A predictive model for real-time LSTM methods: Monitoring dynamic transmembrane pressure improves loop life and anticoagulant therapy accuracy in continuous renal replacement therapy

Abstract

Background

Continuous Renal Replacement Therapy (CRRT), is essential for managing acute kidney injury (AKI) Dynamic monitoring of transmembrane pressure (TMP) during CRRT is crucial for predicting filter clotting and optimizing filter lifespan, which indirectly supports anticoagulation management.

Objective

To prolong the lifespan of CRRT circuits and enhance the precision of anticoagulation therapy by developing a predictive early warning model for CRRT circuit life, based on dynamic TMP monitoring.

Methods

We conducted a retrospective analysis in the ICU of the First Affiliated Hospital of Army Medical University. Leveraging the TMP data recorded by CRRT machines, we established an adaptive real-time predictive modeling framework, termed DTP (Dynamic Transmembrane Pressure Prediction), utilizing Long Short-Term Memory (LSTM) networks. This framework predicts TMP trends as an early indicator of filter clotting. Our models were validated using over 20,000 min of clinical data from 405 CRRT cases, predicting TMP trajectories within 50 min.

Resuits

In simulated treatment evaluations, our LSTM models accurately identified impending TMP increases, achieving recall rates exceeding 0.97 and F2 scores above 0.93. Notably, an average warning time of 23 min was provided prior to the TMP reaching the critical 260 mmHg threshold, indicating substantial filter clotting. An analysis of false alarms revealed patterns consistent with emerging instability and transient artifacts.

Conclusion

The personalized early warning model developed within the DTP framework effectively predicts TMP changes, enhancing the accuracy and timeliness of medical interventions. This improvement reduces the incidence of adverse events, maximizes the lifespan of CRRT circuits, and ultimately decreases treatment and personnel costs.