Artificial intelligence prediction of In-Hospital mortality in patients with dementia: A multi-center study.

BACKGROUND: Prediction of mortality is very important for care planning in hospitalized patients with dementia and artificial intelligence has the potential to serve as a solution; however, this issue remains unclear. Thus, this study was conducted to elucidate this matter. METHODS: We identified 10,573 hospitalized patients aged ≥ 45 years with dementia from three hospitals between 2010 and 2020 for this study. Utilizing 44 feature variables extracted from electronic medical records, an artificial intelligence (AI) model was constructed to predict death during hospitalization. The data was randomly separated into 70 % training set and 30 % testing set. We compared predictive accuracy among six algorithms including logistic regression, random forest, extreme gradient boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), multilayer perceptron (MLP), and support vector machine (SVM). Additionally, another set of data collected in 2021 was used as the validation set to assess the performance of six algorithms. RESULTS: The average age was 79.8 years, with females constituting 54.5 % of the sample. The in-hospital mortality rate was 6.7 %. LightGBM exhibited the highest area under the curve (0.991) for predicting mortality compared to other algorithms (XGBoost: 0.987, random forest: 0.985, logistic regression: 0.918, MLP: 0.898, SVM: 0.897). The accuracy, sensitivity, positive predictive value, and negative predictive value of LightGBM were 0.943, 0.944, 0.943, 0.542, and 0.996, respectively. Among the features in LightGBM, the three most important variables were the Glasgow Coma Scale, respiratory rate, and blood urea nitrogen. In the validation set, the area under the curve of LightGBM reached 0.753. CONCLUSIONS: The AI prediction model demonstrates strong accuracy in predicting in-hospital mortality among patients with dementia, suggesting its potential implementation to enhance future care quality.

Impact of the Home-Based Medical Integrated Program on Health Outcomes and Medical Resource Utilization in Home Healthcare Patients in Taiwan.

Purpose: The home-based medical integrated program (HMIP) is a novel model for home healthcare (HHC) in Taiwan, initiated in 2016 to enhance care quality. However, the outcomes of this program on health outcomes and medical resource utilization in HHC patients remain unclear. Thus, we conducted this study to clarify it. Patients and Methods: The authors utilized the Taiwan National Health Insurance Research Database to identify HHC patients who received HMIP and those who did not between January 2015 and December 2017. A retrospective cohort study design was used. Convenience sampling was employed to select patients who met the inclusion criteria: being part of the HHC program and having complete data for analysis. Results: A total of 4982 HHC patients in the HMIP group and 10,447 patients in the non-HMIP group were identified for this study. The mean age in the HMIP group and non-HMIP group was 77.6 years and 76.1 years, respectively. Compared with the non-HMIP group, the HMIP group had lower total medical costs for HHC, fewer outpatient department visits and lower medical costs, lower medical costs for emergency department visits, fewer hospitalizations, and a lower mortality rate (34.6% vs 41.2%, p<0.001). Conclusion: The HMIP is a promising model for improving care quality and reducing medical resource utilization in HHC patients. While this suggests that the non-HMIP model should be replaced, it's important to note that both non-HMIP and HMIP models currently coexist. The HMIP may serve as an important reference for other nations seeking to improve care quality and reduce medical resource utilization in their own HHC systems.

Hypothyroidism following carbon monoxide poisoning: An epidemiological study supported by animal experiments.

Previous studies have suggested a possible association between carbon monoxide poisoning (COP) and hypothyroidism, but the evidence is limited. Therefore, the aim of this study was to further investigate this relationship. Using data from the Taiwan National Health Research Database, we identified 32,162 COP patients and matched with 96,486 non-COP patients by age and index date for an epidemiological study. The risk of hypothyroidism was compared between the two cohorts until 2018. Independent predictors of hypothyroidism were analyzed using competing risk analysis. An animal study was also conducted to support the findings. COP patients had an increased risk of hypothyroidism compared to non-COP patients in the overall analysis (adjusted hazard ratio [AHR]= 3.88; 95 % confidence interval [CI]: 3.27-4.60) and in stratified analyses by age, sex, and comorbidities. The increase in the overall risk persisted even after more than six years of follow-up (AHR= 4.19; 95 % CI: 3.18-5.53). Independent predictors of hypothyroidism, in addition to COP, included age ≥65 years, female sex, hyperlipidemia, and mental disorder. The animal study showed damages in the hypothalamus, pituitary gland, and thyroid, as well as altered hormone levels 28 days after COP exposure. The epidemiological results showed an increased risk of hypothyroidism in COP patients, which was further supported by the animal study. These findings suggest the need for close monitoring of thyroid function in COP patients, especially in those who are age ≥65 years, female, and have hyperlipidemia or mental disorder.

Heat-related illness and dementia: a study integrating epidemiological and experimental evidence.

BACKGROUND: Heat-related illness (HRI) is commonly considered an acute condition, and its potential long-term consequences are not well understood. We conducted a population-based cohort study and an animal experiment to evaluate whether HRI is associated with dementia later in life. METHODS: The Taiwan National Health Insurance Research Database was used in the epidemiological study. We identified newly diagnosed HRI patients between 2001 and 2015, but excluded those with any pre-existing dementia, as the study cohort. Through matching by age, sex, and the index date with the study cohort, we selected individuals without HRI and without any pre-existing dementia as a comparison cohort at a 1:4 ratio. We followed each cohort member until the end of 2018 and compared the risk between the two cohorts using Cox proportional hazards regression models. In the animal experiment, we used a rat model to assess cognitive functions and the histopathological changes in the hippocampus after a heat stroke event. RESULTS: In the epidemiological study, the study cohort consisted of 70,721 HRI patients and the comparison cohort consisted of 282,884 individuals without HRI. After adjusting for potential confounders, the HRI patients had a higher risk of dementia (adjusted hazard ratio [AHR] = 1.24; 95% confidence interval [CI]: 1.19-1.29). Patients with heat stroke had a higher risk of dementia compared with individuals without HRI (AHR = 1.26; 95% CI: 1.18-1.34). In the animal experiment, we found cognitive dysfunction evidenced by animal behavioral tests and observed remarkable neuronal damage, degeneration, apoptosis, and amyloid plaque deposition in the hippocampus after a heat stroke event. CONCLUSIONS: Our epidemiological study indicated that HRI elevated the risk of dementia. This finding was substantiated by the histopathological features observed in the hippocampus, along with the cognitive impairments detected, in the experimental heat stroke rat model.

Implementation of a novel computer assisted telephone follow-up model for older patients after emergency department discharge in an Asian population.

BACKGROUND: While the impact of telephone follow-up (TFU) for older emergency department (ED) patients is controversial, its effects on the Asian population remain uncertain. In this study, we evaluated the effectiveness of a novel computer assisted TFU model specifically for this demographic. METHODS: At a Taiwanese tertiary medical center, we developed a TFU protocol that included a referral and case management system within the ED hospital information system. We provided TFU to older discharged patients between April 1, 2021, and May 31, 2021. We compared this cohort with a non-TFU cohort of older ED patients and analyzed demographic characteristics and post-ED discharge outcomes. RESULTS: The TFU model was successfully implemented, with 395 patients receiving TFU and 191 without TFU. TFU patients (median age: 76 years, male proportion: 48.9%) differed from non-TFU patients (median age: 74 years, male proportion: 43.5%). Compared with the non-TFU cohort, the multivariate logistic regression analysis revealed that the TFU cohort had a lower total medical expenditure < 1 month (adjusted odds ratio [AOR]: 0.32; 95% CI: 0.21 - 0.47 for amounts exceeding 5,000 New Taiwan Dollars), and higher satisfaction (AOR: 2.80; 95% CI: 1.46 - 5.36 for scores > 3 on a five-point Likert Scale). However, the TFU cohort also had a higher risk of hospitalization < 1 month (AOR: 2.50; 95% CI: 1.31 - 4.77) compared to the non-TFU cohort. CONCLUSION: Computer-assisted TFU appears promising. Further research involving a larger number of patients and validation in other hospitals is necessary to bolster the evidence and extend the findings to a broader context.

Global mortality of chronic liver diseases attributable to Hepatitis B virus and Hepatitis C virus infections from 1990 to 2019 and projections to 2030.

BACKGROUND: The burden of chronic liver disease (CLD) deaths attributable to the hepatitis B virus (HBV) and hepatitis C virus (HCV) remains unknown. Further research is required to elucidate the extent of this burden in the eventual elimination of these diseases. METHODS: Data on liver cancer, cirrhosis, and other CLD among 204 countries and territories between 1990 and 2019 was extracted from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) published in 2019. The Bayesian age-period-cohort model was used to analyze the temporal trend and predict the disease burden by 2030. RESULTS: The number of HCV-related CLD deaths surpassed that of CLD deaths caused by HBV in 2019 (536833 deaths versus 523003 deaths) and is expected to be maintained until 2030 (689124 deaths versus 628824 deaths). East Asia had the highest burden of chronic HBV and HCV infections during the study period. In 2019, the largest age-standardized death rates (ASDR) of CLD deaths caused by HBV and HCV were mainly observed in Western Sub-Saharan Africa (18.75%) and Eastern Sub-Saharan Africa (16.42%), respectively. South Asia and East Asia are predicted to have the highest number of CLD deaths related to HCV and HBV by 2030. Eastern Europe and South Asia show the largest expected increase in disease burden caused by HCV or HBV between 2019 and 2030. No GBD region is projected to achieve the WHO target of a 65% reduction in mortality from chronic HBV and HCV infections by 2030. CONCLUSIONS: Although the mortality of CLD caused by HBV and HCV decreased in the last three decades (from 1990 to 2019), the number of deaths will continue to increase until 2030. Therefore, governments and international organizations need to strengthen the effectiveness of vaccines, screening, and treatment, especially in potential emerging hotspot regions.

Mesenchymal stem cells reduce long-term cognitive deficits and attenuate myelin disintegration and microglia activation following repetitive traumatic brain injury.

The underlying mechanisms for the beneficial effects exerted by bone marrow-mesenchymal stem cells (BM-MSCs) in treating repetitive traumatic brain injury (rTBI)-induced long-term sensorimotor/cognitive impairments are not fully elucidated. Herein, we aimed to explore whether BM-MSCs therapy protects against rTBI-induced long-term neurobehavioral disorders in rats via normalizing white matter integrity and gray matter microglial response. Rats were subjected to repeated mild lateral fluid percussion on day 0 and day 3. On the fourth day post-surgery, MSCs groups received MSCs (4 × 106 cells/ml/kg, intravenously) and were assessed by the radial maze, Y maze, passive avoidance tests, and modified neurological severity scores. Hematoxylin & eosin, and Luxol fast blue stainings were used to examine the histopathology and white matter thickness. At the same time, immunofluorescence staining was used to investigate the numbers of tumor necrosis factor-alpha (TNF-α)-containing microglia in gray matter. Three to nine months after neurotrauma, rats displayed sensorimotor and cognitive impairments, reduced thickness in white matter, and over-accumulation of TNF-α-containing microglia and cellular damage in gray matter. Therapy with BM-MSCs significantly attenuated the rTBI-induced sensorimotor and cognitive impairments and all their complications. Mesenchymal stem cell therapy might accelerate the recovery of sensorimotor and cognitive impairments in rats with rTBI via normalizing myelin integrity and microglia response.

White and gray matter integrity evaluated by MRI-DTI can serve as noninvasive and reliable indicators of structural and functional alterations in chronic neurotrauma.

We aimed to evaluate whether white and gray matter microstructure changes observed with magnetic resonance imaging (MRI)-based diffusion tensor imaging (DTI) can be used to reflect the progression of chronic brain trauma. The MRI-DTI parameters, neuropathologic changes, and behavioral performance of adult male Wistar rats that underwent moderate (2.1 atm on day "0") or repeated mild (1.5 atm on days "0" and "2") traumatic brain injury (TBI or rmTBI) or sham operation were evaluated at 7 days, 14 days, and 1-9 months after surgery. Neurobehavioral tests showed that TBI causes long-term motor, cognitive and neurological deficits, whereas rmTBI results in more significant deficits in these paradigms. Both histology and MRI show that rmTBI causes more significant changes in brain lesion volumes than TBI. In vivo DTI further reveals that TBI and rmTBI cause persistent microstructural changes in white matter tracts (such as the body of the corpus callosum, splenium of corpus callus, internal capsule and/or angular bundle) of both two hemispheres. Luxol fast blue measurements reveal similar myelin loss (as well as reduction in white matter thickness) in ipsilateral and contralateral hemispheres as observed by DTI analysis in injured rats. These data indicate that the disintegration of microstructural changes in white and gray matter parameters analyzed by MRI-DTI can serve as noninvasive and reliable markers of structural and functional level alterations in chronic TBI.

Impact of the Integrated Activation System on Door-to-Balloon Times and Clinical Outcomes in STEMI Patients Receiving Primary PCI.

Background: The door-to-balloon (D2B) time is a critical quality measure in managing ST-segment elevation myocardial infarction (STEMI) patients receiving primary percutaneous coronary intervention (PCI). We developed an integrated STEMI activation system, named Acute Myocardial Infarction Software Aids (AMISTAD), to optimize care for STEMI patients. This study aimed to evaluate the impact of the AMISTAD system on D2B times and clinical outcomes. Methods: We retrospectively collected data of consecutive STEMI patients receiving primary PCI between July 2017 and December 2018 at a single center. The patients were categorized into AMISTAD and non-AMISTAD groups. Outcomes included D2B time, length of hospital stay, and 12-month cardiovascular outcomes. Data were analyzed using multiple regression models; subgroup and sensitivity analyses were applied to examine the robustness of the results. Results: A total of 114 STEMI patients were enrolled (38 AMISTAD, 76 non-AMISTAD). The AMISTAD group had a significantly shorter mean D2B time (66.7 ± 13.2 vs. 76.6 ± 24.9 minutes, p = 0.02) and non-significantly shorter length of hospital stay (4.7 vs. 7.2 days, p = 0.09). The 12-month cardiovascular outcomes between the two groups were not significantly different (adjusted hazard ratio 0.79, 95% confidence interval 0.30-2.09, p = 0.64). Subgroup and sensitivity analyses had consistent outcomes. Conclusions: Integrating the AMISTAD system into the STEMI workflow was associated with a reduced D2B time and shorter hospital stay. Further research involving larger cohorts and extended follow-up periods is needed to assess the generalizability and impact on cardiovascular outcomes. The AMISTAD system has the potential to improve the quality of care for STEMI patients.

Real-time artificial intelligence predicts adverse outcomes in acute pancreatitis in the emergency department: Comparison with clinical decision rule.

OBJECTIVE: Artificial intelligence (AI) prediction is increasingly used for decision making in health care, but its application for adverse outcomes in emergency department (ED) patients with acute pancreatitis (AP) is not well understood. This study aimed to clarify this aspect. METHODS: Data from 8274 ED patients with AP in three hospitals from 2009 to 2018 were analyzed. Demographic data, comorbidities, laboratory results, and adverse outcomes were included. Six algorithms were evaluated, and the one with the highest area under the curve (AUC) was implemented into the hospital information system (HIS) for real-time prediction. Predictive accuracy was compared between the AI model and Bedside Index for Severity in Acute Pancreatitis (BISAP). RESULTS: The mean ± SD age was 56.1 ± 16.7 years, with 67.7% being male. The AI model was successfully implemented in the HIS, with Light Gradient Boosting Machine (LightGBM) showing the highest AUC for sepsis (AUC 0.961) and intensive care unit (ICU) admission (AUC 0.973), and eXtreme Gradient Boosting (XGBoost) showing the highest AUC for mortality (AUC 0.975). Compared to BISAP, the AI model had superior AUC for sepsis (BISAP 0.785), ICU admission (BISAP 0.778), and mortality (BISAP 0.817). CONCLUSIONS: The first real-time AI prediction model implemented in the HIS for predicting adverse outcomes in ED patients with AP shows favorable initial results. However, further external validation is needed to ensure its reliability and accuracy.

Association between chronic pain and acute coronary syndrome in the older population: a nationwide population-based cohort study.

BACKGROUND: Chronic pain (CP) may increase the risk of acute coronary syndrome (ACS); however, this issue in the older population remains unclear. Therefore, this study was conducted to clarify it. METHODS: We used the Taiwan National Health Insurance Research Database to identify older patients with CP between 2001 and 2005 as the study cohort. Comparison cohort was the older patients without CP by matching age, sex, and index date at 1:1 ratio with the study cohort in the same period. We also included common underlying comorbidities in the analyses. The risk of ACS was compared between the two cohorts by following up until 2015. RESULTS: A total of 17241 older patients with CP and 17241 older patients without CP were included in this study. In both cohorts, the mean age (± standard deviation) and female percentage were 73.5 (± 5.7) years and 55.4%, respectively. Spinal disorders (31.9%) and osteoarthritis (27.0%) were the most common causes of CP. Older patients with CP had an increased risk for ACS compared to those without CP after adjusting for all underlying comorbidities (adjusted sub-distribution hazard ratio [sHR] 1.18; 95% confidence interval: 1.07-1.30). The increasement of risk of ACS was more when the follow-up period was longer (adjusted sHR of < 3 years: 1.8 vs. <2 years: 1.75 vs. <1 year: 1.55). CONCLUSIONS: CP was associated with an increased risk of ACS in the older population, and the association was more prominent when the follow-up period was longer. Early detection and intervention for CP are suggested in this population.

Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time.

BACKGROUND: Hyperglycemic crises are associated with high morbidity and mortality. Previous studies have proposed methods to predict adverse outcomes of patients in hyperglycemic crises; however, artificial intelligence (AI) has never been used to predict adverse outcomes. We implemented an AI model integrated with the hospital information system (HIS) to clarify whether AI could predict adverse outcomes. METHODS: We included 2,666 patients with hyperglycemic crises from emergency departments (ED) between 2009 and 2018. The patients were randomized into a 70%/30% split for AI model training and testing. Twenty-two feature variables from the electronic medical records were collected. The performance of the multilayer perceptron (MLP), logistic regression, random forest, Light Gradient Boosting Machine (LightGBM), support vector machine (SVM), and K-nearest neighbor (KNN) algorithms was compared. We selected the best algorithm to construct an AI model to predict sepsis or septic shock, intensive care unit (ICU) admission, and all-cause mortality within 1 month. The outcomes between the non-AI and AI groups were compared after implementing the HIS and predicting the hyperglycemic crisis death (PHD) score. RESULTS: The MLP had the best performance in predicting the three adverse outcomes, compared with the random forest, logistic regression, SVM, KNN, and LightGBM models. The areas under the curves (AUCs) using the MLP model were 0.852 for sepsis or septic shock, 0.743 for ICU admission, and 0.796 for all-cause mortality. Furthermore, we integrated the AI predictive model with the HIS to assist decision making in real time. No significant differences in ICU admission or all-cause mortality were detected between the non-AI and AI groups. The AI model performed better than the PHD score for predicting all-cause mortality (AUC 0.796 vs. 0.693). CONCLUSIONS: A real-time AI predictive model is a promising method for predicting adverse outcomes in ED patients with hyperglycemic crises. Further studies recruiting more patients are warranted.

Real-time artificial intelligence system for bacteremia prediction in adult febrile emergency department patients.

BACKGROUND: Artificial intelligence (AI) holds significant potential to be a valuable tool in healthcare. However, its application for predicting bacteremia among adult febrile patients in the emergency department (ED) remains unclear. Therefore, we conducted a study to provide clarity on this issue. METHODS: Adult febrile ED patients with blood cultures at Chi Mei Medical Center were divided into derivation (January 2017 to June 2019) and validation groups (July 2019 to December 2020). The derivation group was utilized to develop AI models using twenty-one feature variables and five algorithms to predict bacteremia. The performance of these models was compared with qSOFA score. The AI model with the highest area under the receiver operating characteristics curve (AUC) was chosen to implement the AI prediction system and tested on the validation group. RESULTS: The study included 5,647 febrile patients. In the derivation group, there were 3,369 patients with a mean age of 61.4 years, and 50.7% were female, including 508 (13.8%) with bacteremia. The model with the best AUC was built using the random forest algorithm (0.761), followed by logistic regression (0.755). All five models demonstrated better AUC than the qSOFA score (0.560). The random forest model was adopted to build a real-time AI prediction system integrated into the hospital information system, and the AUC achieved 0.709 in the validation group. CONCLUSION: The AI model shows promise to predict bacteremia in adult febrile ED patients; however, further external validation in different hospitals and populations is necessary to verify its effectiveness.

Carpal tunnel syndrome in dentists compared to other populations: A nationwide population-based study in Taiwan.

BACKGROUND: Dentists may be at a higher risk of developing carpal tunnel syndrome (CTS) because of their use of frequent wrist and vibratory instruments at work; however, this issue remains unclear. Therefore, we conducted this study to clarify it. METHODS: Taiwan National Health Insurance Research Database was used for this nationwide population-based study. We identified 11,084 dentists, 74,901 non-dentist healthcare professionals (HCPs), and identical number of age- and sex-matched participants from the general population. Participants who had the diagnosis of CTS before 2007 were excluded. Between 2007 and 2011, the risk of developing CTS among dentists, non-dentist HCPs, and the general population was compared by following their medical histories. RESULTS: The cumulative incidence rate of CTS among dentists was 0.5% during the 5-year follow-up period. In dentists, the risk was higher in women (women: 0.7%; men: 0.4%) and older individuals (≥60 years: 1.0%; <60 years: 0.4%). After adjusting for age, sex, and underlying comorbidities, dentists had a lower risk of CTS than the general population (adjusted odds ratio [AOR]: 0.65, 95% confidence interval [CI]: 0.45-0.92). Dentists had a higher risk for CTS compared with non-dentist HCPs, although the difference was not statistically significant (AOR: 1.21; 95% CI: 0.90-1.64). CONCLUSIONS: In CTS, dentists had a lower risk than the general population and a trend of higher risk than non-dentist HCPs. The difference between dentists and non-dentist HCPs suggests that we should pay attention to dentists for potential occupational risk of this disease. However, further studies are warranted to better clarify it.

External validation of geriatric influenza death score: A multicenter study.

The Geriatric Influenza Death (GID) score was developed to help decision making in older patients with influenza in the emergency department (ED), but external validation is unavailable. Thus, we conducted a study was to fill the data gap. We recruited all older patients (≥65 years) who visited the ED of three hospitals between 2009 and 2018. Demographic data and clinical characteristics were retrospectively collected. Discrimination, goodness of fit, and performance of the GID score were evaluated. Of the 5,508 patients (121 died) with influenza, the mean age was 76.6±7.4 (standard deviation) years, and 49.3% were males. The GID score was higher in the mortality group (1.7±1.1 vs. 0.8±0.8, p <0.01). With 0 as the reference, the odds ratio for morality with score of 1, 2 and ≥3 was 3.08 (95% confidence interval [CI]: 1.66-5.71), 6.69 (95% CI: 3.52-12.71), and 23.68 (95% CI: 11.95-46.93), respectively. The area under the curve was 0.722 (95% CI: 0.677-0.766), and the Hosmer-Lemeshow goodness of fit test was 1.000. The GID score had excellent negative predictive values with different cut-offs. The GID score had good external validity, and further studies are warranted for wider application.

Eradication therapy may decrease the risk of immune thrombocytopenia after Helicobacter pylori infection: a retrospective cohort study in Taiwan.

BACKGROUND: Helicobacter pylori (HP) eradication therapy (HPE) is recommended for patients with unexplained immune thrombocytopenia (ITP); however, the role of HPE in preventing ITP in patients with HP infection remains unclear. Therefore, this study was designed to clarify it. METHODS: This study was conducted at a tertiary medical center and included all adult patients with HP infection between January 1, 2016 and December 31, 2018. We compared the risk of developing ITP between patients with and without HPE. All patients were followed up until December 31, 2020. RESULTS: After excluding patients with thrombocytopenia, 1995 adult patients with HP infection, including 1188 patients with HPE and 807 patients without HPE, were included in this study. The mean age of the patients with HPE was 57.9 years, whereas that of those without HPE was 61.6 years. The percentage of males was 56% in patients with HPE and 59% in those without HPE. Patients without HPE had a higher risk of ITP than those with HPE after adjusting for age, sex, the Charlson Comorbidity Index, and comorbidities [adjusted odds ratio (OR) 1.76; 95% confidence interval (CI) 1.16-2.68]. Stratified analyses showed that the higher risk was found only in males (adjusted OR: 1.70; 95% CI 1.03-2.80). In addition to HPE, male sex and anemia were independent predictors of ITP in patients with HP infection. CONCLUSION: This study showed that adult patients with HP infection not receiving HPE had a higher risk of developing ITP. We suggest that HPE should be considered, particularly in males and those who have anemia, to prevent ITP.

Implementation of a patient-centered mobile shared decision making platform and healthcare workers' evaluation: a case in a medical center.

Shared decision making is a patient-centered clinical decision-making process that allows healthcare workers to share the existing empirical medical outcomes with patients before making critical decisions. This study aims to explore a project in a medical center of developing a mobile SDM in Taiwan. Chi Mei Medical Center developed the mobile SDM platform and conducted a survey of evaluation from healthcare workers. A three-tier platform that based on cloud infrastructure with seven functionalities was developed. The survey revealed that healthcare workers with sufficient SDM knowledge have an antecedent effect on the three perceptive factors of acceptance of mobile SDM. Resistance to change and perceived ease of use show significant effect on behavioral intention. We provided a comprehensive architecture of mobile SDM and observed the implementation in a medical center. The majority of healthcare workers expressed their acceptancem; however, resistance to change still present. It is, therefore, necessary to be eliminated by continuously promoting activities that highlight the advantages of the Mobile SDM platform. In clinical practice, we validated that the mobile SDM provides patients and their families with an easy way to express their concerns to healthcare workers improving significantly their relationship with each other.

Alcohol abuse may increase the risk of autoimmune connective tissue disease: a nationwide population-based cohort study.

Objectives: Altered immune and inflammatory responses resulting from alcohol abuse have been implicated in increasing the risk of autoimmune connective tissue disease (ACTD). However, limited research has been conducted on this topic in the Asian population. Therefore, this study was undertaken to investigate and address this knowledge gap. Methods: Using data from Taiwan's National Health Insurance Research Database, we identified all patients with alcohol abuse between 2000 and 2017. We selected a comparison cohort without alcohol abuse, matching them in terms of age, sex, and index date at a 3:1 ratio. We collected information on common underlying comorbidities for analysis. Both cohorts were followed up until the diagnosis of ACTD or the end of 2018. Results: A total of 57,154 patients with alcohol abuse and 171,462 patients without alcohol abuse were included in the study. The age and sex distributions were similar in both cohorts, with men accounting for 89.8% of the total. After adjusting for underlying comorbidities, patients with alcohol abuse had a higher risk of developing ACTD [adjusted hazard ratio (AHR): 1.12, 95% confidence interval (CI): 1.01-1.25]. The stratified analysis revealed that this increased risk was specific to the male population. Additionally, besides alcohol abuse, liver disease, renal disease, coronary artery disease, and chronic obstructive pulmonary disease were identified as independent predictors for ACTD. Conclusion: This study demonstrates that alcohol abuse increases the risk of developing ACTD in the Asian population, particularly among men. Therefore, it is important to implement alcohol cessation, especially in individuals with liver disease, renal disease, coronary artery disease, and chronic obstructive pulmonary disease.

The interrelationships between neuronal viability, synaptic integrity, microglial responses, and amyloid-beta formation in an in vitro neurotrauma model.

The interrelationships between neuronal viability, synaptic integrity, and microglial responses remain in infancy. In dealing with the question, we induced a stretch injury to evaluate the mechanical effects of trauma on rat primary cortical neurons and BV2 microglial cells in a transwell culture system. The viability of primary neurons and BV2 cells was determined by MTT. Synaptic integrity was evaluated by determining the expression of beta-secretase 1 (BACE1), amyloid-beta (Aß), microtubule-associated protein 2 (MAP2), and synaptophysin (vehicle protein). Both CD16/32-positive (CD16/32+) and CD206-positive (CD206+) microglia cells were detected by immunofluorescence staining. The phagocytic ability of the BV2 cells was determined using pHrodo E. coli BioParticles conjugates and flow cytometry. We found that stretch injury BV2 cells caused reduced viability and synaptic abnormalities characterized by Aß accumulation and reductions of BACE1, MAP2, and synaptophysin in primary neurons. Intact BV2 cells exhibited normal phagocytic ability and were predominantly CD206+ microglia cells, whereas the injured BV2 cells exhibited reduced phagocytic ability and were predominantly CD16/32+ microglial cells. Like a stretch injury, the injured BV2 cells can cause both reduced viability and synaptic abnormalities in primary neurons; intact BV2 cells, when cocultured with primary neurons, can protect against the stretch-injured-induced reduced viability and synaptic abnormalities in primary neurons. We conclude that CD206+ and CD16/32+ BV-2 cells can produce neuroprotective and cytotoxic effects on primary cortical neurons.

Risks of Developing Diabetes and Hyperglycemic Crisis Following Carbon Monoxide Poisoning: A Study Incorporating Epidemiologic Analysis and Animal Experiment.

Purpose: Carbon monoxide (CO) poisoning may damage the pancreas, but the effects of CO poisoning on the development of diabetes and on existing diabetes remain unclear. We conducted a study incorporating data from epidemiologic analyses and animal experiments to clarify these issues. Methods: Using the National Health Insurance Database of Taiwan, we identified CO poisoning patients diagnosed between 2002 and 2016 (CO poisoning cohort) together with references without CO poisoning who were matched by age, sex, and index date at a 1:3 ratio. We followed participants until 2017 and compared the risks of diabetes and hyperglycemic crisis between two cohorts using Cox proportional hazards regressions. In addition, a rat model was used to assess glucose and insulin levels in blood as well as pathological changes in the pancreas and hypothalamus following CO poisoning. Results: Among participants without diabetes history, 29,141 in the CO poisoning cohort had a higher risk for developing diabetes than the 87,423 in the comparison cohort after adjusting for potential confounders (adjusted hazard ratio [AHR]=1.23; 95% confidence interval [CI]: 1.18-1.28). Among participants with diabetes history, 2302 in the CO poisoning cohort had a higher risk for developing hyperglycemic crisis than the 6906 in participants without CO poisoning (AHR = 2.12; 95% CI: 1.52-2.96). In the rat model, CO poisoning led to increased glucose and decreased insulin in blood and damages to pancreas and hypothalamus. Conclusion: Our epidemiological study revealed that CO poisoning increased the risks of diabetes and hyperglycemic crisis, which might be attributable to damages in the pancreas and hypothalamus as shown in the animal experiments.