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.

Recent progress in machine learning approaches for predicting carcinogenicity in drug development

INTRODUCTION

This review explores the transformative impact of machine learning (ML) on carcinogenicity prediction within drug development. It discusses the historical context and recent advancements, emphasizing the significance of ML methodologies in overcoming challenges related to data interpretation, ethical considerations, and regulatory acceptance.

AREAS COVERED

The review comprehensively examines the integration of ML, deep learning, and diverse artificial intelligence (AI) approaches in various aspects of drug development safety assessments. It explores applications ranging from early-phase compound screening to clinical trial optimization, highlighting the versatility of ML in enhancing predictive accuracy and efficiency.

EXPERT OPINION

Through the analysis of traditional approaches such as in vivo rodent bioassays and in vitro assays, the review underscores the limitations and resource intensity associated with these methods. It provides expert insights into how ML offers innovative solutions to address these challenges, revolutionizing safety assessments in drug development.

Personalised prediction of maintenance dialysis initiation in patients with chronic kidney disease stages 3-5: a multicentre study using the machine learning approach

BACKGROUND

Optimal timing for initiating maintenance dialysis in patients with chronic kidney disease (CKD) stages 3-5 is challenging. This study aimed to develop and validate a machine learning (ML) model for early personalised prediction of maintenance dialysis initiation within 1-year and 3-year timeframes among patients with CKD stages 3-5.

METHODS

Retrospective electronic health record data from the Taipei Medical University clinical research database were used. Newly diagnosed patients with CKD stages 3-5 between 2008 and 2017 were identified. The observation period spanned from the diagnosis of CKD stages 3-5 until the maintenance dialysis initiation or a maximum follow-up of 3 years. Predictive models were developed using patient demographics, comorbidities, laboratory data and medications. The dataset was divided into training and testing sets to ensure robust model performance. Model evaluation metrics, including area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value and F1 score, were employed.

RESULTS

A total of 6123 and 5279 patients were included for 1 year and 3 years of the model development. The artificial neural network demonstrated better performance in predicting maintenance dialysis initiation within 1 year and 3 years, with AUC values of 0.96 and 0.92, respectively. Important features such as baseline estimated glomerular filtration rate and albuminuria significantly contributed to the predictive model.

CONCLUSION

This study demonstrates the efficacy of an ML approach in developing a highly predictive model for estimating the timing of maintenance dialysis initiation in patients with CKD stages 3-5. These findings have important implications for personalised treatment strategies, enabling improved clinical decision-making and potentially enhancing patient outcomes.

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.

Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases

Background

Previous studies have identified COVID-19 risk factors, such as age and chronic health conditions, linked to severe outcomes and mortality. However, accurately predicting severe illness in COVID-19 patients remains challenging, lacking precise methods.

Objective

This study aimed to leverage clinical real-world data and multiple machine-learning algorithms to formulate innovative predictive models for assessing the risk of severe outcomes or mortality in hospitalized patients with COVID-19.

Methods

Data were obtained from the Taipei Medical University Clinical Research Database (TMUCRD) including electronic health records from three Taiwanese hospitals in Taiwan. This study included patients admitted to the hospitals who received an initial diagnosis of COVID-19 between January 1, 2021, and May 31, 2022. The primary outcome was defined as the composite of severe infection, including ventilator use, intubation, ICU admission, and mortality. Secondary outcomes consisted of individual indicators. The dataset encompassed demographic data, health status, COVID-19 specifics, comorbidities, medications, and laboratory results. Two modes (full mode and simplified mode) are used; the former includes all features, and the latter only includes the 30 most important features selected based on the algorithm used by the best model in full mode. Seven machine learning was employed algorithms the performance of the models was evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity.

Results

The study encompassed 22,192 eligible in-patients diagnosed with COVID-19. In the full mode, the model using the light gradient boosting machine algorithm achieved the highest AUROC value (0.939), with an accuracy of 85.5%, a sensitivity of 0.897, and a specificity of 0.853. Age, vaccination status, neutrophil count, sodium levels, and platelet count were significant features. In the simplified mode, the extreme gradient boosting algorithm yielded an AUROC of 0.935, an accuracy of 89.9%, a sensitivity of 0.843, and a specificity of 0.902.

Conclusion

This study illustrates the feasibility of constructing precise predictive models for severe outcomes or mortality in COVID-19 patients by leveraging significant predictors and advanced machine learning. These findings can aid healthcare practitioners in proactively predicting and monitoring severe outcomes or mortality among hospitalized COVID-19 patients, improving treatment and resource allocation.

Machine learning-based prediction of medication refill adherence among first-time insulin users with type 2 diabetes

AIMS

The prevalence of Type 2 Diabetes Mellitus (T2DM) is projected to be 7 % in 2030. Despite its need for long-term diabetes care, the adherence rate of injectable medications such as insulin is around 60 %, lower than the acceptable threshold of 80 %. This study aims to create classification models to predict insulin adherence among adult T2DM naïve insulin users.

METHODS

Clinical data were extracted from Taipei Medical University Clinical Research Database (TMUCRD) from January 1st, 2004 to December 30th, 2020. A patient was regarded as adherent if his/her medication possession ratio (MPR) was at least 80 %. Seven domains of predictors were created, including demographics, baseline medications, baseline comorbidities, baseline laboratory data, healthcare resource utilization, index insulins, and the concomitant non-insulin T2DM medications. We built two Xgboost models for internal and external testing respectively.

RESULTS

Using a cohort of 4134 patients from Taiwan, our model achieved the Area Under the curve of the Receiver Operating Characteristic (AUROC) of the internal test was 0.782 and the AUROC of the external test was 0.771. the SHAP (SHapley Additive exPlanations) value showed that the number of prescribed medications, the number of outpatient visits, and laboratory data were predictive of future insulin adherence.

CONCLUSIONS

This is the first study to predict adherence among adult naïve insulin users. The developed model is a potential clinical decision support tool to identify possible non-adherent patients for healthcare providers to design individualized education plans.

Ranitidine Use and Incident Cancer in a Multinational Cohort

Importance

Ranitidine, the most widely used histamine-2 receptor antagonist (H2RA), was withdrawn because of N-nitrosodimethylamine impurity in 2020. Given the worldwide exposure to this drug, the potential risk of cancer development associated with the intake of known carcinogens is an important epidemiological concern.

Objective

To examine the comparative risk of cancer associated with the use of ranitidine vs other H2RAs.

Design, Setting, and Participants

This new-user active comparator international network cohort study was conducted using 3 health claims and 9 electronic health record databases from the US, the United Kingdom, Germany, Spain, France, South Korea, and Taiwan. Large-scale propensity score (PS) matching was used to minimize confounding of the observed covariates with negative control outcomes. Empirical calibration was performed to account for unobserved confounding. All databases were mapped to a common data model. Database-specific estimates were combined using random-effects meta-analysis. Participants included individuals aged at least 20 years with no history of cancer who used H2RAs for more than 30 days from January 1986 to December 2020, with a 1-year washout period. Data were analyzed from April to September 2021.

Exposure

The main exposure was use of ranitidine vs other H2RAs (famotidine, lafutidine, nizatidine, and roxatidine).

Main Outcomes and Measures

The primary outcome was incidence of any cancer, except nonmelanoma skin cancer. Secondary outcomes included all cancer except thyroid cancer, 16 cancer subtypes, and all-cause mortality.

Results

Among 1 183 999 individuals in 11 databases, 909 168 individuals (mean age, 56.1 years; 507 316 [55.8%] women) were identified as new users of ranitidine, and 274 831 individuals (mean age, 58.0 years; 145 935 [53.1%] women) were identified as new users of other H2RAs. Crude incidence rates of cancer were 14.30 events per 1000 person-years (PYs) in ranitidine users and 15.03 events per 1000 PYs among other H2RA users. After PS matching, cancer risk was similar in ranitidine compared with other H2RA users (incidence, 15.92 events per 1000 PYs vs 15.65 events per 1000 PYs; calibrated meta-analytic hazard ratio, 1.04; 95% CI, 0.97-1.12). No significant associations were found between ranitidine use and any secondary outcomes after calibration.

Conclusions and Relevance

In this cohort study, ranitidine use was not associated with an increased risk of cancer compared with the use of other H2RAs. Further research is needed on the long-term association of ranitidine with cancer development.

Assessment of effects of moon phases on hospital outpatient visits: An observational national study

Objectives

A vast amount of literature has been conducted for investigating the association of different lunar phases with human health; and it has mixed reviews for association and non-association of diseases with lunar phases. This study investigates the existence of any impact of moon phases on humans by exploring the difference in the rate of outpatient visits and type of diseases that prevail in either non-moon or moon phases.

Methods

We retrieved dates of non-moon and moon phases for eight years (1st January 2001-31st December 2008) from the timeanddate.com website for Taiwan. The study cohort consisted of 1 million people from Taiwan's National Health Insurance Research Database (NHIRD) followed over eight years (1st January 2001-31st December 2008). We used the two-tailed, paired-t-test to compare the significance of difference among outpatient visits for 1229 moon phase days and 1074 non-moon phase days by using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes from NHIRD records.

Results

We found 58 diseases that showed statistical differences in number of outpatient visits in the non-moon and moon phases.

Conclusions

The results of our study identified diseases that have significant variations during different lunar phases (non-moon and moon phases) for outpatient visits in the hospital. In order to fully understand the reality of the pervasive myth of lunar effects on human health, behaviors and diseases, more in-depth research investigations are required for providing comprehensive evidence covering all the factors, such as biological, psychological and environmental aspects.

Renin-Angiotensin-Aldosterone System Inhibitors and Development of Gynecologic Cancers: A 23 Million Individual Population-Based Study

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 p < 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.

Association between Proton Pump Inhibitor Use and the Risk of Female Cancers: A Nested Case-Control Study of 23 Million Individuals

Background: Firm conclusions about whether long-term proton pump inhibitor (PPI) drug use impacts female cancer risk remain controversial. Objective: We aimed to investigate the associations between PPI use and female cancer risks. Methods: A nationwide population-based, nested case-control study was conducted within Taiwan’s Health and Welfare Data Science Center’s databases (2000−2016) and linked to pathologically confirmed cancer data from the Taiwan Cancer Registry (1979−2016). Individuals without any cancer diagnosis during the 17 years of the study served as controls. Case and control patients were matched 1:4 based on age, gender, and visit date. Conditional logistic regression with 95% confidence intervals (CIs) was applied to investigate the association between PPI exposure and female cancer risks by adjusting for potential confounders such as the Charlson comorbidity index and medication usage (metformin, aspirin, and statins). Results: A total of 233,173 female cancer cases were identified, consisting of 135,437 diagnosed with breast cancer, 64,382 with cervical cancer, 19,580 with endometrial cancer, and 13,774 with ovarian cancer. After matching each case with four controls, we included 932,692 control female patients. The number of controls for patients with breast cancer, cervical cancer, endometrial cancer, and ovarian cancer was 541,748, 257,528, 78,320, and 55,096, respectively. The use of PPIs was significantly associated with reduced risk of breast cancer and ovarian cancer in groups aged 20−39 years (adjusted odds ratio (aOR): 0.69, 95%CI: 0.56−0.84; p < 0.001 and aOR: 0.58, 95%CI: 0.34−0.99; p < 0.05, respectively) and 40−64 years (aOR: 0.89, 95%CI: 0.86−0.94; p < 0.0001 and aOR: 0.87, 95%CI: 0.75−0.99; p < 0.05, respectively). PPI exposure was associated with a significant decrease in cervical and endometrial cancer risks in the group aged 40−64 years (with aOR: 0.79, 95%CI: 0.73−0.86; p < 0.0001 and aOR: 0.72, 95%CI: 0.65−0.81; p < 0.0001, respectively). In contrast, in elderly women, PPI use was found to be insignificantly associated with female cancers among users. Conclusions: Our findings, based on real-world big data, can depict a comprehensive overview of PPI usage and female cancer risk. Further clinical studies are needed to elucidate the effects of PPIs on female cancers.

Determinants of coronavirus disease 2019 infection by artificial intelligence technology: A study of 28 countries

Objectives

The coronavirus disease 2019 pandemic has affected countries around the world since 2020, and an increasing number of people are being infected. The purpose of this research was to use big data and artificial intelligence technology to find key factors associated with the coronavirus disease 2019 infection. The results can be used as a reference for disease prevention in practice.

Methods

This study obtained data from the "Imperial College London YouGov Covid-19 Behaviour Tracker Open Data Hub", covering a total of 291,780 questionnaire results from 28 countries (April 1~August 31, 2020). Data included basic characteristics, lifestyle habits, disease history, and symptoms of each subject. Four types of machine learning classification models were used, including logistic regression, random forest, support vector machine, and artificial neural network, to build prediction modules. The performance of each module is presented as the area under the receiver operating characteristics curve. Then, this study further processed important factors selected by each module to obtain an overall ranking of determinants.

Results

This study found that the area under the receiver operating characteristics curve of the prediction modules established by the four machine learning methods were all >0.95, and the RF had the highest performance (area under the receiver operating characteristics curve is 0.988). Top ten factors associated with the coronavirus disease 2019 infection were identified in order of importance: whether the family had been tested, having no symptoms, loss of smell, loss of taste, a history of epilepsy, acquired immune deficiency syndrome, cystic fibrosis, sleeping alone, country, and the number of times leaving home in a day.

Conclusions

This study used big data from 28 countries and artificial intelligence methods to determine the predictors of the coronavirus disease 2019 infection. The findings provide important insights for the coronavirus disease 2019 infection prevention strategies.

Using Artificial Intelligence for the Early Detection of Micro-Progression of Pressure Injuries in Hospitalized Patients: A Preliminary Nursing Perspective Evaluation

This study established a predictive model for the early detection of micro-progression of pressure injuries (PIs) from the perspective of nurses. An easy and programing-free artificial intelligence modeling tool with professional evaluation capability and it performed independently by nurses was used for this purpose. In the preliminary evaluation, the model achieved an accuracy of 89%. It can bring positive benefits to clinical care. Only the overfitting issue and image subtraction method remain to be addressed.

Contact Laxative Use and the Risk of Arteriovenous Fistula Maturation Failure in Patients Undergoing Hemodialysis: A Multi-Center Cohort Study

Laxatives are commonly prescribed for constipation management; however, they are recognized as an independent factor associated with cardiovascular diseases. Arteriovenous fistula (AVF) is the closest to the ideal model of hemodialysis (HD) vascular access and part of the cardiovascular system. Our study aims to explore the association of contact laxative use with AVF maturation outcomes in patients undergoing HD. We conducted a multi-center cohort study of 480 contact laxative users and 472 non-users who had undergone initial AVF creation. All patients were followed until the outcomes of AVF maturation were confirmed. Multivariable logistic regression models were performed to evaluate the risk of AVF maturation failure imposed by laxatives. Here, we found that patients who used contact laxatives were significantly associated with an increased risk of AVF maturation failure compared to non-users (adjusted odds ratio, 1.64; p = 0.003). Notably, the risk of AVF maturation failure increased when increasing their average daily doses and cumulative treatment days. In conclusion, our study found a significant dose- and duration-dependent relationship between contact laxative use and an increased risk of AVF maturation failure. Thus, laxatives should be prescribed with caution in this population. Further studies are needed to validate these observations and investigate the potential mechanisms.

Analysis of Dual Combination Therapies Used in Treatment of Hypertension in a Multinational Cohort

IMPORTANCE

More than 1 billion adults have hypertension globally, of whom 70% cannot achieve their hypertension control goal with monotherapy alone. Data are lacking on clinical use patterns of dual combination therapies prescribed to patients who escalate from monotherapy.

OBJECTIVE

To investigate the most common dual combinations prescribed for treatment escalation in different countries and how treatment use varies by age, sex, and history of cardiovascular disease.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study used data from 11 electronic health record databases that cover 118 million patients across 8 countries and regions between January 2000 and December 2019. Included participants were adult patients (ages ≥18 years) who newly initiated antihypertensive dual combination therapy after escalating from monotherapy. There were 2 databases included for 3 countries: the Iqvia Longitudinal Patient Database (LPD) Australia and Electronic Practice-based Research Network 2019 linked data set from South Western Sydney Local Health District (ePBRN SWSLHD) from Australia, Ajou University School of Medicine (AUSOM) and Kyung Hee University Hospital (KHMC) databases from South Korea, and Khoo Teck Puat Hospital (KTPH) and National University Hospital (NUH) databases from Singapore. Data were analyzed from June 2020 through August 2021.

EXPOSURES

Treatment with dual combinations of the 4 most commonly used antihypertensive drug classes (angiotensin-converting enzyme inhibitor [ACEI] or angiotensin receptor blocker [ARB]; calcium channel blocker [CCB]; β-blocker; and thiazide or thiazide-like diuretic).

MAIN OUTCOMES AND MEASURES

The proportion of patients receiving each dual combination regimen, overall and by country and demographic subgroup.

RESULTS

Among 970 335 patients with hypertension who newly initiated dual combination therapy included in the final analysis, there were 11 494 patients from Australia (including 9291 patients in Australia LPD and 2203 patients in ePBRN SWSLHD), 6980 patients from South Korea (including 6029 patients in Ajou University and 951 patients in KHMC), 2096 patients from Singapore (including 842 patients in KTPH and 1254 patients in NUH), 7008 patients from China, 8544 patients from Taiwan, 103 994 patients from France, 76 082 patients from Italy, and 754 137 patients from the US. The mean (SD) age ranged from 57.6 (14.8) years in China to 67.7 (15.9) years in the Singapore KTPH database, and the proportion of patients by sex ranged from 24 358 (36.9%) women in Italy to 408 964 (54.3%) women in the US. Among 12 dual combinations of antihypertensive drug classes commonly used, there were significant variations in use across country and patient subgroup. For example starting an ACEI or ARB monotherapy followed by a CCB (ie, ACEI or ARB + CCB) was the most commonly prescribed combination in Australia (698 patients in ePBRN SWSLHD [31.7%] and 3842 patients in Australia LPD [41.4%]) and Singapore (216 patients in KTPH [25.7%] and 439 patients in NUH [35.0%]), while in South Korea, CCB + ACEI or ARB (191 patients in KHMC [20.1%] and 1487 patients in Ajou University [24.7%]), CCB + β-blocker (814 patients in Ajou University [13.5%] and 217 patients in KHMC [22.8%]), and ACEI or ARB + CCB (147 patients in KHMC [15.5%] and 1216 patients in Ajou University [20.2%]) were the 3 most commonly prescribed combinations. The distribution of 12 dual combination therapies were significantly different by age and sex in almost all databases. For example, use of ACEI or ARB + CCB varied from 873 of 3737 patients ages 18 to 64 years (23.4%) to 343 of 2292 patients ages 65 years or older (15.0%) in South Korea's Ajou University database (P for database distribution by age < .001), while use of ACEI or ARB + CCB varied from 2121 of 4718 (44.8%) men to 1721 of 4549 (37.7%) women in Australian LPD (P for drug combination distributions by sex < .001).

CONCLUSIONS AND RELEVANCE

In this study, large variation in the transition between monotherapy and dual combination therapy for hypertension was observed across countries and by demographic group. These findings suggest that future research may be needed to investigate what dual combinations are associated with best outcomes for which patients.

A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record

PURPOSE

To develop deep learning model (Deep-KOA) that can predict the risk of knee osteoarthritis (KOA) within the next year by using the previous three years nonimage-based electronic medical record (EMR) data.

PATIENTS AND METHODS

We randomly selected information of two million patients from the Taiwan National Health Insurance Research Database (NHIRD) from January 1, 1999 to December 31, 2013. During the study period, 132,594 patients were diagnosed with KOA, while 1,068,464 patients without KOA were chosen randomly as control. We constructed a feature matrix by using the three-year history of sequential diagnoses, drug prescriptions, age, and sex. Deep learning methods of convolutional neural network (CNN) and artificial neural network (ANN) were used together to develop a risk prediction model. We used the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and precision to evaluate the performance of Deep-KOA. Then, we explored the important features using stepwise feature selection.

RESULTS

This study included 132,594 KOA patients, 83,111 females (62.68%), 49,483 males (37.32%), mean age 64.2 years, and 1,068,464 non-KOA patients, 545,902 females (51.09%), 522,562 males (48.91%), mean age 51.00 years. The Deep-KOA achieved an overall AUROC, sensitivity, specificity, and precision of 0.97, 0.89, 0.93, and 0.80 respectively. The discriminative analysis of Deep-KOA showed important features from several diseases such as disorders of the eye and adnexa, acute respiratory infection, other metabolic and immunity disorders, and diseases of the musculoskeletal and connective tissue. Age and sex were not found as the most discriminative features, with AUROC of 0.9593 (-0.76% loss) and 0.9644 (-0.25% loss) respectively. Whereas medications including antacid, cough suppressant, and expectorants were identified as discriminative features.

CONCLUSION

Deep-KOA was developed to predict the risk of KOA within one year earlier, which may provide clues for clinical decision support systems to target patients with high risk of KOA to get precision prevention program.

The Effectiveness and Safety of Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer Patients With Stage III/IV: A Multicenter Study

Immune checkpoint inhibitors (ICIs) have been approved to treat patients with various cancer types, including lung cancer, in many countries. This study aims to investigate the effectiveness and safety of ICIs under different treatment conditions of non-small cell lung cancer patients. A population-based retrospective cohort study was conducted using the electronic health records of three medical centers in Taiwan. From January 01, 2016, to November 30, 2018, a total of 91 ICIs and 300 traditional chemotherapy users who had undergone stage III and IV lung cancer treatment were included in the study. We performed the randomized matched pair design by selecting a Chemotherapy subject for each ICI patient in the sample population. All subjects were monitored from the date of taking ICIs or chemotherapy drugs until the event of death, loss to follow-up, or were occurred with any defined adverse events. Kaplan-Meier estimators and cox proportional hazard regression models were used to compute the overall survival, efficacy, and safety of the ICIs group. The median overall survival (OS) in the ICI and Chemo groups after matching was 11.2 months and 10.5 months, respectively. However, the results showed no significant OS differences between ICIs and chemo groups for both before and after matching (HR,1.30; 95%CI, 0.68-2.46; p=0.428 before matching and HR,0.96; 95CI%, 0.64-1.44; p=0.838 after matching). We observed that with the higher amount of PD-L1, the length of the patients’ overall survival was (positive vs. negative PD-L1, HR,0.21; 95%CI, 0.05-0.80; p=0.022). The incidences of serious adverse drug events above grade 3 in the ICIs and traditional chemo groups were 12.7% and 21.5%, respectively. We also found that the number of AEs was less in ICIs than in the Chemo group, and the AEs that occurred after treatments were observed earlier in the ICIs compared to the Chemo group. ICIs drugs were observed to be safer than traditional chemotherapy as they had a lower risk of serious adverse drug events. It is necessary to pay attention to immune-related side effects and provide appropriate treatment. Furthermore, the patient’s physical status and PD-L1 test can be used to evaluate the clinical effectiveness of ICIs.

Risk of cancer in long-term levothyroxine users: Retrospective population-based study

Levothyroxine is a widely prescribed medication for the treatment of an underactive thyroid. The relationship between levothyroxine use and cancer risk is largely underdetermined. To investigate the magnitude of the possible association between levothyroxine use and cancer risk, this retrospective case‐control study was conducted using Taiwan’s Health and Welfare Data Science Center database. Cases were defined as all patients who were aged ≥20 years and had a first‐time diagnosis for cancer at any site for the period between 2001 and 2011. Multivariable conditional logistic regression models were used to calculate an adjusted odds ratio (AOR) to reduce potential confounding factors. A total of 601 733 cases and 2 406 932 controls were included in the current study. Levothyroxine users showed a 50% higher risk of cancer at any site (AOR: 1.50, 95% CI: 1.46‐1.54; P < .0001) compared with non–users. Significant increased risks were also observed for brain cancer (AOR: 1.90, 95% CI: 1.48‐2.44; P < .0001), skin cancer (AOR: 1.42, 95% CI: 1.17‐1.72; P < .0001), pancreatic cancer (AOR: 1.27, 95% CI: 1.01‐1.60; P = .03), and female breast cancer (AOR: 1.24, 95% CI: 1.15‐1.33; P < .0001). Our study results showed that levothyroxine use was significantly associated with an increased risk of cancer, particularly brain, skin, pancreatic, and female breast cancers. Levothyroxine remains a highly effective therapy for hypothyroidism; therefore, physicians should carefully consider levothyroxine therapy and monitor patients’ condition to avoid negative outcomes. Additional studies are needed to confirm these findings and to evaluate the potential biological mechanisms.

Determinants of Health Literacy and Its Associations With Health-Related Behaviors, Depression Among the Older People With and Without Suspected COVID-19 Symptoms: A Multi-Institutional Study

Purpose: We examined factors associated with health literacy among elders with and without suspected COVID-19 symptoms (S-COVID-19-S). Methods: A cross-sectional study was conducted at outpatient departments of nine hospitals and health centers 14 February-2 March 2020. Self-administered questionnaires were used to assess patient characteristics, health literacy, clinical information, health-related behaviors, and depression. A sample of 928 participants aged 60-85 years were analyzed. Results: The proportion of people with S-COVID-19-S and depression were 48.3 and 13.4%, respectively. The determinants of health literacy in groups with and without S-COVID-19-S were age, gender, education, ability to pay for medication, and social status. In people with S-COVID-19-S, one-score increment of health literacy was associated with 8% higher healthy eating likelihood (odds ratio, OR, 1.08; 95% confidence interval, 95%CI, 1.04, 1.13; p < 0.001), 4% higher physical activity likelihood (OR, 1.04; 95%CI, 1.01, 1.08, p = 0.023), and 9% lower depression likelihood (OR, 0.90; 95%CI, 0.87, 0.94; p < 0.001). These associations were not found in people without S-COVID-19-S. Conclusions: The older people with higher health literacy were less likely to have depression and had healthier behaviors in the group with S-COVD-19-S. Potential health literacy interventions are suggested to promote healthy behaviors and improve mental health outcomes to lessen the pandemic's damage in this age group.

Statins use and its impact in EGFR-TKIs resistance to prolong the survival of lung cancer patients: A Cancer registry cohort study in Taiwan

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.

Deep Learning Approach for the Development of a Novel Predictive Model for Prostate Cancer

We developed a deep learning approach for accurate prediction of PCA patients one year earlier with minimal features from electronic health records. The area under the receiver operating curve for prediction of PCA was 0.94. Moreover, the sensitivity and specificity of CNN were 0.87 and 0.88, respectively.

Incidences of Hypothyroidism Associated With Surgical Procedures for Thyroid Disorders: A Nationwide Population-Based Study

Background and Aim: Limited information available about different types of thyroid surgeries with risk for postoperative hypothyroidism. This study aimed to investigate the risk of developing early and late-onset postoperative hypothyroidism in patients with thyroid disorders. Methods: We used a large cohort data from the Taiwan National Health Insurance Research Data Base (NHIRDB) and identified 9,693 (9, 348) patients from January 1998 to December 2010, admitted for thyroid disorder surgeries. We used the surgical procedures time as the index date. Our observational retrospective cohort study excluded the subjects diagnosed with hypoparathyroidism and hypothyroidism before any surgeries. We analyzed the data using the Cox regression model to calculate the hazard ratio. Result: Postoperative hypothyroidism associated with bilateral-total (HR, 4.27; 95% CI, 3.32-5.50), one-side total and another subtotal (HR, 3.16; 95% CI, 2.59-3.86), bilateral-subtotal (HR, 1.65; 95% CI, 1.37-1.98), and unilateral-total (HR, 1.17; 95% CI, 0.95-1.44) surgical procedures. The time intervals for thyroid disorders were 320 cases developed postoperative hypoparathyroidism in eight weeks, 480 cases the second month, and 1000 cases in the first year after surgery. Conclusion: Findings suggest that thyroidectomy was associated with transient postoperative hypothyroidism in thyroid disorder patients. The bilateral-total surgical procedure was strongly associated with temporary postoperative hypothyroidism.

Transcriptomes of the interaction between Fusarium verticillioides and a Streptomyces strain reveal the fungal defense strategy under the pressure of a potential biocontrol agent

The actinobacteria Streptomyces sp. AV05 appears to be a potential biocontrol agent (BCA) against mycotoxigenic fungi. It was found to significantly inhibit F. verticillioides growth and mycotoxin production during their co-cultivation. F. verticillioides growth was durably affected while the decrease of the toxin production levels was reversible, suggesting different BCA actions. The study of both transcriptomes brought useful information on the microbial interaction. RNA-seq data indicated that the dual interaction modified genetic expression of both microorganisms as 18.5 % of the genes were differentially expressed for the fungus against 3.8 % for the actinobacteria. Fungal differentially expressed genes (DEGs) were equally up and down regulated while bacterial ones were mainly upregulated. We especially focused the analysis of DEGs on fungal defense reaction to bacterial attack. For example, if this potential BCA implements a strategy of antibiosis with the over expression of 'siderophore-interacting protein' linked to the production of bacteriocins, the fungus in a state of stress is able to adapt its metabolism by up-regulation of amidase. It could correspond to the induction of resistance gene clusters and suggest a detoxification process. Moreover fumonisins-related pathway appears underexpressed in the presence of Streptomyces that explain the reduction of fumonisin accumulation observed.

Does long-term use of antidiabetic drugs changes cancer risk?

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.

Development of Deep Learning Algorithm for Detection of Colorectal Cancer in EHR Data

We aimed to develop a deep learning model for the prediction of the risk of advanced colorectal cancer in Taiwanese adults. We collected data of 58152 patients from the Taiwan National Health Insurance database from 1999 to 2013. All patients' comorbidities and medications history were included in the development of the convolution neural network (CNN) model. We also used 3-year medical data of all patients before the diagnosed colorectal cancer (CRC) as the dimensional time in the model. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were computed to measure the performance of the model. The results showed the mean (SD) of AUC of the model was 0.922 (0.004). Moreover, the performance of the model observed the sensitivity of 0.837, specificity of 0.867, and 0.532 for PPV value. Our study utilized CNN to develop a prediction model for CRC, based on non-image and multi-dimensional medical records.

Artificial Intelligence in Diabetic Retinopathy: Insights from a Meta-Analysis of Deep Learning

The demand for AI to improve patients outcome has been increased; we, therefore, aim to establish the diagnostic values of AI in diabetic retinopathy by pooling the published studies of deep learning on this subject. A total of eight studies included which evaluated deep learning in a total of 706,922 retinal images. The overall pooled area under receiver operating curve (AUROC) was 98.93% (95%CI:98.37%-99.49%). However, the overall pooled sensitivity and specificity for detecting referable diabetic retinopathy (RDR) was 74% (95% CI: 73%-74%), and 95% (95% CI: 95%-95%). The findings of this study show that deep learning had high sensitivity and specificity for identifying diabetic retinopathy.

A probabilistic model for reducing medication errors: A sensitivity analysis using Electronic Health Records data

OBJECTIVES

Medication-related clinical decision support systems have already been considered as a sophisticated method to improve healthcare quality, however, its importance has not been fully recognized. This paper's aim was to validate an existing probabilistic model that can automatically identify medication errors by performing a sensitivity analysis from electronic medical record data.

METHODS

We first built a knowledge base that consisted of 2.22 million disease-medication (DM) and 0.78 million medication-medication (MM) associations using Taiwan Health and Welfare data science claims data between January 1st, 2009 and December 31st, 2011. Further, we collected 0.6 million outpatient visit prescriptions from six departments across five different medical centers/hospitals. Afterward, we employed the data to our AESOP model and validated it using a sensitivity analysis of 11 various thresholds (α = [0.5; 1.5]) that were used to identify positive DM and MM associations. We randomly selected 2400 randomly prescriptions and compared them to the gold standard of 18 physicians' manual review for appropriateness.

RESULTS

One hundred twenty-one results of 2400 prescriptions with various thresholds were tested by the AESOP model. Validation against the gold standard showed a high accuracy (over 80%), sensitivity (80-96%), and positive predictive value (over 85%). The negative predictive values ranged from 45 to 75% across three departments, cardiology, neurology, and ophthalmology.

CONCLUSION

We performed a sensitivity analysis and validated the AESOP model in different hospitals. Thus, picking the optimal threshold of the model depended on balancing false negatives with false positives and depending on the specialty and the purpose of the system.

Prc1E and Kif4A control microtubule organization within and between large Xenopus egg asters

The cleavage furrow in Xenopus zygotes is positioned by two large microtubule asters that grow out from the poles of the first mitotic spindle. Where these asters meet at the midplane, they assemble a disk-shaped interaction zone consisting of anti-parallel microtubule bundles coated with chromosome passenger complex (CPC) and centralspindlin that instructs the cleavage furrow. Here we investigate the mechanism that keeps the two asters separate and forms a distinct boundary between them, focusing on the conserved cytokinesis midzone proteins Prc1 and Kif4A. Prc1E, the egg orthologue of Prc1, and Kif4A were recruited to anti-parallel bundles at interaction zones between asters in Xenopus egg extracts. Prc1E was required for Kif4A recruitment but not vice versa. Microtubule plus-end growth slowed and terminated preferentially within interaction zones, resulting in a block to interpenetration that depended on both Prc1E and Kif4A. Unexpectedly, Prc1E and Kif4A were also required for radial order of large asters growing in isolation, apparently to compensate for the direction-randomizing influence of nucleation away from centrosomes. We propose that Prc1E and Kif4, together with catastrophe factors, promote "anti-parallel pruning" that enforces radial organization within asters and generates boundaries to microtubule growth between asters.

The impact of different surgical procedures on hypoparathyroidism after thyroidectomy: A population-based study

The main objective of this study is to investigate the outcome between surgical procedures and the risk of development of hypoparathyroidism followed by surgical procedure in patients with thyroid disorders.We analyzed the data acquired from Taiwan's Bureau of National Health Insurance (BNHI) research database from 1998 to 2011 and found 9316 patients with thyroid surgery. Cox regression model was used to calculate the hazard ratio (HR).A count of 314 cases (3.4%) of hypoparathyroidism was identified. The 9 years cumulated incidence of hypoparathyroidism was the highest in patient undergone bilateral total thyroidectomy (13.5%) and the lowest in the patient with unilateral subtotal thyroidectomy (1.2%). However, in the patients who had undergone unilateral subtotal, the risk was the highest in bilateral total (HR: 11.86), followed by radical thyroidectomy with unilateral neck lymph node dissection (HR: 8.56), unilateral total (HR, 4.39), and one side total and another side subtotal (HR: 2.80).The extent of thyroid resection determined the risk of development of hypoparathyroidism. It is suggested that the association of these factors is investigated in future studies.

Exploring association between statin use and breast cancer risk: an updated meta-analysis

PURPOSE

The benefits of statin treatment for preventing cardiac disease are well established. However, preclinical studies suggested that statins may influence mammary cancer growth, but the clinical evidence is still inconsistent. We, therefore, performed an updated meta-analysis to provide a precise estimate of the risk of breast cancer in individuals undergoing statin therapy.

METHODS

For this meta-analysis, we searched PubMed, the Cochrane Library, Web of Science, Embase, and CINAHL for published studies up to January 31, 2017. Articles were included if they (1) were published in English; (2) had an observational study design with individual-level exposure and outcome data, examined the effect of statin therapy, and reported the incidence of breast cancer; and (3) reported estimates of either the relative risk, odds ratios, or hazard ratios with 95% confidence intervals (CIs). We used random-effect models to pool the estimates.

RESULTS

Of 2754 unique abstracts, 39 were selected for full-text review, and 36 studies reporting on 121,399 patients met all inclusion criteria. The overall pooled risks of breast cancer in patients using statins were 0.94 (95% CI 0.86-1.03) in random-effect models with significant heterogeneity between estimates (I ² = 83.79%, p = 0.0001). However, we also stratified by region, the duration of statin therapy, methodological design, statin properties, and individual stain use.

CONCLUSIONS

Our results suggest that there is no association between statin use and breast cancer risk. However, observational studies cannot clarify whether the observed epidemiologic association is a causal effect or the result of some unmeasured confounding variable. Therefore, more research is needed.

The Concomitant Association of Thyroid Disorders and Myasthenia Gravis

Background

Some of the thyroid disorders (TD) and Myasthenia gravis (MG) are autoimmune related disease. The purpose of the study to evaluate the relationship of MG with all morphological and functional thyroid disorders.

Methods

We constructed a population-based cohort study during the period from January 2000-December 2002 by using reimbursement data from the Bureau National Health Insurance (NHI) system in Taiwan. Patients with TD and MG were identified by referring to the ICD-9-CM codes. (ICD-10-CM as reference) .The association of TD with MG occurred only in the same person within the study period. The Q value was used to measure the strength of disease-disease associations.

Results

We obtained 520628 TD and 7965 MG records for analysis. Diffuse toxic goiter had highest association rate, followed by nontoxic nodular goiter, simple goiter, chronic lymphocytic thyroiditis, thyroid cancer, and toxic nodular goiter. Female and older patients had a higher rate than their male and younger counterparts, respectively. Functional abnormalities revealed higher incidence of thyrotoxicosis and hypothyroidism in both sexes. We also found the strongest association in men with chronic thyroiditis, diffuse toxic goiter, thyrotoxicosis, acquired hypothyroidism, thyroid cancer, and simple goiter. While an intermediate association was observed in female with diffuse toxic goiter, in a male with toxic and nontoxic nodular/multinodular goiters, in female with thyrotoxicosis, thyroid cancer and acquired hypothyroidism.

Conclusion

This population based cohort study showed potential association of all types of TD with MG, and observed a higher association rate in female autoimmune TD whereas males showed a higher strength of association.

E-Health Literacy and Health Information Seeking Behavior Among University Students in Bangladesh

Web 2.0 has become a leading health communication platform and will continue to attract young users; therefore, the objective of this study was to understand the impact of Web 2.0 on health information seeking behavior among university students in Bangladesh. A random sample of adults (n = 199, mean 23.75 years, SD 2.87) participated in a cross-sectional, a survey that included the eHealth literacy scale (eHEALS) assessed use of Web 2.0 for health information. Collected data were analyzed using a descriptive statistical method and t-tests. Finally logistic regression analyses were conducted to determine associations between sociodemographic, social determinants, and use of Web 2.0 for seeking and sharing health information. Almost 74% of older Web 2.0 users (147/199, 73.9%) reported using popular Web 2.0 websites, such as Facebook and Twitter, to find and share health information. Current study support that current Web-based health information seeking and sharing behaviors influence health-related decision making.

Benzodiazepine Use and Risk of Dementia in the Elderly Population: A Systematic Review and Meta-Analysis

BACKGROUND

Benzodiazepines are a widely used medication in developed countries, particularly among elderly patients. However, benzodiazepines are known to affect memory and cognition and might thus enhance the risk of dementia. The objective of this review is to synthesize evidence from observational studies that evaluated the association between benzodiazepines use and dementia risk.

SUMMARY

We performed a systematic review and meta-analysis of controlled observational studies to evaluate the risk of benzodiazepines use on dementia outcome. All control observational studies that compared dementia outcome in patients with benzodiazepine use with a control group were included. We calculated pooled ORs using a random-effects model. Ten studies (of 3,696 studies identified) were included in the systematic review, of which 8 studies were included in random-effects meta-analysis and sensitivity analyses. Odds of dementia were 78% higher in those who used benzodiazepines compared with those who did not use benzodiazepines (OR 1.78; 95% CI 1.33-2.38). In subgroup analysis, the higher association was still found in the studies from Asia (OR 2.40; 95% CI 1.66-3.47) whereas a moderate association was observed in the studies from North America and Europe (OR 1.49; 95% CI 1.34-1.65 and OR 1.43; 95% CI 1.16-1.75). Also, diabetics, hypertension, cardiac disease, and statin drugs were associated with increased risk of dementia but negative association was observed in the case of body mass index. There was significant statistical and clinical heterogeneity among studies for the main analysis and most of the sensitivity analyses. There was significant statistical and clinical heterogeneity among the studies for the main analysis and most of the sensitivity analyses. Key Messages: Our results suggest that benzodiazepine use is significantly associated with dementia risk. However, observational studies cannot clarify whether the observed epidemiologic association is a causal effect or the result of some unmeasured confounding variable. Therefore, more research is needed.

Cloud-based BP system integrated with CPOE improves self-management of the hypertensive patients: A randomized controlled trial

BACKGROUND

Less than 50% of patients with hypertensive disease manage to maintain their blood pressure (BP) within normal levels.

OBJECTIVE

The aim of this study is to evaluate whether cloud BP system integrated with computerized physician order entry (CPOE) can improve BP management as compared with traditional care.

METHODS

A randomized controlled trial done on a random sample of 382 adults recruited from 786 patients who had been diagnosed with hypertension and receiving treatment for hypertension in two district hospitals in the north of Taiwan. Physicians had access to cloud BP data from CPOE. Neither patients nor physicians were blinded to group assignment. The study was conducted over a period of seven months.

RESULTS

At baseline, the enrollees were 50% male with a mean (SD) age of 58.18 (10.83) years. The mean sitting BP of both arms was no different. The proportion of patients with BP control at two, four and six months was significantly greater in the intervention group than in the control group. The average capture rates of blood pressure in the intervention group were also significantly higher than the control group in all three check-points.

CONCLUSIONS

Cloud-based BP system integrated with CPOE at the point of care achieved better BP control compared to traditional care. This system does not require any technical skills and is therefore suitable for every age group. The praise and assurance to the patients from the physicians after reviewing the Cloud BP records positively reinforced both BP measuring and medication adherence behaviors.

Interactions between traditional Chinese medicine and western drugs in Taiwan: A population-based study

BACKGROUND

Drug-drug interactions have long been an active research area in clinical medicine. In Taiwan, however, the widespread use of traditional Chinese medicines (TCM) presents additional complexity to the topic. Therefore, it is important to see the interaction between traditional Chinese and western medicine.

OBJECTIVE

(1) To create a comprehensive database of multi-herb/western drug interactions indexed according to the ways in which physicians actually practice and (2) to measure this database's impact on the detection of adverse effects between traditional Chinese medicine compounds and western medicines.

METHODS

First, a multi-herb/western medicine drug interactions database was created by separating each TCM compound into its constituent herbs. Each individual herb was then checked against an existing single-herb/western drug interactions database. The data source comes from the National Health Insurance research database, which spans the years 1998-2011. This study estimated the interaction prevalence rate and further separated the rates according to patient characteristics, distribution by county, and hospital accreditation levels. Finally, this new database was integrated into a computer order entry module of the electronic medical records system of a regional teaching hospital. The effects it had were measured for two months.

RESULTS

The most commonly interacting Chinese herbs were Ephedrae Herba and Angelicae Sinensis Radix/Angelicae Dahuricae Radix. Ephedrae Herba contains active ingredients similar to in ephedrine. 15 kinds of traditional Chinese medicine compounds contain Ephedrae Herba. Angelicae Sinensis Radix and Angelicae Dahuricae Radix contain ingredients similar to coumarin, a blood thinner. 9 kinds of traditional Chinese medicine compounds contained Angelicae Sinensis Radix/Angelicae Dahuricae Radix. In the period from 1998 to 2011, the prevalence of herb-drug interactions related to Ephedrae Herba was 0.18%. The most commonly prescribed traditional Chinese compounds were MA SHING GAN SHYR TANG (23.1%), followed by SHEAU CHING LONG TANG (15.5%) and DINQ CHUAN TANG (13.2%). The prevalence of herb-drug interactions related to Angelicae Sinensis Radix, Angelicae Dahuricae Radix was 4.59%. The most common traditional Chinese compound formula were TSANG EEL SAAN (32%), followed by HUOH SHIANG JENQ CHIH SAAN (31.4%) and SHY WUH TANG (10.7%). Once the multi-herb drug interaction database was deployed in a hospital system, there were 480 prescriptions that indicated a TCM-western drug interaction. Physicians were alerted 24 times during two months. These alerts resulted in a prescription change four times (16.7%).

CONCLUSION

Due to the unique cultural factors that have resulted in widespread acceptance of both western and traditional Chinese medicine, Taiwan stands well positioned to report on the prevalence of interactions between western drugs and traditional Chinese medicine and devise ways to reduce their incidence. This study built a multi-herb/western drug interactions database, embedded inside a hospital clinical information system, and then examined the effects that drug interaction alerts had on clinician prescribing behaviour. The results demonstrated that western drug/traditional Chinese medicine interactions are prevalent and that western-trained physicians tend to change their prescribing behaviour more than traditional Chinese medicine physicians in their response to medication interaction alerts.

mHealth: An updated systematic review with a focus on HIV/AIDS and tuberculosis long term management using mobile phones

OBJECTIVE

To evaluate the utilization of mobile phone technology for treatment adherence, prevention, education, data collection, monitoring long-term management of HIV/AIDS and TB patients.

METHODS

Articles published in English language from January 2005 until now from PubMed/MEDLINE, EMBASE, Web of Science, WHO databases, and clinical trials were included. Data extraction is based on medication adherence, quality of care, prevention, education, motivation for HIV test, data collection from HIV lab test results and patient monitoring. Articles selected for the analysis cover RCTs and non RCTs related to the use of mobile phones for long-term care and treatment of HIV/AIDS and TB patients.

RESULTS

Out of 90 articles selected for the analysis, a large number of studies, 44 (49%) were conducted in developing countries, 24 (26%) studies from developed countries, 12 (13%) are systematic reviews and 10 (11%) did not mention study location. Forty seven (52.2%) articles focused on treatment, 11 (12.2%) on quality of care, 8 (9%) on prevention, 13 (14.4%) on education, 6 (6.6%) on data collection, and 5 (5.5%) on patient monitoring. Overall, 66 (73%) articles reported positive effects, 21 (23%) were neutral and 3 (4%) reported negative results.

CONCLUSIONS

Mobile phone technology is widely reported to be an effective tool for HIV/AIDS and TB long-term care. It can substantially reduce disease burden on health care systems by rendering more efficient prevention, treatment, education, data collection and management support.

Profiling phenome-wide associations: a population-based observational study

OBJECTIVES

To objectively characterize phenome-wide associations observed in the entire Taiwanese population and represent them in a meaningful, interpretable way.

STUDY DESIGN

In this population-based observational study, we analyzed 782 million outpatient visits and 15 394 unique phenotypes that were observed in the entire Taiwanese population of over 22 million individuals. Our data was obtained from Taiwan's National Health Insurance Research Database.Results We stratified the population into 20 gender-age groups and generated 28.8 million and 31.8 million pairwise odds ratios from male and female subpopulations, respectively. These associations can be accessed online at http://associations.phr.tmu.edu.tw. To demonstrate the database and validate the association estimates obtained, we used correlation analysis to analyze 100 phenotypes that were observed to have the strongest positive association estimates with respect to essential hypertension. The results indicated that association patterns tended to have a strong positive correlation between adjacent age groups, while correlation estimates tended to decline as groups became more distant in age, and they diverged when assessed across gender groups.

CONCLUSIONS

The correlation analysis of pairwise disease association patterns across different age and gender groups led to outcomes that were broadly predicted before the analysis, thus confirming the validity of the information contained in the presented database. More diverse individual disease-specific analyses would lead to a better understanding of phenome-wide associations and empower physicians to provide personalized care in terms of predicting, preventing, or initiating an early management of concomitant diseases.

Is long-term use of benzodiazepine a risk for cancer?

The carcinogenicity of benzodiazepines (BZDs) is still unclear. We aimed to assess whether long-term benzodiazepines use is risk for cancer.We conducted a longitudinal population-based case-control study by using 12 years from Taiwan National Health Insurance database and investigated the association between BZDs use and cancer risk of people aged over 20 years. During the study period, 42,500 cases diagnosed with cancer were identified and analyzed for BZDs use. For each case, six eligible controls matched for age, sex, and the index date (ie, free of any cancer in the date of case diagnosis) by using propensity score. For appropriate risk estimation, we observed the outcomes according to their length of exposure (LOE) and defined daily dose (DDD). To mimic bias, we adjusted with potential confounding factors such as medications and comorbid diseases which could influence for cancer risk during the study period. The data was analyzed by using Cox proportional hazard regression and conditional logistic regression.The finding unveils benzodiazepines use into safe and unsafe groups for their carcinogenicity. The use of diazepam (HR, 0.96; 95%CI, 0.92-1.00), chlorodizepoxide (HR, 0.98; 95%CI, 0.92-1.04), medazepam (HR, 1.01; 95%CI, 0.84-1.21), nitrazepam (HR, 1.06; 95%CI, 0.98-1.14), oxazepam (HR, 1.05; 95%CI, 0.94-1.17) found safer among BZDs. However, clonazepam (HR, 1.15; 95%CI, 1.09-1.22) were associated with a higher risk for cancers. Moreover, specific cancer risk among BZDs use was observed significantly increased 98% for brain, 25% for colorectal, and 10% for lung, as compared with non-BZDs use.Diazepam, chlordiazepoxide, medazepam, nitrazepam, and oxazepam are safe among BZDs use for cancer risk. Our findings could help physicians to select safer BZDs and provide an evidence on the carcinogenic effect of benzodiazepines use by considering the LOE and DDD for further research.

LabPush: a pilot study of providing remote clinics with laboratory results via short message service (SMS) in Swaziland, Africa - a qualitative study

BACKGROUND

Developing countries are confronting a steady growth in the prevalence of the infectious diseases. Mobile technologies are widely available and can play an important role in health care at the regional, community, and individual levels. Although labs usually able to accomplish the requested blood test and produce the results within two days after receiving the samples, but the time for the results to be delivered back to clinics is quite variable depending on how often the motorbike transport makes trips between the clinic and the lab.

OBJECTIVE

In this study, we seek to assess factors facilitating as well as factors hindering the adoption of mobile devices in the Swazi healthcare through evaluating the end-users of the LabPush system.

METHODS

A qualitative study with semi-structured and in-depth one on one interviews were conducted over two month period July-August 2012. Purposive sampling was used; participants were those operating and using the LabPush system at the remote clinics, at the national laboratory and the supervisors of users at Swaziland. Interview questions were focused on perceived of ease of use and usefulness of the system. All interviews were recorded and then transcribed.

RESULTS

This study had aimed its primary focus on reducing TAT, prompt patient care, reducing bouncing of patients and defaulting of patients which were challenges that the clinicians have always had. Therefore, the results revealed several barriers and facilitators to the adoption of mobile device by healthcare providers in the Swaziland. The themes Shortens TAT, Technical support, Patient-centered care, Mindset, Improved communication, Missing Reports, Workload, Workflow, Security of smart phone, Human error and Ownership are sorted by facilitators to barriers.

CONCLUSION

Thus the end-users perspective, prompt patient care, reduced bouncing of patients, technical support, better communication, willing participant and social influence were facilitators of the adoption m-health in the Swazi healthcare.

Using hemoglobin A1C as a predicting model for time interval from pre-diabetes progressing to diabetes

OBJECTIVE

The early identification of subjects at high risk for diabetes is essential, thus, random rather than fasting plasma glucose is more useful. We aim to evaluate the time interval between pre-diabetes to diabetes with anti-diabetic drugs by using HbA1C as a diagnostic tool, and predicting it using a mathematic model.

METHODS

We used the Taipei Medical University Affiliated Hospital Patient Profile Database (AHPPD) from January-2007 to June-2011. The patients who progressed and were prescribed anti-diabetic drugs were selected from AHPPD. The mathematical model used to predict the time interval of HbA1C value ranged from 5.7% to 6.5% for diabetes progression.

RESULTS

We predicted an average overall time interval for all participants in between 5.7% to 6.5% during a total of 907 days (standard error, 103 days). For each group found among 5.7% to 6.5% we determined 1169.3 days for the low risk group (i.e. 3.2 years), 1080.5 days (i.e. 2.96 years) for the increased risk group and 729.4 days (i.e. 1.99 years) for the diabetes group. This indicates the patients will take an average of 2.49 years to reach 6.5%.

CONCLUSION

This prediction model is very useful to help prioritize the diagnosis at an early stage for targeting individuals with risk of diabetes. Using patients' HbA1C before anti-diabetes drugs are used we predicted the time interval from pre-diabetes progression to diabetes is 2.49 years without any influence of age and gender. Additional studies are needed to support this model for a long term prediction.

How did national life expectation related to school years in developing countries - an approach using panel data mining

BACKGROUND

The purpose of the study was to probe into the changes in life expectancy associated with schooling years found by the Organization for Economic Co-operation and Development (OECD).

METHODS

The study was based on the OECD database from the period 2000 to 2006. The data of thirty countries were constructed to allow comparisons over time and across these countries. Panel data analysis was used to estimate the relationship of national education, as defined as school years, with life expectancy. The control factors considered were numbers of practicing physicians, practicing nurses, hospital beds, and GDP.

RESULTS

We used fixed effects of both country and time through linear regression, the coefficient of school years in relation to life expectancy was statistically significant but negative. This finding is not in accord with the hypothesis that investing in human capital through education stimulates better health outcomes.

CONCLUSION

Within developing countries, educational attainment is no longer keeping the same pace with life expectancy as before. Therefore, we suggest that an effective education policy should cover diverse topics, for example, balancing economic growth and mental hygiene, to improve national life expectancy.

The relationship between usage intention and adoption of electronic health records at primary care clinics

OBJECTIVE

Despite of emerging evidence that electronic health records (EHRs) can improve the clinical quality, enhances patient safety and efficiency. Most physicians in primary health care clinics in the Taiwan do not currently adopt EHR at their clinic practices. We aim to measure the relationship between usage intention and adoption behavior.

STUDY DESIGN AND METHODS

We used structured questionnaires distributed both EHRs adopter and non-adopter group to the primary health care physicians which participated in the DOH project to establish the information exchange environment across Taiwan. The response rate of adopter and non-adopter is 54.7% and 55.0% respectively.

MEASUREMENTS

EHRs adoption behavior.

RESULTS

The EHRs adopter group has higher intention than non-adopter (p=0.003). From the result of logistic regression analyses, we found the key factors affecting physicians' adoption pattern were intention to use (OR: 2.85; 95% CI: 2.30-3.54). In addition, higher perceived usefulness (OR: 1.29; 95% CI: 1.06-1.56) and perceived ease to use (OR: 1.48; 95% CI: 1.22-1.79) increase adoption of EHR found.

CONCLUSION

The intention to use EHR, perceived usefulness and ease to use of primary care physicians were found as key factors influencing EHRs adoption. Thus, we suggest that government should promote the potential benefits of EHR and enhance physicians' willingness to adopt the EHRs at their clinic practices.

Plasma lipids and cardiovascular risk: a POSCH report. Program on the Surgical Control of the Hyperlipidemias

Quantifying the relationship between changes in lipid variables and clinical endpoints has been difficult. We studied the predictive value of various lipid variables on three endpoints in the Program on the Surgical Control of the Hyperlipidemias (POSCH): overall mortality, coronary heart disease (CHD) mortality, and CHD mortality and confirmed nonfatal myocardial infarction (MI) combined. We measured lipid variables for the annual visits from baseline to 5 years for actual follow-up values, actual and percentage differences between baseline and follow-up values, as well as the parameters comparing baseline only to 5 years for actual differences, percentage differences, and the ratio of baseline to 5 years. The lipid variables included were total cholesterol, low density lipoprotein (LDL) cholesterol, high density lipoprotein (HDL) cholesterol, very low density lipoprotein (VLDL) cholesterol, triglycerides, and the LDL cholesterol/HDL cholesterol ratio. The analytic method used was that of Cox regression, with age and sex as secondary covariates, and each lipid or ratio of lipids as the primary (univariate) covariate. As a result, 108 univariate Cox regressions were conducted. The combined findings for the control and the intervention groups are presented. The number of events for the combined group were: overall mortality, 190; CHD mortality, 119; and CHD mortality and confirmed nonfatal MI, 262. The highest hazard ratios were found for the lipid variable of the LDL cholesterol/HDL cholesterol ratio (e.g. 1.196 for a 1-unit increase). Only for the combined endpoint of CHD mortality and confirmed nonfatal MI was there a substantial number of statistically significant relationships (P<0.01) of lipid variables and parameters of assessment.

The effect of formulation excipients on protein stability and aerosol performance of spray-dried powders of a recombinant humanized anti-IgE monoclonal antibody

PURPOSE

To study the effect of trehalose, lactose, and mannitol on the biochemical stability and aerosol performance of spray-dried powders of an anti-IgE humanized monoclonal antibody.

METHODS

Protein aggregation of spray-dried powders stored at various temperature and relative humidity conditions was assayed by size exclusion chromatography and sodium dodecyl sulfate polyacrylamide gel electrophoresis. Protein glycation was determined by isoelectric focusing and affinity chromatography. Crystallization was examined by X-ray powder diffraction. Aerosol performance was assessed as the fine particle fraction (FPF) of the powders blended with coarse carrier lactose, and was determined using a multiple stage liquid impinger.

RESULTS

Soluble protein aggregation consisting of non-covalent and disulfide-linked covalent dimers and trimers occurred during storage. Aggregate was minimized by formulation with trehalose at or above a molar ratio in the range of 300: 1 to 500:1 (excipient:protein). However, the powders were excessively cohesive and unsuitable for aerosol administration. Lactose had a similar stabilizing effect, and the powders exhibited acceptable aerosol performance, but protein glycation was observed during storage. The addition of mannitol also reduced aggregation, while maintaining the FPF, but only up to a molar ratio of 200:1. Further increased mannitol resulted in crystallization, which had a detrimental effect on protein stability and aerosol performance.

CONCLUSIONS

Protein stability was improved by formulation with carbohydrate. However, a balance must be achieved between the addition of enough stabilizer to improve protein biochemical stability without compromising blended powder aerosol performance.