National trends in retreatment of HCV due to reinfection or treatment failure in Australia.

BACKGROUND & AIMS: Population-level uptake of direct-acting antiviral (DAA) treatment for hepatitis C virus (HCV) infection, including retreatment, can be estimated through administrative pharmaceutical dispensation data. However, the reasons for retreatment are not captured in these data. We developed a machine learning model to classify retreatments as reinfection or treatment failure at a national level. METHODS: Retreatment data from the REACH-C cohort (n = 10,843 treated with DAAs; n = 320 retreatments with known reason), were used to train a random forest model. Nested cross validation was undertaken to assess model performance and to optimise hyperparameters. The model was applied to data on DAA retreatment dispensed during 2016-2021 in Australia, to identify the reason for retreatment (treatment failure or reinfection). RESULTS: Average predictive accuracy, precision, sensitivity, specificity and F1-score for the model were 96.3%, 96.5%, 96.3%, 96.3% and 96.3%, respectively. Nationally, 95,272 individuals initiated DAAs, with treatment uptake declining from 32,454 in 2016 to 6,566 in 2021. Of those treated, 6,980 (7%) were retreated. Our model classified 51.8% (95% CI 46.7-53.6%; n = 3,614) of cases as reinfection and 48.2% (95% CI 46.4-53.3%; n = 3,366) as treatment failure. Retreatment for reinfection increased steadily over the study period from 14 in 2016 to 1,092 in 2020, stabilising in 2021. Retreatment for treatment failure increased from 73 in 2016 to 1,077 in 2019, then declined to 515 in 2021. Among individuals retreated for treatment failure, 50% had discontinued initial treatment. CONCLUSIONS: We used a novel methodology with high classification accuracy to evaluate DAA retreatment patterns at a national level. Increases in retreatment uptake for treatment failure corresponded to the availability of pangenotypic and salvage regimens. Increasing retreatment uptake for reinfection likely reflects increasing reinfection incidence. IMPACT AND IMPLICATIONS: This study used machine learning methodologies to analyse national administrative data and characterise trends in HCV retreatment due to reinfection and treatment failure. Retreatment for reinfection increased over time, reflecting increasing numbers of people at risk for reinfection following HCV cure. Increased retreatment for treatment failure corresponded to the availability of pangenotypic and salvage DAA regimens. The findings of this study can be used by public health agencies and policy makers to guide and assess HCV elimination strategies, while the novel methodology for monitoring trends in HCV retreatment has the potential to be used in other settings, and health conditions.

Generating synthetic clinical data that capture class imbalanced distributions with generative adversarial networks: Example using antiretroviral therapy for HIV.

OBJECTIVE: Clinical data's confidential nature often limits the development of machine learning models in healthcare. Generative adversarial networks (GANs) can synthesise realistic datasets, but suffer from mode collapse, resulting in low diversity and bias towards majority demographics and common clinical practices. This work proposes an extension to the classic GAN framework that includes a variational autoencoder (VAE) and an external memory mechanism to overcome these limitations and generate synthetic data accurately describing imbalanced class distributions commonly found in clinical variables. METHODS: The proposed method generated a synthetic dataset related to antiretroviral therapy for human immunodeficiency virus (ART for HIV). We evaluated it based on five metrics: (1) accurately representing imbalanced class distribution; (2) the realism of the individual variables; (3) the realism among variables; (4) patient disclosure risk; and (5) the utility of the generated dataset for developing downstream machine learning models. RESULTS: The proposed method overcomes the issue of mode collapse and generates a synthetic dataset that accurately describes imbalanced class distributions commonly found in clinical variables. The generated data has a patient disclosure risk of 0.095%, lower than the 9% threshold stated by Health Canada and the European Medicines Agency, making it suitable for distribution to the research community with high security. The generated data also has high utility, indicating the potential of the proposed method to enable the development of downstream machine learning algorithms for healthcare applications using synthetic data. CONCLUSION: Our proposed extension to the classic GAN framework, which includes a VAE and an external memory mechanism, represents a promising approach towards generating synthetic data that accurately describe imbalanced class distributions commonly found in clinical variables. This method overcomes the limitations of GANs and creates more realistic datasets with higher patient cohort diversity, facilitating the development of downstream machine learning algorithms for healthcare applications.

A Machine Learning Approach to Predict the Added-Sugar Content of Packaged Foods.

BACKGROUND: Dietary guidelines recommend limiting the intake of added sugars. However, despite the public health importance, most countries have not mandated the labeling of added-sugar content on packaged foods and beverages, making it difficult for consumers to avoid products with added sugar, and limiting the ability of policymakers to identify priority products for intervention. OBJECTIVE: The aim was to develop a machine learning approach for the prediction of added-sugar content in packaged products using available nutrient, ingredient, and food category information. METHODS: The added-sugar prediction algorithm was developed using k-nearest neighbors (KNN) and packaged food information from the US Label Insight dataset (n = 70,522). A synthetic dataset of Australian packaged products (n = 500) was used to assess validity and generalization. Performance metrics included the coefficient of determination (R2), mean absolute error (MAE), and Spearman rank correlation (ρ). To benchmark the KNN approach, the KNN approach was compared with an existing added-sugar prediction approach that relies on a series of manual steps. RESULTS: Compared with the existing added-sugar prediction approach, the KNN approach was similarly apt at explaining variation in added-sugar content (R2 = 0.96 vs. 0.97, respectively) and ranking products from highest to lowest in added-sugar content (ρ = 0.91 vs. 0.93, respectively), while less apt at minimizing absolute deviations between predicted and true values (MAE = 1.68 g vs. 1.26 g per 100 g or 100 mL, respectively). CONCLUSIONS: KNN can be used to predict added-sugar content in packaged products with a high degree of validity. Being automated, KNN can easily be applied to large datasets. Such predicted added-sugar levels can be used to monitor the food supply and inform interventions aimed at reducing added-sugar intake.

Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients.

PURPOSE: Earlier work showed that IVIM-NETorig , an unsupervised physics-informed deep neural network, was faster and more accurate than other state-of-the-art intravoxel-incoherent motion (IVIM) fitting approaches to diffusion-weighted imaging (DWI). This study presents a substantially improved version, IVIM-NEToptim , and characterizes its superior performance in pancreatic cancer patients. METHOD: In simulations (signal-to-noise ratio [SNR] = 20), the accuracy, independence, and consistency of IVIM-NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, number of hidden layers, dropout, batch normalization, learning rate), by calculating the normalized root-mean-square error (NRMSE), Spearman's ρ, and the coefficient of variation (CVNET ), respectively. The best performing network, IVIM-NEToptim was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM-NEToptim 's performance was evaluated in an independent dataset of 23 patients with pancreatic ductal adenocarcinoma. Fourteen of the patients received no treatment between two repeated scan sessions and nine received chemoradiotherapy between the repeated sessions. Intersession within-subject standard deviations (wSD) and treatment-induced changes were assessed. RESULTS: In simulations (SNR = 20), IVIM-NEToptim outperformed IVIM-NETorig in accuracy (NRMSE(D) = 0.177 vs 0.196; NMRSE(f) = 0.220 vs 0.267; NMRSE(D*) = 0.386 vs 0.393), independence (ρ(D*, f) = 0.22 vs 0.74), and consistency (CVNET (D) = 0.013 vs 0.104; CVNET (f) = 0.020 vs 0.054; CVNET (D*) = 0.036 vs 0.110). IVIM-NEToptim showed superior performance to the LS and Bayesian approaches at SNRs < 50. In vivo, IVIM-NEToptim showed significantly less noisy parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM-NEToptim detected the most individual patients with significant parameter changes compared to day-to-day variations. CONCLUSION: IVIM-NEToptim is recommended for accurate, informative, and consistent IVIM fitting to DWI data.

Psychotropic medicine prescribing and polypharmacy for people with dementia entering residential aged care: the influence of changing general practitioners.

OBJECTIVE: To examine relationships between changing general practitioner after entering residential aged care and overall medicines prescribing (including polypharmacy) and that of psychotropic medicines in particular. DESIGN: Retrospective data linkage study. SETTING, PARTICIPANTS: 45 and Up Study participants in New South Wales with dementia who were PBS concession card holders and entered permanent residential aged care during January 2010 - June 2014 and were alive six months after entry. MAIN OUTCOME MEASURES: Inverse probability of treatment-weighted numbers of medicines dispensed to residents and proportions of residents dispensed antipsychotics, benzodiazepines, and antidepressants in the six months after residential care entry, by most frequent residential care GP category: usual (same as during two years preceding entry), known (another GP, but known to the resident), or new GP. RESULTS: Of 2250 new residents with dementia (mean age, 84.1 years; SD, 7.0 years; 1236 women [55%]), 625 most frequently saw their usual GPs (28%), 645 saw known GPs (29%), and 980 saw new GPs (44%). The increase in mean number of dispensed medicines after residential care entry was larger for residents with new GPs (+1.6 medicines; 95% CI, 1.4-1.9 medicines) than for those attended by their usual GPs (+0.7 medicines; 95% CI, 0.4-1.1 medicines; adjusted rate ratio, 2.42; 95% CI, 1.59-3.70). The odds of being dispensed antipsychotics (adjusted odds ratio [aOR], 1.59; 95% CI, 1.18-2.12) or benzodiazepines (aOR, 1.69; 95% CI, 1.25-2.30), but not antidepressants (aOR, 1.32; 95% CI, 0.98-1.77), were also higher for the new GP group. Differences between the known and usual GP groups were not statistically significant. CONCLUSIONS: Increases in medicine use and rates of psychotropic dispensing were higher for people with dementia who changed GP when they entered residential care. Facilitating continuity of GP care for new residents and more structured transfer of GP care may prevent potentially inappropriate initiation of psychotropic medicines.

Medications used disproportionately during pregnancy: Priorities for research on the risks and benefits of medications when used during pregnancy.

PURPOSE: To identify medications used disproportionately more or less among pregnant women relative to women of childbearing age. METHODS: Medication use among pregnant women in New South Wales, Australia was identified using linked perinatal and pharmaceutical dispensing data from 2006 to 2012. Medication use in women of childbearing age (including pregnant women) was identified using pharmaceutical dispensing data for a 10% random sample of the Australian population. Pregnant social security beneficiaries (n = 111 612) were age-matched (1:3) to female social security beneficiaries in the 10% sample. For each medication, the risk it was dispensed during pregnancy relative to being dispensed during an equivalent time period among matched controls was computed. Medications were mapped to Australian pregnancy risk categories. RESULTS: Of the 181 included medications, 35 were statistically significantly more commonly dispensed to pregnant women than control women. Of these, 23 are categorised as posing no increased risk to the foetus. Among medications suspected of causing harm or having insufficient safety data, the strongest associations were observed for hydralazine, ondansetron, dalteparin sodium and ranitidine. Use was less likely during pregnancy than control periods for 127 medications, with the strongest associations observed for hormonal contraceptives and progestogens. CONCLUSIONS: Most medications found to be used disproportionately more by pregnant women are indicated for pregnancy-related problems. A large number of medications were used disproportionately less among pregnant women, where avoidance of some of these medications may pose a greater risk of harm. For many other medications avoided during pregnancy, current data are insufficient to inform this risk-benefit assessment.

The impact of dementia on aged care service transitions in the last five years of life.

OBJECTIVE: To investigate the impact of dementia on aged care service use at end-of-life. METHODS: Our retrospective data linkage study in New South Wales, Australia, used survey data from participants in the 45 and Up Study who died between July 2011-June 2014 linked to routinely collected administrative data for 2006-2014. We investigated movement between aged care "states" (No Services, Home Care including Home Support and Low-and High-Level Home Care and Residential Care) in the last five years of life. The dementia cohort comprised decedents with a dementia diagnosis recorded in hospital records, death certificates or who had claims for dementia-specific medicines prior to death (n = 2,230). The comparison cohort were decedents with no dementia diagnosis, matched 1:1 on age-at-death, sex, income and location. RESULTS: Compared to those without dementia, people with dementia were more likely to: use home care (67 versus 60%, P < 0.001), enter residential care (72 versus 30%, P < 0.001) and stay longer in residential care (median 17.9 versus 12.7 months, P < 0.001). Five years before death, more people with dementia were within residential care (6 versus 4%; RR = 1.61, 95%CI = 1.23-2.10) and these rates diverged at the end-of-life (69 versus 28%, RR = 2.48, 95%CI = 2.30-2.66). Use of home-based care was higher among people with dementia five years from death (20 versus 17%; RR = 1.15, 95%CI = 1.02-1.30) but lower at end-of-life (13 versus 24%, RR = 0.55, 95%CI = 0.49-0.63). CONCLUSION: Dementia-specific aged care trajectories were dominated by residential care. Home care use declined towards end-of-life for people with dementia and may not be meeting their needs.

Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI.

PURPOSE: This prospective clinical study assesses the feasibility of training a deep neural network (DNN) for intravoxel incoherent motion (IVIM) model fitting to diffusion-weighted MRI (DW-MRI) data and evaluates its performance. METHODS: In May 2011, 10 male volunteers (age range, 29-53 years; mean, 37) underwent DW-MRI of the upper abdomen on 1.5T and 3.0T MR scanners. Regions of interest in the left and right liver lobe, pancreas, spleen, renal cortex, and renal medulla were delineated independently by 2 readers. DNNs were trained for IVIM model fitting using these data; results were compared to least-squares and Bayesian approaches to IVIM fitting. Intraclass correlation coefficients (ICCs) were used to assess consistency of measurements between readers. Intersubject variability was evaluated using coefficients of variation (CVs). The fitting error was calculated based on simulated data, and the average fitting time of each method was recorded. RESULTS: DNNs were trained successfully for IVIM parameter estimation. This approach was associated with high consistency between the 2 readers (ICCs between 50% and 97%), low intersubject variability of estimated parameter values (CVs between 9.2 and 28.4), and the lowest error when compared with least-squares and Bayesian approaches. Fitting by DNNs was several orders of magnitude quicker than the other methods, but the networks may need to be retrained for different acquisition protocols or imaged anatomical regions. CONCLUSION: DNNs are recommended for accurate and robust IVIM model fitting to DW-MRI data. Suitable software is available for download.

Functional and Targeted Lymph Node Imaging in Prostate Cancer: Current Status and Future Challenges.

Patients with prostate cancer who have regional lymph node (LN) metastases face an increased risk of death from disease and are therefore treated aggressively. Surgical LN dissection is the established method of staging regional nodes; however, this invasive technique carries substantial morbidities and a noninvasive imaging method is needed to reduce or eliminate the need for extended pelvic LN dissections (ePLND). Conventional computed tomography and magnetic resonance (MR) imaging have proven insensitive and nonspecific because both use nodal size criteria, which is notoriously inaccurate. Novel imaging techniques such as functional MR imaging by using diffusion-weighted MR imaging, MR lymphography with iron oxide particles, and targeted positron emission tomography imaging are currently under development and appear to improve LN staging of prostate cancer. Although progress is being made in staging nodes with imaging, it has not reached the point of replacing ePLND. In this review, the strengths and limitations of these new functional and targeted LN imaging techniques for prostate cancer are discussed. © RSNA, 2017.

Differentiation of prostate cancer lesions with high and with low Gleason score by diffusion-weighted MRI.

OBJECTIVES: To differentiate prostate cancer lesions with high and with low Gleason score by diffusion-weighted-MRI (DW-MRI). METHODS: This prospective study was approved by the responsible ethics committee. DW-MRI of 84 consenting prostate and/or bladder cancer patients scheduled for radical prostatectomy were acquired and used to compute apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM: the pure diffusion coefficient D t, the pseudo-diffusion fraction F p and the pseudo-diffusion coefficient D p), and high b value (as acquired and Hessian filtered) parameters within the index lesion. These parameters (separately and combined in a logistic regression model) were used to differentiate lesions depending on whether whole-prostate histopathological analysis after prostatectomy determined a high (≥7) or low (6) Gleason score. RESULTS: Mean ADC and D t differed significantly (p of independent two-sample t test < 0.01) between high- and low-grade lesions. The highest classification accuracy was achieved by the mean ADC (AUC 0.74) and D t (AUC 0.70). A logistic regression model based on mean ADC, mean F p and mean high b value image led to an AUC of 0.74 following leave-one-out cross-validation. CONCLUSIONS: Classification by IVIM parameters was not superior to classification by ADC. DW-MRI parameters correlated with Gleason score but did not provide sufficient information to classify individual patients. KEY POINTS: • Mean ADC and diffusion coefficient differ between high- and low-grade prostatic lesions. • Accuracy of trivariate logistic regression is not superior to using ADC alone. • DW-MRI is not a valid substitute for biopsies in clinical routine yet.

Selection for biopsy of kidney transplant patients by diffusion-weighted MRI.

OBJECTIVES: To assess retrospectively whether diffusion-weighted magnetic resonance imaging (DW-MRI) allows physicians to determine the severity of histopathologic findings in biopsies of renal allograft patients with deteriorating renal function. METHODS: Forty consecutive kidney transplant patients underwent DW-MRI and biopsy. Patients were assigned to one group with severe and to another group with normal or mild histopathologic findings. These two groups were compared based on a qualitative DW-MRI assessment (homo-/heterogeneity) and the combination of qualitative and quantitative DW-MRI parameters (ADC, and intravoxel incoherent motion, IVIM, parameters: D, f, D*). Sensitivity, specificity, and accuracy were determined for each parameter. RESULTS: Biopsy findings were severe in 25 patients and normal or mild in 15 patients. Qualitative DW-MRI led to a sensitivity of 44.0% and a specificity of 93.3%. Combined qualitative and quantitative DW-MRI led to an accuracy of 80% for both the minimal ADC (ADCmin) and the minimal perfusion fraction (fmin) with a sensitivity of 84.0% and 92.0% and a specificity of 73.3% and 60.0%, respectively. CONCLUSION: Combined qualitative and quantitative DW-MRI might allow physicians to determine the severity of histopathologic findings in biopsies of a high number of kidney transplant patients. KEY POINTS: • Qualitative DW-MRI is highly specific when predicting the severity of kidney transplant biopsy. • Allografts appearing heterogeneous on ADC are associated with severe histopathologic findings. • Combining qualitative and quantitative DW-MRI parameters improves the classification's sensitivity and accuracy. • Kidney transplant biopsies might be spared by combining qualitative and quantitative DW-MRI.

Comparison of Intravoxel Incoherent Motion Parameters across MR Imagers and Field Strengths: Evaluation in Upper Abdominal Organs.

Purpose To determine the reproducibility of intravoxel incoherent motion (IVIM) parameters measured in upper abdominal organs with magnetic resonance (MR) imagers from different vendors and with different field strengths. Materials and Methods This prospective study was approved by the independent ethics committees of Kanton Bern and Kanton Zurich, and signed informed consent was obtained from all participants. Abdominal diffusion-weighted images in 10 healthy men (mean age, 37 years ± 8 [standard deviation]) were acquired by using 1.5- and 3.0-T MR imagers from three different vendors. Two readers independently delineated regions of interest that were used to measure IVIM parameters (diffusion coefficient [Dt], perfusion fraction [Fp], and pseudodiffusion coefficient [Dp]) in the left and right lobes of the liver, and in the pancreas, spleen, renal cortex, and renal medulla. Measurement reproducibility between readers was assessed with intraclass correlation coefficients (ICCs). Variability across MR imagers was analyzed by using between- and within-subject coefficients of variation (CVs) and analysis of variance (ANOVA). Results Between-reader reproducibility was high for Dt (ICC, 94.6%), intermediate for Fp (ICC, 81.7%), and low for Dp (ICC, 69.5%). Between- and within-subject CVs of Dt were relatively high (>20%) in the left lobe of the liver and relatively low (<10%) in the renal cortex and renal medulla. CVs generally exceeded 15% for Fp values and 20% for Dp. ANOVA indicated significant differences (P < .05) between MR imagers. Conclusion IVIM parameters in the upper abdomen may differ substantially across MR imagers. (©) RSNA, 2015 Online supplemental material is available for this article.

Impact of the calculation algorithm on biexponential fitting of diffusion-weighted MRI in upper abdominal organs.

PURPOSE: To compare the variability, precision, and accuracy of six different algorithms (Levenberg-Marquardt, Trust-Region, Fixed-Dp , Segmented-Unconstrained, Segmented-Constrained, and Bayesian-Probability) for computing intravoxel-incoherent-motion-related parameters in upper abdominal organs. METHODS: Following the acquisition of abdominal diffusion-weighted magnetic resonance images of 10 healthy men, six distinct algorithms were employed to compute intravoxel-incoherent-motion-related parameters in the left and right liver lobe, pancreas, spleen, renal cortex, and renal medulla. Algorithms were evaluated regarding inter-reader and intersubject variability. Comparability of results was assessed by analyses of variance. The algorithms' precision and accuracy were investigated on simulated data. RESULTS: A Bayesian-Probability based approach was associated with very low inter-reader variability (average Intraclass Correlation Coefficients: 96.5-99.6%), the lowest inter-subject variability (Coefficients of Variation [CV] for the pure diffusion coefficient Dt : 3.8% in the renal medulla, 6.6% in the renal cortex, 10.4-12.1% in the left and right liver lobe, 15.3% in the spleen, 15.8% in the pancreas; for the perfusion fraction Fp : 15.5% on average; for the pseudodiffusion coefficient Dp : 25.8% on average), and the highest precision and accuracy. Results differed significantly (P < 0.05) across algorithms in all anatomical regions. CONCLUSION: The Bayesian-Probability algorithm should be preferred when computing intravoxel-incoherent-motion-related parameters in upper abdominal organs.

Diffusion-weighted imaging outside the brain: Consensus statement from an ISMRM-sponsored workshop.

The significant advances in magnetic resonance imaging (MRI) hardware and software, sequence design, and postprocessing methods have made diffusion-weighted imaging (DWI) an important part of body MRI protocols and have fueled extensive research on quantitative diffusion outside the brain, particularly in the oncologic setting. In this review, we summarize the most up-to-date information on DWI acquisition and clinical applications outside the brain, as discussed in an ISMRM-sponsored symposium held in April 2015. We first introduce recent advances in acquisition, processing, and quality control; then review scientific evidence in major organ systems; and finally describe future directions. J. Magn. Reson. Imaging 2016;44:521-540.

Prescription opioid use among people with opioid dependence and concurrent benzodiazepine and gabapentinoid exposure: An analysis of overdose and all-cause mortality.

BACKGROUND: Studies investigating mortality risk associated with use of opioid analgesics, benzodiazepines, gabapentinoids, and opioid agonist treatment (OAT) among people with opioid dependence (PWOD) are lacking. This study addresses this gap using a cohort of 37,994 PWOD initiating opioid analgesics between July 2003 and July 2018 in New South Wales, Australia. METHODS: Linked administrative records provided data on dispensings, sociodemographics, clinical characteristics, OAT, and mortality. Cox proportional hazards models assessed associations between time-varying measures of individual and concurrent medicine use and OAT with all-cause mortality, accidental opioid overdose, non-drug induced accidents, and non-drug-induced suicide. Opioid analgesic dose effects, expressed as oral morphine equivalents (OMEs) per day, were also examined. OUTCOMES: During the study period, 3167 individuals died. Compared with no use, all medicines of interest were associated with increased accidental opioid overdose risk; hazard ratios (HR) ranged from 1.33 (95 % CI: 1.05-1.68) for opioid analgesic use to 6.10 (95 % CI: 4.11-9.06) for opioid analgesic, benzodiazepine and gabapentinoid use. Benzodiazepine use was associated with increased non-drug-induced accidents and non-drug-induced suicides. For all-cause mortality, all combinations of benzodiazepines and gabapentinoids with opioid analgesics were associated with increased risk (aHRs ranged from 1.35 to 2.73). For most medicines/medicine combinations, all-cause mortality risk was reduced when in OAT compared to out of OAT. Higher opioid analgesic doses were associated with increased all-cause mortality (e.g., 90-199 mg vs 1-49 mg OME per day: HR 1.90 [95 % CI: 1.52-2.40]). INTERPRETATION: The increased mortality risk associated with benzodiazepines and gabapentinoids among PWOD appear to be reduced when engaged in OAT. A greater focus on encouraging OAT engagement, providing overdose prevention education, and access and coverage of overdose antidotes is necessary to minimise the unintended consequences of medicines use in this population.

Enriching Data Science and Health Care Education: Application and Impact of Synthetic Data Sets Through the Health Gym Project.

Large-scale medical data sets are vital for hands-on education in health data science but are often inaccessible due to privacy concerns. Addressing this gap, we developed the Health Gym project, a free and open-source platform designed to generate synthetic health data sets applicable to various areas of data science education, including machine learning, data visualization, and traditional statistical models. Initially, we generated 3 synthetic data sets for sepsis, acute hypotension, and antiretroviral therapy for HIV infection. This paper discusses the educational applications of Health Gym's synthetic data sets. We illustrate this through their use in postgraduate health data science courses delivered by the University of New South Wales, Australia, and a Datathon event, involving academics, students, clinicians, and local health district professionals. We also include adaptable worked examples using our synthetic data sets, designed to enrich hands-on tutorial and workshop experiences. Although we highlight the potential of these data sets in advancing data science education and health care artificial intelligence, we also emphasize the need for continued research into the inherent limitations of synthetic data.

Increasing national trend of direct-acting antiviral discontinuation among people treated for HCV 2016-2021.

BACKGROUND: Direct-acting antiviral (DAA) treatment discontinuation may negatively impact HCV elimination efforts. In Australia, DAA therapy is pharmacy dispensed, generally in 4-week amounts, with the approved duration (8-24 wk) and volume dispensed reported in pharmaceutical administrative data. This analysis assessed national HCV treatment discontinuation. METHODS: Individuals commencing DAAs between 2016 and 2021 were assessed for treatment discontinuation. Individuals with a single dispensation of their entire treatment course were excluded. Treatment discontinuation was defined as ≥4 weeks of approved treatment duration not dispensed. Factors associated with treatment discontinuation were assessed using Cox regression. Factors associated with retreatment following treatment discontinuation were assessed using logistic regression. RESULTS: Of 95,275 individuals who were treated, 88,986 were included in the analysis of whom 7532 (9%) discontinued treatment. Treatment discontinuation increased from 6% in the first half of 2016 to 15% in 2021. Longer treatment durations (vs. 8 wk) were associated with increased discontinuation risk (12 wk: adjusted HR = 3.23; 95% CI: 2.90, 3.59; p < 0.001, 16-24 wk: adjusted HR = 6.29; 95% CI: 5.55, 7.14; p < 0.001). Of individuals discontinuing treatment, 24% were retreated. Early discontinuation (4 wk treatment dispensed) increased the likelihood of retreatment (adjusted OR = 3.91; 95% CI: 3.44, 4.44; p < 0.001). Those with early discontinuation of glecaprevir/pibrentasvir 8 weeks (vs. sofosbuvir/velpatasvir 12 wk) had a lower likelihood of retreatment (adjusted OR = 0.62; 95% CI: 0.49, 0.79; p < 0.001). Initial treatment discontinuation was associated with an increased risk of retreatment discontinuation (adjusted HR = 4.41; 3.85, 5.05; p < 0.001). CONCLUSIONS: DAA treatment discontinuation increased over time corresponding to increasing treatment uptake through primary care among people who inject drugs. The use of simplified, short-duration therapies may reduce treatment discontinuation. Access to adherence support and retreatment will be essential for HCV elimination.

Associations of Breast Arterial Calcifications with Cardiovascular Disease.

Cardiovascular diseases (CVD), including coronary artery disease (CAD), continue to be the leading cause of global mortality among women. While traditional CVD/CAD prevention tools play a significant role in reducing morbidity and mortality among both men and women, current tools for preventing CVD/CAD rely on traditional risk factor-based algorithms that often underestimate CVD/CAD risk in women compared with men. In recent years, some studies have suggested that breast arterial calcifications (BAC), which are benign calcifications seen in mammograms, may be linked to CVD/CAD. Considering that millions of women older than 40 years undergo annual screening mammography for breast cancer as a regular activity, innovative risk prediction factors for CVD/CAD involving mammographic data could offer a gender-specific and convenient solution. Such factors that may be independent of, or complementary to, current risk models without extra cost or radiation exposure are worthy of detailed investigation. This review aims to discuss relevant studies examining the association between BAC and CVD/CAD and highlights some of the issues related to previous studies' design such as sample size, population types, method of assessing BAC and CVD/CAD, definition of cardiovascular events, and other confounding factors. The work may also offer insights for future CVD risk prediction research directions using routine mammograms and radiomic features other than BAC such as breast density and macrocalcifications.

Sexual and drug use risk behaviour trajectories among people treated for recent HCV infection: the REACT study.

INTRODUCTION: Exploration of sexual and drug use behaviours following treatment for recent hepatitis C virus (HCV) is limited. This analysis modelled behavioural trajectories following treatment for recent HCV and assessed reinfection. METHODS: Participants treated for recent HCV in an international trial (enrolled 2017-2019) were followed at 3-monthly intervals for up to 2 years to assess longitudinal behaviours. Population-averaged changes were assessed using generalized estimating equations. Distinct behavioural trajectories were identified using group-based trajectory modelling. HCV reinfection incidence was calculated using person-years (PY) of observation. RESULTS: During the follow-up of 212 participants (84% gay and bisexual men [GBM]; 69% HIV; 26% current injecting drug use [IDU]), behavioural trajectories for IDU and stimulant use (past month) did not change. However, population-averaged decreases in the likelihood of daily IDU (adjusted odds ratio [AOR] 0.83; 95% CI 0.72, 0.95) and opioid use (AOR 0.84; 95% CI 0.75, 0.93) were observed. Among GBM, behavioural trajectories for chemsex did not change. Population-averaged decreases in condomless anal intercourse with casual male partners (CAI-CMP) (AOR 0.95; 95% CI 0.90, 0.99) and group-sex (AOR 0.86; 95% CI 0.80, 0.93) were observed, but masked distinct trajectories. While a proportion had a decreased probability of CAI-CMP (23%) and group-sex (59%) post-treatment, a substantial proportion retained a high probability of these behaviours. High HCV reinfection incidence was observed for the sustained high probability IDU (33.0/100 PY; 95% CI 17.7, 61.3) and chemsex (23.3/100 PY; 95% CI 14.5, 37.5) trajectories. CONCLUSIONS: Limited sexual and drug use behavioural change was observed following treatment for recent HCV, supporting access to surveillance and (re)treatment.