Machine Learning Model for Anesthetic Risk Stratification for Gynecologic and Obstetric Patients: A Novel Approach for Early Detection.

BACKGROUND: Preoperative evaluation is important, our study explored the application of machine learning methods for anesthetic risk classification and for the evaluation of the contributions of various factors. To minimize the effects of confounding variables during model training, we used a homogenous group with similar physiological states and ages undergoing similar pelvic organ-related procedures not involving malignancies. OBJECTIVE: Data on women of reproductive age (age = 20-50 years) who underwent gestational or gynecological surgery between January 1, 2017, and December 31, 2021, were obtained from the National Taiwan University Hospital Integrated Medical Database. METHODS: We first performed an exploratory analysis and selected key features. We then performed data preprocessing to acquire relevant features related to preoperative examination. To further enhance predictive performance, we employed the log likelihood ratio algorithm to generate comorbidity patterns. Lastly, we input the processed features into the light gradient boosting machine (LightGBM) model for training and subsequent prediction. RESULTS: A total of 10,892 patients were included. Within this data set, 9893 patients were classified as having low anesthetic risk (American Society of Anesthesiologists physical status score 1-2), and 999 patients were classified as having high anesthetic risk (American Society of Anesthesiologists physical status score > 2). The area under the receiver operating characteristic curve of the LightGBM model was 90.25. CONCLUSIONS: By combining comorbidity information and clinical laboratory data, our methodology based on the LightGBM model provides more accurate predictions for anesthetic risk classification. CLINICALTRIAL: This study was registered with the Research Ethics Committee of the National Taiwan University Hospital with trial number 202204010RINB.

Sex differences in clinical characteristics and long-term clinical outcomes in Asian hospitalized heart failure patients.

AIMS: Sex differences in long-term post-discharge clinical outcomes in Asian patients hospitalized for acute decompensated heart failure (HF) persist despite the world-wide implementation of guideline-directed medical therapy for decades. The present study aims to elucidate the puzzling dilemma and to depict the directions of solution. METHODS AND RESULTS: Between 2011 and 2020, a total of 12 428 patients (6518 men and 5910 women, mean age 73.50 ± 14.85) hospitalized for acute decompensated HF were retrospectively enrolled from a university HF cohort. Compared with men, women hospitalized for acute decompensated HF were older in age (76.40 ± 13.43 vs. 71.20 ± 15.67 years old, P < 0.0001) with more coexisting hypertension, diabetes, hyperlipidaemia and moderate to severe chronic kidney disease, but less with ischaemic heart disease, cerebrovascular disease and chronic obstructive pulmonary disease (P < 0.0001). In echocardiography measurement parameters, women had smaller left ventricular and left atrial dimensions, higher left ventricular mass index, higher left ventricular ejection fraction (LVEF) and more in HF with preserved ejection fraction (EF) category (LVEF > 50%) than men (P < 0.0001). In HF therapy, women compared with men received more guideline-directed medical HF therapies including angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, angiotensin receptor-neprilysin inhibitors and sodium-glucose cotransporter-2 inhibitors, but similar beta-blockers and mineralocorticoid receptor antagonists (P < 0.0001). Post-discharge long-term clinical outcomes after multivariate-adjusted analysis revealed that women compared with men had lower all-cause mortality [adjusted hazard ratio (aHR): 0.89, 95% confidence interval (CI): 0.84-0.93], lower cardiovascular mortality (aHR: 0.89, 95% CI: 0.80-0.99) and lower 1 year mortality (aHR: 0.91, 95% CI: 0.84-0.99) but similar HF rehospitalization rate (aHR: 1.02, 95% CI: 0.95-1.09) over 8 years of follow-up. The superiority of women over men in all-cause mortality was shown in HF with preserved EF (>50%) and HF with mildly reduced EF (40%-50%), but not in HF with reduced EF (<40%) category. Subgroup forest plot analysis showed body mass index, coexisting hypertension and chronic obstructive pulmonary disease as significant interacting factors. CONCLUSIONS: With more coronary risk factors and medical comorbidities, less cardiac remodelling and better adherence to guideline-directed HF therapy, women hospitalized for acute decompensated HF demonstrated superiority over men in long-term post-discharge clinical outcomes, including all-cause mortality, cardiovascular mortality and 1 year mortality, and mainly in HF with preserved and mid-range EF categories, in the Asian HF cohort.

Web-based artificial intelligence to predict cognitive impairment following stroke: A multicenter study.

BACKGROUND AND PURPOSE: Post-stroke cognitive impairment (PSCI) is highly prevalent in modern society. However, there is limited study implying an accurate and explainable machine learning model to predict PSCI. The aim of this study is to develop and validate a web-based artificial intelligence (AI) tool for predicting PSCI. METHODS: The retrospective cohort study design was conducted to develop and validate a web-based prediction model. Adults who experienced a stroke between January 1, 2004, and September 30, 2017, were enrolled, and patients with PSCI were followed up from the stroke index date until their last follow-up. The model's performance metrics, including accuracy, area under the curve (AUC), recall, precision, and F1 score, were compared. RESULTS: A total of 3209 stroke patients were included in the study. The model demonstrated an accuracy of 0.8793, AUC of 0.9200, recall of 0.6332, precision of 0.9664, and F1 score of 0.7651. In the external validation phase, the accuracy improved to 0.9039, AUC to 0.9094, recall to 0.7457, precision to 0.9168, and F1 score to 0.8224. The final model can be accessed at https://psci-calculator.my.id/. CONCLUSION: Our results are able to produce a user-friendly interface that is useful for health practitioners to perform early prediction on PSCI. These findings also suggest that the provided AI model is reliable and can serve as a roadmap for future studies using AI models in a clinical setting.

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.

Salpingectomy for ectopic pregnancy reduces ovarian cancer risk-a nationwide study.

Recent studies propose fallopian tubes as the tissue origin for many ovarian epithelial cancers. To further support this paradigm, we assessed whether salpingectomy for treating ectopic pregnancy had a protective effect using the Taiwan Longitudinal National Health Research Database. We identified 316 882 women with surgical treatment for ectopic pregnancy and 3 168 820 age- and index-date-matched controls from 2000 to 2016. In a nested cohort, 91.5% of cases underwent unilateral salpingectomy, suggesting that most surgically managed patients have salpingectomy. Over a follow-up period of 17 years, the ovarian carcinoma incidence was 0.0069 (95% confidence interval [CI] = 0.0060 to 0.0079) and 0.0089 (95% CI = 0.0086 to 0.0092) in the ectopic pregnancy and the control groups, respectively (P < .001). After adjusting the events to per 100 person-years, the hazard ratio (HR) in the ectopic pregnancy group was 0.70 (95% CI = 0.61 to 0.80). The risk reduction occurred only in epithelial ovarian cancer (HR = 0.73, 95% CI = 0.63 to 0.86) and not in non-epithelial subtypes. These findings show a decrease in ovarian carcinoma incidence after salpingectomy for treating ectopic pregnancy.

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.

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

AIMS: The prevalence of Type 2 Diabetes Mellitus (T2DM) is projected to be 7 % in 2030. Despite its need for long-term diabetes care, the adherence rate of injectable medications such as insulin is around 60 %, lower than the acceptable threshold of 80 %. This study aims to create classification models to predict insulin adherence among adult T2DM naïve insulin users. METHODS: Clinical data were extracted from Taipei Medical University Clinical Research Database (TMUCRD) from January 1st, 2004 to December 30th, 2020. A patient was regarded as adherent if his/her medication possession ratio (MPR) was at least 80 %. Seven domains of predictors were created, including demographics, baseline medications, baseline comorbidities, baseline laboratory data, healthcare resource utilization, index insulins, and the concomitant non-insulin T2DM medications. We built two Xgboost models for internal and external testing respectively. RESULTS: Using a cohort of 4134 patients from Taiwan, our model achieved the Area Under the curve of the Receiver Operating Characteristic (AUROC) of the internal test was 0.782 and the AUROC of the external test was 0.771. the SHAP (SHapley Additive exPlanations) value showed that the number of prescribed medications, the number of outpatient visits, and laboratory data were predictive of future insulin adherence. CONCLUSIONS: This is the first study to predict adherence among adult naïve insulin users. The developed model is a potential clinical decision support tool to identify possible non-adherent patients for healthcare providers to design individualized education plans.

The Effects of Different Types of Sleep Disorder on Colorectal Cancer: A Nationwide Population-Based Cohort Study.

The impact of sleep disorders (SDs), particularly sleep apnea (SA), on the development of colorectal cancer (CRC) has been the subject of significant research. However, the potential contribution of other SDs to the incidence of CRC remains unexplored. The objective of this study was to examine the effects of SDs on the risk of developing CRC. This study assessed CRC risk among individuals diagnosed with SDs compared with age- and sex-matched unaffected individuals. A longitudinal, nationwide, population-based cohort study was conducted using data from the Taiwan National Health Insurance Research Database (NHIRD) encompassing 177,707 individuals diagnosed with SDs and 177,707 matched controls. Cox proportional hazard regression analysis was used to determine the relative increased risk of CRC in individuals with SDs and specific subgroups of SDs. The CRC incidences were 1.32-fold higher (95% CI 1.23-1.42) in the overall SD cohort, 1.17-fold higher (95% CI 0.82-1.68) in the SA cohort, 1.42-fold higher (95% CI 1.31-1.55) in the insomnia cohort, 1.27-fold higher (95% CI 1.17-1.38) in the sleep disturbance cohort, and 1.00-fold higher (95% CI 0.77-1.29) in the other SD cohort, after adjusting for age, sex, and comorbidities.

Effect of Annual Influenza Vaccination on the Risk of Lung Cancer Among Patients With Hypertension: A Population-Based Cohort Study in Taiwan.

Objectives: Lung cancer is a main contributor to all newly diagnosed cancers worldwide. The chemoprotective effect of the influenza vaccine among patients with hypertension remains unclear. Methods: A total of 37,022 patients with hypertension were retrospectively enrolled from the Taiwan National Health Insurance Research Database. These patients were further divided into a vaccinated group (n = 15,697) and an unvaccinated group (n = 21,325). Results: After adjusting for sex, age, comorbidities, medications, level of urbanization and monthly income, vaccinated patients had a significantly lower risk of lung cancer occurrence than unvaccinated patients (adjusted hazard ratio [aHR]: 0.56, 95% confidence interval [CI]: 0.47-0.67). A potential protective effect was observed for both sexes and in the elderly age group. With a greater total number of vaccinations, a potentially greater protective effect was observed (aHR: 0.75, 95% CI 0.60-0.95; aHR: 0.66, 95% CI: 0.53-0.82; aHR: 0.26, 95% CI: 0.19-0.36, after receiving 1, 2-3 and ≥4 vaccinations, respectively). Conclusion: Influenza vaccination was associated with a lower risk of lung cancer among patients with hypertension. The potentially chemoprotective effect appeared to be dose dependent.

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.

Incidence and Mortality of Malignant Brain Tumors after 20 Years of Mobile Use.

(1) Objective: This population-based study was performed to examine the trends of incidence and deaths due to malignant neoplasm of the brain (MNB) in association with mobile phone usage for a period of 20 years (January 2000-December 2019) in Taiwan. (2) Methods: Pearson correlation, regression analysis, and joinpoint regression analysis were used to examine the trends of incidence of MNB and deaths due to MNB in association with mobile phone usage. (3) Results: The findings indicate a trend of increase in the number of mobile phone users over the study period, accompanied by a slight rise in the incidence and death rates of MNB. The compound annual growth rates further support these observations, highlighting consistent growth in mobile phone users and a corresponding increase in MNB incidences and deaths. (4) Conclusions: The results suggest a weaker association between the growing number of mobile phone users and the rising rates of MNB, and no significant correlation was observed between MNB incidences and deaths and mobile phone usage. Ultimately, it is important to acknowledge that conclusive results cannot be drawn at this stage and further investigation is required by considering various other confounding factors and potential risks to obtain more definitive findings and a clearer picture.

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.

The Association between Statins and Liver Cancer Risk in Patients with Heart Failure: A Nationwide Population-Based Cohort Study.

Heart failure (HF) and cancer have similar risk factors. HMG-CoA reductase inhibitors, also known as statins, are chemoprotective agents against carcinogenesis. We aimed to evaluate the chemoprotective effects of statins against liver cancer in patients with HF. This cohort study enrolled patients with HF aged ≥20 years between 1 January 2001 and 31 December 2012 from the National Health Insurance Research Database in Taiwan. Each patient was followed to assess liver cancer risk. A total of 25,853 patients with HF were followed for a 12-year period; 7364 patients used statins and 18,489 did not. The liver cancer risk decreased in statin users versus non-users (adjusted hazard ratio (aHR) = 0.26, 95% confidence interval (CI): 0.20-0.33) in the entire cohort in the multivariate regression analysis. In addition, both lipophilic and hydrophilic statins reduced the liver cancer risk in patients with HF (aHR 0.34, 95% CI: 0.26-0.44 and aHR 0.42, 95% CI: 0.28-0.54, respectively). In the sensitivity analysis, statin users in all dose-stratified subgroups had a reduced liver cancer risk regardless of age, sex, comorbidity, or other concomitant drug use. In conclusion, statins may decrease liver cancer risk in patients with HF.

The Association between Influenza Vaccine and Risk of Chronic Kidney Disease/Dialysis in Patients with Hypertension.

BACKGROUNDS: Influenza vaccination could decrease the risk of major cardiac events in patients with hypertension. However, the vaccine's effects on decreasing the risk of chronic kidney disease (CKD) development in such patients remain unclear. METHODS: We retrospectively analysed the data of 37,117 patients with hypertension (≥55 years old) from the National Health Insurance Research Database during 1 January 2001 to 31 December 2012. After a 1:1 propensity score matching by the year of diagnosis, we divided the patients into vaccinated (n = 15,961) and unvaccinated groups (n = 21,156). RESULTS: In vaccinated group, significantly higher prevalence of comorbidities such as diabetes, cerebrovascular disease, dyslipidemia, heart and liver disease were observed compared with unvaccinated group. After adjusting age, sex, comorbidities, medications (anti-hypertensive agents, metformin, aspirin and statin), level of urbanization and monthly incomes, significantly lower risk of CKD occurrence was observed among vaccinated patients in influenza season, non-influenza season and all season (Adjusted hazard ratio [aHR]: 0.39, 95% confidence level [C.I.]: 0.33-0.46; 0.38, 95% C.I.: 0.31-0.45; 0.38, 95% C.I.: 0.34-0.44, respectively). The risk of hemodialysis significantly decreased after vaccination (aHR: 0.40, 95% C.I.: 0.30-0.53; 0.42, 95% C.I.: 0.31-0.57; 0.41, 95% C.I.: 0.33-0.51, during influenza season, non-influenza season and all season). In sensitivity analysis, patients with different sex, elder and non-elder age, with or without comorbidities and with or without medications had significant decreased risk of CKD occurrence and underwent hemodialysis after vaccination. Moreover, the potential protective effect appeared to be dose-dependent. CONCLUSIONS: Influenza vaccination decreases the risk of CKD among patients with hypertension and also decrease the risk of receiving renal replacement therapy. Its potential protective effects are dose-dependent and persist during both influenza and noninfluenza seasons.

Protective Effects of Influenza Vaccine against Colorectal Cancer in Populations with Chronic Kidney Disease: A Nationwide Population-Based Cohort Study.

Chronic kidney disease (CKD) is associated with malignancy, including colorectal cancer, via the potential mechanism of chronic inflammation status. This study aimed to determine whether influenza vaccines can reduce the risk of colorectal cancer in patients with CKD. Our cohort study enrolled 12,985 patients older than 55 years with a diagnosis of CKD in Taiwan from the National Health Insurance Research Database at any time from 1 January 2001 to 31 December 2012. Patients enrolled in the study were divided into a vaccinated and an unvaccinated group. In this study, 7490 and 5495 patients were unvaccinated and vaccinated, respectively. A propensity score was utilized to reduce bias and adjust the results. Cox proportional hazards regression was used to estimate the correlation between the influenza vaccine and colorectal cancer in patients with CKD. The results showed that the influenza vaccine exerted a protective effect against colorectal cancer in populations with CKD. The incidence rate of colon cancer in the vaccinated group was significantly lower than in the unvaccinated group, with an adjusted hazard rate (HR) of 0.38 (95% CI: 0.30-0.48, p < 0.05). After the propensity score was adjusted for Charlson comorbidity index, age, sex, dyslipidemia, hypertension, diabetes, monthly income, and level of urbanization, the dose-dependent effect was found, and it revealed adjusted HRs of 0.74 (95% CI: 0.54-1.00, p < 0.05), 0.41 (95% CI: 0.30-0.57, p < 0.001), 0.16 (95% CI: 0.11-0.25, p < 0.001) for one, two to three, and four or more vaccinations, respectively. In summary, the influenza vaccine was found to be associated with a reduced risk of colorectal cancer in CKD patients. This study highlights the potential chemopreventive effect of influenza vaccination among patients with CKD. Future studies are required to determine whether the aforementioned relationship is a causal one.

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

The chronic receipt of renin-angiotensin-aldosterone system (RAAS) inhibitors including angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) have been assumed to be associated with a significant decrease in overall gynecologic cancer risks. This study aimed to investigate the associations of long-term RAAS inhibitors use with gynecologic cancer risks. A large population-based case-control study was conducted from claim databases of Taiwan's Health and Welfare Data Science Center (2000-2016) and linked with Taiwan Cancer Registry (1979-2016). Each eligible case was matched with four controls using propensity matching score method for age, sex, month, and year of diagnosis. We applied conditional logistic regression with 95% confidence intervals to identify the associations of RAAS inhibitors use with gynecologic cancer risks. The statistical significance threshold was p < 0.05. A total of 97,736 gynecologic cancer cases were identified and matched with 390,944 controls. The adjusted odds ratio for RAAS inhibitors use and overall gynecologic cancer was 0.87 (95% CI: 0.85-0.89). Cervical cancer risk was found to be significantly decreased in the groups aged 20-39 years (aOR: 0.70, 95% CI: 0.58-0.85), 40-64 years (aOR: 0.77, 95% CI: 0.74-0.81), ≥65 years (aOR: 0.87, 95% CI: 0.83-0.91), and overall (aOR: 0.81, 95% CI: 0.79-0.84). Ovarian cancer risk was significantly lower in the groups aged 40-64 years (aOR: 0.76, 95% CI: 0.69-0.82), ≥65 years (aOR: 0.83, 95% CI: 0.75-092), and overall (aOR: 0.79, 95% CI: 0.74-0.84). However, a significantly increased endometrial cancer risk was observed in users aged 20-39 years (aOR: 2.54, 95% CI: 1.79-3.61), 40-64 years (aOR: 1.08, 95% CI: 1.02-1.14), and overall (aOR: 1.06, 95% CI: 1.01-1.11). There were significantly reduced risks of gynecologic cancers with ACEIs users in the groups aged 40-64 years (aOR: 0.88, 95% CI: 0.84-0.91), ≥65 years (aOR: 0.87, 95% CI: 0.83-0.90), and overall (aOR: 0.88, 95% CI: 0.85-0.80), and ARBs users aged 40-64 years (aOR: 0.91, 95% CI: 0.86-0.95). Our case-control study demonstrated that RAAS inhibitors use was associated with a significant decrease in overall gynecologic cancer risks. RAAS inhibitors exposure had lower associations with cervical and ovarian cancer risks, and increased endometrial cancer risk. ACEIs/ARBs use was found to have a preventive effect against gynecologic cancers. Future clinical research is needed to establish causality.

Utilization of inpatient ophthalmology services in Taiwan-A nationwide population study.

BACKGROUND: This study attempted to illustrate the demographic of inpatient eye careservice from 1997 to 2011 in Taiwan, and also the ophthalmic disease landscape and utilization change over time. These insights might apply to resource allocation planning and trainees' better understandings of ophthalmic inpatient practice. METHODS: This study utilized Taiwan's National Health Insurance Research Database (NHIRD). Admission records of eye service that occurred since 1997 and until 2011 were included. Records were separated into operative and non-operative. The records were further divided according to their time: a group of early time before 2006 and a late one after 2006. RESULTS: Patients' mean age were 56 and 44 years for operative and non-operative records. The sex ratio (male to female) was 1.3, and the average of admission duration was 4 days. The average spending was around 1000 United State Dollars per admission and a gradually upgoing trend was also noted. The number of inpatient eye services decreased over time, from 3,248 to 2,174 in the studied period. Cases admitted for operation primarily underwent cataract surgery, vitrectomy, and scleral buckling during the studied period. Trabeculectomy emerged as another major indication of admission during the later time. Cases admitted for non-operative management were primarily corneal ulcer, glaucoma, and infection, including orbital cellulitis and lid abscess. Corneal ulcers made up a major proportion of admission records in the non-operative group during both periods. CONCLUSIONS: This study described the demographics of inpatient eye service in Taiwan. Ophthalmologist, especially trainees, and officials could make better policies according to the presented results in this study.

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.