K-ras mutation detected by peptide nucleic acid-clamping polymerase chain reaction, Ki-67, S100P, and SMAD4 expression can improve the diagnostic accuracy of inconclusive pancreatic EUS-FNB specimens.

OBJECTIVES: We aimed to assess the diagnostic utility of an immunohistochemical panel including calcium-binding protein P, p53, Ki-67, and SMAD family member 4 and K-ras mutation for diagnosing pancreatic solid lesion specimens obtained by endoscopic ultrasound-guided fine-needle biopsy and to confirm their usefulness in histologically inconclusive cases. METHODS: Immunohistochemistry and peptide nucleic acid-clamping polymerase chain reaction for K-ras mutation were performed on 96 endoscopic ultrasound-guided fine-needle biopsy specimens. The diagnostic efficacy of each marker and the combination of markers was calculated. The diagnostic performances of these markers were evaluated in 27 endoscopic ultrasound-guided fine-needle biopsy specimens with histologically inconclusive diagnoses. A classification tree was constructed. RESULTS: K-ras mutation showed the highest accuracy and consistency. Positivity in more than two or three of the five markers showed high diagnostic accuracy (94.6 % and 93.6 %, respectively), and positivity for more than three markers showed the highest accuracy for inconclusive cases (92.0 %). A classification tree using K-ras mutation, Ki-67, S100P, and SMAD4 showed high diagnostic performance, with only two misclassifications in inconclusive cases. CONCLUSIONS: K-ras mutation detection via peptide nucleic acid-clamping polymerase chain reaction is a stable and accurate method for distinguishing between pancreatic ductal adenocarcinoma and non-pancreatic ductal adenocarcinoma lesions. A classification tree using K-ras mutation, Ki-67, S100P, and SMAD4 helps increase the diagnostic accuracy of cases that are histologically difficult to diagnose.

Beyond One-Size-Fits-All: Tailoring Teicoplanin Regimens for Normal Renal Function Patients Using Population Pharmacokinetics and Monte Carlo Simulation.

In patients with normal renal function, significant teicoplanin dose adjustments are often necessary. This study aimed to develop a population pharmacokinetic (PK) model for teicoplanin in healthy adults and use it to recommend optimal dosage regimens for patients with normal renal function. PK samples were obtained from 12 subjects and analyzed using a population approach. The derived parameters informed Monte Carlo simulations for dosing recommendations. The PK profile was best described using a three-compartment model, in which the estimated glomerular filtration rate calculated via the CKD-EPI equation and adjusted for body surface area was identified as a significant covariate affecting total clearance. For pathogens with a minimum inhibitory concentration of 1 mg/L, a loading dose (LD) of 14 mg/kg administered every 12 h for four doses, followed by a maintenance dose (MD) of 16 mg/kg administered every 24 h, is recommended. These findings indicate the need for dosage adjustments, such as increasing the LD and MD or decreasing the dosing interval of MD in patients with normal renal function. Because of the long half-life of teicoplanin and the requirement for long-term administration, therapeutic drug monitoring at strategic intervals is important to avoid nephrotoxicity associated with elevated trough concentrations.

Intrapulmonary and Systemic Pharmacokinetics of Colistin Following Nebulization of Low-Dose Colistimethate Sodium in Patients with Ventilator-Associated Pneumonia Caused by Carbapenem-Resistant Acinetobacter baumannii.

Colistimethate sodium (CMS) nebulization is associated with reduced systemic toxicity compared to intravenous injection, with potentially enhanced clinical efficacy. This study aimed to assess the pharmacokinetic (PK) properties of colistin during low-dose CMS nebulization in patients with ventilator-associated pneumonia (VAP) caused by carbapenem-resistant Acinetobacter baumannii. A nonlinear mixed-effects modeling approach was applied to develop population PK models for colistin in both epithelial lining fluid (ELF) and plasma. Twenty patients participated, and 80 ELF and 100 plasma samples were used for model development. Median colistin concentrations measured in ELF were 614-fold, 408-fold, and 250-fold higher than in plasma at 1, 3, and 5 h, respectively. Time courses in both ELF and plasma were best described by a one-compartment model with a Weibull absorption process. When the final model was simulated, the maximum free concentration and area under the free colistin concentration-time curve at steady state over 24 h in the plasma were approximately 1/90 and 1/50 of the corresponding values in ELF at steady state, respectively. For an A. baumannii MIC of 1 mg/L, inhaling 75 mg of CMS at 6 h intervals was deemed appropriate, with dose adjustments needed for MICs exceeding 2 mg/L. Using a nebulizer for CMS resulted in a notably higher exposure of colistin in the ELF than plasma, indicating the potential of nebulization to reduce systemic toxicity while effectively treating VAP.

Endoplasmic reticular stress as an emerging therapeutic target for chronic pain: a narrative review.

Chronic pain is a severely debilitating condition with enormous socioeconomic costs. Current treatment regimens with nonsteroidal anti-inflammatory drugs (NSAIDs), steroids, or opioids have been largely unsatisfactory with uncertain benefits or severe long-term side effects. This is mainly because chronic pain has a multifactorial aetiology. Although conventional pain medications can alleviate pain by keeping several dysfunctional pathways under control, they can mask other underlying pathological causes, ultimately worsening nerve pathologies and pain outcome. Recent preclinical studies have shown that endoplasmic reticulum (ER) stress could be a central hub for triggering multiple molecular cascades involved in the development of chronic pain. Several ER stress inhibitors and unfolded protein response modulators, which have been tested in randomised clinical trials or apprpoved by the US Food and Drug Administration for other chronic diseases, significantly alleviated hyperalgesia in multiple preclinical pain models. Although the role of ER stress in neurodegenerative disorders, metabolic disorders, and cancer has been well established, research on ER stress and chronic pain is still in its infancy. Here, we critically analyse preclinical studies and explore how ER stress can mechanistically act as a central node to drive development and progression of chronic pain. We also discuss therapeutic prospects, benefits, and pitfalls of using ER stress inhibitors and unfolded protein response modulators for managing intractable chronic pain. In the future, targeting ER stress to impact multiple molecular networks might be an attractive therapeutic strategy against chronic pain refractory to steroids, NSAIDs, or opioids. This novel therapeutic strategy could provide solutions for the opioid crisis and public health challenge.

Is the Availability of Biosimilar Adalimumab Associated with Budget Savings? A Difference-in-Difference Analysis of 14 Countries.

OBJECTIVE: The aim was to assess the influence of the presence of biosimilar adalimumab on adalimumab budget savings in 14 high- and upper-middle-income countries. METHODS: This study analyzed Multinational Integrated Data Analysis System (MIDAS)-IQVIA data from the fourth quarter (Q4) of 2018 to the Q4 of 2019, comparing adalimumab expenditure (in United States dollars) and consumption (in standard units [SU]) across 14 countries (Australia, Austria, Brazil, Canada, France, Germany, Italy, Japan, Korea, Singapore, South Africa, Spain, Sweden, and Taiwan). The countries were divided into two groups based on the availability of adalimumab biosimilars during the study period. A difference-in-difference design was employed to analyze the groups, focusing on changes from Q4 2018 to Q4 2019. Additionally, changes in adalimumab expenditure were decomposed into price, quantity, and drug mix during the study period. RESULTS: Among countries with adalimumab biosimilars, there was a significant decrease in expenditure (- $371.0 per gross domestic product per capita; p = 0.03) over four quarters, while the consumption significantly increased (1.0 SU per 1000 population; p = 0.02). This was consistent with visual observations and differed from countries without adalimumab biosimilar. Sensitivity analysis with a narrowed list of countries (12 high-income countries) showed a consistent trend. Adalimumab expenditure decreased by 14% during the study period in countries where adalimumab biosimilars were available, mainly due to the price changes (Pt = 0.85; - 15%) and the drug-mix effect (εt  = 0.88; - 12%). Yet, adalimumab expenditure (Et = 1.04; +4%) changed in a quantity-dependent manner (Qt = 1.06; +6%) in countries where adalimumab biosimilars were absent. CONCLUSION: The availability of biosimilars was associated with a decrease in adalimumab expenditure without compromising the consumption of adalimumab.

Bias reduction for semi-competing risks frailty model with rare events: application to a chronic kidney disease cohort study in South Korea.

In a semi-competing risks model in which a terminal event censors a non-terminal event but not vice versa, the conventional method can predict clinical outcomes by maximizing likelihood estimation. However, this method can produce unreliable or biased estimators when the number of events in the datasets is small. Specifically, parameter estimates may converge to infinity, or their standard errors can be very large. Moreover, terminal and non-terminal event times may be correlated, which can account for the frailty term. Here, we adapt the penalized likelihood with Firth's correction method for gamma frailty models with semi-competing risks data to reduce the bias caused by rare events. The proposed method is evaluated in terms of relative bias, mean squared error, standard error, and standard deviation compared to the conventional methods through simulation studies. The results of the proposed method are stable and robust even when data contain only a few events with the misspecification of the baseline hazard function. We also illustrate a real example with a multi-centre, patient-based cohort study to identify risk factors for chronic kidney disease progression or adverse clinical outcomes. This study will provide a better understanding of semi-competing risk data in which the number of specific diseases or events of interest is rare.

The Natural Ficus carica L. (fig) Extract as an Effective Prophylactic Antibacterial Agent for Inflammation-Related Infections.

Klebsiella pneumoniae (K. pneumoniae) is a multidrug-resistance Gram-negative organism responsible for carbapenem-resistant infections. These challenges have inspired studies on the use of natural products as alternatives to conventional drugs. The aim of this study was to analyze the antibacterial and antioxidant effects of Ficus carica L. (fig) branch extracts and to perform in vivo animal experiments to better understand the absorption mechanisms of the antibacterial components during the digestion process after oral administration. The antibacterial components of the fig branch extracts were analyzed via gas chromatography-mass spectrometry (GC-MS). An in vivo animal study and liquid chromatography-triple quadrupole-tandem mass spectrometry (LC-QQQ-MS/MS) analyses were performed to analyze the deacetylation reactions of the fig extracts after oral administration in mice. Ultimately, the antibacterial effects of the fig extracts increased with the fractional distillation time. The fig extracts showed excellent antibacterial effects against K. pneumoniae, as well as Escherichia coli (E. coli), Staphylococcus aureus (S. aureus), and Pseudomonas aeruginosa (P. aeruginosa). The three antibacterial and antioxidant components of the fig extracts were revealed to be eugenol, acetyleugenol, and psoralen. Interestingly, in this study, we identified acetyleugenol in the phenolic compounds of the fig extract for the first time. Through in vivo animal testing, we observed the deacetylation reaction of acetyleugenol to eugenol in the fig extract as digestion proceeded in the internal organs of the mice after oral administration. The results of this study suggest the use of natural fig extract as an effective therapeutic and prophylactic antibacterial agent for inflammation-related infections with a wide variety of biomedical applications.

Effect of subscapularis repair on joint contact forces based on degree of posterior-superior rotator cuff tear severity in reverse shoulder arthroplasty.

Massive irreparable rotator cuff tears (RCTs) affect the clinical outcomes of reverse shoulder arthroplasty (RSA). However, the effects of subscapularis repair on the outcomes of RSA, based on the degree of posterior-superior RCTs, are unclear. This study aimed to examine the effect of subscapularis repair on three-dimensional joint contact forces (JCFs) based on the degree of posterior-superior RCT severity in RSA. Ten human in vivo experimental data were used as input to the musculoskeletal model. A six-degrees-of-freedom (DOF) anatomical shoulder model was developed and validated against three-dimensional JCFs. The 6-DOF musculoskeletal shoulder model of RSA was then developed by importing the reverse shoulder implant into the validated anatomical shoulder model. Based on the various types of posterior-superior RCT severity, inverse dynamic simulations of subscapularis-torn and subscapularis-repaired models of RSA were performed: from isolated supraspinatus tears to partial or massive tears of the infraspinatus and teres minor. The intact rotator cuff model of RSA was also simulated for comparison with the different types of models. Our results showed that the more posterior-superior RCTs progressed in RSA, the more superior JCFs were observed at 90°, 105°, and 120° abduction in the subscapularis-torn model. However, subscapularis repair decreased the superior JCF at those angles sufficiently. In addition, the teres minor muscle-tendon force increased as infraspinatus bundle tears progressed in both the subscapularis-torn and -repaired models, in order to compensate for the reduced force during abduction. However, the teres minor muscle-tendon force was not as high as that of the infraspinatus muscle-tendon, which could result in muscle force imbalance between repaired subscapularis and teres minor. Therefore, our results suggest that repairing the subscapularis and the repairable infraspinatus during RSA can improve glenohumeral joint stability in the superior-inferior direction by restoring muscle force balance between the anterior cuff (i.e., subscapularis) and posterior cuff (i.e., infraspinatus and teres minor). The findings of this study can help clinician decide whether to repair the rotator cuff during RSA to enhance joint stability.

Comparing predictions among competing risks models with rare events: application to KNOW-CKD atudy-a multicentre cohort study of chronic kidney disease.

A prognostic model to determine an association between survival outcomes and clinical risk factors, such as the Cox model, has been developed over the past decades in the medical field. Although the data size containing subjects' information gradually increases, the number of events is often relatively low as medical technology develops. Accordingly, poor discrimination and low predicted ability may occur between low- and high-risk groups. The main goal of this study was to evaluate the predicted probabilities with three existing competing risks models in variation with censoring rates. Three methods were illustrated and compared in a longitudinal study of a nationwide prospective cohort of patients with chronic kidney disease in Korea. The prediction accuracy and discrimination ability of the three methods were compared in terms of the Concordance index (C-index), Integrated Brier Score (IBS), and Calibration slope. In addition, we find that these methods have different performances when the effects are linear or nonlinear under various censoring rates.

Femoral Tunnel Geometry and Graft Inclination Angles in Anterior Cruciate Ligament Reconstruction Using a Flexible Reamer System.

Background and Objectives: The aim of this study is to investigate the femoral tunnel geometry (femoral tunsnel location, femoral graft bending angle, and femoral tunnel length) on three-dimensional (3D) computed tomography (CT) and graft inclination on magnetic resonance imaging (MRI) after anatomic anterior cruciate ligament (ACL) reconstruction using a flexible reamer system. Materials and Methods: A total of 60 patients who underwent anatomical ACL reconstruction (ACLR) using a flexible reamer system were retrospectively reviewed. One day after the ACLR procedure was performed, all patients underwent three-dimensional computed tomography (3D-CT) and magnetic resonance imaging (MRI). The femoral tunnel location, femoral graft bending angle, femoral tunnel length, and graft inclination were assessed. Results: In the 3D-CTs, the femoral tunnel was located at 29.7 ± 4.4% in the posterior to anterior (deep to shallow) direction and at 24.1 ± 5.9% in the proximal to distal (high to low) direction. The mean femoral graft bending angle was 113.9 ± 5.7°, and the mean femoral tunnel length was 35.2 ± 3.1 mm. Posterior wall breakage was observed in five patients (8.3%). In the MRIs, the mean coronal graft inclination was 69.2 ± 4.7°, and the mean sagittal graft inclination was 52.4 ± 4.6°. The results of this study demonstrated that a comparable femoral graft bending angle and longer femoral tunnel length were observed compared with the reported outcomes from previous studies that used the rigid reamer system. Conclusions: ACLR using a flexible reamer system allowed for an anatomic femoral tunnel location and a comparable graft inclination to that of the native ACL. In addition, it achieved a tolerable femoral graft bending angle and femoral tunnel length.

The Growth Characteristics and the Active Compounds of Cudrania tricuspidata Fruits in Different Cultivation Environments in South Korea.

Cudrania tricuspidata is a traditional medicinal herb in East Asia. The compounds of plants vary depending on environmental factors, such as soil, temperature, drainage, and so on. However, few to no studies have been done on the correlation among environment, growth, and compounds in C. tricuspidata. Thus, we aimed to investigate their relationship. Samples of C. tricuspidata fruit and cultivation soil were collected from 28 cultivation sites in October 2021. Six growth characteristics, eleven soil physicochemical properties, seven meteorological data points, and three active compounds were investigated in this study. We developed and validated an optimized method for quantifying active compounds using UPLC and performed correlation analysis of the environment, growth characteristics, and active compounds. The UPLC-UV method for determining active compounds was validated by measuring the linearity, LOD, LOQ, precision, and accuracy using UPLC. The LOD and LOQ were 0.01-0.03 µg/mL and 0.04-0.09 µg/mL, respectively. The precision was acceptable with RSD% values less than 2%. The recoveries ranged from 97.25 to 104.98% with RSD values <2%, within the acceptable limits. The active compounds were negatively correlated with the size of the fruit, and the growth characteristics were negatively correlated with some environmental factors. The results of this study can be used as basic data for the standard cultural practices and quality control of C. tricuspidata fruits.

Revisiting the analysis pipeline for overdispersed Poisson and binomial data.

Overdispersion is a common feature in categorical data analysis and several methods have been developed for detecting and handling it in generalized linear models. The first aim of this study is to clarify the relationships among various score statistics for testing overdispersion and to compare their performances. In addition, we investigate a principled way to correct finite sample bias in the score statistic caused by estimating regression parameters with restricted likelihood. The second aim is to reconsider the current practice for handling overdispersed categorical data. Although the conventional models are based on substantially different mechanisms for generating overdispersion, model selection in practice has not been well studied. We perform an intensive numerical study for determining which method is more robust to various overdispersion mechanisms. In addition, we provide some graphical tools for identifying the better model. The last aim is to reconsider the key assumption for deriving the score statistics. We study the meaning of testing overdispersion when this assumption is violated, and we analytically show the conditions for which it is not appropriate to employ the current statistical practices for analyzing overdispersed data.

Population Pharmacokinetics and Monte Carlo Simulation of Cefepime in Critically Ill Patients with Hospital-Acquired/Ventilator-Associated Pneumonia.

BACKGROUND: This study aimed to investigate the population pharmacokinetics (PK) profile and determine the optimal dosage regimen of cefepime in critically ill adult patients with hospital-acquired pneumonia (HAP) or ventilator-associated pneumonia (VAP). MATERIALS AND METHODS: Population-PK models for cefepime were developed using a nonlinear mixed-effect modeling approach. The percentage of time within 24 h in which the free concentration exceeded the minimum inhibitory concentration (MIC) at a steady state (50%fT>MIC, 100%fT>MIC, and 100%fT>4×MIC) for various combinations of dosage regimens and renal function was explored using Monte Carlo simulation. RESULTS: Twenty-one patients were prospectively enrolled in this study. Cefepime PK was best described using a two-compartment model in which creatinine clearance (CLCR) through Cockcroft-Gault (CG) was a significant covariate for the total clearance of cefepime. The simulation results to determine the optimal cefepime dosing regimen for 50%fT>MIC as treatment target with Cmin <20 mg/L as safety target showed that a dosage regimen of 2 g through intravenous (IV) infusion every 12 h administered over 4 h was optimal at an MIC of 4 mg/L, rather than the currently recommended dosage regimen of 2 g administered through IV infusion every 8 h, in patients with normal renal function (CLCR = 90 - 130 mL/min). For a treatment target of 100%fT>MIC with Cmin <35 mg/L as a safety target, a dosage regimen of 0.75 g administered through continuous infusion over 24 h would be sufficient at an MIC equal to or less than 8 mg/L in patients with renal dysfunction (CLCR = 10 - 30 mL/min). CONCLUSION: Our results suggest that clinicians should consider renal function and potential neurotoxicity when deciding the dosing regimen of cefepime in critically ill patients with HAP or VAP. Therapeutic drug monitoring (TDM) to adjust cefepime trough levels may be useful to improve clinical outcomes and reduce cefepime neurotoxicity.

Evaluation of the color stability of 3D printed resin according to the oxygen inhibition effect and temperature difference in the post-polymerization process.

The aim of this study was to determine the color stability of 3D printed resin according to the post-curing conditions (polymerization conditions and temperature). Specimens were post-polymerized under different conditions of oxygen inhibition, such as on glycerin immersion (GLY), medium-low vacuum environment (VA), and oxygen contact (CON, the control group), and temperature (35 °C, 60 °C, and 80 °C). The degree of conversion (DC), water sorption (Wsp) and solubility (Wsl), surface roughness (Ra) were measured. Additionally, surface free energy (SFE), pH values of colorants were measured. Grape juice (grape), coffee, and curry were used as the colorants, and distilled water (DW) was used as a control. And the color value was measured before and after immersion using a spectrophotometer. Then, Calculated the color change. For statistical methods, The Shapiro-Wilk test performed to check for normality revealed that the data presented a normal distribution (p>0.05). ΔE values were analyzed using three-way ANOVA. DC, Wsp, Wsl, SFE, and Ra were analyzed using two-way ANOVA. To confirm the linear correlation, Pearson's correlation coefficient was determined. The threshold for significance (p) was set at 0.05 (95% confidence interval) for all tests. DC was the highest at 80 °C in the GLY group (95.08 ± 4.88%). And Wsl decreased with increasing temperature, and was lowest at 80 °C in the GLY group (0.46 ± 0.30 um/mm3). After the colorants were immersed for 30 days, as the temperature increased, ΔE decreased in the GLY group but not in the VA and CON groups, and was the lowest at 80 °C in the GLY group: (DW, 0.95 ± 0.45 [mean± SD]; grape, 6.45± 0.69; coffee, 4.50± 0.56; curry, 9.37± 1.40). There was also a significant inverse relation between DC and ΔE. A significant inverse relation was found between Wsl and DC, and a significant positive correlation was found between Wsl and ΔE. Wsp, SFE, and Ra did not affect color stability. In the post-polymerization process, increasing the temperature and GLY were effective in reducing ΔE, which was lowest at 80 °C in the GLY group. It was also observed that a complex mechanism between the DC, Wsl of 3D printed resin affects ΔE of the resin.

Overall assessment for selected markers from high-throughput data.

Reproducibility, a hallmark of science, is typically assessed in validation studies. We focus on high-throughput studies where a large number of biomarkers is measured in a training study, but only a subset of the most significant findings is selected and re-tested in a validation study. Our aim is to get the statistical measures of overall assessment for the selected markers, by integrating the information in both the training and validation studies. Naive statistical measures, such as the combined P $$ P $$ -value by conventional meta-analysis, that ignore the non-random selection are clearly biased, producing over-optimistic significance. We use the false-discovery rate (FDR) concept to develop a selection-adjusted FDR (sFDR) as an overall assessment measure. We describe the link between the overall assessment and other concepts such as replicability and meta-analysis. Some simulation studies and two real metabolomic datasets are considered to illustrate the application of sFDR in high-throughput data analyses.

Multiple-Kernel Support Vector Machine for Predicting Internet Gaming Disorder Using Multimodal Fusion of PET, EEG, and Clinical Features.

Internet gaming disorder (IGD) has become an important social and psychiatric issue in recent years. To prevent IGD and provide the appropriate intervention, an accurate prediction method for identifying IGD is necessary. In this study, we investigated machine learning methods of multimodal neuroimaging data including Positron Emission Tomography (PET), Electroencephalography (EEG), and clinical features to enhance prediction accuracy. Unlike the conventional methods which usually concatenate all features into one feature vector, we adopted a multiple-kernel support vector machine (MK-SVM) to classify IGD. We compared the prediction performance of standard machine learning methods such as SVM, random forest, and boosting with the proposed method in patients with IGD (N = 28) and healthy controls (N = 24). We showed that the prediction accuracy of the optimal MK-SVM using three kinds of modalities was much higher than other conventional machine learning methods, with the highest accuracy being 86.5%, the sensitivity 89.3%, and the specificity 83.3%. Furthermore, we deduced that clinical variables had the highest contribution to the optimal IGD prediction model and that the other two modalities were also indispensable. We found that more efficient integration of multimodal data through kernel combination could contribute to better performance of the prediction model. This study is a novel attempt to integrate each method from different sources and suggests that integrating each method, such as self-administrated reports, PET, and EEG, improves the prediction of IGD.

Predicting Antibiotic Effect of Vancomycin Using Pharmacokinetic/Pharmacodynamic Modeling and Simulation: Dense Sampling versus Sparse Sampling.

This study aimed to investigate the effect of a structural pharmacokinetic (PK) model with fewer compartments developed following sparse sampling on the PK parameter estimation and the probability of target attainment (PTA) prediction of vancomycin. Two- and three-compartment PK models of vancomycin were used for the virtual concentration-time profile simulation. Datasets with reduced blood sampling times were generated to support a model with a lesser number of compartments. Monte Carlo simulation was conducted to evaluate the PTA. For the two-compartment PK profile, the total clearance (CL) of the reduced one-compartment model showed a relative bias (RBias) and relative root mean square error (RRMSE) over 90%. For the three-compartment PK profile, the CL of the reduced one-compartment model represented the largest RBias and RRMSE, while the steady-state volume of distribution of the reduced two-compartment model represented the largest absolute RBias and RRMSE. A lesser number of compartments corresponded to a lower predicted area under the concentration-time curve of vancomycin. The estimated PK parameters and predicted PK/PD index from models built with sparse sampling designs that cannot support the PK profile can be significantly inaccurate and unprecise. This might lead to the misprediction of the PTA and selection of improper dosage regimens when clinicians prescribe antibiotics.

Application of Machine Learning Classification to Improve the Performance of Vancomycin Therapeutic Drug Monitoring.

Bayesian therapeutic drug monitoring (TDM) software uses a reported pharmacokinetic (PK) model as prior information. Since its estimation is based on the Bayesian method, the estimation performance of TDM software can be improved using a PK model with characteristics similar to those of a patient. Therefore, we aimed to develop a classifier using machine learning (ML) to select a more suitable vancomycin PK model for TDM in a patient. In our study, nine vancomycin PK studies were selected, and a classifier was created to choose suitable models among them for patients. The classifier was trained using 900,000 virtual patients, and its performance was evaluated using 9000 and 4000 virtual patients for internal and external validation, respectively. The accuracy of the classifier ranged from 20.8% to 71.6% in the simulation scenarios. TDM using the ML classifier showed stable results compared with that using single models without the ML classifier. Based on these results, we have discussed further development of TDM using ML. In conclusion, we developed and evaluated a new method for selecting a PK model for TDM using ML. With more information, such as on additional PK model reporting and ML model improvement, this method can be further enhanced.