Statins have been shown to be a beneficial treatment as chemotherapy and target therapy for lung cancer. This study aimed to investigate the effectiveness of statins in combination with epidermal ...growth factor receptor‐tyrosine kinase inhibitor therapy for the resistance and mortality of lung cancer patients. A population‐based cohort study was conducted using the Taiwan Cancer Registry database. From January 1, 2007, to December 31, 2012, in total 792 non‐statins and 41 statins users who had undergone EGFR‐TKIs treatment were included in this study. All patients were monitored until the event of death or when changed to another therapy. Kaplan‐Meier estimators and Cox proportional hazards regression models were used to calculate overall survival. We found that the mortality was significantly lower in patients in the statins group compared with patients in the non‐statins group (4‐y cumulative mortality, 77.3%; 95% confidence interval (CI), 36.6%‐81.4% vs. 85.5%; 95% CI, 78.5%‐98%; P = .004). Statin use was associated with a reduced risk of death in patients the group who had tumor sizes <3 cm (hazard ratio HR, 0.51, 95% CI, 0.29‐0.89) and for patients in the group who had CCI scores <3 (HR, 0.6; 95% CI, 0.41‐0.88; P = .009). In our study, statins were found to be associated with prolonged survival time in patients with lung cancer who were treated with EGFR‐TKIs and played a synergistic anticancer role.
In this article, we describe the use of statins on the beneficial mortality of lung cancer patients with EGFR‐TKIs therapy. We found that statins were associated with prolonged survival time in patients with lung cancer who were taking EGFR‐TKIs. These could be playing a synergic anticancer role during the TKI treatment period, as well as improving quality of life worldwide and medical practice overall.
Coronavirus disease 2019 (COVID-19) has already raised serious concern globally as the number of confirmed or suspected cases have increased rapidly. Epidemiological studies reported that obesity is ...associated with a higher rate of mortality in patients with COVID-19. Yet, to our knowledge, there is no comprehensive systematic review and meta-analysis to assess the effects of obesity and mortality among patients with COVID-19. We, therefore, aimed to evaluate the effect of obesity, associated comorbidities, and other factors on the risk of death due to COVID-19. We did a systematic search on PubMed, EMBASE, Google Scholar, Web of Science, and Scopus between January 1, 2020, and August 30, 2020. We followed Cochrane Guidelines to find relevant articles, and two reviewers extracted data from retrieved articles. Disagreement during those stages was resolved by discussion with the main investigator. The random-effects model was used to calculate effect sizes. We included 17 articles with a total of 543,399 patients. Obesity was significantly associated with an increased risk of mortality among patients with COVID-19 (RR
: 1.42 (95%CI: 1.24-1.63,
< 0.001). The pooled risk ratio for class I, class II, and class III obesity were 1.27 (95%CI: 1.05-1.54,
= 0.01), 1.56 (95%CI: 1.11-2.19,
< 0.01), and 1.92 (95%CI: 1.50-2.47,
< 0.001), respectively). In subgroup analysis, the pooled risk ratio for the patients with stroke, CPOD, CKD, and diabetes were 1.80 (95%CI: 0.89-3.64,
= 0.10), 1.57 (95%CI: 1.57-1.91,
< 0.001), 1.34 (95%CI: 1.18-1.52,
< 0.001), and 1.19 (1.07-1.32,
= 0.001), respectively. However, patients with obesity who were more than 65 years had a higher risk of mortality (RR: 2.54; 95%CI: 1.62-3.67,
< 0.001). Our study showed that obesity was associated with an increased risk of death from COVID-19, particularly in patients aged more than 65 years. Physicians should aware of these risk factors when dealing with patients with COVID-19 and take early treatment intervention to reduce the mortality of COVID-19 patients.
The aim of this study was to conduct a nationwide survey of the use of emergency ophthalmology services using a sub-dataset of one million beneficiaries sampled from Taiwan's National Health ...Insurance Research Database (NHIRD) for the years 2008 through 2012. By analyzing this population dataset, the study illustrates the disease landscape of emergency eye care services. The five-year, one-million-person NHIRD sub-dataset for 2008 through 2012 was used to explore emergency visits and ophthalmology specialty visits and to analyze the associated demographics and diagnosis codes based on the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM). Diagnoses were categorized into three groups: urgent, non-urgent, and intermediate. A total of 2454 emergency eye care visits were identified. The mean age of the patients who made these visits was 34.6 years old, and their sex ratio was 1.36 men to women. The percentages of urgent, non-urgent, and intermediate eye care visits in this study were 48.2%, 30.9%, and 20.9%, respectively. The leading diagnoses in the urgent category were corneal abrasions, foreign bodies in the eyes, eye burns, and blunt eye injuries. The leading diagnoses for the non-urgent visits were conjunctivitis, subconjunctival hemorrhages, trichiasis, and dry eye disease. Those for the intermediate category were superficial punctate keratitis, corneal opacity and degeneration, and lid, orbital, and lacrimal drainage infections. The urgent visit category accounted for nearly half of all the visits identified in this study. Compared to outpatient department visitors, the emergency ophthalmology service patients were younger and more predominantly male. These results were consistent with those of previous reports. Low copays have made emergency ophthalmology services highly accessible in Taiwan. However, future policies can be designed to more effectively allocate resources to urgent cases.
This study attempted to illustrate the demographic of inpatient eye careservice from 1997 to 2011 in Taiwan, and also the ophthalmic disease landscape and utilization change over time. These insights ...might apply to resource allocation planning and trainees' better understandings of ophthalmic inpatient practice.
This study utilized Taiwan's National Health Insurance Research Database (NHIRD). Admission records of eye service that occurred since 1997 and until 2011 were included. Records were separated into operative and non-operative. The records were further divided according to their time: a group of early time before 2006 and a late one after 2006.
Patients' mean age were 56 and 44 years for operative and non-operative records. The sex ratio (male to female) was 1.3, and the average of admission duration was 4 days. The average spending was around 1000 United State Dollars per admission and a gradually upgoing trend was also noted. The number of inpatient eye services decreased over time, from 3,248 to 2,174 in the studied period. Cases admitted for operation primarily underwent cataract surgery, vitrectomy, and scleral buckling during the studied period. Trabeculectomy emerged as another major indication of admission during the later time. Cases admitted for non-operative management were primarily corneal ulcer, glaucoma, and infection, including orbital cellulitis and lid abscess. Corneal ulcers made up a major proportion of admission records in the non-operative group during both periods.
This study described the demographics of inpatient eye service in Taiwan. Ophthalmologist, especially trainees, and officials could make better policies according to the presented results in this study.
Introduction
Pancreatic cancer is associated with poor prognosis. Considering the increased global incidence of diabetes cases and that individuals with diabetes are considered a high‐risk ...subpopulation for pancreatic cancer, it is critical to detect the risk of pancreatic cancer within populations of person living = with diabetes. This study aimed to develop a novel prediction model for pancreatic cancer risk among patients with diabetes, using = a real‐world database containing clinical features and employing numerous artificial intelligent approach algorithms.
Methods
This retrospective observational study analyzed data on patients with Type 2 diabetes from a multisite Taiwanese EMR database between 2009 and 2019. Predictors were selected in accordance with the literature review and clinical perspectives. The prediction models were constructed using machine learning algorithms such as logistic regression, linear discriminant analysis, gradient boosting machine, and random forest.
Results
The cohort consisted of 66,384 patients. The Linear Discriminant Analysis (LDA) model generated the highest AUROC of 0.9073, followed by the Voting Ensemble and Gradient Boosting machine models. LDA, the best model, exhibited an accuracy of 84.03%, a sensitivity of 0.8611, and a specificity of 0.8403. The most significant predictors identified for pancreatic cancer risk were glucose, glycated hemoglobin, hyperlipidemia comorbidity, antidiabetic drug use, and lipid‐modifying drug use.
Conclusion
This study successfully developed a highly accurate 4‐year risk model for pancreatic cancer in patients with diabetes using real‐world clinical data and multiple machine‐learning algorithms. Potentially, our predictors offer an opportunity to identify pancreatic cancer early and thus increase prevention and invention windows to impact survival in diabetic patients.
To predict the risk of pancreatic cancer in patients with diabetes is crucial; the study aims to establish a novel prediction model for pancreatic cancer risk among patients with diabetes using real‐world clinical data.
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.
The amount of information being uploaded onto social video platforms, such as YouTube, Vimeo, and Veoh, continues to spiral, making it increasingly difficult to discern reliable health information ...from misleading content. There are thousands of YouTube videos promoting misleading information about anorexia (eg, anorexia as a healthy lifestyle).
The aim of this study was to investigate anorexia-related misinformation disseminated through YouTube videos.
We retrieved YouTube videos related to anorexia using the keywords anorexia, anorexia nervosa, proana, and thinspo on October 10, 2011.Three doctors reviewed 140 videos with approximately 11 hours of video content, classifying them as informative, pro-anorexia, or others. By informative we mean content describing the health consequences of anorexia and advice on how to recover from it; by pro-anorexia we mean videos promoting anorexia as a fashion, a source of beauty, and that share tips and methods for becoming and remaining anorexic. The 40 most-viewed videos (20 informative and 20 pro-anorexia videos) were assessed to gauge viewer behavior.
The interrater agreement of classification was moderate (Fleiss' kappa=0.5), with 29.3% (n=41) being rated as pro-anorexia, 55.7% (n=78) as informative, and 15.0% (n=21) as others. Pro-anorexia videos were favored 3 times more than informative videos (odds ratio OR 3.3, 95% CI 3.3-3.4, P<.001).
Pro-anorexia information was identified in 29.3% of anorexia-related videos. Pro-anorexia videos are less common than informative videos; however, in proportional terms, pro-anorexia content is more highly favored and rated by its viewers. Efforts should focus on raising awareness, particularly among teenagers, about the trustworthiness of online information about beauty and healthy lifestyles. Health authorities producing videos to combat anorexia should consider involving celebrities and models to reach a wider audience. More research is needed to study the characteristics of pro-anorexia videos in order to develop algorithms that will automatically detect and filter those videos before they become popular.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The chronic receipt of renin-angiotensin-aldosterone system (RAAS) inhibitors including angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) have been assumed to ...be associated with a significant decrease in overall gynecologic cancer risks. This study aimed to investigate the associations of long-term RAAS inhibitors use with gynecologic cancer risks. A large population-based case-control study was conducted from claim databases of Taiwan's Health and Welfare Data Science Center (2000-2016) and linked with Taiwan Cancer Registry (1979-2016). Each eligible case was matched with four controls using propensity matching score method for age, sex, month, and year of diagnosis. We applied conditional logistic regression with 95% confidence intervals to identify the associations of RAAS inhibitors use with gynecologic cancer risks. The statistical significance threshold was
< 0.05. A total of 97,736 gynecologic cancer cases were identified and matched with 390,944 controls. The adjusted odds ratio for RAAS inhibitors use and overall gynecologic cancer was 0.87 (95% CI: 0.85-0.89). Cervical cancer risk was found to be significantly decreased in the groups aged 20-39 years (aOR: 0.70, 95% CI: 0.58-0.85), 40-64 years (aOR: 0.77, 95% CI: 0.74-0.81), ≥65 years (aOR: 0.87, 95% CI: 0.83-0.91), and overall (aOR: 0.81, 95% CI: 0.79-0.84). Ovarian cancer risk was significantly lower in the groups aged 40-64 years (aOR: 0.76, 95% CI: 0.69-0.82), ≥65 years (aOR: 0.83, 95% CI: 0.75-092), and overall (aOR: 0.79, 95% CI: 0.74-0.84). However, a significantly increased endometrial cancer risk was observed in users aged 20-39 years (aOR: 2.54, 95% CI: 1.79-3.61), 40-64 years (aOR: 1.08, 95% CI: 1.02-1.14), and overall (aOR: 1.06, 95% CI: 1.01-1.11). There were significantly reduced risks of gynecologic cancers with ACEIs users in the groups aged 40-64 years (aOR: 0.88, 95% CI: 0.84-0.91), ≥65 years (aOR: 0.87, 95% CI: 0.83-0.90), and overall (aOR: 0.88, 95% CI: 0.85-0.80), and ARBs users aged 40-64 years (aOR: 0.91, 95% CI: 0.86-0.95). Our case-control study demonstrated that RAAS inhibitors use was associated with a significant decrease in overall gynecologic cancer risks. RAAS inhibitors exposure had lower associations with cervical and ovarian cancer risks, and increased endometrial cancer risk. ACEIs/ARBs use was found to have a preventive effect against gynecologic cancers. Future clinical research is needed to establish causality.
A well-established lung-cancer-survival-prediction model that relies on multiple data types, multiple novel machine-learning algorithms, and external testing is absent in the literature. This study ...aims to address this gap and determine the critical factors of lung cancer survival. We selected non-small-cell lung cancer patients from a retrospective dataset of the Taipei Medical University Clinical Research Database and Taiwan Cancer Registry between January 2008 and December 2018. All patients were monitored from the index date of cancer diagnosis until the event of death. Variables, including demographics, comorbidities, medications, laboratories, and patient gene tests, were used. Nine machine-learning algorithms with various modes were used. The performance of the algorithms was measured by the area under the receiver operating characteristic curve (AUC). In total, 3714 patients were included. The best performance of the artificial neural network (ANN) model was achieved when integrating all variables with the AUC, accuracy, precision, recall, and F1-score of 0.89, 0.82, 0.91, 0.75, and 0.65, respectively. The most important features were cancer stage, cancer size, age of diagnosis, smoking, drinking status, EGFR gene, and body mass index. Overall, the ANN model improved predictive performance when integrating different data types.
Abstract
Antidiabetic medications are commonly used around the world, but their safety is still unclear. The aim of this study was to investigate whether long-term use of insulin and oral ...antidiabetic medications is associated with cancer risk.
We conducted a well-designed case–control study using 12 years of data from Taiwan's National Health Insurance Research Database and investigated the association between antidiabetic medication use and cancer risk over 20 years. We identified 42,500 patients diagnosed with cancer and calculated each patient's exposure to antidiabetic drugs during the study period. We matched cancer and noncancer subjects matched 1:6 by age, gender, and index date, and used Cox proportional hazard regression and conditional logistic regression, adjusted for potential confounding factors, that is, medications and comorbid diseases that could influence cancer risk during study period.
Pioglitazone (adjusted odds ratio AOR, 1.20; 95% confidence interval CI, 1.05–1.38); and insulin and its analogs for injection, intermediate or long acting combined with fast acting (AOR, 1.22; 95% CI, 1.05–1.43) were significantly associated with a higher cancer risk. However, metformin (AOR, 1.00; 95% CI, 0.93–1.07), glibenclamide (AOR, 0.98; 95% CI, 0.92–1.05), acarbose (AOR, 1.06; 95% CI, 0.96–1.16), and others do not show evidence of association with cancer risk. Moreover, the risk for specific cancers among antidiabetic users as compared with nonantidiabetic medication users was significantly increased for pancreas cancer (by 45%), liver cancer (by 32%), and lung cancer (by 18%).
Antidiabetic drugs do not seem to be associated with an increased cancer risk incidence except for pioglitazone, insulin and its analogs for injection, intermediate or long acting combined with fast acting.
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