Using electronic health records to quantify and stratify the severity of type 2 diabetes in primary care in England: rationale and cohort study design

Diabetes severity and the risk of depression: A nationwide population-based study

BACKGROUND

In consideration of the substantial occurrence rates of diabetes mellitus (DM) and depression, it is imperative to identify patients with DM who are at an elevated risk of developing depression. Accordingly, this study aimed to examine whether the risk of depression escalated proportionally with the severity of diabetes.

METHODS

2,067,017 adults diagnosed with type 2 DM, with the exception of those diagnosed with depression either before or within one year of the index date, were identified from a nationwide population-based cohort in Korea. Severity scores for DM were established based on various factors, including insulin use, DM duration of at least 5 years, use of three or more oral hypoglycemic agents, the presence of chronic kidney disease (CKD), cardiovascular diseases (CVD), or diabetic retinopathy. Each of these attributes was assigned a score of one point for diabetes severity, and their cumulative sum was defined as a diabetes severity score, ranging from 0 to 6.

RESULTS

During a median follow-up of 6.2 years, 407,047 cases of major depression were identified. Each component contributing to the DM severity score was significantly associated with an increased risk of depression (all P-values <0.001), with insulin use and the presence of CVD demonstrating the most significant correlation with depression risk. As the DM severity score increased, the risk of depression was observed to significantly escalate (P for trend <0.001). After adjusting for potential confounding variables, the hazard ratios (95% confidence intervals) of depression were 1.15 (1.14-1.16) in 1 point, 1.28 (1.27-1.29) in 2 points, 1.45 (1.43-1.47) in 3 points, 1.70 (1.67-1.73) in 4 points, 1.91 (1.84-1.98) in 5 points, and 2.01 (1.79-2.26) in 6 points, respectively.

CONCLUSION

The results of this study indicate that diabetes severity is positively associated with an elevated risk of developing major depression. Based on these findings, it is feasible to consider targeting depression screening efforts towards individuals with higher diabetes severity scores.

Association between dysglycemia and the Charlson Comorbidity Index among hospitalized patients with diabetes

AIM

Inpatient dysglycemia has been linked to short-term mortality, but longer-term mortality data are lacking. Our aim was to evaluate the association between inpatient dysglycemia and one-year mortality risk.

METHODS

Retrospective chart review of adults with diabetes hospitalized between 2015 and 2019. The Charlson Comorbidity Index (CCI) was used to estimate 1-year mortality risk, stratified into low (CCI ≤ 5) and high risk (CCI ≥6). Simple and multivariable logistic regression was used to evaluate the association between dysglycemic measures and high mortality risk.

RESULTS

Among 22,639 unique admissions, BG ≥ 180, ≥300, ≤70, <54 and <40 mg/dL were associated with adjusted odds of 1.43 (95 % CI, 1.33, 1.54), 1.58 (95 % CI, 1.48, 1.68), 2.16 (95 % CI, 2.01, 2.32), 2.58 (95 % CI, 2.32, 2.86), and 2.56 (95 % CI, 2.19, 2.99) for high mortality risk, respectively. Older age and Black race were positively associated with hyperglycemia and hypoglycemia. Myocardial infarction, congestive heart failure (CHF), and moderate to severe liver disease were most strongly associated with hyperglycemia, while renal disease, CHF, peripheral vascular disease, and peptic ulcer disease were most strongly associated with hypoglycemia.

CONCLUSIONS

Inpatient hypoglycemia and hyperglycemia were both positively associated with higher one-year mortality risk, with stronger magnitude of association observed for hypoglycemia. The association appears to be mediated mainly by presence of diabetes-related complications.

Current gaps in management and timely referral of cardiorenal complications among people with type 2 diabetes mellitus in the Middle East and African countries: Expert recommendations

The upsurge of type 2 diabetes mellitus is a major public health concern in the Middle East and North Africa (MENA) and Africa (AFR) region, with cardiorenal complications (CRCs) being the predominant cause of premature morbidity and mortality. High prevalence of cardiometabolic risk factors, lack of awareness among patients and physicians, deficient infrastructure, and economic constraints lead to a cascade of CRCs at a significantly earlier age in MENA and AFR. In this review, we present consensus recommendations by experts in MENA and AFR, highlighting region-specific challenges and potential solutions for management of CRCs. Health professionals who understand sociocultural barriers can significantly increase patient awareness and encourage health-seeking behavior through simple educational tools. Increasing physician knowledge on early identification of CRCs and personalized treatment based on risk stratification, alongside optimum glycemic control, can mitigate therapeutic inertia. Early diagnosis of high-risk people with regular and systematic monitoring of cardiorenal parameters, development of region-specific care pathways for timely referral to specialists, followed by guideline-recommended care with novel antidiabetics are imperative. Adherence to guideline-recommended care can catalyze utilization of sodium glucose cotransporter 2 inhibitors and glucagon-like peptide 1 receptor agonists with demonstrated cardiorenal benefits-thus paving the way for overcoming care gaps in a cost-effective manner. Leveraging digital technology like electronic medical records can help generate real-world data and provide insights on voids in adoption of newer antidiabetic medications. A patient-centric approach, collaborative care among physicians from different specialties, alongside involvement of policy makers are key for improving patient outcomes and quality of care in MENA and AFR.

LV Prasad Eye Institute EyeSmart electronic medical record-based analytics of big data: LEAD-Uveitis Report 1: Demographics and clinical features of uveitis in a multi-tier hospital based network in Southern India

Purpose

To describe the demographics and epidemiology of uveitis presenting to a multi-tier ophthalmology hospital network in Southern India.

Methods

Cross-sectional hospital-based study of 19,352 patients with uveitis presenting between March 2012 and August 2018.

Results

In total, 1,734,272 new patients were seen across the secondary and tertiary centers of our multi-tier ophthalmology hospital network during the study period. Among them, 25,353 eyes of 19,352 patients were diagnosed with uveitis and were included in the study. Uveitis constituted 1.11% of all cases. The majority of patients were male (60.33%) and had unilateral (68.09%) affliction. The most common age group was 21-50 years with 12,204 (63.06%) patients. The most common type of uveitis was anterior uveitis, which was seen in 7380 (38.14%) patients, followed by posterior uveitis in 5397 (23.89%) patients. Among the infectious causes, tuberculosis was the most common etiology (2551 patients, 13%) followed by toxoplasmosis (1147 patients, 6%).

Conclusion

Uveitis constituted 1.11% of all cases presenting to our clinics. It was more common in the age group of 21-50 and was predominantly unilateral. Anterior uveitis was the most common subtype seen in 38%.

Population stratification in type 2 diabetes mellitus: A systematic review

AIMS

There is increasing interest in using stratification in type 2 diabetes to target resources, individualise care and improve outcomes. We aim to systematically review and collate literature that has utilised population stratification methods in the study of adults with type 2 diabetes; and to describe and compare stratification methodologies, population characteristics, variables used to stratify and outcome variables.

METHODS

The MEDLINE, EMBASE, CINAHL and Cochrane databases were searched from inception to July 2020. Studies included adults with type 2 diabetes using population stratification methods. The review protocol was registered on PROSPERO (ID: CRD42020206604) and conducted in line with PRISMA guidance. Extracted data included study aims; study setting (primary or secondary care); population characteristics; stratification variables and outcomes; and methodological approach to stratification.

RESULTS

Across 348 included studies, there were a total of 10,776,009 participants with a mean age of 61.0 years (SD 5.94). 6.7% of studies used data-driven methods and the rest employed expert-driven approaches using pre-defined stratification criteria. The commonest variable used to stratify populations was HbA_1c (n = 57, 16.4%); few studies stratified using clinically important non-traditional variables such as health behaviours and beliefs.

CONCLUSIONS

Most studies performing population stratification in type 2 diabetes used expert-driven approaches with the aim of predicting outcomes in glycaemic control, mortality and cardiovascular complications. We identified relatively few studies using data-driven approaches, which offer opportunities generate hypotheses beyond current expert knowledge. We describe important research gaps including stratification with regard to disease remission.

Estimating the population health burden of musculoskeletal conditions using primary care electronic health records

OBJECTIVES

Better indicators from affordable, sustainable data sources are needed to monitor population burden of musculoskeletal conditions. We propose five indicators of musculoskeletal health and assessed if routinely available primary care electronic health records (EHR) can estimate population levels in musculoskeletal consulters.

METHODS

We collected validated patient-reported measures of pain experience, function and health status through a local survey of adults (≥35 years) presenting to English general practices over 12 months for low back pain, shoulder pain, osteoarthritis and other regional musculoskeletal disorders. Using EHR data we derived and validated models for estimating population levels of five self-reported indicators: prevalence of high impact chronic pain, overall musculoskeletal health (based on Musculoskeletal Health Questionnaire), quality of life (based on EuroQoL health utility measure), and prevalence of moderate-to-severe low back pain and moderate-to-severe shoulder pain. We applied models to a national EHR database (Clinical Practice Research Datalink) to obtain national estimates of each indicator for three successive years.

RESULTS

The optimal models included recorded demographics, deprivation, consultation frequency, analgesic and antidepressant prescriptions, and multimorbidity. Applying models to national EHR, we estimated that 31.9% of adults (≥35 years) presenting with non-inflammatory musculoskeletal disorders in England in 2016/17 experienced high impact chronic pain. Estimated population health levels were worse in women, older aged and those in the most deprived neighbourhoods, and changed little over 3 years.

CONCLUSION

National and subnational estimates for a range of subjective indicators of non-inflammatory musculoskeletal health conditions can be obtained using information from routine electronic health records.

The impact of comorbid chronic kidney disease and diabetes on health-related quality-of-life: a 12-year community cohort study

BACKGROUND

Quality-of-life is an essential outcome for clinical care. Both chronic kidney disease (CKD) and diabetes have been associated with poorer quality-of-life. The combined impact of having both diseases is less well understood. As diabetes is the most common cause of CKD, it is imperative that we deepen our understanding of their joint impact.

METHODS

This was a prospective, longitudinal cohort study of community-based Australians aged ≥25 years who participated in the Australian Diabetes, Obesity and Lifestyle study. Quality-of-life was measured by physical component summary (PCS) and mental component summary sub-scores of the Short Form (36) Health Survey. Univariate and multivariate linear mixed effect regressions were performed.

RESULTS

Of the 11 081 participants with quality-of-life measurements at baseline, 1112 had CKD, 1001 had diabetes and of these 271 had both. Of the 1112 with CKD 421 had Stage 1, 314 had Stage 2, 346 had Stage 3 and 31 had Stages 4/5. Adjusted linear mixed effect models showed baseline PCS was lower for those with both CKD and diabetes compared with either disease alone (P < 0.001). Longitudinal analysis demonstrated a more rapid decline in PCS in those with both diseases.

CONCLUSIONS

The combination of CKD and diabetes has a powerful adverse impact on quality-of-life, and participants with both diseases had significantly poorer quality-of-life than those with one condition.

Assessing the severity of cardiovascular disease in 213 088 patients with coronary heart disease: a retrospective cohort study

Objective

Most current cardiovascular disease (CVD) risk stratification tools are for people without CVD, but very few are for prevalent CVD. In this study, we developed and validated a CVD severity score in people with coronary heart disease (CHD) and evaluated the association between severity and adverse outcomes.

Methods

Primary and secondary care data for 213 088 people with CHD in 398 practices in England between 2007 and 2017 were used. The cohort was randomly divided into training and validation datasets (80%/20%) for the severity model. Using 20 clinical severity indicators (each assigned a weight=1), baseline and longitudinal CVD severity scores were calculated as the sum of indicators. Adjusted Cox and competing-risk regression models were used to estimate risks for all-cause and cause-specific hospitalisation and mortality.

Results

Mean age was 64.5±12.7 years, 46% women, 16% from deprived areas, baseline severity score 1.5±1.2, with higher scores indicating a higher burden of disease. In the training dataset, 138 510 (81%) patients were hospitalised at least once, and 39 944 (23%) patients died. Each 1-unit increase in baseline severity was associated with 41% (95% CI 37% to 45%, area under the receiver operating characteristics (AUROC) curve=0.79) risk for 1 year for all-cause mortality; 59% (95% CI 52% to 67%, AUROC=0.80) for cardiovascular (CV)/diabetes mortality; 27% (95% CI 26% to 28%) for any-cause hospitalisation and 37% (95% CI 36% to 38%) for CV/diabetes hospitalisation. Findings were consistent in the validation dataset.

Conclusions

Higher CVD severity score is associated with higher risks for any-cause and cause-specific hospital admissions and mortality in people with CHD. Our reproducible score based on routinely collected data can help practitioners better prioritise management of people with CHD in primary care.

Big data and the eyeSmart electronic medical record system - An 8-year experience from a three-tier eye care network in India

Purpose

To assess the demographic details and distribution of ocular disorders in patients presenting to a three-tier eye care network in India using electronic medical record (EMR) systems across an 8-year period using big data analytics.

Methods

An 8-year retrospective review of all the patients who presented across the three-tier eye care network of L.V. Prasad Eye Institute was performed from August 2010 to August 2018. Data were retrieved using an in-house eyeSmart EMR system. The demographic details and clinical presentation and ocular disease profile of all the patients were analyzed in detail.

Results

In an 8-year period, a total of 2,270,584 patients were captured on the EMR system with 4,730,221 consultations. More than half of the patients presented at tertiary centers (n = 1,174,643, 51.73%), a quarter at the secondary centers (n = 564,251, 24.85%) followed by the vision centers (n = 531,690, 23.42%). The ratio of males and females was 1.18:1. Most common states of presentation were Andhra Pradesh (n = 1,103,733, 48.61%) and Telangana (n = 661,969, 29.15%). In total, 3,721,051 ocular diagnosis instances were documented in the patients. Most common ocular disorders were related to cornea and anterior segment (n = 1,347,754, 36.22%) followed by refractive error (n = 1,133,078, 30.45%).

Conclusion

This study depicts the demographic details and distribution of various ocular disorders in a very large cohort of patients. There is a need to adopt digitization in geographies that cater to large populations to enable insightful research. The implementation of EMR systems enables structured data for research purposes and the development of real-time analytics for the same.

Does the Encounter Type Matter When Defining Diabetes Complications in Electronic Health Records?

BACKGROUND

Electronic health records (EHRs) and claims records are widely used in defining type 2 diabetes mellitus (T2DM) complications across different types of health care encounters.

OBJECTIVE

This study investigates whether using different EHR encounter types to define diabetes complications may lead to different results when examining associations between diabetes complications and their risk factors in patients with T2DM.

RESEARCH DESIGN

The study cohort of 64,855 adult patients with T2DM was created from EHR data from the Research Action for Health Network (REACHnet), using the Surveillance Prevention, and Management of Diabetes Mellitus (SUPREME-DM) definitions. Incidence of coronary heart disease (CHD) and stroke events were identified using International Classification of Diseases (ICD)-9/10 codes and grouped by encounter types: (1) inpatient (IP) or emergency department (ED) type, or (2) any health care encounter type. Cox proportional hazards regression was used to estimate associations between diabetes complications (ie, CHD and stroke) and risk factors (ie, low-density lipoprotein cholesterol and hemoglobin A1c).

RESULTS

The incidence rates of CHD and stroke in all health care settings were more than twice the incidence rates of CHD and stroke in IP/ED settings. The age-adjusted and multivariable-adjusted hazard ratios for incident CHD and stroke across different levels of low-density lipoprotein cholesterol and hemoglobin A1c were similar between IP/ED and all settings.

CONCLUSION

While there are large variations in incidence rates of CHD and stroke as absolute risks, the associations between both CHD and stroke and their respective risk factors measured by hazard ratios as relative risks are similar, regardless of alternative definitions.

Conceptual Framework to Support Clinical Trial Optimization and End-to-End Enrollment Workflow

In this work, we present a conceptual framework to support clinical trial optimization and enrollment workflows and review the current state, limitations, and future trends in this space. This framework includes knowledge representation of clinical trials, clinical trial optimization, clinical trial design, enrollment workflows for prospective clinical trial matching, waitlist management, and, finally, evaluation strategies for assessing improvement.

Development and validation of the DIabetes Severity SCOre (DISSCO) in 139 626 individuals with type 2 diabetes: a retrospective cohort study

OBJECTIVE

Clinically applicable diabetes severity measures are lacking, with no previous studies comparing their predictive value with glycated hemoglobin (HbA_1c). We developed and validated a type 2 diabetes severity score (the DIabetes Severity SCOre, DISSCO) and evaluated its association with risks of hospitalization and mortality, assessing its additional risk information to sociodemographic factors and HbA_1c.

RESEARCH DESIGN AND METHODS

We used UK primary and secondary care data for 139 626 individuals with type 2 diabetes between 2007 and 2017, aged ≥35 years, and registered in general practices in England. The study cohort was randomly divided into a training cohort (n=111 748, 80%) to develop the severity tool and a validation cohort (n=27 878). We developed baseline and longitudinal severity scores using 34 diabetes-related domains. Cox regression models (adjusted for age, gender, ethnicity, deprivation, and HbA_1c) were used for primary (all-cause mortality) and secondary (hospitalization due to any cause, diabetes, hypoglycemia, or cardiovascular disease or procedures) outcomes. Likelihood ratio (LR) tests were fitted to assess the significance of adding DISSCO to the sociodemographics and HbA_1c models.

RESULTS

A total of 139 626 patients registered in 400 general practices, aged 63±12 years were included, 45% of whom were women, 83% were White, and 18% were from deprived areas. The mean baseline severity score was 1.3±2.0. Overall, 27 362 (20%) people died and 99 951 (72%) had ≥1 hospitalization. In the training cohort, a one-unit increase in baseline DISSCO was associated with higher hazard of mortality (HR: 1.14, 95% CI 1.13 to 1.15, area under the receiver operating characteristics curve (AUROC)=0.76) and cardiovascular hospitalization (HR: 1.45, 95% CI 1.43 to 1.46, AUROC=0.73). The LR tests showed that adding DISSCO to sociodemographic variables significantly improved the predictive value of survival models, outperforming the added value of HbA_1c for all outcomes. Findings were consistent in the validation cohort.

CONCLUSIONS

Higher levels of DISSCO are associated with higher risks for hospital admissions and mortality. The new severity score had higher predictive value than the proxy used in clinical practice, HbA_1c. This reproducible algorithm can help practitioners stratify clinical care of patients with type 2 diabetes.

Assessing the severity of Type 2 diabetes using clinical data-based measures: a systematic review

AIMS

To identify and critically appraise measures that use clinical data to grade the severity of Type 2 diabetes.

METHODS

We searched MEDLINE, Embase and PubMed between inception and June 2018. Studies reporting on clinical data-based diabetes-specific severity measures in adults with Type 2 diabetes were included. We excluded studies conducted solely in participants with other types of diabetes. After independent screening, the characteristics of the eligible measures including design and severity domains, the clinical utility of developed measures, and the relationship between severity levels and health-related outcomes were assessed.

RESULTS

We identified 6798 studies, of which 17 studies reporting 18 different severity measures (32 314 participants in 17 countries) were included: a diabetes severity index (eight studies, 44%); severity categories (seven studies, 39%); complication count (two studies, 11%); and a severity checklist (one study, 6%). Nearly 89% of the measures included diabetes-related complications and/or glycaemic control indicators. Two of the severity measures were validated in a separate study population. More severe diabetes was associated with increased healthcare costs, poorer cognitive function and significantly greater risks of hospitalization and mortality. The identified measures differed greatly in terms of the included domains. One study reported on the use of a severity measure prospectively.

CONCLUSIONS

Health records are suitable for assessment of diabetes severity; however, the clinical uptake of existing measures is limited. The need to advance this research area is fundamental as higher levels of diabetes severity are associated with greater risks of adverse outcomes. Diabetes severity assessment could help identify people requiring targeted and intensive therapies and provide a major benchmark for efficient healthcare services.