Impact of Coexisting Risk Factors on Outcomes in Patients with Acute Coronary Syndrome: A Real-World Analysis Using the Taiwan Chang Gung Research Database.

BACKGROUND Diabetes mellitus (DM), chronic kidney disease (CKD), and advanced age are associated with poor outcomes in patients with acute coronary syndrome (ACS). This real-world study utilized data from the Taiwan Chang Gung Research Database (CGRD) to compare outcomes in ACS patients with DM, CKD, and the elderly. MATERIAL AND METHODS The study enrolled 28,613 ACS patients diagnosed based on CGRD medical records between January 2005 and December 2019. Baseline characteristics and clinical outcomes were compared among groups based on patient characteristics. RESULTS Within the ACS cohort, 42.1% had DM, 48.2% had CKD, and 33.6% were elderly. Among them, 10.7% (3,070) were elderly patients with both DM and CKD. Elderly patients with DM and CKD had significantly higher risks of gastrointestinal bleeding (hazard ratio=11.32), cardiovascular events (HR=7.29), and all-cause mortality (HR=8.59). Patients with three or at least two of these risk factors had a 2.20-2.99-fold increased risk of recurrent ACS during the three-year follow-up period. CONCLUSIONS Patients with the combination of DM, CKD, and advanced age (elderly) experienced an 11.32-fold increased risk of gastrointestinal bleeding, 7.29-fold increased risk of cardiovascular events, and 8.59-fold increased risk of all-cause mortality compared to those without these risk factors. Furthermore, patients with two or more of these risk factors had a 2- to 3-fold increased risk of recurrent ACS. These findings emphasize the importance of managing multiple risk factors in ACS patients to improve outcomes.

The Relationship of Conduction Disorder and Prognosis in Patients with Acute Coronary Syndrome.

Objective: Conduction disorders with a widened QRS are associated with poor prognosis in patients with acute coronary syndrome (ACS). Conduction disorders include left bundle branch block (LBBB), right bundle branch block (RBBB), and nonspecific intraventricular conduction delay (NICD). Previous studies did not have conflicting results regarding the type of bundle branch block (BBB) with the worst prognosis, and few studies have focused on the prognosis of patients with NICD. Methods: Patients with ACS were enrolled between January 2005 and December 2019, and their medical history (International Classification of Diseases codes) was obtained from the Chang Gung Research Database. Age, sex, comorbidities, left ventricular ejection fraction (LVEF), and drug use were compared between the patients with and without conduction disorders. The following clinical outcomes were compared between patients with and without conduction disorders: heart failure (HF) hospitalization, cardiovascular (CV) mortality, and all-cause mortality. After propensity score matching, the Kaplan-Meier curve analysis for HF hospitalization, CV mortality, and all-cause mortality were compared among patients with LBBB, RBBB, and NICD. Results: This study enrolled a total of 33970 participants and involved 3392 and 30578 patients with and without conduction disorders, respectively. Older age and a higher prevalence of comorbidities were noted in patients with conduction disorders. Lower mean LVEF was exhibited in the patients with conduction disorders (with vs. without; 44.64 ± 20.73% vs. 49.85 ± 20.63%; p < 0.001). During the 3-year follow-up period, higher incidences of HF hospitalization (21.55% vs. 17.51%; p < 0.001), CV mortality (17.98% vs. 12.14%; p < 0.001), and all-cause mortality (38.86% vs. 31.15%; p < 0.001) were noted in the patients with conduction disorder. After ACS events, 10.0% of patients presented with conduction disorders, with LBBB in 3.3%, RBBB in 6.0%, and NICD in 0.7%. The lowest mean of LVEF was presented in the patients with NICD (LBBB vs. RBBB vs. NICD; 41.00 ± 19.47% vs. 47.73 ± 20.82% vs. 34.57 ± 20.02%; p < 0.001). Among the three groups, the highest incidence of HF hospitalization was noted in patients with LBBB after propensity score matching. The lowest incidence of CV and all-cause mortality was observed in patients with RBBB. After adjustment of age, gender, comorbidities, medication, and mean LVEF, those with LBBB had the highest hazard ratio for major adverse cardiovascular events (MACEs) of 1.113 (p=0.029; 95% CI = 1.013-1.266). Conclusions: In the ACS population, patients with conduction delay had a poor prognosis due to a higher prevalence of comorbidities and lower mean LVEF. Among the patients with LBBB, RBBB, and NICD, those with LBBB and NICD had a higher incidence of HF hospitalization, CV mortality, and all-cause mortality. Patients with NICD had the lowest mean LVEF compared to those with LBBB and RBBB. Patients with LBBB had a significantly highest HR of MACE.

Month of birth and mental disorders: A population-based study and validation using global meta-analysis.

OBJECTIVE: Month of birth (MOB) is associated with specified mental disorders (MDs). However, whether these relationships extend to all MDs remains unclear. We investigate the association using a population-based cohort study and a meta-analysis. METHODS: First, we examined patients with 34 DSM-5-classified MDs in the Taiwan national database. We estimated the relative risk ratios (RR) of each illness in each MOB relative to that in the general population and assessed the periodicity, with six further sensitivity analyses. Second, we searched PubMed, Embase, and Cochrane for related articles through 31 December 2020. We used a random-effects model, pooled RRs with 95% confidence intervals of each MOB from the identified studies, and transformed them from MOB to relative age in a year or season. RESULTS: The cohort included 1,951,777 patients. Except for posttraumatic stress disorder, dissociative disorders, feeding/eating disorders, gender dysphoria, and paraphilic disorders, the other MDs had significant MOB periodicity. The meta-analysis included 51 studies investigating 10 MDs. The youngest age at the start of school owing to MOB was associated with the highest RRs of intellectual disability (1.13), autism (1.05), attention-deficit/hyperactivity disorder (1.13). Winter births had significant risks of schizophrenia (1.04), bipolar I disorder (1.02), and major depressive disorder (1.01), and autumn births had a significant risk of alcohol use disorder (1.02). No significant associations between season of birth and Alzheimer's disease, or eating disorders were found. CONCLUSIONS: MOB is related to the risks of certain MDs. This finding provides a reference for future research on the etiology of MDs.

Fuzzy inference system for modeling the environmental risk map of air pollutants in Taiwan.

This study aimed to improve the uncertainty in spatial data of risk assessment through a Fuzzy inference system (FIS) as a way to conduct an environmental risk map of air pollution in Taiwan. In modeling, the feature inputs of FIS included the geographic coordinates and time, while the outputs are the pollutant concentrations. The outputs are supplements to the concentration contour on the map in comparison with Kriging interpolation. In our model, the FIS was designed using the official open data of air pollutants, including Pb and PM2.5 that were collected from the monitoring stations in mid-southern Taiwan. The model involved data filtration and imputation in the preliminary scheme to extract the historical data for analysis. We used the data of Pb (2001-2013) and PM2.5 (2006-2013) for the training process, and then used the data from 2014 to 2015 for validation. Our model was able to compute the smaller errors of inferred and measured values of Pb and PM2.5 than the conventional method. The approach was applied to deduce the exposure of PM2.5 distributed over the Taiwan Island in accordance with the governmental open data of seventy-three stations during 2006-2016 in order to produce our risk map. The designed model upon Fuzzy inference accesses potential risks of spatiotemporal exposures in the unmeasured locations with feasibility and adaptability for environmental management.

Investigation of electrocatalysis for tiered-tower micro-electro-mechanical-system-based biosensors: application in the early detection of the thrombosis factor trimethylamine N-oxide.

Trimethylamine N-oxide (TMAO) has been recognized as a biomarker for the early detection of thrombosis. However, testing for TMAO typically requires expensive laboratory equipment and skilled technicians, making it unsuitable for home care pre-screening. To enable its widespread use in home applications, it is crucial to develop a scalable and sensitive device capable of catalyzing TMAO metabolism with a specific enzyme that is tailored for point-of-care use. This study presents an investigation of a MEMS-based two-tiered-tower biosensor array with a detection limit of 0.1 µM for TMAO, aiming to diagnose chronic metabolic diseases using urine or serum samples. Based on the augmented Cole-Cole model, the proposed parameters R_catalyzed, C_catalyzed, and Rp_catalyzed can predict the catalytic impedance of enzymatic activities such as the redox effects of analytes and characterize the small-signal current caused by catalysis. The proposed MEMS biosensor, integrated with a readout circuitry, demonstrates a high sensitivity of 41 ADC counts per µM TMAO (or 4.5 mV µM-1 TMAO), a response time of 1 second, a repetition rate of 98.9%, and a drift over time of 0.5 mV. The sensor effectively distinguishes TMAO based on minute capacitance changes induced by the TorA enzyme, resulting in a discernible distinction of 10.6%. These measurements were successfully compared to conventional cyclic voltammetry (CV) results, showing a variance of only 0.024%. The proposed biosensor is well-suited for pre-screening thrombosis factors for the early detection and prevention of thrombosis in point-of-care applications. The device is cost-effective, lightweight, and demonstrates excellent performance, with a conversion rate of 88% of TMAO and a selectivity rate of 97% for the by-product TMA, allowing for the prediction of cardiovascular risks.

Rapid and Direct Detection of Trimethylamine N-oxide Using an Off-Chip Capacitance Biosensor with Readout SoC for Early-Stage Thrombosis and Cardiovascular Disease.

A demonstration of an off-chip capacitance array sensor with a limit of detection of 1 µM trimethylamine N-oxide (TMAO) to diagnose a chronic metabolism disease in urine is presented. The improved Cole-Cole model is employed to determine the parameters of R_catalyzed, C_catalyzed, and Rp_catalyzed, enabling the prediction of the catalytic resistance of enzyme, reduction effects of the analyte, and characterize the small signal alternating current properties of ionic strength caused by catalysis. Based on the standard solutions, we investigate the effects of pixel geometry parameters, driving electrode width, and sensing electrode width on the electrical field change of the off-chip capacitance sensor; the proposed off-chip sensor with readout system-on-chip exhibits a high sensitivity of 21 analog-to-digital converter counts/µM TMAO (or 2.5 mV/µM TMAO), response time of 1 s, repetition of 98.9%, and drift over time of 0.5 mV. The proposed off-chip sensor effectively discriminates TMAO in a phosphate-buffered saline solution based on minute changes in capacitance induced by the TorA enzyme, resulting in a discernible 2.15% distinction. These measurements have been successfully corroborated using the conventional cyclic voltammetry method, demonstrating a mere 0.024% variance. The off-chip sensor is crafted with a specific focus on detecting TMAO, achieved by excluding any reduction reactions between the TMAO-specific enzyme TorA and the compounds creatine and creatinine present in urine. This deliberate omission ensures that the sensor's attention remains solely on TMAO, thereby enhancing its precision in achieving accurate and reliable TMAO detection.

Comparison of Clinical Outcomes Between Ticagrelor and Clopidogrel in East-Asian Patients with Acute Coronary Syndrome: Large Cohort Study.

AIM: A high risk of bleeding is observed in East Asian patients with acute coronary syndrome (ACS). Therefore, the choice between two antiplatelet therapy drugs, ticagrelor and clopidogrel, remains controversial in this population with ACS. This study aimed to use a large cohort database to assess the clinical outcomes of ticagrelor and clopidogrel therapy, including major bleeding, recurrent ACS, and mortality, in this population. METHODS: Between January 2009 and December 2019, 43,696 patients were diagnosed with ACS based on the medical history (International Classification of Diseases [ICD] code) of the Chang Gung Research Database. After excluding patients without percutaneous coronary intervention, with concurrent medical problems, and on non-standard dual antiplatelet therapy (DAPT) or a single antiplatelet agent, 18,046 patients were recruited for analysis. Ticagrelor- and clopidogrel-based DAPT were administered to 3666 patients and 14,380 patients, respectively. Baseline characteristics and clinical outcomes were compared between the two groups. A total of 4225 patients were defined as a high-bleeding-risk subgroup according to Academic Research Consortium for High Bleeding Risk (ARC-HBR) score (met one major or two minor criteria), of which 466 and 3759 patients received ticagrelor- and clopidogrel-based DAPT, respectively. RESULTS: Before propensity score matching (PSM), younger age, higher prevalence of male sex, and higher body mass index were noted in the ticagrelor-based DAPT group in the whole cohort and high-bleeding-risk subgroup. After PSM, no difference in baseline characteristics and comorbidities between ticagrelor-based and clopidogrel-based DAPT groups in the whole cohort and high-bleeding-risk subgroup was noted. The Kaplan-Meier curves of recurrent ACS and major bleeding were significantly lower in the ticagrelor-based DAPT group than in the clopidogrel-based DAPT group, and that of cardiovascular (CV) and all-cause mortality showed no significant differences. After PSM, in the high-bleeding-risk subgroup, the Kaplan-Meier curve of recurrent ACS was significantly lower in the ticagrelor-based DAPT group than in the clopidogrel-based DAPT group, and that of major bleeding, CV, and all-cause mortality showed no significant differences. CONCLUSION: In this large cohort study, patients receiving ticagrelor-based DAPT were at lower risk of recurrent ACS compared to those receiving clopidogrel-based DAPT, especially in the patients with myocardial infarction. Ticagrelor-based DAPT did not result in a higher risk of major bleeding in the whole ACS population and high-bleeding-risk subgroup. The rate of CV and all-cause mortality were similar between both the groups.

Long-term exposure to air pollutants and increased risk of chronic kidney disease in a community-based population using a fuzzy logic inference model.

Background: Fuzzy inference systems (FISs) based on fuzzy theory in mathematics were previously applied to infer supplementary points for the limited number of monitoring sites and improve the uncertainty of spatial data. Therefore we adopted the FIS method to simulate spatiotemporal levels of air pollutants [particulate matter <2.5 µm (PM2.5), sulfur dioxide (SO2) and (NO2)] and investigated the association of levels of air pollutants with the community-based prevalence of chronic kidney disease (CKD). Methods: A Complex Health Screening program was launched during 2012-2013 and a total of 8284 community residents in Chiayi County, which is located in southwestern Taiwan, received a series of standard physical examinations, including measurement of estimated glomerular filtration rate (eGFR). CKD cases were defined as eGFR <60 mL/min/1.73 m2 and were matched for age and gender in a 1:4 ratio of cases:controls. Data on air pollutants were collected from air quality monitoring stations during 2006-2016. The longitude, latitude and recruitment month of the individual case were entered into the trained FIS. The defuzzification process was performed based on the proper membership functions and fuzzy logic rules to infer the concentrations of air pollutants. In addition, we used conditional logistic regression and the distributed lag nonlinear model to calculate the prevalence ratios of CKD and the 95% confidence interval. Confounders including Framingham Risk Score (FRS), diabetes, gout, arthritis, heart disease, metabolic syndrome and vegetables consumption were adjusted in the models. Results: Participants with a high FRS (>10%), diabetes, heart disease, gout, arthritis or metabolic syndrome had significantly increased CKD prevalence. After adjustment for confounders, PM2.5 levels were significantly increased in CKD cases in both single- and two-pollutant models (prevalence ratio 1.31-1.34). There was a positive association with CKD in the two-pollutant models for NO2. However, similar results were not observed for SO2. Conclusions: FIS may be helpful to reduce uncertainty with better interpolation for limited monitoring stations. Meanwhile, long-term exposure to ambient PM2.5 appears to be associated with an increased prevalence of CKD, based on a FIS model.

Prevalence and Outcomes of Major Psychiatric Disorders Preceding Index Surgery for Degenerative Thoracic/Lumbar Spine Disease.

The relationship between preexisting major psychiatric disorders and outcomes of spine surgery for degenerative thoracic/lumbar disease remains unclear. A 5% subset of inpatients was randomly selected from the Taiwan National Health Insurance Research Database. A total of 10,109 inpatients aged 18 years or over with degenerative thoracic/lumbar disease and underwent spine surgery met inclusion criteria. Major psychiatric disorders diagnosed by psychiatrists preceding index surgery, including anxiety disorder, depression disorder, bipolar disorder, schizophrenia and dementia, were identified. The prevalence of psychiatric disorders, and their differential risks on in-hospital and post-discharge outcomes were examined. 10.4% had major psychiatric disorders, of which depression (6.6%) and anxiety (4.9%) were most common. Logistic regression revealed increased risks of ventilator use in depression (OR = 1.62, 95% CI = 1.04-2.54, p < 0.05), extended hospitalization length in bipolar (OR = 1.77, 95% CI = 1.08-2.89, p < 0.05), and higher rehabilitation utilization in depression (OR = 1.25, 95% CI = 1.06-1.47, p < 0.01) and bipolar (OR = 1.69, 95% CI = 1.04-2.76, p < 0.05). Those patients with anxiety had a decreased risk of longer hospitalization duration (OR = 0.77, 95% CI = 0.60-0.98, p < 0.05), while those with dementia and schizophrenia had no change in risks. Preoperative recognition of major psychiatric disorders for risk and treatment assessment is suggested as people with preexisting depression or bipolar disorder have worse outcomes after spine surgery.

Subject-enabled analytics model on measurement statistics in health risk expert system for public health informatics.

PURPOSE: This study applied open source technology to establish a subject-enabled analytics model that can enhance measurement statistics of case studies with the public health data in cloud computing. METHODS: The infrastructure of the proposed model comprises three domains: 1) the health measurement data warehouse (HMDW) for the case study repository, 2) the self-developed modules of online health risk information statistics (HRIStat) for cloud computing, and 3) the prototype of a Web-based process automation system in statistics (PASIS) for the health risk assessment of case studies with subject-enabled evaluation. The system design employed freeware including Java applications, MySQL, and R packages to drive a health risk expert system (HRES). In the design, the HRIStat modules enforce the typical analytics methods for biomedical statistics, and the PASIS interfaces enable process automation of the HRES for cloud computing. The Web-based model supports both modes, step-by-step analysis and auto-computing process, respectively for preliminary evaluation and real time computation. RESULTS: The proposed model was evaluated by computing prior researches in relation to the epidemiological measurement of diseases that were caused by either heavy metal exposures in the environment or clinical complications in hospital. The simulation validity was approved by the commercial statistics software. The model was installed in a stand-alone computer and in a cloud-server workstation to verify computing performance for a data amount of more than 230K sets. Both setups reached efficiency of about 105 sets per second. CONCLUSIONS: The Web-based PASIS interface can be used for cloud computing, and the HRIStat module can be flexibly expanded with advanced subjects for measurement statistics. The analytics procedure of the HRES prototype is capable of providing assessment criteria prior to estimating the potential risk to public health.