 7 . This manual approach is simple, inexpensive, and readily accessible—requiring only a standard microscope and minimal training—thus allowing nephrologists to perform the assessment whenever needed. While a preliminary experience has suggested good agreement between manual and automated counts, robust data defining diagnostic and monitoring cut-off values are still limited 7 . Therefore, this study aimed to validate a direct manual microscopic approach for WBC quantification in peritoneal effluent, comparing its diagnostic accuracy and reproducibility with FCC. By enabling immediate, quantitative, and bedside assessment of peritoneal effluent, this method could significantly improve the timeliness of peritonitis diagnosis, guide therapeutic decisions, and strengthen the practicality of PD as a home-based renal replacement therapy. 2. METHODS 2.1 Participants, study design and data collection This is a single center study. Patients treated by PD at Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico who were evaluated for WBC count of the peritoneal effluent from 1st January 2024 to 30th June 2025 were included in the study. The reasons for the WBC count were one of the following: (1) suspected of peritonitis onset, (2) evaluation of treatment response after diagnosis of peritonitis or (3) control in asymptomatic patients. The patients with suspected peritonitis were referred to our PD clinic when reporting abdominal pain, cloudy dialysis effluent, nausea or diarrhea. After physical examination, those patients underwent blood drawn to evaluate complete blood count and C-reactive protein. Furthermore, on the same day four peritoneal effluent specimens were collected to perform cultures and WBC counts using both DMC and FCC methods (Fig. 1 ). Blood samples were analyzed by Central Laboratory of our Institution which performed also FCC and peritoneal effluent cultures. Data such as age, gender, comorbidities and PD adequacy parameters were collected from clinical records at our unit by reviewing an electronic database (Galenus®, Infogramma s.r.l., Milan, Italy). Patients diagnosed with PD-associated peritonitis underwent multiple follow-up peritoneal effluent WBC counts using both DMC and FCC after the initiation of antibiotic therapy. In contrast, asymptomatic PD patients were evaluated during routine outpatient visits to our center. 2.2 Laboratory tests 2.2.1 Peritoneal effluent white blood cell counts 2.2.1.1 Direct manual microscopic white blood cell counts Manual microscopic count was performed directly in our hospital ward by an expert biologist (GG), together with nephrologists, referring to the work by Lobo et al 7 . After a dwell time of at least two hours, a 10-mL sample was taken from the drainage bag. Then, 50-µL of fluid were directly transferred to a slide, covered with a 22x22 mm coverslip, for DMC. Within 15 minutes from the time of collection, we obtained the results (Fig. 1 ). The sample was examined by light microscopy at a magnification of 400x high power field (HPF). WBC were reported as the total number of elements counted in 20 HPFs (Fig. 2 ). 2.2.1.2 Automated flow cytometry white blood cell counts One 10-mL aliquot of peritoneal effluent from the same drainage bag was sent to the Central Laboratory of our institution for quantitative analysis. Specifically, 88 µL of fluid were automatically aspirated and analyzed by a cytofluorimeter (Sysmex XN-1000, body fluid module). The instrument automatically identified and counted white blood cells, and results were expressed as concentration of cells/µL (Fig. 1 ). 2.2.2 Microbiological investigation Whenever peritonitis was suspected, microbiological examination was promptly accomplished. Culture of peritoneal dialysis effluent was performed using BacTAlert FA Plus Aerobic bottles (Biomerieux, Inc. Durham NC) incubated in Virtuo automated system for 5 days. Positive bottles were subcultured on Blood agar, MacConkey agar, Mannitol salt agar (incubated 48 h at 37 +/- 1ÆCin aerobic conditions), Chocolate agar (incubated 48 h at 37 +/- 1ÆC in 5% CO2), and Sabouraud agar (incubated 48 h at 32 +/- 1ÆC in aerobic conditions). Species identification was obtained with MALDI-TOF Vitek MS (Biomerieux) and antibiotic susceptibility was determined with Vitek2 automated system (Biomerieux) according to EUCAST criteria. 2.3 Definition of peritonitis and recovery from peritonitis Peritonitis was diagnosed when the two following conditions were simultaneously present: (1) clinical features consistent with peritonitis: either abdominal pain and/or cloudy dialysis effluent; (2) dialysis effluent WBC > 100/µL after a dwell time of at least 2 hours, with > 50% polymorphonuclear cells. Similarly, the recovery from peritonitis during follow up was defined in the presence of the two following conditions: (1) absence of clinical features consistent with peritonitis; (2) dialysis effluent WBC < 100/µL after a dwell time of at least 2 hours. These diagnostic criteria were employed to evaluate the sensibility and sensitivity of direct manual microscopic WBC count for the diagnosis and the recovery of peritonitis at the onset and during follow-up using flow cytometry method as reference. 2.4 Statistical analyses Normally distributed variables are presented as mean ± standard deviation, while nonparametric data are presented as median with interquartile range. Categorical variables are expressed as frequency and percentage. Initially, to evaluate the correlation between the two count methods (manual microscopy and cytofluorimetry), we calculated Spearman’s rank correlation coefficient (rho), treating each measurement as independent. To account for within-patient dependence due to multiple samples per individual, we then employed a linear mixed-effects model, specifying one WBC counting technique as the dependent variable, the other as a fixed effect, and patient as a random effect. To assess the discriminative ability of the different WBC counting methods, we performed receiver operating characteristic (ROC) analyses and calculated the area under the curve (AUC). The optimal cut-off value for each method was determined using the maximum Youden index. All the analyses were performed using JMP® Student Edition 18.2.2 (JMP Statistical Discovery LLC, Cary, NC, USA). 2.5 Ethical approval and informed consent Treatments and procedures herein reported were in accordance with the ethical standards of the 1964 Helsinki declaration and its later amendments, or comparable ethical standards. All methods were approved by the ethical committee of Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (approval No. 4764 − 1839/25). The informed consent was collected by all patients. 3. RESULTS 3.1 Indication for white blood cell counts and population characteristics During the study period, a total of 250 peritoneal effluent WBC counts were performed in 40 patients, with a median of 5 evaluations per patient (Q1-Q3, 2–8). Demographic and baseline clinical characteristics of the study population are summarized in Table 1. The majority of patients were male (55%) and most were treated with CAPD (95%). The mean age was 66.3 ± 18.1 years. Patients had been on PD for a median of 24.1 months (IQR 37.2), with a median residual kidney function of 6.5 mL/min/1.73m² (IQR 6.35). The median weekly total Kt/V was 2.2 (IQR 0.7). Among the 250 evaluations, 36 were performed at the time of suspected peritonitis, 187 during treatment follow-up after peritonitis diagnosis, and 27 in asymptomatic patients undergoing routine assessments. 3.2 Correlation between microscopic and flow cytometry white blood cell counts Across all 250 paired evaluations, direct manual microscopy and automated flow cytometry showed a strong correlation (Spearman’s rho = 0.91, p 100 cells/µL, direct microscopy achieved a sensitivity of 94.2% and a specificity of 88.4% at a cut-off of 17 cells/20 HPFs (Youden index = 0.83). The AUC was 0.97 (Fig. 3A). The linear mixed-effects model further confirmed the strong linear association (p < 0.0001), with a slope (β₁) of 6.78 (95% CI 6.28–7.28). 3.3 Evaluations in case of suspected peritonitis Thirty-six patients were evaluated in the PD clinic for suspected PD-related peritonitis. Clinical characteristics at presentation are summarized in Table 2. Most patients reported abdominal pain (91.7%), and more than half presented with cloudy effluent (61.1%). A diagnosis of peritonitis was established in 31 cases (77.8%), of which 3 were associated with tunnel infection. The remaining 5 cases were diagnosed as isolated tunnel infections 8 . Medical cure was achieved in 61.1% of patients (22/36), while 38.9% (14/36) experienced refractory episodes. Catheter removal was required in 36.1% of cases, and cuff removal in one case 9 . Microbiological results are summarized in Table 3. A pathogen was isolated in 25 cases (69.4%), with Gram-positive and Gram-negative bacteria accounting for 60.9% and 39.1% of isolates, respectively. Fungal infections occurred in 2.8% of episodes, similar in frequency to polymicrobial cases. Culture-negative peritonitis represented 19.3% of episodes (6/31). In all 5 cases of isolated tunnel infection, exit-site swabs grew Staphylococcus aureus , including one methicillin-resistant strain. In this subgroup, direct microscopy and flow cytometry again showed a strong correlation (Spearman’s rho = 0.93, p 100 cells/µL), direct microscopy demonstrated a sensitivity of 97.0% and a specificity of 100% at a cut-off of 20 cells/20 HPFs (Youden index = 0.97). The AUC was 0.99 (Fig. 3B). The only discordant case (one false negative) involved a patient who was already receiving oral cephalosporin therapy for urinary symptoms that had developed three days earlier. In this instance, the WBC count measured by flow cytometry was only mildly elevated (335 cells/μL). 3.3 Evaluations during monitoring of treatment response A total of 187 paired measurements were obtained to monitor treatment response following the diagnosis of peritonitis. Direct microscopic and flow cytometric WBC counts showed a strong correlation (Spearman’s ρ = 0.89, p 100 cells/μL for a still positive count), direct microscopy demonstrated a sensitivity of 97.2% and a specificity of 83.4% at an optimal threshold of 12 cells per 20 HPFs (Youden index = 0.81), with an AUC of 0.97 (Fig. 3C). Twenty-one were the discordant cases (nine-teen false positive and two false negative). In the nineteen cases where the DMC remained positive (>12 WBCs/20 HPFs) while the FCC result was already negative (≤100 cells/μL), the median WBC count by DMC was 20 (Q1–Q3, 17–30), compared with 77 (Q1–Q3, 49–89) by FCC. When applying a higher cut-off of 20 cells per 20 HPFs, the corresponding sensitivity and specificity were 88.9% and 90.0%, respectively (Youden index = 0.78). 4. DISCUSSION In this single-center study, we validated a direct manual microscopic method for quantifying WBCs in PD effluent as a diagnostic tool for PD-related peritonitis. Direct manual microscopic demonstrated excellent correlation with automated flow cytometry across all samples, both at peritonitis onset and during treatment follow-up Importantly, the manual approach showed high diagnostic accuracy, with sensitivity exceeding 90% in all analyses and an area under the ROC curve approaching 1.0. These data support the potential utility of DMC as a rapid, reliable, and low-cost bedside alternative to automated cytometry for the diagnosis and monitoring of PD-related peritonitis. A small number of discordant results were observed between DMC and FCC, predominantly during the follow-up phase of peritonitis treatment. Most of these (19 of 21 cases) represented DMC-positive but FCC-negative samples, characterized by low WBC counts near the diagnostic threshold. This discrepancy may reflect the higher sensitivity of direct microscopic evaluation in detecting residual inflammatory cells, especially when total cell counts are declining and unevenly distributed in the effluent. Alternatively, minor differences in sampling or cell sedimentation during slide preparation could contribute to variability at low concentrations. Importantly, these findings may indicate that DMC remains positive slightly longer than FCC, capturing residual inflammation that persists despite early cytometric normalization. Such persistence could offer additional clinical insight into the resolution phase and might assist in assessing the adequacy or duration of antibiotic therapy. Only one false-negative case was observed during the evaluation for suspected peritonitis. This patient had been receiving oral cephalosporin therapy for urinary symptoms for three days prior to presentation, which likely attenuated the local inflammatory response and reduced the leukocyte concentration in the peritoneal effluent. The WBC count measured by flow cytometry in this case was only mildly elevated (335 cells/µL), suggesting that prior antibiotic exposure can partially mask effluent cellularity, leading to underestimation by both microscopic and automated methods. This observation highlights the importance of considering recent antibiotic use when interpreting WBC results in suspected peritonitis. Overall, the minimal rate of discordance supports the robustness of DMC, while the pattern of DMC-persistent positivity suggests potential utility for monitoring treatment response and confirming complete recovery. Our approach is in line with recent advances in point-of-care (POC) and home-based diagnostic technologies designed to improve the timeliness of peritonitis detection. Early studies explored leukocyte esterase reagent strips (PeriScreen®), showing good correlation with effluent WBC counts but only qualitative results 10 , 11 . More recently, Goodlad et al. reported that PERiPLEX®, a lateral-flow device detecting interleukin-6 and matrix metalloproteinase-8, achieved a sensitivity of 97.6% and specificity of 87.7%, enabling rapid infection confirmation within minutes 12 . These findings were corroborated by Htay et al., who observed comparable accuracy in 120 patients, highlighting the value of PERiPLEX® both for diagnosis and for confirming resolution of infection 13 . Similarly, Govindji-Bhatt et al. introduced QuickCheck, an optical light-scatter device providing instantaneous leukocyte quantification, demonstrating 94% accuracy and performance equivalent to flow cytometry 14 . A major advance in this field has been the development of turbidity-based remote monitoring systems (CloudCath®), which continuously assess dialysate clarity in real time and automatically alert patients and clinicians when effluent cloudiness is detected. This innovation exemplifies the growing trend toward POC diagnostics that minimize patient delay and reduce reliance on centralized laboratories. Briggs et al. reported that use of the CloudCath® prototype in a hospital setting achieved a sensitivity of 95.2% and specificity of 91.5% for peritonitis detection 15 . Similarly, Mehrotra et al. demonstrated that the remote, sensor-based CloudCath® system can reliably detect early effluent turbidity in PD patients at home, enabling clinical assessment and intervention up to three days earlier than conventional symptom-based recognition 16 . However, the study also reported a notification rate of 0.23 per patient-year for non-peritonitis events, comparable to the 0.25 notifications per patient-year associated with true peritonitis episodes, highlighting the importance of confirmatory diagnostic methods to distinguish false-positive alerts. Thus, while turbidity sensors provide early qualitative alerts, they do not confirm infection or quantify inflammation. In this context, direct manual microscopy can serve as the elective confirmatory method following a turbidity signal at home or on presentation to the clinic. Direct manual microscopy enables rapid, quantitative measurement of peritoneal WBC count, providing immediate confirmation or exclusion of peritonitis without requiring centralized laboratory analysis. This integration of remote turbidity monitoring and bedside microscopy could create a practical and cost-effective two-step diagnostic pathway—early detection at home, followed by rapid quantitative confirmation in the PD unit. The quantitative nature of DMC not only allows for diagnosis but also makes it a valuable tool for monitoring the response to antibiotic therapy. In our study, DMC and FCC remained highly correlated during follow-up, with DMC accurately tracking WBC decline over the course of treatment. This real-time quantification can help clinicians evaluate therapeutic efficacy, guide the duration of antibiotic therapy, and support decisions about catheter management or treatment modification. Moreover, DMC is simple, inexpensive, and requires only a standard light microscope, making it particularly advantageous in low- and middle-income countries, where access to flow cytometry or automated analyzers may be limited. Given its negligible consumable cost and rapid turnaround (< 15 minutes), DMC could be incorporated into both hospital and community PD programs worldwide, enhancing the feasibility and safety of home-based dialysis. The strengths of this study include the large number of paired DMC–FCC evaluations, inclusion of both diagnostic and follow-up phases, and robust statistical validation. Limitations include its single-center design, absence of interobserver variability assessment, and lack of direct outcome comparisons such as time to antibiotic initiation or clinical resolution. Future studies should explore multicenter validation, inter-operator reproducibility, and the implementation of DMC in combination with home turbidity monitoring systems. As PD care continues to shift toward home-based and remote models, integrating simple bedside diagnostics such as DMC with emerging POC technologies could transform peritonitis management. Combining automated turbidity sensors for early detection with manual microscopy for rapid confirmation and monitoring offers a powerful, scalable framework for timely and equitable PD care. Development of training modules and digital or AI-assisted microscopy may further enhance accuracy and enable remote verification by PD centers. In conclusion, direct manual microscopic WBC counting in PD effluent is a reliable, rapid, and affordable diagnostic alternative to automated cytometry. Our findings align with emerging literature advocating for POC approaches to expedite peritonitis diagnosis and treatment. Given its negligible cost and minimal equipment requirements, DMC could be particularly impactful in low-resource environments, enabling equitable access to timely care and supporting the global expansion of safe, home-based peritoneal dialysis. Declarations ACKNOWLEDGEMENTS Title of Manuscript: INSTANT DIAGNOSIS OF PERITONEAL DIALYSIS-ASSOCIATED PERITONITIS: VALIDATION OF A SIMPLE BEDSIDE MICROSCOPY APPROACH Authors: Luca Nardelli (1,2), Antonio Scalamogna (1), Giuseppe Garigali (1), Anna Sikharulidze (1), Matteo Abinti (1,2), Anxhela Hida (1), Sara Moscardino (1), Federico Alberici (3,4), Giuseppe Castellano (1,2). Institutions: Division of Nephrology, Dialysis and Kidney Transplantation, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (1), Milan, Italy. Department of Clinical Sciences and Community Health, Università degli studi di Milano (2), Milan, Italy. Nephrology Unit, ASST Spedali Civili di Brescia (3), Brescia, Italy. Department of Medical and Surgical Specialities, Radiological Sciences and Public Health, University of Brescia (4), Brescia, Italy. Declaration of Conflicting Interest: We have read and understood Journal of nephrology’s policy on disclosing conflicts of interest and declare that we have none. Funding: None. Ethical Approval: All methods were approved by the ethical committee of Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (approval No. 4764-1839/25). All methods were approved by the ethical committee of Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (approval No. 4764-1839/25). The informed consent was collected by all patients. Informed Consent to Participate: All methods were approved by the ethical committee of Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (approval No. 4764-1839/25). All methods were approved by the ethical committee of Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (approval No. 4764-1839/25). Informed Consent to Publish: The informed consent was collected by all patients. Trial Registration : not applicable. Authorship: Research idea and study design: LN, GG; data acquisition: AS, AH, SM, GG; data analysis/interpretation: LN, AS; statistical analysis: LN, MA; supervision or mentorship: AS, GC, FA. Each author contributed important intellectual content during manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved. Acknowledgements: None. References Bonenkamp AA, van Eck van der Sluijs A, Dekker FW, et al. Technique failure in peritoneal dialysis: modifiable causes and patient-specific risk factors. Perit Dial Int 2023;43:73–83. Boudville N, Kemp A, Clayton P, et al. Recent peritonitis associates with mortality among patients treated with peritoneal dialysis. J Am Soc Nephrol 2012;23:1398–405. Nardelli L, Scalamogna A, Ponzano F, et al. Peritoneal dialysis related peritonitis: insights from a long-term analysis of an Italian center. BMC Nephrol 2024;25:163. Li PK, Chow KM, Cho Y, et al. ISPD peritonitis guideline recommendations: 2022 update on prevention and treatment. Perit Dial Int 2022;42:110–53. Oki R, Tsuji S, Hamasaki Y, et al. Time until treatment initiation is associated with catheter survival in peritoneal dialysis-related peritonitis. Sci Rep 2021;11:6547. Muthucumarana K, Howson P, Crawford D, Burrows S, Swaminathan R, Irish A. The relationship between presentation and the time of initial administration of antibiotics with outcomes of peritonitis in peritoneal dialysis patients: the PROMPT study. Kidney Int Rep 2016;1:65–72. J Lobo, S Montibello, V Castiglia, G B Fogazzi. Direct white cell count in peritoneal effluent. A simple technique to diagnose and monitor peritonitis. Perit Dial Int 2001;21:628. Nardelli L, Scalamogna A, Castellano G. Utility of ultrasonographic examination in catheter-related infections in peritoneal dialysis: a clinical approach. J Nephrol 2023;36:1751–61. Scalamogna A, Nardelli L, Castellano G. The use of mini-invasive surgical techniques to treat refractory exit-site and tunnel infections in peritoneal dialysis patients: a clinical approach. J Nephrol 2023;36:1743–9. Farmer C, Hobbs H, Mann S, et al. Leukocyte esterase reagent strips for early detection of peritonitis in patients on peritoneal dialysis. Perit Dial Int 2000;20:237–9. Fan S, Lane C, Punzalan S. Correlation of periscreen strip results and white cell count in peritoneal dialysis peritonitis. J Ren Care 2010;36:90–5. Goodlad C, George S, Sandoval S, et al. Measurement of innate immune response biomarkers in peritoneal dialysis effluent using a rapid diagnostic point-of-care device as a diagnostic indicator of peritonitis. Kidney Int 2020;97:1253–9. Htay H, Choo JCJ, Huang DH, et al. Rapid point-of-care test for diagnosis of peritonitis in peritoneal dialysis patients. Perit Dial Int 2024;44:413–8. Govindji-Bhatt N, Kennedy SM, Barker MG, et al. Novel Colorimetric and Light Scatter Methods to Identify and Manage Peritoneal Dialysis-Associated Peritonitis at the Point-of-Care. Kidney Int Rep 2024;9:589–600. Briggs B, Garcia-Garcia G, Ibarra-Hernandez M, et al. Performance characteristics of a prototype dialysate turbidity monitoring system to detect peritonitis in patients receiving peritoneal dialysis. Perit Dial Int 2024;44:419–25. Mehrotra R, Williamson DE, Betts CR, et al. A Prospective clinical study to evaluAte the ability of the CloudCath system to detect peritonitis during in-home peritoneal dialysis (CATCH). Kidney Int Rep 2024;9:929–40. Tables Table 1 Baseline demographic and clinical characteristics of the 40 patients who underwent 250 peritoneal effluent white blood cell (WBC) assessments. Patients (n=40) AGE years [mean ± SD] 66.3 ± 18.1 GENDER male [n(%)] 22 (55) CAPD [n(%)] 38 (95) APD [n(%)] 2 (5) BMI kg/m2 [mean ± SD] 23.5 (3.8) DIABETES [n(%)] 9 (22.5) CAD [n(%)] 6 (15) CVD [n(%)] 13 (32.5) COPD [n(%)] 6 (15) MALIGNANCY [n(%)] 12 (30) HEART FAILURE [n(%)] 20 (50) LIVER DISEASE [n(%)] 4 (10) mCCI [mean ± SD] 9,2 ± 5.2 RENAL DISEASE Hypertensive nephropathy [n(%)] 15 (37.5) Glomerulonephritis [(n%)] 8 (20) Diabetic nephropathy [n(%)] 6 (15) ADPKD [n(%)] 4 (10) Others [n(%)] 4 (10) Uknown [n(%)] 3 (7.5) DIALYSIS VINTAGE months [median (IQR)] 24.1 (37.2) RESIDUAL KIDNEY FUNCTION ml/min/1.73m2 [median (IQR)] 6.5 (6.35) DIURESIS VOLUME ml [median (IQR)] 1150 (1337.5) KT/V total [median (IQR)] 2.2 (0.7) D/P [mean ± SD] 0.68 ± 0.08 G/G0 [mean ± SD] 0.22 ± 0.07 ∆Na [mean ± SD] 6,63 ± 3,5 UF litres [mean ± SD] 615 ± 217 ADPKD = autosomal dominant polycystic kidney disease; BMI = body mass index; CAD = coronary artery disease; CAPD = continuous ambulatory peritoneal dialysis; COPD = chronic obstructive pulmonary disease; CVD = cerebral vascular disease; IQR = interquartile range; mCCI = modified Charlson comorbidity index; n = number of patients; SD = standard deviation; D/P = ratio of the concentrations of creatinine in dialysate/plasma; G/G 0 = ratio between the concentrations of glucose at the end/beginning of the test; DNa = Sodium sieving: change in the Na concentration in the fresh dialysate solution and after 60 minutes of testing; UF = peritoneal ultrafiltration. Table 2 Clinical characteristics of the 36 episodes of suspected peritonitis. (n=36) CLINICAL PRESENTATION Abdominal pain [n(%)] 33 (91.7) Cloudy dialysis effluent [n(%)] 22 (61.1) Fever [n(%)] 11 (30.6) Nausea [n(%)] 3 (8.3) Diarrhea [n(%)] 2 (5.6) LABORATORY TESTS White blood cells n/ul [median (IQR)] 7830 (2760) Hemoglobin g/dl [mean ± SD] 11.1 ± 1.4 Neutrophils % [mean ± SD] 75.8 ± 11.2 C-reactive protein mg/dl [median (IQR)] 6.3 (5.1) OUTCOME Peritonitis [n(%)] 28 (77.8) Tunnel infection [n(%)] 5 (13.9) Peritonitis and tunnel infection [n(%)] 3 (8.3) Response to antibiotic therapy [n(%)] 22 (61.1) Cuff removal n(%)] 1 (2.8) Catheter removal [n(%)] 13 (36.1) IQR = interquartile range; n = number of episodes; SD = standard deviation. Table 3 Microbiological results of the 36 cases of suspected peritonitis Organism ALL n=36, (%**) n (%*) Staphylococcus other species 5 (13.9) GRAM + 14 (60.9) MSSA 3 (8.3) MRSE 2 (5.6) MSSE 1 (2.8) Brevibacterium sanguinis 1 (2.8) Enterococcus faecalis 1 (2.8) Corinebacterium striatum 1 (2.8) GRAM - 9 (39.1) Pseudomonas aeruginosa 5 (13.9) Citrobacter Koseri 2 (5.6) Escherichia coli 1 (2.8) Klebsiella Pneumoniae 1 (2.8) FUNGI Candida parapsilosis 1 (2.8) OTHERS Polymicrobial 1 (2.8) Negative 11 (30.5) *Percentage calculated on the total of isolated bacteria (n=23); **Percentage calculated on the total of accomplished peritoneal effluent cultures (n=36) Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8821797","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590116642,"identity":"62fcd704-371f-4e24-b446-13b722f9a337","order_by":0,"name":"Luca Nardelli","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACPijN2MDO3HDgA5DFxk5ACxtcCzNjw8EZIBFmUrQw84CYBLWwnz3AXFBzT7afmbHxsM2vbfJ8zAyMHz7m4NHCk5fAPONYsfHMZsaGw7l9tw3bmBmYJWduw6NFgseAmYctIXHDYZCWntuMQC1szLwEtfxLSNwP0mLZc9ueOC28bUBbgN4/zPDjdiJhLTw5Bod5+xKMZwBtOdjbcDu5jZmxGa9f+NnPGD7m+ZYg29/efPjDjz+3bee3Nx/88BGPFhA4AGcxtoHJBvzqUcEfUhSPglEwCkbBSAEADXdLWsjlQL4AAAAASUVORK5CYII=","orcid":"","institution":"Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico","correspondingAuthor":true,"prefix":"","firstName":"Luca","middleName":"","lastName":"Nardelli","suffix":""},{"id":590116643,"identity":"889ccf98-e5cf-4816-86d2-98302962ea4d","order_by":1,"name":"Antonio Scalamogna","email":"","orcid":"","institution":"Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Scalamogna","suffix":""},{"id":590116644,"identity":"50dbeeef-003f-43c2-9afb-694db3a1b44e","order_by":2,"name":"Giuseppe Garigali","email":"","orcid":"","institution":"Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico","correspondingAuthor":false,"prefix":"","firstName":"Giuseppe","middleName":"","lastName":"Garigali","suffix":""},{"id":590116645,"identity":"463b3ab0-e2fd-4db0-8cfb-bec4a1573ce9","order_by":3,"name":"Anna Sikharulidze","email":"","orcid":"","institution":"Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Sikharulidze","suffix":""},{"id":590116646,"identity":"a7c7426d-e408-44a2-b138-60c6940e9726","order_by":4,"name":"Matteo Abinti","email":"","orcid":"","institution":"Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico","correspondingAuthor":false,"prefix":"","firstName":"Matteo","middleName":"","lastName":"Abinti","suffix":""},{"id":590116647,"identity":"b40a80ac-59ce-4580-800b-1fec6d3a5d3d","order_by":5,"name":"Anxhela Hida","email":"","orcid":"","institution":"Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico","correspondingAuthor":false,"prefix":"","firstName":"Anxhela","middleName":"","lastName":"Hida","suffix":""},{"id":590116648,"identity":"a649590d-6524-44d1-aeaf-2b312926dd22","order_by":6,"name":"Sara Moscardino","email":"","orcid":"","institution":"Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Moscardino","suffix":""},{"id":590116649,"identity":"2118d5ee-6a8b-47d5-9035-2b807060a31d","order_by":7,"name":"Federico Alberici","email":"","orcid":"","institution":"Spedali Civili di Brescia","correspondingAuthor":false,"prefix":"","firstName":"Federico","middleName":"","lastName":"Alberici","suffix":""},{"id":590116650,"identity":"6f8cbe2a-a663-4b95-afc3-7a599a1a6cf1","order_by":8,"name":"Giuseppe Castellano","email":"","orcid":"","institution":"Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico","correspondingAuthor":false,"prefix":"","firstName":"Giuseppe","middleName":"","lastName":"Castellano","suffix":""}],"badges":[],"createdAt":"2026-02-08 13:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8821797/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8821797/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11255-026-05140-1","type":"published","date":"2026-04-15T15:56:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102839473,"identity":"05cc58dd-b201-4af8-9625-41281dee3b5c","added_by":"auto","created_at":"2026-02-17 11:45:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":337197,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMethodology of peritoneal effluent white blood cell counts: fluorescence flow cytometry and direct manual microscopy.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each evaluation, two aliquots of peritoneal effluent were obtained from the same dialysis bag: one was analyzed immediately by light microscopy using the direct manual white blood cell (WBCs) count method (mean number of cells per 20 high-power fields [HPFs]), and the other was processed by fluorescence flow cytometry to determine the WBC concentration (cells/μL).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8821797/v1/fadea3b4ba315e5b81eca27d.png"},{"id":102839472,"identity":"85387190-e466-4747-aa95-00ce304d52ee","added_by":"auto","created_at":"2026-02-17 11:45:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":289813,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentative light microscopy images (400× high-power field) of peritoneal effluent white blood cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom left to right: a negative sample, a moderately positive sample, and a markedly positive sample\u003c/p\u003e\n\u003cp\u003eHPF = high-power field.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8821797/v1/0da9910495971c1fa25cc5fc.png"},{"id":102839481,"identity":"d5d3fb19-aac7-45d5-babd-64ca41935d1a","added_by":"auto","created_at":"2026-02-17 11:45:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":638945,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between microscopic and flow cytometry white blood cell counts: Spearman’ s rank correlation and receiver operating characteristic curve analyses.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) all analyses; (B) only evaluations in case of suspected peritonitis; (C) only evaluations during monitoring of treatment response.\u003c/p\u003e\n\u003cp\u003eAUC = area under the curve; HPF = high power field.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8821797/v1/b75d9eb1c513fce1aa0998fb.png"},{"id":107350712,"identity":"56febb2e-160f-4c8b-bbba-605745410910","added_by":"auto","created_at":"2026-04-20 16:00:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1765151,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8821797/v1/20825bdb-1923-4470-8220-c84e3c74d5e2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eInstant Diagnosis of Peritoneal Dialysis- Associated Peritonitis: Validation of a Simple Bedside Microscopy Approach\u003c/p\u003e","fulltext":[{"header":"SUMMARY AT GLANCE","content":"\u003cp\u003eThis study validates a direct manual microscopic method for bedside white blood cell quantification in peritoneal dialysis effluent. The method showed excellent agreement with flow cytometry and high diagnostic accuracy for peritonitis onset and treatment monitoring. Its simplicity and rapid availability support timely, point-of-care peritonitis management.\u003c/p\u003e"},{"header":"1. INTRODUCTION","content":"\u003cp\u003ePeritoneal dialysis (PD)\u0026ndash;related peritonitis remains the leading cause of PD technique failure, morbidity, and mortality\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Despite substantial advances in catheter design, connection systems, and patient education, the global incidence of peritonitis continues to limit PD utilization and confidence in home-based therapy.\u003c/p\u003e \u003cp\u003eThe clinical management of PD-related peritonitis typically involves a sequence of steps: symptom recognition and visual assessment of effluent cloudiness by the patient, presentation to the PD clinic for evaluation and laboratory testing, and subsequent initiation of antibiotic therapy\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The appearance of cloudy effluent is an early indicator of PD\u0026ndash;related peritonitis and reflects the presence of leukocytes in the dialysate. However, this sign is not universally present\u0026mdash;some patients experience abdominal pain without effluent cloudiness, whereas others have cloudy effluent in the absence of symptoms. Once peritonitis is suspected, prompt initiation of antibiotic therapy is crucial\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, as each hour of delay from the onset of symptoms increases the risk of PD failure or death by approximately 6.8%\u003csup\u003e6\u003c/sup\u003e. Additional delays may occur when effluent samples are sent to centralized laboratories for leukocyte quantification, further extending the time to diagnosis and treatment.\u003c/p\u003e \u003cp\u003eAccording to the International Society for Peritoneal Dialysis guidelines, PD-related peritonitis is diagnosed when at least two of the following three criteria are met\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e: (1) clinical features consistent with peritonitis, such as abdominal pain and/or cloudy effluent; (2) a dialysis effluent white blood cell (WBC) count greater than 100/\u0026micro;L (after a dwell time of at least two hours), with more than 50% polymorphonuclear cells; and (3) a positive dialysate culture. Among these, the WBC count in the peritoneal effluent plays a pivotal role, as microbiological culture results require several days to become available.\u003c/p\u003e \u003cp\u003eCurrently, WBC quantification is primarily performed using automated instruments based on flow cytometry. Although highly accurate, this method requires trained personnel, specialized equipment, and considerable processing time. Because samples must be transported to centralized laboratories, delays in transport, analysis, and reporting are frequent. Consequently, this approach is unsuitable for bedside or point-of-care use, particularly in outpatient or home-based PD management.\u003c/p\u003e \u003cp\u003eGiven that most PD-related peritonitis cases are managed outside the hospital, rapid bedside diagnosis is essential to ensure timely antibiotic initiation and optimize patient outcomes. However, a widely available and validated point-of-care method for effluent WBC quantification remains lacking.\u003c/p\u003e \u003cp\u003eIn this context, direct manual microscopy (DMC) of peritoneal effluent has been proposed as a practical alternative to automated flow cytometry (FCC)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. This manual approach is simple, inexpensive, and readily accessible\u0026mdash;requiring only a standard microscope and minimal training\u0026mdash;thus allowing nephrologists to perform the assessment whenever needed. While a preliminary experience has suggested good agreement between manual and automated counts, robust data defining diagnostic and monitoring cut-off values are still limited\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to validate a direct manual microscopic approach for WBC quantification in peritoneal effluent, comparing its diagnostic accuracy and reproducibility with FCC. By enabling immediate, quantitative, and bedside assessment of peritoneal effluent, this method could significantly improve the timeliness of peritonitis diagnosis, guide therapeutic decisions, and strengthen the practicality of PD as a home-based renal replacement therapy.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Participants, study design and data collection\u003c/h2\u003e\n \u003cp\u003eThis is a single center study. Patients treated by PD at Fondazione IRCCS Ca\u0026apos; Granda Ospedale Maggiore Policlinico who were evaluated for WBC count of the peritoneal effluent from 1st January 2024 to 30th June 2025 were included in the study. The reasons for the WBC count were one of the following: (1) suspected of peritonitis onset, (2) evaluation of treatment response after diagnosis of peritonitis or (3) control in asymptomatic patients.\u003c/p\u003e\n \u003cp\u003eThe patients with suspected peritonitis were referred to our PD clinic when reporting abdominal pain, cloudy dialysis effluent, nausea or diarrhea. After physical examination, those patients underwent blood drawn to evaluate complete blood count and C-reactive protein. Furthermore, on the same day four peritoneal effluent specimens were collected to perform cultures and WBC counts using both DMC and FCC methods (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Blood samples were analyzed by Central Laboratory of our Institution which performed also FCC and peritoneal effluent cultures.\u003c/p\u003e\n \u003cp\u003eData such as age, gender, comorbidities and PD adequacy parameters were collected from clinical records at our unit by reviewing an electronic database (Galenus\u0026reg;, Infogramma s.r.l., Milan, Italy).\u003c/p\u003e\n \u003cp\u003ePatients diagnosed with PD-associated peritonitis underwent multiple follow-up peritoneal effluent WBC counts using both DMC and FCC after the initiation of antibiotic therapy. In contrast, asymptomatic PD patients were evaluated during routine outpatient visits to our center.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Laboratory tests\u003c/h2\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1 Peritoneal effluent white blood cell counts\u003c/h2\u003e\n \u003cdiv id=\"Sec6\" class=\"Section4\"\u003e\n \u003ch2\u003e2.2.1.1 Direct manual microscopic white blood cell counts\u003c/h2\u003e\n \u003cp\u003eManual microscopic count was performed directly in our hospital ward by an expert biologist (GG), together with nephrologists, referring to the work by Lobo et al\u003csup\u003e7\u003c/sup\u003e. After a dwell time of at least two hours, a 10-mL sample was taken from the drainage bag. Then, 50-\u0026micro;L of fluid were directly transferred to a slide, covered with a 22x22 mm coverslip, for DMC. Within 15 minutes from the time of collection, we obtained the results (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The sample was examined by light microscopy at a magnification of 400x high power field (HPF). WBC were reported as the total number of elements counted in 20 HPFs (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section4\"\u003e\n \u003ch2\u003e2.2.1.2 Automated flow cytometry white blood cell counts\u003c/h2\u003e\n \u003cp\u003eOne 10-mL aliquot of peritoneal effluent from the same drainage bag was sent to the Central Laboratory of our institution for quantitative analysis. Specifically, 88 \u0026micro;L of fluid were automatically aspirated and analyzed by a cytofluorimeter (Sysmex XN-1000, body fluid module). The instrument automatically identified and counted white blood cells, and results were expressed as concentration of cells/\u0026micro;L (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.2 Microbiological investigation\u003c/h2\u003e\n \u003cp\u003eWhenever peritonitis was suspected, microbiological examination was promptly accomplished. Culture of peritoneal dialysis effluent was performed using BacTAlert FA Plus Aerobic bottles (Biomerieux, Inc. Durham NC) incubated in Virtuo automated system for 5 days. Positive bottles were subcultured on Blood agar, MacConkey agar, Mannitol salt agar (incubated 48 h at 37 +/- 1\u0026AElig;Cin aerobic conditions), Chocolate agar (incubated 48 h at 37 +/- 1\u0026AElig;C in 5% CO2), and Sabouraud agar (incubated 48 h at 32 +/- 1\u0026AElig;C in aerobic conditions). Species identification was obtained with MALDI-TOF Vitek MS (Biomerieux) and antibiotic susceptibility was determined with Vitek2 automated system (Biomerieux) according to EUCAST criteria.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Definition of peritonitis and recovery from peritonitis\u003c/h2\u003e\n \u003cp\u003ePeritonitis was diagnosed when the two following conditions were simultaneously present: (1) clinical features consistent with peritonitis: either abdominal pain and/or cloudy dialysis effluent; (2) dialysis effluent WBC\u0026thinsp;\u0026gt;\u0026thinsp;100/\u0026micro;L after a dwell time of at least 2 hours, with \u0026gt;\u0026thinsp;50% polymorphonuclear cells. Similarly, the recovery from peritonitis during follow up was defined in the presence of the two following conditions: (1) absence of clinical features consistent with peritonitis; (2) dialysis effluent WBC\u0026thinsp;\u0026lt;\u0026thinsp;100/\u0026micro;L after a dwell time of at least 2 hours. These diagnostic criteria were employed to evaluate the sensibility and sensitivity of direct manual microscopic WBC count for the diagnosis and the recovery of peritonitis at the onset and during follow-up using flow cytometry method as reference.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Statistical analyses\u003c/h2\u003e\n \u003cp\u003eNormally distributed variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while nonparametric data are presented as median with interquartile range. Categorical variables are expressed as frequency and percentage. Initially, to evaluate the correlation between the two count methods (manual microscopy and cytofluorimetry), we calculated Spearman\u0026rsquo;s rank correlation coefficient (rho), treating each measurement as independent. To account for within-patient dependence due to multiple samples per individual, we then employed a linear mixed-effects model, specifying one WBC counting technique as the dependent variable, the other as a fixed effect, and patient as a random effect. To assess the discriminative ability of the different WBC counting methods, we performed receiver operating characteristic (ROC) analyses and calculated the area under the curve (AUC). The optimal cut-off value for each method was determined using the maximum Youden index. All the analyses were performed using JMP\u0026reg; Student Edition 18.2.2 (JMP Statistical Discovery LLC, Cary, NC, USA).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.5 Ethical approval\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eand informed consent\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTreatments and procedures herein reported were in accordance with the ethical standards of the 1964 Helsinki declaration and its later amendments, or comparable ethical standards. All methods were approved by the ethical committee of Fondazione IRCCS Ca\u0026apos; Granda Ospedale Maggiore Policlinico (approval No. 4764\u0026thinsp;\u0026minus;\u0026thinsp;1839/25). The informed consent was collected by all patients.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003e\u003cstrong\u003e3.1 Indication for white blood cell counts and population characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the study period, a total of 250 peritoneal effluent WBC counts were performed in 40 patients, with a median of 5 evaluations per patient (Q1-Q3, 2–8). Demographic and baseline clinical characteristics of the study population are summarized in Table 1. The majority of patients were male (55%) and most were treated with CAPD (95%). The mean age was 66.3 ± 18.1 years. Patients had been on PD for a median of 24.1 months (IQR 37.2), with a median residual kidney function of 6.5 mL/min/1.73m² (IQR 6.35). The median weekly total Kt/V was 2.2 (IQR 0.7).\u003cbr\u003e\u0026nbsp;Among the 250 evaluations, 36 were performed at the time of suspected peritonitis, 187 during treatment follow-up after peritonitis diagnosis, and 27 in asymptomatic patients undergoing routine assessments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Correlation between microscopic and flow cytometry white blood cell counts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross all 250 paired evaluations, direct manual microscopy and automated flow cytometry showed a strong correlation (Spearman’s rho = 0.91, p \u0026lt; 0.0001). Using flow cytometry as the reference standard and a positivity threshold of \u0026gt;100 cells/µL, direct microscopy achieved a sensitivity of 94.2% and a specificity of 88.4% at a cut-off of 17 cells/20 HPFs (Youden index = 0.83). The AUC was 0.97 (Fig. 3A). The linear mixed-effects model further confirmed the strong linear association (p \u0026lt; 0.0001), with a slope (β₁) of 6.78 (95% CI 6.28–7.28).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Evaluations in case of suspected peritonitis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThirty-six patients were evaluated in the PD clinic for suspected PD-related peritonitis. Clinical characteristics at presentation are summarized in Table 2. Most patients reported abdominal pain (91.7%), and more than half presented with cloudy effluent (61.1%). A diagnosis of peritonitis was established in 31 cases (77.8%), of which 3 were associated with tunnel infection. The remaining 5 cases were diagnosed as isolated tunnel infections\u003csup\u003e8\u003c/sup\u003e. Medical cure was achieved in 61.1% of patients (22/36), while 38.9% (14/36) experienced refractory episodes. Catheter removal was required in 36.1% of cases, and cuff removal in one case\u003csup\u003e9\u003c/sup\u003e. Microbiological results are summarized in Table 3. A pathogen was isolated in 25 cases (69.4%), with Gram-positive and Gram-negative bacteria accounting for 60.9% and 39.1% of isolates, respectively. Fungal infections occurred in 2.8% of episodes, similar in frequency to polymicrobial cases. Culture-negative peritonitis represented 19.3% of episodes (6/31). In all 5 cases of isolated tunnel infection, exit-site swabs grew \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, including one methicillin-resistant strain. In this subgroup, direct microscopy and flow cytometry again showed a strong correlation (Spearman’s rho = 0.93, p \u0026lt; 0.0001). Using flow cytometry as reference (\u0026gt;100 cells/µL), direct microscopy demonstrated a sensitivity of 97.0% and a specificity of 100% at a cut-off of 20 cells/20 HPFs (Youden index = 0.97). The AUC was 0.99 (Fig. 3B). The only discordant case (one false negative) involved a patient who was already receiving oral cephalosporin therapy for urinary symptoms that had developed three days earlier. In this instance, the WBC count measured by flow cytometry was only mildly elevated (335 cells/μL).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Evaluations during monitoring of treatment response\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 187 paired measurements were obtained to monitor treatment response following the diagnosis of peritonitis. Direct microscopic and flow cytometric WBC counts showed a strong correlation (Spearman’s ρ = 0.89, p \u0026lt; 0.0001). Using flow cytometry as the reference standard (\u0026gt;100 cells/μL for a still positive count), direct microscopy demonstrated a sensitivity of 97.2% and a specificity of 83.4% at an optimal threshold of 12 cells per 20 HPFs (Youden index = 0.81), with an AUC of 0.97 (Fig. 3C). Twenty-one were the discordant cases (nine-teen false positive and two false negative). In the nineteen cases where the DMC remained positive (\u0026gt;12 WBCs/20 HPFs) while the FCC result was already negative (≤100 cells/μL), the median WBC count by DMC was 20 (Q1–Q3, 17–30), compared with 77 (Q1–Q3, 49–89) by FCC. When applying a higher cut-off of 20 cells per 20 HPFs, the corresponding sensitivity and specificity were 88.9% and 90.0%, respectively (Youden index = 0.78).\u0026nbsp;\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eIn this single-center study, we validated a direct manual microscopic method for quantifying WBCs in PD effluent as a diagnostic tool for PD-related peritonitis. Direct manual microscopic demonstrated excellent correlation with automated flow cytometry across all samples, both at peritonitis onset and during treatment follow-up Importantly, the manual approach showed high diagnostic accuracy, with sensitivity exceeding 90% in all analyses and an area under the ROC curve approaching 1.0. These data support the potential utility of DMC as a rapid, reliable, and low-cost bedside alternative to automated cytometry for the diagnosis and monitoring of PD-related peritonitis.\u003c/p\u003e \u003cp\u003eA small number of discordant results were observed between DMC and FCC, predominantly during the follow-up phase of peritonitis treatment. Most of these (19 of 21 cases) represented DMC-positive but FCC-negative samples, characterized by low WBC counts near the diagnostic threshold. This discrepancy may reflect the higher sensitivity of direct microscopic evaluation in detecting residual inflammatory cells, especially when total cell counts are declining and unevenly distributed in the effluent. Alternatively, minor differences in sampling or cell sedimentation during slide preparation could contribute to variability at low concentrations. Importantly, these findings may indicate that DMC remains positive slightly longer than FCC, capturing residual inflammation that persists despite early cytometric normalization. Such persistence could offer additional clinical insight into the resolution phase and might assist in assessing the adequacy or duration of antibiotic therapy.\u003c/p\u003e \u003cp\u003eOnly one false-negative case was observed during the evaluation for suspected peritonitis. This patient had been receiving oral cephalosporin therapy for urinary symptoms for three days prior to presentation, which likely attenuated the local inflammatory response and reduced the leukocyte concentration in the peritoneal effluent. The WBC count measured by flow cytometry in this case was only mildly elevated (335 cells/\u0026micro;L), suggesting that prior antibiotic exposure can partially mask effluent cellularity, leading to underestimation by both microscopic and automated methods. This observation highlights the importance of considering recent antibiotic use when interpreting WBC results in suspected peritonitis.\u003c/p\u003e \u003cp\u003eOverall, the minimal rate of discordance supports the robustness of DMC, while the pattern of DMC-persistent positivity suggests potential utility for monitoring treatment response and confirming complete recovery.\u003c/p\u003e \u003cp\u003eOur approach is in line with recent advances in point-of-care (POC) and home-based diagnostic technologies designed to improve the timeliness of peritonitis detection. Early studies explored leukocyte esterase reagent strips (PeriScreen\u0026reg;), showing good correlation with effluent WBC counts but only qualitative results\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMore recently, Goodlad et al. reported that PERiPLEX\u0026reg;, a lateral-flow device detecting interleukin-6 and matrix metalloproteinase-8, achieved a sensitivity of 97.6% and specificity of 87.7%, enabling rapid infection confirmation within minutes\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. These findings were corroborated by Htay et al., who observed comparable accuracy in 120 patients, highlighting the value of PERiPLEX\u0026reg; both for diagnosis and for confirming resolution of infection\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSimilarly, Govindji-Bhatt et al. introduced QuickCheck, an optical light-scatter device providing instantaneous leukocyte quantification, demonstrating 94% accuracy and performance equivalent to flow cytometry\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA major advance in this field has been the development of turbidity-based remote monitoring systems (CloudCath\u0026reg;), which continuously assess dialysate clarity in real time and automatically alert patients and clinicians when effluent cloudiness is detected. This innovation exemplifies the growing trend toward POC diagnostics that minimize patient delay and reduce reliance on centralized laboratories. Briggs et al. reported that use of the CloudCath\u0026reg; prototype in a hospital setting achieved a sensitivity of 95.2% and specificity of 91.5% for peritonitis detection\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Similarly, Mehrotra et al. demonstrated that the remote, sensor-based CloudCath\u0026reg; system can reliably detect early effluent turbidity in PD patients at home, enabling clinical assessment and intervention up to three days earlier than conventional symptom-based recognition\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, the study also reported a notification rate of 0.23 per patient-year for non-peritonitis events, comparable to the 0.25 notifications per patient-year associated with true peritonitis episodes, highlighting the importance of confirmatory diagnostic methods to distinguish false-positive alerts. Thus, while turbidity sensors provide early qualitative alerts, they do not confirm infection or quantify inflammation. In this context, direct manual microscopy can serve as the elective confirmatory method following a turbidity signal at home or on presentation to the clinic. Direct manual microscopy enables rapid, quantitative measurement of peritoneal WBC count, providing immediate confirmation or exclusion of peritonitis without requiring centralized laboratory analysis. This integration of remote turbidity monitoring and bedside microscopy could create a practical and cost-effective two-step diagnostic pathway\u0026mdash;early detection at home, followed by rapid quantitative confirmation in the PD unit.\u003c/p\u003e \u003cp\u003eThe quantitative nature of DMC not only allows for diagnosis but also makes it a valuable tool for monitoring the response to antibiotic therapy. In our study, DMC and FCC remained highly correlated during follow-up, with DMC accurately tracking WBC decline over the course of treatment. This real-time quantification can help clinicians evaluate therapeutic efficacy, guide the duration of antibiotic therapy, and support decisions about catheter management or treatment modification.\u003c/p\u003e \u003cp\u003eMoreover, DMC is simple, inexpensive, and requires only a standard light microscope, making it particularly advantageous in low- and middle-income countries, where access to flow cytometry or automated analyzers may be limited. Given its negligible consumable cost and rapid turnaround (\u0026lt;\u0026thinsp;15 minutes), DMC could be incorporated into both hospital and community PD programs worldwide, enhancing the feasibility and safety of home-based dialysis.\u003c/p\u003e \u003cp\u003eThe strengths of this study include the large number of paired DMC\u0026ndash;FCC evaluations, inclusion of both diagnostic and follow-up phases, and robust statistical validation. Limitations include its single-center design, absence of interobserver variability assessment, and lack of direct outcome comparisons such as time to antibiotic initiation or clinical resolution. Future studies should explore multicenter validation, inter-operator reproducibility, and the implementation of DMC in combination with home turbidity monitoring systems.\u003c/p\u003e \u003cp\u003eAs PD care continues to shift toward home-based and remote models, integrating simple bedside diagnostics such as DMC with emerging POC technologies could transform peritonitis management. Combining automated turbidity sensors for early detection with manual microscopy for rapid confirmation and monitoring offers a powerful, scalable framework for timely and equitable PD care. Development of training modules and digital or AI-assisted microscopy may further enhance accuracy and enable remote verification by PD centers.\u003c/p\u003e \u003cp\u003eIn conclusion, direct manual microscopic WBC counting in PD effluent is a reliable, rapid, and affordable diagnostic alternative to automated cytometry. Our findings align with emerging literature advocating for POC approaches to expedite peritonitis diagnosis and treatment. Given its negligible cost and minimal equipment requirements, DMC could be particularly impactful in low-resource environments, enabling equitable access to timely care and supporting the global expansion of safe, home-based peritoneal dialysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTitle of Manuscript: INSTANT DIAGNOSIS OF PERITONEAL DIALYSIS-ASSOCIATED PERITONITIS: VALIDATION OF A SIMPLE BEDSIDE MICROSCOPY APPROACH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors:\u0026nbsp;\u003c/strong\u003eLuca Nardelli (1,2), Antonio Scalamogna (1), Giuseppe Garigali (1), Anna Sikharulidze (1), Matteo Abinti (1,2), Anxhela Hida (1), Sara Moscardino (1), Federico Alberici (3,4), Giuseppe Castellano (1,2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutions:\u0026nbsp;\u003c/strong\u003eDivision of Nephrology, Dialysis and Kidney Transplantation, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (1), Milan, Italy. Department of Clinical Sciences and Community Health, Università degli studi di Milano (2), Milan, Italy. Nephrology Unit, ASST Spedali Civili di Brescia (3), Brescia, Italy. Department of Medical and Surgical Specialities, Radiological Sciences and Public Health, University of Brescia (4), Brescia, Italy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Conflicting Interest:\u0026nbsp;\u003c/strong\u003eWe have read and understood Journal of nephrology’s policy on disclosing conflicts of interest and declare that we have none.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval:\u003c/strong\u003e All methods were approved by the ethical committee of Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (approval No. 4764-1839/25).\u0026nbsp;All methods were approved by the ethical committee of Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (approval No. 4764-1839/25). The informed consent was collected by all patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent to Participate:\u0026nbsp;\u003c/strong\u003eAll methods were approved by the ethical committee of Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (approval No. 4764-1839/25). All methods were approved by the ethical committee of Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (approval No. 4764-1839/25).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent to Publish:\u003c/strong\u003e The informed consent was collected by all patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial Registration\u003c/strong\u003e: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship:\u003c/strong\u003e Research idea and study design: LN, GG; data acquisition: AS, AH, SM, GG; data analysis/interpretation: LN, AS; statistical analysis: LN, MA; supervision or mentorship: AS, GC, FA. Each author contributed important intellectual content during manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBonenkamp AA, van Eck van der Sluijs A, Dekker FW, et al. Technique failure in peritoneal dialysis: modifiable causes and patient-specific risk factors. Perit Dial Int 2023;43:73\u0026ndash;83.\u003c/li\u003e\n \u003cli\u003eBoudville N, Kemp A, Clayton P, et al. Recent peritonitis associates with mortality among patients treated with peritoneal dialysis. J Am Soc Nephrol 2012;23:1398\u0026ndash;405.\u003c/li\u003e\n \u003cli\u003eNardelli L, Scalamogna A, Ponzano F, et al. Peritoneal dialysis related peritonitis: insights from a long-term analysis of an Italian center. BMC Nephrol 2024;25:163.\u003c/li\u003e\n \u003cli\u003eLi PK, Chow KM, Cho Y, et al. ISPD peritonitis guideline recommendations: 2022 update on prevention and treatment. Perit Dial Int 2022;42:110\u0026ndash;53.\u003c/li\u003e\n \u003cli\u003eOki R, Tsuji S, Hamasaki Y, et al. Time until treatment initiation is associated with catheter survival in peritoneal dialysis-related peritonitis. Sci Rep 2021;11:6547.\u003c/li\u003e\n \u003cli\u003eMuthucumarana K, Howson P, Crawford D, Burrows S, Swaminathan R, Irish A. The relationship between presentation and the time of initial administration of antibiotics with outcomes of peritonitis in peritoneal dialysis patients: the PROMPT study. Kidney Int Rep 2016;1:65\u0026ndash;72.\u003c/li\u003e\n \u003cli\u003eJ Lobo, S Montibello, V Castiglia, G B Fogazzi. Direct white cell count in peritoneal effluent. A simple technique to diagnose and monitor peritonitis. Perit Dial Int 2001;21:628.\u003c/li\u003e\n \u003cli\u003eNardelli L, Scalamogna A, Castellano G. Utility of ultrasonographic examination in catheter-related infections in peritoneal dialysis: a clinical approach. J Nephrol 2023;36:1751\u0026ndash;61.\u003c/li\u003e\n \u003cli\u003eScalamogna A, Nardelli L, Castellano G. The use of mini-invasive surgical techniques to treat refractory exit-site and tunnel infections in peritoneal dialysis patients: a clinical approach. J Nephrol 2023;36:1743\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eFarmer C, Hobbs H, Mann S, et al. Leukocyte esterase reagent strips for early detection of peritonitis in patients on peritoneal dialysis. Perit Dial Int 2000;20:237\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eFan S, Lane C, Punzalan S. Correlation of periscreen strip results and white cell count in peritoneal dialysis peritonitis. J Ren Care 2010;36:90\u0026ndash;5.\u003c/li\u003e\n \u003cli\u003eGoodlad C, George S, Sandoval S, et al. Measurement of innate immune response biomarkers in peritoneal dialysis effluent using a rapid diagnostic point-of-care device as a diagnostic indicator of peritonitis. Kidney Int 2020;97:1253\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eHtay H, Choo JCJ, Huang DH, et al. Rapid point-of-care test for diagnosis of peritonitis in peritoneal dialysis patients. Perit Dial Int 2024;44:413\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eGovindji-Bhatt N, Kennedy SM, Barker MG, et al. Novel Colorimetric and Light Scatter Methods to Identify and Manage Peritoneal Dialysis-Associated Peritonitis at the Point-of-Care. Kidney Int Rep 2024;9:589\u0026ndash;600.\u003c/li\u003e\n \u003cli\u003eBriggs B, Garcia-Garcia G, Ibarra-Hernandez M, et al. Performance characteristics of a prototype dialysate turbidity monitoring system to detect peritonitis in patients receiving peritoneal dialysis. Perit Dial Int 2024;44:419\u0026ndash;25.\u003c/li\u003e\n \u003cli\u003eMehrotra R, Williamson DE, Betts CR, et al. A Prospective clinical study to evaluAte the ability of the CloudCath system to detect peritonitis during in-home peritoneal dialysis (CATCH). Kidney Int Rep 2024;9:929\u0026ndash;40.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Baseline demographic and clinical characteristics of the 40 patients who underwent 250 peritoneal effluent white blood cell (WBC) assessments.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"690\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003ePatients (n=40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eAGE years [mean \u0026plusmn; SD]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e66.3 \u0026plusmn; 18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eGENDER male [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e22 (55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eCAPD [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e38 (95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eAPD [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e2 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eBMI kg/m2 [mean \u0026plusmn; SD]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e23.5 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eDIABETES [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e9 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eCAD [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e6 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eCVD [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e13 (32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eCOPD [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e6 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eMALIGNANCY [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e12 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eHEART FAILURE [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e20 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eLIVER DISEASE [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e4 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003emCCI [mean \u0026plusmn; SD]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e9,2 \u0026plusmn; 5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eRENAL DISEASE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eHypertensive nephropathy [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e15 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eGlomerulonephritis [(n%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e8 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eDiabetic nephropathy [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e6 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eADPKD [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e4 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eOthers [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e4 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eUknown [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e3 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eDIALYSIS VINTAGE months [median (IQR)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e24.1 (37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eRESIDUAL KIDNEY FUNCTION ml/min/1.73m2 [median (IQR)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e6.5 (6.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eDIURESIS VOLUME ml [median (IQR)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e1150 (1337.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eKT/V total [median (IQR)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e2.2 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eD/P [mean \u0026plusmn; SD]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e0.68 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eG/G0 [mean \u0026plusmn; SD]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e0.22 \u0026plusmn; 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003e∆Na [mean \u0026plusmn; SD]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e6,63 \u0026plusmn; 3,5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 375px;\"\u003e\n \u003cp\u003eUF litres [mean \u0026plusmn; SD]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e615 \u0026plusmn; 217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eADPKD = autosomal dominant polycystic kidney disease; BMI = body mass index; CAD = coronary artery disease; CAPD = continuous ambulatory peritoneal dialysis; COPD = chronic obstructive pulmonary disease; CVD = cerebral vascular disease; IQR = interquartile range; mCCI = modified Charlson comorbidity index; n = number of patients; SD = standard deviation; D/P = ratio of the concentrations of creatinine in dialysate/plasma; G/G\u003csub\u003e0\u003c/sub\u003e = ratio between the concentrations of glucose at the end/beginning of the test; DNa = Sodium sieving: change in the Na concentration in the fresh dialysate solution and after 60 minutes of testing; UF = peritoneal ultrafiltration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Clinical characteristics of the 36 episodes of suspected peritonitis.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"657\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026nbsp;(n=36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eCLINICAL PRESENTATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eAbdominal pain [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e33 (91.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eCloudy dialysis effluent [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e22 (61.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eFever [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e11 (30.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eNausea [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e3 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eDiarrhea [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e2 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eLABORATORY TESTS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eWhite blood cells n/ul [median (IQR)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e7830 (2760)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eHemoglobin g/dl [mean \u0026plusmn; SD]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e11.1 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eNeutrophils % [mean \u0026plusmn; SD]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e75.8 \u0026plusmn; 11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eC-reactive protein mg/dl [median (IQR)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e6.3 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eOUTCOME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003ePeritonitis [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e28 (77.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eTunnel infection [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e5 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003ePeritonitis and tunnel infection [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e3 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eResponse to antibiotic therapy [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e22 (61.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eCuff removal n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e1 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eCatheter removal [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e13 (36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIQR = interquartile range; n = number of episodes; SD = standard deviation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Microbiological results of the 36 cases of suspected peritonitis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"681\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 271px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003eOrganism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eALL n=36, (%**)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 271px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003en (%*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 166px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 271px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 166px;\"\u003e\n \u003cp\u003eStaphylococcus other species\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e5 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 271px;\"\u003e\n \u003cp\u003eGRAM +\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 71px;\"\u003e\n \u003cp\u003e14 (60.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 166px;\"\u003e\n \u003cp\u003eMSSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 166px;\"\u003e\n \u003cp\u003eMRSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 166px;\"\u003e\n \u003cp\u003eMSSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 166px;\"\u003e\n \u003cp\u003eBrevibacterium sanguinis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 166px;\"\u003e\n \u003cp\u003eEnterococcus faecalis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 166px;\"\u003e\n \u003cp\u003eCorinebacterium striatum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 271px;\"\u003e\n \u003cp\u003eGRAM -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 71px;\"\u003e\n \u003cp\u003e9 (39.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 166px;\"\u003e\n \u003cp\u003ePseudomonas aeruginosa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e5 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 166px;\"\u003e\n \u003cp\u003eCitrobacter Koseri\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 166px;\"\u003e\n \u003cp\u003eEscherichia coli\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 166px;\"\u003e\n \u003cp\u003eKlebsiella Pneumoniae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 271px;\"\u003e\n \u003cp\u003eFUNGI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 166px;\"\u003e\n \u003cp\u003eCandida parapsilosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 271px;\"\u003e\n \u003cp\u003eOTHERS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 166px;\"\u003e\n \u003cp\u003ePolymicrobial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 166px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e11 (30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Percentage calculated on the total of isolated bacteria (n=23); **Percentage calculated on the total of accomplished peritoneal effluent cultures (n=36)\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"peritoneal dialysis, peritonitis, white blood cells, peritoneal effluent, microscopy, flow cytometry, point of care, bedside","lastPublishedDoi":"10.21203/rs.3.rs-8821797/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8821797/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Peritoneal dialysis (PD)–related peritonitis remains a major cause of technique failure, morbidity, and mortality. Timely diagnosis is crucial, yet conventional white blood cell (WBC) quantification by flow cytometry (FCC) requires centralized laboratory processing, delaying treatment. This study aimed to validate a direct manual microscopic (DMC) method for bedside quantification of WBCs in peritoneal effluent as a rapid, low-cost diagnostic alternative.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eIn this single-center study, 40 PD patients underwent 250 paired WBC assessments between January 2024 and June 2025. Peritoneal effluent samples were analyzed by both DMC and FCC at peritonitis onset, during treatment follow-up, and in asymptomatic controls. Diagnostic performance was evaluated using Spearman’s correlation, receiver operating characteristic (ROC) analysis, and the Youden index, employing FCC as the reference standard (\u0026gt;100 cells/μL).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Across all evaluations, DMC and FCC showed a strong correlation (ρ = 0.91, p \u0026lt; 0.0001). In patients evaluated for suspected peritonitis (n = 36), DMC achieved a sensitivity of 97.0% and specificity of 100% at a cut-off of 20 cells/20 HPFs (AUC = 0.99). During follow-up (n = 187), correlation remained high (ρ = 0.89, p \u0026lt; 0.0001), with sensitivity 97.2% and specificity 83.4% at 12 cells/20 HPFs (AUC = 0.97).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e DMC provides a reliable, rapid, and quantitative alternative to automated cytometry for diagnosing and monitoring PD-related peritonitis. Its simplicity, affordability, and bedside applicability make it particularly suitable for integration with home turbidity monitoring systems and for use in low-resource settings to improve timely peritonitis management.\u003c/p\u003e","manuscriptTitle":"Instant Diagnosis of Peritoneal Dialysis- Associated Peritonitis: Validation of a Simple Bedside Microscopy Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 11:45:48","doi":"10.21203/rs.3.rs-8821797/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e418f4ec-1179-4306-a90e-952b090eea80","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:00:21+00:00","versionOfRecord":{"articleIdentity":"rs-8821797","link":"https://doi.org/10.1007/s11255-026-05140-1","journal":{"identity":"international-urology-and-nephrology","isVorOnly":false,"title":"International Urology and Nephrology"},"publishedOn":"2026-04-15 15:56:54","publishedOnDateReadable":"April 15th, 2026"},"versionCreatedAt":"2026-02-17 11:45:48","video":"","vorDoi":"10.1007/s11255-026-05140-1","vorDoiUrl":"https://doi.org/10.1007/s11255-026-05140-1","workflowStages":[]},"version":"v1","identity":"rs-8821797","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8821797","identity":"rs-8821797","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}