The Bitemporal Lens Model-toward a holistic approach to chronic disease prevention with digital biomarkers.

Objectives: We introduce the Bitemporal Lens Model, a comprehensive methodology for chronic disease prevention using digital biomarkers. Materials and Methods: The Bitemporal Lens Model integrates the change-point model, focusing on critical disease-specific parameters, and the recurrent-pattern model, emphasizing lifestyle and behavioral patterns, for early risk identification. Results: By incorporating both the change-point and recurrent-pattern models, the Bitemporal Lens Model offers a comprehensive approach to preventive healthcare, enabling a more nuanced understanding of individual health trajectories, demonstrated through its application in cardiovascular disease prevention. Discussion: We explore the benefits of the Bitemporal Lens Model, highlighting its capacity for personalized risk assessment through the integration of two distinct lenses. We also acknowledge challenges associated with handling intricate data across dual temporal dimensions, maintaining data integrity, and addressing ethical concerns pertaining to privacy and data protection. Conclusion: The Bitemporal Lens Model presents a novel approach to enhancing preventive healthcare effectiveness.

Immersive virtual reality-based learning as a supplement for biomedical engineering labs: challenges faced and lessons learned.

Introduction: Immersive virtual reality (VR) based laboratory demonstrations have been gaining traction in STEM education as they can provide virtual hands-on experience. VR can also facilitate experiential and visual learning and enhanced retention. However, several optimizations of the implementation, in-depth analyses of advantages and trade-offs of the technology, and assessment of receptivity of modern techniques in STEM education are required to ensure better utilization of VR-based labs. Methods: In this study, we developed VR-based demonstrations for a biomolecular engineering laboratory and assessed their effectiveness using surveys containing free responses and 5-point Likert scale-based questions. Insta360 Pro2 camera and Meta Quest 2 headsets were used in combination with an in-person lab. A cohort of 53 students watched the experimental demonstration on VR headsets in the lab after a brief lab overview in person and then performed the experiments in the lab. Results: Only 28.29% of students reported experiencing some form of discomfort after using the advanced VR equipment as opposed to 63.63% of students from the previous cohort. About 40% of the students reported that VR eliminated or reduced auditory and visual distractions from the environment, the length of the videos was appropriate, and they received enough information to understand the tasks. Discussion: The traditional lab method was found to be more suitable for explaining background information and lab concepts while the VR was found to be suitable for demonstrating lab procedures and tasks. Analyzing open-ended questions revealed several factors and recommendations to overcome the potential challenges and pitfalls of integrating VR with traditional modes of learning. This study provides key insights to help optimize the implementation of immersive VR to effectively supplement in-person learning experiences.

A systematic review and meta-analysis on sodium tanshinone IIA sulfonate injection for the adjunctive therapy of pulmonary heart disease.

AIMS: Sodium tanshinone IIA sulfonate (STS) injection has been widely used as adjunctive therapy for pulmonary heart disease (PHD) in China. Nevertheless, the efficacy of STS injection has not been systematically evaluated so far. Hence, the efficacy of STS injection as adjunctive therapy for PHD was explored in this study. METHODS: Randomized controlled trials (RCTs) were screened from China Science and Technology Journal Database, China National Knowledge Infrastructure, Wanfang Database, PubMed, Sino-Med, Google Scholar, Medline, Chinese Biomedical Literature Database, Cochrane Library, Embase and Chinese Science Citation Database until 20 January 2024. Literature searching, data collection and quality assessment were independently performed by two investigators. The extracted data was analyzed with RevMan 5.4 and STATA 14.0. Basing on the methodological quality, dosage of STS injection, control group measures and intervention time, sensitivity analysis and subgroup analysis were performed. RESULTS: 19 RCTs with 1739 patients were included in this study. Results showed that as adjunctive therapy, STS injection combined with Western medicine showed better therapeutic efficacy than Western medicine alone for PHD by increasing the clinical effective rate (RR = 1.22; 95% CI, 1.17 to 1.27; p < 0.001), partial pressure of oxygen (MD = 10.16; 95% CI, 5.07 to 15.24; p < 0.001), left ventricular ejection fraction (MD = 8.66; 95% CI, 6.14 to 11.18; p < 0.001) and stroke volume (MD = 13.10; 95% CI, 11.83 to 14.38; p < 0.001), meanwhile decreasing the low shear blood viscosity (MD = -1.16; 95% CI, -1.57 to -0.74; p < 0.001), high shear blood viscosity (MD = -0.64; 95% CI, -0.86 to -0.42; p < 0.001), plasma viscosity (MD = -0.23; 95% CI, -0.30 to -0.17; p < 0.001), hematokrit (MD = -8.52; 95% CI, -11.06 to -5.98; p < 0.001), fibrinogen (MD = -0.62; 95% CI, -0.87 to -0.37; p < 0.001) and partial pressure of carbon dioxide (MD = -8.56; 95% CI, -12.09 to -5.02; p < 0.001). CONCLUSION: STS injection as adjunctive therapy seemed to be more effective than Western medicine alone for PHD. However, due to low quality of the included RCTs, more well-designed RCTs were necessary to verify the efficacy of STS injection.

Evidence mapping of traditional Chinese medicine in diabetic peripheral neuropathy treatment.

Objective: Diabetic peripheral neuropathy (DPN) stands as a crucial complication of diabetes, significantly affecting patients' quality of life. This study aims to elucidate the evidence distribution from clinical randomized controlled trials (RCTs) on DPN treatment with traditional Chinese medicine (TCM) through evidence mapping. Methods: A comprehensive search was conducted from January 2017 to October 2022 in databases such as Wanfang (China Online Journals), CNKI (China National Knowledge Infrastructure), VIP (China Science and Technology Journal Database), SinoMed (Chinese Biomedical Literature Database), PubMed, Web of Science, and Cochrane Library. Literature related to the treatment of DPN with TCM was selected. From the 1,229 RCTs identified over the past 6 years, relevant data were extracted. The evidence mapping approach was utilized, and trends in publications, study scales, intervention types, and evaluation indicators were analyzed using descriptive text combined with tables and bubble charts. Results: Research on the treatment of DPN with TCM is extensive. The publication trend remains relatively stable with predominantly smaller sample sizes. The main treatments encompass oral Chinese medicine and traditional external treatments. The most common evaluation indicators are neurophysiological, efficiency rate, symptom signs, neuropathy scores, and traditional Chinese symptoms, with less focus on psychological status and the ankle-brachial index (ABI). Conclusion: Shedding light on contemporary research, this study explores the current RCTs evaluating TCM's efficacy in treating DPN. The findings not only highlight the potential role of TCM in addressing diabetic complications but also underscore areas that could benefit from refined research approaches, expanded intervention methods, and broader assessment criteria. Our observations aim to inform and inspire future research directions and clinical practices concerning TCM's role in managing diabetes-associated complications.

Assessment of Reader Technologies for Over-the-Counter Diagnostic Testing.

Background: Over-the-counter (OTC) diagnostic testing is on the rise with many in vitro diagnostic tests being lateral flow assays (LFAs). A growing number of these are adopting reader technologies, which provides an alternative to visual readouts for results interpretation, allowing for improved accessibility of OTC diagnostics. As the reader technology market develops, there are many technologies entering the market, but no clear, single solution has yet been identified. The purpose of this research is to identify and discuss important parameters for the assessment of LFA reader technologies for consideration by manufacturers or researchers. Methods: As part of The National Institute of Biomedical Imaging and Bioengineering's Rapid Acceleration of Diagnostics (RADx) Tech program, reader manufacturers were interviewed to investigate the current state of reader technology development through several parameters identified as important industry standards. Readers were categorized by technology type and parameters including cost, detection method, multiplex capabilities, assay type, maturity, and use case were all assessed. Results: Fifteen reader manufacturers were identified and interviewed, and information on a total of 19 technologies was assessed. Reader technology type was found to be predictive of other attributes, whether the reader is smart technology only, a standalone reader, a reader with smart technology required, or a reader with smart technology optional. Conclusions: Pairing reader technology with OTC diagnostic tests is important for improving existing COVID-19 tests and can be utilized in other diagnostics as the OTC use case grows in popularity. Reader technology type, which is predictive of core reader attributes, should be considered when selecting a reader technology for a specific LFA test within the context of regulatory guidance. As diagnostics increase in complexity, readers provide solutions to accessibility challenges, facilitate public health reporting, and ease the transition to multiplex testing, therefore increasing market availability.

Institutional Priority-Setting for Novel Drugs and Therapeutics: A Qualitative Systematic Review.

BACKGROUND: There is a lack of guidance on approaches to formulary management and funding for high-cost drugs and therapeutics by individual healthcare institutions. The objective of this review was to assess institutional approaches to resource allocation for such therapeutics, with a particular focus on paediatric and rare disease populations. METHODS: A search of Embase and MEDLINE was conducted for studies relevant to decision-making for off-formulary, high-cost drugs and therapeutics. Abstracts were evaluated for inclusion based on the Simple Multiple-Attribute Rating Techniques (SMART) criteria. A framework of 30 topics across 4 categories was used to guide data extraction and was based on findings from the initial abstract review and previous health technology assessment (HTA) publications. Reflexive thematic analysis was conducted using QSR NVivo 12 software. RESULTS: A total of 168 studies were included for analysis. Only 4 (2%) focused on paediatrics, while 21 (12%) centred on adults and the remainder (85%) did not specify. Thirty-two (19%) studies discussed the importance of high-cost therapeutics and 34 (23%) focused on rare/orphan drugs. Five themes were identified as being relevant to institutional decision-making for high-cost therapeutics: institutional strategy, substantive criteria, procedural considerations, guiding principles and frameworks, and operational activities. Each of these themes encompassed several sub-themes and was complemented by a sixth category specific to paediatrics and rare diseases. CONCLUSION: The rising cost of novel drugs and therapeutics underscores the need for robust, evidence-based, and ethically defensible decision-making processes for health technology funding, particularly at the hospital level. Our study highlights practices and themes to aid decision-makers in thinking critically about institutional, substantive, procedural, and operational considerations in support of legitimate decisions about institutional funding of high-cost drugs and therapeutics, as well as opportunities and challenges that exist for paediatric and rare disease populations.

Design of a multiple criteria decision analysis framework for prioritizing high-impact health technologies in a regional health service.

OBJECTIVES: This study aims to develop a framework for establishing priorities in the regional health service of Murcia, Spain, to facilitate the creation of a comprehensive multiple criteria decision analysis (MCDA) framework. This framework will aid in decision-making processes related to the assessment, reimbursement, and utilization of high-impact health technologies. METHOD: Based on the results of a review of existing frameworks for MCDA of health technologies, a set of criteria was proposed to be used in the context of evaluating high-impact health technologies. Key stakeholders within regional healthcare services, including clinical leaders and management personnel, participated in a focus group (n = 11) to discuss the proposed criteria and select the final fifteen. To elicit the weights of the criteria, two surveys were administered, one to a small sample of healthcare professionals (n = 35) and another to a larger representative sample of the general population (n = 494). RESULTS: The responses obtained from health professionals in the weighting procedure exhibited greater consistency compared to those provided by the general public. The criteria more highly weighted were "Need for intervention" and "Intervention outcomes." The weights finally assigned to each item in the multicriteria framework were derived as the equal-weighted sum of the mean weights from the two samples. CONCLUSIONS: A multi-attribute function capable of generating a composite measure (multicriteria) to assess the value of high-impact health interventions has been developed. Furthermore, it is recommended to pilot this procedure in a specific decision context to evaluate the efficacy, feasibility, usefulness, and reliability of the proposed tool.

Give Due Consideration .

The new EU Regulation on health technology assessment (HTAR) provides for joint clinical assessments (JCA) of health technologies at EU level. When Member States carry out health technology assessments (HTA) at the national level, they shall give due consideration to the results of a JCA and comply with other obligations of the Regulation. This article aims to clarify what these obligations mean for the Member States and whether JCA results have to be considered outside a national health technology assessment as well. In this context, the question of which processes qualify as 'national HTA' and which requirements need to be fulfilled to trigger the obligations under Article 13 HTAR are discussed in more detail in this paper.

Application of natural language processing to predict final recommendation of Brazilian health technology assessment reports.

INTRODUCTION: Health technology assessment (HTA) plays a vital role in healthcare decision-making globally, necessitating the identification of key factors impacting evaluation outcomes due to the significant workload faced by HTA agencies. OBJECTIVES: The aim of this study was to predict the approval status of evaluations conducted by the Brazilian Committee for Health Technology Incorporation (CONITEC) using natural language processing (NLP). METHODS: Data encompassing CONITEC's official report summaries from 2012 to 2022. Textual data was tokenized for NLP analysis. Least Absolute Shrinkage and Selection Operator, logistic regression, support vector machine, random forest, neural network, and extreme gradient boosting (XGBoost), were evaluated for accuracy, area under the receiver operating characteristic curve (ROC AUC) score, precision, and recall. Cluster analysis using the k-modes algorithm categorized entries into two clusters (approved, rejected). RESULTS: The neural network model exhibited the highest accuracy metrics (precision at 0.815, accuracy at 0.769, ROC AUC at 0.871, and recall at 0.746), followed by XGBoost model. The lexical analysis uncovered linguistic markers, like references to international HTA agencies' experiences and government as demandant, potentially influencing CONITEC's decisions. Cluster and XGBoost analyses emphasized that approved evaluations mainly concerned drug assessments, often government-initiated, while non-approved ones frequently evaluated drugs, with the industry as the requester. CONCLUSIONS: NLP model can predict health technology incorporation outcomes, opening avenues for future research using HTA reports from other agencies. This model has the potential to enhance HTA system efficiency by offering initial insights and decision-making criteria, thereby benefiting healthcare experts.

Enzyme-Responsive Fluorescent Labeling Strategy for In Vivo Imaging of Gut Bacteria.

Myrosinase (Myr), as a unique ß-thioglucosidase enzyme capable of converting natural and gut bacterial metabolite glucosinolates into bioactive agents, has recently attracted a great deal of attention because of its essential functions in exerting homeostasis dynamics and promoting human health. Such nutraceutical and biomedical significance demands unique and reliable strategies for specific identification of Myr enzymes of gut bacterial origin in living systems, whereas the dearth of methods for bacterial Myr detection and visualization remains a challenging concern. Herein, we present a series of unique molecular probes for specific identification and imaging of Myr-expressing gut bacterial strains. Typically, an artificial glucosinolate with an azide group in aglycone was synthesized and sequentially linked with the probe moieties of versatile channels through simple click conjugation. Upon gut bacterial enzymatic cleavage, the as-prepared probe molecules could be converted into reactive isothiocyanate forms, which can further act as reactive electrophiles for the covalent labeling of gut bacteria, thus realizing their localized fluorescent imaging within a wide range of wavelength channels in live bacterial strains and animal models. Overall, our proposed method presents a novel technology for selective gut bacterial Myr enzyme labeling in vitro and in vivo. We envision that such a rational probe design would serve as a promising solution for chemoprevention assessment, microflora metabolic mechanistic study, and gut bacterium-mediated physiopathological exploration.

Synthesis of relevant information around non-core domains to support Multi-Criteria Decision Analysis (MCDA) for decision making.

Countries fundamentally base macro and micro decision making in the field of health on economic considerations, the budgetary impact of technologies being a major criterion. Nevertheless, the value of the technology of interest and its dimensions are more complex if we seek to take decisions based on the value itself. The use of structured and explicit approaches that require the assessment of multiple criteria that reflect the dimensions of this value may significantly improve the quality of the decision making. Multi-criteria decision analysis (MCDA) is a complementary decision-making tool that is able to systematically incorporate dimensions or domains such as ethical, organisational, legal, environmental and social considerations, as well as costs and benefits of medical interventions, together with the distinct perspectives of the interested parties. The objective of this article is to propose the implementation of analysis of non-core domains, in reports of Health Technology Assessment (HTA) agencies/units. To assess the scientific evidence on MCDA techniques a systematic review was conducted using structured searches in biomedical databases and websites of various HTA organisations. A consensus group was held using the nominal group technique and involving users of healthcare services, providers, managers and academics. Complementary, a survey was sent to HTA agencies to ascertain the degree of implementation of MCDA in their methods. 42 articles reporting the use of non-core criteria for the assessment of health technologies were included in the analysis. From these articles, a total of 216 non-core criteria were retrieved and categorised into domains by the researchers, and of these, 56 were classified as socioeconomic, 59 as organisational, 10 as legal, 8 as environmental and 47 as ethical, while 36 were considered to relate to other domains. The consensus group, based on the 216 non-core criteria obtained from the systematic review, proposed, and defined 26 criteria that participants considered necessary for decision making in healthcare. The consensus group did not consider that any of the domains should be given more weight than others or that any individual criteria should dominate. These approaches can serve as a framework of reference for a well-structured systematic discussion concerning the basis of individual criteria and the evidence supporting them.

Upper-level inter-disciplinary microbiology CUREs increase student's scientific self-efficacy, scientific identity, and self-assessed skills.

Course-based undergraduate research experiences (CUREs) provide opportunities for undergraduate students to engage in authentic research and generally increase the participation rate of students in research. Students' participation in research has a positive impact on their science identity and self-efficacy, both of which can predict integration of students in Science, Technology, Engineering, and Math (STEM), especially for underrepresented students. The main goal of this study was to investigate instructor-initiated CUREs implemented as upper-level elective courses in the Biomedical Sciences major. We hypothesized that these CUREs would (i) have a positive impact on students' scientific identity and self-efficacy and (ii) result in gains in students' self-assessed skills in laboratory science, research, and science communication. We used Likert-type surveys developed by Estrada et al. (14) under the Tripartite Integration Model of Social Influence to measure scientific identity, self-efficacy, and scientific value orientation. When data from all CUREs were combined, our results indicate that students' self-efficacy and science identity significantly increased after completion. Students' self-assessment of research and lab-related skills was significantly higher after completion of the CUREs. We also observed that prior to participation in the CUREs, students' self-assessment of molecular and bioinformatic skills was low, when compared with microbiological skills. This may indicate strengths and gaps in our curriculum that could be explored further.

Challenges and Opportunities in Interdisciplinary Research and Real-World Data for Treatment Sequences in Health Technology Assessments.

With an ever-increasing number of treatment options, the assessment of treatment sequences has become crucial in health technology assessment (HTA). This review systematically explores the multifaceted challenges inherent in evaluating sequences, delving into their interplay and nuances that go beyond economic model structures. We synthesised a 'roadmap' of literature from key methodological studies, highlighting the evolution of recent advances and emerging research themes. These insights were compared against HTA guidelines to identify potential avenues for future research. Our findings reveal a spectrum of challenges in sequence evaluation, encompassing selecting appropriate decision-analytic modelling approaches and comparators, deriving appropriate clinical effectiveness evidence in the face of data scarcity, scrutinising effectiveness assumptions and statistical adjustments, considering treatment displacement, and optimising model computations. Integrating methodologies from diverse disciplines-statistics, epidemiology, causal inference, operational research and computer science-has demonstrated promise in addressing these challenges. An updated review of application studies is warranted to provide detailed insights into the extent and manner in which these methodologies have been implemented. Data scarcity on the effectiveness of treatment sequences emerged as a dominant concern, especially because treatment sequences are rarely compared in clinical trials. Real-world data (RWD) provide an alternative means for capturing evidence on effectiveness and future research should prioritise harnessing causal inference methods, particularly Target Trial Emulation, to evaluate treatment sequence effectiveness using RWD. This approach is also adaptable for analysing trials harbouring sequencing information and adjusting indirect comparisons when collating evidence from heterogeneous sources. Such investigative efforts could lend support to reviews of HTA recommendations and contribute to synthesising external control arms involving treatment sequences.

Image testing based on biomedical drug loading in cardiovascular drug care simulation for coronary heart disease.

Coronary heart disease is a common cardiovascular disease, and its therapeutic effect is affected by the distribution and absorption of drugs in the body. Biomedical drug-carrying image testing technology can provide a quantitative assessment of drug distribution and absorption in the body. This study aims to explore the application of biomedical drug-carrying image testing technology in the simulation of cardiovascular drug care in coronary heart disease, so as to provide reference for the optimization of drug treatment plan and individualized treatment. The study collected clinical data and medication regiments of patients with coronary heart disease. Then, the imaging examination of patients was carried out by selecting appropriate drug loading markers using the biomedical drug loading image examination technology. Then quantitative analysis was used to process the image data to quantitatively evaluate the distribution and absorption of drugs in the cardiovascular system. The quantitative data of drug distribution and absorption in patients with coronary heart disease have been obtained successfully by means of biomedical imaging. These data reveal the dynamic changes of drugs in the cardiovascular system, and help doctors optimize drug therapy, improve treatment effectiveness, and achieve personalized treatment.

The impact of willingness-to-pay threshold on price reduction recommendations for oncology drugs: a review of assessments conducted by the Canadian Agency for Drugs and Technologies in Health.

Since late 2020, the Canadian Agency of Drugs and Technologies in Health (CADTH) has been using a threshold of $50,000 (CAD) per quality-adjusted life-year (QALY) for both oncology and non-oncology drugs. When used for oncology products, this threshold is hypothesized to have a higher impact on the time to access these drugs in Canada. We studied the impact of price reductions on time to engagement and negotiation with the pan-Canadian Pharmaceutical Alliance for oncology drugs reviewed by CADTH between January 2020 and December 2022. Overall, 103 assessments reported data on price reductions recommended by CADTH to meet the cost-effectiveness threshold for reimbursement. Of these assessments, 57% (59/103) recommendations included a price reduction of greater than 70% off the list price. Eight percent (8/103) were not cost-effective even at a 100% price reduction. Of the 47 assessments that had a clear benefit, in 21 (45%) CADTH recommended a price reduction of at least 70%. The median time to price negotiation (not including time to engagement) for assessments that received at least 70% vs >70% price reduction was 2.6 vs 4.8 months. This study showed that there is a divergence between drug sponsor's incremental cost-effectiveness ratio (ICER) and CADTH revised ICER leading to a price reduction to meet the $50,000/QALY threshold. For the submissions with clear clinical benefit the median length of engagement (2.5 vs 3.3 months) and median length of negotiation (3.1 vs 3.6 months) were slightly shorter compared with the submissions where uncertainties were noted in the clinical benefit according to CADTH. This study shows that using a $50,000 per QALY threshold for oncology products potentially impacts timely access to life saving medications.

High-precision sensor for glucose solution using active multidimensional feature THz spectroscopy.

Terahertz waves are known for their bio-safety and spectral fingerprinting features, and terahertz spectroscopy technology holds great potential for both qualitative and quantitative identification in the biomedical field. There has been a substantial amount of research utilizing this technology in conjunction with machine learning algorithms for substance identification. However, due to the strong absorption of water for terahertz waves, the single-dimensional features of the sample become indistinct, thereby diminishing the efficiency of the algorithmic recognition. Building upon this, we propose a method that employs terahertz time-domain spectroscopy (THz-TDS) in conjunction with multidimensional feature spectrum identification for the detection of blood sugar and glucose mixtures. Our research indicates that combining THz-TDS with multidimensional feature spectrum and linear discriminant analysis (LDA) algorithms can effectively identify glucose concentrations and detect adulteration. By integrating the multidimensional feature spectrum, the identification success rate increased from 68.9% to 96.0%. This method offers an economical, rapid, and safe alternative to traditional methods and can be applied in blood sugar monitoring, sweetness assessment, and food safety.

Photon-counting CT in Thoracic Imaging: Early Clinical Evidence and Incorporation Into Clinical Practice.

Photon-counting CT (PCCT) is an emerging advanced CT technology that differs from conventional CT in its ability to directly convert incident x-ray photon energies into electrical signals. The detector design also permits substantial improvements in spatial resolution and radiation dose efficiency and allows for concurrent high-pitch and high-temporal-resolution multienergy imaging. This review summarizes (a) key differences in PCCT image acquisition and image reconstruction compared with conventional CT; (b) early evidence for the clinical benefit of PCCT for high-spatial-resolution diagnostic tasks in thoracic imaging, such as assessment of airway and parenchymal diseases, as well as benefits of high-pitch and multienergy scanning; (c) anticipated radiation dose reduction, depending on the diagnostic task, and increased utility for routine low-dose thoracic CT imaging; (d) adaptations for thoracic imaging in children; (e) potential for further quantitation of thoracic diseases; and (f) limitations and trade-offs. Moreover, important points for conducting and interpreting clinical studies examining the benefit of PCCT relative to conventional CT and integration of PCCT systems into multivendor, multispecialty radiology practices are discussed.

Predicting the onset of end-stage knee osteoarthritis over two- and five-years using machine learning.

OBJECTIVE: Identifying participants who will progress to advanced stage in knee osteoarthritis (KOA) trials remains a significant challenge. Current tools, relying on total knee replacements (TKR), fall short in reliability due to the extraneous factors influencing TKR decisions. Acknowledging these limitations, our study identifies a critical need for a more robust metric to assess severe KOA. The end-stage KOA (esKOA) measure, which combines symptomatic and radiographic criteria, serves as a solid indicator. To enhance future trials that use esKOA as an endpoint, our study focuses on developing and validating a machine-learning tool to identify individuals likely to develop esKOA within 2 to 5 years. DESIGN: Utilizing the Osteoarthritis Initiative (OAI) data, we trained models on 3,114 participants and validated them with 606 participants for the right knee, and similarly for the left knee, with external validation from the Multicentre Osteoarthritis Study (MOST) involving 1,602 participants. We aimed to predict esKOA onset at 2-to-2.5 years and 4-to-5 years, defining esKOA by severe radiographic KOA with moderate/severe symptoms or mild/moderate radiographic KOA with persistent/intense symptoms. Our analysis considered 51 candidate predictors, including demographics, clinical history, physical examination, and X-ray evaluations. An online tool predicting esKOA progression, based on models with ten and nine predictors for the right and left knees, respectively, was developed. RESULTS: External validation (MOST) for the right knee at 2.5 years yielded an Area Under Curve (AUC) of 0.847 (95 % CI 0.811 to 0.882), and at 5 years, 0.853 (95 % CI 0.823 to 0.881); for the left knee at 2.5 years, AUC was 0.824 (95 % CI 0.782 to 0.857), and at 5 years, 0.807 (95 % CI 0.768 to 0.843). Models with fewer predictors demonstrated comparable performance. The online tool is available at: https://eskoa.shinyapps.io/webapp/. CONCLUSION: Our study unveils a robust, externally validated machine learning tool proficient in predicting the onset of esKOA over the next 2 to 5 years. Our tool can lead to more efficient KOA trials.

The Relationship Between Adverse Childhood Experiences and Postpartum Depression: A Systematic Review and Meta-Analysis.

Although numerous factors have been found to influence postpartum depression (PPD), no previous meta-analysis have systematically explored whether it is affected by adverse childhood experiences (ACEs). This study aimed to explore the influence of ACEs and their subtypes on PPD. A systematic literature search was conducted using Web of Science, PubMed, Embase, Wan Fang, China Science and Technology Journal Database, Chinese Biomedical Database, and China National Knowledge Infrastructure, and literature was screened according to inclusion and exclusion criteria. Methodological quality assessment and data extraction were performed on the included studies. A random-effects model was used to pool the effects. In total, 24 studies were included, and 73 independent effects were extracted from them. The meta-analysis revealed that ACE was a risk factor for PPD (odds ratio [OR] = 2.31, 95% confidence interval [CI] [2.04, 2.63]). The subgroup analysis results showed that emotional abuse was the ACE subtype most strongly related to the occurrence of PPD (OR = 2.95, 95% CI [2.08, 4.20]), followed by emotional neglect (OR = 2.87, 95% CI [1.89, 4.36]) and sexual abuse (OR = 2.81, 95% CI [1.93, 4.09]). In addition, family member incarceration (OR = 2.62, 95% CI [1.51, 4.54]), physical abuse (OR = 2.31, 95% CI [1.67, 3.19]), and physical neglect (OR = 2.15, 95% CI [1.36, 3.39]) also have strong effects on PPD. ACE is a risk factor for PPD. Early screening of ACE plays an important role in the prevention and intervention of PPD.

Leveraging Generative AI Tools to Support the Development of Digital Solutions in Health Care Research: Case Study.

BACKGROUND: Generative artificial intelligence has the potential to revolutionize health technology product development by improving coding quality, efficiency, documentation, quality assessment and review, and troubleshooting. OBJECTIVE: This paper explores the application of a commercially available generative artificial intelligence tool (ChatGPT) to the development of a digital health behavior change intervention designed to support patient engagement in a commercial digital diabetes prevention program. METHODS: We examined the capacity, advantages, and limitations of ChatGPT to support digital product idea conceptualization, intervention content development, and the software engineering process, including software requirement generation, software design, and code production. In total, 11 evaluators, each with at least 10 years of experience in fields of study ranging from medicine and implementation science to computer science, participated in the output review process (ChatGPT vs human-generated output). All had familiarity or prior exposure to the original personalized automatic messaging system intervention. The evaluators rated the ChatGPT-produced outputs in terms of understandability, usability, novelty, relevance, completeness, and efficiency. RESULTS: Most metrics received positive scores. We identified that ChatGPT can (1) support developers to achieve high-quality products faster and (2) facilitate nontechnical communication and system understanding between technical and nontechnical team members around the development goal of rapid and easy-to-build computational solutions for medical technologies. CONCLUSIONS: ChatGPT can serve as a usable facilitator for researchers engaging in the software development life cycle, from product conceptualization to feature identification and user story development to code generation. TRIAL REGISTRATION: ClinicalTrials.gov NCT04049500; https://clinicaltrials.gov/ct2/show/NCT04049500.