Developing a machine learning model for predicting venlafaxine active moiety concentration: a retrospective study using real-world evidence.

BACKGROUND: Venlafaxine is frequently prescribed for patients with depression. To control the concentration of venlafaxine within the therapeutic window for the best treatment effect, a model to predict venlafaxine concentration is necessary. AIM: Our objective was to develop a prediction model for venlafaxine concentration using real-world evidence based on machine learning and deep learning techniques. METHOD: Patients who underwent venlafaxine treatment between November 2019 and August 2022 were included in the study. Important variables affecting venlafaxine concentration were identified using a combination of univariate analysis, sequential forward selection, and machine learning techniques. Predictive performance of nine machine learning and deep learning algorithms were assessed, and the one with the optimal performance was selected for modeling. The final model was interpreted using SHapley Additive exPlanations. RESULTS: A total of 330 eligible patients were included. Five influential variables that affect venlafaxine concentration were venlafaxine daily dose, sex, age, hyperlipidemia, and adenosine deaminase. The venlafaxine concentration prediction model was developed using the eXtreme Gradient Boosting algorithm (R2 = 0.65, mean absolute error = 77.92, root mean square error = 93.58). In the testing cohort, the accuracy of the predicted concentration within ± 30% of the actual concentration was 73.49%. In the subgroup analysis, the prediction accuracy was 69.39% within the recommended therapeutic range of venlafaxine concentration within ± 30% of the actual value. CONCLUSION: The XGBoost model for predicting blood concentration of venlafaxine using real-world evidence was developed, guiding the adjustment of regimen in clinical practice.

Analysis of Factors Affecting Concentrations and Concentration-To-Dose Ratios of Trazodone.

BACKGROUND: Trazodone is prescribed for several clinical conditions. Multiple factors may affect trazodone to reach its therapeutic reference range. The concentration-to-dose (C/D) ratio can be used to facilitate the therapeutic drug monitoring of trazodone. The study aimed to investigate factors on the concentrations and C/D ratio of trazodone. METHODS: This study analyzed the therapeutic drug monitoring electronic case information of inpatients in the First Hospital of Hebei Medical University from October 2021 to July 2023. Factors that could affect the concentrations and C/D ratio of trazodone were analyzed, including body mass index, sex, age, smoking, drinking, drug manufacturers, and concomitant drugs. RESULTS: A total of 255 patients were analyzed. The mean age was 52.44 years, and 142 (55.69%) were women. The mean dose of trazodone was 115.29 mg. The mean concentration of trazodone was 748.28 ng/mL, which was in the therapeutic reference range (700-1000 ng/mL). 50.20% of patients reached the reference range, and some patients (36.86%) had concentrations below the reference range. The mean C/D ratio of trazodone was 6.76 (ng/mL)/(mg/d). A significant positive correlation was found between daily dose and trazodone concentrations (r 2 = 0.2885, P < 0.001). Trazodone concentrations were significantly affected by dosage, sex, smoking, drinking, and concomitant drugs of duloxetine or fluoxetine. After dosage emendation, besides the above factors, it was influenced by age ( P < 0.05, P < 0.01, or P < 0.001). CONCLUSIONS: This study identified factors affecting trazodone concentrations and C/D ratio. The results can help clinicians closely monitor patients on trazodone therapy and maintain concentrations within the reference range.

Study on Mechanical Properties of Hybrid Polypropylene-Steel Fiber RPC and Computational Method of Fiber Content.

On the basis of determining the optimum content of polypropylene fiber reactive powder concrete (RPC), the influence of different steel fiber content on the compressive strength and splitting tensile strength of hybrid polypropylene-steel fiber RPC was studied. The particle morphology and pore parameters of hybrid polypropylene-steel fiber RPC were analyzed by combining scanning electron microscope (SEM) with image-pro plus (IPP) software. The results showed that the RPC ductility can be further improved on the basis of polypropylene fiber RPC, the compressive strength and splitting tensile strength of polypropylene fiber. The optimum content of hybrid polypropylene-steel fiber RPC is 0.15% polypropylene fiber, 1.75% steel fiber. Hybrid polypropylene-steel fiber RPC is mainly composed of particles with small particle size. The particle area ratio first increased and decreased with the increase of steel fiber content, and the maximum steel fiber content is 1.75%. The pore area ratio first decreased and increased with the increase of steel fiber content, and the pore area ratio is the smallest when the steel fiber content is 1.75%. The calculation methods of polypropylene fiber content and steel fiber content and 28-day RPC compressive strength and splitting tensile strength are proposed to select polypropylene fiber content and steel fiber content flexibly according to different engineering requirements, which can provide important guidance for the popularization and application of RPC in practical engineering.