#1944 How the patient characteristics and the prescription can be accounted for in patient-specific predictive HD models?
Authors: Casagrande Giustina , Costantino Marialaura , Pizzi Marialavinia , Ambrosini Andrea , Colturi Carla , Grosse Philipp , Venzin Reto Martin , Melfa Gianvincenzo , Pianca Silvio 
Published: May 2024

Abstract

Background and Aims
Compartmental models in HemoDialysis (HD) allow for understanding and predicting the patient-specific response to the treatment. These models, after training with consistent clinical data, can be adapted to each single patient through the estimation of a set of parameters.
This work explores the relationship between the sets of estimated parameters and the anamneses of the enrolled patients, together with the settings of the HD sessions, and their time evolution. The finding of similar relationships will help properly set the models even avoiding the training phase.

Method
The data acquired during the INTERREG InterACTIVE-HD 2.0 observational longitudinal study, in partnership with 5 Italian and Switzerland hospitals (ASSTs Lariana, SetteLaghi, Valtellina e Alto Lario, Regional Hospital of Lugano, and Kantonsspital Graubünden of Chur Dialysis Units), were used (576 sessions). Clinical prescription, machine settings, and blood composition at the start, end, and every hour during the HDs were monitored over 7 months. The parametric multi-compartmental, multi-solute model optimized during the study was used. The patient-specific parameters are:
Their default values (literature-based) are:
Patient-specific parameters have been identified for each patient, by training the model with 3 session's data. Then the model can be used to predict the patient's response by knowing the clinical data at the beginning of the HD and setting the parameters equal to the mean of the 3 identified values.
A correlation analysis between the patient-dependent parameters and the anamneses of the enrolled patients, together with the settings of the HD sessions, has been performed. The optimization orders have been also considered.

Results
k(K+), k(Urea), and k(Crea) are the first optimized and remain generally of the same magnitude. k(Na+), k(Cl-), k(HCO3-), k(Mg2+), and k(Glu), correlate with the UF rate, the therapy technique, or the concentrations in dialysate bags. They are later optimized and characterized by wider variability in values and optimization orders. k(Ca2+) remains at the default in 98% of the sessions, being optimized last. More convective therapies seem to induce patient-specific variations in active and passive cellular transport for specific solutes.
η relevant correlations refer to the dialysate bag composition and the baseline conditions of the patients. They are in general late optimized, in HDF before than in SHD. η(Urea) and η(Crea) in 99% of the cases do not move from the default, suggesting a lower interaction of the non-ionic species with the filter membrane. They are optimized only in non-hypertensive insulin-dependent diabetic patients. No correlation between η and the filter material or the initial serum albumin was instead observed.
ρ is related to the therapy; in HDF sessions it is over the range boundaries ([0,10]). For females undergoing SHD, it assumes the highest values (&gt;9). ρ seems also to be affected by the use of beta-blockers (p &lt; 0.01) and antihyperglycemic ((p &lt; 0.01).
Ca_buff coefficients for females (higher values) and males are statistically different. Diabetics using anti-hyperglycemia drugs are characterized by lower values.

Conclusion
Compartmental patient-specific models, if successfully trained become a useful support tool for therapy customization, mainly for ESRD patients with multiple comorbidities.
The observed correlations represent the first step to causally relate the values of the modulating parameters with the patient characteristics implying the reduction or amplification of a specific response to the treatment. The next step will be an analysis of combined effects. The final goal is to be able to correctly set up the models even before the training phase is completed.