Impact of Terminology Mapping on Population Health Cohorts IMPaCt

Background and Objectives: The population health care delivery model uses phenotype algorithms in the electronic health record (EHR) system to identify patient cohorts targeted for clinical interventions such as laboratory tests, and procedures. The standard terminology used to identify disease cohorts may contribute to significant variation in error rates for patient inclusion or exclusion. The United States requires EHR systems to support two diagnosis terminologies, the International Classification of Disease (ICD) and the Systematized Nomenclature of Medicine (SNOMED). Terminology mapping enables the retrieval of diagnosis data using either terminology. There are no standards of practice by which to evaluate and report the operational characteristics of ICD and SNOMED value sets used to select patient groups for population health interventions. Establishing a best practice for terminology selection is a step forward in ensuring that the right patients receive the right intervention at the right time. The research question is, “How does the diagnosis retrieval terminology (ICD vs SNOMED) and terminology map maintenance impact population health cohorts?” Aim 1 and 2 explore this question, and Aim 3 informs practice and policy for population health programs. Methods Aim 1: Quantify impact of terminology choice (ICD vs SNOMED) ICD and SNOMED phenotype algorithms for diabetes, chronic kidney disease (CKD), and heart failure were developed using matched sets of codes from the Value Set Authority Center. The performance of the diagnosis-only phenotypes was compared to published reference standard that included diagnosis codes, laboratory results, procedures, and medications. Aim 2: Measure terminology maintenance impact on SNOMED cohorts For each disease state, the performance of a single SNOMED algorithm before and after terminology updates was evaluated in comparison to a reference standard to identify and quantify cohort changes introduced by terminology maintenance. Aim 3: Recommend methods for improving population health interventions The socio-technical model for studying health information technology was used to inform best practice for the use of population health interventions. Results Aim 1: ICD-10 value sets had better sensitivity than SNOMED for diabetes (.829, .662) and CKD (.242, .225) (N=201,713, p Aim 2: Following terminology maintenance the SNOMED algorithm for diabetes increased in sensitivity from (.662 to .683 (p Aim 3: Based on observed social and technical challenges to population health programs, including and in addition to the development and measurement of phenotypes, a practical method was proposed for population health intervention development and reporting

Quality of care in incident type 2 diabetes and initial presentation of vascular complications: Prospective cohort study using linked electronic health records from CALIBER research platform

Background. Numbers of new cases of type 2 diabetes (T2D) are increasing rapidly. Early and continuing intervention after T2D presentation is crucial for best possible outcomes, ensuring that the existing high burden of T2D will not be aggravated. Identification of patterns of continuous care and predictors for meeting key targets for T2D management can improve quality of care. Glycaemic control is particularly important for primary prevention of vascular complications but its relationship with contemporary cardiovascular diseases (CVDs) has been less explored. More importantly, long-term glycaemic control can be assessed from routine monitoring, potentially providing new insight into T2D management to prevent vascular complications. Linked electronic health records are invaluable data resources for investigating these issues. Objective. To examine the quality of care in an incident T2D cohort through assessment of temporal trends of care, predictors of glycaemic, blood pressure and lipid control, and associations of short-term and long-term glycaemic control with chronic vascular complications. Methods. The data source for studies in this thesis was CALIBER which links electronic health records from primary care, hospitalisation, myocardial infarction and mortality registries. Patients newly diagnosed with T2D between 1998 and 2010 were followed-up until a censoring administrative date or initial occurrence of vascular complications. Trends in receipt of care and attainment of glycaemic, blood pressure and total cholesterol targets were examined. Predictors for meeting the targets were explored using multinomial logistic regressions. Association of early glycaemic control with a range of specific cardiovascular complications were investigated using Cox regressions. A longitudinal metric for glycaemic control was developed by quantifying time spent at target during follow-up and was tested for its association with cardiovascular and microvascular outcomes using mixed logistic regressions. Results. A total of 52,379 incident T2D patients were identified with a median follow-up of over 4 years. Positive trends were observed for blood pressure and total cholesterol control, but not for glycaemic control, whilst attainment of HbA1c and blood pressure targets over time consistently fell short. Older age at diagnosis was an important predictor for meeting the key targets. In 36,149 patients free from prior CVD, early glycaemic and blood pressure control was associated with lower risk for heart failure and peripheral arterial disease, whereas cholesterol control with myocardial infarction and transient ischaemic attack. Shorter duration at glycaemic target was associated with higher risk of major adverse cardiovascular events, cardiovascular death and diabetic retinopathy. Conclusions. This thesis highlights missed opportunities and inequality in T2D care. Both short-term and long-term glycaemic control are important for reducing risk of vascular complications. Limitations and implications of the findings for clinical practice and research were discussed

Genetics in chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference

Numerous genes for monogenic kidney diseases with classical patterns of inheritance, as well as genes for complex kidney diseases that manifest in combination with environmental factors, have been discovered. Genetic findings are increasingly used to inform clinical management of nephropathies, and have led to improved diagnostics, disease surveillance, choice of therapy, and family counseling. All of these steps rely on accurate interpretation of genetic data, which can be outpaced by current rates of data collection. In March of 2021, Kidney Diseases: Improving Global Outcomes (KDIGO) held a Controversies Conference on "Genetics in Chronic Kidney Disease (CKD)" to review the current state of understanding of monogenic and complex (polygenic) kidney diseases, processes for applying genetic findings in clinical medicine, and use of genomics for defining and stratifying CKD. Given the important contribution of genetic variants to CKD, practitioners with CKD patients are advised to "think genetic," which specifically involves obtaining a family history, collecting detailed information on age of CKD onset, performing clinical examination for extrarenal symptoms, and considering genetic testing. To improve the use of genetics in nephrology, meeting participants advised developing an advanced training or subspecialty track for nephrologists, crafting guidelines for testing and treatment, and educating patients, students, and practitioners. Key areas of future research, including clinical interpretation of genome variation, electronic phenotyping, global representation, kidney-specific molecular data, polygenic scores, translational epidemiology, and open data resources, were also identified

Targeted Metabolomics for Clinical Biomarker Discovery in Multifactorial Diseases

Diseases & disorder