scholarly journals A Machine Learning Model to Predict Risperidone Active Moiety Concentration Based on Initial Therapeutic Drug Monitoring

2021 ◽  
Vol 12 ◽  
Author(s):  
Wei Guo ◽  
Ze Yu ◽  
Ya Gao ◽  
Xiaoqian Lan ◽  
Yannan Zang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Therapeutic Drug Monitoring ◽  
Prediction Model ◽  
Drug Monitoring ◽  
Validation Cohort ◽  
Univariate Analysis ◽  
Support Vector ◽  
Therapeutic Drug ◽  
Data Set ◽  
Active Moiety

Risperidone is an efficacious second-generation antipsychotic (SGA) to treat a wide spectrum of psychiatric diseases, whereas its active moiety (risperidone and 9-hydroxyrisperidone) concentration without a therapeutic reference range may increase the risk of adverse drug reactions. We aimed to establish a prediction model of risperidone active moiety concentration in the next therapeutic drug monitoring (TDM) based on the initial TDM information using machine learning methods. A total of 983 patients treated with risperidone between May 2017 and May 2018 in Beijing Anding Hospital were collected as the data set. Sixteen predictors (the initial TDM value, dosage, age, WBC, PLT, BUN, weight, BMI, prolactin, ALT, MECT, Cr, AST, Ccr, TDM interval, and RBC) were screened from 26 variables through univariate analysis (p < 0.05) and XGBoost (importance score >0). Ten algorithms (XGBoost, LightGBM, CatBoost, AdaBoost, Random Forest, support vector machine, lasso regression, ridge regression, linear regression, and k-nearest neighbor) compared the model performance, and ultimately, XGBoost was chosen to establish the prediction model. A cohort of 210 patients treated with risperidone between March 1, 2019, and May 31, 2019, in Beijing Anding Hospital was used to validate the model. Finally, the prediction model was evaluated, obtaining R2 (0.512 in test cohort; 0.374 in validation cohort), MAE (10.97 in test cohort; 12.07 in validation cohort), MSE (198.55 in test cohort; 324.15 in validation cohort), RMSE (14.09 in test cohort; 18.00 in validation cohort), and accuracy of the predicted TDM within ±30% of the actual TDM (54.82% in test cohort; 60.95% in validation cohort). The prediction model has promising performance to facilitate rational risperidone regimen on an individualized level and provide reference for other antipsychotic drugs' risk prediction.

scholarly journals Personalized drug concentration predictions with machine learning: an exploratory study

2020 ◽  
Vol 9 (6) ◽  
pp. 980
Author(s):  
Danish Shakeel ◽  
Shakeel Ahmad Mir
Keyword(s):  
Machine Learning ◽  
Therapeutic Drug Monitoring ◽  
Regression Model ◽  
Drug Monitoring ◽  
Drug Level ◽  
Therapeutic Drug ◽  
Reference Ranges ◽  
Dose Individualization ◽  
Out Of Sample ◽  
Blood Drug Level

Background: The dose individualization by therapeutic drug monitoring (TDM) can be improved if population-based reference ranges are available, as there is large inter- and intrapatient variability. If these ranges are not available, dose individualization may not be optimal. Machine learning can help achieve accurate drug dose settings and predict the resultant levels.Methods: Two random forest models, a multi-class classifier to predict dose and a regression model to predict blood drug level were trained on 320 patients’ data, consisting of their age, sex, dose and blood drug level. The classifier consisted of 1000 estimators (decision trees) and the regression model consisted of 1300 estimators. The model was evaluated on randomly split test set having 10% of the total dataset size. The regression model was compared against k-Nearest neighbor and linear regression models. The classifier was evaluated using accuracy, precision, and F1 Score; the regression model was evaluated using R2, Root mean squared error, and mean absolute error.Results: The classifier had an out-of-sample accuracy of 68.75%, average precision of 0.7567, and an average F1 score of 0.6907. The regression model had an out-of-sample R2 value of 0.2183, root mean squared value of 3.7359, and a mean absolute error of 2.5156. These values signify an average classification performance, and a below-average regression performance due to small dataset.Conclusions: It is possible for machine learning algorithms to be used in therapeutic drug monitoring. With a well-structured, rich, and large dataset, a very accurate model can be built.

scholarly journals Malaria Prediction Model Using Machine Learning Algorithms

2021 ◽  
Vol 12 (10) ◽  
pp. 7488-7496
Author(s):  
Yusuf Aliyu Adamu, Et. al.
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Prediction Model ◽  
Public Awareness ◽  
Health Sector ◽  
Weather Condition ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
African Countries ◽  
Data Set

Measures have been taking to ensure the safety of individuals from the burden of vector-borne disease but it remains the causative agent of death than any other diseases in Africa. Many human lives are lost particularly of children below five years regardless of the efforts made. The effect of malaria is much more challenging mostly in developing countries. In 2019, 51% of malaria fatality happen in Africa which it increased by 20% in 2020 due to the covid-19 pandemic. The majority of African countries lack a proper or a sound health care system, proper environmental settlement, economic hardship, limited funding in the health sector, and absence of good policies to ensure the safety of individuals. Information has to become available to the peoples on the effect of malaria by making public awareness program to make sure people become acquainted with the disease so that certain measure can be maintained. The prediction model can help the policymakers to know more about the expected time of the malaria occurrence based on the existing features so that people will get to know the information regarding the disease on time, health equipment and medication to be made available by government through it policy. In this research weather condition, non-climatic features, and malaria cases are considered in designing the model for prediction purposes and also the performance of six different machine learning classifiers for instance Support Vector Machine, K-Nearest Neighbour, Random Forest, Decision Tree, Logistic Regression, and Naïve Bayes is identified and found that Random Forest is the best with accuracy (97.72%), AUC (98%) AUC, and (100%) precision based on the data set used in the analysis.  

Therapeutic Drug Monitoring of Venlafaxine and Impact of Age, Gender, BMI, and Diagnosis

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
M. Krivosova ◽  
M. Kertys ◽  
M. Grendar ◽  
I. Ondrejka ◽  
I. Hrtanek ◽  
...  
Keyword(s):  
Therapeutic Drug Monitoring ◽  
Drug Monitoring ◽  
Active Metabolite ◽  
Common Mental Disorder ◽  
Plasma Concentrations ◽  
Therapeutic Drug ◽  
Active Moiety ◽  
Drug Concentrations ◽  
First Choice ◽  
Significant Difference

AbstractDepression is a common mental disorder affecting more than 264 million people in the world and 5.1% of the Slovak population. Although various antidepressant approaches have been used; still, about 40% of patients do not respond to a first-choice drug administration and one third of patients do not achieve total remission. Therapeutic drug monitoring (TDM) is a method used for quantification and interpreting the drug concentrations in plasma in order to optimize the pharmacotherapy. The aim of this study was to measure the plasma concentrations of venlafaxine, the fourth most prescribed antidepressant in Slovakia, as well as its active metabolite and interpret them with the relevant patients’ characteristics.The study was of retrospective nature and 28 adult patients in total were included. The concentrations of venlafaxine and its active metabolite O-desmethylvenlafaxine (ODV) in plasma were quantified using the validated UHPLC-MS/MS method. The effects of potential influencing factors were evaluated by a multivariate linear regression model.Only 39% of patients reached the venlafaxine active moiety concentrations within the recommended therapeutic range. Plasma concentrations were dependent on age, gender, and duration of the therapy. Venlafaxine metabolism expressed as a metabolite-to-parent concentrations ratio was influenced by a combination of age, gender, and body mass index (BMI). We did not observe any significant difference in plasma concentrations between the patients with a single and recurrent diagnosis of depression. Combining variables made an additive effect on plasma concentrations, for example, active moiety plasma concentrations were higher in older women. In contrast, drug metabolism was higher in older men and men with lower BMI. TDM of venlafaxine is recommended in clinical practice, especially in the elderly when beginning the pharmacotherapy.

scholarly journals Development and validation of an online model to predict critical COVID-19 with immune-inflammatory parameters

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Yue Gao ◽  
Lingxi Chen ◽  
Jianhua Chi ◽  
Shaoqing Zeng ◽  
Xikang Feng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Critical Illness ◽  
Prediction Model ◽  
Validation Cohort ◽  
External Validation ◽  
Validation Dataset ◽  
Support Vector ◽  
Internal Validation ◽  
Inflammatory Parameters ◽  
Severity Prediction

Abstract Background Immune and inflammatory dysfunction was reported to underpin critical COVID-19(coronavirus disease 2019). We aim to develop a machine learning model that enables accurate prediction of critical COVID-19 using immune-inflammatory features at admission. Methods We retrospectively collected 2076 consecutive COVID-19 patients with definite outcomes (discharge or death) between January 27, 2020 and March 30, 2020 from two hospitals in China. Critical illness was defined as admission to intensive care unit, receiving invasive ventilation, or death. Least Absolute Shrinkage and Selection Operator (LASSO) was applied for feature selection. Five machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosted Decision Tree (GBDT), K-Nearest Neighbor (KNN), and Neural Network (NN) were built in a training dataset, and assessed in an internal validation dataset and an external validation dataset. Results Six features (procalcitonin, [T + B + NK cell] count, interleukin 6, C reactive protein, interleukin 2 receptor, T-helper lymphocyte/T-suppressor lymphocyte) were finally used for model development. Five models displayed varying but all promising predictive performance. Notably, the ensemble model, SPMCIIP (severity prediction model for COVID-19 by immune-inflammatory parameters), derived from three contributive algorithms (SVM, GBDT, and NN) achieved the best performance with an area under the curve (AUC) of 0.991 (95% confidence interval [CI] 0.979–1.000) in internal validation cohort and 0.999 (95% CI 0.998–1.000) in external validation cohort to identify patients with critical COVID-19. SPMCIIP could accurately and expeditiously predict the occurrence of critical COVID-19 approximately 20 days in advance. Conclusions The developed online prediction model SPMCIIP is hopeful to facilitate intensive monitoring and early intervention of high risk of critical illness in COVID-19 patients. Trial registration This study was retrospectively registered in the Chinese Clinical Trial Registry (ChiCTR2000032161). Graphical abstracthelper lymphocytve vv

Salivary Monitoring of Antiepileptic Drugs

2007 ◽  
Vol 20 (2) ◽  
pp. 147-157 ◽  
Author(s):  
Robert J. Baumann
Keyword(s):  
Therapeutic Drug Monitoring ◽  
Drug Monitoring ◽  
Lower Cost ◽  
Home Monitoring ◽  
Therapeutic Drug ◽  
Serum Free ◽  
Active Moiety ◽  
Free Level ◽  
Anticonvulsant Treatment ◽  
Wide Potential

Therapeutic drug monitoring is widely used in the anticonvulsant treatment of persons with epilepsy. Most monitoring uses serum, but many anticonvulsant drugs can as easily be monitored using saliva, including phenobarbital, phenytoin, carbamazepine, lamotrigine, oxcarbazepine, topiramate, levetiracetam, and gabapentin. For highly protein-bound medications such as phenobarbital, phenytoin, and carbamazepine, saliva has the advantage of providing an approximation of the serum free level, the free level presumably being the active moiety. Salivary therapeutic drug monitoring offers a number of advantages over serum therapeutic drug monitoring, including lack of pain, lower cost, and wide potential acceptability by patients and physicians. It has the potential to open new approaches to treatment with strategic at-home monitoring at the time a seizure or adverse event occurs and to allow the collection of cohort-based, pharmacokinetic, and pharmcodynamic data for populations of persons of varying ages and with different medical conditions who require anticonvulsant medications.

Diagnose und Monitoring bei chronisch-entzündlicher Darmerkrankung

2018 ◽  
Vol 75 (5) ◽  
pp. 316-328
Author(s):  
Christian Ansprenger ◽  
Emanuel Burri
Keyword(s):  
Therapeutic Drug Monitoring ◽  
Drug Monitoring ◽  
Morbus Crohn ◽  
Therapeutic Drug ◽  
Körperliche Untersuchung

Zusammenfassung. Die Diagnose und auch die Überwachung von chronisch entzündlichen Darmerkrankungen ruht auf mehreren Säulen: Anamnese, körperliche Untersuchung, Laborwerte (im Blut und Stuhl), Endoskopie, Histologie und Bildgebung. Die Diagnose kann nicht anhand eines einzelnen Befundes gestellt werden. In den letzten Jahren hat sich das Therapieziel weg von klinischen Endpunkten hin zu endoskopischen und sogar histologischen Endpunkten entwickelt. Für einige dieser neuen Therapieziele existiert allerdings noch keine allgemein gültige Definition. Regelmässige Endoskopien werden von Patienten schlecht toleriert, weshalb Surrogat-Marker wie Calprotectin untersucht wurden und eine gute Korrelation mit der mukosalen Entzündungsaktivität nachgewiesen werden konnte. Entsprechend zeigte sich bei Morbus Crohn eine Algorithmus-basierte Therapiesteuerung – unter anderem basierend auf Calprotectin – einer konventionellen Therapiesteuerung überlegen. Die Überwachung der medikamentösen Therapie («Therapeutic Drug Monitoring» [TDM]) ist ein zweites Standbein des Monitoring von chronisch entzündlichen Darmerkrankungen. Mit zunehmendem Einsatz vor allem der Biologika-Therapien wurden sowohl reaktives TDM (in Patienten mit klinischem Rezidiv) als auch proaktives TDM (in Patienten in Remission / stabiler Erkrankung) untersucht und haben (teilweise) Eingang in aktuelle Richtlinien gefunden. Zukünftige Studien werden die vorgeschlagenen Therapieziele besser definieren und den Nutzen der medikamentösen Therapieüberwachung auf den Krankheitsverlauf weiter untersuchen müssen.

Therapeutic drug monitoring (TDM) of the recent antipsychotic ziprasidone in dried blood spots (DBS) and plasma

2011 ◽  
Vol 44 (06) ◽  
Author(s):  
L Mercolini ◽  
G Fulgenzi ◽  
M Melis ◽  
G Boncompagni ◽  
LJ Albers ◽  
...  
Keyword(s):  
Therapeutic Drug Monitoring ◽  
Drug Monitoring ◽  
Dried Blood Spots ◽  
Therapeutic Drug ◽  
Blood Spots
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