Abstract
Routine monitoring of lithium levels is common clinical practice. This is because the lithium prediction strategies available developed by previous studies are still limited due to insufficient prediction performance. Thus, we used machine learning approaches to predict lithium concentration in a large real-world dataset. Real-world data from multicenter electronic medical records were used in different machine learning algorithms to predict: (1) whether the serum level was 0.6–1.2 mmol/L or 0.0–0.6 mmol/L (binary prediction), and (2) its concentration value (continuous prediction). We developed models from 1505 samples through 5-fold cross-validation and used 204 independent samples to test their performance by evaluating their accuracy. Moreover, we ranked the most important clinical features in different models and reconstructed three reduced models with fewer clinical features. For binary and continuous predictions, the average accuracy of these models was 0.70–0.73 and 0.68–0.75, respectively. Seven features were listed as important features related to serum lithium levels of 0.6–1.2 mmol/L or higher lithium concentration, namely older age, lower systolic blood pressure, higher daily and last doses of lithium prescription, concomitant psychotropic drugs with valproic acid and -pine drugs, and comorbid substance-related disorders. After reducing the features in the three new predictive models, the binary or continuous models still had an average accuracy of 0.67–0.74. Machine learning processes complex clinical data and provides a potential tool for predicting lithium concentration. This may help in clinical decision-making and reduce the frequency of serum level monitoring.
Highlights
Lithium is an important therapeutic drug and is considered an archetypal mood stabilizer for the management of mood disorders or schizoaffective disorder [1,2,3].Many international guidelines recommend lithium as a first-line drug, especially for bipolar disorder [4,5]
Patients’ lithium concentration records had to meet the following inclusion criteria: (1) Patients should take the same daily dose for at least 5-day consecutively before the blood test was performed to achieve the steady-state concentration of lithium therapy based on its half-life [22]; (2) Had no chronic kidney disease [23]; (3) Data on serum lithium level ranging from 0.0–1.2 mmol/L [4]; (4) Serum lithium samples were drawn
Our findings show that this flexible feature combination of algorithm-driven machine learning models is clinically more practical
Summary
Lithium is an important therapeutic drug and is considered an archetypal mood stabilizer for the management of mood disorders or schizoaffective disorder [1,2,3].Many international guidelines recommend lithium as a first-line drug, especially for bipolar disorder [4,5]. Even though lithium has clear beneficial effects, the therapeutic range of its serum levels is narrow (
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