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 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.

Association between dietary antioxidant and fatty acids and low muscle mass in obese and non-obese community-dwelling older adults: An NHANES analysis.

Sarcopenia increases disability, hospital stays, readmissions, and mortality in older adults. Antioxidative nutrients and fatty acids consumption may help maintain muscle mass by reducing oxidative stress. This study aims to assess the association between antioxidant and fatty acid intake and low muscle mass in community-dwelling older people. This retrospective analysis used data from the National Health and Nutrition Examination Survey from 1999 to 2004. Participants ≥ 60 years with information on muscle mass measured by Dual energy X-ray absorptiometry (DXA) were included. Appendicular skeletal muscle mass was assessed. Associations between antioxidants and fatty acids intake, and low muscle mass were evaluated using logistic regressions. 3648 (1748 men and 1900 women) were included. The prevalence of low muscle mass was 41% and 26% among men and women ≥ 75 years, and 45.2% and 28.4% among obese men and women. In obese males, a natural-log-unit increase of vitamin A (aOR = 0.806, 95% CI: 0.652-0.996), vitamin C (aOR = 0.878, 95% CI: 0.779-0.990), selenium intake (aOR = 0.716, 95% CI: 0.517-0.993), and higher saturated fatty acids (aOR = 0.956, 95% CI: 0.915-0.998) and monounsaturated fatty acids (aOR = 0.959, 95% CI: 0.925-0.994) intake were associated with decreased odds for low muscle mass. Among obese females, a natural-log-unit increase of vitamin E (P = .036), vitamin B12 (P = .014), total folate (P = .015), zinc (P = .005), and selenium intake (P = .018) were associated with increased odds of low muscle mass, whereas higher saturated fatty acids (P < .001), monounsaturated fatty acids (P = .001), and polyunsaturated fatty acids intake (P = .006) were associated with decreased odds for low muscle mass. Antioxidants (vitamin A, C, E, B6, B12, total folate, zinc, magnesium, selenium) intake does not consistently relate to low muscle mass across age and sex. Higher intake of saturated, monounsaturated, and polyunsaturated fatty acids are independently associated with reduced likelihood of low muscle mass in both obese older men and women.

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.

Examining Physical Wellness as the Fundamental Element for Achieving Holistic Well-Being in Older Persons: Review of Literature and Practical Application in Daily Life.

This review examines the impact of physical activity, nutrition, and sleep evaluations on the physical wellness (PW) and overall well-being of older individuals. A comprehensive search was conducted in databases like PubMed, Google Scholar, and EBSCO Information Services. The search spanned from January 2000 to December 2022, resulting in 19,400 articles, out of which 98 review articles met the inclusion criteria. Through the analysis of these articles, key characteristics of the literature were summarized, and opportunities to enhance the practical application of physical activity (PA), nutrition, and sleep evaluations in the daily lives of older persons were identified. Regular physical activity is crucial for older persons to maintain their physical, mental, and emotional well-being and prevent age-related health issues. Older persons have specific nutritional needs, including increased protein, vitamin D, calcium, and vitamin B12 intake. Poor sleep quality in older persons is associated with negative health outcomes such as cognitive decline, physical disability, and mortality. This review emphasizes the significance of considering physical wellness as a fundamental element for achieving holistic well-being in older persons and highlights the importance of physical activity, nutrition, and sleep evaluations in improving their overall health and well-being. By understanding and implementing these findings, we can enhance the quality of life and promote healthy aging in older persons.

Artificial Intelligence in Dementia: A Bibliometric Study.

The applications of artificial intelligence (AI) in dementia research have garnered significant attention, prompting the planning of various research endeavors in current and future studies. The objective of this study is to provide a comprehensive overview of the research landscape regarding AI and dementia within scholarly publications and to suggest further studies for this emerging research field. A search was conducted in the Web of Science database to collect all relevant and highly cited articles on AI-related dementia research published in English until 16 May 2023. Utilizing bibliometric indicators, a search strategy was developed to assess the eligibility of titles, utilizing abstracts and full texts as necessary. The Bibliometrix tool, a statistical package in R, was used to produce and visualize networks depicting the co-occurrence of authors, research institutions, countries, citations, and keywords. We obtained a total of 1094 relevant articles published between 1997 and 2023. The number of annual publications demonstrated an increasing trend over the past 27 years. Journal of Alzheimer's Disease (39/1094, 3.56%), Frontiers in Aging Neuroscience (38/1094, 3.47%), and Scientific Reports (26/1094, 2.37%) were the most common journals for this domain. The United States (283/1094, 25.86%), China (222/1094, 20.29%), India (150/1094, 13.71%), and England (96/1094, 8.77%) were the most productive countries of origin. In terms of institutions, Boston University, Columbia University, and the University of Granada demonstrated the highest productivity. As for author contributions, Gorriz JM, Ramirez J, and Salas-Gonzalez D were the most active researchers. While the initial period saw a relatively low number of articles focusing on AI applications for dementia, there has been a noticeable upsurge in research within this domain in recent years (2018-2023). The present analysis sheds light on the key contributors in terms of researchers, institutions, countries, and trending topics that have propelled the advancement of AI in dementia research. These findings collectively underscore that the integration of AI with conventional treatment approaches enhances the effectiveness of dementia diagnosis, prediction, classification, and monitoring of treatment progress.

Proton Pump Inhibitors and Risk of Chronic Kidney Disease: Evidence from Observational Studies.

Previous epidemiological studies have raised the concern that the use of proton pump inhibitors (PPIs) is associated with an increased risk of kidney diseases. To date, no comprehensive meta-analysis has been conducted to assess the association between PPIs and the risk of chronic kidney disease (CKD). Therefore, we conducted a systematic review and meta-analysis to address the association between PPIs and CKD. The primary search was conducted in the most popular databases, such as PubMed, Scopus, and Web of Science. All observational studies evaluated the risk of CKD among PPI users, and non-users were considered for inclusion. Two reviewers conducted data extraction and assessed the risk of bias. Random-effect models were used to calculate pooled effect sizes. A total of 6,829,905 participants from 10 observational studies were included. Compared with non-PPI use, PPI use was significantly associated with an increased risk of CKD (RR 1.72, 95% CI: 1.02-2.87, p = 0.03). This updated meta-analysis showed that PPI was significantly associated with an increased risk of CKD. Association was observed in the same among moderate-quality studies. Until further randomized control trials (RCTs) and biological studies confirm these results, PPI therapy should not stop patients with gastroesophageal reflux disease (GERD). However, caution should be used when prescribing to patients with high-risk kidney disease.

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.

Statins and the Risk of Gastric Cancer: A Systematic Review and Meta-Analysis.

Previous epidemiological studies have reported that the use of statins is associated with a decreased risk of gastric cancer, although the beneficial effects of statins on the reduction of gastric cancer remain unclear. Therefore, we conducted a systematic review and meta-analysis to investigate the association between the use of statins and the risk of gastric cancer. Electronic databases such as PubMed, EMBASE, Scopus, and Web of Science were searched between 1 January 2000 and 31 August 2022. Two authors used predefined selection criteria to independently screen all titles, abstracts, and potential full texts. Observational studies (cohort and case-control) or randomized control trials that assessed the association between statins and gastric cancer were included in the primary and secondary analyses. The pooled effect sizes were calculated using the random-effects model. The Meta-analysis of Observational Studies in Epidemiology (MOOSE) reporting guidelines were followed to conduct this study. The total sample size across the 20 included studies was 11,870,553. The use of statins was associated with a reduced risk of gastric cancer (RRadjusted: 0.72; 95%CI: 0.64−0.81, p < 0.001). However, the effect size of statin use on the risk of gastric cancer was lower in Asian studies compared to Western studies (RRAsian: 0.62; 95%CI: 0.53−0.73 vs. RRwestern: 0.88; 95%CI: 0.79−0.99). These findings suggest that the use of statins is associated with a reduced risk of gastric cancer. This reverse association was even stronger among Asian people than Western individuals.

Association between systemic rheumatic diseases and dementia risk: A meta-analysis.

Background and aims: Epidemiological studies have been conducted on the relationship between systemic rheumatic diseases (SRDs) and dementia. Therefore, we focused on determining the extent of alliances bounded by SRDs, along with the risk of dementia. Materials and methods: Two independent reviewers assessed all studies retrieved from the PubMed, EMBASE, Scopus, and Web of Science databases between January 1, 2000 and November 30, 2021. Only observational studies that estimated the possibility of dementia in participants with SRD were considered. The random-effects model was applied to forecast pooled risk ratios (RRs) and 95% confidence intervals (CI). Heterogeneity among the studies was evaluated using the Q and I2 statistics. The quality of the included studies was assessed using the Newcastle-Ottawa Scale. Funnel plots were used to calculate the risk of bias. Results: Seventeen observational studies with 17,717,473 participants were recruited. Our findings showed that among the participants with SRDs, those with osteoarthritis, systemic lupus erythematosus, and Sjogren's syndrome were highly related to an elevated risk of dementia (pooled RR: 1.31; 95% CI: 1.15-1.49, p<0.001; pooled RR: 1.43; 95% CI: 1.19-1.73, p<0.001; and pooled RR: 1.26; 95% CI: 1.14-1.39, p<0.001, respectively). However, participants with rheumatoid arthritis (RA) were not associated with an increased risk of dementia (pooled RR: 0.98; 95% CI: 0.90-1.07, p<0.001). Conclusion: This systematic review and meta-analysis demonstrated an increased dementia risk among SRDs participants, except for RA.

Dietary inflammatory index and metabolic syndrome in US children and adolescents: evidence from NHANES 2001-2018.

BACKGROUND: An increasing number of children and adolescents are affected by metabolic syndrome (MetS). Dietary inflammatory index (DII) was associated with MetS in adult population. This study aimed to determine the associations between DII scores, MetS, and MetS components among children and adolescents. METHODS: Data of children and adolescents in the National Health and Nutrition Examination Survey (NHANES) database 2001-2008 were obtained. DII was calculated for each participant based on the 24-h dietary recall interview. Univariate and multivariate logistic regression were conducted to determine the associations between DII, the other study variables and abnormal MetS components. RESULTS: A total of 5,656 US children and adolescents (mean age = 15.49) in the 2001-2018 NHANES database were included. After adjusting for all confounders in the multivariate analysis, the top DII quartile was significantly and independently associated with increased odds of high blood pressure (BP) (aOR = 2.27, 95% CI: 1.02-5.07) as compared with the lowest DII quartile. DII in quartile 2, 3 or 4 were not significantly associated with increased odds of MetS, high waist circumference (WC), low high density lipoprotein-cholesterol (HDL-c), triglyceride (TG) or fasting plasma glucose (FPG) as compared with the lowest quartile. In stratified analysis by recommended physical activity level for children and adolescents, no significant association was observed between higher DII and MetS. CONCLUSIONS: Among US children and adolescents, high DII is associated with prevalent high BP but not MetS. The finding may contribute to future policymaking in promoting children's health.

mHealth Research for Weight Loss, Physical Activity, and Sedentary Behavior: Bibliometric Analysis.

BACKGROUND: Research into mobile health (mHealth) technologies on weight loss, physical activity, and sedentary behavior has increased substantially over the last decade; however, no research has been published showing the research trend in this field. OBJECTIVE: The purpose of this study was to provide a dynamic and longitudinal bibliometric analysis of recent trends of mHealth research for weight loss, physical activity, and sedentary behavior. METHODS: A comprehensive search was conducted through Web of Science to retrieve all existing relevant documents published in English between January 1, 2010, and November 1, 2021. We developed appropriate research questions; based on the proven bibliometric approaches, a search strategy was formulated to screen the title for eligibility. Finally, we conducted bibliometric analyses to explore the growth rate of publications; publication patterns; and the most productive authors, institutions, and countries, and visualized the trends in the field using a keyword co-occurrence network. RESULTS: The initial search identified 8739 articles, of which 1035 were included in the analyses. Our findings show an exponential growth trend in the number of annual publications of mHealth technology research in these fields. JMIR mHealth and uHealth (n=214, 20.67%), Journal of Medical Internet Research (n=71, 6.86%), and BMC Public Health (n=36, 3.47%) were the top 3 journals, publishing higher numbers of articles. The United States remained the leading contributor in these areas (n=405, 39.13%), followed by Australia (n=154, 14.87%) and England (n=125, 12.07%). Among the universities, the University of Sydney (n=36, 3.47%) contributed the most mHealth technology research in these areas; however, Deakin University (n=25, 2.41%) and the National University of Singapore (n=23, 2.22%) were in the second and third positions, respectively. CONCLUSIONS: Although the number of papers published on mobile technologies for weight loss, physical activity, and sedentary behavior was initially low, there has been an overall increase in these areas in recent years. The findings of the study indicate that mobile apps and technologies have substantial potential to reduce weight, increase physical activity, and change sedentary behavior. Indeed, this study provides a useful overview of the publication trends and valuable guidance on future research directions and perspectives in this rapidly developing field.

Combination of Acupoints for Alzheimer's Disease: An Association Rule Analysis.

Background: Alzheimer's disease (AD) is an ongoing neurological degeneration characterized by amnesia and a decline in cognitive abilities. Hippocampal neurogenesis is the leading cause of AD. Mild cognitive impairment (MCI), a prodromal state of AD, is mainly due to the degradation of neuropsychiatric manifestations. Previous systematic reviews demonstrated that treatment with acupuncture with Chinese herbs is tolerable and effective in improving cognitive function in patients with AD. Our investigation aimed to discover the main acupoint combination for AD management based on a preceding systematic review and meta-analysis of randomized control trials (RCTs). Materials and Methods: Our investigation was executed using association rule analysis, which is a common data mining technique accessible within R. Our study elucidated acupoint locations as binary data from 15 of the included studies using the Apriori algorithm. Results: Thirty-two acupoints were selected from 15 RCTs. The 10 most frequent acupoints were selected. We inspected 503 association rules using the interpreted acupuncture data. The obtained results showed that {SP6, BI10} ≥ {HT7} and {HT7, BI10} ≥ {SP6} were the most associated rules in 15 RCTs. Conclusion: The combination of acupoints ({SP6, BI10} ≥ {HT7} and {HT7, BI10} ≥ {SP6}) can be acknowledged as a core combination for future acupuncture regimens of AD.

Association between Statin Use and Risk of Parkinson's Disease: Evidence from 18 Observational Studies Comprising 3.7 Million Individuals.

The potential impact of statins on the risk of Parkinson's disease (PD) is still controversial; therefore, we conducted a comprehensive meta-analysis of observational studies to examine the effect of statin use on the risk of PD. We searched electronic databases, such as PubMed, EMBASE, Scopus, and Web of Science, for articles published between 1 January 2000 and 15 March 2022. Cohort studies which examined the association between statins and PD risk in the general population were also included. Two authors assessed the data and extracted all potential information for analysis. Random effects meta-analyses were performed to measure the risk ratio (RR) and 95% confidence intervals (CIs). Eighteen cohort studies including 3.7 million individuals with 31,153 PD participants were identified. In statin users, compared with non-users, the RR for PD was 0.79 (95% CI: 0.68-0.91). In a subgroup analysis of PD, this association was observed with medium and high quality, and the studies were adjusted for age, gender, and smoking status. When the data were stratified according to the duration of exposure, long-duration statin use was associated with a decreased risk of PD (RR = 0.49; 95% CI: 0.26-0.92). There was no significant decrease in the risk of PD in short-term statin users (RR = 0.94; 95% CI: 0.67-1.31). Moreover, no significant difference in the reduction in the risk of PD was observed between men (RR = 0.80; 95% CI: 0.75-0.86) and women (RR = 0.80; 95% CI: 0.75-0.86). Although our findings confirm a reduction in the PD risk associated with statin treatment and suggest that statins play a clinically favorable role, these findings should be interpreted with caution. Future randomized control trials with an ad hoc design are needed to confirm the potential utility of statins in reducing the risk of PD.

A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record.

PURPOSE: To develop deep learning model (Deep-KOA) that can predict the risk of knee osteoarthritis (KOA) within the next year by using the previous three years nonimage-based electronic medical record (EMR) data. PATIENTS AND METHODS: We randomly selected information of two million patients from the Taiwan National Health Insurance Research Database (NHIRD) from January 1, 1999 to December 31, 2013. During the study period, 132,594 patients were diagnosed with KOA, while 1,068,464 patients without KOA were chosen randomly as control. We constructed a feature matrix by using the three-year history of sequential diagnoses, drug prescriptions, age, and sex. Deep learning methods of convolutional neural network (CNN) and artificial neural network (ANN) were used together to develop a risk prediction model. We used the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and precision to evaluate the performance of Deep-KOA. Then, we explored the important features using stepwise feature selection. RESULTS: This study included 132,594 KOA patients, 83,111 females (62.68%), 49,483 males (37.32%), mean age 64.2 years, and 1,068,464 non-KOA patients, 545,902 females (51.09%), 522,562 males (48.91%), mean age 51.00 years. The Deep-KOA achieved an overall AUROC, sensitivity, specificity, and precision of 0.97, 0.89, 0.93, and 0.80 respectively. The discriminative analysis of Deep-KOA showed important features from several diseases such as disorders of the eye and adnexa, acute respiratory infection, other metabolic and immunity disorders, and diseases of the musculoskeletal and connective tissue. Age and sex were not found as the most discriminative features, with AUROC of 0.9593 (-0.76% loss) and 0.9644 (-0.25% loss) respectively. Whereas medications including antacid, cough suppressant, and expectorants were identified as discriminative features. CONCLUSION: Deep-KOA was developed to predict the risk of KOA within one year earlier, which may provide clues for clinical decision support systems to target patients with high risk of KOA to get precision prevention program.

Anemia and the Risk of Cognitive Impairment: An Updated Systematic Review and Meta-Analysis.

BACKGROUND: Cognitive impairment is one of the most common, burdensome, and costly disorders in the elderly worldwide. The magnitude of the association between anemia and overall cognitive impairment (OCI) has not been established. OBJECTIVE: We aimed to update and expand previous evidence of the association between anemia and the risk of OCI. METHODS: We conducted an updated systematic review and meta-analysis. We searched electronic databases, including EMBASE, PubMed, and Web of Science for published observational studies and clinical trials between 1 January 1990 and 1 June 2020. We excluded articles that were in the form of a review, letter to editors, short reports, and studies with less than 50 participants. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines were followed. We estimated summary risk ratios (RRs) with random effects. RESULTS: A total of 20 studies, involving 6558 OCI patients were included. Anemia was significantly associated with an increased risk of OCI (adjusted RR (aRR) 1.39 (95% CI, 1.25-1.55; p < 0.001)). In subgroup analysis, anemia was also associated with an increased risk of all-cause dementia (adjusted RR (aRR), 1.39 (95% CI, 1.23-1.56; p < 0.001)), Alzheimer's disease [aRR, 1.59 (95% CI, 1.18-2.13; p = 0.002)], and mild cognitive impairment (aRR, 1.36 (95% CI, 1.04-1.78; p = 0.02)). CONCLUSION: This updated meta-analysis shows that patients with anemia appear to have a nearly 1.39-fold risk of developing OCI than those without anemia. The magnitude of this risk underscores the importance of improving anemia patients' health outcomes, particularly in elderly patients.

Association between Anemia and Risk of Parkinson Disease.

METHODS: We systematically searched articles on electronic databases such as PubMed, Embase, Scopus, and Google Scholar between January 1, 2000 and July 30, 2020. Articles were independently evaluated by two authors. We included observational studies (case-control and cohort) and calculated the risk ratios (RRs) for associated with anemia and PD. Heterogeneity among the studies was assessed using the Q and I 2 statistic. We utilized the random-effect model to calculate the overall RR with 95% CI. RESULTS: A total of 342 articles were identified in the initial searches, and 7 full-text articles were evaluated for eligibility. Three articles were further excluded for prespecified reasons including insufficient data and duplications, and 4 articles were included in our systematic review and meta-analysis. A random effect model meta-analysis of all 4 studies showed no increased risk of PD in patients with anemia (N = 4, RRadjusted = 1.17 (95% CI: 0.94-1.45, p = 0.15). However, heterogeneity among the studies was significant (I 2 = 92.60, p = <0.0001). The pooled relative risk of PD in female patients with anemia was higher (N = 3, RRadjusted = 1.14 (95% CI: 0.83-1.57, p = 0.40) as compared to male patients with anemia (N = 3, RRadjusted = 1.09 (95% CI: 0.83-1.42, p = 0.51). CONCLUSION: This is the first meta-analysis that shows that anemia is associated with higher risk of PD when compared with patients without anemia. However, more studies are warranted to evaluate the risk of PD among patients with anemia.

Statin Use Is Associated with a Decreased Risk of Mortality among Patients with COVID-19.

BACKGROUND: Recent epidemiological studies remain controversial regarding the association between statin use and reducing the risk of mortality among individuals with COVID-19. OBJECTIVE: The objective of this study was to clarify the association between statin use and the risk of mortality among patients with COVID-19. METHODS: We conducted a systematic articles search of online databases (PubMed, EMBASE, Scopus, and Web of Science) between 1 February 2020 and 20 February 2021, with no restriction on language. The following search terms were used: "Statins" and "COVID-19 mortality or COVID19 mortality or SARS-CoV-2 related mortality". Two authors individually examined all articles and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for study inclusion and exclusion. The overall risk ratio (RRs) with 95% confidence interval (CI) was calculated to show the strength of the association and the heterogeneity among the studies was presented Q and I2 statistic. RESULTS: Twenty-eight studies were assessed for eligibility and 22 studies met the inclusion criteria. Statin use was associated with a significantly decreased risk of mortality among patients with COVID-19 (RR adjusted = 0.64; 95% CI: 0.57-0.72, p < 0.001). Moreover, statin use both before and after the admission was associated with lowering the risk of mortality among the COVID-19 patients (RR adjusted;before = 0.69; 95% CI: 0.56-0.84, p < 0.001 and RR adjusted;after = 0.57; 95% CI: 0.54-0.60, p < 0.001). CONCLUSION: This comprehensive study showed that statin use is associated with a decreased risk of mortality among individuals with COVID-19. A randomized control trial is needed to confirm and refute the association between them.

Deep Learning Classifier with Patient's Metadata of Dermoscopic Images in Malignant Melanoma Detection.

BACKGROUND: Incidence of skin cancer is one of the global burdens of malignancies that increase each year, with melanoma being the deadliest one. Imaging-based automated skin cancer detection still remains challenging owing to variability in the skin lesions and limited standard dataset availability. Recent research indicates the potential of deep convolutional neural networks (CNN) in predicting outcomes from simple as well as highly complicated images. However, its implementation requires high-class computational facility, that is not feasible in low resource and remote areas of health care. There is potential in combining image and patient's metadata, but the study is still lacking. OBJECTIVE: We want to develop malignant melanoma detection based on dermoscopic images and patient's metadata using an artificial intelligence (AI) model that will work on low-resource devices. METHODS: We used an open-access dermatology repository of International Skin Imaging Collaboration (ISIC) Archive dataset consist of 23,801 biopsy-proven dermoscopic images. We tested performance for binary classification malignant melanomas vs nonmalignant melanomas. From 1200 sample images, we split the data for training (72%), validation (18%), and testing (10%). We compared CNN with image data only (CNN model) vs CNN for image data combined with an artificial neural network (ANN) for patient's metadata (CNN+ANN model). RESULTS: The balanced accuracy for CNN+ANN model was higher (92.34%) than the CNN model (73.69%). Combination of the patient's metadata using ANN prevents the overfitting that occurs in the CNN model using dermoscopic images only. This small size (24 MB) of this model made it possible to run on a medium class computer without the need of cloud computing, suitable for deployment on devices with limited resources. CONCLUSION: The CNN+ANN model can increase the accuracy of classification in malignant melanoma detection even with limited data and is promising for development as a screening device in remote and low resources health care.

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.