Long short-term memory algorithm for personalized tacrolimus dosing: A simple and effective time series forecasting approach post-lung transplantation.

Abstract

Background: Management of tacrolimus trough levels (TTLs) influences morbidity and mortality after lung transplantation. Several studies have explored pharmacokinetic and artificial intelligence models to monitor tacrolimus levels. However, many models depend on a wide range of variables, some of which, like genetic polymorphisms, are not commonly tested for in regular clinical practice. This study aimed to verify the efficacy of a novel approach simply utilizing time series data of tacrolimus dosing, with the objective of accurately predicting trough levels in a variety of clinical settings.

Methods: Data encompassing 36 clinical variables for each patient were gathered, and a multivariate long short-term memory algorithm was applied to forecast subsequent TTLs based on the selected clinical variables. The tool was developed using a dataset of 87,112 data points from 117 patients, and its efficacy was confirmed using 6 additional cases.

Results: Shapley additive explanations revealed a significant correlation between trough levels and prior dose-concentration data. By using simple trend learning of dose, administration route, and previous trough levels of tacrolimus, we could predict values within 30% of the actual values for 88.5% of time points, which facilitated the creation of a tool for simulating TTLs in response to dosage adjustments. The tool exhibited the potential for rectifying clinical misjudgments in a simulation cohort.

Conclusions: Utilizing our time series forecasting tool, precise prediction of trough levels is attainable independently of other clinical variables through the analysis of historical tacrolimus dose-concentration trends alone.