Plant-based polyphenol rich protein supplementation attenuated skeletal muscle loss and lowered the LDL level via gut microbiota remodeling in Taiwan's community-dwelling elderly.

Sarcopenia, characterized by muscle loss, negatively affects the elderly's physical activity and survival. Enhancing protein and polyphenol intake, possibly through the supplementation of fermented black soybean koji product (BSKP), may alleviate sarcopenia by addressing anabolic deficiencies and gut microbiota dysbiosis because of high contents of polyphenols and protein in BSKP. This study aimed to examine the effects of long-term supplementation with BSKP on mitigating sarcopenia in the elderly and the underlying mechanisms. BSKP was given to 46 participants over 65 years old with early sarcopenia daily for 10 weeks. The participants' physical condition, serum biochemistry, inflammatory cytokines, antioxidant activities, microbiota composition, and metabolites in feces were evaluated both before and after the intervention period. BSKP supplementation significantly increased the appendicular skeletal muscle mass index and decreased the low-density lipoprotein level. BSKP did not significantly alter the levels of inflammatory factors, but significantly increased the activity of antioxidant enzymes. BSKP changed the beta diversity of gut microbiota and enhanced the relative abundance of Ruminococcaceae_UCG_013, Lactobacillus_murinus, Algibacter, Bacillus, Gordonibacter, Porphyromonas, and Prevotella_6. Moreover, BSKP decreased the abundance of Akkermansia and increased the fecal levels of butyric acid. Positive correlations were observed between the relative abundance of BSKP-enriched bacteria and the levels of serum antioxidant enzymes and fecal short chain fatty acids (SCFAs), and Gordonibacter correlated negatively with serum low-density lipoprotein. In summary, BSKP attenuated age-related sarcopenia by inducing antioxidant enzymes and SCFAs via gut microbiota regulation. Therefore, BSKP holds potential as a high-quality nutrient source for Taiwan's elderly, especially in conditions such as sarcopenia.

Association between liver stiffness measurement by transient elastography and chronic kidney disease.

ABSTRACT: Transient elastography or elastometry (TE) is widely used for clinically cirrhosis and liver steatosis examination. Liver fibrosis and fatty liver had been known to share some co-morbidities that may result in chronic impairment in renal function. We conducted a study to analyze the association between scores of 2 TE parameters, liver stiffness measurement (LSM) and controlled attenuation parameter (CAP), with chronic kidney disease among health checkup population.This was a retrospective, cross-sectional study. Our study explored the data of the health checkup population between January 2009 and the end of June 2018 in a regional hospital. All patients were aged more than 18 year-old. Data from a total of 1940 persons were examined in the present study. The estimated glomerular filtration rate (eGFR) was calculated by the modification of diet in renal disease (MDRD-simplify-GFR) equation. Chronic kidney disease (CKD) was defined as eGFR < 60 mL/min/1.73 m2.The median of CAP and LSM score was 242, 265.5, and 4.3, 4.95 in non-CKD (eGFR > 60) and CKD (eGFR < 60) group, respectively. In stepwise regression model, we adjust for LSM, CAP, inflammatory markers, serum biochemistry markers of liver function, and metabolic risks factors. The P value of LSM score, ALT, AST, respectively is .005, <.001, and <.001 in this model.The LSM score is an independent factor that could be used to predict renal function impairment according to its correlation with eGFR. This result can further infer that hepatic fibrosis may be a risk factor for CKD.

Probiotic supplementation attenuates age-related sarcopenia via the gut-muscle axis in SAMP8 mice.

BACKGROUND: Age-related muscle dysfunctions are common disorders resulting in poor quality of life in the elderly. Probiotic supplementation is a potential strategy for preventing age-related sarcopenia as evidence suggests that probiotics can enhance muscle function via the gut-muscle axis. However, the effects and mechanisms of probiotics in age-related sarcopenia are currently unknown. In this study, we examined the effects of Lactobacillus casei Shirota (LcS), a probiotic previously reported to improve muscle function in young adult mice. METHODS: We administered LcS (1 × 108 or 1 × 109  CFU/mouse/day) by oral gavage to senescence-accelerated mouse prone-8 mice for 12 weeks (16- to 28-week-old). Sixteen-week-old and 28-week-old SMAP8 mice were included as non-aged and aged controls, respectively. Muscle condition was evaluated using dual-energy X-ray absorptiometry for muscle mass, holding impulse and grip strength tests for muscle strength, and oxygen consumption rate, gene expressions of mitochondrial biogenesis, and mitochondrial number assays for mitochondria function. Inflammatory cytokines were determined using enzyme-linked immunosorbent assay. Gas chromatography-mass spectrometry was utilized to measure the short-chain fatty acid levels. The gut microbiota was analysed based on the data of 16S rRNA gene sequencing of mouse stool. RESULTS: The LcS supplementation reduced age-related declines in muscle mass (>94.6%, P < 0.04), strength (>66% in holding impulse and >96.3% in grip strength, P < 0.05), and mitochondrial function (P < 0.05). The concentration of short-chain fatty acids (acetic, isobutyric, butyric, penic, and hexanoic acid) was recovered by LcS (>65.9% in the mice given high dose of LcS, P < 0.05) in the aged mice, and LcS attenuated age-related increases in inflammation (P < 0.05) and reactive oxygen species (>89.4%, P < 0.001). The high dose of LcS supplementation was also associated with distinct microbiota composition as indicated by the separation of groups in the beta-diversity analysis (P = 0.027). LcS supplementation altered predicted bacterial functions based on the gut microbiota. Apoptosis (P = 0.026), p53 signalling (P = 0.017), and non-homologous end-joining (P = 0.031) were significantly reduced, whereas DNA repair and recombination proteins (P = 0.043), RNA polymerase (P = 0.008), and aminoacyl-tRNA biosynthesis (P = 0.003) were increased. Finally, the genera enriched by high-dose LcS [linear discriminant analysis (LDA) score > 2.0] were positively correlated with healthy muscle and physiological condition (P < 0.05), while the genera enriched in aged control mice (LDA score > 2.0) were negatively associated with healthy muscle and physiological condition (P < 0.05). CONCLUSIONS: Lactobacillus casei Shirota represents an active modulator that regulates the onset and progression of age-related muscle impairment potentially via the gut-muscle axis.

Correction: A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study.

[This corrects the article DOI: 10.2196/27806.].

Identify the Characteristics of Metabolic Syndrome and Non-obese Phenotype: Data Visualization and a Machine Learning Approach.

Introduction: A third of the world's population is classified as having Metabolic Syndrome (MetS). Traditional diagnostic criteria for MetS are based on three or more of five components. However, the outcomes of patients with different combinations of specific metabolic components are undefined. It is challenging to be discovered and introduce treatment in advance for intervention, since the related research is still insufficient. Methods: This retrospective cohort study attempted to establish a method of visualizing metabolic components by using unsupervised machine learning and treemap technology to discover the relations between predicting factors and different metabolic components. Several supervised machine-learning models were used to explore significant predictors of MetS and to construct a powerful prediction model for preventive medicine. Results: The random forest had the best performance with accuracy and c-statistic of 0.947 and 0.921, respectively, and found that body mass index, glycated hemoglobin, and controlled attenuation parameter (CAP) score were the optimal primary predictors of MetS. In treemap, high triglyceride level plus high fasting blood glucose or large waist circumference group had higher CAP scores (>260) than other groups. Moreover, 32.2% of patients with high CAP scores during 3 years of follow-up had metabolic diseases are observed. This reveals that the CAP score may be used for detecting MetS, especially for the non-obese MetS phenotype. Conclusions: Machine learning and data visualization can illustrate the complicated relationships between metabolic components and potential risk factors for MetS.

Exploring and predicting mortality among patients with end-stage liver disease without cancer: a machine learning approach.

OBJECTIVE: End-stage liver disease is a global public health problem with a high mortality rate. Early identification of people at risk of poor prognosis is fundamental for decision-making in clinical settings. This study created a machine learning prediction system that provides several related models with visualized graphs, including decision trees, ensemble learning and clustering, to predict mortality in patients with end-stage liver disease. METHODS: A retrospective cohort study was conducted: the training data were from patients enrolled from January 2009 to December 2010 and followed up to December 2014; validation data were from patients enrolled from January 2015 to December 2016 and followed up to January 2019. Hospitalized patients with noncancer-related chronic liver disease were identified from the hospital's electrical medical records. RESULTS: In traditional multivariable logistic regression and Cox proportional hazard model, prothrombin time of international normalized ratio, which was significant with P value = 0.002, odds ratio = 2.790 and hazard ratio 1.363. Besides, blood urea nitrogen and C-reactive protein were also significant, with P value <0.001 and 0.026. The area under the curve was 0.771 in the receiver operating characteristic curve. In machine learning, blood urea nitrogen and age were regarded as the primary factors for predicting mortality. Creatinine, prothrombin time of international normalized ratio and bilirubin were also significant mortality predictors. The area under the curve of the random forest and AdaBoost was 0.838 and 0.792. CONCLUSION: The machine learning techniques provided a comprehensive assessment of patient conditions; it could help physicians make an accurate diagnosis of chronic liver disease and improve healthcare management.

A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study.

BACKGROUND: More than 79.2 million confirmed COVID-19 cases and 1.7 million deaths were caused by SARS-CoV-2; the disease was named COVID-19 by the World Health Organization. Control of the COVID-19 epidemic has become a crucial issue around the globe, but there are limited studies that investigate the global trend of the COVID-19 pandemic together with each country's policy measures. OBJECTIVE: We aimed to develop an online artificial intelligence (AI) system to analyze the dynamic trend of the COVID-19 pandemic, facilitate forecasting and predictive modeling, and produce a heat map visualization of policy measures in 171 countries. METHODS: The COVID-19 Pandemic AI System (CPAIS) integrated two data sets: the data set from the Oxford COVID-19 Government Response Tracker from the Blavatnik School of Government, which is maintained by the University of Oxford, and the data set from the COVID-19 Data Repository, which was established by the Johns Hopkins University Center for Systems Science and Engineering. This study utilized four statistical and deep learning techniques for forecasting: autoregressive integrated moving average (ARIMA), feedforward neural network (FNN), multilayer perceptron (MLP) neural network, and long short-term memory (LSTM). With regard to 1-year records (ie, whole time series data), records from the last 14 days served as the validation set to evaluate the performance of the forecast, whereas earlier records served as the training set. RESULTS: A total of 171 countries that featured in both databases were included in the online system. The CPAIS was developed to explore variations, trends, and forecasts related to the COVID-19 pandemic across several counties. For instance, the number of confirmed monthly cases in the United States reached a local peak in July 2020 and another peak of 6,368,591 in December 2020. A dynamic heat map with policy measures depicts changes in COVID-19 measures for each country. A total of 19 measures were embedded within the three sections presented on the website, and only 4 of the 19 measures were continuous measures related to financial support or investment. Deep learning models were used to enable COVID-19 forecasting; the performances of ARIMA, FNN, and the MLP neural network were not stable because their forecast accuracy was only better than LSTM for a few countries. LSTM demonstrated the best forecast accuracy for Canada, as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 2272.551, 1501.248, and 0.2723075, respectively. ARIMA (RMSE=317.53169; MAPE=0.4641688) and FNN (RMSE=181.29894; MAPE=0.2708482) demonstrated better performance for South Korea. CONCLUSIONS: The CPAIS collects and summarizes information about the COVID-19 pandemic and offers data visualization and deep learning-based prediction. It might be a useful reference for predicting a serious outbreak or epidemic. Moreover, the system undergoes daily updates and includes the latest information on vaccination, which may change the dynamics of the pandemic.

Machine-Learning Monitoring System for Predicting Mortality Among Patients With Noncancer End-Stage Liver Disease: Retrospective Study.

BACKGROUND: Patients with end-stage liver disease (ESLD) have limited treatment options and have a deteriorated quality of life with an uncertain prognosis. Early identification of ESLD patients with a poor prognosis is valuable, especially for palliative care. However, it is difficult to predict ESLD patients that require either acute care or palliative care. OBJECTIVE: We sought to create a machine-learning monitoring system that can predict mortality or classify ESLD patients. Several machine-learning models with visualized graphs, decision trees, ensemble learning, and clustering were assessed. METHODS: A retrospective cohort study was conducted using electronic medical records of patients from Wan Fang Hospital and Taipei Medical University Hospital. A total of 1214 patients from Wan Fang Hospital were used to establish a dataset for training and 689 patients from Taipei Medical University Hospital were used as a validation set. RESULTS: The overall mortality rate of patients in the training set and validation set was 28.3% (257/907) and 22.6% (145/643), respectively. In traditional clinical scoring models, prothrombin time-international normalized ratio, which was significant in the Cox regression (P<.001, hazard ratio 1.288), had a prominent influence on predicting mortality, and the area under the receiver operating characteristic (ROC) curve reached approximately 0.75. In supervised machine-learning models, the concordance statistic of ROC curves reached 0.852 for the random forest model and reached 0.833 for the adaptive boosting model. Blood urea nitrogen, bilirubin, and sodium were regarded as critical factors for predicting mortality. Creatinine, hemoglobin, and albumin were also significant mortality predictors. In unsupervised learning models, hierarchical clustering analysis could accurately group acute death patients and palliative care patients into different clusters from patients in the survival group. CONCLUSIONS: Medical artificial intelligence has become a cutting-edge tool in clinical medicine, as it has been found to have predictive ability in several diseases. The machine-learning monitoring system developed in this study involves multifaceted analyses, which include various aspects for evaluation and diagnosis. This strength makes the clinical results more objective and reliable. Moreover, the visualized interface in this system offers more intelligible outcomes. Therefore, this machine-learning monitoring system provides a comprehensive approach for assessing patient condition, and may help to classify acute death patients and palliative care patients. Upon further validation and improvement, the system may be used to help physicians in the management of ESLD patients.

Correction: Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach.

[This corrects the article DOI: 10.2196/18585.].

Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach.

BACKGROUND: In the era of information explosion, the use of the internet to assist with clinical practice and diagnosis has become a cutting-edge area of research. The application of medical informatics allows patients to be aware of their clinical conditions, which may contribute toward the prevention of several chronic diseases and disorders. OBJECTIVE: In this study, we applied machine learning techniques to construct a medical database system from electronic medical records (EMRs) of subjects who have undergone health examination. This system aims to provide online self-health evaluation to clinicians and patients worldwide, enabling personalized health and preventive health. METHODS: We built a medical database system based on the literature, and data preprocessing and cleaning were performed for the database. We utilized both supervised and unsupervised machine learning technology to analyze the EMR data to establish prediction models. The models with EMR databases were then applied to the internet platform. RESULTS: The validation data were used to validate the online diagnosis prediction system. The accuracy of the prediction model for metabolic syndrome reached 91%, and the area under the receiver operating characteristic (ROC) curve was 0.904 in this system. For chronic kidney disease, the prediction accuracy of the model reached 94.7%, and the area under the ROC curve (AUC) was 0.982. In addition, the system also provided disease diagnosis visualization via clustering, allowing users to check their outcome compared with those in the medical database, enabling increased awareness for a healthier lifestyle. CONCLUSIONS: Our web-based health care machine learning system allowed users to access online diagnosis predictions and provided a health examination report. Users could understand and review their health status accordingly. In the future, we aim to connect hospitals worldwide with our platform, so that health care practitioners can make diagnoses or provide patient education to remote patients. This platform can increase the value of preventive medicine and telemedicine.

Palliative medicine family conferences and caregiver psychological distress during prolonged mechanical ventilation.

OBJECTIVES: Little is known about the experience of family caregivers of patients who require prolonged mechanical ventilation (PMV). We examined the perspectives of caregivers of patients who died after PMV to explore the role of palliative care and the quality of dying and death (QODD) in patients and understand the psychological symptoms of these caregivers. METHODS: A longitudinal study was performed in five hospitals in Taipei, Taiwan. Routine palliative care family conferences and optional consultation with a palliative care specialist were provided, and family caregivers were asked to complete surveys. RESULTS: In total, 136 family caregivers of 136 patients receiving PMV were recruited and underwent face-to-face baseline interviews in 2016-2017. By 2018, 61 (45%) of 136 patients had died. We successfully interviewed 30 caregivers of patients' death to collect information on the QODD of patients and administer the Impact of Event Scale (IES), Hospital Anxiety and Depression Scale (HADS) and Center for Epidemiologic Studies Depression (CES-D) scale to caregivers. We observed that more frequent palliative care family conferences were associated with poorer QODD in patients (coefficients: -44.04% and 95% CIs -75.65 to -12.44), and more psychological symptoms among caregivers (coefficient: 9.77% and 95% CI 1.63 to 17.90 on CES-D and coefficient: 7.67% and 95% CI 0.78 to 14.55 on HADS). A higher caregiver burden at baseline correlated with lower psychological symptoms (coefficient: -0.35% and 95% CI -0.58 to -0.11 on IES and coefficient: -0.22% and 95% CI -0.40 to -0.05 on CES-D) among caregivers following the patients' death. Caregivers' who accepted the concept of palliative care had fewer psychological symptoms after patients' death (coefficient: -3.29% and 95% CI -6.32 to -0.25 on IES and coefficient: -3.22% and 95% CI -5.24 to -1.20 on CES-D). CONCLUSIONS: Palliative care conferences were more common among family members with increased distress. Higher caregiver burden and caregiver acceptance of palliative care at baseline both predicted lower levels of caregiver distress after death.

Dipeptidyl peptidase-4 inhibitors and the risks of autoimmune diseases in type 2 diabetes mellitus patients in Taiwan: a nationwide population-based cohort study.

AIMS: Dipeptidyl peptidase-4, a transmembrane glycoprotein expressed in various cell types, serves as a co-stimulator molecule to influence immune response. This study aimed to investigate associations between DPP-4 inhibitors and risk of autoimmune disorders in patients with type 2 diabetes mellitus in Taiwan. METHODS: This retrospective cohort study used the nationwide data from the diabetes subsection of Taiwan National Health Insurance Research Database between January 1, 2009, and December 31, 2013. Cox proportional hazards models were developed to compare the risk of autoimmune disorders and the subgroup analyses between the DPP-4i and DPP-4i-naïve groups. RESULTS: A total of 774,198 type 2 diabetic patients were identified. The adjusted HR of the incidence for composite autoimmune disorders in DPP-4i group was 0.56 (95% CI 0.53-0.60; P < 0.001). The subgroup analysis demonstrated that the younger patients (aged 20-40 years: HR 0.47, 95% CI 0.35-0.61; aged 41-60 years: HR 0.50, 95% CI 0.46-0.55; aged 61-80 years: HR 0.63, 95% CI 0.58-0.68, P = 0.0004) and the lesser duration of diabetes diagnosed (0-5 years: HR 0.48, 95% CI 0.44-0.52; 6-10 years: HR 0.48, 95% CI 0.43-0.53; ≧ 10 years: HR 0.86, 95% CI 0.78-0.96, P < 0.0001), the more significant the inverse association of DPP-4 inhibitors with the incidence of composite autoimmune diseases. CONCLUSIONS: DPP-4 inhibitors are associated with lower risk of autoimmune disorders in type 2 diabetes mellitus patients in Taiwan, especially for the younger patients and the lesser duration of diabetes diagnosed. The significant difference was found between the four types of DPP-4 inhibitors and the risk of autoimmune diseases. This study provides clinicians with useful information regarding the use of DPP-4 inhibitors for treating diabetic patients.

Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study.

BACKGROUND: Metabolic syndrome is a cluster of disorders that significantly influence the development and deterioration of numerous diseases. FibroScan is an ultrasound device that was recently shown to predict metabolic syndrome with moderate accuracy. However, previous research regarding prediction of metabolic syndrome in subjects examined with FibroScan has been mainly based on conventional statistical models. Alternatively, machine learning, whereby a computer algorithm learns from prior experience, has better predictive performance over conventional statistical modeling. OBJECTIVE: We aimed to evaluate the accuracy of different decision tree machine learning algorithms to predict the state of metabolic syndrome in self-paid health examination subjects who were examined with FibroScan. METHODS: Multivariate logistic regression was conducted for every known risk factor of metabolic syndrome. Principal components analysis was used to visualize the distribution of metabolic syndrome patients. We further applied various statistical machine learning techniques to visualize and investigate the pattern and relationship between metabolic syndrome and several risk variables. RESULTS: Obesity, serum glutamic-oxalocetic transaminase, serum glutamic pyruvic transaminase, controlled attenuation parameter score, and glycated hemoglobin emerged as significant risk factors in multivariate logistic regression. The area under the receiver operating characteristic curve values for classification and regression trees and for the random forest were 0.831 and 0.904, respectively. CONCLUSIONS: Machine learning technology facilitates the identification of metabolic syndrome in self-paid health examination subjects with high accuracy.

Clustering Heatmap for Visualizing and Exploring Complex and High-dimensional Data Related to Chronic Kidney Disease.

BACKGROUND: Preventive medicine and primary health care are essential for patients with chronic kidney disease (CKD) because the symptoms of CKD may not appear until the renal function is severely compromised. Early identification of the risk factors of CKD is critical for preventing kidney damage and adverse outcomes. Early recognition of rapid progression to advanced CKD in certain high-risk populations is vital. METHODS: This is a retrospective cohort study, the population screened and the site where the study has been performed. Multivariate statistical analysis was used to assess the prediction of CKD as many potential risk factors are involved. The clustering heatmap and random forest provides an interactive visualization for the classification of patients with different CKD stages. RESULTS: uric acid, blood urea nitrogen, waist circumference, serum glutamic oxaloacetic transaminase, and hemoglobin A1c (HbA1c) were significantly associated with CKD. CKD was highly associated with obesity, hyperglycemia, and liver function. Hypertension and HbA1c were in the same cluster with a similar pattern, whereas high-density lipoprotein cholesterol had an opposite pattern, which was also verified using heatmap. Early staged CKD patients who are grouped into the same cluster as advanced staged CKD patients could be at high risk for rapid decline of kidney function and should be closely monitored. CONCLUSIONS: The clustering heatmap provided a new predictive model of health care management for patients at high risk of rapid CKD progression. This model could help physicians make an accurate diagnosis of this progressive and complex disease.

Evaluation of urate-lowering therapy in hyperuricemia patients: a systematic review and Bayesian network meta-analysis of randomized controlled trials.

OBJECTIVE: Hyperuricemia is a strong precursor of gout, which deteriorates patients' health and quality of life. Sustained adherence to urate-lowering therapies (ULTs) is crucial for efficacy and therapeutic cost-effectiveness. Recently, several new ULTs have been proposed. We performed a systematic review and meta-analysis of randomized controlled trials (RCTs) to reassess the efficacy and safety of the current ULTs, focusing on adherence attrition-related adverse event reporting. METHOD: The Bayesian network meta-analysis was applied to compare ULTs. Drug efficacy and safety were measured by whether the target level of serum urate acid was achieved and whether any adverse events occurred. The results were summarized using the pooled estimates of effect sizes (odds ratios), their precisions (95% credible interval), and the ranking probabilities. RESULTS AND CONCLUSIONS: Thirty-nine RCTs were identified, accumulating 19,401 patients. Consistent with previous studies, febuxostat (≥ 40 mg/day) was superior to other monoagent ULTs. The new findings were as follows: (i) dual-agent ULTs were superior to febuxostat alone, and further surveillance on the adverse effects when lesinurad is uptitrated is needed, and (ii) terminalia bellerica 500 mg/day, a novel xanthine oxidase inhibitor (XOI) made of natural fruit extracts, and topiroxostat ≥ 80 mg/day, an XOI used mostly in Japan, could be new effective options for lowering the occurrence of adherence attrition events. Evidence from RCTs regarding second-line agents, such as probenecid and pegloticase, remains insufficient for clinical decision-making.Key Points• Dual-agent ULTs were superior to febuxostat alone, and further surveillance on the adverse-effects when lesinurad is uptitrated is needed.• Terminalia bellerica 500 mg/day, a novel xanthine oxidase inhibitor (XOI) made of natural fruit extracts, and topiroxostat 80 mg/day, an XOI used mostly in Japan, could be new effective options for lowering the occurrence of adherence attrition events.

Risk Factors for Myocardial Infarction and Stroke Among Sepsis Survivors: A Competing Risks Analysis.

OBJECTIVES: Predictors for post-sepsis myocardial infarction (MI) and stroke are yet to be identified due to the competing risk of death. METHODS: This study included all hospitalized patients with sepsis from National Health Insurance Research Database of Taiwan between 2000 and 2011. The primary outcome was the first occurrence of MI and stroke requiring hospitalization within 180 days following hospital discharge from the index sepsis episode. The association between predictors and post-sepsis MI and stroke were analyzed using cumulative incidence competing risk model that controlled for the competing risk of death. RESULTS: Among 42 316 patients with sepsis, 1012 (2.4%) patients developed MI and stroke within 180 days of hospital discharge. The leading 5 predictors for post-sepsis MI and stroke are prior cerebrovascular diseases (hazard ratio [HR]: 2.02, 95% confidence interval [CI]: 1.74-2.32), intra-abdominal infection (HR: 1.94, 95% CI: 1.71-2.20), previous MI (HR: 1.81, 95% CI: 1.53-2.15), lower respiratory tract infection (HR: 1.62, 95% CI: 1.43-1.85), and septic encephalopathy (HR: 1.61, 95% CI: 1.26-2.06). CONCLUSIONS: Baseline comorbidities and sources of infection were associated with an increased risk of post-sepsis MI and stroke. The identified risk factors may help physicians select a group of patients with sepsis who may benefit from preventive measures, antiplatelet treatment, and other preventive measures for post-sepsis MI and stroke.

Blockchain-Enabled iWellChain Framework Integration With the National Medical Referral System: Development and Usability Study.

BACKGROUND: Medical referral is the transfer of a patient's care from one physician to another upon request. This process involves multiple steps that require provider-to-provider and provider-to-patient communication. In Taiwan, the National Health Insurance Administration (NHIA) has implemented a national medical referral (NMR) system, which encourages physicians to refer their patients to different health care facilities to reduce unnecessary hospital visits and the financial stress on the national health insurance. However, the NHIA's NMR system is a government-based electronic medical referral service, and its referral data access and exchange are limited to authorized clinical professionals using their national health smart cards over the NHIA virtual private network. Therefore, this system lacks scalability and flexibility and cannot establish trusting relationships among patients, family doctors, and specialists. OBJECTIVE: To eliminate the existing restrictions of the NHIA's NMR system, this study developed a scalable, flexible, and blockchain-enabled framework that leverages the NHIA's NMR referral data to build an alliance-based medical referral service connecting health care facilities. METHODS: We developed a blockchain-enabled framework that can integrate patient referral data from the NHIA's NMR system with electronic medical record (EMR) and electronic health record (EHR) data of hospitals and community-based clinics to establish an alliance-based medical referral service serving patients, clinics, and hospitals and improve the trust in relationships and transaction security. We also developed a blockchain-enabled personal health record decentralized app (DApp) based on our blockchain-enabled framework for patients to acquire their EMR and EHR data; DApp access logs were collected to assess patients' behavior and investigate the acceptance of our personal authorization-controlled framework. RESULTS: The constructed iWellChain Framework was installed in an affiliated teaching hospital and four collaborative clinics. The framework renders all medical referral processes automatic and paperless and facilitates efficient NHIA reimbursements. In addition, the blockchain-enabled iWellChain DApp was distributed for patients to access and control their EMR and EHR data. Analysis of 3 months (September to December 2018) of access logs revealed that patients were highly interested in acquiring health data, especially those of laboratory test reports. CONCLUSIONS: This study is a pioneer of blockchain applications for medical referral services, and the constructed framework and DApp have been applied practically in clinical settings. The iWellChain Framework has the scalability to deploy a blockchain environment effectively for health care facilities; the iWellChain DApp has potential for use with more patient-centered applications to collaborate with the industry and facilitate its adoption.

Noninvasive and Convenient Screening of Metabolic Syndrome Using the Controlled Attenuation Parameter Technology: An Evaluation Based on Self-Paid Health Examination Participants.

BACKGROUND: There is a medical need for an easy, fast, and non-invasive method for metabolic syndrome (MetS) screening. This study aimed to assess the ability of FibroScan to detect MetS, in participants who underwent a self-paid health examination. METHODS: A retrospective cohort study was conducted on all adults who underwent a self-paid health examination comprising of an abdominal transient elastography inspection using FibroScan 502 Touch from March 2015 to February 2019. FibroScan can assess the level of liver fibrosis by using a liver stiffness score, and the level of liver steatosis by using the controlled attenuation parameter (CAP) score. The logistic regression analysis and receiver operating characteristic curve were applied to select significant predictors and assess their predictability. A final model that included all significant predictors that are found by univariate analysis, and a convenient model that excluded all invasive parameters were created. RESULTS: Of 1983 participants, 13.6% had a physical status that fulfilled MetS criteria. The results showed that the CAP score solely could achieve an area under the curve (AUC) of 0.79 (0.76-0.82) in predicting MetS, and the AUC can be improved to 0.88 (0.85-0.90) in the final model. An AUC of 0.85 (0.83-0.88) in predicting MetS was obtained in the convenient model, which includes only 4 parameters (CAP score, gender, age, and BMI). A panel of predictability indices (the ranges of sensitivity, specificity, positive and negative likelihood ratio: 0.78-0.89, 0.66-0.82, 2.64-4.47, and 0.17-0.26) concerning gender- and BMI-specific CAP cut-off values (range: 191.65-564.95) were presented for practical reference. CONCLUSIONS: Two prediction systems were proposed for identifying individuals with a physical status that fulfilled the MetS criteria, and a panel of predictability indices was presented for practical reference. Both systems had moderate predictive performance. The findings suggested that FibroScan evaluation is appropriate as a first-line MetS screening; however, the variation in prediction performance of such systems among groups with varying metabolic derangements warrants further studies in the future.

Impact of post-sepsis cardiovascular complications on mortality in sepsis survivors: a population-based study.

BACKGROUND: It remains unclear whether sepsis-related cardiovascular complications have an adverse impact on survival independent of pre-existing comorbidities. To investigate the survival impact of post-sepsis cardiovascular complications among sepsis survivors, we conducted a population-based study using the National Health Insurance Database of Taiwan. METHODS: We identified sepsis patients from the National Health Insurance Research Database of Taiwan using ICD-9-CM codes involving infection and organ dysfunction between 2000 and 2011. Post-sepsis incident myocardial infarction (MI) and stroke were ascertained by ICD-9-CM codes and antiplatelet treatment. We constructed a non-sepsis comparison cohort using propensity score matching to ascertain the association between sepsis and cardiovascular complications. Furthermore, we compared the 180-day mortality and 365-day mortality between patients surviving sepsis with or without post-sepsis MI or stroke within 70 days of hospital discharge. We constructed Cox regression models adjusting for pre-existing comorbidities to evaluate the independent survival impact of post-sepsis MI or stroke among sepsis survivors. RESULTS: We identified 42,316 patients hospitalized for sepsis, from which we matched 42,151 patients 1:1 with 42,151 patients hospitalized without sepsis. Compared to patients hospitalized without sepsis, patients hospitalized with sepsis had an increased risk of MI or stroke (adjusted odds ratio 1.72, 95% CI 1.60-1.85). Among 42,316 patients hospitalized for sepsis, 486 (1.15%) patients developed incident stroke and 108 (0.26%) developed incident MI within 70 days of hospital discharge. Compared to sepsis survivors without cardiovascular complications, sepsis survivors with incident MI or stroke had a higher mortality rate at 180 days (11.68% vs. 4.44%, P = 0.003) and at 365 days (16.75% vs. 7.11%, P = 0.005). Adjusting for age, sex, and comorbidities, post-sepsis MI or stroke was independently associated with increased 180-day (adjusted hazard ratio [HR] 2.16, 95% CI 1.69-2.76) and 365-day (adjusted HR 1.90, 95% CI 1.54-2.32) mortality. CONCLUSIONS: Compared to sepsis patients without incident MI or stroke, sepsis patients with incident MI or stroke following hospital discharge had an increased risk of mortality for up to 365 days of follow-up. This increased risk cannot be explained by pre-sepsis comorbidities.

Epidemiology of Emergency Department Sepsis: A National Cohort Study Between 2001 and 2012.

The aim of this study is to examine the incidence trend of sepsis over 11 years and compared mortality outcomes among Taiwanese patients with sepsis admitted from emergency department (ED) and non-ED routes. We used a nationwide health insurance database from Taiwan, which comprise of 23 million beneficiaries. Patients with sepsis were identified by ICD-9 CM codes for infection and organ dysfunction from 2001 to 2012. We performed propensity score matching and compared mortality rates between ED-admitted and non ED-admitted patients.During the 11-year study period, we identified 1,256,684 patients with sepsis. 493,397 (29.3%) were admitted through the ED, and 763,287 (70.7%) were admitted directly to the floor. For patients with sepsis, mortality in ED-admitted patients decreased from 27.2% in 2002 to 21.1% in 2012 while that in non-ED admitted patients decreased from 35.3% in 2002 to 30.7% in 2012. Although patients with sepsis admitted through the ED had a higher incidence of organ dysfunction than patients who were directly admitted, they had more favorable outcomes in mortality, length of intensive care unit stay, and hospital stay. After propensity score matching, ED-admitted patients had a 7% lower risk of 90-day mortality (HR, 0.93, 95% CI, 0.89-0.97) compared with directly admitted patients. During the study period, mortality declined faster among ED admitted sepsis patients than directly admitted sepsis patients. Results of this study should be interpreted in light of limitations. Like other administrative database studies, treatment details are not available. Further clinical studies evaluating the treatment and outcome difference between ED and non-ED admitted sepsis patients are warranted.