Peritonitis Tracking and Outcomes

Peritonitis Tracking and Outcomes

Reducing peritonitis has been a key strategy to improve patient outcomes in our PD population and increase the number of patient who can successfully stay in the program.

We implemented a slew of enhancements to reduce peritonitis, including more detailed case tracking using the RenalConnect web app.  Using RenalConnect, I decided to take a quick look at our performance using the reports dashboard.

Our peritonitis rate is quite reasonable at 1 in 32 months (over the past 12 months).


This is definite improvement compared to 2009 when we started working on our peritonitis strategy – that year our peritonitis rate was 1 in 22 months.

However, our culture negative rate is 18% which is a higher than we’d like to see.

Perhaps most exciting is our rate of fungal peritonitis.  In the past, we had detected a worrisome rate of fungal peritonitis which prompted the initiation of a fluconazole prophylaxis program.  In this program, we encouraged all clinicians managing patients on PD to use Fluconazole 100 mg q2d when patients were exposed to courses of antibiotics.  In the first year of the program we detected a significant reduction in cases of fungal peritonitis.

In reviewing our data from the past year, I’m pleased to report 0 (zero) cases of fungal peritonitis.

Have a look at our breakdown of pathogens in peritonitis cases over the past year:

You won’t see one case of fungal peritonitis, which is incredibly exciting and suggests that our fluconazole prophylaxis may be effective.   You will notice 23% of cases are Coagulase Negative Staph and 16% Culture Negative.  This suggests we’ll need to expend some additional effort looking at patient technique and training to reduce the Coagulase Negative Staph infections as well as look at our collection and culture techniques to ensure we’re identifying as many organisms as possible.

Thanks again to Dimas Yusuf  who built such a powerful analytics and case management tool for us.  Using RenalConnect, I was able to do the above analysis in about 60 seconds.


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