Online identification for characterizing renal pressure–flow dynamics during ex vivo rabbit kidney NMP: A proof-of-concept study

Abstract

ObjectiveEx vivo normothermic machine perfusion (NMP) supports kidney preservation and assessment, but conventional measurements mainly describe operating-point or endpoint conditions. This proof-of-concept study examined whether controlled flow perturbation and online system identification can characterize renal pressure–flow dynamics during rabbit kidney NMP.

MethodsThree isolated rabbit kidneys ($n=3$) were perfused on a custom NMP platform. Pseudo-random binary sequence perturbations were applied to nominal pump-commanded perfusion flow, with renal arterial pressure as the output. Second-, third-, and fourth-order transfer-function models were identified using recursive least squares with adaptive multiple forgetting factors (RLS-MFF). Chronological online tracking was assessed using FIT and RMSE; fixed training-end models were evaluated using AIC/BIC, residual autocorrelation, and Ljung–Box testing. Parameter trajectories and frequency responses were also examined.

ResultsOnline adaptive tracking accuracy increased with model order. For the selected third-order structure, online-evaluation FIT ranged from 84.81% to 86.00%, with a mean RMSE of 1.48 mmHg. Fourth-order models achieved the highest FIT and lowest RMSE but required two additional coefficients. AIC/BIC favored the third-order model in two records, while both third- and fourth-order structures satisfied the primary 10-lag Ljung–Box criterion in two of three records.

ConclusionControlled hydraulic perturbation and adaptive online identification provided kidney-specific pressure–flow dynamic descriptors during rabbit kidney NMP. The third-order model offered a parsimonious common structure across records. With only three kidneys and no independent kidney-quality reference outcome, the findings demonstrate methodological feasibility rather than validated organ assessment.