#5803 ARTERIOVENOUS FISTULA BLOOD FLOW ESTIMATION THROUGH AI MODEL EXPLOITING THE PATIENT'S ROUTINELY COLLECTED DATA

Abstract Background and Aims Clinical monitoring and surveillance are key pillars of Arteriovenous Fistula (AVF) management in hemodialysis patients. They are aimed at limiting the risk of suboptimal dialysis dose and Vascular Access (VA) failure. AVF blood flow (Qa) is commonly used to assess VA function. Several methods have been proposed for the measurement of Qa including doppler ultrasound, and Body Thermal Monitor, among others. All these diagnostic tests are, in general, time-consuming for clinical staff, operator dependent, and costly for healthcare providers. To overcome limitations in the uptake of current Qa measurement techniques, we used data automatically recorded by dialysis machine sensors and medical information captured in electronic charts records to estimate Qa using a machine learning technique. Method For this historical cohort study, we analyzed electronic health records (EHR) of adult patients from four different European countries (Czech Republic, Portugal, Slovakia, and Spain), receiving in-center hemodialysis therapy in Fresenius Medical Care dialysis clinics between January 1st, 2015 and June 30th, 2022 registered in the European Clinical Database (EuCliD®). All patients consented their pseudo-anonymized data be used for secondary data analysis. The input dataset included 49 variables representing the health status of the patients, functional parameters of AVF function and the HD treatment parameters. The input variables were collected in the 90 days before the Qa measure. We constructed metrics representing the 90-day average and trend of each functional and medical parameter. Qa was classified in three levels: < 525 ml/h (very low), 525 ml/h - 925 ml/h (low), > 925 ml/h (normal). We estimated Qa as ordinal classification problem. Therefore, we used 2 binary classifiers based on the XGBoost algorithm. The estimation of the first-class, P(Qa<525), is given by the first classifier and the estimation of the third class, P(Qa≥925), is given by the second classifier. The probability of the middle class is computed as P(Qa≥525)⋅(1-P(Qa≥925)). The Qa estimation accuracy was assessed computing mean absolute error (MAE), precision and recall, F1-score and confusion matrix. Results Our dataset included 46,292 Qa measurements referred to 5,940 different hemodialysis patients. We obtained an overall precision of 0.77, a recall of 0.76, a F1-score of 0.76, a MAE of 0.27. The same metrics for each class are shown in Table 1. The model was able to detect fistula with “very low flow” with a precision of 0.9 and 16% of the missed “very low flow” AVFs are predicted as “low flow” and 4.7% are predicted as normal flow. The Confusion Matrix for each class is shown in Figure 1. Conclusion In this study we showed that clinically relevant Qa classes can be accurately predicted by resorting to routinely collected clinical data extracted from a electronic health record without any additional effort from healthcare professionals, training or instrumentation. Qa assessment is an important parameter in the evaluation of AVF function. Our algorithm accurately discriminated patients with “very low flow” that may be referred to vascular surgeon evaluation. It might help the AVF surveillance process, without adding time-consuming procedures for clinical staff or costs for healthcare provider.