Precision Non-Alcoholic Fatty Liver Disease (NAFLD) Diagnosis: Leveraging Ensemble Machine Learning and Gender Insights for Cost-Effective Detection.

Non-Alcoholic Fatty Liver Disease (NAFLD) is characterized by the accumulation of excess fat in the liver. If left undiagnosed and untreated during the early stages, NAFLD can progress to more severe conditions such as inflammation, liver fibrosis, cirrhosis, and even liver failure. In this study, machine learning techniques were employed to predict NAFLD using affordable and accessible laboratory test data, while the conventional technique hepatic steatosis index (HSI)was calculated for comparison. Six algorithms (random forest, K-nearest Neighbors, Logistic Regression, Support Vector Machine, extreme gradient boosting, decision tree), along with an ensemble model, were utilized for dataset analysis. The objective was to develop a cost-effective tool for enabling early diagnosis, leading to better management of the condition. The issue of imbalanced data was addressed using the Synthetic Minority Oversampling Technique Edited Nearest Neighbors (SMOTEENN). Various evaluation metrics including the F1 score, precision, accuracy, recall, confusion matrix, the mean absolute error (MAE), receiver operating characteristics (ROC), and area under the curve (AUC) were employed to assess the suitability of each technique for disease prediction. Experimental results using the National Health and Nutrition Examination Survey (NHANES) dataset demonstrated that the ensemble model achieved the highest accuracy (0.99) and AUC (1.00) compared to the machine learning techniques that we used and HSI. These findings indicate that the ensemble model holds potential as a beneficial tool for healthcare professionals to predict NAFLD, leveraging accessible and cost-effective laboratory test data.

Determination of speed and assessment of conditioning in horses submitted to a lactate minimum test-alternative approaches.

The maximal lactate steady state (MLSS) is a well-known gold standard method for determining the aerobic capacity of athletic horses. Owing to its high cost and complex execution, there is a search for standardized exercise tests that can predict this value in a single session. One of the methods described for this purpose is the lactate minimum test (LMT), which could be more accurate despite being adequate to predict MLSS. This study aimed to examine the impact of training on the speed corresponding to lactate minimum speed (LMS) and to apply new mathematical methods to evaluate the fitness level of horses based on the curve obtained by the LMT. Ten Arabian horses underwent a 6-week training program based on LMS calculated by second-degree polynomial regression (LMSP). In addition, the LMS was also determined by visual inspection (LMSV), bi-segmented linear regression (LMSBI) and spline regression (LMSS). From the curve obtained during the LMT, it was possible to calculate angles α, ß and ω, as well as the total area under the curve (AUCTOTAL) before (AUCPRELMS) and after (AUCPOSLMS) the LMS. The methods for determining the LMS were evaluated by ANOVA, intraclass correlation coefficient (ICC) and effect size (ES) by Cohen's d test. The Pearson correlation coefficient (r) between the proposed LMS determination methods and other mathematical methods was also calculated. Despite showing a good correlation (ICC >0.7), the LMS determination methods differed from each other (p < 0.05), albeit without a significant difference resulting from conditioning. There were reductions in α:ß ratio, angle α, and AUCPOSTLMS, with the latter indicating lower lactate accumulation in the incremental phase of LMT after conditioning, in addition to an improvement in the animals' aerobic capacity. Considering that the most common methods for determining the LMS are applicable yet with low sensitivity for conditioning assessment, the approaches proposed herein can aid in analyzing the aerobic capacity of horses subjected to LMT. The mathematical models presented in this paper have the potential to be applied in human lactate-guided training program trials with a comparable study basis.

ISTH bleeding assessment tool and platelet function analyzer in children with mild inherited platelet function disorders.

OBJECTIVES: To evaluate the diagnostic performance of platelet function analyzer (PFA) and The International Society on Thrombosis and Hemostasis bleeding-assessment-tool (ISTH-BAT) in detecting mild inherited platelet function disorders (IPFDs) in children with suspected bleeding disorders. METHODS: Prospective single-center diagnostic study including consecutive patients <18 years with suspected bleeding disorder and performing a standardized workup for platelet function defects including ISTH-BAT, PFA, platelet aggregation testing, blood smear-based immunofluorescence, and next-generation sequencing-based genetic screening for IPFDs. RESULTS: We studied 97 patients, of which 34 von Willebrand disease (VWD, 22 type-1, 11 type-2), 29 IPFDs (including delta-/alpha-storage pool disease, Glanzmann thrombasthenia, Hermansky-Pudlak syndrome) and 34 with no diagnosis. In a model combining PFA-adenosine diphosphate (ADP), PFA-epinephrine (EPI), and ISTH-BAT overall performance to diagnose IPFDs was low with area under the curves of 0.56 (95% CI 0.44, 0.69) compared with 0.84 (95% CI 0.76, 0.92) for VWD. Correlation of PFA-EPI/-ADP and ISTH-BAT was low with 0.25/0.39 Spearman's correlation coefficients. PFA were significantly prolonged in patients with VWD and Glanzmann thrombasthenia. ISTH-BAT-scores were only positive in severe bleeding disorders, but not in children with mild IPFDs or VWD. CONCLUSION: Neither ISTH-BAT nor PFA or the combination of both help diagnosing mild IPFDs in children. PFA is suited to exclude severe IPFDs or VWD and is in this regard superior to ISTH-BAT in children.

A machine learning approach to predict daptomycin exposure from two concentrations based on Monte Carlo simulations.

Daptomycin is a concentration-dependent lipopeptide antibiotic for which exposure/effect relationships have been shown. Machine learning (ML) algorithms, developed to predict the individual exposure to drugs, have shown very good performances in comparison to maximum a posteriori Bayesian estimation (MAP-BE). The aim of this work was to predict the area under the blood concentration curve (AUC) of daptomycin from two samples and a few covariates using XGBoost ML algorithm trained on Monte Carlo simulations. Five thousand one hundred fifty patients were simulated from two literature population pharmacokinetics models. Data from the first model were split into a training set (75%) and a testing set (25%). Four ML algorithms were built to learn AUC based on daptomycin blood concentration samples at pre-dose and 1 h post-dose. The XGBoost model (best ML algorithm) with the lowest root mean square error (RMSE) in a 10-fold cross-validation experiment was evaluated in both the test set and the simulations from the second population pharmacokinetic model (validation). The ML model based on the two concentrations, the differences between these concentrations, and five other covariates (sex, weight, daptomycin dose, creatinine clearance, and body temperature) yielded very good AUC estimation in the test (relative bias/RMSE = 0.43/7.69%) and validation sets (relative bias/RMSE = 4.61/6.63%). The XGBoost ML model developed allowed accurate estimation of daptomycin AUC using C0, C1h, and a few covariates and could be used for exposure estimation and dose adjustment. This ML approach can facilitate the conduct of future therapeutic drug monitoring (TDM) studies.

Exploring various measures of the area under the curve for the assessment of dose-proportionality and estimation of bioavailability.

OBJECTIVE: In pharmacokinetics, the area under the concentration versus time curve (AUC) extrapolated to infinity (AUC0-∞) is the preferred metric but it is not always possible to have a reliable estimate of the terminal phase half-life. Here we sought to explore the accuracy of three different area measures to accurately identify dose proportionality and bioavailability. METHODS: One to three compartment model simulations with different doses for dose-proportionality or different rates and/or extents of bioavailability. Area measures evaluated were AUC0-∞, to the last quantifiable concentration (AUCtlast), and to a common time value (AUCt'). RESULTS: Under linear pharmacokinetics, AUCt' provided the most accurate measure of dose proportionality. Except for the one compartment model where AUC0-∞ provided the best predictor of the true measure, there was no clear advantage to the use of either of the three measures of AUC. CONCLUSION: With uncertainty about the terminal phase half-life, the use of AUCt' can be a very useful and even the preferred measure of exposure for use in assessing proportionality in exposure between doses. The choice of AUC measure in bioavailability is less clear and may depend on compartmental nature of the drug, and study parameters including assay sensitivity and sampling protocols.

ACR Appropriateness Criteria® Staging and Post-Therapy Assessment of Head and Neck Cancer.

Imaging of head and neck cancer at initial staging and as part of post-treatment surveillance is a key component of patient care as it guides treatment strategy and aids determination of prognosis. Head and neck cancer includes a heterogenous group of malignancies encompassing several anatomic sites and histologies, with squamous cell carcinoma the most common. Together this comprises the seventh most common cancer worldwide. At initial staging comprehensive imaging delineating the anatomic extent of the primary site, while also assessing the nodal involvement of the neck is necessary. The treatment of head and neck cancer often includes a combination of surgery, radiation, and chemotherapy. Post-treatment imaging is tailored for the evaluation of treatment response and early detection of local, locoregional, and distant recurrent tumor. Cross-sectional imaging with CT or MRI is recommended for the detailed anatomic delineation of the primary site. PET/CT provides complementary metabolic information and can map systemic involvement. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Practitioner Review: Clinical utility of the QbTest for the assessment and diagnosis of attention-deficit/hyperactivity disorder - a systematic review and meta-analysis.

BACKGROUND: Several computerised cognitive tests (e.g. continuous performance test) have been developed to support the clinical assessment of attention-deficit/hyperactivity disorder (ADHD). Here, we appraised the evidence-base underpinning the use of one of these tests - the QbTest - in clinical practice, by conducting a systematic review and meta-analysis investigating its accuracy and clinical utility. METHODS: Based on a preregistered protocol (CRD42022377671), we searched PubMed, Medline, Ovid Embase, APA PsycINFO and Web of Science on 15th August 2022, with no language/type of document restrictions. We included studies reporting accuracy measures (e.g. sensitivity, specificity, or Area under the Receiver Operating Characteristics Curve, AUC) for QbTest in discriminating between people with and without DSM/ICD ADHD diagnosis. Risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2). A generic inverse variance meta-analysis was conducted on AUC scores. Pooled sensitivity and specificity were calculated using a random-effects bivariate model in R. RESULTS: We included 15 studies (2,058 participants; 48.6% with ADHD). QbTest Total scores showed acceptable, rather than good, sensitivity (0.78 [95% confidence interval: 0.69; 0.85]) and specificity (0.70 [0.57; 0.81]), while subscales showed low-to-moderate sensitivity (ranging from 0.48 [0.35; 0.61] to 0.65 [0.52; 0.75]) and moderate-to-good specificity (from 0.65 [0.48; 0.78] to 0.83 [0.60; 0.94]). Pooled AUC scores suggested moderate-to-acceptable discriminative ability (Q-Total: 0.72 [0.57; 0.87]; Q-Activity: 0.67 [0.58; 0.77); Q-Inattention: 0.66 [0.59; 0.72]; Q-Impulsivity: 0.59 [0.53; 0.64]). CONCLUSIONS: When used on their own, QbTest scores available to clinicians are not sufficiently accurate in discriminating between ADHD and non-ADHD clinical cases. Therefore, the QbTest should not be used as stand-alone screening or diagnostic tool, or as a triage system for accepting individuals on the waiting-list for clinical services. However, when used as an adjunct to support a full clinical assessment, QbTest can produce efficiencies in the assessment pathway and reduce the time to diagnosis.

Development and Assessment of Vancomycin Dosing Utilizing AUC/MIC Protocol in Patients With End-Stage Renal Disease (ESRD) on Intermittent Hemodialysis (IHD).

Purpose: Vancomycin is commonly prescribed for the treatment of methicillin-resistant Staphylococcus aureus (MRSA) infections, including patients with end stage renal disease (ESRD) receiving intermittent hemodialysis (IHD). Infection is the second-leading cause of mortality in this patient population; therefore, optimizing vancomycin dosing is essential. New guidelines recommend using the ratio of area under the curve (AUC)/minimal inhibitory concentration (MIC) dosing with a target of 400-600 to maximize efficacy and minimize vancomycin nephrotoxicity. Summary: A retrospective chart review was performed to assess the current protocol for vancomycin dosing in ESRD patients on IHD at a community hospital in North Mississippi. A protocol was developed for dosing vancomycin utilizing AUC/MIC targets in this patient population. The study included all inpatient adults with ESRD receiving IHD at least 3 times weekly and receiving vancomycin. Data collection occurred in two phases. The first phase of data collection occurred before implementation of the new protocol and assessed the current vancomycin protocol effectiveness. In phase II of data collection, an assessment of the newly developed protocol utilizing similar data collected in phase I was conducted. Conclusions: It is thought that the difference in treatment outcomes and AUC/MIC targets is due to decreased immune function in this population. For this reason, we set our goal pre-dialysis level at 20-25 mg/dL, rather than 17-25 mg/dL, which correlates with an AUC/MIC of 480-600. It is important to quickly achieve therapeutic levels for the patients that do have MRSA to improve outcomes, to sustain these levels, and to reduce adverse events and costs.

Debris flow susceptibility assessment based on information value and machine learning coupling method: from the perspective of sustainable development.

Accurately assessing the susceptibility of debris flow disasters is of great significance for reducing the cost of disaster prevention and mitigation, as well as disaster losses. Machine learning (ML) models have been widely used in the susceptibility assessment of debris flow disasters. However, these models often have randomness in the selection of non-disaster data, which can lead to redundant information and poor applicability and accuracy of susceptibility evaluation results. To address this issue, this paper focuses on debris flow disasters in Yongji County, Jilin Province, China; optimizes the sampling method of non-disaster datasets in machine learning susceptibility assessment; and proposes a susceptibility prediction model that couples information value (IV) with artificial neural network (ANN) and logistic regression (LR) models. A debris flow disaster susceptibility distribution map with higher accuracy was drawn based on this model. The model's performance is evaluated using the area under the receiver operating characteristic curve (AUC), information gain ratio (IGR), and typical disaster point verification methods. The results show that the rainfall and topography were found to be decisive factors in the occurrence of debris flow disasters, and the IV-ANN model established in this study had the highest accuracy (AUC = 0.968). Compared to traditional machine learning models, the coupling model produced an increase in economic benefit of about 25% while reducing the average disaster prevention and control investment cost by about 8%. Based on model's susceptibility map, this paper proposes practical disaster prevention and control suggestions that promote sustainable development in the region, such as establishing monitoring systems and information platforms to aid disaster management.

Integrated assessment of groundwater potential zones and artificial recharge sites using GIS and Fuzzy-AHP: a case study in Peddavagu watershed, India.

Groundwater is a crucial natural resource for providing reliable and long-lasting water supplies across the globe. The integrated approach used in the current study involved the use of multiple techniques to assess groundwater potential zones (GWPZs) and identify suitable areas for artificial recharge sites. The methods used in the study were a combination of geographic information system (GIS), analytic hierarchy process (AHP), and fuzzy analytic hierarchy process (Fuzzy-AHP) to accomplish this goal. The study considered multiple thematic maps, such as drainage density, elevation, geomorphology, slope, curvature, topographic wetness index (TWI), geology, distance from the river, land use and land cover (LULC), and rainfall, to determine the GWPZs. AHP and Fuzzy-AHP were used to weight thematic maps based on their relative importance in controlling groundwater availability and recharge, and then a weighted overly analysis in a GIS environment was utilized to derive the final GWPZs map. After completing the weighting of thematic maps, both AHP and Fuzzy-AHP models categorized GWPZs into low, moderate, and high categories in the study area. In this study area, GWPZs were classified as poor, moderate, and high using both the AHP and Fuzzy-AHP models. According to the AHP model, 5.41% of the area's GWPZs were categorized as poor, 70.68% as moderate, and 23.91% as high. The Fuzzy-AHP model, on the other hand, categorized 4.92% as poor, 69.75% as moderate, and 25.33% as high. To validate these results, the receiver operating characteristic curve (ROC) and area under the curve (AUC) were used to explore the prediction accuracy, resulting in an accuracy rate of 70.1% for AHP and 71% for Fuzzy-AHP. These findings suggest that the Fuzzy-AHP model is effective in accurately identifying GWPZs in this area. Additionally, using remote sensing (RS) and GIS, the current study created a map by overlaying the lineament and drainage maps to determine suitable locations for artificial recharge. One-hundred-forty suitable locations for artificial recharge sites were identified based on Fuzzy-AHP. The study's reliable findings assist decision-makers and water users in the research area to use groundwater resources sustainably. This information aids in sustainable planning and management of groundwater resources, ensuring their availability and sustainability for future generations.

ACR Appropriateness Criteria® Headache: 2022 Update.

Headache is an ancient problem plaguing a large proportion of the population. At present, headache disorders rank third among the global causes of disability, accounting for over $78 billion per year in direct and indirect costs in the United States. Given the prevalence of headache and the wide range of possible etiologies, the goal of this document is to help clarify the most appropriate initial imaging guidelines for headache for eight clinical scenarios/variants, which range from acute onset, life-threatening etiologies to chronic benign scenarios. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Deep learning-based automatic left atrial appendage filling defects assessment on cardiac computed tomography for clinical and subclinical atrial fibrillation patients.

Rationale and objectives: Selecting region of interest (ROI) for left atrial appendage (LAA) filling defects assessment can be time consuming and prone to subjectivity. This study aimed to develop and validate a novel artificial intelligence (AI), deep learning (DL) based framework for automatic filling defects assessment on CT images for clinical and subclinical atrial fibrillation (AF) patients. Materials and methods: A total of 443,053 CT images were used for DL model development and testing. Images were analyzed by the AI framework and expert cardiologists/radiologists. The LAA segmentation performance was evaluated using Dice coefficient. The agreement between manual and automatic LAA ROI selections was evaluated using intraclass correlation coefficient (ICC) analysis. Receiver operating characteristic (ROC) curve analysis was used to assess filling defects based on the computed LAA to ascending aorta Hounsfield unit (HU) ratios. Results: A total of 210 patients (Group 1: subclinical AF, n = 105; Group 2: clinical AF with stroke, n = 35; Group 3: AF for catheter ablation, n = 70) were enrolled. The LAA volume segmentation achieved 0.931-0.945 Dice scores. The LAA ROI selection demonstrated excellent agreement (ICC ≥0.895, p < 0.001) with manual selection on the test sets. The automatic framework achieved an excellent AUC score of 0.979 in filling defects assessment. The ROC-derived optimal HU ratio threshold for filling defects detection was 0.561. Conclusion: The novel AI-based framework could accurately segment the LAA region and select ROIs while effectively avoiding trabeculae for filling defects assessment, achieving close-to-expert performance. This technique may help preemptively detect the potential thromboembolic risk for AF patients.

A comparison of statistical methods for animal oncology studies.

In pre-clinical oncology studies, tumor-bearing animals are treated and observed over a period of time in order to measure and compare the efficacy of one or more cancer-intervention therapies along with a placebo/standard of care group. A data analysis is typically carried out by modeling and comparing tumor volumes, functions of tumor volumes, or survival. Data analysis on tumor volumes is complicated because animals under observation may be euthanized prior to the end of the study for one or more reasons, such as when an animal's tumor volume exceeds an upper threshold. In such a case, the tumor volume is missing not-at-random for the time remaining in the study. To work around the non-random missingness issue, several statistical methods have been proposed in the literature, including the rate of change in log tumor volume and partial area under the curve. In this work, an examination and comparison of the test size and statistical power of these and other popular methods for the analysis of tumor volume data is performed through realistic Monte Carlo computer simulations. The performance, advantages, and drawbacks of popular statistical methods for animal oncology studies are reported. The recommended methods are applied to a real data set.

Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis.

Purpose: To evaluate the diagnostic accuracy of machine learning (ML) techniques applied to radiomic features extracted from OCT and OCT angiography (OCTA) images for diabetes mellitus (DM), diabetic retinopathy (DR), and referable DR (R-DR) diagnosis. Design: Cross-sectional analysis of a retinal image dataset from a previous prospective OCTA study (ClinicalTrials.govNCT03422965). Participants: Patients with type 1 DM and controls included in the progenitor study. Methods: Radiomic features were extracted from fundus retinographies, OCT, and OCTA images in each study eye. Logistic regression, linear discriminant analysis, support vector classifier (SVC)-linear, SVC-radial basis function, and random forest models were created to evaluate their diagnostic accuracy for DM, DR, and R-DR diagnosis in all image types. Main Outcome Measures: Area under the receiver operating characteristic curve (AUC) mean and standard deviation for each ML model and each individual and combined image types. Results: A dataset of 726 eyes (439 individuals) were included. For DM diagnosis, the greatest AUC was observed for OCT (0.82, 0.03). For DR detection, the greatest AUC was observed for OCTA (0.77, 0.03), especially in the 3 × 3 mm superficial capillary plexus OCTA scan (0.76, 0.04). For R-DR diagnosis, the greatest AUC was observed for OCTA (0.87, 0.12) and the deep capillary plexus OCTA scan (0.86, 0.08). The addition of clinical variables (age, sex, etc.) improved most models AUC for DM, DR and R-DR diagnosis. The performance of the models was similar in unilateral and bilateral eyes image datasets. Conclusions: Radiomics extracted from OCT and OCTA images allow identification of patients with DM, DR, and R-DR using standard ML classifiers. OCT was the best test for DM diagnosis, OCTA for DR and R-DR diagnosis and the addition of clinical variables improved most models. This pioneer study demonstrates that radiomics-based ML techniques applied to OCT and OCTA images may be an option for DR screening in patients with type 1 DM. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

Risk Stratification in Acute Coronary Syndrome by Comprehensive Morphofunctional Assessment With Optical Coherence Tomography.

Background: Artificial intelligence enables simultaneous evaluation of plaque morphology and computational physiology from optical coherence tomography (OCT). Objectives: This study sought to appraise the predictive value of major adverse cardiovascular events (MACE) by combined plaque morphology and computational physiology. Methods: A total of 604 patients with acute coronary syndrome who underwent OCT imaging in ≥1 nonculprit vessel during index coronary angiography were retrospectively enrolled. A novel morphologic index, named the lipid-to-cap ratio (LCR), and a functional parameter to evaluate the physiologic significance of coronary stenosis from OCT, namely, the optical flow ratio (OFR), were calculated from OCT, together with classical morphologic parameters, like thin-cap fibroatheroma (TCFA) and minimal lumen area. Results: The 2-year cumulative incidence of a composite of nonculprit vessel-related cardiac death, cardiac arrest, acute myocardial infarction, and ischemia-driven revascularization (NCV-MACE) at 2 years was 4.3%. Both LCR (area under the curve [AUC]: 0.826; 95% CI: 0.793-0.855) and OFR (AUC: 0.838; 95% CI: 0.806-0.866) were superior to minimal lumen area (AUC: 0.618; 95% CI: 0.578-0.657) in predicting NCV-MACE at 2 years. Patients with both an LCR of >0.33 and an OFR of ≤0.84 had significantly higher risk of NCV-MACE at 2 years than patients in whom at least 1 of these 2 parameters was normal (HR: 42.73; 95% CI: 12.80-142.60; P < 0.001). The combination of thin-cap fibroatheroma and OFR also identified patients at higher risk of future events (HR: 6.58; 95% CI: 2.83-15.33; P < 0.001). Conclusions: The combination of LCR with OFR permits the identification of a subgroup of patients with 43-fold higher risk of recurrent cardiovascular events in the nonculprit vessels after acute coronary syndrome.

ACR Appropriateness Criteria® Lower Extremity Arterial Claudication-Imaging Assessment for Revascularization: 2022 Update.

Arterial claudication is a common manifestation of peripheral artery disease. This document focuses on necessary imaging before revascularization for claudication. Appropriate use of ultrasound, invasive arteriography, MR angiography, and CT angiography are discussed. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Critical assessment of the revised guidelines for vancomycin therapeutic drug monitoring.

BACKGROUND: The revised vancomycin guidelines recommend replacing trough-only with trough or peak/trough Bayesian and first-order equations monitoring, citing their better AUC predictions and poor AUC-trough R2. Yet, evidence suggesting good AUC-trough correlation has been overlooked, and the optimality of peak/trough samples has been doubted. The guidelines recommend Bayesian programs implement richly-sampled PopPK priors despite their scarcity. Therefore, whether complex Bayesian and sample-demanding first-order equations can bring significant advantages to the practice over simple trough-only monitoring is worth weighing. OBJECTIVES: The primary aim is to compare the predictive performance of the AUC monitoring methods. Then, we investigate the impact of not adhering to trough sampling on the Bayesian-based predictions. Moreover, we report the nature of PopPK priors used in Bayesian programs to assess the applicability of the guideline recommendations. METHODS: We calculated the predictive performance of the monitoring methods using a standard PopPK modeling and simulation approach. We thoroughly explored the prior PK models implemented in Bayesian programs. RESULTS: Predictive performances of the monitoring methods were comparable at steady-state relative to the number of samples. Contrary to the recommendation, Bayesian trough monitoring did not result in better predictive performances compared to using random levels. Very few programs implemented richly-sampled priors. CONCLUSION: All the monitoring methods can be, relatively, reliable at steady-state, if properly implemented. Although only Bayesian-based monitoring can be used pre-steady-state, its predictive performance can be modest. Trough-only monitoring is the simplest approach. Constraints regarding trough sampling times could be relaxed. The scarcity of richly-sampled Bayesian priors questions the applicability of the revised guidelines recommendation.

Towards a More Evidence-Based Risk Assessment for People in the Criminal Justice System: the Case of OxRec in the Netherlands.

Risk assessment tools are widely used throughout the criminal justice system to assist in making decisions about sentencing, supervision, and treatment. In this article, we discuss several methodological and practical limitations associated with risk assessment tools currently in use. These include variable predictive performance due to the exclusion of important background predictors; high costs, including the need for regular staff training, in order to use many tools; development of tools using suboptimal methods and poor transparency in how they create risk scores; included risk factors being based on dated evidence; and ethical concerns highlighted by legal scholars and criminologists, such as embedding systemic biases and uncertainty about how these tools influence judicial decisions. We discuss the potential that specific predictors, such as living in a deprived neighbourhood, may indirectly select for individuals in racial or ethnic minority groups. To demonstrate how these limitations and ethical concerns can be addressed, we present the example of OxRec, a risk assessment tool used to predict recidivism for individuals in the criminal justice system. OxRec was developed in Sweden and has been externally validated in Sweden and the Netherlands. The advantages of OxRec include its predictive accuracy based on rigorous multivariable testing of predictors, transparent reporting of results and the final model (including how the probability score is derived), scoring simplicity (i.e. without the need for additional interview), and the reporting of a wide range of performance measures, including those of discrimination and calibration, the latter of which is rarely reported but a key metric. OxRec is intended to be used alongside professional judgement, as a support for decision-making, and its performance measures need to be interpreted in this light. The reported calibration of the tool in external samples clearly suggests no systematic overestimation of risk, including in large subgroups.

Pharmacokinetic assessment of vancomycin in critically ill patients and nephrotoxicity prediction using individualized pharmacokinetic parameters.

Introduction: Therapeutic drug monitoring (TDM) and pharmacokinetic assessments of vancomycin would be essential to avoid vancomycin-associated nephrotoxicity and obtain optimal therapeutic and clinical responses. Different pharmacokinetic parameters, including trough concentration and area under the curve (AUC), have been proposed to assess the safety and efficacy of vancomycin administration. Methods: Critically ill patients receiving vancomycin at Nemazee Hospital were included in this prospective study. Four blood samples at various time intervals were taken from each participated patient. Vancomycin was extracted from plasma samples and analyzed using a validated HPLC method. Results: Fifty-three critically ill patients with a total of 212 blood samples from June 2019 to June 2021 were included in this study. There was a significant correlation between baseline GFR, baseline serum creatinine, trough and peak concentrations, AUCτ, AUC24h, Cl, and Vd values with vancomycin-induced AKI. Based on trough concentration values, 66% of patients were under-dosed (trough concentration <15 µg/ml) and 18.9% were over-dosed (trough concentration ≥20 µg/ml). Also, based on AUC24h values, about 52.2% were under-dosed (AUC24h < 400 µg h/ml), and 21.7% were over-dosed (AUC24h > 600 µg h/ml) that emphasizes on the superiority of AUC-based monitoring approach for TDM purposes to avoid nephrotoxicity occurrence. Conclusion: The AUC-based monitoring approach would be superior in terms of nephrotoxicity prediction. Also, to avoid vancomycin-induced AKI, trough concentration and AUCτ values should be maintained below the cut-off points.

Assessment of Various Machine Learning Models for Peach Maturity Prediction Using Non-Destructive Sensor Data.

To date, many machine learning models have been used for peach maturity prediction using non-destructive data, but no performance comparison of the models on these datasets has been conducted. In this study, eight machine learning models were trained on a dataset containing data from 180 'Suncrest' peaches. Before the models were trained, the dataset was subjected to dimensionality reduction using the least absolute shrinkage and selection operator (LASSO) regularization, and 8 input variables (out of 29) were chosen. At the same time, a subgroup consisting of the peach ground color measurements was singled out by dividing the set of variables into three subgroups and by using group LASSO regularization. This type of variable subgroup selection provided valuable information on the contribution of specific groups of peach traits to the maturity prediction. The area under the receiver operating characteristic curve (AUC) values of the selected models were compared, and the artificial neural network (ANN) model achieved the best performance, with an average AUC of 0.782. The second-best machine learning model was linear discriminant analysis with an AUC of 0.766, followed by logistic regression, gradient boosting machine, random forest, support vector machines, a classification and regression trees model, and k-nearest neighbors. Although the primary parameter used to determine the performance of the model was AUC, accuracy, F1 score, and kappa served as control parameters and ultimately confirmed the obtained results. By outperforming other models, ANN proved to be the most accurate model for peach maturity prediction on the given dataset.