Taipei Medical University Clinical Research Database: a collaborative hospital EHR database aligned with international common data standards.

OBJECTIVE: The objective of this paper is to provide a comprehensive overview of the development and features of the Taipei Medical University Clinical Research Database (TMUCRD), a repository of real-world data (RWD) derived from electronic health records (EHRs) and other sources. METHODS: TMUCRD was developed by integrating EHRs from three affiliated hospitals, including Taipei Medical University Hospital, Wan-Fang Hospital and Shuang-Ho Hospital. The data cover over 15 years and include diverse patient care information. The database was converted to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) for standardisation. RESULTS: TMUCRD comprises 89 tables (eg, 29 tables for each hospital and 2 linked tables), including demographics, diagnoses, medications, procedures and measurements, among others. It encompasses data from more than 4.15 million patients with various medical records, spanning from the year 2004 to 2021. The dataset offers insights into disease prevalence, medication usage, laboratory tests and patient characteristics. DISCUSSION: TMUCRD stands out due to its unique advantages, including diverse data types, comprehensive patient information, linked mortality and cancer registry data, regular updates and a swift application process. Its compatibility with the OMOP CDM enhances its usability and interoperability. CONCLUSION: TMUCRD serves as a valuable resource for researchers and scholars interested in leveraging RWD for clinical research. Its availability and integration of diverse healthcare data contribute to a collaborative and data-driven approach to advancing medical knowledge and practice.

Machine-Learning Based Risk Assessment for Cancer Therapy-Related Cardiac Adverse Events Among Breast Cancer Patients.

The study aims to develop machine-learning models to predict cardiac adverse events in female breast cancer patients who receive adjuvant therapy. We selected breast cancer patients from a retrospective dataset of the Taipei Medical University Clinical Research Database and Taiwan Cancer Registry between January 2004 and December 2020. Patients were monitored at the date of prescribed chemo- and/or -target therapies until cardiac adverse events occurred during a year. Variables were used, including demographics, comorbidities, medications, and lab values. Logistics regression (LR) and artificial neural network (ANN) were used. The performance of the algorithms was measured by the area under the receiver operating characteristic curve (AUC). In total, 1321 patients (an equal 15039 visits) were included. The best performance of the artificial neural network (ANN) model was achieved with the AUC, precision, recall, and F1-score of 0.89, 0.14, 0.82, and 0.2, respectively. The most important features were a pre-existing cardiac disease, tumor size, estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), cancer stage, and age at index date. Further research is necessary to determine the feasibility of applying the algorithm in the clinical setting and explore whether this tool could improve care and outcomes.

A novel prediction model of the risk of pancreatic cancer among diabetes patients using multiple clinical data and machine learning.

INTRODUCTION: Pancreatic cancer is associated with poor prognosis. Considering the increased global incidence of diabetes cases and that individuals with diabetes are considered a high-risk subpopulation for pancreatic cancer, it is critical to detect the risk of pancreatic cancer within populations of person living = with diabetes. This study aimed to develop a novel prediction model for pancreatic cancer risk among patients with diabetes, using = a real-world database containing clinical features and employing numerous artificial intelligent approach algorithms. METHODS: This retrospective observational study analyzed data on patients with Type 2 diabetes from a multisite Taiwanese EMR database between 2009 and 2019. Predictors were selected in accordance with the literature review and clinical perspectives. The prediction models were constructed using machine learning algorithms such as logistic regression, linear discriminant analysis, gradient boosting machine, and random forest. RESULTS: The cohort consisted of 66,384 patients. The Linear Discriminant Analysis (LDA) model generated the highest AUROC of 0.9073, followed by the Voting Ensemble and Gradient Boosting machine models. LDA, the best model, exhibited an accuracy of 84.03%, a sensitivity of 0.8611, and a specificity of 0.8403. The most significant predictors identified for pancreatic cancer risk were glucose, glycated hemoglobin, hyperlipidemia comorbidity, antidiabetic drug use, and lipid-modifying drug use. CONCLUSION: This study successfully developed a highly accurate 4-year risk model for pancreatic cancer in patients with diabetes using real-world clinical data and multiple machine-learning algorithms. Potentially, our predictors offer an opportunity to identify pancreatic cancer early and thus increase prevention and invention windows to impact survival in diabetic patients.

Ranitidine Use and Incident Cancer in a Multinational Cohort.

Importance: Ranitidine, the most widely used histamine-2 receptor antagonist (H2RA), was withdrawn because of N-nitrosodimethylamine impurity in 2020. Given the worldwide exposure to this drug, the potential risk of cancer development associated with the intake of known carcinogens is an important epidemiological concern. Objective: To examine the comparative risk of cancer associated with the use of ranitidine vs other H2RAs. Design, Setting, and Participants: This new-user active comparator international network cohort study was conducted using 3 health claims and 9 electronic health record databases from the US, the United Kingdom, Germany, Spain, France, South Korea, and Taiwan. Large-scale propensity score (PS) matching was used to minimize confounding of the observed covariates with negative control outcomes. Empirical calibration was performed to account for unobserved confounding. All databases were mapped to a common data model. Database-specific estimates were combined using random-effects meta-analysis. Participants included individuals aged at least 20 years with no history of cancer who used H2RAs for more than 30 days from January 1986 to December 2020, with a 1-year washout period. Data were analyzed from April to September 2021. Exposure: The main exposure was use of ranitidine vs other H2RAs (famotidine, lafutidine, nizatidine, and roxatidine). Main Outcomes and Measures: The primary outcome was incidence of any cancer, except nonmelanoma skin cancer. Secondary outcomes included all cancer except thyroid cancer, 16 cancer subtypes, and all-cause mortality. Results: Among 1 183 999 individuals in 11 databases, 909 168 individuals (mean age, 56.1 years; 507 316 [55.8%] women) were identified as new users of ranitidine, and 274 831 individuals (mean age, 58.0 years; 145 935 [53.1%] women) were identified as new users of other H2RAs. Crude incidence rates of cancer were 14.30 events per 1000 person-years (PYs) in ranitidine users and 15.03 events per 1000 PYs among other H2RA users. After PS matching, cancer risk was similar in ranitidine compared with other H2RA users (incidence, 15.92 events per 1000 PYs vs 15.65 events per 1000 PYs; calibrated meta-analytic hazard ratio, 1.04; 95% CI, 0.97-1.12). No significant associations were found between ranitidine use and any secondary outcomes after calibration. Conclusions and Relevance: In this cohort study, ranitidine use was not associated with an increased risk of cancer compared with the use of other H2RAs. Further research is needed on the long-term association of ranitidine with cancer development.

Age and gender differences in misperceptions of body shape in a Taiwanese population.

OBJECTIVES: Most studies of body size perception have been performed in adolescents, and most focus on gender differences in accurate perception of body size. This study investigated misperceptions of body sizes among males and females at different stages of adulthood in Taiwan. DESIGNS: In-person home interviews were used to proportionally and randomly select 2095 adult men and women to answer the East Asian Social Survey. Participants were divided into 18-39, 40-64, and 65 + age groups. The main variables analyzed were self-perceived body size and standardized BMI. RESULTS: Women, unlike men, were more likely to misperceive their body size as being overweight (OR = 2.92; p < .001). People with higher self-perceived social status were less likely to misperceive themselves as overweight (OR = 0.91; p = .01). People with college educations were 2.35 times more likely to overestimate their body size as being heavier than they were (p < .001) and less likely to underestimate it as being thinner than they were (OR = 0.45; p < .001). Women 18-35 and 36-64 years old were 6.96 and 4.31 times more likely (p < .001) to misperceive themselves as being overweight than women 65 or older, who were more likely to misperceive themselves as being too thin. There were no significant differences in body size misperceptions among the three age groups of adult men (p > .05). We found no different significant discrepancies between self-perceived body size and actual BMI between the older men and women (p = .16). However, younger and middle-aged men were 6.67 and 3.1 times more likely to misperceive themselves as being too thin than women in their same age groups (OR = 0.15 and OR = 0.32, respectively). CONCLUSIONS: Age and gender affect self-perceptions of body size in Taiwan. Overall, women are more likely than men to misperceive themselves as being too big, and men are more likely than women to misperceive themselves as too thin. Older women, however, were more likely to misperceive themselves as being too thin. Clinicians and health educators should know that people's perceptions and concerns regarding their body size vary by age and gender.

Machine learning approaches for predicting 5-year breast cancer survival: A multicenter study.

The study used clinical data to develop a prediction model for breast cancer survival. Breast cancer prognostic factors were explored using machine learning techniques. We conducted a retrospective study using data from the Taipei Medical University Clinical Research Database, which contains electronic medical records from three affiliated hospitals in Taiwan. The study included female patients aged over 20 years who were diagnosed with primary breast cancer and had medical records in hospitals between January 1, 2009 and December 31, 2020. The data were divided into training and external testing datasets. Nine different machine learning algorithms were applied to develop the models. The performances of the algorithms were measured using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. A total of 3914 patients were included in the study. The highest AUC of 0.95 was observed with the artificial neural network model (accuracy, 0.90; sensitivity, 0.71; specificity, 0.73; PPV, 0.28; NPV, 0.94; and F1-score, 0.37). Other models showed relatively high AUC, ranging from 0.75 to 0.83. According to the optimal model results, cancer stage, tumor size, diagnosis age, surgery, and body mass index were the most critical factors for predicting breast cancer survival. The study successfully established accurate 5-year survival predictive models for breast cancer. Furthermore, the study found key factors that could affect breast cancer survival in Taiwanese women. Its results might be used as a reference for the clinical practice of breast cancer treatment.

Prescription patterns of granulocyte colony-stimulating factors in patients with breast cancer: A real-world study.

BACKGROUND AND OBJECTIVES: Myelosuppressive chemotherapy is effective for breast cancer but carries a potential risk of febrile neutropenia (FN). Clinical practice guidelines have recommended prophylaxis with granulocyte colony-stimulating factor (G-CSF) to reduce the incidence of FN in patients receiving chemotherapy. We aimed to examine the use of G-CSFs for primary prophylaxis for FN and to see whether it follows the guidelines. In addition, we examined the changes in the use of long-acting and short-acting G-CSFs in patients with breast cancer over the past ten years. METHODS: This was a retrospective observational real-world study. The data were obtained from the clinical research database of three hospitals affiliated with Taipei Medical University. Patients with breast cancer who initiated their first chemotherapy regimen between January 1, 2011, and December 31, 2020, were identified by the ICD codes and their use of filgrastim or pegfilgrastim was identified by the Anatomical Therapeutic Chemical codes. Whether and how G-CSF was prescribed during the study patients' first chemotherapy regimen was examined, and the annual change in the total number of short- and long-acting G-CSFs prescribed to the study patients from 2011 to 2020 was analyzed. RESULTS: Among the 2,444 patients who were prescribed at least one of the examined 15 breast cancer chemotherapy drugs, 1,414 did not use any G-CSFs during their first chemotherapy regimen while 145 used G-CSFs for primary prophylaxis and 185 for treatment. Among the patients receiving high FN risk regimens, only 8.6% used G-CSF for primary prophylaxis. The average (± SD) number of days for short-acting G-CSF use was 2.3 (± 1.5) days with a median of 2 days. In addition, it was found that there was a significant reduction in long-acting G-CSF use (p = 0.03) whereas the changes in short-acting G-CSF use over time were not significant (p = 0.50). CONCLUSIONS: Our study results show that G-CSFs are used for primary prophylaxis in a small percentage of patients with breast cancer and the duration of short-acting G-CSF use is relatively short. Considering the significant clinical and economic impact of FN, it is hoped that the prescription patterns of G-CSFs observed can provide an important reference for future clinical practice and reimbursement policy.

Comparison of local ablative therapies, including radiofrequency ablation, microwave ablation, stereotactic ablative radiotherapy, and particle radiotherapy, for inoperable hepatocellular carcinoma: a systematic review and meta-analysis.

Surgical intervention is the first-line treatment in well-selected hepatocellular carcinoma (HCC) patients. However, only a few patients are suitable to receive radical surgery. We conducted a systematic review and meta-analysis to evaluate local control among four local ablative therapies in inoperable HCC patients, including radiofrequency ablation therapy (RFA), microwave ablation therapy (MWA), stereotactic ablative radiotherapy (SABR), and particle radiotherapy. The primary outcome was the local control rate and the secondary were regional and distant progression rates, overall survival rate, and adverse events. We included twenty-six studies from PubMed, EMBASE, and Cochrane Library databases. MWA (p < 0.001) and particle radiotherapy (p < 0.001) showed better performance of local control compared to RFA, while SABR (p = 0.276) showed a non-significant trend. However, SABR (p = 0.002) and particle radiotherapy (p < 0.001) showed better performance than RFA in HCCs of ≥ 30 mm in size. MWA showed a similar result to RFA while SABR and particle radiotherapy showed a lower survival rate in the 2-, 3-, and 4-year overall survival rates. Our results indicate that MWA, SABR and particle radiotherapy were safe and no inferior to RFA in local control rate. Besides, the local control rates of SABR and particle radiotherapy are better than RFA in HCC of ≥ 30 mm in size. As a result, we suggested that MWA, SABR and particle radiotherapy to be effective alternatives to RFA for inoperable HCC. Moreover, the tumor size should be taken into consideration for optimal treatment selection between local ablative therapies.

Emotion recognition in doctor-patient interactions from real-world clinical video database: Initial development of artificial empathy.

BACKGROUND AND OBJECTIVE: The promising use of artificial intelligence (AI) to emulate human empathy may help a physician engage with a more empathic doctor-patient relationship. This study demonstrates the application of artificial empathy based on facial emotion recognition to evaluate doctor-patient relationships in clinical practice. METHODS: A prospective study used recorded video data of doctor-patient clinical encounters in dermatology outpatient clinics, Taipei Municipal Wanfang Hospital, and Taipei Medical University Hospital collected from March to December 2019. Two cameras recorded the facial expressions of four doctors and 348 adult patients during regular clinical practice. Facial emotion recognition was used to analyze the basic emotions of doctors and patients with a temporal resolution of 1 second. In addition, a physician-patient satisfaction questionnaire was administered after each clinical session, and two standard patients gave impartial feedback to avoid bias. RESULTS: Data from 326 clinical session videos showed that (1) Doctors expressed more emotions than patients (t [326] > = 2.998, p < = 0.003), including anger, happiness, disgust, and sadness; the only emotion that patients showed more than doctors was surprise (t [326] = -4.428, p < .001) (p < .001). (2) Patients felt happier during the latter half of the session (t [326] = -2.860, p = .005), indicating a good doctor-patient relationship. CONCLUSIONS: Artificial empathy can offer objective observations on how doctors' and patients' emotions change. With the ability to detect emotions in 3/4 view and profile images, artificial empathy could be an accessible evaluation tool to study doctor-patient relationships in practical clinical settings.

Near infrared imaging system for preventing blood vision obstruction in endoscopy.

Spinal endoscopy procedure is commonly used in the diagnosis and treatment of various health problems and is effective. Bleeding is one of the most common complications of spinal endoscopy procedures. Blood vision obstruction (BVO), that is, obstruction of the endoscopic camera lens caused by the accumulation of blood in the surgical field, is a serious problem in endoscopic procedures. This study presents what we believe to be a new approach to addressing BVO with external multispectral imaging. The study was completed using a BVO simulation model, and the results reveal that this technology can be used to effectively overcome BVO and provide clear images of the anatomy, enabling more effective diagnosis and treatment. This technique may enable improvement of the outcomes of endoscopic procedures and could have far-reaching implications in the field of endoscopy.

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

Background: Previous studies have identified COVID-19 risk factors, such as age and chronic health conditions, linked to severe outcomes and mortality. However, accurately predicting severe illness in COVID-19 patients remains challenging, lacking precise methods. Objective: This study aimed to leverage clinical real-world data and multiple machine-learning algorithms to formulate innovative predictive models for assessing the risk of severe outcomes or mortality in hospitalized patients with COVID-19. Methods: Data were obtained from the Taipei Medical University Clinical Research Database (TMUCRD) including electronic health records from three Taiwanese hospitals in Taiwan. This study included patients admitted to the hospitals who received an initial diagnosis of COVID-19 between January 1, 2021, and May 31, 2022. The primary outcome was defined as the composite of severe infection, including ventilator use, intubation, ICU admission, and mortality. Secondary outcomes consisted of individual indicators. The dataset encompassed demographic data, health status, COVID-19 specifics, comorbidities, medications, and laboratory results. Two modes (full mode and simplified mode) are used; the former includes all features, and the latter only includes the 30 most important features selected based on the algorithm used by the best model in full mode. Seven machine learning was employed algorithms the performance of the models was evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity. Results: The study encompassed 22,192 eligible in-patients diagnosed with COVID-19. In the full mode, the model using the light gradient boosting machine algorithm achieved the highest AUROC value (0.939), with an accuracy of 85.5%, a sensitivity of 0.897, and a specificity of 0.853. Age, vaccination status, neutrophil count, sodium levels, and platelet count were significant features. In the simplified mode, the extreme gradient boosting algorithm yielded an AUROC of 0.935, an accuracy of 89.9%, a sensitivity of 0.843, and a specificity of 0.902. Conclusion: This study illustrates the feasibility of constructing precise predictive models for severe outcomes or mortality in COVID-19 patients by leveraging significant predictors and advanced machine learning. These findings can aid healthcare practitioners in proactively predicting and monitoring severe outcomes or mortality among hospitalized COVID-19 patients, improving treatment and resource allocation.

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

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

Comparison Between Lumbar Endoscopic Unilateral Laminotomy for Bilateral Decompression and Other Decompression Approaches for Lumbar Spinal Stenosis: A Systematic Review.

BACKGROUND: Lumbar spinal stenosis affects numerous people globally. Full-endoscopic uniportal interlaminar decompression (FEUID) for lumbar spinal stenosis results in satisfactory outcomes. In this systematic review, we compared technical methods, surgical outcomes, and complications among different types of surgical techniques and discussed the effect of different surgical skill levels. METHODS: A systematic review of studies published from 1990 to January 2022 was performed. Studies related to FEUID were identified using the keywords "interlaminar decompression," "endoscopy," "uniportal," and "percutaneous." The outcomes measured were operative time, blood loss, hospital stay, complications, visual analog scale scores, Oswestry Disability Index scores, and the Macnab criteria. RESULTS: Ten of 306 studies were eligible for inclusion. For FEUID, data for 580 patients and more than 367 levels were collected. All the studies reported significant improvement in mean visual analog scale and Oswestry Disability Index scores, and the mean overall complication rate was 9.5%. Compared with other surgical techniques, FEUID resulted in lower visual analog scale and Oswestry Disability Index scores, complication rates, and blood loss and shorter hospital stay. These surgical parameters were considerably affected by a surgeon's skill level. CONCLUSION: FEUID results in better patient satisfaction with more favorable surgical outcomes and fewer complications. Although more prospective randomized controlled studies are required to confirm these findings, our results indicate that FEUID is a reasonable alternative to traditional lumbar spinal surgery.

Development and Validation of Novel Deep-Learning Models Using Multiple Data Types for Lung Cancer Survival.

A well-established lung-cancer-survival-prediction model that relies on multiple data types, multiple novel machine-learning algorithms, and external testing is absent in the literature. This study aims to address this gap and determine the critical factors of lung cancer survival. We selected non-small-cell lung cancer patients from a retrospective dataset of the Taipei Medical University Clinical Research Database and Taiwan Cancer Registry between January 2008 and December 2018. All patients were monitored from the index date of cancer diagnosis until the event of death. Variables, including demographics, comorbidities, medications, laboratories, and patient gene tests, were used. Nine machine-learning algorithms with various modes were used. The performance of the algorithms was measured by the area under the receiver operating characteristic curve (AUC). In total, 3714 patients were included. The best performance of the artificial neural network (ANN) model was achieved when integrating all variables with the AUC, accuracy, precision, recall, and F1-score of 0.89, 0.82, 0.91, 0.75, and 0.65, respectively. The most important features were cancer stage, cancer size, age of diagnosis, smoking, drinking status, EGFR gene, and body mass index. Overall, the ANN model improved predictive performance when integrating different data types.

Comparison of long-term outcomes of spinal fusion surgeries supplemented with "topping-off" implants in lumbar degenerative diseases: A systematic review and network meta-analysis.

Background Context: Spinal fusion surgery is a common treatment for lumbar degenerative diseases and has been associated with the long-term complication of adjacent segment disease (ASD). In recent years, the "topping-off" technique has emerged as a new surgical method, combining spinal fusion with a hybrid stabilization device (HSD) or interspinous process device (IPD) proximal to the fused vertebrae. Methods: A literature search using the PubMed, Cochrane Central Register of Controlled Trials, EMBASE, and Web of Science databases identified eligible studies comparing topping-off implant(s) with spinal fusion surgery for lumbar degenerative diseases. Risk of bias was assessed using the Cochrane RoB 2.0 tool for randomized controlled trials and the Newcastle-Ottawa scale for retrospective studies. Each outcome was analyzed using the statistical Confidence in NMA (CINeMA) 1.9.0 software. Results: 17 RCTs and retrospective studies that included 1255 participants and five interventions were identified. The topping-off implants device for intervertebral assisted motion (DIAM; OR = 0.235, p < 0.001), Dynesys (OR = 0.413, p < 0.001), and Coflex (OR = 0.417, p < 0.01) significantly lowered the incidence of radiographic adjacent segment degeneration (RASDeg) compared with spinal fusion surgery alone. Spinal fusion supplemented with DIAM significantly reduced the incidence of clinical adjacent segment disease (CASD) (OR = 0.358, p = 0.032). Conclusions: Spinal fusion supplemented with DIAM substantially reduced the incidence of radiographic and clinical adjacent segment disease. No significant difference was observed between the treatment comparators for reoperation due to ASD and back pain relief score.

Effectiveness and Safety of Immunosuppressants and Biological Therapy for Chronic Spontaneous Urticaria: A Network Meta-Analysis.

Chronic spontaneous urticaria (CSU) is the most common phenotype of chronic urticaria. We compared treatment effects and safety profiles of the medications in patients with CSU. We searched PubMed, MEDLINE, and Web of Science for randomized control trials (RCTs), from 1 January 2000 to 31 July 2021, which evaluated omalizumab and immunosuppressants. Network meta-analyses (NMAs) were performed with a frequentist approach. Outcome assessments considered the efficacy (Dermatology Life Quality Index (DLQI) and weekly urticaria activity score (UAS7)) and tolerability profiles with evaluations of study quality, inconsistencies, and heterogeneity. We identified 14 studies which we included in our direct and indirect quantitative analyses. Omalizumab demonstrated better efficacy in DLQI and UAS7 outcomes compared to a placebo, and UAS7 assessments also demonstrated better outcomes compared to cyclosporine. Alongside this, omalizumab demonstrated relatively lower incidences of safety concerns compared to the other immunosuppressants. Cyclosporin was also associated with higher odds of adverse events than other treatment options. Our findings indicate that omalizumab resulted in greater improvements in terms of the DLQI and UAS7 with good tolerability in CSU patients compared to the other immunosuppressants.

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

OBJECTIVES: The coronavirus disease 2019 pandemic has affected countries around the world since 2020, and an increasing number of people are being infected. The purpose of this research was to use big data and artificial intelligence technology to find key factors associated with the coronavirus disease 2019 infection. The results can be used as a reference for disease prevention in practice. METHODS: This study obtained data from the "Imperial College London YouGov Covid-19 Behaviour Tracker Open Data Hub", covering a total of 291,780 questionnaire results from 28 countries (April 1~August 31, 2020). Data included basic characteristics, lifestyle habits, disease history, and symptoms of each subject. Four types of machine learning classification models were used, including logistic regression, random forest, support vector machine, and artificial neural network, to build prediction modules. The performance of each module is presented as the area under the receiver operating characteristics curve. Then, this study further processed important factors selected by each module to obtain an overall ranking of determinants. RESULTS: This study found that the area under the receiver operating characteristics curve of the prediction modules established by the four machine learning methods were all >0.95, and the RF had the highest performance (area under the receiver operating characteristics curve is 0.988). Top ten factors associated with the coronavirus disease 2019 infection were identified in order of importance: whether the family had been tested, having no symptoms, loss of smell, loss of taste, a history of epilepsy, acquired immune deficiency syndrome, cystic fibrosis, sleeping alone, country, and the number of times leaving home in a day. CONCLUSIONS: This study used big data from 28 countries and artificial intelligence methods to determine the predictors of the coronavirus disease 2019 infection. The findings provide important insights for the coronavirus disease 2019 infection prevention strategies.

Association of Air Pollution and Weather Factors with Traffic Injury Severity: A Study in Taiwan.

Exposure to air pollutants may elevate the injury severity scores (ISSs) for road traffic injuries (RTIs). This multicenter cross-sectional study aimed to investigate the associations between air pollution, weather conditions, and RTI severity. This retrospective study was performed in Taiwan in 2018. The location of each road traffic accident (RTA) was used to determine the nearest air quality monitoring and weather station, and the time of each RTA was matched to the corresponding hourly air pollutant concentration and weather factors. Five multiple logistic regression models were used to compute the risk of sustaining severe injury (ISS ≥ 9). Of the 14,973 patients with RTIs, 2853 sustained severe injury. Moderate or unhealthy air quality index, higher exposure to particulate matter ≤2.5 µm in diameter, bicyclists or pedestrians, greater road width, nighttime, and higher temperature and relative humidity were significant risk factors for severe injury. Exposure to nitrogen oxide and ozone did not increase the risk. Auto occupants and scene-to-hospital time were the protective factors. Sensitivity analyses showed consistent results between air pollutants and the risk of severe injury. Poor air quality and hot and humid weather conditions were associated with severe RTIs. Active commuters were at higher risk of sustaining severe RTI.

Parsing the role of NSP1 in SARS-CoV-2 infection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) leads to shutoff of protein synthesis, and nsp1, a central shutoff factor in coronaviruses, inhibits cellular mRNA translation. However, the diverse molecular mechanisms employed by nsp1 as well as its functional importance are unresolved. By overexpressing various nsp1 mutants and generating a SARS-CoV-2 mutant, we show that nsp1, through inhibition of translation and induction of mRNA degradation, targets translated cellular mRNA and is the main driver of host shutoff during infection. The propagation of nsp1 mutant virus is inhibited exclusively in cells with intact interferon (IFN) pathway as well as in vivo, in hamsters, and this attenuation is associated with stronger induction of type I IFN response. Therefore, although nsp1's shutoff activity is broad, it plays an essential role, specifically in counteracting the IFN response. Overall, our results reveal the multifaceted approach nsp1 uses to shut off cellular protein synthesis and uncover nsp1's explicit role in blocking the IFN response.