[Clinical characteristics and treatment outcomes of first peritonitis in patients receiving long-term peritoneal dialysis: a multicenter study]

Application of metagenomic next-generation sequencing technology in the etiological diagnosis of peritoneal dialysis-associated peritonitis

Pathogens detected by metagenomic next-generation sequencing (mNGS) and the laboratory blood culture flask method were compared to understand the advantages and clinical significance of mNGS assays in the etiological diagnosis of peritoneal dialysis-associated peritonitis (PDAP). The study involved a total of 37 patients from the hospital's peritoneal dialysis centre, six of whom were patients with non-peritoneal dialysis-associated peritonitis. Peritoneal dialysis samples were collected from the 37 patients, who were divided into two groups. One group's samples were cultured using conventional blood culture flasks, and the other samples underwent pathogen testing using mNGS. The results showed that the positive rate of mNGS was 96.77%, while that of the blood culture flask method was 70.97% (p < 0.05). A total of 29 pathogens were detected by mNGS, namely 24 bacteria, one fungus, and four viruses. A total of 10 pathogens were detected using the bacterial blood culture method, namely nine bacteria and one fungus. The final judgment of the PDAP's causative pathogenic microorganism was made by combining the clinical condition, response to therapy, and the whole-genome sequencing findings. For mNGS, the sensitivity was 96.77%, the specificity was 83.33%, the positive predictive value was 96.77%, and the negative predictive value was 83.33%. For the blood culture flask method, the sensitivity was 70.97%, the specificity was 100%, the positive predictive value was 100%, and the negative predictive value was 0%. In conclusion, mNGS had a shorter detection time for diagnosing peritoneal dialysis-related peritonitis pathogens, with a higher positive rate than traditional bacterial cultures, providing significant advantages in diagnosing rare pathogens.

Multidrug-resistant organism-peritoneal dialysis associated peritonitis: clinical and microbiological features and risk factors of treatment failure

Background

Multidrug-resistant (MDR) bacterial infection causes difficulty in the therapy of peritoneal dialysis-associated peritonitis (PDAP); however, there are few studies on multidrug-resistant organism (MDRO)-PDAP. In view of growing concerns about MDRO-PDAP, the aim of this study was to investigate the clinical features, risk factors of treatment failure, and causative pathogens of MDRO-PDAP.

Methods

In total, 318 patients who underwent PD between 2013 and 2019 were included in this multicenter retrospective study. Clinical features, patient outcomes, factors related to treatment failure, and microbiological profiles associated with MDRO-PDAP were analyzed and risk factors for treatment failure associated with MDR-Escherichia coli (E. coli) were further discussed.

Results

Of 1,155 peritonitis episodes, 146 eligible episodes of MDRO-PDAP, which occurred in 87 patients, were screened. There was no significant difference in the composition ratio of MDRO-PDAP between 2013-2016 and 2017-2019 (p > 0.05). E. coli was the most prevalent MDRO-PDAP isolate, with high sensitivity to meropenem (96.0%) and piperacillin/tazobactam (89.1%). Staphylococcus aureus was the second most common isolate and was susceptible to vancomycin (100%) and linezolid (100%). Compared to non-multidrug-resistant organism-PDAP, MDRO-PDAP was associated with a lower cure rate (66.4% vs. 85.5%), higher relapse rate (16.4% vs. 8.0%), and higher treatment failure rate (17.1% vs.6.5%). Dialysis age [odds ratio (OR): 1.034, 95% confidence interval (CI): 1.016-1.052, p < 0.001] and >2 previous peritonitis episodes (OR: 3.400, 95% CI: 1.014-11.400, p = 0.047) were independently associated with treatment failure. Furthermore, longer dialysis age (OR: 1.033, 95% CI: 1.003-1.064, p = 0.031) and lower blood albumin level (OR: 0.834, 95% CI: 0.700-0.993, p = 0.041) increased the risk of therapeutic failure for MDR-E. coli infection.

Conclusion

The proportion of MDRO-PDAP has remained high in recent years. MDRO infection is more likely to result in worse outcomes. Dialysis age and previous multiple peritonitis infections were significantly associated with treatment failure. Treatment should be promptly individualized based on local empirical antibiotic and drug sensitivity analyses.

Development and Validation of a Prediction Model for the Cure of Peritoneal Dialysis-Associated Peritonitis: A Multicenter Observational Study

Aim

Peritoneal dialysis (PD)-associated peritonitis (PDAP) is a severe complication of PD. It is an important issue about whether it can be cured. At present, there is no available prediction model for peritonitis cure. Therefore, this study aimed to develop and validate a prediction model for peritonitis cure in patients with PDAP.

Methods

Patients with PD who developed PDAP from four dialysis centers in Northeast China were followed up. According to the region of PD, data were divided into training and validation datasets. Initially, a nomogram for peritonitis cure was established based on the training dataset. Later, the nomogram performance was assessed by discrimination (C-statistic), calibration, and decision curves.

Results

Totally, 1,011 episodes of peritonitis were included in the final analysis containing 765 in the training dataset and 246 in the validation dataset. During the follow-up period, peritonitis cure was reported in 615 cases from the training dataset and 198 from the validation dataset. Predictors incorporated in the final nomogram included PD duration, serum albumin, antibiotics prior to admission, white cell count in peritoneal dialysate on day 5 (/μl) ≥ 100/μl, and type of causative organisms. The C-statistic values were 0.756 (95% CI: 0.713–0.799) in the training dataset and 0.756 (95% CI: 0.681–0.831) in the validation dataset. The nomogram exhibited favorable performance in terms of calibration in both the training and validation datasets.

Conclusion

This study develops a practical and convenient nomogram for the prediction of peritonitis cure in patients with PDAP, which assists in clinical decision-making.

[Development and validation of a prediction model for treatment failure in peritoneal dialysis-associated peritonitis patients: a multicenter study]

OBJECTIVE

To develop and validate a risk prediction model of treatment failure in patients with peritoneal dialysis-associated peritonitis (PDAP).

METHODS

We retrospectively analyzed the data of patients undergoing peritoneal dialysis (PD) in 3 dialysis centers in Jilin Province who developed PDAP between January 1, 2013 and December 31, 2019. The data collected from the Second Hospital of Jilin University and Second Division of First Hospital of Jilin University) were used as the training dataset and those from Jilin Central Hospital as the validation dataset. We developed a nomogram for predicting treatment failure using a logistic regression model with backward elimination. The performance of the nomogram was assessed by analyzing the C-statistic and the calibration plots. We also plotted decision curves to evaluate the clinical efficacy of the nomogram.

RESULTS

A total of 977 episodes of PDAP were included in the analysis (625 episodes in the training dataset and 352 episodes in the validation dataset). During follow-up, 78 treatment failures occurred in the training dataset and 35 in the validation dataset. A multivariable logistic regression prediction model was established, and the predictors in the final nomogram model included serum albumin, peritoneal dialysate white cell count on day 5, PD duration, and type of causative organisms. The nomogram showed a good performance in predicting treatment failure, with a C-statistic of 0.827 (95% CI: 0.784-0.871) in the training dataset and of 0.825 (95% CI: 0.743-0.908) in the validation dataset. The nomogram also performed well in calibration in both the training and validation datasets.

CONCLUSION

The established nomogram has a good accuracy in estimating the risk of treatment failure in PDAP patients.