ArticleCritical care (London, England)2007
A novel approach for prediction of tacrolimus blood concentration in liver transplantation patients in the intensive care unit through support vector regression.
Article in Critical care (London, England), 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Clinical-oriented tacrolimus dosing algorithms in kidney transplant based on genetic algorithm and deep forest.Frontiers in pharmacology · 2025Article
- Machine-learning model to predict the tacrolimus concentration and suggest optimal dose in liver transplantation recipients: a multicenter retrospective cohort study.Scientific reports · 2024Article
- Effects ofBiomedicines · 2023Article
- Commentary: Predicting blood concentration of tacrolimus in patients with autoimmune diseases using machine learning techniques based on real-world evidence.Frontiers in pharmacology · 2022Article
- A hybrid machine learning/pharmacokinetic approach outperforms maximum a posteriori Bayesian estimation by selectively flattening model priors.CPT: pharmacometrics & systems pharmacology · 2021Article
- Predicting Blood Concentration of Tacrolimus in Patients With Autoimmune Diseases Using Machine Learning Techniques Based on Real-World Evidence.Frontiers in pharmacology · 2021Article
- Applications of machine learning in acute care research.Journal of the American College of Emergency Physicians open · 2020Article
- A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus Using the DMETInternational journal of molecular sciences · 2020Article
- The effect of multiple single nucleotide polymorphisms in the folic acid pathway genes on homocysteine metabolism.BioMed research international · 2014Article
- Predictive data mining on monitoring data from the intensive care unit.Journal of clinical monitoring and computing · 2013Article
- Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies.BMC medical informatics and decision making · 2008Article
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7 authors.
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Abstract
introductionTacrolimus is an important immunosuppressive drug for organ transplantation patients. It has a narrow therapeutic range, toxic side effects, and a blood concentration with wide intra- and interindividual variability. Hence, it is of the utmost importance to monitor tacrolimus blood concentration, thereby ensuring clinical effect and avoiding toxic side effects. Prediction models for tacrolimus blood concentration can improve clinical care by optimizing monitoring of these concentrations, especially in the initial phase after transplantation during intensive care unit (ICU) stay. This is the first study in the ICU in which support vector machines, as a new data modeling technique, are investigated and tested in their prediction capabilities of tacrolimus blood concentration. Linear support vector regression (SVR) and nonlinear radial basis function (RBF) SVR are compared with multiple linear regression (MLR).
methodsTacrolimus blood concentrations, together with 35 other relevant variables from 50 liver transplantation patients, were extracted from our ICU database. This resulted in a dataset of 457 blood samples, on average between 9 and 10 samples per patient, finally resulting in a database of more than 16,000 data values. Nonlinear RBF SVR, linear SVR, and MLR were performed after selection of clinically relevant input variables and model parameters. Differences between observed and predicted tacrolimus blood concentrations were calculated. Prediction accuracy of the three methods was compared after fivefold cross-validation (Friedman test and Wilcoxon signed rank analysis).
resultsLinear SVR and nonlinear RBF SVR had mean absolute differences between observed and predicted tacrolimus blood concentrations of 2.31 ng/ml (standard deviation [SD] 2.47) and 2.38 ng/ml (SD 2.49), respectively. MLR had a mean absolute difference of 2.73 ng/ml (SD 3.79). The difference between linear SVR and MLR was statistically significant (p < 0.001). RBF SVR had the advantage of requiring only 2 input variables to perform this prediction in comparison to 15 and 16 variables needed by linear SVR and MLR, respectively. This is an indication of the superior prediction capability of nonlinear SVR.
conclusionPrediction of tacrolimus blood concentration with linear and nonlinear SVR was excellent, and accuracy was superior in comparison with an MLR model.
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