The effect of colchicine on cancer risk in patients with immune-mediated inflammatory diseases: a time-dependent study based on the Taiwan's National Health Insurance Research Database.

BACKGROUND: To determine the effect of colchicine on cancer risk in patients with the immune-mediated inflammatory diseases (IMIDs)-related to colchicine use. METHODS: This is a time-dependent propensity-matched general population study based on the National Health Insurance Research Database (NHIRD) of Taiwan. We identified the IMIDs patients (n = 111,644) newly diagnosed between 2000 and 2012 based on the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)-274,712, 135, 136.1, 279.49, 518.3, 287.0, 696.0, 696.1, 696.8, 420, 429.4, 710.0, 710.1, 710.3, 710.4, 714.0, 720, 55.0, 55.1, 55.9, 556. INCLUSION CRITERIA: aged ≧ 20 years, if a patient had at least these disease diagnosis requirements within 1 year of follow-up, and, these patients had at least two outpatient visits or an inpatient visit. After propensity-matched according to age, sex, comorbidities, medications and index date, the IMIDs patients enter into colchicine users (N = 16,026) and colchicine nonusers (N = 16,026). Furthermore, time-dependent Cox models were used to analyze cancer risk in propensity-matched colchicine users compared with the nonusers. The cumulative cancer incidence was analyzed using Cox proportional regression analysis. We calculated adjusted hazard ratios (aHRs) and their 95% confidence intervals (95% CIs) for cancer after adjusting for sex, age, comorbidities, and use of medicine including acetylcysteine, medication for smoking cessation such as nicotine replacement medicines (the nicotine patch) and pill medicines (varenicline), anti-inflammatory drugs and immunosuppressant drugs. RESULTS: Comparing the colchicine nonusers, all cancer risk were mildly attenuated, the (aHR (95% CI)) of all cancer is (0.84 (0.55, 0.99)). Meanwhile, the colchicine users were associated with the lower incidence of the colorectal cancer, the (aHRs (95% CI)) is (0.22 (0.19, 0.89)). Those aged < 65 years and male/female having the colchicine users were associated with lower risk the colorectal cancer also. Moreover, the colchicine > 20 days use with the lower aHR for colorectal cancer. CONCLUSION: Colchicine was associated with the lower aHR of the all cancer and colorectal cancer formation in patients with the IMIDs.

Relationship between Glucagon-like Peptide-1 Receptor Agonists and Cardiovascular Disease in Chronic Respiratory Disease and Diabetes.

The purpose of this paper is to assess the effect of glucagon-like peptide-1 receptor agonists (GLP-1RAs) on stroke or heart disease in patients having chronic respiratory disease and diabetes (CD) with underlying diseases related to COVID-19. From 1998 to 2019, we adjusted competing risk by assessing the effect of GLP-1RAs on stroke or heart disease in a CD cohort after propensity matching based on the Taiwan National Health Insurance Research Database. We also used the time-dependent method to examine the results. GLP-1 RA and non-GLP-1 RA user groups included 15,801 patients (53% women and 46% men with a mean age of 52.6 ± 12.8 years). The time between the diagnoses of DM and the initial use of the GLP-1 RA among the stroke subcohort (<2000 days) was shorter than that of the heart disease subcohort (>2000 days) (all p-values < 0.05). The overall risks of stroke, ischemic, and hemorrhagic stroke were significantly lower in GLP-1 RA users than nonusers. The adjusted subhazard ratio (aSHR) was 0.76 [95% CI 0.65-0.90], 0.77 [95% CI 0.64-0.92], and 0.69 [95% CI 0.54-0.88] (p < 0.05 for all). Furthermore, a ≥351-day use had a significantly lower stroke risk than GLP-1 RA nonusers (aSHR 0.35 [95% CI 0.26-0.49]). The time-dependent method revealed the same result, such as lower stroke, and ischemic or hemorrhagic stroke risk. In contrast, the cardiac arrhythmia incidence was higher in GLP-1 RA users with an aSHR of 1.36 [95% CI 1.16-1.59]. However, this risk disappeared after the ≥351-day use with 1.21 (0.98, 1.68) aSHR. Longer GLP-1 RA use was associated with a decreased risk of ischemic or hemorrhagic stroke and the risk of cardiac arrhythmia disappears in a CD cohort. Both a shorter lag time use of the GLP-1 RA and a longer time use of GLP-1 RA were associated with a decreased risk of ischemic or hemorrhagic stroke in the CD cohort. The GLP-1 RA use in the early stage and optimal time use in the CD cohort may avoid the stroke risk.

Exploring the proficiency of ChatGPT-4: An evaluation of its performance in the Taiwan advanced medical licensing examination.

Background: Taiwan is well-known for its quality healthcare system. The country's medical licensing exams offer a way to evaluate ChatGPT's medical proficiency. Methods: We analyzed exam data from February 2022, July 2022, February 2023, and July 2033. Each exam included four papers with 80 single-choice questions, grouped as descriptive or picture-based. We used ChatGPT-4 for evaluation. Incorrect answers prompted a "chain of thought" approach. Accuracy rates were calculated as percentages. Results: ChatGPT-4's accuracy in medical exams ranged from 63.75% to 93.75% (February 2022-July 2023). The highest accuracy (93.75%) was in February 2022's Medicine Exam (3). Subjects with the highest misanswered rates were ophthalmology (28.95%), breast surgery (27.27%), plastic surgery (26.67%), orthopedics (25.00%), and general surgery (24.59%). While using "chain of thought," the "Accuracy of (CoT) prompting" ranged from 0.00% to 88.89%, and the final overall accuracy rate ranged from 90% to 98%. Conclusion: ChatGPT-4 succeeded in Taiwan's medical licensing exams. With the "chain of thought" prompt, it improved accuracy to over 90%.

Performance of ChatGPT on Stage 1 of the Taiwanese medical licensing exam.

Introduction: Since its release by OpenAI in November 2022, numerous studies have subjected ChatGPT to various tests to evaluate its performance in medical exams. The objective of this study is to evaluate ChatGPT's accuracy and logical reasoning across all 10 subjects featured in Stage 1 of Senior Professional and Technical Examinations for Medical Doctors (SPTEMD) in Taiwan, with questions that encompass both Chinese and English. Methods: In this study, we tested ChatGPT-4 to complete SPTEMD Stage 1. The model was presented with multiple-choice questions extracted from three separate tests conducted in February 2022, July 2022, and February 2023. These questions encompass 10 subjects, namely biochemistry and molecular biology, anatomy, embryology and developmental biology, histology, physiology, microbiology and immunology, parasitology, pharmacology, pathology, and public health. Subsequently, we analyzed the model's accuracy for each subject. Result: In all three tests, ChatGPT achieved scores surpassing the 60% passing threshold, resulting in an overall average score of 87.8%. Notably, its best performance was in biochemistry, where it garnered an average score of 93.8%. Conversely, the performance of the generative pre-trained transformer (GPT)-4 assistant on anatomy, parasitology, and embryology was not as good. In addition, its scores were highly variable in embryology and parasitology. Conclusion: ChatGPT has the potential to facilitate not only exam preparation but also improve the accessibility of medical education and support continuous education for medical professionals. In conclusion, this study has demonstrated ChatGPT's potential competence across various subjects within the SPTEMD Stage 1 and suggests that it could be a helpful tool for learning and exam preparation for medical students and professionals.

Performance of ChatGPT incorporated chain-of-thought method in bilingual nuclear medicine physician board examinations.

Objective: This research explores the performance of ChatGPT, compared to human doctors, in bilingual, Mandarin Chinese and English, medical specialty exam in Nuclear Medicine in Taiwan. Methods: The study employed generative pre-trained transformer (GPT-4) and integrated chain-of-thoughts (COT) method to enhance performance by triggering and explaining the thinking process to answer the question in a coherent and logical manner. Questions from the Taiwanese Nuclear Medicine Specialty Exam served as the basis for testing. The research analyzed the correctness of AI responses in different sections of the exam and explored the influence of question length and language proportion on accuracy. Results: AI, especially ChatGPT with COT, exhibited exceptional capabilities in theoretical knowledge, clinical medicine, and handling integrated questions, often surpassing, or matching human doctor performance. However, AI struggled with questions related to medical regulations. The analysis of question length showed that questions within the 109-163 words range yielded the highest accuracy. Moreover, an increase in the proportion of English words in questions improved both AI and human accuracy. Conclusions: This research highlights the potential and challenges of AI in the medical field. ChatGPT demonstrates significant competence in various aspects of medical knowledge. However, areas like medical regulations require improvement. The study also suggests that AI may help in evaluating exam question difficulty and maintaining fairness in examinations. These findings shed light on AI role in the medical field, with potential applications in healthcare education, exam preparation, and multilingual environments. Ongoing AI advancements are expected to further enhance AI utility in the medical domain.

Machine Learning and Radiomics of Bone Scintigraphy: Their Role in Predicting Recurrence of Localized or Locally Advanced Prostate Cancer.

BACKGROUND: Machine-learning (ML) and radiomics features have been utilized for survival outcome analysis in various cancers. This study aims to investigate the application of ML based on patients' clinical features and radiomics features derived from bone scintigraphy (BS) and to evaluate recurrence-free survival in local or locally advanced prostate cancer (PCa) patients after the initial treatment. METHODS: A total of 354 patients who met the eligibility criteria were analyzed and used to train the model. Clinical information and radiomics features of BS were obtained. Survival-related clinical features and radiomics features were included in the ML model training. Using the pyradiomics software, 128 radiomics features from each BS image's region of interest, validated by experts, were extracted. Four textural matrices were also calculated: GLCM, NGLDM, GLRLM, and GLSZM. Five training models (Logistic Regression, Naive Bayes, Random Forest, Support Vector Classification, and XGBoost) were applied using K-fold cross-validation. Recurrence was defined as either a rise in PSA levels, radiographic progression, or death. To assess the classifier's effectiveness, the ROC curve area and confusion matrix were employed. RESULTS: Of the 354 patients, 101 patients were categorized into the recurrence group with more advanced disease status compared to the non-recurrence group. Key clinical features including tumor stage, radical prostatectomy, initial PSA, Gleason Score primary pattern, and radiotherapy were used for model training. Random Forest (RF) was the best-performing model, with a sensitivity of 0.81, specificity of 0.87, and accuracy of 0.85. The ROC curve analysis showed that predictions from RF outperformed predictions from other ML models with a final AUC of 0.94 and a p-value of <0.001. The other models had accuracy ranges from 0.52 to 0.78 and AUC ranges from 0.67 to 0.84. CONCLUSIONS: The study showed that ML based on clinical features and radiomics features of BS improves the prediction of PCa recurrence after initial treatment. These findings highlight the added value of ML techniques for risk classification in PCa based on clinical features and radiomics features of BS.

Imaging prediction of KRAS mutation in patients with rectal cancer through deep metric learning using pretreatment [18F]Fluorodeoxyglucose positron emission tomography/computed tomography.

OBJECTIVES: To predict KRAS mutation in rectal cancer (RC) through computer vision of [18F]fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) by using metric learning (ML). METHODS: This study included 160 patients with RC who had undergone preoperative PET/CT. KRAS mutation was identified through polymerase chain reaction analysis. This model combined ML with the deep-learning framework to analyze PET data with or without CT images. The Batch Balance Wrapper framework and K-fold cross-validation were employed during the learning process. A receiver operating characteristic (ROC) curve analysis was performed to assess the model's predictive performance. RESULTS: Genetic alterations in KRAS were identified in 82 (51%) tumors. Both PET and CT images were used, and the proposed model had an area under the ROC curve of 0.836 for its ability to predict a mutation status. The sensitivity, specificity, and accuracy were 75.3%, 79.3%, and 77.5%, respectively. When PET images alone were used, the area under the curve was 0.817, whereas the sensitivity, specificity, and accuracy were 73.2%, 79.6%, and 76.2%, respectively. CONCLUSIONS: The ML model presented herein revealed that baseline 18F-FDG PET/CT images could provide supplemental information to determine KRAS mutation in RC. Additional studies are required to maximize the predictive accuracy. ADVANCES IN KNOWLEDGE: The results of the ML model presented herein indicate that baseline 18F-FDG PET/CT images could provide supplemental information for determining KRAS mutation in RC.The predictive accuracy of the model was 77.5% when both image types were used and 76.2% when PET images alone were used. Additional studies are required to maximize the predictive accuracy.

Denosumab treatment and infection risks in patients with osteoporosis: propensity score matching analysis of a national-wide population-based cohort study.

Introduction: Denosumab demonstrates efficacy in reducing the incidence of hip, vertebral, and nonvertebral fractures in postmenopausal women with osteoporosis. We present a population-based national cohort study to evaluate the infection risks in patients with osteoporosis after long-term denosumab therapy. Methods: We used the Taiwan National Health Insurance Research Database (NHIRD) to identify patients with osteoporosis. The case cohort comprised patients treated with denosumab. Propensity score (PS) matching was used to select denosumab nonusers for the control cohort. The study period was between August 2011 and December 2017. Our study comprised 30,106 pairs of case and control patients. Results: Patients receiving denosumab therapy had high risks of the following infections: pneumonia and influenza (adjusted hazard ratio [aHR]: 1.33; 95% confidence interval [CI]: 1.27 -1.39), urinary tract infection (aHR: 1.36; 95% CI:1.32 -1.40), tuberculosis (aHR: 1.60; 95% CI: 1.36 -1.87), fungal infection (aHR: 1.67; 95% CI:1.46 -1.90), candidiasis (aHR: 1.68; 95% CI: 1.47 -1.93), herpes zoster infection (aHR: 1.27; 95% CI: 1.19 -1.35), sepsis (aHR: 1.54; 95% CI:1.43 -1.66), and death (aHR: 1.26; 95% CI: 1.20 -1.32). However, the longer the duration of denosumab treatment, the lower the risk patients had of developing infections. Discussion: Denosumab therapy is associated with a higher infection risk at the early periods of treatment. Nevertheless, the risk attenuates significantly after the 2nd year of therapy. Clinicians should closely monitor infection status in patients with osteoporosis during the initial stages of denosumab therapy.

Machine Learning Assisting the Prediction of Clinical Outcomes following Nucleoplasty for Lumbar Degenerative Disc Disease.

BACKGROUND: Lumbar degenerative disc disease (LDDD) is a leading cause of chronic lower back pain; however, a lack of clear diagnostic criteria and solid LDDD interventional therapies have made predicting the benefits of therapeutic strategies challenging. Our goal is to develop machine learning (ML)-based radiomic models based on pre-treatment imaging for predicting the outcomes of lumbar nucleoplasty (LNP), which is one of the interventional therapies for LDDD. METHODS: The input data included general patient characteristics, perioperative medical and surgical details, and pre-operative magnetic resonance imaging (MRI) results from 181 LDDD patients receiving lumbar nucleoplasty. Post-treatment pain improvements were categorized as clinically significant (defined as a ≥80% decrease in the visual analog scale) or non-significant. To develop the ML models, T2-weighted MRI images were subjected to radiomic feature extraction, which was combined with physiological clinical parameters. After data processing, we developed five ML models: support vector machine, light gradient boosting machine, extreme gradient boosting, extreme gradient boosting random forest, and improved random forest. Model performance was measured by evaluating indicators, such as the confusion matrix, accuracy, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC), which were acquired using an 8:2 allocation of training to testing sequences. RESULTS: Among the five ML models, the improved random forest algorithm had the best performance, with an accuracy of 0.76, a sensitivity of 0.69, a specificity of 0.83, an F1 score of 0.73, and an AUC of 0.77. The most influential clinical features included in the ML models were pre-operative VAS and age. In contrast, the most influential radiomic features had the correlation coefficient and gray-scale co-occurrence matrix. CONCLUSIONS: We developed an ML-based model for predicting pain improvement after LNP for patients with LDDD. We hope this tool will provide both doctors and patients with better information for therapeutic planning and decision-making.

Bivalent mRNA COVID-19 Vaccine-Related Pericarditis on 18 F-FDG PET/CT.

ABSTRACT: A 13-year-old boy was suspected with pericarditis after a second booster dose of bivalent mRNA COVID-19 vaccine. After specific preparation for cardiac inflammation with carbohydrate-free, high-fat diet, the 18 F-FDG PET/CT successfully demonstrated simultaneous presentation of vaccination-related axillary lymphadenopathy and pericarditis without the interference of physiological myocardial uptake.

Predicting Overall Survival with Deep Learning from 18F-FDG PET-CT Images in Patients with Hepatocellular Carcinoma before Liver Transplantation.

Positron emission tomography and computed tomography with 18F-fluorodeoxyglucose (18F-FDG PET-CT) were used to predict outcomes after liver transplantation in patients with hepatocellular carcinoma (HCC). However, few approaches for prediction based on 18F-FDG PET-CT images that leverage automatic liver segmentation and deep learning were proposed. This study evaluated the performance of deep learning from 18F-FDG PET-CT images to predict overall survival in HCC patients before liver transplantation (LT). We retrospectively included 304 patients with HCC who underwent 18F-FDG PET/CT before LT between January 2010 and December 2016. The hepatic areas of 273 of the patients were segmented by software, while the other 31 were delineated manually. We analyzed the predictive value of the deep learning model from both FDG PET/CT images and CT images alone. The results of the developed prognostic model were obtained by combining FDG PET-CT images and combining FDG CT images (0.807 AUC vs. 0.743 AUC). The model based on FDG PET-CT images achieved somewhat better sensitivity than the model based on CT images alone (0.571 SEN vs. 0.432 SEN). Automatic liver segmentation from 18F-FDG PET-CT images is feasible and can be utilized to train deep-learning models. The proposed predictive tool can effectively determine prognosis (i.e., overall survival) and, thereby, select an optimal candidate of LT for patients with HCC.

Artificial Intelligence of Object Detection in Skeletal Scintigraphy for Automatic Detection and Annotation of Bone Metastases.

BACKGROUND: When cancer has metastasized to bone, doctors must identify the site of the metastases for treatment. In radiation therapy, damage to healthy areas or missing areas requiring treatment should be avoided. Therefore, it is necessary to locate the precise bone metastasis area. The bone scan is a commonly applied diagnostic tool for this purpose. However, its accuracy is limited by the nonspecific character of radiopharmaceutical accumulation. The study evaluated object detection techniques to improve the efficacy of bone metastases detection on bone scans. METHODS: We retrospectively examined the data of 920 patients, aged 23 to 95 years, who underwent bone scans between May 2009 and December 2019. The bone scan images were examined using an object detection algorithm. RESULTS: After reviewing the image reports written by physicians, nursing staff members annotated the bone metastasis sites as ground truths for training. Each set of bone scans contained anterior and posterior images with resolutions of 1024 × 256 pixels. The optimal dice similarity coefficient (DSC) in our study was 0.6640, which differs by 0.04 relative to the optimal DSC of different physicians (0.7040). CONCLUSIONS: Object detection can help physicians to efficiently notice bone metastases, decrease physician workload, and improve patient care.

Real-World Testing of a Machine Learning-Derived Visual Scale for Tc99m TRODAT-1 for Diagnosing Lewy Body Disease: Comparison with a Traditional Approach Using Semiquantification.

Objectives: Abnormal dopamine transporter (DAT) uptake is an important biomarker for diagnosing Lewy body disease (LBD), including Parkinson's disease (PD) and dementia with Lewy bodies (DLB). We evaluated a machine learning-derived visual scale (ML-VS) for Tc99m TRODAT-1 from one center and compared it with the striatal/background ratio (SBR) using semiquantification for diagnosing LBD in two other centers. Patients and Methods: This was a retrospective analysis of data from a history-based computerized dementia diagnostic system. MT-VS and SBR among normal controls (NCs) and patients with PD, PD with dementia (PDD), DLB, or Alzheimer's disease (AD) were compared. Results: We included 715 individuals, including 122 NCs, 286 patients with PD, 40 with AD, 179 with DLB, and 88 with PDD. Compared with NCs, patients with PD exhibited a significantly higher prevalence of abnormal DAT uptake using all methods. Compared with the AD group, PDD and DLB groups exhibited a significantly higher prevalence of abnormal DAT uptake using all methods. The distribution of ML-VS was significantly different between PD and NC, DLB and AD, and PDD and AD groups (all p < 0.001). The correlation coefficient of ML-VS/SBR in all participants was 0.679. Conclusions: The ML-VS designed in one center is useful for differentiating PD from NC, DLB from AD, and PDD from AD in other centers. Its correlation with traditional approaches using different scanning machines is also acceptable. Future studies should develop models using data pools from multiple centers for increasing diagnostic accuracy.

Neck Lymph Node Recurrence in HNC Patients Might Be Predicted before Radiotherapy Using Radiomics Extracted from CT Images and XGBoost Algorithm.

The five-year overall survival rate of patients without neck lymph node recurrence is over 50% higher than those with lymph node metastasis. This study aims to investigate the prognostic impact of computed tomogram (CT)-based radiomics on the outcome of metastatic neck lymph nodes in patients with head and neck cancer (HNC) receiving definitive radiotherapy or chemoradiotherapy for organ preservation. The pretreatment 18F-FDG PET/CT of 79 HNC patients was retrospectively analyzed with radiomics extractors. The imbalanced data was processed using two techniques: over-sampling and under-sampling, after which the prediction model was established with a machine learning model using the XGBoost algorithm. The imbalanced dataset strategies slightly decreased the specificity but greatly improved the sensitivity. To have a higher chance of predicting neck cancer recurrence, however, clinical data combined with CT-based radiomics provides the best prediction effect. The original dataset performed was as follows: accuracy = 0.76 ± 0.07, sensitivity = 0.44 ± 0.22, specificity = 0.88 ± 0.06. After we used the over-sampling technique, the accuracy, sensitivity, and specificity values were 0.80 ± 0.05, 0.67 ± 0.11, and 0.84 ± 0.05, respectively. Furthermore, after using the under-sampling technique, the accuracy, sensitivity, and specificity values were 0.71 ± 0.09, 0.73 ± 0.13, and 0.70 ± 0.13, respectively. The outcome of metastatic neck lymph nodes in patients with HNC receiving radiotherapy for organ preservation can be predicted based on the results of machine learning. This way, patients can be treated alternatively. A further external validation study is required to verify our findings.

Prediction of All-Cause Mortality Based on Stress/Rest Myocardial Perfusion Imaging (MPI) Using Deep Learning: A Comparison between Image and Frequency Spectra as Input.

BACKGROUND: Cardiovascular management and risk stratification of patients is an important issue in clinics. Patients who have experienced an adverse cardiac event are concerned for their future and want to know the survival probability. METHODS: We trained eight state-of-the-art CNN models using polar maps of myocardial perfusion imaging (MPI), gender, lung/heart ratio, and patient age for 5-year survival prediction after an adverse cardiac event based on a cohort of 862 patients who had experienced adverse cardiac events and stress/rest MPIs. The CNN model outcome is to predict a patient's survival 5 years after a cardiac event, i.e., two classes, either yes or no. RESULTS: The best accuracy of all the CNN prediction models was 0.70 (median value), which resulted from ResNet-50V2, using image as the input in the baseline experiment. All the CNN models had better performance after using frequency spectra as the input. The accuracy increment was about 7~9%. CONCLUSIONS: This is the first trial to use pure rest/stress MPI polar maps and limited clinical data to predict patients' 5-year survival based on CNN models and deep learning. The study shows the feasibility of using frequency spectra rather than images, which might increase the performance of CNNs.

CHA2DS2-VASc score as an independent outcome predictor in patients hospitalized with acute ischemic stroke.

PURPOSE: Atrial fibrillation (AF) is a significant independent risk factor for 1-year mortality in patients with first acute ischemic stroke (AIS). The CHA2DS2-VASc score was initially developed to assess the risk of stroke in patients with AF. Recently, this scoring system has been demonstrated to have clinical value for predicting long-term clinical outcomes in AIS but the evidence is insufficient. This large-scale prospective cohort study investigated the independent predictive value of the score in such patients. METHODS: We included patients with AIS from the Taiwan Stroke Registry (TSR) during 2006-2016 as the present study population. Patients were divided into those with high (≥2) and low (<2) CHA2DS2-VASc scores. We further analyzed and classified patients according to the presence of AF. The clinical endpoint was major adverse cardiac and cerebrovascular events (MACCEs) at 1 year after the index AIS. RESULTS: A total of 62,227 patients with AIS were enrolled. The median age was 70.3 years, and 59% of the patients were women. After confounding factors were controlled, patients with high CHA2DS2-VASc scores had significantly higher incidence of 1-year MACCEs (adjusted hazard ratio [HR] = 1.63; 95% confidence interval [CI] = 1.52, 1.76), re-stroke (adjusted HR = 1.28; 95% CI = 1.16, 1.42), and all-cause mortality (adjusted HR = 2.03; 95% CI = 1.83, 2.24) than those with low CHA2DS2-VASc scores did. In the comparison between AF and non-AF groups, the AF group had increased MACCEs (adjusted HR = 1.74; 95% CI = 1.60, 1.89), myocardial infarction (adjusted HR = 4.86; 95% CI = 2.07, 11.4), re-stroke (adjusted HR = 1.47; 95% CI = 1.26, 1.71), and all-cause mortality (adjusted HR = 1.90; 95% CI = 1.72, 2.10). The Kaplan-Meier curve revealed that both CHA2DS2-VASc scores and AF were independent risk predictors for 1-year MACCEs and mortality. CONCLUSIONS: The CHA2DS2-VASc score and AF appeared to consistently predict 1-year MACCEs of AIS patients and provide more accurate risk stratification. Therefore, increased use of the CHA2DS2-VASc score may help improve the holistic clinical assessment of AIS patients with or without AF.

Colchicine Is a Weapon for Managing the Heart Disease Among Interstitial Lung Disease With Viral Infection: Have We Found the Holy Grail?

Background: This study investigated the effect of colchicine use on the risks of heart disease (HD), pericarditis, endocarditis, myocarditis, cardiomyopathy, cardiac arrhythmia, and cardiac failure in patients having interstitial lung disease (ILD) with virus infection (ILD cohort). Methods: We retrospectively enrolled ILD cohort between 2000 and 2013 from the Longitudinal Health Insurance Database and divided them into colchicine users (n = 12,253) and colchicine non-users (n = 12,253) through propensity score matching. The event of interest was the diagnosis of HD. The incidence of HD was analyzed using multivariate Cox proportional hazards models between colchicine users and the comparison cohort after adjustment for age, sex, medication, comorbidities, and index date based on the time-dependent analysis. Results: Colchicine users had a significantly lower risk of HD (aHR = 0.87, 95% confidence interval (CI]) = 0.82-0.92) than did the colchicine non-user. For colchicine non-users as the reference, the aHR (95% CI) of the patients who received colchicine of 2-7, 8-30, 31-150, and > 150 days were 0.89 (0.81-0.98), 0.84 (0.76-0.94), 090 (0.80-0.99), and 0.83 (0.74-0.93), respectively; regardless of duration use, the lower risk of HD persisted in colchicine users. The cumulative incidence of HD in colchicine users was significantly lower than that in the colchicine non-users (log-rank p < 0.001). Conclusion: The addition of short-term or long-term colchicine to standard medical therapy may have benefits to prevent the HD among the ILD patients concurrent with a virus infection or comorbidities even in elderly patients.

Hydroxychloroquine on the Pulmonary Vascular Diseases in Interstitial Lung Disease: Immunologic Effects, and Virus Interplay.

To investigate the effects of hydroxychloroquine (HCQ) drug use on the risk of pulmonary vascular disease (PVD) in an interstitial lung disease cohort (ILD cohort, ILD+ virus infection), we retrospectively enrolled the ILD cohort with HCQ (HCQ users, N = 4703) and the ILD cohort without HCQ (non-HCQ users, N = 4703) by time-dependence after propensity score matching. Cox models were used to analyze the risk of PVD. We calculated the adjusted hazard ratios (aHRs) and their 95% confidence intervals (CIs) for PVD after adjusting for sex, age, comorbidities, index date and immunosuppressants, such as steroids, etc. Compared with the HCQ nonusers, in HCQ users, the aHRs (95% CIs) for PVD were (2.24 (1.42, 3.54)), and the women's aHRs for PVD were (2.54, (1.49, 4.35)). The aHRs based on the days of HCQ use for PVD of 28−30 days, 31−120 days, and >120 days were (1.27 (0.81, 1.99)), (3.00 (1.81, 4.87)) and (3.83 (2.46, 5.97)), respectively. The medium or long-term use of HCQ or young women receiving HCQ were associated with a higher aHR for PVD in the ILD cohort. These findings indicated interplay of the primary immunologic effect of ILD, comorbidities, women, age and virus in the HCQ users.

Effects of colchicine use on ischemic and hemorrhagic stroke risk in diabetic patients with and without gout.

This study aimed to determine the effect of colchicine use on the risk of stroke among patients with diabetes mellitus (DM). We retrospectively enrolled patients with DM between 2000 and 2013 from the Longitudinal Health Insurance Database and divided them into a colchicine cohort (n = 8761) and noncolchicine cohort (n = 8761) by using propensity score matching (PSM). The event of interest was a stroke, including ischemic stroke and hemorrhagic stroke. The incidence of stroke was analyzed using multivariate Cox proportional hazards models between the colchicine cohort and the comparison cohort after adjustment for several confounding factors. The subdistribution hazard model was also performed for examination of the competing risk. The colchicine cohort had a significantly lower incidence of stroke [adjusted hazard ratios (aHR), 95% confidence intervals (95%CI)] (aHR = 0.61, 95%CI = 0.55-0.67), ischemic stroke (aHR = 0.59, 95%CI = 0.53-0.66), and hemorrhagic stroke (aHR = 0.66, 95%CI = 0.53-0.82) compared with the noncolchicine cohort. Drug analysis indicated that patients in the colchicine cohort who received colchicine of cumulative daily defined dose (cDDD) > 14 and duration > 28 days had a lower risk of stroke and ischemic stroke compared with nonusers. The colchicine cohort (cDDD > 150, duration > 360 days) also had a lower risk of stroke, ischemic stroke, and hemorrhagic stroke. The cumulative incidence of stroke, ischemic stroke, and hemorrhagic stroke in the colchicine cohort was significantly lower than that in the noncolchicine cohort (log-rank P < 0.001). However, the subdistribution hazard model reveal the colchicine was not associated with the hemorrhagic stroke in DM patients without gout (aHR = 0.69, 95%CI = 0.47-1.00). Colchicine use with cDDD > 14 and duration > 28 days was associated with lower risk of stroke and ischemic stroke, and colchicine use with cDDD > 150 and duration > 360 days played an auxiliary role in the prevention of stroke, ischemic stroke, and hemorrhagic stroke in patients with DM. The colchicine for the hemorrhagic stroke in DM patients without gout seem to be null effect.