Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: “definite AD” with diagnostic codes and dementia medication (n = 614) and “probable AD” with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings

In Vivo Reprogramming Using Yamanaka Factors in the CNS: A scoping review

This scoping review aims to explore in vivo reprogramming for the central nervous system (CNS) using Yamanaka factors, mapping the existing literature and identify areas for future research

Association between Rheumatoid Arthritis and Respiratory Allergic Diseases in Korean Adults: A Propensity Score Matched Case-Control Study

Rheumatoid arthritis (RA) and allergic diseases are result of a poor functioning immune system, giving dominance to either T-helper 1 (Th1) or T-helper 2 (Th2) diseases, respectively. Studies have stated that there seems to be a relationship present between the immune response subsets. This study was designed to examine the association between RA and respiratory allergic diseases in Korean adults. The study utilized the KNHANES 2013–2015 data and excluded individuals diagnosed with RA before being diagnosed with allergic diseases, using age at clinical diagnosis. Total of 253 RA patients were matched 1 : 1 with non-RA patients by a propensity score, using sex and age as matched variables. Multivariate conditional logistic regression analyses were used to evaluate for association between RA and respiratory allergic diseases in the matched 506 participants. RA was associated with an increased risk of prevalence of respiratory allergic diseases with an OR of 1.51 (95% CI, 1.31–1.75), adjusted for socioeconomic demographic variables. The adjusted OR for prevalence of RA among participants with prevalence of asthma and allergic rhinitis was as follows: 3.12 (95% CI, 2.77–3.51) and 1.39 (95% CI, 1.16–1.67). Participants with prevalence of asthma in particular had an increased risk of developing prevalence of RA. Based on our findings, Th1 and Th2 diseases may indeed coexist, and one pathway may stimulate or contribute towards the onset of the other

Value of Online Videos as a Shoulder Injection Training Tool for Physicians and Usability of Current Video Evaluation Tools

This study aimed to evaluate the reliability, overall quality, and educational value of online videos for learning the techniques related to shoulder injection treatments and analyzing the usability of video evaluation tools for musculoskeletal injections. Online video searches were performed in February 2022 using the terms “shoulder injection”, “glenohumeral joint injection”, “acromioclavicular joint injection”, and “subacromial bursa injection.” Included videos were scored by modified DISCERN (mDISCERN), global quality score (GQS), and shoulder injection score (SIS). Correlations between scoring systems were analyzed. Of the 150 videos, 49 (32.67%) contained highly reliable information. Regarding the assessment of overall quality by the GQS, 109 (72.67%) videos were of low quality. Regarding SIS, 114 (76.00%) scored not >5, of which 77 (51.33%) scored <3. Most of the SIS domains were fully explained in <40% of the included videos. A weak positive relationship was noted between the mDISCERN and SIS (r2 = 0.38), while a moderately positive relationship was observed between the GQS and SIS (r2 = 0.49). The majority of online videos about shoulder injection treatment showed low reliability, overall quality, and educational value. Additionally, a new scoring system is required to accurately evaluate musculoskeletal injection videos for educational purposes

Reliability, Quality, and Educational Suitability of TikTok Videos as a Source of Information about Scoliosis Exercises: A Cross-Sectional Study

This study aimed to systematically assess the informational reliability, quality, and educational suitability of videos introducing scoliosis exercises on TikTok. We retrieved and screened 1904 TikTok videos with the hashtags: “#scoliosis”, “#scoliosisexercise”, and “#scoliosistips”, before collecting a final sample of 171 scoliosis exercises in March 2022. Then, two independent raters assessed the reliability and quality of the videos using the DISCERN instrument and evaluated the educational suitability of the information using “Scoliosis Exercise Education Score” (SEES; exercise cycle, target, effect, precaution, and rationale). None of the videos were rated as excellent or good according to DISCERN. The mean SEES score was 2.02 out of 5. Videos uploaded by health organizations had significantly lower DISCERN and SEES scores than those by general users and healthcare professionals. Regarding the propriety of physiotherapeutic scoliosis-specific exercises (PSSE), DISCERN and SEES scores were significantly higher in the PSSE proper group than in the PSSE non-proper group. Although TikTok has become a popular source of scoliosis-related information, the overall information quality, reliability, and educational suitability of videos on scoliosis exercises in TikTok appear to be low, suggesting that TikTok is not suitable source for obtaining scoliosis exercise information

Mobile applications for cognitive training: Content analysis and quality review

Background: As the number of individuals suffering from cognitive diseases continues to rise, dealing with the diminished cognitive function that comes with age has become a serious public health concern. While the use of mobile applications (apps) as digital treatments for cognitive training shows promise, the analysis of their content and quality remains unclear. Objective: The aim of this study was to systematically search and assess cognitive training apps using the multidimensional mobile app rating scale (MARS) to rate objective quality and identify critical points. Methods: A search was conducted on the Google Play Store and Apple App Store in February 2022 using the terms “cognitive training” and “cognitive rehabilitation.” The cognitive domains provided by each app were analyzed, and the frequency and percentage according to the apps were obtained. The MARS, a mHealth app quality rating tool including multidimensional measures, was used to analyze the quality of the apps. The relationship between the MARS score, the number of reviews, and 5-star ratings were examined. Results: Of the 53 apps, 52 (98 %) included memory function, 48 (91 %) included attention function, 24 (45 %) included executive function, and 19 (36 %) included visuospatial function. The mean (SD) scores of MARS, 5-star ratings, and reviews of 53 apps were 3.09 (0.61), 4.33 (0.30), and 62,415.43 (121,578.77). From the between-section comparison, engagement (mean 2.97, SD 0.68) obtained lower scores than functionality (mean 3.18, SD 0.62), aesthetics (mean 3.13, SD 0.72), and information (mean 3.11, SD 0.54). The mean quality score and reviews showed a statistically significant association (r = 0.447 and P = .001*). As the number of domains increased, the mean quality score showed a statistically significant increasing trend (P = .002*). Conclusions: Most apps provided training for the memory and attention domains, but few apps included executive function or visuospatial domains. The quality of the apps improved significantly when more domains were provided, and was positively associated with the number of reviews received. These results could be useful for the future development of mobile apps for cognitive training

In Vivo Reprogramming Using Yamanaka Factors in the CNS: A Scoping Review

Central nervous system diseases, particularly neurodegenerative disorders, pose significant challenges in medicine. These conditions, characterized by progressive neuronal loss, have remained largely incurable, exacting a heavy toll on individuals and society. In recent years, in vivo reprogramming using Yamanaka factors has emerged as a promising approach for central nervous system regeneration. This technique involves introducing transcription factors, such as Oct4, Sox2, Klf4, and c-Myc, into adult cells to induce their conversion into neurons. This review summarizes the current state of in vivo reprogramming research in the central nervous system, focusing on the use of Yamanaka factors. In vivo reprogramming using Yamanaka factors has shown promising results in several animal models of central nervous system diseases. Studies have demonstrated that this approach can promote the generation of new neurons, improve functional outcomes, and reduce scar formation. However, there are still several challenges that need to be addressed before this approach can be translated into clinical practice. These challenges include optimizing the efficiency of reprogramming, understanding the cell of origin for each transcription factor, and developing methods for reprogramming in non-subventricular zone areas. Further research is needed to overcome the remaining challenges, but this approach has the potential to revolutionize the way we treat central nervous system disorders

Usefulness of YouTube in Sharing Information about New Gene Therapy for Spinal Muscular Atrophy: A Content Analysis

This study aimed to objectively assess YouTube videos’ quality, reliability, and information delivery capability regarding novel spinal muscular atrophy treatments. Using the keywords “nusinersen”, “spinraza”, “ridisplam”, “evrysdi”, “onasemnogene abeparvovec”, and “zolgensma”, we were able to retrieve and screen 360 videos before settling on a final sample of 99 on 25 September 2022. Then, two independent raters used the mDISCERN and GQS instruments to evaluate the videos’ reliability and quality and the Information Delivery Capability (IDC) score to assess the videos’ accuracy and patient-friendliness. The quality, reliability, and information delivery capability of the videos about the new treatment for SMA were quite heterogeneous, with an average mDISCERN, GQS, and IDC score of 3.172 ± 0.899, 2.980 ± 1.025, and 4.141 ± 1.747, respectively. In-depth analysis showed that healthcare expert videos that explained contents while showing infographic supplements had good quality, reliability, and information delivery capability. As YouTube is already a dominant media platform, the public may obtain new information about novel therapeutics for SMA through YouTube. It is necessary to consider how SMA patients and caregivers can choose trusted sources with reliable information on YouTube, and our results can provide clues. Additionally, experts should strive to provide more accurate, reliable, and patient-oriented videos

Electronic Health Records Based Prediction of Future Incidence of Alzheimer’s Disease Using Machine Learning

Background: Prediction of future incidence of Alzheimer’s disease may facilitate intervention strategy to delay disease onset. Existing AD risk prediction models require collection of biospecimen (genetic, CSF, or blood samples), cognitive testing, or brain imaging. Conversely, EHR provides an opportunity to build a completely automated risk prediction model based on individuals’ history of health and healthcare. We tested machine learning models to predict future incidence of AD using administrative EHR in individuals aged 65 or older. Methods: We obtained de-identified EHR from Korean elders age above 65 years old (N=40,736) collected between 2002 and 2012 in the Korean National Health Insurance Service database system. Consisting of Participant Insurance Eligibility database, Healthcare Utilization database, and Health Screening database, this EHR contain 4,894 unique clinical features including ICD-9/10 codes, medication codes, laboratory values, history of personal and family illness, and socio-demographics. Our event of interest was new incidence of AD defined from the EHR based on both AD codes and prescription of anti-dementia medication. Two definitions were considered: a more stringent one requiring a diagnosis and dementia medication resulting in n=614 cases (“definite AD”) and a more liberal one requiring only diagnostic codes (n=2,026; “probable AD”). We trained and validated a random forest, support vector machine, and logistic regression to predict incident AD in 1,2,3, and 4 subsequent years using the EHR available since 2002. The length of the EHR used in the models ranged from 1,571 to 2,239 days. Data was randomly split into training (60%), validation (20%), and test sets (20%) so that AUC values represent true out of sample prediction are based on the test set. Results: Average duration of EHR was 1,936 days in AD and 2,694 days in controls. For predicting future incidence of AD using the “definite AD” outcome, the machine learning models showed the best performance in 1 year prediction with AUC of 0.781; in 2 year, 0.739; in 3 year, 0.686; in 4 year, 0.662. Using “probable AD” outcome, the machine learning models showed the best performance in 1 year prediction with AUC of 0.730; in 2 year, 0.645; in 3 year, 0.575; in 4 year, 0.602. Important clinical features selected in logistic regression included hemoglobin level (b=-0.902), age (b=0.689), urine protein level (b=0.303), prescription of Lodopin (antipsychotic drug) (b=0.303), and prescription of Nicametate Citrate (vasodilator) (b=-0.297). Conclusion: This study demonstrates that EHR can i detect risk for incident AD. This approach could enable risk-specific stratification of elders for better targeted clinical trials

Long-term clinical outcomes of oral antidiabetic drugs as fixed-dose combinations: A nationwide retrospective cohort study

Aim To compare treatment patterns and clinical outcomes of single-pill fixed-dose combination (FDC) and two-pill combination (TPC) therapies using real-world data. Methods We conducted a nationwide retrospective cohort study using South Korea's healthcare database (2002-2015). We identified two cohorts of incident patients with type 2 diabetes who initiated FDC or TPC therapy within 4 months of their first prescription for metformin or sulphonylurea. We examined persistence and adherence patterns and the clinical outcome of a composite endpoint of death or hospitalization for acute myocardial infarction, heart failure or stroke and compared the differences in treatment patterns and clinical outcomes using Cox models. Results Of 5143 and 10 973 patients who initiated FDC and TPC therapy, respectively, we identified 5143 patient pairs after propensity score matching. The FDC group exhibited greater median time to treatment discontinuation (163 vs. 146 days), and proportion of days covered at 12 months (mean 0.60 vs. 0.57, P < .0001) and at 24 months (0.53 vs. 0.51, P = .014) than the TPC group. The FDC group, compared with the TPC group, had reduced risks of the composite clinical outcome (hazard ratio 0.86, 95% confidence intervals 0.77-0.97) and hospitalization for stroke (0.80, 0.67-0.96). Conclusion FDC therapy may provide favourable cardiovascular benefits, especially reducing the risk of hospitalization for stroke, and has better medication adherence among patients with type 2 diabetes.N