Clinical epidemiology in the era of big data: new opportunities, familiar challenges

Data harmonization and federated learning for multi-cohort dementia research using the OMOP common data model: A Netherlands consortium of dementia cohorts case study

BACKGROUND

Establishing collaborations between cohort studies has been fundamental for progress in health research. However, such collaborations are hampered by heterogeneous data representations across cohorts and legal constraints to data sharing. The first arises from a lack of consensus in standards of data collection and representation across cohort studies and is usually tackled by applying data harmonization processes. The second is increasingly important due to raised awareness for privacy protection and stricter regulations, such as the GDPR. Federated learning has emerged as a privacy-preserving alternative to transferring data between institutions through analyzing data in a decentralized manner.

METHODS

In this study, we set up a federated learning infrastructure for a consortium of nine Dutch cohorts with appropriate data available to the etiology of dementia, including an extract, transform, and load (ETL) pipeline for data harmonization. Additionally, we assessed the challenges of transforming and standardizing cohort data using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) and evaluated our tool in one of the cohorts employing federated algorithms.

RESULTS

We successfully applied our ETL tool and observed a complete coverage of the cohorts' data by the OMOP CDM. The OMOP CDM facilitated the data representation and standardization, but we identified limitations for cohort-specific data fields and in the scope of the vocabularies available. Specific challenges arise in a multi-cohort federated collaboration due to technical constraints in local environments, data heterogeneity, and lack of direct access to the data.

CONCLUSION

In this article, we describe the solutions to these challenges and limitations encountered in our study. Our study shows the potential of federated learning as a privacy-preserving solution for multi-cohort studies that enhance reproducibility and reuse of both data and analyses.

The use of routinely collected healthcare records for outcome assessment in clinical trials: a UK perspective

The use of routinely collected electronic healthcare records (EHR) for outcome assessment in clinical trials has been described as a 'disruptive' new technique more than a decade ago. Despite this potential, significant methodological issues and regulatory barriers have hampered the progress in this area. This article discusses the key considerations that trialists should take into account when incorporating EHR into their trials. These include considerations of the clinical relevance of the outcome, data timeliness and quality, ethical and regulatory issues, and some practical considerations for clinical trials units. In addition, this article describes the benefits of using EHR which include cost, reduced trial burden for participants and staff, follow up efficiencies, and improved health economic evaluation procedures. We also describe the major regulatory and start up costs of using EHR in clinical trials. This article focuses on the UK specific EHR landscape in clinical trials and would help researchers and trials units considering the use of this method of outcome data collection in their next trial. If the issues described are mitigated, this method will be a formidable tool for conducting pragmatic clinical trials.

Assessing cardiovascular disease risk in women with a history of hypertensive disorders of pregnancy: A guidance paper for studies using administrative data

BACKGROUND

Hypertensive disorders of pregnancy (HDP) are a major cause of maternal morbidity and mortality, and their association with increased cardiovascular disease (CVD) risk represents a major public health concern. However, assessing CVD risk in women with a history of these conditions presents unique challenges, especially when studies are carried out using routinely collected data.

OBJECTIVES

To summarise and describe key challenges related to the design and conduct of administrative studies assessing CVD risk in women with a history of HDP and provide concrete recommendations for addressing them in future research.

METHODS

This is a methodological guidance paper.

RESULTS

Several conceptual and methodological factors related to the data-generating mechanism and study conceptualisation, design/data management and analysis, as well as the interpretation and reporting of study findings should be considered and addressed when designing and carrying out administrative studies on this topic. Researchers should develop an a priori conceptual framework within which the research question is articulated, important study variables are identified and their interrelationships are carefully considered.

CONCLUSIONS

To advance our understanding of CVD risk in women with a history of HDP, future studies should carefully consider and address the conceptual and methodological considerations outlined in this guidance paper. In highlighting these challenges, and providing specific recommendations for how to address them, our goal is to improve the quality of research carried out on this topic.

Predicting incident heart failure from population-based nationwide electronic health records: protocol for a model development and validation study

INTRODUCTION

Heart failure (HF) is increasingly common and associated with excess morbidity, mortality, and healthcare costs. Treatment of HF can alter the disease trajectory and reduce clinical events in HF. However, many cases of HF remain undetected until presentation with more advanced symptoms, often requiring hospitalisation. Predicting incident HF is challenging and statistical models are limited by performance and scalability in routine clinical practice. An HF prediction model implementable in nationwide electronic health records (EHRs) could enable targeted diagnostics to enable earlier identification of HF.

METHODS AND ANALYSIS

We will investigate a range of development techniques (including logistic regression and supervised machine learning methods) on routinely collected primary care EHRs to predict risk of new-onset HF over 1, 5 and 10 years prediction horizons. The Clinical Practice Research Datalink (CPRD)-GOLD dataset will be used for derivation (training and testing) and the CPRD-AURUM dataset for external validation. Both comprise large cohorts of patients, representative of the population of England in terms of age, sex and ethnicity. Primary care records are linked at patient level to secondary care and mortality data. The performance of the prediction model will be assessed by discrimination, calibration and clinical utility. We will only use variables routinely accessible in primary care.

ETHICS AND DISSEMINATION

Permissions for CPRD-GOLD and CPRD-AURUM datasets were obtained from CPRD (ref no: 21_000324). The CPRD ethical approval committee approved the study. The results will be submitted as a research paper for publication to a peer-reviewed journal and presented at peer-reviewed conferences.

TRIAL REGISTRATION DETAILS

The study was registered on Clinical Trials.gov (NCT05756127). A systematic review for the project was registered on PROSPERO (registration number: CRD42022380892).

[Urban Epidemiology as an Integrative Approach to Sustainable and Healthy Urban Development]

Understanding the complex relationships between the physical and social environment and health in urban areas is essential for the development of appropriate measures of health promotion, disease prevention, and health protection. This article aims to characterize the comparatively new approach of urban epidemiology with its relevance for research and practice of urban health. Research in urban epidemiology provides important data and methodological foundations for integrated reporting, health impact assessments, and evaluation of interventions. Current challenges and solutions are outlined and initial recommendations for research, practice, and education and training are presented for discussion. Methods and findings of urban epidemiology can contribute in many ways to health-promoting, sustainable urban development.

Systematic review of validated case definitions to identify hypertensive disorders of pregnancy in administrative healthcare databases

BACKGROUND

Administrative data are frequently used to study cardiovascular disease (CVD) risk in women with hypertensive disorders of pregnancy (HDP). Little is known about the validity of case-finding definitions (CFDs, eg, disease classification codes/algorithms) designed to identify HDP in administrative databases.

METHODS

A systematic review of the literature. We searched MEDLINE, Embase, CINAHL, Web of Science and grey literature sources for eligible studies. Two independent reviewers screened articles for eligibility and extracted data. Quality of reporting was assessed using checklists; risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool, adapted for administrative studies. Findings were summarised descriptively.

RESULTS

Twenty-six studies were included; most (62%) validated CFDs for a variety of maternal and/or neonatal outcomes. Six studies (24%) reported reference standard definitions for all HDP definitions validated; seven reported all 2×2 table values for ≥1 CFD or they were calculable. Most CFDs (n=83; 58%) identified HDP with high specificity (ie, ≥98%); however, sensitivity varied widely (3%-100%). CFDs validated for any maternal hypertensive disorder had the highest median sensitivity (91%, range: 15%-97%). Quality of reporting was generally poor, and all studies were at unclear or high risk of bias on ≥1 QUADAS-2 domain.

CONCLUSIONS

Even validated CFDs are subject to bias. Researchers should choose the CFD(s) that best align with their research objective, while considering the relative importance of high sensitivity, specificity, negative predictive value and/or positive predictive value, and important characteristics of the validation studies from which they were derived (eg, study prevalence of HDP, spectrum of disease studied, methodological rigour, quality of reporting and risk of bias). Higher quality validation studies on this topic are urgently needed.

PROSPERO REGISTRATION NUMBER

CRD42021239113.

Hematologic Cancer Detection Using White Blood Cancerous Cells Empowered with Transfer Learning and Image Processing

Lymphoma and leukemia are fatal syndromes of cancer that cause other diseases and affect all types of age groups including male and female, and disastrous and fatal blood cancer causes an increased savvier death ratio. Both lymphoma and leukemia are associated with the damage and rise of immature lymphocytes, monocytes, neutrophils, and eosinophil cells. So, in the health sector, the early prediction and treatment of blood cancer is a major issue for survival rates. Nowadays, there are various manual techniques to analyze and predict blood cancer using the microscopic medical reports of white blood cell images, which is very steady for prediction and causes a major ratio of deaths. Manual prediction and analysis of eosinophils, lymphocytes, monocytes, and neutrophils are very difficult and time-consuming. In previous studies, they used numerous deep learning and machine learning techniques to predict blood cancer, but there are still some limitations in these studies. So, in this article, we propose a model of deep learning empowered with transfer learning and indulge in image processing techniques to improve the prediction results. The proposed transfer learning model empowered with image processing incorporates different levels of prediction, analysis, and learning procedures and employs different learning criteria like learning rate and epochs. The proposed model used numerous transfer learning models with varying parameters for each model and cloud techniques to choose the best prediction model, and the proposed model used an extensive set of performance techniques and procedures to predict the white blood cells which cause cancer to incorporate image processing techniques. So, after extensive procedures of AlexNet, MobileNet, and ResNet with both image processing and without image processing techniques with numerous learning criteria, the stochastic gradient descent momentum incorporated with AlexNet is outperformed with the highest prediction accuracy of 97.3% and the misclassification rate is 2.7% with image processing technique. The proposed model gives good results and can be applied for smart diagnosing of blood cancer using eosinophils, lymphocytes, monocytes, and neutrophils.

Application of big data in ophthalmology

The advents of information technologies have led to the creation of ever-larger datasets. Also known as big data, these large datasets are characterized by its volume, variety, velocity, veracity, and value. More importantly, big data has the potential to expand traditional research capabilities, inform clinical practice based on real-world data, and improve the health system and service delivery. This review first identified the different sources of big data in ophthalmology, including electronic medical records, data registries, research consortia, administrative databases, and biobanks. Then, we provided an in-depth look at how big data analytics have been applied in ophthalmology for disease surveillance, and evaluation on disease associations, detection, management, and prognostication. Finally, we discussed the challenges involved in big data analytics, such as data suitability and quality, data security, and analytical methodologies.

Study Types in Orthopaedics Research: Is My Study Design Appropriate for the Research Question?

When performing orthopaedic clinical research, alternative study designs can be more appropriate depending on the research question, availability of data, and feasibility. The most common observational study designs in total joint arthroplasty research are cohort and cross-sectional studies. This article describes methodological considerations for different study designs with examples from the total joint arthroplasty literature. We highlight the advantages and feasibility of experimental and observational study designs using real-world examples. We illustrate how to avoid common mistakes, such as incorrect labeling of matched cohort studies as case-control studies. We further guide investigators through a step-by-step design of a case-control study. We conclude with considerations when choosing between alternative study designs. Please visit the followinghttps://youtu.be/Zvce61cMYi8for videos that explain the highlights of the article in practical terms.

Measurement Error and Misclassification in Orthopedics: When Study Subjects are Categorized in the Wrong Exposure or Outcome Groups

Datasets available for orthopedic research often contain measurement and misclassification errors due to errors in data collection or missing data. These errors can have different effects on the study results. Measurement error refers to inaccurate measurement of continuous variables (eg, body mass index), whereas misclassification refers to assigning subjects in the wrong exposure and/or outcome groups (eg, obesity categories). Misclassification of any type can result in underestimation or overestimation of the association between exposures and outcomes. In this article, we offer practical guidelines to avoid, identify, and account for measurement and misclassification errors. We also provide an illustrative example on how to perform a validation study to address misclassification based on real-world orthopedic data. Please visit the followinghttps://youtu.be/9-ekW2NnWrsor videos that explain the highlights of the article in practical terms.

Establishing a National Cardiovascular Disease Surveillance System in the United States Using Electronic Health Record Data: Key Strengths and Limitations

Cardiovascular disease surveillance involves quantifying the evolving population-level burden of cardiovascular outcomes and risk factors as a data-driven initial step followed by the implementation of interventional strategies designed to alleviate this burden in the target population. Despite widespread acknowledgement of its potential value, a national surveillance system dedicated specifically to cardiovascular disease does not currently exist in the United States. Routinely collected health care data such as from electronic health records (EHRs) are a possible means of achieving national surveillance. Accordingly, this article elaborates on some key strengths and limitations of using EHR data for establishing a national cardiovascular disease surveillance system. Key strengths discussed include the: (1) ubiquity of EHRs and consequent ability to create a more "national" surveillance system, (2) existence of a common data infrastructure underlying the health care enterprise with respect to data domains and the nomenclature by which these data are expressed, (3) longitudinal length and detail that define EHR data when individuals repeatedly patronize a health care organization, and (4) breadth of outcomes capable of being surveilled with EHRs. Key limitations discussed include the: (1) incomplete ascertainment of health information related to health care-seeking behavior and the disconnect of health care data generated at separate health care organizations, (2) suspect data quality resulting from the default information-gathering processes within the clinical enterprise, (3) questionable ability to surveil patients through EHRs in the absence of documented interactions, and (4) the challenge in interpreting temporal trends in health metrics, which can be obscured by changing clinical and administrative processes.

Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed

Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science. These initiatives offer greater transparency in science, with the opportunity for external research groups to reproduce, replicate, and extend research findings. Further, larger datasets offer the opportunity to identify homogeneous patterns within subgroups of individuals, where these patterns may be obscured by the heterogeneity of the neurobiological measure in smaller samples. However, data sharing and data pooling initiatives are not without their challenges, especially with new laws that may at first glance appear quite restrictive for open science initiatives. Interestingly, what is key to some of these new laws (i.e, the European Union's general data protection regulation) is that they provide greater control of data to those who "give" their data for research purposes. Thus, the most important element in data sharing is allowing the participants to make informed decisions about how they want their data to be used, and, within the law of the specific country, to follow the participants' wishes. This framework encompasses obtaining thorough informed consent and allowing the participant to determine the extent that they want their data shared, many of the ethical and legal obstacles are reduced to just monsters under the bed. In this manuscript we discuss the many options and obstacles for data sharing, from fully open, to federated learning, to fully closed. Importantly, we highlight the intersection of data sharing, privacy, and data ownership and highlight specific examples that we believe are informative to the neuroimaging community.

Constructing Epidemiologic Cohorts from Electronic Health Record Data

In the United States, electronic health records (EHR) are increasingly being incorporated into healthcare organizations to document patient health and services rendered. EHRs serve as a vast repository of demographic, diagnostic, procedural, therapeutic, and laboratory test data generated during the routine provision of health care. The appeal of using EHR data for epidemiologic research is clear: EHRs generate large datasets on real-world patient populations in an easily retrievable form permitting the cost-efficient execution of epidemiologic studies on a wide array of topics. Constructing epidemiologic cohorts from EHR data involves as a defining feature the development of data machinery, which transforms raw EHR data into an epidemiologic dataset from which appropriate inference can be drawn. Though data machinery includes many features, the current report focuses on three aspects of machinery development of high salience to EHR-based epidemiology: (1) selecting study participants; (2) defining “baseline” and assembly of baseline characteristics; and (3) follow-up for future outcomes. For each, the defining features and unique challenges with respect to EHR-based epidemiology are discussed. An ongoing example illustrates key points. EHR-based epidemiology will become more prominent as EHR data sources continue to proliferate. Epidemiologists must continue to improve the methods of EHR-based epidemiology given the relevance of EHRs in today’s healthcare ecosystem.

Predicting patient-level new-onset atrial fibrillation from population-based nationwide electronic health records: protocol of FIND-AF for developing a precision medicine prediction model using artificial intelligence

INTRODUCTION

Atrial fibrillation (AF) is a major cardiovascular health problem: it is common, chronic and incurs substantial healthcare expenditure because of stroke. Oral anticoagulation reduces the risk of thromboembolic stroke in those at higher risk; but for a number of patients, stroke is the first manifestation of undetected AF. There is a rationale for the early diagnosis of AF, before the first complication occurs, but population-based screening is not recommended. Previous prediction models have been limited by their data sources and methodologies. An accurate model that uses existing routinely collected data is needed to inform clinicians of patient-level risk of AF, inform national screening policy and highlight predictors that may be amenable to primary prevention.

METHODS AND ANALYSIS

We will investigate the application of a range of deep learning techniques, including an adapted convolutional neural network, recurrent neural network and Transformer, on routinely collected primary care data to create a personalised model predicting the risk of new-onset AF over a range of time periods. The Clinical Practice Research Datalink (CPRD)-GOLD dataset will be used for derivation, and the CPRD-AURUM dataset will be used for external geographical validation. Both comprise a sizeable representative population and are linked at patient-level to secondary care databases. The performance of the deep learning models will be compared against classic machine learning and traditional statistical predictive modelling methods. We will only use risk factors accessible in primary care and endow the model with the ability to update risk prediction as it is presented with new data, to make the model more useful in clinical practice.

ETHICS AND DISSEMINATION

Permissions for CPRD-GOLD and CPRD-AURUM datasets were obtained from CPRD (ref no: 19_076). The CPRD ethical approval committee approved the study. The results will be submitted as a research paper for publication to a peer-reviewed journal and presented at peer-reviewed conferences.

TRIAL REGISTRATION DETAILS

A systematic review to incorporate within the overall project was registered on PROSPERO (registration number CRD42021245093). The study was registered on ClinicalTrials.gov (NCT04657900).

Using Electronic Medical Record Data for Research in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 Hospital in Beijing: Cross-sectional Study

BACKGROUND

With the proliferation of electronic medical record (EMR) systems, there is an increasing interest in utilizing EMR data for medical research; yet, there is no quantitative research on EMR data utilization for medical research purposes in China.

OBJECTIVE

This study aimed to understand how and to what extent EMR data are utilized for medical research purposes in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 hospital in Beijing, China. Obstacles and issues in the utilization of EMR data were also explored to provide a foundation for the improved utilization of such data.

METHODS

For this descriptive cross-sectional study, cluster sampling from Xuanwu Hospital, one of two Stage 7 hospitals in Beijing, was conducted from 2016 to 2019. The utilization of EMR data was described as the number of requests, the proportion of requesters, and the frequency of requests per capita. Comparisons by year, professional title, and age were conducted by double-sided chi-square tests.

RESULTS

From 2016 to 2019, EMR data utilization was poor, as the proportion of requesters was 5.8% and the frequency was 0.1 times per person per year. The frequency per capita gradually slowed and older senior-level staff more frequently used EMR data compared with younger staff.

CONCLUSIONS

The value of using EMR data for research purposes is not well studied in China. More research is needed to quantify to what extent EMR data are utilized across all hospitals in Beijing and how these systems can enhance future studies. The results of this study also suggest that young doctors may be less exposed or have less reason to access such research methods.

Sex, Age, and Socioeconomic Differences in Nonfatal Stroke Incidence and Subsequent Major Adverse Outcomes

Supplemental Digital Content is available in the text.

Data about variations in stroke incidence and subsequent major adverse outcomes are essential to inform secondary prevention and prioritizing resources to those at the greatest risk of major adverse end points. We aimed to describe the age, sex, and socioeconomic differences in the rates of first nonfatal stroke and subsequent major adverse outcomes.

A Review on the Application and Limitations of Administrative Health Care Data for the Study of Acute Kidney Injury Epidemiology and Outcomes in Children

Administrative health care databases contain valuable patient information generated by health care encounters. These "big data" repositories have been increasingly used in epidemiological health research internationally in recent years as they are easily accessible and cost-efficient and cover large populations for long periods. Despite these beneficial characteristics, it is also important to consider the limitations that administrative health research presents, such as issues related to data incompleteness and the limited sensitivity of the variables. These barriers potentially lead to unwanted biases and pose threats to the validity of the research being conducted. In this review, we discuss the effectiveness of health administrative data in understanding the epidemiology of and outcomes after acute kidney injury (AKI) among adults and children. In addition, we describe various validation studies of AKI diagnostic or procedural codes among adults and children. These studies reveal challenges of AKI research using administrative data and the lack of this type of research in children and other subpopulations. Additional pediatric-specific validation studies of administrative health data are needed to promote higher volume and increased validity of this type of research in pediatric AKI, to elucidate the large-scale epidemiology and patient and health systems impacts of AKI in children, and to devise and monitor programs to improve clinical outcomes and process of care.

Demographic and clinical characterization of multiple sclerosis in Mexico: The REMEMBer study

BACKGROUND

Multiple sclerosis (MS) is a chronic neurodegenerative disease of the central nervous system with high prevalence in young adults around the world. The vast majority of epidemiological studies and statistics are based on European and American data, so most clinical guidelines and medical consensus are based on this information. There is very limited evidence in Mexico regarding demographic and clinical aspects of MS. Therefore, this study comprehensively described the epidemiological and clinical features of MS in a large cohort of patients from eight tertiary-level centers in Mexico.

METHODS

A cross-sectional multicenter study was conducted. A group of neurologists, the "Registro Mexicano de Esclerosis Multiple" (REMEMBer) group, compiled the information of MS patients (January to December 2019) from eight tertiary-level centers. Clinical and demographic data were extracted.

RESULTS

A total of 1,185 patients were included. The mean age was 40.65 ± 11.43 years old. Women represented more than half of the whole cohort (64.9% vs. 35.1%). Of the whole cohort, forty-three percent of MS patients had a relative with at least one autoimmune disease (MS: 24%, other autoimmune disorders: 74.9%) or thyroid disease (28%). Furthermore, the mean age of clinical onset was 31.23 ± 9.71 (range: 16-68) years old, and the disease duration was 9.33 ± 7.25 (0.46-40.19) years. The most prevalent phenotype of MS was relapsing-remitting (87.76%). Primary (1.18%) and secondary (9.11%) progressive, as well as clinically isolated syndrome (CIS, 1.43%), were also found. Clinical phenotypes (facial, hearing, and speech disorders, and movement impairment and ataxia) and the frequency of thyroid disorders were different between genders.

CONCLUSION

In Mexico, the frequency of MS seems to be higher in the female gender (2:1 women/men ratio) compared to other series. In addition, there was a predominance of facial, hearing and speech disorders, as well as movement impairment and ataxia. Thyroid diseases were more common in women with multiple sclerosis than men.

Probabilistic Quantification of Bias to Combine the Strengths of Population-Based Register Data and Clinical Cohorts-Studying Mortality in Osteoarthritis

We propose combining population-based register data with a nested clinical cohort to correct misclassification and unmeasured confounding through probabilistic quantification of bias. We have illustrated this approach by estimating the association between knee osteoarthritis and mortality. We used the Swedish Population Register to include all persons resident in the Skåne region in 2008 and assessed whether they had osteoarthritis using data from the Skåne Healthcare Register. We studied mortality through year 2017 by estimating hazard ratios. We used data from the Malmö Osteoarthritis Study (MOA), a small cohort study from Skåne, to derive bias parameters for probabilistic quantification of bias, to correct the hazard ratio estimate for differential misclassification of the knee osteoarthritis diagnosis and confounding from unmeasured obesity. We included 292,000 persons in the Skåne population and 1,419 from the MOA study. The adjusted association of knee osteoarthritis with all-cause mortality in the MOA sample had a hazard ratio of 1.10 (95% confidence interval (CI): 0.80, 1.52) and was thus inconclusive. The naive association in the Skåne population had a hazard ratio of 0.95 (95% CI: 0.93, 0.98), while the bias-corrected estimate was 1.02 (95% CI: 0.59, 1.52), suggesting high uncertainty in bias correction. Combining population-based register data with clinical cohorts provides more information than using either data source separately.

Approaches to addressing missing values, measurement error, and confounding in epidemiologic studies

OBJECTIVES

Epidemiologic studies often suffer from incomplete data, measurement error (or misclassification), and confounding. Each of these can cause bias and imprecision in estimates of exposure-outcome relations. We describe and compare statistical approaches that aim to control all three sources of bias simultaneously.

STUDY DESIGN AND SETTING

We illustrate four statistical approaches that address all three sources of bias, namely, multiple imputation for missing data and measurement error, multiple imputation combined with regression calibration, full information maximum likelihood within a structural equation modeling framework, and a Bayesian model. In a simulation study, we assess the performance of the four approaches compared with more commonly used approaches that do not account for measurement error, missing values, or confounding.

RESULTS

The results demonstrate that the four approaches consistently outperform the alternative approaches on all performance metrics (bias, mean squared error, and confidence interval coverage). Even in simulated data of 100 subjects, these approaches perform well.

CONCLUSION

There can be a large benefit of addressing measurement error, missing values, and confounding to improve the estimation of exposure-outcome relations, even when the available sample size is relatively small.

A decision support framework for prediction of avian influenza

For years, avian influenza has influenced economies and human health around the world. The emergence and spread of avian influenza virus have been uncertain and sudden. The virus is likely to spread through several pathways such as poultry transportation and wild bird migration. The complicated and global spread of avian influenza calls for surveillance tools for timely and reliable prediction of disease events. These tools can increase situational awareness and lead to faster reaction to events. Here, we aimed to design and evaluate a decision support framework that aids decision makers by answering their questions regarding the future risk of events at various geographical scales. Risk patterns were driven from pre-built components and combined in a knowledge base. Subsequently, questions were answered by direct queries on the knowledge base or through a built-in algorithm. The evaluation of the system in detecting events resulted in average sensitivity and specificity of 69.70% and 85.50%, respectively. The presented framework here can support health care authorities by providing them with an opportunity for early control of emergency situations.

Complications in pulmonary vein isolation in the Netherlands Heart Registration differ with sex and ablation technique

Aims 

Pulmonary vein isolation (PVI) has become a cornerstone of the invasive treatment of atrial fibrillation. Severe complications are reported in 1–3% of patients. This study aims to compare complications and follow-up outcome of PVI in patients with atrial fibrillation.

Methods and results 

The data were extracted from the Netherlands Heart Registration. Procedural and follow-up outcomes in patients treated with conventional radiofrequency (C-RF), multielectrode phased RF (Ph-RF), or cryoballoon (CB) ablation from 2012 to 2017 were compared. Subgroup analysis was performed to identify variables associated with complications and repeat ablations. In total, 13 823 patients (69% male) were included. The reported complication incidence was 3.6%. Patients treated with C-RF developed more cardiac tamponades (C-RF 0.8% vs. Ph-RF 0.3% vs. CB 0.3%, P ≤ 0.001) and vascular complications (C-RF 1.7% vs. Ph-RF 1.2% vs. CB 1.3%, P ≤ 0.001). Ph-RF was associated with fewer bleeding complications (C-RF: 1.0% vs. Ph-RF: 0.4% vs. CB: 0.7%, P = 0.020). Phrenic nerve palsy mainly occurred in patients treated with CB (C-RF: 0.1% vs. Ph-RF: 0.2% vs. CB: 1.5%, P ≤ 0.001). In total, 18.4% of patients were referred for repeat ablation within 1 year. Female sex, age, and CHA2DS2-VASc were independent risk factors for cardiac tamponade and bleeding complications, with an adjusted OR for female patients of 2.97 (95% CI 1.98–4.45) and 2.02 (95% CI 1.03–4.00) respectively.

Conclusion 

The reported complication rate during PVI was low. Patients treated with C-RF ablation were more likely to develop cardiac tamponades and vascular complications. Female sex was associated with more cardiac tamponade and bleeding complications.

Healthcare Applications of Artificial Intelligence and Analytics: A Review and Proposed Framework

Healthcare is considered as one of the most promising application areas for artificial intelligence and analytics (AIA) just after the emergence of the latter. AI combined to analytics technologies is increasingly changing medical practice and healthcare in an impressive way using efficient algorithms from various branches of information technology (IT). Indeed, numerous works are published every year in several universities and innovation centers worldwide, but there are concerns about progress in their effective success. There are growing examples of AIA being implemented in healthcare with promising results. This review paper summarizes the past 5 years of healthcare applications of AIA, across different techniques and medical specialties, and discusses the current issues and challenges, related to this revolutionary technology. A total of 24,782 articles were identified. The aim of this paper is to provide the research community with the necessary background to push this field even further and propose a framework that will help integrate diverse AIA technologies around patient needs in various healthcare contexts, especially for chronic care patients, who present the most complex comorbidities and care needs.

Impact of industry 4.0 to create advancements in orthopaedics

Scientists and health professional are focusing on improving the medical sciences for the betterment of patients. The fourth industrial revolution, which is commonly known as Industry 4.0, is a significant advancement in the field of engineering. Industry 4.0 is opening a new opportunity for digital manufacturing with greater flexibility and operational performance. This development is also going to have a positive impact in the field of orthopaedics. The purpose of this paper is to present various advancements in orthopaedics by the implementation of Industry 4.0. To undertake this study, we have studied the available literature extensively on Industry 4.0, technologies of Industry 4.0 and their role in orthopaedics. Paper briefly explains about Industry 4.0, identifies and discusses the major technologies of Industry 4.0, which will support development in orthopaedics. Finally, from the available literature, the paper identifies twelve significant advancements of Industry 4.0 in orthopaedics. Industry 4.0 uses various types of digital manufacturing and information technologies to create orthopaedics implants, patient-specific tools, devices and innovative way of treatment. This revolution is to be useful to perform better spinal surgery, knee and hip replacement, and invasive surgeries.

Big Data in Ophthalmology

Big data is the fuel of mankind's fourth industrial revolution. Coupled with new technology such as artificial intelligence and deep learning, the potential of big data is poised to be harnessed to its maximal in years to come. In ophthalmology, given the data-intensive nature of this specialty, big data will similarly play an important role. Electronic medical records, administrative and health insurance databases, mega national biobanks, crowd source data from mobile applications and social media, and international epidemiology consortia are emerging forms of "big data" in ophthalmology. In this review, we discuss the characteristics of big data, its potential applications in ophthalmology, and the challenges in leveraging and using these data. Importantly, in the next phase of work, it will be pertinent to further translate "big data" findings into real-world applications, to improve quality of eye care, and cost-effectiveness and efficiency of health services in ophthalmology.

Hospitalization burden and comorbidities of patients with rheumatoid arthritis in Spain during the period 2002-2017

Background

Rheumatoid arthritis (RA) is a chronic autoimmune rheumatic disease that is associated with multiple comorbidities and has a significant economic impact on the Spanish health system. The objective of this study was to estimate the rates of hospitalization of rheumatoid arthritis in Spain, and describing hospitalization rates and their changing by age, region, RA variant, and when RA as a main cause of hospitalization or a comorbidity.

Methods

Observational descriptive study that reviewed hospital records from the CMBD. We included all hospitalizations of patients in Spain whose main diagnosis or comorbidity in the ICD-9-CM was rheumatoid arthritis during the period of 2002–2017.

Results

A total of 315,190 hospitalizations with the RA code were recorded; 67.3% were in women. The mean age of the patients was 68.5 ± 13.9 years. The median length of hospital stay was 7 days (IQR 3–11 days). In 29,809 of the admissions, RA was coded as the main diagnosis (9.4%). When RA was not coded as the main diagnosis, the most frequent main diagnoses were diseases of the circulatory system (18.9%) and diseases of the respiratory system (17.4%). The hospitalization rate during the period of 2002–2017 was 43.8 (95% CI: 43.7–44.0) per 100,000 inhabitants and constantly increased during the period. The total cost for the healthcare system was 1.476 million euros, with a median of 3542 euros per hospitalization (IQR 2646–5222 euros).

Conclusions

In Spain, the hospitalization rate of patients with RA increased during the study period, despite the decrease in the hospitalization rate when RA was the main diagnosis.

Data mining-based model and risk prediction of colorectal cancer by using secondary health data: A systematic review

OBJECTIVE

Prevention and early detection of colorectal cancer (CRC) can increase the chances of successful treatment and reduce burden. Various data mining technologies have been utilized to strengthen the early detection of CRC in primary care. Evidence synthesis on the model's effectiveness is scant. This systematic review synthesizes studies that examine the effect of data mining on improving risk prediction of CRC.

METHODS

The PRISMA framework guided the conduct of this study. We obtained papers via PubMed, Cochrane Library, EMBASE and Google Scholar. Quality appraisal was performed using Downs and Black's quality checklist. To evaluate the performance of included models, the values of specificity and sensitivity were comparted, the values of area under the curve (AUC) were plotted, and the median of overall AUC of included studies was computed.

RESULTS

A total of 316 studies were reviewed for full text. Seven articles were included. Included studies implement techniques including artificial neural networks, Bayesian networks and decision trees. Six articles reported the overall model accuracy. Overall, the median AUC is 0.8243 [interquartile range (IQR): 0.8050-0.8886]. In the two articles that reported comparison results with traditional models, the data mining method performed better than the traditional models, with the best AUC improvement of 10.7%.

CONCLUSIONS

The adoption of data mining technologies for CRC detection is at an early stage. Limited numbers of included articles and heterogeneity of those studies implied that more rigorous research is expected to further investigate the techniques' effects.

Cloud-Based Multicenter Data Collection and Epidemiologic Analysis of Bisphosphonate-Related Osteonecrosis of the Jaws in a Central European Population

Objective: Bisphosphonate-related osteonecrosis of the jaws is considered to be a rare but severe complication of bisphosphonate therapy. To understand this condition better, data collection is essential. Although the number of scientific papers about this subject is large, to date only a few multicenter reports have been published. Study design: We present a novel cloud-based data collection system for the evaluation of the risk factors of bisphosphonate-related osteonecrosis of the jaws. Web-based questionnaire and database have been set up and made available to voluntary researchers and clinicians in oral and maxillofacial surgery in Hungary and Slovakia. Results: To date, fifteen colleagues from eight maxillofacial units have joined the study. Data of 180 patients have been recorded. Collected data were statistically analysed and evaluated from an epidemiological point of view. Conclusions: Authors consider cloud-based multicenter data collection a useful tool that allows for real-time collaboration between users, facilitates fast data entry and analysis, and thus considerably contributes to widening our knowledge of bisphosphonate-related osteonecrosis of the jaws.

Cloud-Based Multicenter Data Collection and Epidemiologic Analysis of Bisphosphonate-Related Osteonecrosis of the Jaws in a Central European Population

Objective: Bisphosphonate-related osteonecrosis of the jaws is considered to be a rare but severe complication of bisphosphonate therapy. To understand this condition better, data collection is essential. Although the number of scientific papers about this subject is large, to date only a few multicenter reports have been published. Study design: We present a novel cloud-based data collection system for the evaluation of the risk factors of bisphosphonate-related osteonecrosis of the jaws. Web-based questionnaire and database have been set up and made available to voluntary researchers and clinicians in oral and maxillofacial surgery in Hungary and Slovakia. Results: To date, fifteen colleagues from eight maxillofacial units have joined the study. Data of 180 patients have been recorded. Collected data were statistically analysed and evaluated from an epidemiological point of view. Conclusions: Authors consider cloud-based multicenter data collection a useful tool that allows for real-time collaboration between users, facilitates fast data entry and analysis, and thus considerably contributes to widening our knowledge of bisphosphonate-related osteonecrosis of the jaws.

Novel Data Linkages to Characterize Palliative and End-Of-Life Care: Challenges and Considerations

CONTEXT

Working groups have called for linkages of existing and diverse databases to improve quality measurement in palliative and end-of-life (EOL) care, but limited data are available on the challenges of using different data sources to measure such care.

OBJECTIVES

To assess concordance of data obtained from different sources in a novel linkage of death certificates, electronic health records (EHRs), cancer registry data, and insurance claims for patients who died with cancer.

METHODS

We joined a database of Washington State death certificates and EHR to a data repository of commercial health plan enrollment and claims files linked to registry records from Puget Sound Cancer Surveillance System. We assessed care in the last month including hospitalizations, intensive care unit (ICU) admissions, emergency department visits, imaging scans, radiation, and hospice, plus chemotherapy in the last 14 days. We used a Chi-squared test to compare differences between health care in EHR and claims.

RESULTS

Records of hospitalization, ICU use, and emergency department use were 33%, 15%, and 33% lower in EHR versus claims, respectively. Radiation, hospice, and imaging were 6%, 14%, and 28% lower, respectively, in EHR, but chemotherapy was 4% higher than that in claims. These differences were statistically different for hospice (P < 0.02), hospitalization, ICU, ER, and imaging (all P < 0.01) but not radiation (P = 0.12) or chemotherapy (P = 0.29).

CONCLUSION

We found substantial variation between EHR and claims for EOL health-care use. Reliance on EHR will miss some health-care use, while claims will not capture the complex clinical details in EHR that can help define the quality of palliative care and EOL health-care utilization.

What Can We Learn About Drug Safety and Other Effects in the Era of Electronic Health Records and Big Data That We Would Not Be Able to Learn From Classic Epidemiology?

As more and more health systems have converted to the use of electronic health records, the amount of searchable and analyzable data is exploding. This includes not just provider or laboratory created data but also data collected by instruments, personal devices, and patients themselves, among others. This has led to more attention being paid to the analysis of these data to answer previously unaddressed questions. This is especially important given the number of therapies previously found to be beneficial in clinical trials that are currently being re-scrutinized. Because there are orders of magnitude more information contained in these data sets, a fundamentally different approach needs to be taken to their processing and analysis and the generation of knowledge. Health care and medicine are drivers of this phenomenon and will ultimately be the main beneficiaries. Concurrently, many different types of questions can now be asked using these data sets. Research groups have become increasingly active in mining large data sets, including nationwide health care databases, to learn about associations of medication use and various unrelated diseases such as cancer. Given the recent increase in research activity in this area, its promise to radically change clinical research, and the relative lack of widespread knowledge about its potential and advances, we surveyed the available literature to understand the strengths and limitations of these new tools. We also outline new databases and techniques that are available to researchers worldwide, with special focus on work pertaining to the broad and rapid monitoring of drug safety and secondary effects.

The Norwegian Patient Registry and the Norwegian Registry for Primary Health Care: Research potential of two nationwide health-care registries

In Norway, the Directorate of Health is responsible for two nationwide registries – the Norwegian Patient Registry (NPR) and the Norwegian Registry for Primary Health Care (NRPHC) – which together cover all governmental-funded health care. The NPR (specialist health care) was established in 2008, while the NRPHC (primary health care) was established in 2017. Data from the NPR are extensively used in a large variety of studies. We expect that data from the NRPHC will increase in importance when the registry covers a longer time period. The NRPHC will be especially important for studying conditions mainly treated in primary care and for investigation of patient trajectories. The main aim of this paper is to give an overview of the history and content of the NPR and its research possibilities. In addition, we introduce the NRPHC as a possible future research tool and the potential for studying patient trajectories when combining data from the two registries.

Hypocrisy Around Medical Patient Data: Issues of Access for Biomedical Research, Data Quality, Usefulness for the Purpose and Omics Data as Game Changer

Whether due to simplicity or hypocrisy, the question of access to patient data for biomedical research is widely seen in the public discourse only from the angle of patient privacy. At the same time, the desire to live and to live without disability is of much higher value to the patients. This goal can only be achieved by extracting research insight from patient data in addition to working on model organisms, something that is well understood by many patients. Yet, most biomedical researchers working outside of clinics and hospitals are denied access to patient records when, at the same time, clinicians who guard the patient data are not optimally prepared for the data’s analysis. Medical data collection is a time- and cost-intensive process that is most of all tedious, with few elements of intellectual and emotional satisfaction on its own. In this process, clinicians and bioinformaticians, each group with their own interests, have to join forces with the goal to generate medical data sets both from clinical trials and from routinely collected electronic health records that are, as much as possible, free from errors and obvious inconsistencies. The data cleansing effort as we have learned during curation of Singaporean clinical trial data is not a trivial task. The introduction of omics and sophisticated imaging modalities into clinical practice that are only partially interpreted in terms of diagnosis and therapy with today’s level of knowledge warrant the creation of clinical databases with full patient history. This opens up opportunities for re-analyses and cross-trial studies at future time points with more sophisticated analyses of the same data, the collection of which is very expensive.

Defining and Adopting Clinical Performance Measures in Graduate Medical Education: Where Are We Now and Where Are We Going?

Assessment and evaluation of trainees' clinical performance measures is needed to ensure safe, high-quality patient care. These measures also aid in the development of reflective, high-performing clinicians and hold graduate medical education (GME) accountable to the public. Although clinical performance measures hold great potential, challenges of defining, extracting, and measuring clinical performance in this way hinder their use for educational and quality improvement purposes. This article provides a way forward by identifying and articulating how clinical performance measures can be used to enhance GME by linking educational objectives with relevant clinical outcomes. The authors explore four key challenges: defining as well as measuring clinical performance measures, using electronic health record and clinical registry data to capture clinical performance, and bridging silos of medical education and health care quality improvement. The authors also propose solutions to showcase the value of clinical performance measures and conclude with a research and implementation agenda. Developing a common taxonomy of uniform specialty-specific clinical performance measures, linking these measures to large-scale GME databases, and applying both quantitative and qualitative methods to create a rich understanding of how GME affects quality of care and patient outcomes is important, the authors argue. The focus of this article is primarily GME, yet similar challenges and solutions will be applicable to other areas of medical and health professions education as well.

How to validate a diagnosis recorded in electronic health records

Over the last decades, the adoption of electronic health records (EHR) by health services worldwide has facilitated the construction of large population-based patient databases. These routinely generated longitudinal records have an enormous potential for epidemiological and clinical research [1–3]. EHR contain information on the health of an individual and are an electronic version of a patient's medical history. This contrasts with administrative claims data, whose main purpose is administration of reimbursement of medical services to healthcare providers. Due to the immense size of EHR, they can offer high statistical power and can often be representative of a population. Linkage between different EHR can further improve the completeness of the data. However, the primary raison d’être of most of these EHR is for clinical, administrative or audit purposes, which is a major challenge to their use for health research. Data elements that would be useful for research can therefore be wrongly classified, insufficiently specified or missing. Misclassified data can lead to systematic measurement errors. Missing data can lead to selection bias and counteract the statistical power provided by the magnitude of EHR [4].

Systematic measurement errors in electronic health record databases can lead to large inferential errors. Validation techniques can help determine the degree of these errors and therefore aid in the interpretation of findings.http://ow.ly/iHQ630np4xU

Overview of changes in RMD epidemiology and outcome development in the last 10 years

Epidemiological studies have been affected by environmental (or technological and societal) changes in the last 10 years, such as the emergence of registries, big data and machine learning algorithms, epigenetics, data protection regulations or a more solid presence of the patient perspective in outcomes research. As a consequence we, epidemiologists, are facing challenges in the design, conduct, and analysis of the studies, as well as on the interpretation of the results. Not everything that is new may be better than the old ways of doing epidemiology. In this article, we will review pros and cons of new technologies and regulations on epidemiological research, as well as ways to tackle obstacles and co-living of old and new methods.

Opioid-Related Harms: Simplistic Solutions to the Crisis Ineffective and Cause Collateral Damage

The narrative of the opioid crisis is that ill-informed and careless prescribing by physicians has led to increases in opioid-related harms including overdose deaths. Focusing on reducing the access to prescribed opioids without treating substance use disorder has led to increases in use of heroin and illicitly produced fentanyl. Overall prescribing of opioids has declined causing collateral damage to those who use opioids appropriately to reduce pain and improve function. The complexity of this issue requires a change in focus and broad changes in society's approach to substance abuse and mental health.

Phenome-wide association studies across large population cohorts support drug target validation

Phenome-wide association studies (PheWAS) have been proposed as a possible aid in drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we select 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease indications. We interrogate these SNPs by PheWAS in four large cohorts with extensive health information (23andMe, UK Biobank, FINRISK, CHOP) for association with 1683 binary endpoints in up to 697,815 individuals and conduct meta-analyses for 145 mapped disease endpoints. Our analyses replicate 75% of known GWAS associations (P < 0.05) and identify nine study-wide significant novel associations (of 71 with FDR < 0.1). We describe associations that may predict ADEs, e.g., acne, high cholesterol, gout, and gallstones with rs738409 (p.I148M) in PNPLA3 and asthma with rs1990760 (p.T946A) in IFIH1. Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery.

Testing the association between genetic variants and a range of phenotypes can assist drug development. Here, in a phenome-wide association study in up to 697,815 individuals, Diogo et al. identify genotype–phenotype associations predicting efficacy, alternative indications or adverse drug effects.

A review of Big Data analytics and potential for implementation in the delivery of global neurosurgery

Global access to neurosurgical care is still a work in progress, with many patients in low-income countries not able to access potentially lifesaving neurosurgical procedures. “Big Data” is an increasingly popular data collection and analytical technique predicated on collecting large amounts of data across multiple data sources and types for future analysis. The potential applications of Big Data to global outreach neurosurgery are myriad: from assessing the overall burden of neurosurgical disease to planning cost-effective improvements in access to neurosurgical care, and collecting data on conditions which are rare in developed countries. Although some global neurosurgical outreach programs have intelligently implemented Big Data principles in their global neurosurgery initiatives already, there is still significant progress that remains to be made. Big Data has the potential to drive the efficient improvement of access to neurosurgical care across low- and medium-income countries.

The identification of cases of major hemorrhage during hospitalization in patients with acute leukemia using routinely recorded healthcare data

INTRODUCTION

Electronic health care data offers the opportunity to study rare events, although detecting these events in large datasets remains difficult. We aimed to develop a model to identify leukemia patients with major hemorrhages within routinely recorded health records.

METHODS

The model was developed using routinely recorded health records of a cohort of leukemia patients admitted to an academic hospital in the Netherlands between June 2011 and December 2015. Major hemorrhage was assessed by chart review. The model comprised CT-brain, hemoglobin drop, and transfusion need within 24 hours for which the best discriminating cut off values were taken. External validation was performed within a cohort of two other academic hospitals.

RESULTS

The derivation cohort consisted of 255 patients, 10,638 hospitalization days, of which chart review was performed for 353 days. The incidence of major hemorrhage was 0.22 per 100 days in hospital. The model consisted of CT-brain (yes/no), hemoglobin drop of ≥0.8 g/dl and transfusion of ≥6 units. The C-statistic was 0.988 (CI 0.981-0.995). In the external validation cohort of 436 patients (19,188 days), the incidence of major hemorrhage was 0.46 per 100 hospitalization days and the C-statistic was 0.975 (CI 0.970-0.980). Presence of at least one indicator had a sensitivity of 100% (CI 95.8-100) and a specificity of 90.7% (CI 90.2-91.1). The number of days to screen to find one case decreased from 217.4 to 23.6.

INTERPRETATION

A model based on information on CT-brain, hemoglobin drop and need of transfusions can accurately identify cases of major hemorrhage within routinely recorded health records.

Challenges of evaluating chronic heart failure and acute heart failure events in research studies using large health care databases

Epidemiological studies on heart failure (HF) using large health care databases are becoming increasingly frequent, as they represent an invaluable opportunity to characterize the importance and risk factors of HF from a population perspective. Nevertheless, because of its complex diagnosis and natural history, the heterogeneous use of the relevant terminology in routine clinical practice, and the limitations of some disease coding systems, HF can be a challenging condition to assess using large health care databases as the main source of information. In this narrative review, we discuss some of the challenges that researchers may face, with a special focus on the identification and validation of chronic HF cases and acute HF decompensations. For each of these challenges, we present some potential solutions inspired by the literature and/or based on our research experience, aimed at increasing the internal validity of research and at informing its interpretation. We also discuss future directions on the field, presenting constructive recommendations aimed at facilitating the conduct of valid epidemiological studies on HF in the coming years.

Analysis of an Internet Community about Pneumothorax and the Importance of Accurate Information about the Disease

Background

The huge improvements in the speed of data transmission and the increasing amount of data available as the Internet has expanded have made it easy to obtain information about any disease. Since pneumothorax frequently occurs in young adolescents, patients often search the Internet for information on pneumothorax.

Methods

This study analyzed an Internet community for exchanging information on pneumothorax, with an emphasis on the importance of accurate information and doctors’ role in providing such information.

Results

This study assessed 599,178 visitors to the Internet community from June 2008 to April 2017. There was an average of 190 visitors, 2.2 posts, and 4.5 replies per day. A total of 6,513 posts were made, and 63.3% of them included questions about the disease. The visitors mostly searched for terms such as ‘pneumothorax,’ ‘recurrent pneumothorax,’ ‘pneumothorax operation,’ and ‘obtaining a medical certification of having been diagnosed with pneumothorax.’ However, 22% of the pneumothorax-related posts by visitors contained inaccurate information.

Conclusion

Internet communities can be an important source of information. However, incorrect information about a disease can be harmful for patients. We, as doctors, should try to provide more in-depth information about diseases to patients and to disseminate accurate information about diseases in Internet communities.

Precision Medicine in Type 2 Diabetes: Clinical Markers of Insulin Resistance Are Associated With Altered Short- and Long-term Glycemic Response to DPP-4 Inhibitor Therapy

OBJECTIVE

A precision approach to type 2 diabetes therapy would aim to target treatment according to patient characteristics. We examined if measures of insulin resistance and secretion were associated with glycemic response to dipeptidyl peptidase 4 (DPP-4) inhibitor therapy.

RESEARCH DESIGN AND METHODS

We evaluated whether markers of insulin resistance and insulin secretion were associated with 6-month glycemic response in a prospective study of noninsulin-treated participants starting DPP-4 inhibitor therapy (Predicting Response to Incretin Based Agents [PRIBA] study; n = 254), with replication for routinely available markers in U.K. electronic health care records (Clinical Practice Research Datalink [CPRD]; n = 23,001). In CPRD, we evaluated associations between baseline markers and 3-year durability of response. To test the specificity of findings, we repeated analyses for glucagon-like peptide 1 (GLP-1) receptor agonists (PRIBA, n = 339; CPRD, n = 4,464).

RESULTS

In PRIBA, markers of higher insulin resistance (higher fasting C-peptide [P = 0.03], HOMA2 insulin resistance [P = 0.01], and triglycerides [P < 0.01]) were associated with reduced 6-month HbA_1c response to DPP-4 inhibitors. In CPRD, higher triglycerides and BMI were associated with reduced HbA_1c response (both P < 0.01). A subgroup defined by obesity (BMI ≥30 kg/m²) and high triglycerides (≥2.3 mmol/L) had reduced 6-month response in both data sets (PRIBA HbA_1c reduction 5.3 [95% CI 1.8, 8.6] mmol/mol [0.5%] [obese and high triglycerides] vs. 11.3 [8.4, 14.1] mmol/mol [1.0%] [nonobese and normal triglycerides]; P = 0.01). In CPRD, the obese, high- triglycerides subgroup also had less durable response (hazard ratio 1.28 [1.16, 1.41]; P < 0.001). There was no association between markers of insulin resistance and response to GLP-1 receptor agonists.

CONCLUSIONS

Markers of higher insulin resistance are consistently associated with reduced glycemic response to DPP-4 inhibitors. This finding provides a starting point for the application of a precision diabetes approach to DPP-4 inhibitor therapy.