Real-world data: a brief review of the methods, applications, challenges and opportunities

Comparing Machine Learning and Advanced Methods with Traditional Methods to Generate Weights in Inverse Probability of Treatment Weighting: The INFORM Study

Purpose

Observational research provides valuable insights into treatments used in patient populations in real-world settings. However, confounding is likely to occur if there are differences in patient characteristics associated with both the exposure and outcome between the groups being evaluated. One approach to reduce confounding and facilitate unbiased comparisons is inverse probability of treatment weighting (IPTW) using propensity scores. Machine learning (ML) and entropy balancing can potentially be used in generating propensity scores for IPTW, but there is limited literature on this application. We aimed to assess the feasibility of applying these methods for reducing confounding in observational studies. These methods were assessed in a study comparing cardiovascular outcomes in adults with type 2 diabetes and established atherosclerotic cardiovascular disease taking once-weekly glucagon-like peptide-1 receptor agonists or dipeptidyl peptidase-4 inhibitors.

Methods

We applied advanced methods to generate the propensity scores compared to the original logistic regression method in terms of covariate balance. After calculating weights, a weighted Cox proportional hazards model was used to calculate the sample average treatment effect. Support Vector Classification, Support Vector Regression, XGBoost, and LightGBM were the ML models used. Entropy balancing was also performed on features identified in the original cardiovascular outcomes study.

Results

Accuracy (range: 0.71 to 0.73), area under the curve (0.77 to 0.79), precision (0.53 to 0.60), recall (0.66 to 0.68), and F1 score (0.60 to 0.64) were similar between all of the advanced propensity score methods and traditional logistic regression. Among ML models, only XGBoost achieved balance in all measured baseline characteristics between the two treatment groups, closely approximating the performance of the original logistic regression. Entropy balancing weights provided the best performance among all models in balancing baseline characteristics, achieving near perfect balancing.

Conclusion

Among the advanced methods examined, entropy balancing weights performed the best for optimizing balancing and can produce similar results compared to traditional logistic regression.

FOUNTAIN: a modular research platform for integrated real-world evidence generation

BACKGROUND

Real-world evidence (RWE) plays a key role in regulatory and healthcare decision-making, but the potentially fragmentated nature of generated evidence may limit its utility for clinical decision-making. Heterogeneity and a lack of reproducibility in RWE resulting from inconsistent application of methodologies across data sources should be minimized through harmonization.

METHODS

This paper's aim is to describe and reflect upon a multidisciplinary research platform (FOUNTAIN; FinerenOne mUlti-database NeTwork for evidence generAtIoN) with coordinated studies using diverse RWE generation approaches and explore the platform's strengths and limitations. With guidance from an executive advisory committee of multidisciplinary experts and patient representatives, the goal of the FOUNTAIN platform is to harmonize RWE generation across a portfolio of research projects, including research partner collaborations and a common data model (CDM)-based program. FOUNTAIN's overarching objectives as a research platform are to establish long-term collaborations among pharmacoepidemiology research partners and experts and to integrate diverse approaches for RWE generation, including global protocol execution by research partners in local data sources and common protocol execution in multiple data sources through federated data networks, while ensuring harmonization of medical definitions, methodology, and reproducible artifacts across all studies. Specifically, the aim of the multiple studies run within the frame of FOUNTAIN is to provide insight into the real-world utilization, effectiveness, and safety of finerenone across its life-cycle.

RESULTS

Currently, the FOUNTAIN platform includes 9 research partner collaborations and 8 CDM-mapped data sources from 7 countries (United States, United Kingdom, China, Japan, The Netherlands, Spain, and Denmark). These databases and research partners were selected after a feasibility fit-for-purpose evaluation. Six multicountry, multidatabase, cohort studies are ongoing to describe patient populations, current standard of care, comorbidity profiles, healthcare resource use, and treatment effectiveness and safety in different patient populations with chronic kidney disease and type 2 diabetes. Strengths and potential limitations of FOUNTAIN are described in the context of valid RWE generation.

CONCLUSION

The establishment of the FOUNTAIN platform has allowed harmonized execution of multiple studies, promoting consistency both within individual studies that employ multiple data sources and across all studies run within the platform's framework. FOUNTAIN presents a proposal to efficiently improve the consistency and generalizability of RWE on finerenone.

Data Science Opportunities To Improve Radiotherapy Planning and Clinical Decision Making

Radiotherapy aims to achieve a high tumor control probability while minimizing damage to normal tissues. Personalizing radiotherapy treatments for individual patients, therefore, depends on integrating physical treatment planning with predictive models of tumor control and normal tissue complications. Predictive models could be improved using a wide range of rich data sources, including tumor and normal tissue genomics, radiomics, and dosiomics. Deep learning will drive improvements in classifying normal tissue tolerance, predicting intra-treatment tumor changes, tracking accumulated dose distributions, and quantifying the tumor response to radiotherapy based on imaging. Mechanistic patient-specific computer simulations ('digital twins') could also be used to guide adaptive radiotherapy. Overall, we are entering an era where improved modeling methods will allow the use of newly available data sources to better guide radiotherapy treatments.

Insulin resistance, clinical presentation and resistance to selective serotonin and noradrenaline reuptake inhibitors in major depressive disorder

BACKGROUND

The understanding of mechanisms underlying non-response to antidepressants is limited. The latest data highlights the role of insulin resistance (IR) in major depressive disorder (MDD) pathophysiology, presentation, and treatment efficacy. This work aimed to assess IR in MDD and explore the relationships between IR, MDD presentation and non-response to selective serotonin and noradrenaline reuptake inhibitors (SNRI).

METHODS

67 MDD individuals: 36 responsive (MDD T[+]), 31 non-responsive (MDD T[-]) to SNRI and 30 healthy controls were recruited. The treatment response criteria were: Clinical Global Impression Scale-Improvement score of 1 or 2 after ≥ 8 weeks of treatment. Participants were assessed by physician and self-report tools measuring depression, anhedonia, anxiety, bipolarity, sleep quality. Blood samples were collected to assess fasting glucose and insulin levels and calculate HOMA-IR (homeostasis model assessment of insulin resistance).

RESULTS

MDD T[-] vs. MDD T[+] had significantly higher body mass index, insulin levels, and HOMA-IR. MDD T[-] presented higher levels of depressed mood, appetite/weight changes, loss of interest, energy, overall depressive symptoms, and sleep impairment; some evaluations suggested higher anhedonia and anxiety in MDD T[-] vs. MDD T[+]. Insulin and IR were weakly but significantly correlated with the severity of psychomotor symptoms, energy level, thoughts of death/suicide, self-criticism, appetite/weight, depressed mood symptoms, sleep problems. IR was weakly but significantly correlated with anhedonia.

CONCLUSION

IR appears to be linked to depressive symptoms characteristic of the "metabolic" MDD subtype, such as psychomotor changes, energy level, anhedonia, sleep problems, appetite/weight changes, state and trait anxiety, sleep quality, and non-response to SNRI.

Maintenance of zilucoplan efficacy in patients with generalised myasthenia gravis up to 24 weeks: a model-informed analysis

Background

Clinical efficacy of zilucoplan has been demonstrated in a 12-week, placebo-controlled, phase III study in patients with acetylcholine receptor autoantibody-positive generalised myasthenia gravis (gMG). However, placebo-controlled zilucoplan data past 12 weeks are not available.

Objectives

Predict the treatment effect of zilucoplan versus control (placebo or standard of care) in patients with gMG up to 24 weeks.

Design

A model-informed analysis (MIA) within a Bayesian framework.

Methods

Part 1 of the MIA comprised a control meta-regression using aggregate data on control response over time from randomised studies and a national myasthenia gravis (MG) registry. In Part 2, a combined Bayesian analysis of individual patient-level data from the phase II (NCT03315130), RAISE (NCT04115293) and RAISE-XT (NCT04225871) studies of zilucoplan was conducted using posterior distributions from Part 1 as informative priors. Population mean treatment effect in the change from baseline (CFB) at week 24 in MG-Activities of Daily Living (MG-ADL) and quantitative MG (QMG) scores for zilucoplan versus control were assessed.

Results

At week 24, the predicted mean CFB in MG-ADL score was -4.55 (95% credible interval: -6.04, -3.13) with zilucoplan versus -2.00 (-3.35, -0.64) with control (difference: -2.55 [-3.76, -1.40]). The probability of a favourable treatment effect as measured by MG-ADL score at week 24 with zilucoplan versus control was >99.9%. There was an 82.8% probability that the difference in the predicted mean CFB in MG-ADL score at week 24 was greater than the clinically meaningful threshold (⩾2.0-point improvement). Comparable results were observed with QMG.

Conclusion

This MIA demonstrates the maintenance of efficacy with zilucoplan versus control up to 24 weeks. Through combining real-world evidence with data from randomised studies, this novel method to estimate long-term treatment efficacy facilitated reduced exposure to placebo in the phase III RAISE study. This methodology could be used to reduce the length of future placebo-controlled studies.

Proxy endpoints - bridging clinical trials and real world data

OBJECTIVE

Disease severity scores, or endpoints, are routinely measured during Randomized Controlled Trials (RCTs) to closely monitor the effect of treatment. In real-world clinical practice, although a larger set of patients is observed, the specific RCT endpoints are often not captured, which makes it hard to utilize real-world data (RWD) to evaluate drug efficacy in larger populations.

METHODS

To overcome this challenge, we developed an ensemble technique which learns proxy models of disease endpoints in RWD. Using a multi-stage learning framework applied to RCT data, we first identify features considered significant drivers of disease available within RWD. To create endpoint proxy models, we use Explainable Boosting Machines (EBMs) which allow for both end-user interpretability and modeling of non-linear relationships.

RESULTS

We demonstrate our approach on two diseases, rheumatoid arthritis (RA) and atopic dermatitis (AD). As we show, our combined feature selection and prediction method achieves good results for both disease areas, improving upon prior methods proposed for predictive disease severity scoring.

CONCLUSION

Having disease severity over time for a patient is important to further disease understanding and management. Our results open the door to more use cases in the space of RA and AD such as treatment effect estimates or prognostic scoring on RWD. Our framework may be extended beyond RA and AD to other diseases where the severity score is not well measured in electronic health records.

Utility analysis and demonstration of real-world clinical texts: A case study on Japanese cancer-related EHRs

Real-world data (RWD) in the medical field, such as electronic health records (EHRs) and medication orders, are receiving increasing attention from researchers and practitioners. While structured data have played a vital role thus far, unstructured data represented by text (e.g., discharge summaries) are not effectively utilized because of the difficulty in extracting medical information. We evaluated the information gained by supplementing structured data with clinical concepts extracted from unstructured text by leveraging natural language processing techniques. Using a machine learning-based pretrained named entity recognition tool, we extracted disease and medication names from real discharge summaries in a Japanese hospital and linked them to medical concepts using medical term dictionaries. By comparing the diseases and medications mentioned in the text with medical codes in tabular diagnosis records, we found that: (1) the text data contained richer information on patient symptoms than tabular diagnosis records, whereas the medication-order table stored more injection data than text. In addition, (2) extractable information regarding specific diseases showed surprisingly small intersections among text, diagnosis records, and medication orders. Text data can thus be a useful supplement for RWD mining, which is further demonstrated by (3) our practical application system for drug safety evaluation, which exhaustively visualizes suspicious adverse drug effects caused by the simultaneous use of anticancer drug pairs. We conclude that proper use of textual information extraction can lead to better outcomes in medical RWD mining.

Cluster analysis of adult individuals with type 1 diabetes: Treatment pathways and complications over a five-year follow-up period

AIMS

To identify subgroups of adults with type 1 diabetes and analyse their treatment pathways and risk of diabetes-related complications over a 5-year follow-up.

METHODS

We performed a k-means cluster analysis using the T1DExchange Registry (n = 6,302) to identify subgroups based on demographic and clinical characteristics. Annual reassessments linked treatment trajectories with these clusters, considering drug and technology use. Complication risks were analysed using Cox regression.

RESULTS

Five clusters were identified: 1) A favourable combination of all variables (31.67 %); 2) Longer diabetes duration (22.63 %); 3) Higher HbA1c levels (13.28 %); 4) Higher BMI (15.25 %); 5) Older age at diagnosis (17.17 %). Two-thirds of patients remained in their initial cluster annually. Technology adoption showed improved glycaemic control over time. Cox proportional hazards showed different risk patterns: Cluster 1 had low complication risk; Cluster 2 had the highest risk for retinopathy, coronary artery disease and autonomic neuropathy; Cluster 3 had the highest risk for albuminuria, depression and diabetic ketoacidosis; Cluster 4 had increased risk for multiple complications; Cluster 5 had the highest risk for hypertension and severe hypoglycaemia, with elevated coronary artery disease risk.

CONCLUSIONS

Clinical characteristics can identify subgroups of patients with T1DM showing differences in treatment and complications during follow-up.

Effect of Nusinersen on Respiratory and Bulbar Function in Children with Spinal Muscular Atrophy: Real-World Experience from a Single Center

BACKGROUND

 Due to the limited data from clinical trials and real-world settings in the realm of nusinersen, there is a need for further evidence. This study seeks to assess the impact of nusinersen, when combined with standard care, on bulbar function, respiratory function, and the necessity for respiratory support among pediatric patients with spinal muscular atrophy (SMA).

METHODS

 Prospective observational study, involving pediatric SMA patients (Types 1-3) undergoing nusinersen treatment at the Hospital Universitario Virgen del Rocío in Spain over at least 24 months. The cohort included 11 SMA type 1 patients, comprising 6 type 1b and 5 type 1c, 12 SMA type 2 patients, and 5 SMA type 3 patients.

RESULTS

 Twenty-eight pediatric patients were enrolled with the majority being male (n = 20). Patients with type 1 were diagnosed and received treatment significantly earlier than those with types 2 and 3 (p < 0.001). Additionally, there was a longer period between diagnosis and the start of treatment in types 2 and 3 (p = 0.002). Follow-up revealed statistically improved functional and respiratory outcomes associated with earlier initiation of nusinersen treatment at 6, 12, and 24 months in all phenotypes. The ability to swallow and feed correctly remained unchanged throughout the study, with SMA type 1c patients maintaining oral feeding in contrast to patients with SMA type 1b. Notably, no deaths were recorded.

CONCLUSIONS

 This study provides important insights into the real-world clinical progress of pediatric SMA patients and their response to nusinersen treatment, highlighting the significance of early intervention for better functional and respiratory outcomes.

Data Quality-Driven Improvement in Health Care: Systematic Literature Review

BACKGROUND

The promise of real-world evidence and the learning health care system primarily depends on access to high-quality data. Despite widespread awareness of the prevalence and potential impacts of poor data quality (DQ), best practices for its assessment and improvement are unknown.

OBJECTIVE

This review aims to investigate how existing research studies define, assess, and improve the quality of structured real-world health care data.

METHODS

A systematic literature search of studies in the English language was implemented in the Embase and PubMed databases to select studies that specifically aimed to measure and improve the quality of structured real-world data within any clinical setting. The time frame for the analysis was from January 1945 to June 2023. We standardized DQ concepts according to the Data Management Association (DAMA) DQ framework to enable comparison between studies. After screening and filtering by 2 independent authors, we identified 39 relevant articles reporting DQ improvement initiatives.

RESULTS

The studies were characterized by considerable heterogeneity in settings and approaches to DQ assessment and improvement. Affiliated institutions were from 18 different countries and 18 different health domains. DQ assessment methods were largely manual and targeted completeness and 1 other DQ dimension. Use of DQ frameworks was limited to the Weiskopf and Weng (3/6, 50%) or Kahn harmonized model (3/6, 50%). Use of standardized methodologies to design and implement quality improvement was lacking, but mainly included plan-do-study-act (PDSA) or define-measure-analyze-improve-control (DMAIC) cycles. Most studies reported DQ improvements using multiple interventions, which included either DQ reporting and personalized feedback (24/39, 61%), IT-related solutions (21/39, 54%), training (17/39, 44%), improvements in workflows (5/39, 13%), or data cleaning (3/39, 8%). Most studies reported improvements in DQ through a combination of these interventions. Statistical methods were used to determine significance of treatment effect (22/39, 56% times), but only 1 study implemented a randomized controlled study design. Variability in study designs, approaches to delivering interventions, and reporting DQ changes hindered a robust meta-analysis of treatment effects.

CONCLUSIONS

There is an urgent need for standardized guidelines in DQ improvement research to enable comparison and effective synthesis of lessons learned. Frameworks such as PDSA learning cycles and the DAMA DQ framework can facilitate this unmet need. In addition, DQ improvement studies can also benefit from prioritizing root cause analysis of DQ issues to ensure the most appropriate intervention is implemented, thereby ensuring long-term, sustainable improvement. Despite the rise in DQ improvement studies in the last decade, significant heterogeneity in methodologies and reporting remains a challenge. Adopting standardized frameworks for DQ assessment, analysis, and improvement can enhance the effectiveness, comparability, and generalizability of DQ improvement initiatives.

Next-generation sequencing in oncology: challenges in economic evaluations

INTRODUCTION

Next-generation sequencing (NGS) identifies genetic variants to inform personalized treatment plans. Insufficient evidence of cost-effectiveness impedes the integration of NGS into routine cancer care. The complexity of personalized treatment challenges conventional economic evaluation. Clearly delineating challenges informs future cost-effectiveness analyses to better value and contextualize health, preference-, and equity-based outcomes.

AREAS COVERED

We conducted a scoping review to characterize the applied methods and outcomes of economic evaluations of NGS in oncology and identify existing challenges. We included 27 articles published since 2016 from a search of PubMed, Embase, and Web of Science. Identified challenges included defining the evaluative scope, managing evidentiary limitations including lack of causal evidence, incorporating preference-based utility, and assessing distributional and equity-based impacts. These challenges reflect the difficulty of generating high-quality clinical effectiveness and real-world evidence (RWE) for NGS-guided interventions.

EXPERT OPINION

Adapting methodological approaches and developing life-cycle health technology assessment (HTA) guidance using RWE is crucial for implementing NGS in oncology. Healthcare systems, decision-makers, and HTA organizations are facing a pivotal opportunity to adapt to an evolving clinical paradigm and create innovative regulatory and reimbursement processes that will enable more sustainable, equitable, and patient-oriented healthcare.

Time from diagnosis to treatment has no impact on survival in newly diagnosed acute myeloid leukemia treated with venetoclax-based regimens

In newly diagnosed acute myeloid leukemia (AML), immediate initiation of treatment is standard of care. However, deferral of antileukemic therapy may be indicated to assess comorbidities or pretherapeutic risk factors. We explored the impact of time from diagnosis to treatment on outcomes in newly diagnosed AML undergoing venetoclax-based therapy in two distinct cohorts. By querying the Study Alliance Leukemia database and the global health network TriNetX, we identified 138 and 717 patients respectively with an average age of 76 and 72 years who received venetoclax-based first-line therapy. When comparing patients who started treatment earlier or later than 10 days after initial diagnosis, no significant difference in median overall survival was observed - neither in the SAL cohort (7.7 vs. 9.6 months; P=0.42) nor in the TriNetX cohort (7.5 vs. 7.2 months; P=0.41). Similarly, severe infections, bleeding, and thromboembolic events were equally observed between early and later treatments, both in the overall patient groups and specific subgroups (age ≥75 years or leukocytes ≥20x109/L). This retrospective analysis indicates that delaying the start of venetoclax-based therapy in newly diagnosed AML might be a safe option for selected patients, provided that close clinical monitoring is performed.

Data Science Methods for Real-World Evidence Generation in Real-World Data

In the healthcare landscape, data science (DS) methods have emerged as indispensable tools to harness real-world data (RWD) from various data sources such as electronic health records, claim and registry data, and data gathered from digital health technologies. Real-world evidence (RWE) generated from RWD empowers researchers, clinicians, and policymakers with a more comprehensive understanding of real-world patient outcomes. Nevertheless, persistent challenges in RWD (e.g., messiness, voluminousness, heterogeneity, multimodality) and a growing awareness of the need for trustworthy and reliable RWE demand innovative, robust, and valid DS methods for analyzing RWD. In this article, I review some common current DS methods for extracting RWE and valuable insights from complex and diverse RWD. This article encompasses the entire RWE-generation pipeline, from study design with RWD to data preprocessing, exploratory analysis, methods for analyzing RWD, and trustworthiness and reliability guarantees, along with data ethics considerations and open-source tools. This review, tailored for an audience that may not be experts in DS, aspires to offer a systematic review of DS methods and assists readers in selecting suitable DS methods and enhancing the process of RWE generation for addressing their specific challenges.

Incidence of Infections, Explantations, and Displacements/Mechanical Complications of Spinal Cord Stimulation During the Past Eight Years

OBJECTIVES

The overall awareness and potential of real-world data have drastically increased in the medical field, with potential implications for postmarket medical device surveillance. The goal of this study was to evaluate real-world data on incidence of infections, explantations, and displacements/mechanical complications of spinal cord stimulation (SCS) during the past eight years and to forecast point estimates for the upcoming three years on the basis of the identified patterns.

MATERIALS AND METHODS

Based on electronic health records from 80 healthcare organizations within the TriNetX data base in the USA, data of 11,934 patients who received SCS as treatment for persistent spinal pain syndrome type 2 (PSPS T2) were extracted. Events of interest were explantations and displacements/mechanical complications of both the lead and implanted pulse generator (IPG), in addition to infection rates from 2015 to 2022. Mann-Kendall tests were performed to detect monotonic trends in the time series. Forecasts were conducted for the upcoming three years for every event of interest.

RESULTS

Statistically significant increasing time trends were revealed for the annual incidence of IPG and lead displacements/mechanical complications in patients with PSPS T2 over the past eight years. These time trends were visible in both male and female patients and in smokers and nonsmokers. For annual incidence of explantations and infections, no significant time effect was observed. In 2025, the incidence of displacements/mechanical complications of the lead (3.07%) is predicted to be the highest, followed by explantations of the IPG (2.67%) and lead (2.02%).

CONCLUSIONS

Based on real world data, device explantation was the most frequent event of interest, with negative peaks in the time series in 2016 and 2020, presumably due to the introduction of rechargeable pulse generators and to the COVID-19 pandemic, respectively.

Model-informed drug development in pediatric, pregnancy and geriatric drug development: States of the art and future

The challenges of drug development in pediatric, pregnant and geriatric populations are a worldwide concern shared by regulatory authorities, pharmaceutical companies, and healthcare professionals. Model-informed drug development (MIDD) can integrate and quantify real-world data of physiology, pharmacology, and disease processes by using modeling and simulation techniques to facilitate decision-making in drug development. In this article, we reviewed current MIDD policy updates, reflected on the integrity of physiological data used for MIDD and the effects of physiological changes on the drug PK, as well as summarized current MIDD strategies and applications, so as to present the state of the art of MIDD in pediatric, pregnant and geriatric populations. Some considerations are put forth for the future improvements of MIDD including refining regulatory considerations, improving the integrity of physiological data, applying the emerging technologies, and exploring the application of MIDD in new therapies like gene therapies for special populations.

Mortality and cardiovascular events in diabetes mellitus patients at dialysis initiation treated with glucagon-like peptide-1 receptor agonists

BACKGROUND

Glucagon-like Peptide-1 Receptor Agonists (GLP-1RAs) have demonstrated efficacy in improving mortality and cardiovascular (CV) outcomes. However, the impact of GLP-1RAs therapy on cardiorenal outcomes of diabetic patients at the commencement of dialysis remains unexplored.

PURPOSE

This study aimed to investigate the long-term benefits of GLP-1RAs in type 2 diabetic patients at dialysis commencement.

METHODS

A cohort of type 2 diabetic patients initializing dialysis was identified from the TriNetX global database. Patients treated with GLP-1RAs and those treated with long-acting insulin (LAI) were matched by propensity score. We focused on all-cause mortality, four-point major adverse cardiovascular events (4p-MACE), and major adverse kidney events (MAKE).

RESULTS

Among 82,041 type 2 diabetic patients initializing dialysis, 2.1% (n = 1685) patients were GLP-1RAs users (mean ages 59.3 years; 55.4% male). 1682 patients were included in the propensity-matched group, treated either with GLP-1RAs or LAI. The main causes of acute dialysis in this study were ischemic heart disease (17.2%), followed by heart failure (13.6%) and sepsis (6.5%). Following a median follow-up of 1.4 years, GLP-1RAs uses at dialysis commencement was associated with a reduced risk of mortality (hazard ratio [HR] = 0.63, p < 0.001), 4p-MACE (HR = 0.65, p < 0.001), and MAKE (HR = 0.75, p < 0.001). This association was particularly notable in long-acting GLP-1RAs users, with higher BMI, lower HbA1c, and those with eGFR > 15 ml/min/1.73m2. GLP-1RAs' new use at dialysis commencement was significantly associated with a lower risk of MACE (p = 0.047) and MAKE (p = 0.004). Additionally, GLP-1RAs use among those who could discontinue from acute dialysis or long-term RAs users was associated with a lower risk of mortality, 4p-MACE, and MAKE.

CONCLUSION

Given to the limitations of this observational study, use of GLP-1RAs at the onset of dialysis was associated with a decreased risk of MACE, MAKE, and all-cause mortality. These findings show the lack of harm associated with the use of GLP-1RAs in diabetic patients at the initiation of acute dialysis.

Surgical treatment of liver cancer and pancreatic cancer under the China Healthcare Security Diagnosis Related Groups payment system

BACKGROUND

Data from the World Health Organization's International Agency for Research on Cancer reported that China had the highest prevalence of cancer and cancer deaths in 2022. Liver and pancreatic cancers accounted for the highest number of new cases. Real-world data (RWD) is now widely preferred to traditional clinical trials in various fields of medicine and healthcare, as the traditional research approach often involves highly selected populations and interventions and controls that are strictly regulated. Additionally, research results from the RWD match global reality better than those from traditional clinical trials.

AIM

To analyze the cost disparity between surgical treatments for liver and pancreatic cancer under various factors.

METHODS

This study analyzed RWD 1137 cases within the HB1 group (patients who underwent pancreatectomy, hepatectomy, and/or shunt surgery) in 2023. It distinguished different expenditure categories, including medical, nursing, technical, management, drug, and consumable costs. Additionally, it assessed the contribution of each expenditure category to total hospital costs and performed cross-group comparisons using the non-parametric Kruskal-Wallis test. This study used the Steel-Dwass test for post-hoc multiple comparisons and the Spearman correlation coefficient to examine the relationships between variables.

RESULTS

The study found that in HB11 and HB13, the total hospitalization costs were significantly higher for pancreaticoduodenectomy than for pancreatectomy and hepatectomy. Although no significant difference was observed in the length of hospital stay between patients who underwent pancreaticoduodenectomy and pancreatectomy, both were significantly longer than those who underwent liver resection. In HB15, no significant difference was observed in the total cost of hospitalization between pancreaticoduodenectomy and pancreatectomy; however, both were significantly higher than those in hepatectomy. Additionally, the length of hospital stay was significantly longer for patients who underwent pancreaticoduodenectomy than for those who underwent pancreatectomy or liver resection.

CONCLUSION

China Healthcare Security Diagnosis Related Groups payment system positively impacts liver and pancreatic cancer surgeries by improving medical quality and controlling costs. Further research could refine this grouping system and ensure continuous effectiveness and sustainability.

What public health challenges and unmet medical needs would benefit from interdisciplinary collaboration in the EU? A survey and multi-stakeholder debate

In the past decade, significant European calls for research proposals have supported translational collaborative research on non-communicable and infectious diseases within the biomedical life sciences by bringing together interdisciplinary and multinational consortia. This research has advanced our understanding of disease pathophysiology, marking considerable scientific progress. Yet, it is crucial to retrospectively evaluate these efforts' societal impact. Research proposals should be thoughtfully designed to ensure that the research findings can be effectively translated into actionable policies. In addition, the choice of scientific methods plays a pivotal role in shaping the societal impact of research discoveries. Understanding the factors responsible for current unmet public health issues and medical needs is crucial for crafting innovative strategies for research policy interventions. A multistakeholder survey and a roundtable helped identify potential needs for consideration in the EU research and policy agenda. Based on survey findings, mental health disorders, metabolic syndrome, cancer, antimicrobial resistance, environmental pollution, and cardiovascular diseases were considered the public health challenges deserving prioritisation. In addition, early diagnosis, primary prevention, the impact of environmental pollution on disease onset and personalised medicine approaches were the most selected unmet medical needs. Survey findings enabled the formulation of some research-policies interventions (RPIs), which were further discussed during a multistakeholder online roundtable. The discussion underscored recent EU-level activities aligned with the survey-derived RPIs and facilitated an exchange of perspectives on public health and biomedical research topics ripe for interdisciplinary collaboration and warranting attention within the EU's research and policy agenda. Actionable recommendations aimed at facilitating the translation of knowledge into transformative, science-based policies are also provided.

Real-World Research on Retinal Diseases Using Health Claims Database: A Narrative Review

Real-world data (RWD) has emerged as a crucial component in understanding and improving patient outcomes across various medical conditions, including retinal diseases. Health claims databases, generated from healthcare reimbursement claims, offer a comprehensive source of RWD, providing insights into patient outcomes, healthcare utilization, and treatment effectiveness. However, the use of these databases for research also presents unique challenges. This narrative review explores the role of real-world research on retinal diseases using health claims databases, highlighting their advantages, limitations, and potential contributions to advancing our understanding and management of the diseases. The review examines the applications of health claims databases in retinal disease research, including epidemiological studies, comparative effectiveness and safety analyses, economic burden assessments, and evaluations of patient outcomes and quality of care. Previous findings demonstrate the value of these databases in generating prevalence and incidence estimates, identifying risk factors and predictors, evaluating treatment effectiveness and safety, and understanding healthcare utilization patterns and costs associated with retinal diseases. Despite their strengths, health claims databases face challenges related to data limitations, biases, privacy concerns, and methodological issues. Accordingly, the review also explores future directions and opportunities, including advancements in data collection and analysis, integration with electronic health records, collaborative research networks and consortia, and the evolving regulatory landscape. These developments are expected to enhance the utility of health claims databases for retinal disease research, resulting in more comprehensive and impactful findings across diverse retinal disorders and robust real-world insights from a large population.

UK Stakeholder Perspectives on Surrogate Endpoints in Cancer, and the Potential for UK Real-World Datasets to Validate Their Use in Decision-Making

Duration of overall survival in patients with cancer has lengthened due to earlier detection and improved treatments. However, these improvements have created challenges in assessing the impact of newer treatments, particularly those used early in the treatment pathway. As overall survival remains most decision-makers' preferred primary endpoint, therapeutic innovations may take a long time to be introduced into clinical practice. Moreover, it is difficult to extrapolate findings to heterogeneous populations and address the concerns of patients wishing to evaluate everyday quality and extension of life. There is growing interest in the use of surrogate or interim endpoints to demonstrate robust treatment effects sooner than is possible with measurement of overall survival. It is hoped that they could speed up patients' access to new drugs, combinations, and sequences, and inform treatment decision-making. However, while surrogate endpoints have been used by regulators for drug approvals, this has occurred on a case-by-case basis. Evidence standards are yet to be clearly defined for acceptability in health technology appraisals or to shape clinical practice. This article considers the relevance of the use of surrogate endpoints in cancer in the UK context, and explores whether collection and analysis of real-world UK data and evidence might contribute to validation.

Methods to advance health equity and social justice in healthcare: Protocol for a scoping review on the utilisation of routinely collected data

BACKGROUND

Many areas of healthcare are impacted by a paucity of research that is translatable to clinical practice. Research utilising real-world data, such as routinely collected patient data, may be one option to efficiently create evidence to inform practice and service delivery. Such studies are also valuable for exploring (in)equity of services and outcomes, and benefit from using non-selected samples representing the diversity of the populations served in the 'real world'. This scoping review aims to identify and map the published research which utilises routinely collected clinical healthcare data. A secondary aim is to explore the extent to which this literature supports the pursuit of social justice in health, including health inequities and intersectional approaches.

METHOD

This review utilises Arksey and O'Malley's methodological framework for scoping reviews and draws on the recommended enhancements of this framework to promote a team-based and mixed methods approach. This includes searching electronic databases and screening papers based on a pre-specified inclusion and exclusion criteria. Data relevant to the research aims will be extracted from included papers, including the clinical/professional area of the topic, the source of data that was used, and whether it addresses elements of social justice. All screening and reviewing will be collaborative and iterative, drawing on strengths of the research team and responsive changes to challenges will be made. Quantitative data will be analysed descriptively, and conceptual content analysis will be utilised to understand qualitative data. These will be collectively synthesised in alignment to the research aims.

CONCLUSION

Our findings will highlight the extent to which such research is being conducted and published, including gaps and make recommendations for future endeavours for real-world data studies. The findings from this scoping review will be relevant for practitioners and researchers, as well as health service managers, commissioners, and research funders.

Tailoring the treatment of inflammatory rheumatic diseases by a better stratification and characterization of the clinical patient heterogeneity. Findings from a systematic literature review and experts' consensus

Inflammatory rheumatic diseases are different pathologic conditions associated with a deregulated immune response, codified along a spectrum of disorders, with autoinflammatory and autoimmune diseases as two-end phenotypes of this continuum. Despite pathogenic differences, inflammatory rheumatic diseases are commonly managed with a limited number of immunosuppressive drugs, sometimes with partial evidence or transferring physicians' knowledge in different patients. In addition, several randomized clinical trials, enrolling these patients, did not meet the primary pre-established outcomes and these findings could be linked to the underlying molecular diversities along the spectrum of inflammatory rheumatic disorders. In fact, the resulting patient heterogeneity may be driven by differences in underlying molecular pathology also resulting in variable responses to immunosuppressive drugs. Thus, the identification of different clinical subsets may possibly overcome the major obstacles that limit the development more effective therapeutic strategies for these patients with inflammatory rheumatic diseases. This clinical heterogeneity could require a diverse therapeutic management to improve patient outcomes and increase the frequency of clinical remission. Therefore, the importance of better patient stratification and characterization is increasingly pointed out according to the precision medicine principles, also suggesting a new approach for disease treatment. In fact, based on a better proposed patient profiling, clinicians could more appropriately balance the therapeutic management. On these bases, we synthetized and discussed the available literature about the patient profiling in regard to therapy in the context of inflammatory rheumatic diseases, mainly focusing on randomized clinical trials. We provided an overview of the importance of a better stratification and characterization of the clinical heterogeneity of patients with inflammatory rheumatic diseases identifying this point as crucial in improving the management of these patients.

Development of quality assessment tool for systematic reviews and meta-analyses of real-world studies: a Delphi consensus survey

The increasing adoption of real-world studies in healthcare for decision making and planning has further necessitated the need for a specific quality assessment tool for evidence synthesis. This study aimed to develop a quality assessment tool for systematic reviews (SR) and meta-analysis (MA) involving real-world studies (QATSM-RWS) using a formal consensus method. Based on scoping review, the authors identified a list of items for possible inclusion in the quality assessment tool. A Delphi survey was formulated based on the identified items. A total of 89 experts, purposively recruited, with research experience in real-world data were invited to participate in the first round of Delphi survey. The participants who responded in the first Delphi round were invited to participate (n = 15) in the phrasing of the items. Strong level of agreement was found on the proposed list of items after the first round of Delphi. A rate of agreement ≥ 0.70 was used to define which items to keep in the tool. A list of 14 items emerged as suitable for QATSM-RWS. The items were structured under five domains: introduction, methods, results, discussions, and others. All participants agreed with the proposed phrasing of the items. This is the first study that has developed a specific tool that can be used to appraise the quality of SR and MA involving real-world studies. QATSM-RWS may be used by policymakers, clinicians, and practitioners when evaluating and generating real-world evidence. This tool is now undergoing validation process.

Harnessing EHR data for health research

With the increasing availability of rich, longitudinal, real-world clinical data recorded in electronic health records (EHRs) for millions of patients, there is a growing interest in leveraging these records to improve the understanding of human health and disease and translate these insights into clinical applications. However, there is also a need to consider the limitations of these data due to various biases and to understand the impact of missing information. Recognizing and addressing these limitations can inform the design and interpretation of EHR-based informatics studies that avoid confusing or incorrect conclusions, particularly when applied to population or precision medicine. Here we discuss key considerations in the design, implementation and interpretation of EHR-based informatics studies, drawing from examples in the literature across hypothesis generation, hypothesis testing and machine learning applications. We outline the growing opportunities for EHR-based informatics studies, including association studies and predictive modeling, enabled by evolving AI capabilities-while addressing limitations and potential pitfalls to avoid.

Strengths, weaknesses, opportunities, and threats of using AI-enabled technology in sleep medicine: a commentary

Over the past few years, artificial intelligence (AI) has emerged as a powerful tool used to efficiently automate several tasks across multiple domains. Sleep medicine is perfectly positioned to leverage this tool due to the wealth of physiological signals obtained through sleep studies or sleep tracking devices and abundance of accessible clinical data through electronic medical records. However, caution must be applied when utilizing AI, due to intrinsic challenges associated with novel technology. The Artificial Intelligence in Sleep Medicine Committee of the American Academy of Sleep Medicine reviews advancements in AI within the sleep medicine field. In this article, the Artificial Intelligence in Sleep Medicine committee members provide a commentary on the scope of AI technology in sleep medicine. The commentary identifies 3 pivotal areas in sleep medicine that can benefit from AI technologies: clinical care, lifestyle management, and population health management. This article provides a detailed analysis of the strengths, weaknesses, opportunities, and threats associated with using AI-enabled technologies in each pivotal area. Finally, the article broadly reviews barriers and challenges associated with using AI-enabled technologies and offers possible solutions.

CITATION

Bandyopadhyay A, Oks M, Sun H, et al. Strengths, weaknesses, opportunities, and threats of using AI-enabled technology in sleep medicine: a commentary. J Clin Sleep Med. 2024;20(7):1183-1191.

Generalizability in real-world trials

Real-world evidence (RWE) trials have a key advantage over conventional randomized controlled trials (RCTs) due to their potentially better generalizability. High generalizability of study results facilitates new biological insights and enables targeted therapeutic strategies. Random sampling of RWE trial participants is regarded as the gold standard for generalizability. Additionally, the use of sample correction procedures can increase the generalizability of trial results, even when using nonrandomly sampled real-world data (RWD). This study presents descriptive evidence on the extent to which the design of currently planned or already conducted RWE trials takes sampling into account. It also examines whether random sampling or procedures for correcting nonrandom samples are considered. Based on text mining of publicly available metadata provided during registrations of RWE trials on clinicaltrials.gov, EU-PAS, and the OSF-RWE registry, it is shown that the share of RWE trial registrations with information on sampling increased from 65.27% in 2002 to 97.43% in 2022, with a corresponding increase from 14.79% to 28.30% for trials with random samples. For RWE trials with nonrandom samples, there is an increase from 0.00% to 0.95% of trials in which sample correction procedures are used. We conclude that the potential benefits of RWD in terms of generalizing trial results are not yet being fully realized.

Ethical considerations for real-world evidence studies

Real-world evidence (RWE) studies are conducted on patient's data primarily collected for monitoring of health status of patients. The use of real-world data to generate evidence in academic research or for regulatory submission raises a variety of ethical issues such as privacy, confidentiality, data protection, data de-identification, data sharing, scientific design of study, and informed consent requirements. The investigators-researchers and sponsors should adhere to current standards of ethics whilst planning and conduct of RWE studies. The ethics committees should consider ethical issues specific to RWE studies before approval.

May the 4th be with you: mixed-methods best-evidence synthesis on 4th-generation alumina-zirconia ceramic bearings in total hip arthroplasty

Purpose

To assess utility, benefits, and risks of 4th-generation alumina-zirconia ceramic pairings in elective total hip arthroplasty (THA).

Methods

A comprehensive mixed-methods best-evidence synthesis using data from systematic reviews, randomized controlled trials (RCTs), prospective and retrospective cohort studies, as well as joint replacement registries, was conducted to estimate overall revision and survival rates, periprosthetic infection, bearing fractures, and noise phenomena with 4th-generation alumina-zirconia ceramic versus other tribological couplings in elective THA. The systematic review part across multiple databases was registered with PROSPERO (CRD42023418076), and individual study data were extracted for statistical re-analysis.

Results

Twenty overlapping systematic reviews, 7, 17, and 8 references from RCTs, cohort studies, and joint replacement registries form the basis of this work. According to current best evidence, it is (i) 15-33 times more likely that 4th-generation alumina-zirconia pairings avoid a revision for infection than causing a revision for audible noise, (ii) 38-85 times more likely that 4th-generation alumina-zirconia pairings avoid a revision for infection than causing a revision for ceramic head fractures, and (iii) three to six times more likely that 4th-generation alumina-zirconia pairings avoid a revision for infection than cause a revision for ceramic liner fractures.

Conclusion

Fourth-generation alumina-zirconia pairings in THA show a favorable benefit-risk ratio, with rare compound-specific adverse events and complications significantly outbalanced by long-term advantages, such as a markedly lower incidence of revision for infection.

Sex- and operation-dependent effects on 5-year weight loss results of bariatric surgery

BACKGROUND

Weight loss response after bariatric surgery is highly variable, and several demographic factors are associated with differential responses to surgery. Preclinical studies demonstrate numerous sex-specific responses to bariatric surgery, but whether these responses are also operation dependent is unknown.

OBJECTIVE

To examine sex-specific weight loss outcomes up to 5 years after laparoscopic Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG).

SETTING

Single center, university, United States.

METHODS

Retrospective, observational cohort study including RYGB (n = 5057) and vertical SG (n = 2041) patients from a single, academic health center. Percentage total weight loss (TWL) over time was examined with generalized linear mixed models to determine the main and interaction effects of surgery type on weight loss by sex.

RESULTS

TWL demonstrated a strong sex-by-procedure interaction, with women having a significant advantage with RYGB compared with SG (adjusted difference at 5 yr: 8.0% [95% CI: 7.5-8.5]; P < .001). Men also experienced greater TWL over time with RYGB or SG, but the difference was less and clinically insignificant (adjusted difference at 5 yr: 2.9% [2.0-3.8]; P < .001; P interaction between sex and procedure type = .0001). Overall, women had greater TWL than men, and RYGB patients had greater TWL than SG patients (adjusted difference at 5 yr: 3.1% [2.4-3.2] and 6.9% [6.5-7.3], respectively; both P < .0001). Patients with diabetes lost less weight compared with those without (adjusted difference at 5 yr: 3.0% [2.7-3.2]; P < .0001).

CONCLUSIONS

Weight loss after bariatric surgery is sex- and procedure-dependent. There is an association suggesting a clinically insignificant difference in weight loss between RYGB and SG among male patients at both the 2- and 5-year postsurgery time points.

The Role of Artificial Intelligence in Nutrition Research: A Scoping Review

Artificial intelligence (AI) refers to computer systems doing tasks that usually need human intelligence. AI is constantly changing and is revolutionizing the healthcare field, including nutrition. This review's purpose is four-fold: (i) to investigate AI's role in nutrition research; (ii) to identify areas in nutrition using AI; (iii) to understand AI's future potential impact; (iv) to investigate possible concerns about AI's use in nutrition research. Eight databases were searched: PubMed, Web of Science, EBSCO, Agricola, Scopus, IEEE Explore, Google Scholar and Cochrane. A total of 1737 articles were retrieved, of which 22 were included in the review. Article screening phases included duplicates elimination, title-abstract selection, full-text review, and quality assessment. The key findings indicated AI's role in nutrition is at a developmental stage, focusing mainly on dietary assessment and less on malnutrition prediction, lifestyle interventions, and diet-related diseases comprehension. Clinical research is needed to determine AI's intervention efficacy. The ethics of AI use, a main concern, remains unresolved and needs to be considered for collateral damage prevention to certain populations. The studies' heterogeneity in this review limited the focus on specific nutritional areas. Future research should prioritize specialized reviews in nutrition and dieting for a deeper understanding of AI's potential in human nutrition.

Streamlining intersectoral provision of real-world health data: a service platform for improved clinical research and patient care

Introduction

Obtaining real-world data from routine clinical care is of growing interest for scientific research and personalized medicine. Despite the abundance of medical data across various facilities - including hospitals, outpatient clinics, and physician practices - the intersectoral exchange of information remains largely hindered due to differences in data structure, content, and adherence to data protection regulations. In response to this challenge, the Medical Informatics Initiative (MII) was launched in Germany, focusing initially on university hospitals to foster the exchange and utilization of real-world data through the development of standardized methods and tools, including the creation of a common core dataset. Our aim, as part of the Medical Informatics Research Hub in Saxony (MiHUBx), is to extend the MII concepts to non-university healthcare providers in a more seamless manner to enable the exchange of real-world data among intersectoral medical sites.

Methods

We investigated what services are needed to facilitate the provision of harmonized real-world data for cross-site research. On this basis, we designed a Service Platform Prototype that hosts services for data harmonization, adhering to the globally recognized Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) international standard communication format and the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Leveraging these standards, we implemented additional services facilitating data utilization, exchange and analysis. Throughout the development phase, we collaborated with an interdisciplinary team of experts from the fields of system administration, software engineering and technology acceptance to ensure that the solution is sustainable and reusable in the long term.

Results

We have developed the pre-built packages "ResearchData-to-FHIR," "FHIR-to-OMOP," and "Addons," which provide the services for data harmonization and provision of project-related real-world data in both the FHIR MII Core dataset format (CDS) and the OMOP CDM format as well as utilization and a Service Platform Prototype to streamline data management and use.

Conclusion

Our development shows a possible approach to extend the MII concepts to non-university healthcare providers to enable cross-site research on real-world data. Our Service Platform Prototype can thus pave the way for intersectoral data sharing, federated analysis, and provision of SMART-on-FHIR applications to support clinical decision making.

Assessing the reach and safety of an innovative urgent care service tailored to older adults

Traditional emergency departments (EDs) are overcrowded and sometimes not suitable for older adults with complex needs. Specialised geriatric urgent care pathways for selected patients can alleviate ED demand and improve patient experience. To address urgent care needs for older adults in Southern Adelaide, the Complex And RestorativE (CARE) service was established. CARE offers alternative, geriatrician-led treatment pathways consisting of a ward-based treatment centre and an in-home visiting team called Eyes on Scene (EoS). Both pathways offer medical, nursing and allied health treatment. Routinely collected clinical data were analysed to explore the reach of the service and the incidence and nature of adverse events. Between September 2021 and March 2023, the CARE service attended to 5324 older adults requiring urgent care. A significant proportion of patients were discharged to or remained in their regular place of residence, with few requiring inpatient admissions or referrals to other facilities. A total of 7% of patients required transfer to ED and adverse events were rare. The CARE service demonstrates a feasible and safe alternative model of urgent care for older Australians.

Improving Diagnosis and Care for Patients With Sarcoma: Do Real-World General Practitioners Data and Prospective Data Collections Have a Place Next to Clinical Trials?

There has been growing interest in the use of real-world data (RWD) to address clinically and policy-relevant (research) questions that cannot be answered with data from randomized controlled trials (RCTs) alone. This is, for example, the case in rare malignancies such as sarcomas as limited patient numbers pose challenges in conducting RCTs within feasible timeliness, a manageable number of collaborators, and statistical power. This narrative review explores the potential of RWD to generate real-world evidence (RWE) in sarcoma research, elucidating its application across different phases of the patient journey, from prediagnosis to the follow-up/survivorship phase. For instance, examining electronic health records (EHRs) from general practitioners (GPs) enables the exploration of consultation frequency and presenting symptoms in primary care before a sarcoma diagnosis. In addition, alternative study designs that integrate RWD with well-designed observational RCTs may offer relevant information on the effectiveness of clinical treatments. As, especially in cases of ultrarare sarcomas, it can be an extreme challenge to perform well-powered randomized prospective studies. Therefore, it is crucial to support the adaptation of novel study designs. Regarding the follow-up/survivorship phase, examining EHR from primary and secondary care can provide valuable insights into identifying the short- and long-term effects of treatment over an extended follow-up period. The utilization of RWD also comes with several challenges, including issues related to data quality and privacy, as described in this study. Notwithstanding these challenges, this study underscores the potential of RWD to bridge, at least partially, gaps between evidence and practice and holds promise in contributing to the improvement of sarcoma care.

Enhancing data practices for Whole Health: Strategies for a transformative future

We explored the challenges and solutions for managing data within the Whole Health System (WHS), which operates as a Learning Health System and a patient-centered healthcare approach that combines conventional and complementary approaches. Addressing these challenges is critical for enhancing patient care and improving outcomes within WHS. The proposed solutions include prioritizing interoperability for seamless data exchange, incorporating patient-centered comparative clinical effectiveness research and real-world data to personalize treatment plans and validate integrative approaches, and leveraging advanced data analytics tools to incorporate patient-reported outcomes, objective metrics, robust data platforms. Implementing these measures will enable WHS to fulfill its mission as a holistic and patient-centered healthcare model, promoting greater collaboration among providers, boosting the well-being of patients and providers, and improving patient outcomes.

Wearable Sensors in Other Medical Domains with Application Potential for Orthopedic Trauma Surgery-A Narrative Review

The use of wearable technology is steadily increasing. In orthopedic trauma surgery, where the musculoskeletal system is directly affected, focus has been directed towards assessing aspects of physical functioning, activity behavior, and mobility/disability. This includes sensors and algorithms to monitor real-world walking speed, daily step counts, ground reaction forces, or range of motion. Several specific reviews have focused on this domain. In other medical fields, wearable sensors and algorithms to monitor digital biometrics have been used with a focus on domain-specific health aspects such as heart rate, sleep, blood oxygen saturation, or fall risk. This review explores the most common clinical and research use cases of wearable sensors in other medical domains and, from it, derives suggestions for the meaningful transfer and application in an orthopedic trauma context.

Data-Fusion-Based Quality Enhancement for HR Measurements Collected by Wearable Sensors

The advancements of Internet of Things (IoT) technologies have enabled the implementation of smart and wearable sensors, which can be employed to provide older adults with affordable and accessible continuous biophysiological status monitoring. The quality of such monitoring data, however, is unsatisfactory due to excessive noise induced by various disturbances, such as motion artifacts. Existing methods take advantage of summary statistics, such as mean or median values, for denoising, without taking into account the biophysiological patterns embedded in data. In this research, a functional data analysis modeling method was proposed to enhance the data quality by learning individual subjects' diurnal heart rate (HR) patterns from historical data, which were further improved by fusing newly collected data. This proposed data-fusion approach was developed based on a Bayesian inference framework. Its effectiveness was demonstrated in an HR analysis from a prospective study involving older adults residing in assisted living or home settings. The results indicate that it is imperative to conduct personalized healthcare by estimating individualized HR patterns. Furthermore, the proposed calibration method provides a more accurate (smaller mean errors) and more precise (smaller error standard deviations) HR estimation than raw HR and conventional methods, such as the mean.

Maximizing the Value of Real-World Data and Real-World Evidence to Accelerate Healthcare Transformation in China: Summary of External Advisory Committee Meetings

Use of real-world data (RWD) is gaining wide attention. To bridge the gap between diverse healthcare stakeholders and to leverage the impact of Chinese real-world evidence (RWE) globally, a multi-stakeholder External Advisory Committee (EAC) and EAC meetings were initiated, aiming to elucidate the current and evolving RWD landscape in China, articulate the values of RWE in ensuring Chinese patients' equitable access to affordable medicines and solutions, and identify strategic opportunities and partnerships for expansion of RWE generation in China. Chinese and international experts who are clinicians and academic researchers were selected as EAC members based on their professional background and familiarity with RWD/RWE. Three EAC meetings were held quarterly in 2023. Various topics were presented and discussed for insights and suggestions. Nine experts from China, one from South Korea, and two from Europe were selected as EAC members and attended these meetings. Experts' presentations were summarized by theme, including the RWD landscape and RWE enablement in China, as well as global development of a patient-centric ecosystem. Experts' insights and suggestions on maximizing the RWD/RWE value to accelerate healthcare transformation in China were collected. We concluded that though data access, sharing, and quality are still challenging, RWD is developing to support evidence generation in the medicinal product lifecycle, inform clinical practice, and empower patient management in China. RWD/RWE creates value, accelerates healthcare transformation, and improves patient outcomes. Fostering a patient-centric ecosystem across healthcare stakeholders and maintaining global partnerships and collaboration are essential for unlocking the power of RWD/RWE.

The Amager Project IV: suicidal behavior and aftercare: real world data (RWD) from a prospective observational project

BACKGROUND

In an RCT study, OPAC (outreach, problem solving, adherence, continuity) approach to aftercare after suicide attempts had an effect. The present study used the OPAC method in a clinical setting on Amager Copenhagen to patients after suicide attempt (Group 1) and patients with suicide ideation (Group 2) in a real-world data (RWD) study.

AIM

To study whether the OPAC method could provide real world evidence (RWE) for results from the RCT study and long-time prospects.

METHOD

This RWD study included 506 patients and followed them for 5 years. Kaplan-Meyer showed 5 years results. Risk factors for 5 years were calculated.

RESULTS

206 males (mean age 37.9) and 300 females (mean age 35.2) participated. A decline in survival accelerated after 3 years. After a 2-year follow-up, Group 1 had an attempted suicide rate of 12,2% and Group 2 5,4%. After 5 years the numbers were 18% and 10%. There were 3 completed suicides. Risk factors were: earlier suicide attempts, one or both parents or they themselves were alcohol/drug abusers, and a poor social network. Group 1 showed the same result as the intervention group in our earlier RCT study. Group 2 did better. Both groups did better than the control group from our RCT study.

CONCLUSION

The OPAC effect was translated into the daily clinic. Risk factors were previous suicide attempts, alcohol and drug abuse and poor social networks. More specific therapy is needed for some patients to prevent relapse. Focus on enhancing a sense of belongingness and/or treating substance abuse.

Optimal clinical practice in IPF and PPF: Integrating the scientific ethos and clinical reasoning

See related article

Clinical Care Pathway and Management of Major Bleeding Associated with Nonvitamin K Antagonist Oral Anticoagulants: A Modified Delphi Consensus from Saudi Arabia and UAE

Background

The nonvitamin K antagonist oral anticoagulants (NOACs) have become the mainstay anticoagulation therapy for patients requiring oral anticoagulants (OACs) in the Gulf Council Cooperation (GCC) countries. The frequency of NOAC-associated major bleeding is expected to increase in the Emergency Department (ED). Nonetheless, we still lack local guidelines and recommendations for bleeding management in the region. The present Delphi-based consensus aims to establish a standardized and evidence-based clinical care pathway for managing NOAC-associated major bleeding in the Kingdom of Saudi Arabia (KSA) and the United Arab Emirates (UAE).

Methods

We adopted a three-step modified Delphi method to develop evidence-based recommendations through two voting rounds and an advisory meeting between the two rounds. A panel of 11 experts from the KSA and UAE participated in the consensus development.

Results

Twenty-eight statements reached the consensus level. These statements addressed key aspects of managing major bleeding events associated with NOACs, including the increased use of NOAC in clinical practice, clinical care pathways, and treatment options.

Conclusion

The present Delphi consensus provides evidence-based recommendations and protocols for the management of NOAC-associated bleeding in the region. Patients with major DOAC-induced bleeding should be referred to a well-equipped ED with standardized management protocols. A multidisciplinary approach is recommended for establishing the association between NOAC use and major bleeding. Treating physicians should have prompt access to specific reversal agents to optimize patient outcomes. Real-world evidence and national guidelines are needed to aid all stakeholders involved in NOAC-induced bleeding management.

COVID-19 vaccine acceptance in the general population and under-resourced communities from high-income countries: realist review

OBJECTIVE

To compare vaccination willingness before rollout and 1 year post-rollout uptake among the general population and under-resourced communities in high-income countries.

DESIGN

A realist review.

DATA SOURCES

Embase, PubMed, Dimensions ai and Google Scholar.

SETTING

High-income countries.

DEFINITIONS

We defined vaccination willingness as the proportion of participants willing or intending to receive vaccines prior to availability. We defined vaccine uptake as the real proportion of the population with complete vaccination as reported by each country until November 2021.

RESULTS

We included data from 62 studies and 18 high-income countries. For studies conducted among general populations, the proportion of vaccination willingness was 67% (95% CI 62% to 72%). In real-world settings, the overall proportion of vaccine uptake among those countries was 73% (95% CI 69% to 76%). 17 studies reported pre-rollout willingness for under-resourced communities. The summary proportion of vaccination willingness from studies reporting results among people from under-resourced communities was 52% (95% CI 0.46% to 0.57%). Real-world evidence about vaccine uptake after rollout among under-resourced communities was limited.

CONCLUSION

Our review emphasises the importance of realist reviews for assessing vaccine acceptance. Limited real-world evidence about vaccine uptake among under-resourced communities in high-income countries is a call to context-specific actions and reporting.

Emergent Patterns, Collaborative Networks and Thematic Evolution on Black Stain and Dental Caries: A Scientometric Study

AIM

To conduct a scientometric analysis on black stains, and dental caries, with a focus on identifying emergent patterns, collaborative networks, and thematic evolution.

MATERIALS AND METHODS

A retrospective, descriptive, observational study was conducted using a scientometric approach. The Scopus database was utilized for literature search, selecting articles published between 1976 and 2023. The literature search was carried out on February 8, 2024, using AND and OR Boolean operators which were adapted to the search strategy. Finally, SciVal used different scientometric indicators such as "Scholarly Output", "Views Count", "Field-Weighted Citation Impact" and the "Citation Count".

RESULTS

A total of 13 documents from 10 different sources were analyzed, covering a period from 1976 to 2023. Despite an annual growth rate of 0%, each document has an average of 21.08 citations, indicating their relevance in the field. The documents have an average age of 12.9 years and contain 336 references. A total of 143 additional keywords and 26 author keywords were identified. The documents were written by 62 different authors, with an average of 5 coauthors per document and 30.77% international coauthorships.

CONCLUSION

The results indicate a significant relevance of the documents in the field, a notable collaboration among authors, and a significant thematic evolution in the research topics. The mentioned institutions and sources produced documents with notable results, indicating a significant interest and impact in the field.

CLINICAL SIGNIFICANCE

This study provides a detailed view of the trends and patterns in the research of dental caries and black stains. The findings can assist dental health professionals in better understanding the prevalence and impact of these conditions. Furthermore, it can guide future research and treatment strategies in this field. How to cite this article: Mayta-Tovalino F, Espinoza-Carhuancho F, Reyes-Carmona J, et al. Emergent Patterns, Collaborative Networks and Thematic Evolution on Black Stain and Dental Caries: A Scientometric Study. J Contemp Dent Pract 2024;25(4):326-330.

The emerging adulthood gap in diabetes prevention research

Emerging adulthood, the period between ages 18 and 25, is distinct from older and younger populations in terms of both physiology and social circumstances. As a critical developmental window with long-lasting repercussions, emerging adulthood presents a key opportunity for prevention of cardiovascular disease. Despite significant advances in diabetes and cardiovascular disease prevention for other age groups, 18-25 year-olds remain underrepresented in research. Using diabetes prevention as an example, we analyzed the cited evidence behind four major guidelines that influence US clinical practice on screening and management of prediabetes in children and adults, revealing that the majority of these studies in both the pediatric and adult literature do not include emerging adults in their study populations. This gap between the pediatric and adult diabetes prevention literature creates a missing link connecting childhood risks to adult chronic disease. In this article, we draw attention to this often overlooked age group, and provide tangible recommendations as a path forward for both pediatric and adult researchers to increase the representation of emerging adults in diabetes prevention and other cardiovascular disease prevention studies.

Artificial intelligence / machine-learning tool for post-market surveillance of in vitro diagnostic assays

The study compares an artificial intelligence technology with traditional manual search of literature databases to assess the accuracy and efficiency of retrieving relevant articles for post-market surveillance of in vitro diagnostic and medical devices under the Medical Device Regulation and In Vitro Diagnostic Medical Device Regulation. Over a 3-year period, literature searches and technical assessment searches were performed manually or using the Huma.AI platform to retrieve relevant articles related to the safety and performance of selected in vitro diagnostic and medical devices. The manual search involved refined keyword searches, screening of titles/abstracts / full text, and extraction of relevant information. The Huma.AI search utilized advanced caching techniques and a natural language processing system to identify relevant reports. Searches were conducted on PubMed and PubMed Central. The number of identified relevant reports, precision rates, and time requirements for each approach were analyzed. The Huma.AI system outperformed the manual search in terms of the number of identified relevant articles in almost all cases. The average precision rates per year were significantly higher and more consistent with the Huma.AI search compared with the manual search. The Huma.AI system also took significantly less time to perform the searches and analyze the outputs than the manual search. The study demonstrated that the Huma.AI platform was more effective and efficient in identifying relevant articles compared with the manual approach.

Celebrating the Data from 100,000 Real-World Users of the MiniMed™ 780G System in Europe, Middle East, and Africa Collected Over 3 Years: From Data to Clinical Evidence

Introduction: The present report celebrates the benchmarking of 100,000 MiniMed™ 780G system users in Europe, Middle East, and Africa (EMEA) and summarizes the major insights into the usability and outcomes of this system. Methods: Carelink Personal data (August 2020-August 2023) of users living in EMEA were analyzed. Continuous glucose monitoring-based endpoints were aggregated for (1) the full cohort and (2) a 12-month longitudinal cohort. Subanalyses were done for users on optimal settings (those spending ≥95% of time with glucose target of 100 mg/dL, and ≥95% of time with active insulin time of 2 h), for self-reported age groups (≤15 and ≥56 years) and for various countries/regions. Results: Data from 101,629 users (34 countries) were analyzed. Mean time in range (TIR) was 72.3%, glucose management indicator (GMI) was 7%, time below 70 mg/dL (TBR70) was 2.0% and time below 54 mg/dL (TBR54) was 0.4%. In terms of international targets, 59.6% of users achieved a GMI <7%, 62.5% a TIR >70%, 88.4% a TBR70 < 4%, and 90.0% a TBR54 < 1%. Data improved impressively in optimal setting users (TIR = 78.8%, and users reaching TIR >70% = 86.3%) while safety remained (TBR70 = 2.2% and TBR54 = 0.4%). Data showed consistency across self-reported age groups and geographies. In the longitudinal cohort, TIR reached 75.5% in the first month and remained 73.3% or higher over the 12-month period. Conclusion: Over 100,000 users of the MiniMed™ 780G system have demonstrated consistency in achieving target control of glycemia.

Artificial intelligence and machine learning for clinical pharmacology

Artificial intelligence (AI) will impact many aspects of clinical pharmacology, including drug discovery and development, clinical trials, personalized medicine, pharmacogenomics, pharmacovigilance and clinical toxicology. The rapid progress of AI in healthcare means clinical pharmacologists should have an understanding of AI and its implementation in clinical practice. As with any new therapy or health technology, it is imperative that AI tools are subject to robust and stringent evaluation to ensure that they enhance clinical practice in a safe and equitable manner. This review serves as an introduction to AI for the clinical pharmacologist, highlighting current applications, aspects of model development and issues surrounding evaluation and deployment. The aim of this article is to empower clinical pharmacologists to embrace and lead on the safe and effective use of AI within healthcare.

The rising trends of self-harm in Brazil: an ecological analysis of notifications, hospitalisations, and mortality between 2011 and 2022

Background

Self-harm is considered an important public health issue and is comprised by a wide range of definitions and behaviours. It is estimated that suicide affects more than 700,000 individuals every year, although, globally, there is a lack of evidence on other self-harm behaviour, such as attempted suicide. The objective of this study is to report and examine temporal trends of notifications, hospitalisations related to self-harm and suicide rates in Brazil between 2011 and 2022, as well as investigating differences in sociodemographic characteristics, methods, and region.

Methods

This ecological study used secondary, Brazilian Health Information System data between 2011 and 2022. Self-harm notifications were collected from the Notifiable Diseases Information System (SINAN); self-harm hospitalisations from the Hospital Information System (SIH), and suicide data from the Mortality Information System (SIM). We calculated self-harm notifications, hospitalisations, and suicide rates by sex, age, race, region, and overall. We assessed time-related trends using Joinpoint regression analyses.

Findings

From 2011 to 2022, 720,480 self-harm notifications, 104,458 self-harm hospitalisations, and 147,698 suicides were recorded in Brazil. In this period, self-harm notifications (AAPC: 21.13 (CI: 17.50, 25.33)) and suicide (AAPC: 3.70 (CI: 3.05, 4.38)) have increased in the country. Male adults (25-59 years old) and the elderly (>60 years old) continue to be the groups most affected by suicide, with respective rates of 9.59 and 8.60/100,000 in 2022. However, the largest percentage increases have been seen in young people (AAPC: 6.14 (CI: 4.57, 7.88)). The Indigenous population had the highest self-harm notification (103.72, 10,000) and suicide (16.58/100,000) rates, but the lowest hospitalisation rates (1.14/100,000) in 2022), compared to the overall population (70.06, 7.27, and 4.69/100,000, respectively, in 2022).

Interpretation

The observed trend of increased self-harm notifications and suicide rates suggests the need for a greater allocation of resources to strategies to prevent self-harm and suicide. The Indigenous population seems to be the group with less access to healthcare.

Funding

Research reported in this publication was supported by the National Institute of Mental Health of the National Institutes of Health under award number R01MH128911-01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Real-world data: a comprehensive literature review on the barriers, challenges, and opportunities associated with their inclusion in the health technology assessment process

Objective: This review aimed to assess the current use and acceptance of real-world data (RWD) and real-world evidence (RWE) in health technology assessment (HTA) process. It additionally aimed to discern stakeholders' viewpoints concerning RWD and RWE in HTA and illuminate the obstacles, difficulties, prospects, and consequences associated with the incorporation of RWD and RWE into the realm of HTA. Methods: A comprehensive PRISMA-based systematic review was performed in July 2022 in PubMed/Medline, Scopus, IDEAS-RePEc, International HTA database, and Centre for Reviews and Dissemination with ad hoc supplementary search in Google Scholar and international organization websites. The review included pre-determined inclusion criteria while the selection of eligible studies, the data extraction process and quality assessment were carried out using standardized and transparent methods. Results: Twenty-nine (n = 29) studies were included in the review out of 2,115 studies identified by the search strategy. In various global contexts, disparities in RWD utilization were evident, with randomized controlled trials (RCTs) serving as the primary evidence source. RWD and RWE played pivotal roles, surpassing relative effectiveness assessments (REAs) and significantly influencing decision-making and cost-effectiveness analyses. Identified challenges impeding RWD integration into HTA encompassed limited local data access, complexities in non-randomized trial design, data quality, privacy, and fragmentation. Addressing these is imperative for optimal RWD utilization. Incorporating RWD/RWE in HTA yields multifaceted advantages, enhancing understanding of treatment efficacy, resource utilization, and cost analysis, particularly via patient registries. RWE complements assessments of advanced therapy medicinal products (ATMPs) and rare diseases. Local data utilization strengthens HTA, bridging gaps when RCT data is lacking. RWD aids medical device decision-making, cancer drug reassessment, and indirect treatment comparisons. Challenges include data availability, stakeholder acceptance, expertise, and privacy. However, standardization, training, collaboration, and guidance can surmount these barriers, fostering enhanced RWD utilization in HTA. Conclusion: This study highlights the intricate global landscape of RWD and RWE acceptance in HTA. Recognizing regional nuances, addressing methodological challenges, and promoting collaboration are pivotal, among others, for leveraging RWD and RWE effectively in healthcare decision-making.

Clinical data mining: challenges, opportunities, and recommendations for translational applications

Clinical data mining of predictive models offers significant advantages for re-evaluating and leveraging large amounts of complex clinical real-world data and experimental comparison data for tasks such as risk stratification, diagnosis, classification, and survival prediction. However, its translational application is still limited. One challenge is that the proposed clinical requirements and data mining are not synchronized. Additionally, the exotic predictions of data mining are difficult to apply directly in local medical institutions. Hence, it is necessary to incisively review the translational application of clinical data mining, providing an analytical workflow for developing and validating prediction models to ensure the scientific validity of analytic workflows in response to clinical questions. This review systematically revisits the purpose, process, and principles of clinical data mining and discusses the key causes contributing to the detachment from practice and the misuse of model verification in developing predictive models for research. Based on this, we propose a niche-targeting framework of four principles: Clinical Contextual, Subgroup-Oriented, Confounder- and False Positive-Controlled (CSCF), to provide guidance for clinical data mining prior to the model's development in clinical settings. Eventually, it is hoped that this review can help guide future research and develop personalized predictive models to achieve the goal of discovering subgroups with varied remedial benefits or risks and ensuring that precision medicine can deliver its full potential.

Use of Metadata-Driven Approaches for Data Harmonization in the Medical Domain: Scoping Review

BACKGROUND

Multisite clinical studies are increasingly using real-world data to gain real-world evidence. However, due to the heterogeneity of source data, it is difficult to analyze such data in a unified way across clinics. Therefore, the implementation of Extract-Transform-Load (ETL) or Extract-Load-Transform (ELT) processes for harmonizing local health data is necessary, in order to guarantee the data quality for research. However, the development of such processes is time-consuming and unsustainable. A promising way to ease this is the generalization of ETL/ELT processes.

OBJECTIVE

In this work, we investigate existing possibilities for the development of generic ETL/ELT processes. Particularly, we focus on approaches with low development complexity by using descriptive metadata and structural metadata.

METHODS

We conducted a literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We used 4 publication databases (ie, PubMed, IEEE Explore, Web of Science, and Biomed Center) to search for relevant publications from 2012 to 2022. The PRISMA flow was then visualized using an R-based tool (Evidence Synthesis Hackathon). All relevant contents of the publications were extracted into a spreadsheet for further analysis and visualization.

RESULTS

Regarding the PRISMA guidelines, we included 33 publications in this literature review. All included publications were categorized into 7 different focus groups (ie, medicine, data warehouse, big data, industry, geoinformatics, archaeology, and military). Based on the extracted data, ontology-based and rule-based approaches were the 2 most used approaches in different thematic categories. Different approaches and tools were chosen to achieve different purposes within the use cases.

CONCLUSIONS

Our literature review shows that using metadata-driven (MDD) approaches to develop an ETL/ELT process can serve different purposes in different thematic categories. The results show that it is promising to implement an ETL/ELT process by applying MDD approach to automate the data transformation from Fast Healthcare Interoperability Resources to Observational Medical Outcomes Partnership Common Data Model. However, the determining of an appropriate MDD approach and tool to implement such an ETL/ELT process remains a challenge. This is due to the lack of comprehensive insight into the characterizations of the MDD approaches presented in this study. Therefore, our next step is to evaluate the MDD approaches presented in this study and to determine the most appropriate MDD approaches and the way to integrate them into the ETL/ELT process. This could verify the ability of using MDD approaches to generalize the ETL process for harmonizing medical data.