Predicting quetiapine dose in patients with depression using machine learning techniques based on real-world evidence
Abstract Background Being one of the most widespread, pervasive, and troublesome illnesses in the world, depression causes dysfunction in various spheres of individual and social life. Regrettably, despite obtaining evidence-based antidepressant medication, up to 70% of people are going to continue...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | Annals of General Psychiatry |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12991-023-00483-w |
| _version_ | 1797362978179252224 |
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| author | Yupei Hao Jinyuan Zhang Jing Yu Ze Yu Lin Yang Xin Hao Fei Gao Chunhua Zhou |
| author_facet | Yupei Hao Jinyuan Zhang Jing Yu Ze Yu Lin Yang Xin Hao Fei Gao Chunhua Zhou |
| author_sort | Yupei Hao |
| collection | DOAJ |
| description | Abstract Background Being one of the most widespread, pervasive, and troublesome illnesses in the world, depression causes dysfunction in various spheres of individual and social life. Regrettably, despite obtaining evidence-based antidepressant medication, up to 70% of people are going to continue to experience troublesome symptoms. Quetiapine, as one of the most commonly prescribed antipsychotic medication worldwide, has been reported as an effective augmentation strategy to antidepressants. The right quetiapine dose and personalized quetiapine treatment are frequently challenging for clinicians. This study aimed to identify important influencing variables for quetiapine dose by maximizing the use of data from real world, and develop a predictive model of quetiapine dose through machine learning techniques to support selections for treatment regimens. Methods The study comprised 308 depressed patients who were medicated with quetiapine and hospitalized in the First Hospital of Hebei Medical University, from November 1, 2019, to August 31, 2022. To identify the important variables influencing the dose of quetiapine, a univariate analysis was applied. The prediction abilities of nine machine learning models (XGBoost, LightGBM, RF, GBDT, SVM, LR, ANN, DT) were compared. Algorithm with the optimal model performance was chosen to develop the prediction model. Results Four predictors were selected from 38 variables by the univariate analysis (p < 0.05), including quetiapine TDM value, age, mean corpuscular hemoglobin concentration, and total bile acid. Ultimately, the XGBoost algorithm was used to create a prediction model for quetiapine dose that had the greatest predictive performance (accuracy = 0.69) out of nine models. In the testing cohort (62 cases), a total of 43 cases were correctly predicted of the quetiapine dose regimen. In dose subgroup analysis, AUROC for patients with daily dose of 100 mg, 200 mg, 300 mg and 400 mg were 0.99, 0.75, 0.93 and 0.86, respectively. Conclusions In this work, machine learning techniques are used for the first time to estimate the dose of quetiapine for patients with depression, which is valuable for the clinical drug recommendations. |
| first_indexed | 2024-03-08T16:14:54Z |
| format | Article |
| id | doaj.art-0292feea95214dfeb5ff9ae1cd4d4fe9 |
| institution | Directory Open Access Journal |
| issn | 1744-859X |
| language | English |
| last_indexed | 2024-03-08T16:14:54Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Annals of General Psychiatry |
| spelling | doaj.art-0292feea95214dfeb5ff9ae1cd4d4fe92024-01-07T12:38:11ZengBMCAnnals of General Psychiatry1744-859X2024-01-0123111310.1186/s12991-023-00483-wPredicting quetiapine dose in patients with depression using machine learning techniques based on real-world evidenceYupei Hao0Jinyuan Zhang1Jing Yu2Ze Yu3Lin Yang4Xin Hao5Fei Gao6Chunhua Zhou7Department of Clinical Pharmacy, the First Hospital of Hebei Medical UniversityBeijing Medicinovo Technology Co., LtdDepartment of Clinical Pharmacy, the First Hospital of Hebei Medical UniversityBeijing Medicinovo Technology Co., LtdDepartment of Clinical Pharmacy, the First Hospital of Hebei Medical UniversityDalian Medicinovo Technology Co., LtdBeijing Medicinovo Technology Co., LtdDepartment of Clinical Pharmacy, the First Hospital of Hebei Medical UniversityAbstract Background Being one of the most widespread, pervasive, and troublesome illnesses in the world, depression causes dysfunction in various spheres of individual and social life. Regrettably, despite obtaining evidence-based antidepressant medication, up to 70% of people are going to continue to experience troublesome symptoms. Quetiapine, as one of the most commonly prescribed antipsychotic medication worldwide, has been reported as an effective augmentation strategy to antidepressants. The right quetiapine dose and personalized quetiapine treatment are frequently challenging for clinicians. This study aimed to identify important influencing variables for quetiapine dose by maximizing the use of data from real world, and develop a predictive model of quetiapine dose through machine learning techniques to support selections for treatment regimens. Methods The study comprised 308 depressed patients who were medicated with quetiapine and hospitalized in the First Hospital of Hebei Medical University, from November 1, 2019, to August 31, 2022. To identify the important variables influencing the dose of quetiapine, a univariate analysis was applied. The prediction abilities of nine machine learning models (XGBoost, LightGBM, RF, GBDT, SVM, LR, ANN, DT) were compared. Algorithm with the optimal model performance was chosen to develop the prediction model. Results Four predictors were selected from 38 variables by the univariate analysis (p < 0.05), including quetiapine TDM value, age, mean corpuscular hemoglobin concentration, and total bile acid. Ultimately, the XGBoost algorithm was used to create a prediction model for quetiapine dose that had the greatest predictive performance (accuracy = 0.69) out of nine models. In the testing cohort (62 cases), a total of 43 cases were correctly predicted of the quetiapine dose regimen. In dose subgroup analysis, AUROC for patients with daily dose of 100 mg, 200 mg, 300 mg and 400 mg were 0.99, 0.75, 0.93 and 0.86, respectively. Conclusions In this work, machine learning techniques are used for the first time to estimate the dose of quetiapine for patients with depression, which is valuable for the clinical drug recommendations.https://doi.org/10.1186/s12991-023-00483-wQuetiapineMachine learningDosePrediction modelDepression |
| spellingShingle | Yupei Hao Jinyuan Zhang Jing Yu Ze Yu Lin Yang Xin Hao Fei Gao Chunhua Zhou Predicting quetiapine dose in patients with depression using machine learning techniques based on real-world evidence Annals of General Psychiatry Quetiapine Machine learning Dose Prediction model Depression |
| title | Predicting quetiapine dose in patients with depression using machine learning techniques based on real-world evidence |
| title_full | Predicting quetiapine dose in patients with depression using machine learning techniques based on real-world evidence |
| title_fullStr | Predicting quetiapine dose in patients with depression using machine learning techniques based on real-world evidence |
| title_full_unstemmed | Predicting quetiapine dose in patients with depression using machine learning techniques based on real-world evidence |
| title_short | Predicting quetiapine dose in patients with depression using machine learning techniques based on real-world evidence |
| title_sort | predicting quetiapine dose in patients with depression using machine learning techniques based on real world evidence |
| topic | Quetiapine Machine learning Dose Prediction model Depression |
| url | https://doi.org/10.1186/s12991-023-00483-w |
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