Risk of cancer in long-term levothyroxine users: Retrospective population-based study.

Levothyroxine is a widely prescribed medication for the treatment of an underactive thyroid. The relationship between levothyroxine use and cancer risk is largely underdetermined. To investigate the magnitude of the possible association between levothyroxine use and cancer risk, this retrospective case-control study was conducted using Taiwan's Health and Welfare Data Science Center database. Cases were defined as all patients who were aged ≥20 years and had a first-time diagnosis for cancer at any site for the period between 2001 and 2011. Multivariable conditional logistic regression models were used to calculate an adjusted odds ratio (AOR) to reduce potential confounding factors. A total of 601 733 cases and 2 406 932 controls were included in the current study. Levothyroxine users showed a 50% higher risk of cancer at any site (AOR: 1.50, 95% CI: 1.46-1.54; P < .0001) compared with non-users. Significant increased risks were also observed for brain cancer (AOR: 1.90, 95% CI: 1.48-2.44; P < .0001), skin cancer (AOR: 1.42, 95% CI: 1.17-1.72; P < .0001), pancreatic cancer (AOR: 1.27, 95% CI: 1.01-1.60; P = .03), and female breast cancer (AOR: 1.24, 95% CI: 1.15-1.33; P < .0001). Our study results showed that levothyroxine use was significantly associated with an increased risk of cancer, particularly brain, skin, pancreatic, and female breast cancers. Levothyroxine remains a highly effective therapy for hypothyroidism; therefore, physicians should carefully consider levothyroxine therapy and monitor patients' condition to avoid negative outcomes. Additional studies are needed to confirm these findings and to evaluate the potential biological mechanisms.

Association between Use of Statin and Risk of Dementia: A Meta-Analysis of Observational Studies.

BACKGROUND AND AIMS: The impact of statin on dementia risk reduction has been a subject of debate over the last decade, but the evidence remains inconclusive. Therefore, we performed a meta-analysis of relevant observational studies to quantify the magnitude of the association between statin therapy and the risk of dementia. METHODS: We systematically searched for relevant studies published from January 2000 to March 2018 using EMBASE, Google, Google Scholar, PubMed, Scopus, and Web of Science. Two authors performed study selection, data abstraction, and risk of bias assessment. We then extracted data from the selected studies and performed meta-analysis of observational studies using a random-effects model. Subgroup and sensitivity analyses were also conducted. RESULTS: A total of 30 observational studies, including 9,162,509 participants (84,101 dementia patients), met the eligibility criteria. Patients with statin had a lower all-caused dementia risk than those without statin (risk ratio [RR] 0.83, 95% CI 0.79-0.87, I2 = 57.73%). The overall pooled reduction of Alzheimer disease in patients with statin use was RR 0.69 (95% CI 0.60-0.80, p < 0.0001), and the overall pooled RR of statin use and vascular dementia risk was RR 0.93 (95% CI 0.74-1.16, p = 0.54). CONCLUSION: This study suggests that the use of statin is significantly associated with a decreased risk of dementia. Future studies measuring such outcomes would provide useful information to patients, clinicians, and policymakers. Until further evidence is established, clinicians need to make sure that statin use should remain restricted to the treatment of cardiovascular disease.

Increase Risk of Multiple Sclerosis in Patients with Psoriasis Disease: An Evidence of Observational Studies.

BACKGROUND: Psoriasis, a common chronic inflammatory disease, increases the risk of developing multiple sclerosis (MS), but evidence for this outcome is still unclear. However, we performed a meta-analysis of relevant studies to quantify the magnitude of the association between psoriasis and MS. It will help to assess the current state of knowledge, fill the gaps in our existing concern, and make a recommendation for future research. METHODS: PubMed, EMBASE, and the bibliographies of articles were searched for studies published between January 1, 1990, and November 1, 2017, which reported on the association between psoriasis and MS. Articles were included if they (1) were published in English, (2) reported patients with psoriasis, and the outcome of interest was MS, (3) provided OR/RR/HR with 95% CI or sufficient information to calculate the 95% CI, and (4) if ≥50 patients. All abstracts, full-text articles, and sources were reviewed, with duplicate data excluded. Summary relative risk (ORs) with 95% CI was pooled using a random-effects model. Subgroup and sensitivity analyses were also conducted. RESULTS: We selected 11 articles out of 785 unique abstracts for full-text review using our predetermined selection criteria, and 9 out of these 11 studies met all of our inclusion criteria. The overall pooled increased of developing MS in patients with psoriasis was RR 1.607 (95% CI 1.322-1.953, p < 0.0001) with low heterogeneity (I2 = 37.41%, Q = 12.782, τ2 = 0.027) for the random effect model. In the subgroup analysis, the MS risk in the patient with psoriasis was also significantly higher in the 6 studies from Europe RR 1.57 (95% CI 1.26-1.94, p < 0.001) with moderate heterogeneity (I2 = 50.66%, Q = 10.13, τ2 = 0.03) for the random effect model. CONCLUSION: Our results showed that psoriasis is significantly associated with an increased risk of developing MS. Physicians should carefully be observed symptoms and empower their patients to improve existing knowledge and quality of life. Further studies are warranted to establish the mechanisms underlying this relationship.

Non-steroidal anti-inflammatory drugs and risk of Parkinson's disease in the elderly population: a meta-analysis.

PURPOSE: Several studies have explored the impact of non-steroidal anti-inflammatory drugs (NSAIDs) and the risk of Parkinson disease (PD). However, the extent to which NSAIDs may increase or decrease the risk of PD remains unresolved. We, therefore, performed a meta-analysis of relevant studies to quantify the magnitude of the association between NSAID use and PD risk in the elderly population. METHODS: The electronic databases such as PubMed, EMBASE, Scopus, Google Scholar, and Web of Science were used to search the relevant articles published between January 1990 and December 2017. Large (n ≥ 1000) observational design studies with a follow-up at least 1 year were considered. Two authors independently extracted information from the included studies. Random effect model was used to calculate risk ratios (RRs) with 95% confidence interval (Cl). RESULTS: A total of 17 studies with 2,498,258 participants and nearly 14,713 PD patients were included in the final analysis. The overall pooled RR of PD was 0.95 (95%CI 0.860-1.048) with significant heterogeneity (I2 = 63.093, Q = 43.352, p < 0.0001). In the subgroup analysis, the overall pooled RR of PD was 0.90 (95%CI 0.738-1.109), 0.96 (95%CI 0.882-1.055), and 0.99 (95%CI 0.841-0.982) from the studies of North America, Europe, and Asia. Additionally, long-term use, study design, individual NSAID use, and risk of PD were also evaluated. CONCLUSION: Despite the neuroprotective potential of NSAIDs demonstrated in some experimental studies, our findings suggest that there is no association between NSAIDs and the risk of Parkinson disease at the population level. Until further evidence is established, clinicians need to be vigilant ensuring that the use of NSAIDs remains restricted to their approved anti-inflammatory and analgesic effect.

Risk of Hemorrhagic Stroke in Patients Exposed to Nonsteroidal Anti-Inflammatory Drugs: A Meta-Analysis of Observational Studies.

BACKGROUND AND AIM: Nonsteroidal anti-inflammatory drugs (NSAIDs) are one of the most common pain relief medications, but the risk of hemorrhagic stroke in patients taking these medications is unclear. In this study, our aim was to systematically review, synthesize, and critique the epidemiological studies that evaluate the association between NSAIDs and hemorrhagic stroke risk. We therefore assessed the current state of knowledge, filling the gaps in our existing concern, and make a recommendation for future research. METHODS: We searched for articles in PubMed, EMBASE, Scopus, and Web of Science between January 1, 1990, and July 30, 2017, which reported on the association between the use of NSAIDs and hemorrhagic stroke. The search was limited to studies published in English. The quality of the included studies was assessed in accordance with the Cochrane guidelines and the Newcastle-Ottawa criteria. Summary risk ratios (RRs) with 95% CI were pooled using a random-effects model. Subgroup and sensitivity analyses were also conducted. RESULTS: We selected 15 out of the 785 unique abstracts for full-text review using our selection criteria, and 13 out of these 15 studies met all of our inclusion criteria. The overall pooled RR of hemorrhagic stroke was 1.332 (95% CI 1.105-1.605, p = 0.003) for the random effect model. In the subgroup analysis, a significant risk was observed among meloxicam, diclofenac, and indomethacin users (RR 1.48; 95% CI 1.149-1.912, RR 1.392; 95% CI 1.107-1.751, and RR 1.363; 95% CI 1.088-1.706). In addition, a greater risk was found in studies from Asia (RR 1.490, 95% CI 1.226-1.811) followed by Europe (RR 1.393, 95% CI 1.104-1.757) and Australia (RR 1.361, 95% CI 0.755-2.452). CONCLUSION: Our results indicated that the use of NSAIDs is significantly associated with a higher risk of developing hemorrhagic stroke. These results should be interpreted with caution because they may be confounded owing to the observational design of the individual studies. Nevertheless, we recommend that NSAIDs should be used judiciously, and their efficacy and safety should be monitored proactively.

Levothyroxine use and the risk of breast cancer: a nation-wide population-based case-control study.

PURPOSE: To investigate whether the use of levothyroxine was associated with breast cancer risk. METHODS: We conducted a population-based case-control study in Taiwan. Cases consisted of all patients who were aged 20 years and older, and had a first-time diagnosis of breast cancer for the period between 2001 and 2011. The controls were matched to the cases by age, sex, year, and month of diagnosis. Adjusted odd ratios (ORs) and 95% confidence intervals (CIs) were estimated by a conditional logistic regression. RESULTS: We examined 65,491 breast cancer cases and 261,964 controls. We found that use of levothyroxine was associated with a significant increase in breast cancer risk (OR 1.24, 95% CI 1.15-1.33; P < 0.001). Compared with no use levothyroxine, the adjusted odd ratio was 1.22 (95% CI 1.11-1.35; P = 0.01) for the group having been prescribed levothyroxine 2 months to 1 year, and 1.26 (95% CI 1.12-1.41; P < 0.01) for the group with more than 1 year. When stratified by age, the adjusted odd ratio was 1.45 (95% CI 1.23-1.71; P < 0.01) for the patients aged 65 years or more and 1.19 (95% CI 1.09-1.29, P < 0.01) for the patients aged less than 65 years. CONCLUSION: The results of the present study are the first to suggest that levothyroxine use increased the risk of breast cancer. However, a larger long-term prospective randomized-controlled trial specifically designed to assess the effect of levothyroxine use on the risk of developing breast cancer is needed.

Exploring the Association between Statin Use and the Risk of Parkinson's Disease: A Meta-Analysis of Observational Studies.

BACKGROUND: Parkinson's disease (PD) is a progressive disorder of the central nervous system. The prevalence of PD varies considerably by age group; it has a higher prevalence in patients aged 60 years and more. Several studies have shown that statin, a cholesterol-lowering medication, reduces the risk of developing PD, but evidence for this is so far inconclusive. The objective of this study is to evaluate the association between statin use and the risk of developing PD. METHODS: PubMed, EMBASE, and the bibliographies of articles were searched for studies published between January 1, 1990, and January 1, 2017, which reported on the association between statin use and PD. Articles were included if they (1) were published in English, (2) reported patients treated with statin, and the outcome of interest was PD, (3) provided OR/HR with 95% CI or sufficient information to calculate the 95% CI. All abstracts, full-text articles, and sources were reviewed, with duplicate data excluded. Summary relative risk (RRs) with 95% CI was pooled using a random-effects model. Subgroup and sensitivity analyses were also conducted. RESULTS: We selected 16 out of 529 unique abstracts for full-text review using our selection criteria, and 13 out of these 16 studies, comprising 4,877,059 persons, met all of our inclusion criteria. The overall pooled RR of PD was 0.70 (95% CI 0.58-0.84) with significant heterogeneity between estimates (I2 = 93.41%, p = 0.000) for the random-effects model. In subgroup analysis, the greater decreased risk was found in studies from Asia (RR 0.62 95% CI 0.51-0.76), whereas a moderate reduction was observed in studies from North America (RR 0.69 95% CI 0.47-1.00), but less reduction was observed in studies from Europe (RR 0.86 95% CI 0.80-0.92). Also, long-term statin use, simvastatin, and atorvastatin showed a higher rate of reduction with significance heterogeneity. CONCLUSION: Our results showed that statin use is significantly associated with a lower risk of developing PD. Physicians should consider statin drug therapy, monitor its outcomes, and empower their patients to improve their knowledge, therapeutic outcomes, and quality of life. However, preventive measures and their associated mechanisms must be further assessed and explored.

Gender-based personalized pharmacotherapy: a systematic review.

PURPOSE: In general, male and female are prescribed the same amount of dosage even if most of the cases female required less dosage than male. Physicians are often facing problem on appropriate drug dosing, efficient treatment, and drug safety for a female in general. To identify and synthesize evidence about the effectiveness of gender-based therapy; provide the information to patients, providers, and health system intervention to ensure safety treatment; and minimize adverse effects. METHODS: We performed a systematic review to evaluate the effect of gender difference on pharmacotherapy. Published articles from January 1990 to December 2015 were identified using specific term in MEDLINE (PubMed), EMBASE, and the Cochrane library according to search strategies that strengthen the reporting of observational and clinical studies. RESULTS: Twenty-six studies fulfilled the inclusion criteria for this systematic review, yielding a total of 6309 subjects. We observed that female generally has a lower the gastric emptying time, gastric PH, lean body mass, and higher plasma volume, BMI, body fat, as well as reduce hepatic clearance, difference in activity of Cytochrome P450 enzyme, and metabolize drugs at different rate compared with male. Other significant factors such as conjugation, protein binding, absorption, and the renal elimination could not be ignored. However, these differences can lead to adverse effects in female especially for the pregnant, post-menopausal, and elderly women. CONCLUSION: This systematic review provides an evidence for the effectiveness of dosage difference to ensure safety and efficient treatment. Future studies on the current topic are, therefore, recommended to reduce the adverse effect of therapy.

Exploring association between statin use and breast cancer risk: an updated meta-analysis.

PURPOSE: The benefits of statin treatment for preventing cardiac disease are well established. However, preclinical studies suggested that statins may influence mammary cancer growth, but the clinical evidence is still inconsistent. We, therefore, performed an updated meta-analysis to provide a precise estimate of the risk of breast cancer in individuals undergoing statin therapy. METHODS: For this meta-analysis, we searched PubMed, the Cochrane Library, Web of Science, Embase, and CINAHL for published studies up to January 31, 2017. Articles were included if they (1) were published in English; (2) had an observational study design with individual-level exposure and outcome data, examined the effect of statin therapy, and reported the incidence of breast cancer; and (3) reported estimates of either the relative risk, odds ratios, or hazard ratios with 95% confidence intervals (CIs). We used random-effect models to pool the estimates. RESULTS: Of 2754 unique abstracts, 39 were selected for full-text review, and 36 studies reporting on 121,399 patients met all inclusion criteria. The overall pooled risks of breast cancer in patients using statins were 0.94 (95% CI 0.86-1.03) in random-effect models with significant heterogeneity between estimates (I 2 = 83.79%, p = 0.0001). However, we also stratified by region, the duration of statin therapy, methodological design, statin properties, and individual stain use. CONCLUSIONS: Our results suggest that there is no association between statin use and breast cancer risk. However, observational studies cannot clarify whether the observed epidemiologic association is a causal effect or the result of some unmeasured confounding variable. Therefore, more research is needed.

Benzodiazepine Use and Risk of Dementia in the Elderly Population: A Systematic Review and Meta-Analysis.

BACKGROUND: Benzodiazepines are a widely used medication in developed countries, particularly among elderly patients. However, benzodiazepines are known to affect memory and cognition and might thus enhance the risk of dementia. The objective of this review is to synthesize evidence from observational studies that evaluated the association between benzodiazepines use and dementia risk. SUMMARY: We performed a systematic review and meta-analysis of controlled observational studies to evaluate the risk of benzodiazepines use on dementia outcome. All control observational studies that compared dementia outcome in patients with benzodiazepine use with a control group were included. We calculated pooled ORs using a random-effects model. Ten studies (of 3,696 studies identified) were included in the systematic review, of which 8 studies were included in random-effects meta-analysis and sensitivity analyses. Odds of dementia were 78% higher in those who used benzodiazepines compared with those who did not use benzodiazepines (OR 1.78; 95% CI 1.33-2.38). In subgroup analysis, the higher association was still found in the studies from Asia (OR 2.40; 95% CI 1.66-3.47) whereas a moderate association was observed in the studies from North America and Europe (OR 1.49; 95% CI 1.34-1.65 and OR 1.43; 95% CI 1.16-1.75). Also, diabetics, hypertension, cardiac disease, and statin drugs were associated with increased risk of dementia but negative association was observed in the case of body mass index. There was significant statistical and clinical heterogeneity among studies for the main analysis and most of the sensitivity analyses. There was significant statistical and clinical heterogeneity among the studies for the main analysis and most of the sensitivity analyses. Key Messages: Our results suggest that benzodiazepine use is significantly associated with dementia risk. However, observational studies cannot clarify whether the observed epidemiologic association is a causal effect or the result of some unmeasured confounding variable. Therefore, more research is needed.

Application of AI in Sepsis: Citation Network Analysis and Evidence Synthesis.

BACKGROUND: Artificial intelligence (AI) has garnered considerable attention in the context of sepsis research, particularly in personalized diagnosis and treatment. Conducting a bibliometric analysis of existing publications can offer a broad overview of the field and identify current research trends and future research directions. OBJECTIVE: The objective of this study is to leverage bibliometric data to provide a comprehensive overview of the application of AI in sepsis. METHODS: We conducted a search in the Web of Science Core Collection database to identify relevant articles published in English until August 31, 2023. A predefined search strategy was used, evaluating titles, abstracts, and full texts as needed. We used the Bibliometrix and VOSviewer tools to visualize networks showcasing the co-occurrence of authors, research institutions, countries, citations, and keywords. RESULTS: A total of 259 relevant articles published between 2014 and 2023 (until August) were identified. Over the past decade, the annual publication count has consistently risen. Leading journals in this domain include Critical Care Medicine (17/259, 6.6%), Frontiers in Medicine (17/259, 6.6%), and Scientific Reports (11/259, 4.2%). The United States (103/259, 39.8%), China (83/259, 32%), United Kingdom (14/259, 5.4%), and Taiwan (12/259, 4.6%) emerged as the most prolific countries in terms of publications. Notable institutions in this field include the University of California System, Emory University, and Harvard University. The key researchers working in this area include Ritankar Das, Chris Barton, and Rishikesan Kamaleswaran. Although the initial period witnessed a relatively low number of articles focused on AI applications for sepsis, there has been a significant surge in research within this area in recent years (2014-2023). CONCLUSIONS: This comprehensive analysis provides valuable insights into AI-related research conducted in the field of sepsis, aiding health care policy makers and researchers in understanding the potential of AI and formulating effective research plans. Such analysis serves as a valuable resource for determining the advantages, sustainability, scope, and potential impact of AI models in sepsis.

Artificial Intelligence in Kidney Disease: A Comprehensive Study and Directions for Future Research.

Artificial intelligence (AI) has emerged as a promising tool in the field of healthcare, with an increasing number of research articles evaluating its applications in the domain of kidney disease. To comprehend the evolving landscape of AI research in kidney disease, a bibliometric analysis is essential. The purposes of this study are to systematically analyze and quantify the scientific output, research trends, and collaborative networks in the application of AI to kidney disease. This study collected AI-related articles published between 2012 and 20 November 2023 from the Web of Science. Descriptive analyses of research trends in the application of AI in kidney disease were used to determine the growth rate of publications by authors, journals, institutions, and countries. Visualization network maps of country collaborations and author-provided keyword co-occurrences were generated to show the hotspots and research trends in AI research on kidney disease. The initial search yielded 673 articles, of which 631 were included in the analyses. Our findings reveal a noteworthy exponential growth trend in the annual publications of AI applications in kidney disease. Nephrology Dialysis Transplantation emerged as the leading publisher, accounting for 4.12% (26 out of 631 papers), followed by the American Journal of Transplantation at 3.01% (19/631) and Scientific Reports at 2.69% (17/631). The primary contributors were predominantly from the United States (n = 164, 25.99%), followed by China (n = 156, 24.72%) and India (n = 62, 9.83%). In terms of institutions, Mayo Clinic led with 27 contributions (4.27%), while Harvard University (n = 19, 3.01%) and Sun Yat-Sen University (n = 16, 2.53%) secured the second and third positions, respectively. This study summarized AI research trends in the field of kidney disease through statistical analysis and network visualization. The findings show that the field of AI in kidney disease is dynamic and rapidly progressing and provides valuable information for recognizing emerging patterns, technological shifts, and interdisciplinary collaborations that contribute to the advancement of knowledge in this critical domain.

Machine Learning Models for Predicting Mortality in Critically Ill Patients with Sepsis-Associated Acute Kidney Injury: A Systematic Review.

While machine learning (ML) models hold promise for enhancing the management of acute kidney injury (AKI) in sepsis patients, creating models that are equitable and unbiased is crucial for accurate patient stratification and timely interventions. This study aimed to systematically summarize existing evidence to determine the effectiveness of ML algorithms for predicting mortality in patients with sepsis-associated AKI. An exhaustive literature search was conducted across several electronic databases, including PubMed, Scopus, and Web of Science, employing specific search terms. This review included studies published from 1 January 2000 to 1 February 2024. Studies were included if they reported on the use of ML for predicting mortality in patients with sepsis-associated AKI. Studies not written in English or with insufficient data were excluded. Data extraction and quality assessment were performed independently by two reviewers. Five studies were included in the final analysis, reporting a male predominance (>50%) among patients with sepsis-associated AKI. Limited data on race and ethnicity were available across the studies, with White patients comprising the majority of the study cohorts. The predictive models demonstrated varying levels of performance, with area under the receiver operating characteristic curve (AUROC) values ranging from 0.60 to 0.87. Algorithms such as extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR) showed the best performance in terms of accuracy. The findings of this study show that ML models hold immense ability to identify high-risk patients, predict the progression of AKI early, and improve survival rates. However, the lack of fairness in ML models for predicting mortality in critically ill patients with sepsis-associated AKI could perpetuate existing healthcare disparities. Therefore, it is crucial to develop trustworthy ML models to ensure their widespread adoption and reliance by both healthcare professionals and patients.

Artificial intelligence in diabetic retinopathy: Bibliometric analysis.

BACKGROUND: The use of artificial intelligence in diabetic retinopathy has become a popular research focus in the past decade. However, no scientometric report has provided a systematic overview of this scientific area. AIMS: We utilized a bibliometric approach to identify and analyse the academic literature on artificial intelligence in diabetic retinopathy and explore emerging research trends, key authors, co-authorship networks, institutions, countries, and journals. We further captured the diabetic retinopathy conditions and technology commonly used within this area. METHODS: Web of Science was used to collect relevant articles on artificial intelligence use in diabetic retinopathy published between January 1, 2012, and December 31, 2022 . All the retrieved titles were screened for eligibility, with one criterion that they must be in English. All the bibliographic information was extracted and used to perform a descriptive analysis. Bibliometrix (R tool) and VOSviewer (Leiden University) were used to construct and visualize the annual numbers of publications, journals, authors, countries, institutions, collaboration networks, keywords, and references. RESULTS: In total, 931 articles that met the criteria were collected. The number of annual publications showed an increasing trend over the last ten years. Investigative Ophthalmology & Visual Science (58/931), IEEE Access (54/931), and Computers in Biology and Medicine (23/931) were the most journals with most publications. China (211/931), India (143/931, USA (133/931), and South Korea (44/931) were the most productive countries of origin. The National University of Singapore (40/931), Singapore Eye Research Institute (35/931), and Johns Hopkins University (34/931) were the most productive institutions. Ting D. (34/931), Wong T. (28/931), and Tan G. (17/931) were the most productive researchers. CONCLUSION: This study summarizes the recent advances in artificial intelligence technology on diabetic retinopathy research and sheds light on the emerging trends, sources, leading institutions, and hot topics through bibliometric analysis and network visualization. Although this field has already shown great potential in health care, our findings will provide valuable clues relevant to future research directions and clinical practice.

Increased risk of atrial fibrillation in patients with psoriasis: A meta-analysis of observational studies.

BACKGROUND: Several epidemiological studies have shown that psoriasis increases the risk of developing atrial fibrillation but evidence of this is still scarce. AIMS: Our objective was to systematically review, synthesise and critique the epidemiological studies that provided information about the relationship between psoriasis and atrial fibrillation risk. METHODS: We searched through PubMed, EMBASE and the bibliographies for articles published between 1 January 2000, and 1 November 2017, that reported on the association between psoriasis and atrial fibrillation. All abstracts, full-text articles and sources were reviewed with duplicate data excluded. Summary relative risks (RRs) with 95% CI were pooled using a random effects model. RESULTS: We identified 252 articles, of these eight unique abstracts underwent full-text review. We finally selected six out of these eight studies comprising 11,187 atrial fibrillation patients. The overall pooled relative risk (RR) of atrial fibrillation was 1.39 (95% CI: 1.257-1.523, P < 0.0001) with significant heterogeneity (I2 = 80.316, Q = 45.723, τ2 = 0.017, P < 0.0001) for the random effects model. In subgroup analysis, the greater risk was found in studies from North America, RR 1.482 (95% CI: 1.119-1.964, P < 0.05), whereas a moderate risk was observed in studies from Europe RR 1.43 (95% CI: 1.269-1.628, P < 0.0001). LIMITATIONS: We were only able to include six studies with 11,178 atrial fibrillation patients, because only a few such studies have been published. CONCLUSION: Our results showed that psoriasis is significantly associated with an increased risk of developing atrial fibrillation. Therefore, physicians should monitor patient's physical condition on a timely basis.

Benzodiazepine Use and the Risk of Dementia in the Elderly Population: An Umbrella Review of Meta-Analyses.

The prevalence of dementia among the elderly is high, and it is the leading cause of death globally. However, the relationship between benzodiazepine use and dementia risk has produced inconsistent results, necessitating an updated review of the evidence. To address this, we conducted an umbrella review of meta-analyses to summarize the available evidence on the association between benzodiazepine use and dementia risk and evaluate its credibility. We systematically evaluated the meta-analyses of observational studies that examined the connection between benzodiazepine use and dementia risk. For each meta-analysis, we collected the overall effect size, heterogeneity, risk of bias, and year of the most recent article and graded the evidence based on pre-specified criteria. We also used AMSTAR, a measurement tool to evaluate systematic reviews, to assess the methodological quality of each study. Our review included five meta-analyses encompassing 30 studies, and the effect size of the association between benzodiazepine use and dementia risk ranged from 1.38 to 1.78. Nonetheless, the evidence supporting this relationship was weak, and the methodological quality of the studies included was low. In conclusion, our findings revealed limited evidence of a link between benzodiazepine use and dementia risk, and more research is required to determine a causal connection. Physicians should only prescribe benzodiazepine for appropriate indications.

A Meta-Analysis of Proton Pump Inhibitor Use and the Risk of Acute Kidney Injury: Geographical Differences and Associated Factors.

Proton pump inhibitors (PPIs) are widely prescribed in medical practice for the treatment of several gastrointestinal disorders. Previous epidemiology studies have reported the association between PPI use and the risk of AKI, although the magnitude of the association between PPIs and the risk of acute kidney injury (AKI) remains uncertain. Therefore, we conducted a meta-analysis to determine the relationship between PPI therapy and the risk of AKI. We systematically searched for relevant articles published before January 2023 on PubMed, Scopus, and Web of Science. In addition, we conducted a manual search of the bibliographies of potential articles. Two independent reviewers examined the appropriateness of all studies for inclusion. We pooled studies that compared the risk of AKI with PPI against their control using a random effect model. The search criteria based on PRISMA guidelines yielded 568 articles. Twelve observational studies included 2,492,125 individuals. The pooled adjusted RR demonstrated a significant positive association between PPI therapy and the risk of AKI (adjusted RR 1.75, 95% CI: 1.40-2.19, p < 0.001), and it was consistent across subgroups. A visual presentation of the funnel plot and Egger's regression test showed no evidence of publication bias. Our meta-analysis indicated that persons using PPIs exhibited an increased risk of AKI. North American individuals had a higher risk of AKI compared to Asian and European individuals. However, the pooled effect from observational studies cannot clarify whether the observed association is a causal effect or the result of some unmeasured confounding factors. Hence, the biological mechanisms underlying this association are still unclear and require further research.

Early Diabetes Prediction: A Comparative Study Using Machine Learning Techniques.

Most screening tests for Diabetes Mellitus (DM) in use today were developed using electronically collected data from Electronic Health Record (EHR). However, developing and under-developing countries are still struggling to build EHR in their hospitals. Due to the lack of HER data, early screening tools are not available for those countries. This study develops a prediction model for early DM by direct questionnaires for a tertiary hospital in Bangladesh. Information gain technique was used to reduce irreverent features. Using selected variables, we developed logistic regression, support vector machine, K-nearest neighbor, Naïve Bayes, random forest (RF), and neural network models to predict diabetes at an early stage. RF outperformed other machine learning algorithms achieved 100% accuracy. These findings suggest that a combination of simple questionnaires and a machine learning algorithm can be a powerful tool to identify undiagnosed DM patients.

Clinical Usefulness of Drug-Disease Interaction Alerts from a Clinical Decision Support System, MedGuard, for Patient Safety: A Single Center Study.

Clinical decision support systems have been widely used in healthcare, yet few studies have concurrently measured the clinical effectiveness of CDSSs, and the appropriateness of alerts with physicians' response to alerts. We conducted a retrospective analysis of prescriptions caused disease-medication related alerts. Medication orders for outpatients' prescriptions, all aged group were included in this study. All the prescriptions were reviewed, and medication orders compared with a widely used medication reference (UpToDate) and other standard guidelines. We reviewed 1,409 CDS alerts (2.67% alert rate) on 52,654 prescriptions ordered during the study period. 545 (38.70%) of alerts were overridden. Override appropriateness was 2.20% overall. However, the rate of alert acceptance was higher, ranging from 11.11 to 92.86%. The MedGuard system had a lower overridden rate than other systems reported in previous studies. The acceptance rate of alerts by physicians was high. Moreover, false-positive rate was low. The MedGuard system has the potential to reduce alert fatigue and to minimize the risk of patient harm.

Proton Pump Inhibitors Use and the Risk of Pancreatic Cancer: Evidence from Eleven Epidemiological Studies, Comprising 1.5 Million Individuals.

Previous epidemiological studies have shown that proton pump inhibitor (PPI) may modify the risk of pancreatic cancer. We conducted an updated systematic review and meta-analysis of observational studies assessing the effect of PPI on pancreatic cancer. PubMed, Embase, Scopus, and Web of Science were searched for studies published between 1 January 2000, and 1 May 2022. We only included studies that assessed exposure to PPI, reported pancreatic cancer outcomes, and provided effect sizes (hazard ratio or odds ratio) with 95% confidence intervals (CIs). We calculated an adjusted pooled risk ratio (RR) with 95%CIs using the random-effects model. Eleven studies (eight case-control and three cohorts) that reported 51,629 cases of pancreatic cancer were included. PPI was significantly associated with a 63% increased risk of pancreatic cancer (RRadj. 1.63, 95%CI: 1.19-2.22, p = 0.002). Subgroup analysis showed that the pooled RR for rabeprazole and lansoprazole was 4.08 (95%CI: 0.61-26.92) and 2.25 (95%CI: 0.83-6.07), respectively. Moreover, the risk of pancreatic cancer was established for both the Asian (RRadj. 1.37, 95%CI: 0.98-1.81) and Western populations (RRadj.2.76, 95%CI: 0.79-9.56). The findings of this updated meta-analysis demonstrate that the use of PPI was associated with an increased risk of pancreatic cancer. Future studies are needed to improve the quality of evidence through better verification of PPI status (e.g., patient selection, duration, and dosages), adjusting for possible confounders, and ensuring long-term follow-up.