Bepridil is potent against SARS-CoV-2 in vitro.

Guided by a computational docking analysis, about 30 Food and Drug Administration/European Medicines Agency (FDA/EMA)-approved small-molecule medicines were characterized on their inhibition of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro). Of these small molecules tested, six displayed a concentration that inhibits response by 50% (IC50) value below 100 µM in inhibiting Mpro, and, importantly, three, that is, pimozide, ebastine, and bepridil, are basic molecules that potentiate dual functions by both raising endosomal pH to interfere with SARS-CoV-2 entry into the human cell host and inhibiting Mpro in infected cells. A live virus-based modified microneutralization assay revealed that bepridil possesses significant anti-SARS-CoV-2 activity in both Vero E6 and A459/ACE2 cells in a dose-dependent manner with low micromolar effective concentration, 50% (EC50) values. Therefore, the current study urges serious considerations of using bepridil in COVID-19 clinical tests.

Machine learning approaches for predicting 5-year breast cancer survival: A multicenter study.

The study used clinical data to develop a prediction model for breast cancer survival. Breast cancer prognostic factors were explored using machine learning techniques. We conducted a retrospective study using data from the Taipei Medical University Clinical Research Database, which contains electronic medical records from three affiliated hospitals in Taiwan. The study included female patients aged over 20 years who were diagnosed with primary breast cancer and had medical records in hospitals between January 1, 2009 and December 31, 2020. The data were divided into training and external testing datasets. Nine different machine learning algorithms were applied to develop the models. The performances of the algorithms were measured using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. A total of 3914 patients were included in the study. The highest AUC of 0.95 was observed with the artificial neural network model (accuracy, 0.90; sensitivity, 0.71; specificity, 0.73; PPV, 0.28; NPV, 0.94; and F1-score, 0.37). Other models showed relatively high AUC, ranging from 0.75 to 0.83. According to the optimal model results, cancer stage, tumor size, diagnosis age, surgery, and body mass index were the most critical factors for predicting breast cancer survival. The study successfully established accurate 5-year survival predictive models for breast cancer. Furthermore, the study found key factors that could affect breast cancer survival in Taiwanese women. Its results might be used as a reference for the clinical practice of breast cancer treatment.

Near infrared imaging system for preventing blood vision obstruction in endoscopy.

Spinal endoscopy procedure is commonly used in the diagnosis and treatment of various health problems and is effective. Bleeding is one of the most common complications of spinal endoscopy procedures. Blood vision obstruction (BVO), that is, obstruction of the endoscopic camera lens caused by the accumulation of blood in the surgical field, is a serious problem in endoscopic procedures. This study presents what we believe to be a new approach to addressing BVO with external multispectral imaging. The study was completed using a BVO simulation model, and the results reveal that this technology can be used to effectively overcome BVO and provide clear images of the anatomy, enabling more effective diagnosis and treatment. This technique may enable improvement of the outcomes of endoscopic procedures and could have far-reaching implications in the field of endoscopy.

Exact simultaneous confidence intervals for logical selection of a biomarker cut-point.

This article proposes four new principles for logical biomarker cut-point selection methods to adhere to: subgroup sensibility, sensitivity, specificity, and target monotonicity. At every cut-point value, our method gives confidence intervals not only for the efficacy at that cut-point value, but also efficacies in the marker-positive and marker-negative subgroups defined by that cut-point. These confidence intervals are given simultaneously for all possible cut-point values. Using Alzheimer's disease (AD) and type 2 diabetes (T2DM) as examples, we show our method achieves the four principles. Our method strongly controls familywise type I error rate (FWER) across both levels of multiplicity: the multiplicity of having marker-positive and marker-negative subgroups at each cut-point, and the multiplicity of searching through infinitely many cut-points. This is in contrast to other available methods. The confidence level of our simultaneous confidence intervals is in fact exact (not conservative). An application (app) is available, which implements the method we propose.

Rejoinder for discussions on correct and logical causal inference for binary and time-to-event outcomes in randomized controlled trials.

Our paper differs from previous literature in two ways: 1.We think in terms of clinical consequences, what benefits patients, what harms patients. Our main message is: using a not logic-respecting efficacy measure can potentially harm patients in a randomized controlled trial (RCT), as we prove analytically, and demonstrate with the OAK blood-based tumor mutational burden (bTMB) study. 2.We follow nature, which mixes effects within each treatment arm. Our secondary message is that following nature to mix within each treatment arm first before calculating any efficacy measure between treatments resolves issues. For example, following natural mixing to prove ratio of time is logic-respecting avoids the issue that weights of efficacy measures are implicit solution to an equation that depends on the unknown prognostic effect. More importantly, coding subgroup mixable estimation (SEM) by mixing within each treatment arm first and then calculating efficacy will make marginal and conditional efficacy agree, for logic-respecting efficacy measures (be it a ratio or a difference), no matter the outcome is continuous, binary, or time-to-event. One does not have to choose between marginal and conditional.

Correct and logical causal inference for binary and time-to-event outcomes in randomized controlled trials.

Targeted therapies tend to have biomarker defined subgroups that derive differential efficacy from treatments. This article corrects three prevailing oversights in stratified analyses comparing treatments in randomized controlled trials (RCTs) with binary and time-to-event outcomes: 1.Using efficacy measures such as odds ratio (OR) and hazard ratio (HR) can make a prognostic biomarker appear predictive, targeting wrong patients, because the inference is affected by a confounding/covert factor even with ignorable treatment assignment in an RCT. As shown analytically and with real immunotherapy patient level data, OR and HR cannot meet the causal Estimand requirement of ICH E9R1. 2.Mixing efficacy in subgroups by prevalence, the prevailing practice, can give misleading results also, for any efficacy measured as a ratio. However, mixing relative response (RR) and ratio of median (RoM) survival times by the prognostic effect, the confounding/covert factor hiding in plain sight, will give causal inference in an RCT. 3.Effects in subgroups should not be mixed on the logarithmic scale, because it creates an artificial Estimand for the whole population which changes depending on how the population is divided into subgroups. Current computer package implementations contain all these oversights. Probabilities, including survival curve probabilities, naturally average within each treatment arm by prevalence. The subgroup mixable estimation (SME) principle fixes the oversights by first averaging probabilities (not their logarithms) within each treatment arm, then computing simultaneous confidence intervals for ratio efficacy in subgroups and their mixtures based on rigorous mathematical derivation, to finally provide causal inference in the form of apps.

Identification and inference for subgroups with differential treatment efficacy from randomized controlled trials with survival outcomes through multiple testing.

With the uptake of targeted therapies, instead of the "one-fits-all" approach, modern randomized controlled trials (RCTs) often aim to develop treatments that target a subgroup of patients. Motivated by analyzing the Age-Related Eye Disease Study (AREDS) data, a large RCT to study the efficacy of nutritional supplements in delaying the progression of an eye disease, age-related macular degeneration (AMD), we develop a simultaneous inference procedure to identify and infer subgroups with differential treatment efficacy in RCTs with time-to-event outcomes. Specifically, we formulate the multiple testing problem through contrasts and construct their simultaneous confidence intervals, which appropriately control both within- and across-marker multiplicity. Realistic simulations are conducted using real genotype data to evaluate the method performance under various scenarios. The method is then applied to AREDS to assess the efficacy of antioxidants and zinc combination in delaying AMD progression. Multiple gene regions including ESRRB-VASH1 on chromosome 14 have been identified with subgroups showing differential efficacy. We further validate our findings in an independent subsequent RCT, AREDS2, by discovering consistent differential treatment responses in the targeted and non-targeted subgroups identified from AREDS. This multiple-testing-based simultaneous inference approach provides a step forward to confidently identify and infer subgroups in modern drug development.

Association Between Social Media Use and Cancer Screening Awareness and Behavior for People Without a Cancer Diagnosis: Matched Cohort Study.

BACKGROUND: The use of social media in communications regarding cancer prevention is rapidly growing. However, less is known about the general population's social media use related to cancer screening awareness and behavior for different cancers. OBJECTIVE: We aimed to examine the relationship between social media use and cancer screening awareness and behavior among people without a cancer diagnosis. METHODS: Data were collected from the Health Information National Trends Survey 5 Cycle 1 to 3 in the United States (n=12,227). Our study included 10,124 participants without a cancer diagnosis and 3 measures of screening awareness (those who had heard of hepatitis C virus [HCV], human papillomavirus [HPV], and the HPV vaccine) and 4 measures of behavior (those who had prostate-specific antigen tests, Papanicolaou tests for cervical cancer, as well as breast cancer and colon cancer tests). Propensity-score matching was conducted to adjust for the sociodemographic variables between the social media user and nonuser participants. Multivariable logistic regression was used to assess the association of social media use by gender. Jackknife replicate weights were incorporated into the analyses. RESULTS: Of the 3794 matched participants, 1861 (57.6% weighted) were male, and the mean age was 55.5 (SD 0.42) years. Compared to social media nonusers, users were more likely to have heard of HCV (adjusted odds ratio [aOR]=2.27, 95% CI, 1.29-3.98 and aOR=2.86, 95% CI, 1.51-5.40, for male and female users, respectively) and HPV (aOR=1.82, 95% CI, 1.29-2.58 and aOR=2.35, 95% CI, 1.65-3.33, for male and female users, respectively). In addition, female users were more likely to have heard of the HPV vaccine (aOR=2.06, 95% CI, 1.41-3.00). No significant associations were found between social media use and prostate-specific antigen tests in males, Papanicolaou tests and breast cancer tests in females, or colon cancer tests in both male and female users. CONCLUSIONS: While social media services can potentially promote cancer screening awareness in the general population, but they did not improve screening behavior after adjusting for socioeconomic status. These findings strengthened our understanding of social media use in targeting health communications for different cancers.

Budget impact analysis of osteoporosis medications for primary prevention of fractures in Taiwan.

INTRODUCTION: Taiwan's national health insurance currently only covers the use of osteoporosis drugs for the secondary prevention of fractures and does not provide coverage for primary prevention. The purpose of this study is to develop a model for analyzing the budgetary impact of the use of osteoporosis medications of primary prevention. METHODS: The budget impact model in this study is the "actual medication cost" minus the "medical expenses for all types of fractures that can be avoided by taking osteoporosis medications." We developed six possible insurance payment plans for primary prevention based on the age of the patients and T-scores and performed eleven steps to estimate the budget impact of each payment plan. RESULTS: The results of this study indicated that there may be 71,220 (T-score ≤ - 3.0, 75 + y/o) to 157,515 (T-score ≤ - 2.5, 65 + y/o) people using the drugs, and the budget impact may be US$26.28-58.98 million in 2019. However, the payment plans may avoid 492-766 fracture events and save medical expenditures for fracture treatment by US$1.30-2.02 million. The average costs for primary prevention within a year will be US$53,386-77,006. CONCLUSION: The budget impact of using osteoporosis medications to primary prevention of fractures is significant, but it can be compensated due to savings in fracture treatment costs.

Health inequality: a longitudinal study on geographic variations in lung cancer incidence and mortality in Taiwan.

BACKGROUND: This study is aimed toward an analysis of the variations in lung cancer incidence and mortality, adjusted by population factors (age, gender, and year), between administrative areas. METHODS: This is a retrospective study, using 2005-2014 data in each administrative area from the Taiwan Cancer Registry database organized by the Health Promotion Administration. The yearly age-standardized (overall) and crude (stratified by gender and age) incidence/mortality (and their growth rates) for each administrative area were collected and calculated. We used a mixed model to analyze the repeated measurements of yearly incidence and mortality rates and used general linear regression to analyze their growth rates. RESULTS: It was found that male and elderly populations had significantly higher lung cancer incidence and mortality in Taiwan. After adjusting for gender, age, and calendar year, there were no significant variations in incidence among the administrative areas, while the mortality in Yilan County was significantly higher than that in Taipei City (the capital city of Taiwan). On the other hand, the incidence in the female and younger population and mortality growth rates were higher. The incidence growth rate in Keelung City was significantly lower than that in Taipei City, while there were no significant variations in mortality growth rate among administrative areas. CONCLUSIONS: This study found an inequality in the lung cancer burden among cities in Taiwan, which can serve as the basis for future resource allocations for lung cancer prevention and treatment in Taiwan.

Correct and logical inference on efficacy in subgroups and their mixture for binary outcomes.

Targeted therapies are becoming more common. In targeted therapy development, suppose its companion diagnostic test divides patients into a marker-positive subgroup and its complementary marker-negative subgroup. To find the right patient population for the therapy to target, inference on efficacy in the marker-positive and marker-negative subgroups as well as efficacy in the overall mixture population are all of interest. Depending on the type of clinical endpoints, inference on mixture population can be nontrivial and commonly used efficacy measures may not be suitable for a mixture population. Correlations among estimates of efficacy in the marker-positive, marker-negative, and overall mixture population play a crucial role in using an earlier phase study to inform on the design of a confirmatory study (e.g., determination of sample size). This article first shows that when the clinical endpoint is binary (such as respond or not), odds ratio is inappropriate as an efficacy measure in this setting, but relative response (RR) is appropriate. We show a safe way of calculating estimated correlations is to consider mixing subgroup response probabilities within each treatment arm first, and then derive the joint distribution of RR estimates. We also show, if one calculates RR within each subgroup first, how wrong the correlations can be if the Delta method derivation fails to take randomness of estimating the mixing coefficient into account.

Geographic Variations and Time Trends in Cancer Treatments in Taiwan.

BACKGROUND: Targeted therapies have become important treatment options for cancer care in many countries. This study aimed to examine recent trends in utilization of antineoplastic drugs, particularly the use of targeted therapies for treatment of cancer, by geographic region in Taiwan (northern, midwestern, southern, and eastern regions and the outer islands). METHODS: This was a retrospective observational study of antineoplastic agents using 2009-2012 quarterly claims data from Taiwan's National Health Insurance Research Database. Yearly market shares by prescription volume and costs for targeted therapies among total antineoplastic agents by region were estimated. We used multivariate regression model and ANOVA to examine variations in utilization of targeted therapies between geographic regions and used ARIMA models to estimate longitudinal trends. RESULTS: Population-adjusted use and costs of antineoplastic drugs (including targeted therapies) were highest in the southern region of Taiwan and lowest in the outer islands. We found a 4-fold difference in use of antineoplastic drugs and a 49-fold difference in use of targeted therapies between regions if the outer islands were included. There were minimal differences in use of antineoplastic drugs between other regions with about a 2-fold difference in use of targeted therapies. Without considering the outer islands, the market share by prescription volume and costs of targeted therapies increased almost 2-fold (1.84-1.90) and 1.5-fold (1.26-1.61) respectively between 2009 and 2012. Furthermore, region was not significantly associated with use of antineoplastic agents or use of targeted therapies after adjusting for confounders. Region was associated with costs of antineoplastic agents but it was not associated with costs of targeted therapies after confounding adjustments. CONCLUSIONS: Use of antineoplastic drugs overall and use of targeted therapies for treatment of cancer varied somewhat between regions in Taiwan; use was notably low in the outer islands. Strategies might be needed to ensure access to cancer care in each region as economic burden of cancer care increase due to growing use of targeted therapies.

Subgroup mixable inference on treatment efficacy in mixture populations, with an application to time-to-event outcomes.

In tailored drug development, the patient population is thought of as a mixture of two or more subgroups that may derive differential treatment efficacy. To find the right patient population for the drug to target, it is necessary to infer treatment efficacy in subgroups and combinations of subgroups. A fundamental consideration in this inference process is that the logical relationships between treatment efficacy in subgroups and their combinations should be respected (for otherwise the assessment of treatment efficacy may become paradoxical). We show that some commonly used efficacy measures are not suitable for a mixture population. We also show that the current practice of over-simply extending the least squares means concept when estimating the efficacy in a mixture population is inappropriate. Proposing a new principle called subgroup mixable estimation, we establish the logical relationships among parameters that represent efficacy and develop a simultaneous inference procedure to confidently infer efficacy in subgroups and their combinations. Using oncology studies with time-to-event outcomes as an example, we show that the hazard ratio is not suitable for measuring treatment efficacy in a mixture population and provide appropriate efficacy measures with a rigorous inference procedure.

Effects of safety warnings and risk management plan for Thiazolidinediones in Taiwan.

PURPOSE: To evaluate changes in thiazolidinedione use and quality of prescription following safety warnings for thiazolidinediones and cardiac risk in 2007, Risk Management Plan (RMP) policy for rosiglitazone in 2010, and warning for pioglitazone and bladder cancer risk in 2010 in Taiwan. METHODS: We obtained 2003-2011 claims data from Taiwan's National Health Insurance Research Database. Using an interrupted time series design and segmented regression, we estimated changes in monthly prescribing rates for thiazolidinediones among all and prevalent diabetes patients with and without cardiovascular disease history (CV history). We also compared time to prescription of thiazolidinediones among new diabetes patients with CV history before and after each regulatory action using survival analysis. RESULTS: Among prevalent patients with and without CV history, the prescribing rates of rosiglitazone decreased 36.88% and 28.92% after safety warnings in 2007 respectively. Pioglitazone prescriptions increased 13% among patients with CV history, but no changes were detected among patients without CV history. After rosiglitazone's RMP policy in 2010, large reductions in prescriptions were observed in patients with CV history (-101.67%) and those without CV history (-88.04%). Among new diabetes patients with CV history, cardiac safety warnings in 2007 significantly delayed the prescription of rosiglitazone, but no significant change was found for pioglitazone. CONCLUSIONS: The Taiwan FDA regulatory actions for thiazolidinediones communicated possible risks of cardiac events and bladder cancer. Different safety regulatory actions had differential impacts on the use of rosiglitazone and pioglitazone and the quality use of these drugs among the high-risk patients.

Cost-effectiveness analysis of granulocyte colony-stimulating factors for the prophylaxis of chemotherapy-induced febrile neutropenia in patients with breast cancer in Taiwan.

OBJECTIVES: To examine the cost-effectiveness of using granulocyte colony-stimulating factor (G-CSF) for primary or secondary prophylaxis in patients with breast cancer from the perspective of Taiwan's National Health Insurance Administration. METHODS: A Markov model was constructed to simulate the events that may occur during and after a high-risk chemotherapy treatment. Various G-CSF prophylaxis strategies and medications were compared in the model. Effectiveness data were derived from the literature and an analysis of the National Health Insurance Research Database (NHIRD). Cost data were obtained from a published NHIRD study, and health utility values were also obtained from the literature. Sensitivity analyses were performed to assess the uncertainty of the cost-effectiveness results. RESULTS: In the base-case analysis, primary prophylaxis with pegfilgrastim had an incremental cost-effectiveness ratio (ICER) of NT$269,683 per quality-adjusted life year (QALY) gained compared to primary prophylaxis with lenograstim. The ICER for primary prophylaxis with lenograstim versus no G-CSF prophylaxis was NT$61,995 per QALY gained. The results were most sensitive to variations in relative risk of febrile neutropenia (FN) for pegfilgrastim versus no G-CSF prophylaxis. Furthermore, in the probabilistic sensitivity analysis, at a willingness-to-pay threshold of one times Taiwan's gross domestic product per capita, the probability of being cost-effective was 88.1% for primary prophylaxis with pegfilgrastim. CONCLUSIONS: Our study suggests that primary prophylaxis with either short- or long-acting G-CSF could be considered cost-effective for FN prevention in breast cancer patients receiving high-risk regimens.

Anti-osteoporosis drugs reduce mortality in cancer patients: A national cohort study of elderly with vertebral fractures.

Introduction: The most prevalent type of fragility fractures is osteoporotic vertebral fractures (OVFs). However, only a few studies have examined the relationship between anti-osteoporosis treatments and malignancy-related mortality following an OVF. The goal of this study is to determine the effect of anti-osteoporosis therapy on mortality in OVF patients with and without cancer. Method: Data from older people over the age of 65 who were hospitalised for OVFs between 1 January 2003 and 31 December 2018 were analysed retrospectively. A total of 6139 persons getting osteoporosis treatment and 28,950 who did not receive treatment were analysed, together with 2 sets of patients, comprising cancer patients (794) and cancer-free patients (5342), using anti-osteoporosis medication or not, in 1:1 propensity score-matched analyses. The hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. Results: In all, 35,089 patients with OVFs were included in the population; 29,931 people (85.3%) were women, and the mean (standard deviation) age was 78.13 (9.27) years. Overall survival was considerably higher in those undergoing osteoporosis therapy. This was true both for those without cancer (adjusted HR 0.55; 95% CI 0.51-0.59; P<.0001) as well as those with cancer (adjusted HR 0.72; 95% CI 0.62-0.84; P<.0001). Even among cancer patients, those who received anti-osteoporotic drugs had a lower mortality rate than those who did not. Conclusion: Our findings suggest that anti-osteoporosis therapy should be initiated regardless of the presence of cancer in the elderly, as it increases survival following OVFs.

Machine-Learning Based Risk Assessment for Cancer Therapy-Related Cardiac Adverse Events Among Breast Cancer Patients.

The study aims to develop machine-learning models to predict cardiac adverse events in female breast cancer patients who receive adjuvant therapy. We selected breast cancer patients from a retrospective dataset of the Taipei Medical University Clinical Research Database and Taiwan Cancer Registry between January 2004 and December 2020. Patients were monitored at the date of prescribed chemo- and/or -target therapies until cardiac adverse events occurred during a year. Variables were used, including demographics, comorbidities, medications, and lab values. Logistics regression (LR) and artificial neural network (ANN) were used. The performance of the algorithms was measured by the area under the receiver operating characteristic curve (AUC). In total, 1321 patients (an equal 15039 visits) were included. The best performance of the artificial neural network (ANN) model was achieved with the AUC, precision, recall, and F1-score of 0.89, 0.14, 0.82, and 0.2, respectively. The most important features were a pre-existing cardiac disease, tumor size, estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), cancer stage, and age at index date. Further research is necessary to determine the feasibility of applying the algorithm in the clinical setting and explore whether this tool could improve care and outcomes.

Taipei Medical University Clinical Research Database: a collaborative hospital EHR database aligned with international common data standards.

OBJECTIVE: The objective of this paper is to provide a comprehensive overview of the development and features of the Taipei Medical University Clinical Research Database (TMUCRD), a repository of real-world data (RWD) derived from electronic health records (EHRs) and other sources. METHODS: TMUCRD was developed by integrating EHRs from three affiliated hospitals, including Taipei Medical University Hospital, Wan-Fang Hospital and Shuang-Ho Hospital. The data cover over 15 years and include diverse patient care information. The database was converted to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) for standardisation. RESULTS: TMUCRD comprises 89 tables (eg, 29 tables for each hospital and 2 linked tables), including demographics, diagnoses, medications, procedures and measurements, among others. It encompasses data from more than 4.15 million patients with various medical records, spanning from the year 2004 to 2021. The dataset offers insights into disease prevalence, medication usage, laboratory tests and patient characteristics. DISCUSSION: TMUCRD stands out due to its unique advantages, including diverse data types, comprehensive patient information, linked mortality and cancer registry data, regular updates and a swift application process. Its compatibility with the OMOP CDM enhances its usability and interoperability. CONCLUSION: TMUCRD serves as a valuable resource for researchers and scholars interested in leveraging RWD for clinical research. Its availability and integration of diverse healthcare data contribute to a collaborative and data-driven approach to advancing medical knowledge and practice.