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 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.

Prediction of Coronary Artery Disease Severity by Using CHA2DS2-VASC-HSF Score in Patients with ST-Elevation Myocardial Infarction

CHADS₂ and CHA₂DS₂-VASc scores are widely used in clinical practice and include similar risk factors for the development of coronary artery disease (CAD). It is known that the factors comprising the newly defined CHA₂DS₂-VASC-HSF score promote atherosclerosis and associated with severity of CAD. Objective of the study was to find out the association of the CHA₂DS₂-VASC-HSF score with the severity of CAD in patients with ST elevation myocardial infarction (STEMI). One hundred (100) patients with STEMI were enrolled in this study after considering inclusion and exclusion criteria over a one year period from October, 2017 to September, 2018 in the Department of Cardiology, National Institute of Cardiovascular Diseases, Dhaka, Bangladesh. Coronary angiogram was done within index hospitalization and coronary artery disease severity was assessed by SYNTAX score system. Patients were divided into two groups on the basis of SYNTAX score. Patients with SYNTAX score ≥23 assigned as Group I and SYNTAX score <23 assigned as Group II. The CHA₂DS₂-VASC-HSF score was calculated. Cut-off value of high CHA₂DS₂-VASC-HSF score was ≥4.0. In this study mean age of study population was 51.8±9.8, male patients were predominant (79.0%). Among the studied patients, highest percentage had history of smoking followed by hypertension, diabetes mellitus and family history of CAD in Group I patients. It was found that DM and family history of CAD and history of stroke/TIA were significantly higher in Group I than Group II. An increasing trend of SYNTAX score was observed according to the CHA₂DS₂-VASc-HSF score. SYNTAX score was significantly higher in CHA2DS2-VASc-HSF score ≥4 than CHA₂DS₂-VASc-HSF score <4 (26.3±6.3 vs. 12.1±7.7, p<0.001). Patients with CHA₂DS₂-VASC-HSF score ≥4 had severe coronary artery disease than CHA₂DS₂-VASC-HSF score <4 assessed by SYNTAX score with 84.4% sensitivity and 81.9% specificity (AUC:0.83, 95% CI: 0.746-0.915, p<0.001). CHA₂DS₂-VASc-HSF score was positively correlated with the severity of CAD. This score could be considered as a predictor of coronary artery disease severity.

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.

Diagnostic Dilemma of Frozen Shoulder in Post CABG Patient: A Case Report

Frozen shoulder, also known as adhesive capsulitis, is a condition featured by stiffness and pain in shoulder joint. In this report, we present a case of 58 years old diabetic male patient with the history of coronary artery bypass grafting (CABG) 06 months back. He presented with persistent right shoulder pain for 05 months. Clinical examinations reveal restriction of the right shoulder joint movement in all directions and wasting of the right supraspinatus, infraspinatus and trapezius muscles. Both active and passive range of motions was restricted with painful right shoulder joint. Pain free abduction range was about 40 degrees in right shoulder. Plain X-ray of right shoulder joint and other relevant investigations show normal findings. Considering the clinical and laboratory findings decision was taken to treat the patient with exercise, pain killer and ultrasound therapy which were found to be optimistic.

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.

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.

Cohort profile: the ESC EURObservational Research Programme Non-ST-segment elevation myocardial infraction (NSTEMI) Registry

AIMS

The European Society of Cardiology (ESC) EURObservational Research Programme (EORP) Non-ST-segment elevation myocardial infarction (NSTEMI) Registry aims to identify international patterns in NSTEMI management in clinical practice and outcomes against the 2015 ESC Guidelines for the management of acute coronary syndromes in patients presenting without ST-segment-elevation.

METHODS AND RESULTS

Consecutively hospitalised adult NSTEMI patients (n = 3620) were enrolled between 11 March 2019 and 6 March 2021, and individual patient data prospectively collected at 287 centres in 59 participating countries during a two-week enrolment period per centre. The registry collected data relating to baseline characteristics, major outcomes (in-hospital death, acute heart failure, cardiogenic shock, bleeding, stroke/transient ischaemic attack, and 30-day mortality) and guideline-recommended NSTEMI care interventions: electrocardiogram pre- or in-hospital, pre-hospitalization receipt of aspirin, echocardiography, coronary angiography, referral to cardiac rehabilitation, smoking cessation advice, dietary advice, and prescription on discharge of aspirin, P2Y12 inhibition, angiotensin converting enzyme inhibitor (ACEi)/angiotensin receptor blocker (ARB), beta-blocker, and statin.

CONCLUSION

The EORP NSTEMI Registry is an international, prospective registry of care and outcomes of patients treated for NSTEMI, which will provide unique insights into the contemporary management of hospitalised NSTEMI patients, compliance with ESC 2015 NSTEMI Guidelines, and identify potential barriers to optimal management of this common clinical presentation associated with significant morbidity and mortality.

Multifaceted role of polyphenols in the treatment and management of neurodegenerative diseases

Neurodegenerative diseases (NDDs) are conditions that cause neuron structure and/or function to deteriorate over time. Genetic alterations may be responsible for several NDDs. However, a multitude of physiological systems can trigger neurodegeneration. Several NDDs, such as Huntington's, Parkinson's, and Alzheimer's, are assigned to oxidative stress (OS). Low concentrations of reactive oxygen and nitrogen species are crucial for maintaining normal brain activities, as their increasing concentrations can promote neural apoptosis. OS-mediated neurodegeneration has been linked to several factors, including notable dysfunction of mitochondria, excitotoxicity, and Ca²⁺ stress. However, synthetic drugs are commonly utilized to treat most NDDs, and these treatments have been known to have side effects during treatment. According to providing empirical evidence, studies have discovered many occurring natural components in plants used to treat NDDs. Polyphenols are often safer and have lesser side effects. As, epigallocatechin-3-gallate, resveratrol, curcumin, quercetin, celastrol, berberine, genistein, and luteolin have p-values less than 0.05, so they are typically considered to be statistically significant. These polyphenols could be a choice of interest as therapeutics for NDDs. This review highlighted to discusses the putative effectiveness of polyphenols against the most prevalent NDDs.

A Case of Bilateral Middle Cerebral Artery Stenosis with Unilateral M1 Stenting

Stroke is not only the third leading cause of death in Bangladesh but it also causes a high number of disability adjusted life years loss (485 per 10,000 people). Appropriate treatment of recurrent stroke has to be established. Here we present A 60 year's old male patient with history of recurrent stroke who came in our out patient department. He had progressing bilateral middle cerebral artery atherosclerotic stenosis (100.0% on right side and 52.0% on left side) which was less severs but symptomatic at dominant hemisphere (left). Percutaneous transluminal angioplasty with stenting was done to prevent further ischemia and to increase the blood supply of remaining brain parenchyma. On post stenting follow-up patient did not suffered from any new event of stroke for about 22 months. Percutaneous transluminal angioplasty with stenting is an effective procedure to prevent recurrent stroke for intracranial atherosclerotic disease.

Observation of Myocardial Involvement in Patients Recovered From COVID-19 by Using Cardiac Magnetic Resonance Imaging, In a Tertiary Care Hospital, Bangladesh

It was previously reported that coronavirus caused myocardial injury in hospitalized patients. However, delayed cardiac involvement in symptomatic patient recovery from COVID-19 is not yet well known. The objective of this study was to evaluate cardiac involvement by using cardiac magnetic resonance (CMR) in symptomatic post-COVID-19 recovered patients. Thirty (30) patients who recovered from COVID-19 and had recently reported cardiac symptoms were studied in a prospective observational study performed at Popular Medical College Hospital, Dhaka, Bangladesh from March 2021 to September 2021. They underwent CMR examinations. CMR scanning protocol included the following: black blood, cine sequence, both short-axis and long-axis, T2-weight short tau inversion recovery (STIR) sequence, T2- weighted imaging (T2WI) and late gadolinium enhancement (LGE) and quantitative mapping sequences-native T1/T2 mapping and post-contrast T1 mapping. Myocardial edema and late gadolinium enhancement were assessed in all patients. Quantitative evaluation of native T1/T2 and ECV value and cardiac function were evaluated. There were 30 people in all in this study. The average age of the participants in the study was 36.6 years. Fourteen (46.6%) of the patients had abnormal cardiac MRI results, while the remaining 15(53.3%) had negative CMR findings. Among positive findings patients, 8(57.1%) of 14 had increased T2 signal. Increased myocardial edema was found in the same no of patients, involving 53.2% (128 of 224) of LV segments. Only 2 cases (2 of 14) showed mid myocardial and subepicardial LGE, involving 18 of 224, 8.03% of myocardial segments. Global native T1, T2 and ECV values are significantly elevated in all CMR positive findings patients. Native T1 1231ms (IQR: 1281.25-1257.5 versus 1155.5 (IQR: 1137.25-1172.75), T2 40 (IQR: 34.5-43.25) versus 35.5 (IQR: 34-37), ECV 31 (29.75-33.25) versus 23.5 (21.25-24.0), p<0.001; p<0.011 and p<0.001 respectively. Reduced RV functional were found in positive as compared with negative CMR findings patients, EF, 32.05 (IQR: 25.25-39.0) versus 54.5 (IQR: 52.0-57.75) and EDV, 117.5 (IQR: 102.0-134.25) versus 95.0 (IQR: 71.75-99.75), p<0.001 and p<0.001 respectively. In this study cardiac involvement was found in the post-COVID-19 recovered patient with cardiac symptoms. Cardiac MRI findings included myocardial edema, fibrosis and reduced right ventricular function. So attention should be paid to symptomatic post-COVID-19 recovered patients.

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.

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.

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.

Sequential coupling of dry and wet COVID-19 screening to reduce the number of quarantined individuals

INTRODUCTION

Currently, several countries are facing severe public health and policy challenges when designing their COVID-19 screening strategy. A quantitative analysis of the potential impact that combing the Rapid Antigen Test (RAT; Wet screening) and digital checker (Dry screening) can have on the healthcare system is lacking.

METHOD

We created a hypothetical COVID-19 cohort for the analysis. The population size was set as 10 million with three levels of disease prevalence (10%, 1%, or 0.1%) under the assumption that a positive test result will lead to quarantine. A digital checker and two RATs are used for analysis. We further hypothesized two scenarios: RAT only and RAT plus digital checker. We then calculated the number of quarantined in both scenarios and compared the two to understand the benefits of sequential coupling of a digital checker with a RAT.

RESULT

Sequential coupling of the digital checker and RAT can significantly reduce the number of individuals quarantined to 0.95-1.33M, 0.86-1.29M, and 0.86-1.29M, respectively, under the three different prevalence levels.

CONCLUSION

Sequential coupling of digital checker and RAT at a population level for COVID-19 positive test to reduce the number of people who require quarantine and alleviating stress on the overburdened healthcare systems during the COVID-19 pandemic.

Quality of Life of Bangladeshi Doctors in the COVID-19 era: Are We Taking Good Care of Our Carers?

The Coronavirus pandemic has been affecting our healthcare professionals physically as well as psychologically since March 2020. Whilst various measures have been taken to protect their physical health, their mental wellbeing has not been brought into attention. We aimed to assess the well-being of Bangladeshi doctors and identify the high-risk group using a core-10 validated form. We performed an observational cross-sectional survey among Bangladeshi healthcare professionals. One hundred one (101) doctors filled out the core-10 form. We collected data over a 1-month-period during the first peak of COVID-19. According to our data, majority of the participants (49%) felt anxious or nervous at some point. Thirty one percent (31%) felt hopeless, unhappy even panic attacks but most significant finding was that 21% participants had at least once thought about ending their lives. This is a matter of concern and the workplaces should identify the vulnerable professionals so that they can be supported better mentally and socially. More than half of the participants (55%) were found to have moderate to severe depression in the first peak of COVID 19 pandemic. This is not over yet; more waves are coming. Therefore, it is really important that we address this issue before it is too late and ensure regular counselling, better childcare for working parents, safety measures to protect their families and financial security. Otherwise, we might exhaust our carers to a level where even they cannot help us survive this global challenge.

Predicting Hepatocellular Carcinoma With Minimal Features From Electronic Health Records: Development of a Deep Learning Model

BACKGROUND

Hepatocellular carcinoma (HCC), usually known as hepatoma, is the third leading cause of cancer mortality globally. Early detection of HCC helps in its treatment and increases survival rates.

OBJECTIVE

The aim of this study is to develop a deep learning model, using the trend and severity of each medical event from the electronic health record to accurately predict the patients who will be diagnosed with HCC in 1 year.

METHODS

Patients with HCC were screened out from the National Health Insurance Research Database of Taiwan between 1999 and 2013. To be included, the patients with HCC had to register as patients with cancer in the catastrophic illness file and had to be diagnosed as a patient with HCC in an inpatient admission. The control cases (non-HCC patients) were randomly sampled from the same database. We used age, gender, diagnosis code, drug code, and time information as the input variables of a convolution neural network model to predict those patients with HCC. We also inspected the highly weighted variables in the model and compared them to their odds ratio at HCC to understand how the predictive model works.

RESULTS

We included 47,945 individuals, 9553 of whom were patients with HCC. The area under the receiver operating curve (AUROC) of the model for predicting HCC risk 1 year in advance was 0.94 (95% CI 0.937-0.943), with a sensitivity of 0.869 and a specificity 0.865. The AUROC for predicting HCC patients 7 days, 6 months, 1 year, 2 years, and 3 years early were 0.96, 0.94, 0.94, 0.91, and 0.91, respectively.

CONCLUSIONS

The findings of this study show that the convolutional neural network model has immense potential to predict the risk of HCC 1 year in advance with minimal features available in the electronic health records.

1214 A Combined Approach to Prioritise Patients for Colonoscopy

Aim

Correlating colonoscopy finding with presenting features to assess the diagnostic yield of different symptoms.

Method

We looked at findings of 100 patients retrospectively who had colonoscopy in Dhaka Medical College Hospital, Bangladesh during first peak of COVID (August 2020 to December 2020). We reviewed NICE guideline for high-risk symptoms and NHS guideline for prioritisation of colonoscopy patients.

Results

100 cases were reviewed, 67% were male among the patients, average age was 42.11 (4 – 75 years). 47 were found to have significant pathology. Findings included colorectal malignancy (28%), Polyp (9%), IBD (6%), tuberculosis (2%) etc. PR bleed had highest diagnostic yield (21.27%), followed by abdominal lump (17.02%) and lower abdominal pain (14.89%). Weight loss showed lowest diagnostic yield (4.25%). 28% colonoscopy findings were normal. Patients were chosen based on clinical assessments and imaging results, as stool biochemical marker tests (FIT test, faecal calprotectin) are not available in DMCH.

Conclusions

Being an aerosol generating procedure which has a considerable amount of risk of transmitting COVID infection from patient to clinician or vice versa, it is important to triage patients with lower GI symptoms for colonoscopy. In a developing country like Bangladesh, it is essential to make the most reasonable use of limited resources. Symptoms-based triaging systems are poor predictors of clinically significant disease on colonoscopy. Therefore, a more holistic and novel approach needs to be studied and formulated using a combination of symptoms, blood, and stool biomarkers in order to reduce the need for a ‘negative’ colonoscopy and avoid unnecessary risks.

Improved diagnosis-medication association mining to reduce pseudo-associations

BACKGROUND AND OBJECTIVE

Association rule mining has been adopted to medical fields to discover prescribing patterns or relationships among diseases and/or medications; however, it has generated unreasonable associations among these entities. This study aims to identify the real-world profile of disease-medication (DM) associations using the modified mining algorithm and assess its performance in reducing DM pseudo-associations.

METHODS

We retrieved data from outpatient records between January 2011 and December 2015 in claims databases maintained by the Health and Welfare Data Science Center, Ministry of Health and Welfare, Taiwan. The association rule mining's lift (Q-value) was adopted to quantify DM associations, referred to as Q₁ for the original algorithm and as Q₂ for the modified algorithm. One thousand DM pairs with positive Q₁-values (Q₁⁺) and negative or no Q₂-values (Q₂^- or Q₂^∅) were selected as the validation dataset, in which two pharmacists assessed the DM associations.

RESULTS

A total of 3,120,449 unique DM pairs were identified, of which there were 333,347 Q₁⁺Q₂^- pairs and 429,931 Q₁⁺Q₂^∅ pairs. Q₁⁺Q₂^- rates were relatively high in ATC classes C (29.91%) and R (30.24%). Classes L (69.91%) and V (52.52%) demonstrated remarkably high Q₁⁺Q₂^∅ rates. For the 1000 pairs in the validation, 93.7% of the Q₁⁺Q₂^- or Q₁⁺Q₂^∅ DM pairs were assessed as pseudo-associations. However, classes M (5.3%), H (4.5%), and B (4.1%) showed the highest rates of plausible associations falsely given Q₂^- or Q₂^∅ by the modified algorithm.

CONCLUSIONS

The modified algorithm demonstrated high accuracy to identify pseudo-associations regarded as positive associations by the original algorithm and would potentially be applied to improve secondary databases to facilitate research on real-world prescribing patterns and further enhance drug safety.

Frequency of Eosinophilic Esophagitis among Patients with Gastroesophageal Reflux Symptoms in an Academic Hospital of Bangladesh: A Cross Sectional Study

Eosinophilic esophagitis (EoE) is a disease of modern era. It was first described 40 years back. Since then it has drawn an immense interest among the clinicians. It is diagnosed by the presence of eosinophils count ≥15/HPF on esophageal biopsied mucosa in patients with symptoms of esopohageal dysfunction. It is more prevalent among patients with gastroesophageal reflux disease. As its symptoms overlap with that of gastroesophageal reflux disease (GERD), it is frequently overlooked & misdiagnosed which increases patients' sufferings. No data is available in Bangladesh. The objective of the study was to find out the frequency of eosinophilic esophagitis among patients with gastroesophageal reflux symptoms. The study was conducted at the Outpatient department of the department of Gastroenterology of Dhaka Medical College Hospital, Dhaka, Bangladesh from September 2018 to April 2019. One hundred and thirty three (133) consecutive patients with symptoms suggestive of gastroesophageal reflux disease based on validated questionnaire underwent upper GI endoscopy. Biopsies were taken from proximal and distal esophagus as well as any other endoscopically abnormal esophageal mucosal lesion. Among 133 patients with gastroesophageal reflux symptoms, 7 patients (5.3%) were found to be positive for eosinophilic esophagitis. Mean age at diagnosis was 37.28±13.38 years. It was more common in younger age group. Female patients (56%) were more than male patients (44%). Heart burn was the major symptom followed by acid regurgitation. Nocturnal cough showed statistically significant relationship with eosinophilic esophagitis. Although the frequency is low, it may be considered as a differential diagnosis among patients with GERD.

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.

Application of Artificial Intelligence for Screening COVID-19 Patients Using Digital Images: Meta-analysis

BACKGROUND

The COVID-19 outbreak has spread rapidly and hospitals are overwhelmed with COVID-19 patients. While analysis of nasal and throat swabs from patients is the main way to detect COVID-19, analyzing chest images could offer an alternative method to hospitals, where health care personnel and testing kits are scarce. Deep learning (DL), in particular, has shown impressive levels of performance when analyzing medical images, including those related to COVID-19 pneumonia.

OBJECTIVE

The goal of this study was to perform a systematic review with a meta-analysis of relevant studies to quantify the performance of DL algorithms in the automatic stratification of COVID-19 patients using chest images.

METHODS

A search strategy for use in PubMed, Scopus, Google Scholar, and Web of Science was developed, where we searched for articles published between January 1 and April 25, 2020. We used the key terms "COVID-19," or "coronavirus," or "SARS-CoV-2," or "novel corona," or "2019-ncov," and "deep learning," or "artificial intelligence," or "automatic detection." Two authors independently extracted data on study characteristics, methods, risk of bias, and outcomes. Any disagreement between them was resolved by consensus.

RESULTS

A total of 16 studies were included in the meta-analysis, which included 5896 chest images from COVID-19 patients. The pooled sensitivity and specificity of the DL models in detecting COVID-19 were 0.95 (95% CI 0.94-0.95) and 0.96 (95% CI 0.96-0.97), respectively, with an area under the receiver operating characteristic curve of 0.98. The positive likelihood, negative likelihood, and diagnostic odds ratio were 19.02 (95% CI 12.83-28.19), 0.06 (95% CI 0.04-0.10), and 368.07 (95% CI 162.30-834.75), respectively. The pooled sensitivity and specificity for distinguishing other types of pneumonia from COVID-19 were 0.93 (95% CI 0.92-0.94) and 0.95 (95% CI 0.94-0.95), respectively. The performance of radiologists in detecting COVID-19 was lower than that of the DL models; however, the performance of junior radiologists was improved when they used DL-based prediction tools.

CONCLUSIONS

Our study findings show that DL models have immense potential in accurately stratifying COVID-19 patients and in correctly differentiating them from patients with other types of pneumonia and normal patients. Implementation of DL-based tools can assist radiologists in correctly and quickly detecting COVID-19 and, consequently, in combating the COVID-19 pandemic.

Serum Iron Profile and Red Cell Indices in Children with Severe Acute Malnutrition in A Tertiary Level Hospital

This cross-sectional analytical study was conducted in the department of Paediatrics, Mymensingh Medical College Hospital (MMCH), Mymensingh, Bangladesh from March 2017 to August 2018 to assess the pattern of serum iron profile and red cell indices in children with severe acute malnutrition. Seventy children having severe acute malnutrition were compared with 70 age matched children those had normal growth. Age range of the studied children was 6 months to 59 completed months. Male was found predominant (54.3%) in both study group and comparison group. Mean serum iron, serum ferritin, serum total iron binding capacity and transferrin saturation in severely malnourished children were 45.3±19.3μg/dl, 26.5±20.0ng/ml, 246.3±47.5μg/dl and 16.4±2.0% respectively which were significantly lower than that of healthy children (p<0.05). Mean Hb level in children with severe acute malnutrition was found 8.3±1.6gm/dl which was also found significantly lower than that of normal children (p<0.05). Anaemia was found in all (100%) severely malnourished children compared to 25.7% of children in comparison group. Mean MCV, MCH and MCHC in children with severe acute malnutrition was found 71.7±13.5fl, 24.0±5.8pg and 31.4±4.0gm/dl respectively which were significantly lower than that of comparison group (p<0.05). Serum iron profile and red cell indices should be routinely done in severely malnourished children for early intervention and management of iron deficiency anaemia.

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.

Development of a Web-Based System for Exploring Cancer Risk With Long-term Use of Drugs: Logistic Regression Approach

Background

Existing epidemiological evidence regarding the association between the long-term use of drugs and cancer risk remains controversial.

Objective

We aimed to have a comprehensive view of the cancer risk of the long-term use of drugs.

Methods

A nationwide population-based, nested, case-control study was conducted within the National Health Insurance Research Database sample cohort of 1999 to 2013 in Taiwan. We identified cases in adults aged 20 years and older who were receiving treatment for at least two months before the index date. We randomly selected control patients from the patients without a cancer diagnosis during the 15 years (1999-2013) of the study period. Case and control patients were matched 1:4 based on age, sex, and visit date. Conditional logistic regression was used to estimate the association between drug exposure and cancer risk by adjusting potential confounders such as drugs and comorbidities.

Results

There were 79,245 cancer cases and 316,980 matched controls included in this study. Of the 45,368 associations, there were 2419, 1302, 662, and 366 associations found statistically significant at a level of P<.05, P<.01, P<.001, and P<.0001, respectively. Benzodiazepine derivatives were associated with an increased risk of brain cancer (adjusted odds ratio [AOR] 1.379, 95% CI 1.138-1.670; P=.001). Statins were associated with a reduced risk of liver cancer (AOR 0.470, 95% CI 0.426-0.517; P<.0001) and gastric cancer (AOR 0.781, 95% CI 0.678-0.900; P<.001). Our web-based system, which collected comprehensive data of associations, contained 2 domains: (1) the drug and cancer association page and (2) the overview page.

Conclusions

Our web-based system provides an overview of comprehensive quantified data of drug-cancer associations. With all the quantified data visualized, the system is expected to facilitate further research on cancer risk and prevention, potentially serving as a stepping-stone to consulting and exploring associations between the long-term use of drugs and cancer risk.

Clinical Characteristics and Neonatal Outcomes of Pregnant Patients With COVID-19: A Systematic Review

Background and Objective: Coronavirus disease 2019 (COVID-19) characterized by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created serious concerns about its potential adverse effects. There are limited data on clinical, radiological, and neonatal outcomes of pregnant women with COVID-19 pneumonia. This study aimed to assess clinical manifestations and neonatal outcomes of pregnant women with COVID-19.

Methods: We conducted a systematic article search of PubMed, EMBASE, Scopus, Google Scholar, and Web of Science for studies that discussed pregnant patients with confirmed COVID-19 between January 1, 2020, and April 20, 2020, with no restriction on language. Articles were independently evaluated by two expert authors. We included all retrospective studies that reported the clinical features and outcomes of pregnant patients with COVID-19.

Results: Forty-seven articles were assessed for eligibility; 13 articles met the inclusion criteria for the systematic review. Data is reported for 235 pregnant women with COVID-19. The age range of patients was 25–40 years, and the gestational age ranged from 8 to 40 weeks plus 6 days. Clinical characteristics were fever [138/235 (58.72%)], cough [111/235 (47.23%)], and sore throat [21/235 (8.93%)]. One hundred fifty six out of 235 (66.38%) pregnant women had cesarean section, and 79 (33.62%) had a vaginal delivery. All the patients showed lung abnormalities in CT scan images, and none of the patients died. Neutrophil cell count, C-reactive protein (CRP) concentration, ALT, and AST were increased but lymphocyte count and albumin levels were decreased. Amniotic fluid, neonatal throat swab, and breastmilk samples were taken to test for SARS-CoV-2 but all found negativ results. Recent published evidence showed the possibility of vertical transmission up to 30%, and neonatal death up to 2.5%. Pre-eclampsia, fetal distress, PROM, pre-mature delivery were the major complications of pregnant women with COVID-19.

Conclusions: Our study findings show that the clinical, laboratory and radiological characteristics of pregnant women with COVID-19 were similar to those of the general populations. The possibility of vertical transmission cannot be ignored but C-section should not be routinely recommended anymore according to latest evidences and, in any case, decisions should be taken after proper discussion with the family. Future studies are needed to confirm or refute these findings with a larger number of sample sizes and a long-term follow-up period.

Machine Learning Approach to Reduce Alert Fatigue Using a Disease Medication-Related Clinical Decision Support System: Model Development and Validation

Background

Computerized physician order entry (CPOE) systems are incorporated into clinical decision support systems (CDSSs) to reduce medication errors and improve patient safety. Automatic alerts generated from CDSSs can directly assist physicians in making useful clinical decisions and can help shape prescribing behavior. Multiple studies reported that approximately 90%-96% of alerts are overridden by physicians, which raises questions about the effectiveness of CDSSs. There is intense interest in developing sophisticated methods to combat alert fatigue, but there is no consensus on the optimal approaches so far.

Objective

Our objective was to develop machine learning prediction models to predict physicians’ responses in order to reduce alert fatigue from disease medication–related CDSSs.

Methods

We collected data from a disease medication–related CDSS from a university teaching hospital in Taiwan. We considered prescriptions that triggered alerts in the CDSS between August 2018 and May 2019. Machine learning models, such as artificial neural network (ANN), random forest (RF), naïve Bayes (NB), gradient boosting (GB), and support vector machine (SVM), were used to develop prediction models. The data were randomly split into training (80%) and testing (20%) datasets.

Results

A total of 6453 prescriptions were used in our model. The ANN machine learning prediction model demonstrated excellent discrimination (area under the receiver operating characteristic curve [AUROC] 0.94; accuracy 0.85), whereas the RF, NB, GB, and SVM models had AUROCs of 0.93, 0.91, 0.91, and 0.80, respectively. The sensitivity and specificity of the ANN model were 0.87 and 0.83, respectively.

Conclusions

In this study, ANN showed substantially better performance in predicting individual physician responses to an alert from a disease medication–related CDSS, as compared to the other models. To our knowledge, this is the first study to use machine learning models to predict physician responses to alerts; furthermore, it can help to develop sophisticated CDSSs in real-world clinical settings.

Development of an Artificial Intelligence-Based Automated Recommendation System for Clinical Laboratory Tests: Retrospective Analysis of the National Health Insurance Database

BACKGROUND

Laboratory tests are considered an essential part of patient safety as patients' screening, diagnosis, and follow-up are solely based on laboratory tests. Diagnosis of patients could be wrong, missed, or delayed if laboratory tests are performed erroneously. However, recognizing the value of correct laboratory test ordering remains underestimated by policymakers and clinicians. Nowadays, artificial intelligence methods such as machine learning and deep learning (DL) have been extensively used as powerful tools for pattern recognition in large data sets. Therefore, developing an automated laboratory test recommendation tool using available data from electronic health records (EHRs) could support current clinical practice.

OBJECTIVE

The objective of this study was to develop an artificial intelligence-based automated model that can provide laboratory tests recommendation based on simple variables available in EHRs.

METHODS

A retrospective analysis of the National Health Insurance database between January 1, 2013, and December 31, 2013, was performed. We reviewed the record of all patients who visited the cardiology department at least once and were prescribed laboratory tests. The data set was split into training and testing sets (80:20) to develop the DL model. In the internal validation, 25% of data were randomly selected from the training set to evaluate the performance of this model.

RESULTS

We used the area under the receiver operating characteristic curve, precision, recall, and hamming loss as comparative measures. A total of 129,938 prescriptions were used in our model. The DL-based automated recommendation system for laboratory tests achieved a significantly higher area under the receiver operating characteristic curve (AUROCmacro and AUROCmicro of 0.76 and 0.87, respectively). Using a low cutoff, the model identified appropriate laboratory tests with 99% sensitivity.

CONCLUSIONS

The developed artificial intelligence model based on DL exhibited good discriminative capability for predicting laboratory tests using routinely collected EHR data. Utilization of DL approaches can facilitate optimal laboratory test selection for patients, which may in turn improve patient safety. However, future study is recommended to assess the cost-effectiveness for implementing this model in real-world clinical settings.

Status of Plasma Vitamin-D Level in Predicting Adverse In-Hospital Outcome in Patients with First Attack of Acute Myocardial Infarction

Acute myocardial infarction has many risk factors and etiologies. Different factors are responsible for adverse in-hospital outcome after acute MI. Status of plasma vitamin D level has been found to be a good predictor of future adverse cardiovascular outcomes in patients with acute MI. Plasma vitamin D level has been considered as a potential marker for identifying individuals under risk of CAD and associated events. This study was done to investigate the role of plasma vitamin D level in predicting in-hospital adverse cardiac events in patients with acute MI. This cross sectional descriptive type of study was conducted in the cardiology department of Mymensingh Medical College Hospital, Mymensingh, Bangladesh from October 2017 to March 2019. Total 257 patients of first attack of acute MI were included considering inclusion and exclusion criteria. Fasting blood samples were analyzed for plasma vitamin D level. Sample population were grouped at first into two, normal and low vitamin D level, taking 30ng/ml as cut-off value, low vitamin D level is further subdivided into insufficiency (21-29ng/ml), deficiency (10-20ng/ml) and severe deficiency (<10ng/ml). Adverse in-hospital cardiac outcomes were observed. In-hospital adverse outcomes occurred in 42.9% patients having normal vitamin D level (>30ng/ml), 66.2% of patients having vitamin D insufficiency (21-29ng/ml), 78.2% of patients having vitamin D deficiency (10-20ng/ml) and 94.4% patients having severe vitamin D deficiency (<10ng/ml), which was statistically significant (p<0.05). Heart failure (30.3%, 47.7%, 63.6% and 77.8%, p<0.05), cardiogenic shock (12.6%, 27.7%, 34.5% and 33.3%, p<0.05), Arrhythmias (14.3%, 21.5%, 23.6% and 22.2%, p>0.05), death (2.5%, 0%, 3.6% and 11.1%, p>0.05) occurred more in low vitamin D groups. Mean vitamin D level was significantly different between Group I and Group II (42.59±10.08 vs. 18.64±6.54, p<0.0001). Multivariate regression analysis showed vitamin D is an independent predictor of in-hospital adverse cardiac events (p=0.001). Age (p=0.001) and obesity (p=0.048) were also other predictors of in-hospital adverse cardiac events. Low plasma vitamin D level is an important predictor for in-hospital adverse cardiac events in patients hospitalized with first attack of acute MI.

Relationship of Plasma Vitamin-D Level with Left Ventricular Ejection Fraction in Patients with First Attack of Acute Myocardial Infarction

It has been widely reported that vitamin D deficiency is associated with Coronary heart disease (CHD), especially acute Myocardial infarction (MI). Many factors are responsible for reduced Left ventricular ejection fraction (LVEF) and acute Left ventricular fraction (LVF) after acute MI. This cross sectional descriptive type of study was conducted in the Cardiology department of Mymensingh Medical College Hospital from October 2017 to March 2019 to investigate the relationship of plasma vitamin D with LVEF in patients with first attack of acute MI. Total 185 patients of first attack of acute MI were included considering inclusion and exclusion criteria. Fasting blood samples were analyzed for plasma vitamin D level. Sample population were grouped at first into two, normal and low vitamin D level, taking 30ng/ml as cut-off value, low vitamin D level is further subdivided into insufficiency (21-29ng/ml), deficiency (10-20ng/ml) and severe deficiency (<10ng/ml). LVEF among the patients was observed. LVEF was found 49.88±8.58% patients having normal vitamin D level (>30ng/ml), 47.60±8.24% of patients having vitamin D insufficiency (21-29ng/ml), 44.38±8.12% of patients having vitamin D deficiency (10-20ng/ml) and 40.61±8.64% patients having severe vitamin D deficiency (<10ng/ml), which was statistically significant (p<0.05). So, low plasma vitamin D level is associated with reduced LVEF in patients hospitalized with first attack of acute MI.

Use of Mobile Phone App Interventions to Promote Weight Loss: Meta-Analysis

BACKGROUND

Obesity and lack of physical activity are major health risk factors for many life-threatening diseases, such as cardiovascular diseases, type 2 diabetes, and cancer. The use of mobile app interventions to promote weight loss and boost physical activity among children and adults is fascinating owing to the demand for cutting-edge and more efficient interventions. Previously published studies have examined different types of technology-based interventions and their impact on weight loss and increase in physical activity, but evidence regarding the impact of only a mobile phone app on weight loss and increase in physical activity is still lacking.

OBJECTIVE

The main objective of this study was to assess the efficacy of a mobile phone app intervention for reducing body weight and increasing physical activity among children and adults.

METHODS

PubMed, Google Scholar, Scopus, EMBASE, and the Web of Science electronic databases were searched for studies published between January 1, 2000, and April 30, 2019, without language restrictions. Two experts independently screened all the titles and abstracts to find the most appropriate studies. To be included, studies had to be either a randomized controlled trial or a case-control study that assessed a mobile phone app intervention with body weight loss and physical activity outcomes. The Cochrane Collaboration Risk of Bias tool was used to examine the risk of publication bias.

RESULTS

A total of 12 studies involving a mobile phone app intervention were included in this meta-analysis. Compared with the control group, the use of a mobile phone app was associated with significant changes in body weight (-1.07 kg, 95% CI -1.92 to -0.21, P=.01) and body mass index (-0.45 kg/m2, 95% CI -0.78 to -0.12, P=.008). Moreover, a nonsignificant increase in physical activity was observed (0.17, 95% CI -2.21 to 2.55, P=.88).

CONCLUSIONS

The findings of this study demonstrate the promising and emerging efficacy of using mobile phone app interventions for weight loss. Future studies are needed to explore the long-term efficacy of mobile app interventions in larger samples.

Use of Mobile Phone App Interventions to Promote Weight Loss: Meta-Analysis

Background

Obesity and lack of physical activity are major health risk factors for many life-threatening diseases, such as cardiovascular diseases, type 2 diabetes, and cancer. The use of mobile app interventions to promote weight loss and boost physical activity among children and adults is fascinating owing to the demand for cutting-edge and more efficient interventions. Previously published studies have examined different types of technology-based interventions and their impact on weight loss and increase in physical activity, but evidence regarding the impact of only a mobile phone app on weight loss and increase in physical activity is still lacking.

Objective

The main objective of this study was to assess the efficacy of a mobile phone app intervention for reducing body weight and increasing physical activity among children and adults.

Methods

PubMed, Google Scholar, Scopus, EMBASE, and the Web of Science electronic databases were searched for studies published between January 1, 2000, and April 30, 2019, without language restrictions. Two experts independently screened all the titles and abstracts to find the most appropriate studies. To be included, studies had to be either a randomized controlled trial or a case-control study that assessed a mobile phone app intervention with body weight loss and physical activity outcomes. The Cochrane Collaboration Risk of Bias tool was used to examine the risk of publication bias.

Results

A total of 12 studies involving a mobile phone app intervention were included in this meta-analysis. Compared with the control group, the use of a mobile phone app was associated with significant changes in body weight (−1.07 kg, 95% CI −1.92 to −0.21, P=.01) and body mass index (−0.45 kg/m2, 95% CI −0.78 to −0.12, P=.008). Moreover, a nonsignificant increase in physical activity was observed (0.17, 95% CI −2.21 to 2.55, P=.88).

Conclusions

The findings of this study demonstrate the promising and emerging efficacy of using mobile phone app interventions for weight loss. Future studies are needed to explore the long-term efficacy of mobile app interventions in larger samples.

Appropriateness of Overridden Alerts in Computerized Physician Order Entry: Systematic Review

BACKGROUND

The clinical decision support system (CDSS) has become an indispensable tool for reducing medication errors and adverse drug events. However, numerous studies have reported that CDSS alerts are often overridden. The increase in override rates has raised questions about the appropriateness of CDSS application along with concerns about patient safety and quality of care.

OBJECTIVE

The aim of this study was to conduct a systematic review to examine the override rate, the reasons for the alert override at the time of prescribing, and evaluate the appropriateness of overrides.

METHODS

We searched electronic databases, including Google Scholar, PubMed, Embase, Scopus, and Web of Science, without language restrictions between January 1, 2000 and March 31, 2019. Two authors independently extracted data and crosschecked the extraction to avoid errors. The quality of the included studies was examined following Cochrane guidelines.

RESULTS

We included 23 articles in our systematic review. The range of average override alerts was 46.2%-96.2%. An average of 29.4%-100% of the overrides alerts were classified as appropriate, and the rate of appropriateness varied according to the alert type (drug-allergy interaction 63.4%-100%, drug-drug interaction 0%-95%, dose 43.9%-88.8%, geriatric 14.3%-57%, renal 27%-87.5%). The interrater reliability for the assessment of override alerts appropriateness was excellent (kappa=0.79-0.97). The most common reasons given for the override were "will monitor" and "patients have tolerated before."

CONCLUSIONS

The findings of our study show that alert override rates are high, and certain categories of overrides such as drug-drug interaction, renal, and geriatric were classified as inappropriate. Nevertheless, large proportions of drug duplication, drug-allergy, and formulary alerts were appropriate, suggesting that these groups of alerts can be primary targets to revise and update the system for reducing alert fatigue. Future efforts should also focus on optimizing alert types, providing clear information, and explaining the rationale of the alert so that essential alerts are not inappropriately overridden.

Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs: A systematic review and meta-analysis

BACKGROUND

Diabetic retinopathy (DR) is one of the leading causes of blindness globally. Earlier detection and timely treatment of DR are desirable to reduce the incidence and progression of vision loss. Currently, deep learning (DL) approaches have offered better performance in detecting DR from retinal fundus images. We, therefore, performed a systematic review with a meta-analysis of relevant studies to quantify the performance of DL algorithms for detecting DR.

METHODS

A systematic literature search on EMBASE, PubMed, Google Scholar, Scopus was performed between January 1, 2000, and March 31, 2019. The search strategy was based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines, and DL-based study design was mandatory for articles inclusion. Two independent authors screened abstracts and titles against inclusion and exclusion criteria. Data were extracted by two authors independently using a standard form and the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used for the risk of bias and applicability assessment.

RESULTS

Twenty-three studies were included in the systematic review; 20 studies met inclusion criteria for the meta-analysis. The pooled area under the receiving operating curve (AUROC) of DR was 0.97 (95%CI: 0.95-0.98), sensitivity was 0.83 (95%CI: 0.83-0.83), and specificity was 0.92 (95%CI: 0.92-0.92). The positive- and negative-likelihood ratio were 14.11 (95%CI: 9.91-20.07), and 0.10 (95%CI: 0.07-0.16), respectively. Moreover, the diagnostic odds ratio for DL models was 136.83 (95%CI: 79.03-236.93). All the studies provided a DR-grading scale, a human grader (e.g. trained caregivers, ophthalmologists) as a reference standard.

CONCLUSION

The findings of our study showed that DL algorithms had high sensitivity and specificity for detecting referable DR from retinal fundus photographs. Applying a DL-based automated tool of assessing DR from color fundus images could provide an alternative solution to reduce misdiagnosis and improve workflow. A DL-based automated tool offers substantial benefits to reduce screening costs, accessibility to healthcare and ameliorate earlier treatments.

Vitamin D Profile in Juvenile Idiopathic Arthritis Patients in a Tertiary Care Hospital in Bangladesh

There are multi-factorial causes of decrease in bone mass in Juvenile Idiopathic Arthritis (JIA) patients who correlate with the duration of active disease. By measuring the vitamin D level we can assess the deficiency or insufficiency earlier and can predict the risk of osteoporotic bone fracture & can give appropriate supplementation of vitamin D & calcium. This study was done to determine the status of serum 25(OH)D in patients with JIA and to see the relationship among various subtypes and disease duration. In this cross sectional study 30 (Thirty) newly diagnosed cases of JIA attending the pediatric rheumatology clinic of Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, Bangladesh from July 2014 to December 2015 were included. Thirty age and sex matched control were selected and serum 25(OH)D was measured in cases and controls. Among JIA patients, 60% and among controls 33% had hypo-vitaminosis D. In JIA group the mean level of serum 25(OH)D was lower than control group and the result is statistically significant in cases of poly-articular JIA and systemic JIA (SJIA). There is significant difference of the mean values of vitamin D levels in JIA and control groups for the cases of hypo-vitaminosis D. Level of serum 25(OH)D significantly decreased as disease duration continue increased. More than half of JIA patients had hypo-vitaminosis D. It is more significant in cases of poly-articular JIA and systemic JIA (SJIA). There was negative relationship between serum 25(OH)D level and disease duration.

Platelet Count as a Severity of Chronic Obstructive Pulmonary Disease

Chronic Obstructive Pulmonary disease (COPD) is a heterogenous respiratory disease characterized by a progressive, not fully reversible airflow limitation associated with an abnormal inflammatory response of the lung to noxious stimuli. It is a disease presenting with pulmonary inflammation as well as a systemic one. Measurement of inflammatory marker is difficult but platelet count estimation is easy and less costly. This descriptive, cross-sectional study was carried out at Department of Medicine, Mymensingh Medical college Hospital, Mymensingh, Bangladesh for a period of twelve months among fifty-nine COPD patients. Data were collected through interview, physical examination and laboratory investigations. Statistical analysis was performed using SPSS version 22.0 for consistency and completeness. Age range of the patients was 40 to 49 years with a mean of 56.3±10.9 years. Age group 40-49 years contained the highest number (19; 32.3%) of patients. Majority 57(96.6%) of the respondents were male. Thirty seven (62.7%) of patients were illiterate. Majority 56(94.9%) of patients resided in rural area, of them most 38(64.4%) were farmers. According to Spirometric measurement among 59 respondents of COPD patient, 3(5.1%) were in GOLD stage-I, 9(15.3%) in GOLD stage-II, 27(45.8%) in GOLD stage-III and 20(33.9%) in GOLD stage IV group. Mean platelet count (10³/μl), 241.6±86.5 was found in mild, whereas 315.0±47.7 in moderate, 337.2±76.3 in severe, and 412.4±67.5 in very severe group of COPD patients. So increase in platelet count is statistically significant in severity of COPD. In conclusion, platelet count measurement is less costly to categorize COPD and may be a diagnostic marker.

Mutual-Aid Mobile App for Emergency Care: Feasibility Study

BACKGROUND

Improving the quality of patient care through the use of mobile devices is one of the hot topics in the health care field. In unwanted situations like an accident, ambulances and rescuers often require a certain amount of time to arrive at the scene. Providing immediate cardiopulmonary resuscitation (CPR) to patients might improve survival.

OBJECTIVE

The primary objective of this study was to evaluate the feasibility of an emergency and mutual-aid app model in Taiwan and to provide a reference for government policy.

METHODS

A structured questionnaire was developed as a research tool. All questionnaires were designed according to the technology acceptance model, and a Likert scale was used to measure the degree of agreement or disagreement. Moreover, in-depth interviews were conducted with six experts from medical, legal, and mobile app departments. Each expert was interviewed once to discuss feasible countermeasures and suggestions. Statistical Package for the Social Sciences (SPSS version 19; IBM Corp, Armonk, New York) was used to perform all statistical analyses, including descriptive statistics, independent sample t-tests, variance analysis, and Pearson correlation analysis.

RESULTS

We conducted this study between October 20, 2017, and November 10, 2017, at the Taipei Medical University Hospital. Questionnaires were distributed to medical personnel, visiting guests, family members, and volunteers. A total of 113 valid questionnaires were finally obtained after the exclusion of incomplete questionnaires. Cronbach α values for self-efficacy (perceived ease of use), use attitude (perceived usefulness), and use willingness and frequency were above .85, meeting the criterion of greater than .70. We observed that the reliability of each subquestion was acceptable and the values for use attitude (perceive usefulness) and use willingness and frequency were more than .90.

CONCLUSIONS

The findings suggest that perceived ease of use and perceived usefulness of the app model affect use willingness. However, perceived usefulness had an intermediary influence on use willingness. Experts in law, medical, and technology fields consider that an emergency and mutual-aid model can be implemented in Taiwan. Along with the development of an emergency and mutual-aid app model, we recommend an increase in the number of automated external defibrillators per region and promotion of correct knowledge about CPR in order to decrease morbidity and mortality.

Exploring the relationship between age at first drink, low-risk drinking knowledge and drinks counting: six rounds of a country-wide survey in Australia

OBJECTIVE

Although a reasonable volume of research has been conducted around health impacts of age at first drink of alcohol on adverse health outcomes, the effects of age at first drink of alcohol on knowledge of low-risk drinking and drinks counting habits were rarely researched. The objective of this study is to examine the associations between age at first full serve of alcohol, knowledge of low-risk drinking and frequency of drinks counting.

STUDY DESIGN

This is a cross-sectional study.

METHODS

Data of six rounds of the National Drug Strategy Household Survey, conducted during the period 2001-2016, were analysed. Multivariable log-binomial regression models were used to explore the associations.

RESULTS

Most people drank the first full serve of alcohol during adolescence, and the age at first full serve of alcohol was consumed increased over time. The levels of knowledge of low-risk drinking and frequency of drinks counting increased with age at first drink of alcohol more steeply during adolescence than in the later period. Participants' age at drinking the first full serve of alcohol was significantly associated with knowledge of low-risk drinking and drinks counting. There was an increasing trend of significant risk ratio between knowledge score and the frequency of drinks counting.

CONCLUSION

It seems conceivable that those who reported drinking the first full serve of alcohol before 16 years of age were indifferent to drinks counting, and they lacked necessary knowledge of standard drink of alcohol or low-risk drinking. Tailored prevention programs are recommended among adolescents to delay age at first drink of alcohol and to enhance their knowledge base on low-risk drinking.

Association Between Atrial Fibrillation and Dementia: A Meta-Analysis

Background: A potential evidence from previous epidemiological studies remains conflicting findings regarding the association between atrial fibrillation (AF) and dementia risk. We, therefore, carried out a meta-analysis of relevant studies to investigate the magnitude of the association between AF and dementia risk.

Methods: We performed a systematic literature search of PubMed, EMBASE, and Google Scholar for potential studies between January 1, 1990, and December 31, 2018, with no restriction on the publication language. All potential studies were independently assessed by two reviewers. We only included observational studies that calculated the odds ratio (OR)/hazards ratio (HR) for dementia associated with atrial fibrillation. We first assessed the heterogeneity among study-specific HRs using the Q statistic and I² statistic. We then used the random-effects model to obtain the overall HR and its 95% CI for all studies. We also tested and corrected for publication bias by funnel plot–based methods. The quality of each study was assessed with the Newcastle Ottawa Scale.

Results: A total of 16 studies with 2,415,356 individuals, and approximately 200,653 cases of incidence dementia were included in this study. Patients with AF had a greater risk of incidence dementia than those without AF (random-effect hazard ratio HR: 1.36, 95% CI: 1.23–1.51, p < 0.0001; I² = 83.58). Funnel plot and Egger test did not reveal significant publication bias. However, limitations of the study included high heterogeneity and varying degrees of confounder adjustment across individual studies.

Conclusion: This study serves as added evidence supporting the hypothesis that AF is associated with an increased risk of dementia. More studies are needed to establish whether optimal treatment of AF can reduce or mitigate the risk of dementia.

Development of Simple, Objective Chair-Standing Assessment of Physical Function in Older Individuals Using a KinectTM Sensor

BACKGROUND

With increasing interest in addressing quality of life of older individuals, tests such as the Functional Independence Measure (FIM) are widely used measures of infirmity and burden of care. However, these scales are largely qualitative and especially problematic when assessing movement-based tasks. While effective, reliable analysis of human movement is technically complicated and expensive; an infrared depth sensor is potentially a low-cost, portable devise which may provide a quantitative aspect to clinical testing.

OBJECTIVE

to assess the utility of the KinectTM sensor in providing an objective evaluation of human movement using an oft measured ADL (chair stand).

DESIGN

Cross-sectional study.

SETTING

Community, geriatric day-care center in Japan.

PARTICIPANTS

Men (n=136) and women (n=266) between 50 and 93 years of age, consisting of healthy (HE; n=312) and physically frail (FR; n= 90) individuals.

MEASUREMENTS

Subjects completed two trials of the chair stand, conducted without assistance. Trials were timed and recorded with KinectTM v2. Coronal plane angle (CPA) was determined by a line transecting the shoulder-center and waist relative to the vertical axis and was used to assess quality of the chair stand movement pattern.

RESULTS

Age, height, and body mass were not different between groups. CPA was significantly greater in FR (29.3 ± 8.3°) than HE (19.5 ± 6.5°). CPA and age were significantly related (r=0.148, p<0.01). An optimal threshold for CPA identifying frailty was determined by a receiver-operator characteristic curve with a CPA of 23.1° providing the greatest combination of sensitivity (79%) and specificity (73%).

CONCLUSION

During the chair stand, frail older adults adopted a forward lean position (increased CPA) compared to healthy older adults. This compensatory posture appears to facilitate torso rotation while reducing lower-limb muscular effort during standing. As such, CPA serves as an indicator of reduced lower-body function in older, frail adults.

Development of Deep Learning Algorithm for Detection of Colorectal Cancer in EHR Data

We aimed to develop a deep learning model for the prediction of the risk of advanced colorectal cancer in Taiwanese adults. We collected data of 58152 patients from the Taiwan National Health Insurance database from 1999 to 2013. All patients' comorbidities and medications history were included in the development of the convolution neural network (CNN) model. We also used 3-year medical data of all patients before the diagnosed colorectal cancer (CRC) as the dimensional time in the model. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were computed to measure the performance of the model. The results showed the mean (SD) of AUC of the model was 0.922 (0.004). Moreover, the performance of the model observed the sensitivity of 0.837, specificity of 0.867, and 0.532 for PPV value. Our study utilized CNN to develop a prediction model for CRC, based on non-image and multi-dimensional medical records.

Risk of Acute Myocardial Infarction in Patients with Rheumatic Arthritis: A National-Wide Population-Based Cohort Study

We performed a cohort study to quantify the association between rheumatic arthritis (RA) and acute myocardial infarction (AMI) risk. ICD-9 was used to identify AMI and RA patients, and the Cox proportional hazards model with adjusted confounding factors was used to quantify the risk. The overall risk of AMI for RA patients was an aHR of 1.05 (95% CI 1.01-1.09). We found RA was associated with an increased risk for AMI.

Artificial Intelligence in Diabetic Retinopathy: Insights from a Meta-Analysis of Deep Learning

The demand for AI to improve patients outcome has been increased; we, therefore, aim to establish the diagnostic values of AI in diabetic retinopathy by pooling the published studies of deep learning on this subject. A total of eight studies included which evaluated deep learning in a total of 706,922 retinal images. The overall pooled area under receiver operating curve (AUROC) was 98.93% (95%CI:98.37%-99.49%). However, the overall pooled sensitivity and specificity for detecting referable diabetic retinopathy (RDR) was 74% (95% CI: 73%-74%), and 95% (95% CI: 95%-95%). The findings of this study show that deep learning had high sensitivity and specificity for identifying diabetic retinopathy.

Effects of arsenic and phosphorus on the growth and nutrient concentration in rice plant

A pot experiment was carried out with arsenic (As) viz. 0, 0.1, 1 and 2 mgL-1 as sodium arsenite and phosphorus viz, 0, 15 and 30 μgmL-1 as ammonium dihydrogen phosphate to evaluate their effects on dry matter yield and nutrients concentration in rice plants (Oryza sativa L.) in the net house. Arsenic toxicity caused more damage to root than to shoot. As reduced plant height and dry matter yields but lower level increased the same significantly. A maximum diminution of 26.70% shoot weight and 32.30% root weights were observed where 2 mgL-1As and 0 μgmL-1 P were applied. Micronutrients were found to be more strongly antagonized by arsenic than the macronutrients. Maximum and minimum accumulation of different nutrients was found at 30 μgmL-1 and 0 μgmL-1 P applications, respectively. The lowest concentration of most of the nutrients were found at 2 mgL-1As and 0 μgmL-1 P. Experiment revealed that the concentrations of nitrogen, potassium, sulphur, iron, copper, zinc and manganese in the root and shoot of rice plants showed an antagonistic effect with As and synergistic effect with P. J. Biodivers. Conserv. Bioresour. Manag. 2019, 5(1): 31-38

An artificial intelligence approach to early predict non-ST-elevation myocardial infarction patients with chest pain

BACKGROUND AND AIMS

Hospital admission rate for the patients with chest pain has already been increased worldwide but no existing risk score has been designed to stratify non-ST-elevation myocardial infarction (NSTEMI) from non-cardiogenic chest pain. Clinical diagnosis of chest pain in the emergency department is always highly subjective and variable. We, therefore, aimed to develop an artificial intelligence approach to predict stable NSTEMI that would give valuable insight to reduce misdiagnosis in the real clinical setting.

METHODS

A standard protocol was developed to collect data from chest pain patients who had visited the emergency department between December 2016 and February 2017. All the chest pain patients with aged <20 years were primarily included in this study. However, STEMI, previous history of ACS, and out-of-hospital cardiac arrest were excluded from our study. An artificial neural network (ANN) model was then developed to predict NSTEMI patients. The accuracy, sensitivity, specificity, and receiver operating characteristic curve was used to measure the performance of this model.

RESULTS

A total of 268 chest pain patients were included in this study; of those, 47 (17.5%) was stable NSTEMI, and 221 (82.5%) was unstable angina patients. Serval risk factors such as cardiac risk factor, systolic blood pressure, hemoglobin, corrected QT interval (QTc), PR interval, glutamic-oxaloacetic transaminase, glutamic pyruvic transaminase and troponin were independently associated with stable NSTEMI. The area under the receiver operating characteristic (AUROC) and accuracy of ANN were 98.4, and 92.86. Additionally, the sensitivity, specificity, positive predictive value, and negative predictive value of the ANN model was 90.91, 93.33, 76.92, and 97.67 respectively.

CONCLUSION

Our prediction model showed a higher accuracy to predict NSTEMI patients. This model has a potential application in disease detection, monitoring, and prognosis of chest pain at risk of AMI.

Prediction of sepsis patients using machine learning approach: A meta-analysis

STUDY OBJECTIVE

Sepsis is a common and major health crisis in hospitals globally. An innovative and feasible tool for predicting sepsis remains elusive. However, early and accurate prediction of sepsis could help physicians with proper treatments and minimize the diagnostic uncertainty. Machine learning models could help to identify potential clinical variables and provide higher performance than existing traditional low-performance models. We therefore performed a meta-analysis of observational studies to quantify the performance of a machine learning model to predict sepsis.

METHODS

A comprehensive literature search was conducted through the electronic database (e.g. PubMed, Scopus, Google Scholar, EMBASE, etc.) between January 1, 2000, and March 1, 2018. All the studies published in English and reporting the sepsis prediction using machine learning algorithms were considered in this study. Two authors independently extracted valuable information from the included studies. Inclusion and exclusion of studies were based on the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines.

RESULTS

A total of 7 out of 135 studies met all of our inclusion criteria. For machine learning models, the pooled area under receiving operating curve (SAUROC) for predicting sepsis onset 3 to 4 h before, was 0.89 (95%CI: 0.86-0.92); sensitivity 0.81 (95%CI:0.80-0.81), and specificity 0.72 (95%CI:0.72-0.72) whereas the pooled SAUROC for SIRS, MEWS, and SOFA was 0.70, 0.50, and 0.78. Additionally, diagnostic odd ratio for machine learning, SIRS, MEWS, and SOFA was 15.17 (95%CI: 9.51-24.20), 3.23 (95%CI: 1.52-6.87), 31.99 (95% CI: 1.54-666.74), and 3.75(95%CI: 2.06-6.83).

CONCLUSION

Our study findings suggest that the machine learning approach had a better performance than the existing sepsis scoring systems in predicting sepsis.

Prediction of fatty liver disease using machine learning algorithms

BACKGROUND AND OBJECTIVE

Fatty liver disease (FLD) is a common clinical complication; it is associated with high morbidity and mortality. However, an early prediction of FLD patients provides an opportunity to make an appropriate strategy for prevention, early diagnosis and treatment. We aimed to develop a machine learning model to predict FLD that could assist physicians in classifying high-risk patients and make a novel diagnosis, prevent and manage FLD.

METHODS

We included all patients who had an initial fatty liver screening at the New Taipei City Hospital between 1st and 31st December 2009. Classification models such as random forest (RF), Naïve Bayes (NB), artificial neural networks (ANN), and logistic regression (LR) were developed to predict FLD. The area under the receiver operating characteristic curve (ROC) was used to evaluate performances among the four models.

RESULTS

A total of 577 patients were included in this study; of those 377 patients had fatty liver. The area under the receiver operating characteristic (AUROC) of RF, NB, ANN, and LR with 10 fold-cross validation was 0.925, 0.888, 0.895, and 0.854 respectively. Additionally, The accuracy of RF, NB, ANN, and LR 87.48, 82.65, 81.85, and 76.96%.

CONCLUSION

In this study, we developed and compared the four classification models to predict fatty liver disease accurately. However, the random forest model showed higher performance than other classification models. Implementation of a random forest model in the clinical setting could help physicians to stratify fatty liver patients for primary prevention, surveillance, early treatment, and management.

A probabilistic model for reducing medication errors: A sensitivity analysis using Electronic Health Records data

OBJECTIVES

Medication-related clinical decision support systems have already been considered as a sophisticated method to improve healthcare quality, however, its importance has not been fully recognized. This paper's aim was to validate an existing probabilistic model that can automatically identify medication errors by performing a sensitivity analysis from electronic medical record data.

METHODS

We first built a knowledge base that consisted of 2.22 million disease-medication (DM) and 0.78 million medication-medication (MM) associations using Taiwan Health and Welfare data science claims data between January 1st, 2009 and December 31st, 2011. Further, we collected 0.6 million outpatient visit prescriptions from six departments across five different medical centers/hospitals. Afterward, we employed the data to our AESOP model and validated it using a sensitivity analysis of 11 various thresholds (α = [0.5; 1.5]) that were used to identify positive DM and MM associations. We randomly selected 2400 randomly prescriptions and compared them to the gold standard of 18 physicians' manual review for appropriateness.

RESULTS

One hundred twenty-one results of 2400 prescriptions with various thresholds were tested by the AESOP model. Validation against the gold standard showed a high accuracy (over 80%), sensitivity (80-96%), and positive predictive value (over 85%). The negative predictive values ranged from 45 to 75% across three departments, cardiology, neurology, and ophthalmology.

CONCLUSION

We performed a sensitivity analysis and validated the AESOP model in different hospitals. Thus, picking the optimal threshold of the model depended on balancing false negatives with false positives and depending on the specialty and the purpose of the system.

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.