Simplified calculation of month-on-month annualized peritoneal dialysis associated peritonitis rate – Validation in ANZDATA, NZ PD and RDPLF registries

Simplified Calculation of Monthly PD Peritonitis Rate

Abstract

Peritonitis is the most important therapy-related complication of peritoneal dialysis (PD). Monthly or quarterly PD peritonitis rate statistics are used to identify special cause variation within or between individual PD centres, to highlight any need for quality improvement. Unfortunately, many PD centres do not accurately “patient flow” (i.e., when patients start and finish on PD), and therefore cannot measure PD peritonitis rate. In this study, we validate an estimating formula for month-on-month annualised PD peritonitis rate, that calculates time-at-risk from “patient stock” (i.e., the number of prevalent patients on PD at the beginning and end of the month). We compared centers’ estimated peritonitis rates with gold-standard measurements in the Australia and New Zealand Dialysis and Transplant Registry / New Zealand PD Registry, and Le Registre de Dialyse Péritonéale de Langue Française et hémodialyse à domicile. A total of 268 centers from 9 countries with 1,020,260 patient-months of follow-up and 19,669 episodes of peritonitis were modeled. Overall agreement was excellent between estimates and gold-standard measurements with a concordance correlation coefficient (CCC) of 0.998 (95% confidence interval [CI] 0.998-0.998) in both registries. There was statistically significant lower agreement for smaller centers, although the CCC was still greater than 0.995. There were no instances of clinically significant misclassification of centers as being compliant or non-compliant with PD peritonitis standards with the use of the estimating formula. The simplified method of calculating the PD peritonitis rate is accurate and will allow more centers around the world to measure, report, and work on reducing PD peritonitis rates.

INTRODUCTION

The identification of excess peritonitis or high peritonitis rates is essential for quality control within peritoneal dialysis (PD) services. Traditionally, this is ascertained as special cause variation in PD peritonitis rates at a unit level, calculating rates of PD peritonitis from the number of episodes, as a function of PD patient time-at-risk[1]. The recommended computation uses “patient flow” data over a period of observation - that is, calculating PD time-at-risk function as the number of PD patient-days at risk, as the cumulative total of each patient’s number of days on PD from their starting and finishing dates.

\( \documentclass{article} \usepackage{amsmath} \begin{document} \displaystyle \frac{\text{Number of PD peritonitis episodes during Year for N patients}}{\sum_{i=1}^{N} \left( \frac{\text{Days on PD during Year for Patient } i}{365.25} \right)} \end{document} \)Eq1

Recently, we validated a simplified method of calculating PD peritonitis rate using patient time-at-risk from “patient stock” data - that is, calculating PD patient-days from the number of prevalent PD patients at the center at the start of the period of observation and the corresponding number at the end. This enables calculations of PD peritonitis rates in the absence of accurate “patient flow” data (that is, the dates when patients start and finish PD), so long as there is reliable “patient stock” data (that is, the numbers of prevalent patients on PD at given points in time)[2].

\( \documentclass{article} \usepackage{amsmath} \begin{document} \displaystyle \frac{\text{Number of PD peritonitis episodes during Year for a given center}}{\left( \frac{N_{\text{Year Start}} + N_{\text{Year Finish}}}{2} \right)} \end{document} \)Eq2

While this estimating formula has been validated for annual PD peritonitis rates, it would be convenient to extend this concept to the calculation of month-on-month annualized PD peritonitis rates (here-after referred to as “monthly peritonitis rates”), a commonly used PD quality indicator. This simplification also replaces the traditional time-at-risk denominator with one calculated from “patient stock” - that is, the number of prevalent PD patients in a center at the beginning and end of each month, a more easily accessible statistic for most PD centers. The algebraic equivalence between the estimates and gold-standard measurements relies on two key assumptions: namely, that patients start and finish PD at a uniform rate throughout the months (that is, at random), and that the number of patients who start on PD after the beginning of the month and also finish before the end of the month is small.

\( \documentclass{article} \usepackage{amsmath} \begin{document} \displaystyle \left[ \frac{\text{Number of PD peritonitis episodes during Month for a given center}}{\left( \frac{N_{\text{Month Start}} + N_{\text{Month Finish}}}{2} \right)} \right] \times 12 \end{document} \)Eq3

In this paper, we explore the accuracy of the estimating formula in two databases. The first is Australia and New Zealand Dialysis and Transplant Registry (ANZDATA) / New Zealand (NZ) PD Registry. The second is Le Registre de Dialyse Péritonéale de Langue Française et hémodialyse à domicile (the RDPLF).

METHODS

Study Design

We performed an observational cohort study to measure agreement between gold-standard annual PD peritonitis rates and those estimated using the simplified formula. The National (NZ) Health and Disability Ethics Committee (IORG0000895) approved the study protocol, and waived the need for patient consent under the provisions for observational research.

Patient Participants and Data Source

The ANZDATA Registry collects data on all kidney failure (KF) patients in Australia and New Zealand. For the purposes of this study, PD patients are defined as those with a diagnosis of KF for whom PD is an indefinite treatment. Data on PD peritonitis has been collected since 2004 (in NZ, directly by ANZDATA until June 2021, but through data linkage with the NZ PD Registry thereafter). Details of the structure and methods of all registries are reported elsewhere (www.anzdata.org.au, www.pdregsitry.org.nz,[3];[4];[5]).

The RDPLF collects corresponding data on all KF patient on PD in Mainland France, as well as data from larger PD centres in Algeria, Francophone Belgium, the Kingdom of Morocco and Southern Provinces, Luxembourg, Francophone Switzerland, and Tunisia. PD peritonitis has been collected since the registry’s inception in 1986. Details of the structure and methods of the RDPLF is reported elsewhere (https://rdplf.org/[6]).

We created a study cohort from the two registries. In ANZDATA, this comprised children and adults with KF on PD from 1-Jan-2004 to 31-December-2019. In the RDPLF, the study cohort comprised corresponding adult patients between the dates of 1-Jan-2000 and 31-December-2020.

Primary Exposure and Outcome Variables

The primary exposure in this study is PD peritonitis, as recorded in the respective registries based upon the opinion of the treating physician / PD team. Gold-standard month-on-month annualized PD peritonitis rate measurements were performed using Equation 1 above (modified for a monthly calculation), and estimates from Equation 3.

Data Measurement and Quantitative Variables

We also used patient and center characteristics in our models, to identify any effect modification on concordance statistics arising from variation in patient case mix between centers. In ANZDATA / NZ PD Registry models, potential effect modifiers were: country, age at PD inception, PD sub-modality (automated PD continuous ambulatory PD ), gender, ethnicity (Caucasian / other, Aboriginal or Torres Strait Islander, Asian, NZ Maori, Pacific peoples), primary kidney disease (diabetic nephropathy, ischemic / hypertensive nephropathy, glomerulonephritis, other), late referral for nephrology pre-dialysis care (<3 months before dialysis inception), and rurality (living in a major city, living in a regional town or remotely). Corresponding characteristics in RDPLF models were: country, age at PD inception, PD sub-modality

.....