Validating the Effectiveness of Switching the Vancomycin TDM Analysis Software Based on the Predictive Accuracy

Imai, Susumu; Yamada, Takehiro; Ishiguro, Nobuhisa; Miyamoto, Terumasa; Kagami, Keisuke; Tomiyama, Naoki; Niinuma, Yusuke; Nagasaki, Daisuke; Suzuki, Kôji; Yamagami, Akira; Kasashi, Kumiko; Kobayashi, Masaki; Iseki, Ken

doi:10.1248/yakushi.17-00080

Cited by 9 publications

(18 citation statements)

References 8 publications

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“…Model Evaluation Using the testing group, we evaluated the rates of attaining the therapeutic range (VCM trough value = 10-15 and 10-20 mg/L), recommended daily dose, and predicted trough value of the DT algorithm and three conventional dose-setting methods, which included two TDM analysis software programs incorporating Japanese PPK parameters [5][6][7] and one nomogram: (i) SHIONOGI-VCM-TDM ver. 2009 (VCM-TDM), (ii) vancomycin MEEK TDM analysis software ver.…”

Section: = ×mentioning

confidence: 99%

“…Generally, dose settings of drugs such as antibiotics are made on the basis of population pharmacokinetics (PPK) parameters. 5,6) For example, in Japan, the initial dose settings of vancomycin (VCM), an anti-methicillin-resistant Staphylococcus aureus (anti-MRSA) agent, are determined using therapeutic drug monitoring (TDM) analysis software that incorporates Japanese PPK parameters. [5][6][7] However, there are no reports of applying ML to drug dose setting, although it is possible that ML can help achieve accurate drug dose settings.…”

Section: Introductionmentioning

confidence: 99%

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A New Algorithm Optimized for Initial Dose Settings of Vancomycin Using Machine Learning

Imai

Takekuma

Miyai

et al. 2020

Biological & Pharmaceutical Bulletin

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This study aimed to construct an optimal algorithm for initial dose settings of vancomycin (VCM) using machine learning (ML) with decision tree (DT) analysis. Patients who were administered intravenous VCM and underwent therapeutic drug monitoring (TDM) at the Hokkaido University Hospital were enrolled. The study period was November 2011 to March 2019. In total, 654 patients were included in the study. Patients were divided into two groups, training (patients who received VCM from November 2011 to December 2017; n 496) and testing (patients who received VCM from January 2018 to March 2019; n 158) groups. For the training group, DT analysis of the classification and regression tree algorithm was performed to construct an algorithm (called DT algorithm) for the initial dose settings of VCM. For the testing group, the rates of attaining the VCM therapeutic range (trough value 10-15 and 10-20 mg/L) with the DT algorithm and three conventional dose-setting methods were compared for model evaluation. The DT algorithm was constructed to be used for patients with estimated glomerular filtration rate ≥50 mL/min and body weight ≥40 kg. As a result, the recommended daily doses ranged from 20.0 to 58.1 mg/kg. In model evaluation, the DT algorithm obtained the highest rates of attaining the VCM therapeutic range compared to conventional dose-setting methods. Therefore, our DT algorithm can be applied to clinical practice. In addition, ML is useful for setting drug doses.

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confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Algorithm Optimized for Initial Dose Settings of Vancomycin Using Machine Learning

Imai

Takekuma

Miyai

et al. 2020

Biological & Pharmaceutical Bulletin

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“…Duration of therapy ≥15 days was selected as the second splitting variable. To our knowledge, there are no reports of long‐term administration of GCV as a predictor of neutropaenia, possibly because of its associated side effects with oral valganciclovir and other drugs . Occurrence of HSCTs was selected as the third splitting variable.…”

Section: Discussionmentioning

confidence: 99%

“…We used the Cockcroft‐Gault equation to calculate CCr . Among the concomitant medications, vasopressor drugs were as follows: etilefrine, norepinephrine, olprinone, milrinone, dopamine and dobutamine …”

Section: Methodsmentioning

confidence: 99%

“…The first splitting variable, baseline ANC <3854 cells/mm 3 , was almost consistent with a previous report.Duration of therapy ≥15 days was selected as the second splitting variable. To our knowledge, there are no reports of long-term administration of GCV as a predictor of neutropaenia, possibly because of its associated side effects with oral valganciclovir and other drugs 16,22,23. Occurrence of HSCTs was selected as the third splitting TA B L E 2 Univariate and multivariable analyses of risk factors for neutropaenia…”

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Construction of a flow chart–like risk prediction model of ganciclovir‐induced neutropaenia including severity grade: A data mining approach using decision tree

et al. 2019

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What is known and objective Haematological toxicities such as neutropaenia are a common side effect of ganciclovir (GCV); however, risk factors for GCV‐induced neutropaenia have not been well established. Decision tree (DT) analysis is a typical technique of data mining consisting of a flow chart–like framework that shows various outcomes from a series of decisions. By following the flow chart, users can estimate combinations of risk factors that may increase the probability of certain events. In our previous study, we demonstrated the usefulness of this approach in the evaluation of adverse drug reactions. Therefore, we aimed to construct a risk prediction model of GCV‐induced neutropaenia including severity grade. Methods We performed a retrospective study at the Hokkaido University Hospital and enrolled patients who received GCV between April 2008 and March 2018. Neutropaenia was defined as an absolute neutrophil count (ANC) <1500 cells/mm3 and a decrease to <75% relative to baseline. We classified the patients who developed neutropaenia in three groups (Grades 2‐4) based on the National Cancer Institute‐Common Terminology Criteria for Adverse Events. Data collection was achieved through the retrieval of medical records. We employed a chi‐squared automatic interaction detection algorithm to construct the DT model and compared the accuracies to the logistic regression model (a conventional statistical method) to evaluate the established model. Results and discussion In total, 396 adult patients were included in the study; 61 (15.4%) developed neutropaenia. Three predictive factors (hematopoietic stem cell transplantation, baseline ANC <3854 cells/mm3 and duration of therapy ≥15 days) were extracted using the DT analysis to produce five subgroups, the incidence of neutropaenia ranged between 1.7% and 52.8%. In each subgroup, patients who developed neutropaenia were categorized based on the severity. The accuracies of each model were the same (84.6%), which indicated precision. What is new and conclusion We successfully built a risk prediction model of GCV‐induced neutropaenia including severity grade. This model is expected to assist decision‐making in the clinical setting.

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Prediction of vancomycin dose on high-dimensional data using machine learning techniques

Huang

Yu²,

Wei

et al. 2021

Expert Review of Clinical Pharmacology

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Objectives: Despite therapeutic vancomycin is regularly monitored, its dose requirements vary considerably between individuals. Various innovative vancomycin dosing strategies have been developed for dose optimization; however, the utilization of individual factors and extensibility is insufficient. We aimed to develop an optimal dosing algorithm for vancomycin based on the high-dimensional data using the proposed variable engineering and machine-learning methods. Methods: This study proposed a variable engineering process that automatically generates secondorder variable interactions. We performed an initial examination of independent variables and interactive variables using eXtreme Gradient Boosting. The vancomycin dose prediction model was established based on the derived variables. Results: Based on the evaluation of the model performance in the validation cohort, our algorithm accounted for 67.5% of variations in the vancomycin doses. Subgroup analysis showed better performance in patients with medium and high body weight (with the ideal predictive percentage of 72.7% and 73.7%), and low and medium levels of serum creatinine (with the ideal predictive percentage of 77.8% and 73.1%) than in other groups. Conclusion:The new vancomycin dose prediction model is potentially useful for patients whose population profiles are similar to those of our patients and yielded desired reference of clinical indicators with specific breakpoints.

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Validating the Effectiveness of Switching the Vancomycin TDM Analysis Software Based on the Predictive Accuracy

Cited by 9 publications

References 8 publications

A New Algorithm Optimized for Initial Dose Settings of Vancomycin Using Machine Learning

A New Algorithm Optimized for Initial Dose Settings of Vancomycin Using Machine Learning

Construction of a flow chart–like risk prediction model of ganciclovir‐induced neutropaenia including severity grade: A data mining approach using decision tree

Prediction of vancomycin dose on high-dimensional data using machine learning techniques

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