MO682TAILORING AI-BASED REFERRAL TO INTENSIFIED INTERVENTION PROGRAMS FOR PERITONITIS PREVENTION WITH COST-EFFECTIVENESS SIMULATION
Authors: Bellocchio Francesco , Neri Luca , Ion Titapiccolo Jasmine , Garbelli Mario , Stuard Stefano 
Published: May 2021

Background and Aims

Peritonitis is a common and potentially severe complication for peritoneal dialysis (PD) patients. It is associated with mortality and technique failure risk and contributes significantly to their healthcare cost. Despite several peritonitis prevention programs based on education and training have been implemented worldwide, it has been reported a large variability of efficacy across patients groups and healthcare settings. In order to avoid unnecessary treatment of low risk patients, healthcare prevention programs should be personalized based on accurate patients’ risk profiling, so that high risk patients may be addressed with intensified prevention programs.

However, referral strategy (i.e. defining when risk is too much and deserves special attention) depends the availability, efficacy and cost of medical interventions.

In this study, we demonstrate through a program implementation simulator, how different referral strategies to inform peritonitis prevention program among PD patients informed by AI-based risk stratification tools, produce different healthcare and health economics outcomes. In particular, the simulation considers a prevention program characterized by standard of care, which affects all patients as well as an intensive intervention for a subset of high-risk patients (e.g. special training or medical treatment).

Method

The Peritonitis Risk Score model was trained and validated among 9325 PD patients treated in FMC network (Model accuracy, AUC=0.86). The pharmaco-economic model simulation was performed considering a cohort of 22,900 adult PD patients, treated in Fresenius Medical Care dialysis network between January 1, 2011 and December 31, 2018, for which the Peritonitis Risk Score was computed at a given date. The occurrence of an acute peritonitis in the month after prediction has been registered. We simulated the program outcomes in terms of proportion of referrals to the intensified prevention program, false omission rate, peritonitis risk reduction, overall cost-savings, number needed to treat.

We considered the following scenario based on previous cost-effectiveness analysis on peritonitis risk prevention:

Results

Given the action threshold selected, 5.3% of patients entered the intensified intervention program (PPV=9.5%); the false omission rate was 2.2%. Cost savings for the intensified healthcare where generated when the effect size of the intensified intervention exceeded 1.4 (figure 1A). For that effect size the number needed to treat for each prevented peritonitis was NNT=23.4. Overall, 162 peritonitis/month could be prevented in the whole network (peritonitis with no intervention=592; Peritonitis after intervention=430). When a less conservative threshold was selected, 12.2% of patients entered the intensified prevention program (PPV=7.3%), generating a false omission rate=1.9%. Cost savings were never generated (i.e. the intensified program needed investment to be sustained) but with the same effect size of 1.4 additional 24 peritonitis/months could be saved in the whole network (peritonitis with no intervention=592; Peritonitis after intervention=406). The number needed to treat for the intensified program was NTT=30.4 (figure 1B).

Conclusion

Cost-effectiveness simulating tool provides a rational evaluation framework for AI-based referral to peritonitis preventive programs. This tool can be easily adapted for any healthcare program based on patient risk score.