Incorrect and incomplete coding and classification of diabetes: a systematic review

Phenotype execution and modeling architecture to support disease surveillance and real-world evidence studies: English sentinel network evaluation

Objective

To evaluate Phenotype Execution and Modelling Architecture (PhEMA), to express sharable phenotypes using Clinical Quality Language (CQL) and intensional Systematised Nomenclature of Medicine (SNOMED) Clinical Terms (CT) Fast Healthcare Interoperability Resources (FHIR) valuesets, for exemplar chronic disease, sociodemographic risk factor, and surveillance phenotypes.

Method

We curated 3 phenotypes: Type 2 diabetes mellitus (T2DM), excessive alcohol use, and incident influenza-like illness (ILI) using CQL to define clinical and administrative logic. We defined our phenotypes with valuesets, using SNOMED's hierarchy and expression constraint language, and CQL, combining valuesets and adding temporal elements where needed. We compared the count of cases found using PhEMA with our existing approach using convenience datasets. We assessed our new approach against published desiderata for phenotypes.

Results

The T2DM phenotype could be defined as 2 intensionally defined SNOMED valuesets and a CQL script. It increased the prevalence from 7.2% to 7.3%. Excess alcohol phenotype was defined by valuesets that added qualitative clinical terms to the quantitative conceptual definitions we currently use; this change increased prevalence by 58%, from 1.2% to 1.9%. We created an ILI valueset with SNOMED concepts, adding a temporal element using CQL to differentiate new episodes. This increased the weekly incidence in our convenience sample (weeks 26-38) from 0.95 cases to 1.11 cases per 100 000 people.

Conclusions

Phenotypes for surveillance and research can be described fully and comprehensibly using CQL and intensional FHIR valuesets. Our use case phenotypes identified a greater number of cases, whilst anticipated from excessive alcohol this was not for our other variable. This may have been due to our use of SNOMED CT hierarchy. Our new process fulfilled a greater number of phenotype desiderata than the one that we had used previously, mostly in the modeling domain. More work is needed to implement that sharing and warehousing domains.

Reduced incidence of diabetes during the COVID-19 pandemic in Alberta: A time-segmented longitudinal study of Alberta's Tomorrow Project

AIM

To characterize the impact of the COVID-19 pandemic on diabetes diagnosis using data from Alberta's Tomorrow Project (ATP), a population-based cohort study of chronic diseases in Alberta, Canada.

MATERIALS AND METHODS

The ATP participants who were free of diabetes on 1 April 2018 were included in the study. A time-segmented regression model was used to compare incidence rates of diabetes before the COVID-19 pandemic, during the first two COVID-19 states of emergency, and in the period when the state of emergency was relaxed, after adjusting for seasonality, sociodemographic factors, socioeconomic status, and lifestyle behaviours.

RESULTS

Among 43 705 ATP participants free of diabetes (65.5% females, age 60.4 ± 9.5 years in 2018), the rate of diabetes was 4.75 per 1000 person-year (PY) during the COVID-19 pandemic (up to 31 March 2021), which was 32% lower (95% confidence interval [CI] 21%, 42%; p < 0.001) than pre-pandemic (6.98 per 1000 PY for the period 1 April 2018 to 16 March 2020). In multivariable regression analysis, the first COVID-19 state of emergency (first wave) was associated with an 87.3% (95% CI -98.6%, 13.9%; p = 0.07) reduction in diabetes diagnosis; this decreasing trend was sustained to the second COVID-19 state of emergency and no substantial rebound (increase) was observed when the COVID-19 state of emergency was relaxed.

CONCLUSIONS

The COVID-19 public health emergencies had a negative impact on diabetes diagnosis in Alberta. The reduction in diabetes diagnosis was likely due to province-wide health service disruptions during the COVID-19 pandemic. Systematic plans to close the post-COVID-19 diagnostic gap are required in diabetes to avoid substantial downstream sequelae of undiagnosed disease.

Performance of European prediction models for classification of type 1 and type 2 diabetes in Indians

AIM

We aimed to determine the performance of European prediction models in an Indian population to classify type 1 diabetes(T1D) and type 2 diabetes(T2D).

METHODS

We assessed discrimination and calibration of published models of diabetes classification, using retrospective data from electronic medical records of 83309 participants aged 18-50 years living in India. Diabetes type was defined based on C-peptide measurement and early insulin requirement. Models assessed combinations of clinical measurements: age at diagnosis, body mass index(mean = 26.6 kg/m2), sex(male = 64.9 %), Glutamic acid decarboxylase(GAD) antibody, serum cholesterol, serum triglycerides, and high-density lipoprotein(HDL) cholesterol.

RESULTS

67955 participants met inclusion criteria, of whom 0.8 % had T1D, which was markedly lower than model development cohorts. Model discrimination for clinical features was broadly similar in our Indian cohort compared to the European cohort: area under the receiver operating characteristic curve(AUC ROC) was 0.90 vs. 0.90 respectively, but was lower in the subset of young participants with measured GAD antibodies(n = 2404): and an AUC ROC of 0.87 when clinical features, sex, lipids and GAD antibodies were combined. All models substantially overestimated the likelihood of T1D, reflecting the lower prevalence of T1D in the Indian population. However, good model performance was achieved after recalibration by updating the model intercept and slope.

CONCLUSION

Models for diabetes classification maintain the discrimination of T1D and T2D in this Indian population, where T2D is far more common, but require recalibration to obtain appropriate model probabilities. External validation and recalibration are needed before these tools can be used in non-European populations.

Identifying type 1 and 2 diabetes in research datasets where classification biomarkers are unavailable: assessing the accuracy of published approaches

OBJECTIVES

We aimed to compare the performance of approaches for classifying insulin-treated diabetes within research datasets without measured classification biomarkers, evaluated against two independent biological definitions of diabetes type.

STUDY DESIGN AND SETTING

We compared accuracy of ten reported approaches for classifying insulin-treated diabetes into type 1 (T1D) and type 2 (T2D) diabetes in two cohorts: UK Biobank (UKBB) n = 26,399 and Diabetes Alliance for Research in England (DARE) n = 1,296. The overall performance for classifying T1D and T2D was assessed using: a T1D genetic risk score and genetic stratification method (UKBB); C-peptide measured at >3 years diabetes duration (DARE).

RESULTS

Approaches' accuracy ranged from 71% to 88% (UKBB) and 68% to 88% (DARE). When classifying all participants, combining early insulin requirement with a T1D probability model (incorporating diagnosis age and body image issue [BMI]), and interview-reported diabetes type (UKBB available in only 15%) consistently achieved high accuracy (UKBB 87% and 87% and DARE 85% and 88%, respectively). For identifying T1D with minimal misclassification, models with high thresholds or young diagnosis age (<20 years) had highest performance. Findings were incorporated into an online tool identifying optimum approaches based on variable availability.

CONCLUSION

Models combining continuous features with early insulin requirement are the most accurate methods for classifying insulin-treated diabetes in research datasets without measured classification biomarkers.

Developing services to support the delivery of care to people with early-onset type 2 diabetes

Early-onset type 2 diabetes occurring in childhood or early adulthood carries a significant excess burden of microvascular diabetes complications, cardiovascular disease and premature death, compared to later onset type 2 diabetes along with adverse pregnancy outcomes in women of child-bearing age. National audit data in England reveal that 122,780 individuals under the age of 40 years are currently living with type 2 diabetes, with an over-representation of people from minority ethnicities and those in the most socioeconomically deprived quintiles. A diagnosis of type 2 diabetes earlier in life poses some unique challenges to healthcare providers that are not routinely encountered when type 2 diabetes presents later. These include; (1) the need to ensure correct diabetes classification in an age group that carries a higher probability of other types of diabetes, (2) overcoming difficulties in engaging with individuals who are of working age or in full-time education, (3) appreciating and addressing the lower attainment of diabetes treatment targets and (4) proactively supporting women of child-bearing age to optimise their future pregnancy outcomes through better preparation for pregnancy, including achieving optimum glycaemic control at the time of conception. Meanwhile, approaches to prevent type 2 diabetes in younger age groups are challenged by difficulties in identifying those at highest risk, by poorer attendance at lifestyle interventions to prevent or delay the onset of type 2 diabetes and by attenuation of associated weight loss in those that do attend. In this article, we discuss the importance of recognising and addressing the distinct challenges in delivering healthcare to those with early-onset type 2 diabetes, the greater challenges in preventing type 2 diabetes at younger ages, and key components of strategies that might address these challenges to drive improvements in pregnancy outcomes, microvascular and cardiovascular outcomes.

Uncontrolled diabetes mellitus increases risk of infection in patients with advanced cirrhosis

BACKGROUND

Diabetes mellitus (DM) is common in patients with cirrhosis and is associated with increased risk of infection.

AIM

To analyze the impact of uncontrolled DM on infection and mortality among inpatients with advanced cirrhosis.

METHODS

This study utilized the Nationwide Inpatient Sample from 1998 to 2014. We defined advanced cirrhosis using a validated ICD-9-CM algorithm requiring a diagnosis of cirrhosis and clinically significant portal hypertension or decompensation. The primary outcome was bacterial infection. Secondary outcomes included inpatient mortality stratified by elderly age (age≥70). Multivariable logistic regression analyzed outcomes.

RESULTS

906,559 (29.2%) patients had DM and 109,694 (12.1%) were uncontrolled. Patients who had uncontrolled DM were younger, had less ascites, but more encephalopathy. Bacterial infection prevalence was more common in uncontrolled DM (34.2% vs. 28.4%, OR 1.33, 95% CI 1.29-1.37, p<0.001). Although uncontrolled DM was not associated with mortality, when stratified by age, elderly patients with uncontrolled DM had a significantly higher risk of inpatient mortality (OR 1.62, 95% CI 1.46-1.81).

CONCLUSIONS

Uncontrolled DM is associated with increased risk of infection, and when combined with elderly age is associated with increased risk of inpatient mortality. Glycemic control is a modifiable target to improve morbidity and mortality in patients with advanced cirrhosis.

National diabetes registries: do they make a difference?

AIMS

The global epidemic of diabetes mellitus continues to expand, including its large impact on national health care. Measuring diabetes outcomes and their causes of variation highlights areas for improvement in care and efficiency gains; large registries carry this potential. By means of a systematic review, we aimed to give an overview of national registries worldwide by quantifying their data and assessing their influence on diabetes care.

METHODS

The literature on MEDLINE up to March 31, 2020, was searched, using keywords diabetes mellitus, national, registry, registration, and/or database. National disease-specific registries from corresponding articles were included. Database characteristics and clinical variables were obtained. All registries were compared to the ICHOM standard set of outcomes.

RESULTS

We identified 12 national clinical diabetes registries, comprising a total of 7,181,356 diabetic patients worldwide. Nearly all registries recorded weight, HbA1c, lipid profile, and insulin treatment; the recording of other variables varied to a great extent. Overall, registries corresponded fairly well with the ICHOM set. Most registries proved to monitor and improve the quality of diabetes care using guidelines as a benchmark. The effects on national healthcare policy were more variable and often less clear.

CONCLUSIONS

National diabetes registries confer clear insights into diagnostics, complications, and treatment. The extent to which registries influenced national healthcare policy was less clear. A globally implemented standard outcome set has the potential to improve concordance between national registries, enhance the comparison and exchange of diabetes outcomes, and allocate resources and interventions where most needed.

Impact of Metabolic Surgery on Type-2 Diabetes Remission

Diabetes Mellitus is characterized by numerous metabolic disorders, which have in common the serum elevation of glucose, caused for a pancreatic malfunction in insulin secretion and / or its action. It is a non-communicable disease, considered major public health problems and generalized growth worldwide, being a chronic disease, which can generate a high treatment cost. Metabolic surgery is a safe treatment, regulated by the Federal Council of Medicine and useful in treating people with BMI over 30 years of age, who are unable to control pathologies associated with obesity, primarily type 2 diabetes. The general objective of this study is to understand through a literature review the main impacts of metabolic surgery about the remission of DM 2. This present study it is an exploratory and descriptive study carried out through a literature review. Data were collected through research in virtual health databases, at the Virtual Health Library - VHL, Latin American and Caribbean Health Sciences Information System, LILACS, National Library of Medicine - MEDLINE, Scielo, USP database, PUBMED theses and books. Metabolic surgery proof be a good and effective treatment for having and maintaining good weight loss, as well as a significant clinical and metabolic improvement that extends beyond weight loss. Metabolic surgery is a satisfactory way of achieving long-term weight reduction in obese individuals, increasing survival for these patients. Obese patients with DM2 have a long-term remission of DM2 after bariatric / metabolic surgery. Therefore, it concludes that such procedure is effective in the treatment of the disease and other diseases associated with obesity.

Systematic Review of Polygenic Risk Scores for Type 1 and Type 2 Diabetes

Recent studies have led to considerable advances in the identification of genetic variants associated with type 1 and type 2 diabetes. An approach for converting genetic data into a predictive measure of disease susceptibility is to add the risk effects of loci into a polygenic risk score. In order to summarize the recent findings, we conducted a systematic review of studies comparing the accuracy of polygenic risk scores developed during the last two decades. We selected 15 risk scores from three databases (Scopus, Web of Science and PubMed) enrolled in this systematic review. We identified three polygenic risk scores that discriminate between type 1 diabetes patients and healthy people, one that discriminate between type 1 and type 2 diabetes, two that discriminate between type 1 and monogenic diabetes and nine polygenic risk scores that discriminate between type 2 diabetes patients and healthy people. Prediction accuracy of polygenic risk scores was assessed by comparing the area under the curve. The actual benefits, potential obstacles and possible solutions for the implementation of polygenic risk scores in clinical practice were also discussed. Develop strategies to establish the clinical validity of polygenic risk scores by creating a framework for the interpretation of findings and their translation into actual evidence, are the way to demonstrate their utility in medical practice.

Development and validation of multivariable clinical diagnostic models to identify type 1 diabetes requiring rapid insulin therapy in adults aged 18-50 years

OBJECTIVE

To develop and validate multivariable clinical diagnostic models to assist distinguishing between type 1 and type 2 diabetes in adults aged 18-50.

DESIGN

Multivariable logistic regression analysis was used to develop classification models integrating five pre-specified predictor variables, including clinical features (age of diagnosis, body mass index) and clinical biomarkers (GADA and Islet Antigen 2 islet autoantibodies, Type 1 Diabetes Genetic Risk Score), to identify type 1 diabetes with rapid insulin requirement using data from existing cohorts.

SETTING

UK cohorts recruited from primary and secondary care.

PARTICIPANTS

1352 (model development) and 582 (external validation) participants diagnosed with diabetes between the age of 18 and 50 years of white European origin.

MAIN OUTCOME MEASURES

Type 1 diabetes was defined by rapid insulin requirement (within 3 years of diagnosis) and severe endogenous insulin deficiency (C-peptide <200 pmol/L). Type 2 diabetes was defined by either a lack of rapid insulin requirement or, where insulin treated within 3 years, retained endogenous insulin secretion (C-peptide >600 pmol/L at ≥5 years diabetes duration). Model performance was assessed using area under the receiver operating characteristic curve (ROC AUC), and internal and external validation.

RESULTS

Type 1 diabetes was present in 13% of participants in the development cohort. All five predictor variables were discriminative and independent predictors of type 1 diabetes (p<0.001 for all) with individual ROC AUC ranging from 0.82 to 0.85. Model performance was high: ROC AUC range 0.90 (95% CI 0.88 to 0.93) (clinical features only) to 0.97 (95% CI 0.96 to 0.98) (all predictors) with low prediction error. Results were consistent in external validation (clinical features and GADA ROC AUC 0.93 (0.90 to 0.96)).

CONCLUSIONS

Clinical diagnostic models integrating clinical features with biomarkers have high accuracy for identifying type 1 diabetes with rapid insulin requirement, and could assist clinicians and researchers in accurately identifying patients with type 1 diabetes.

Recent advances and perspectives in next generation sequencing application to the genetic research of type 2 diabetes

Type 2 diabetes (T2D) mellitus is a common complex disease that currently affects more than 400 million people worldwide and has become a global health problem. High-throughput sequencing technologies such as whole-genome and whole-exome sequencing approaches have provided numerous new insights into the molecular bases of T2D. Recent advances in the application of sequencing technologies to T2D research include, but are not limited to: (1) Fine mapping of causal rare and common genetic variants; (2) Identification of confident gene-level associations; (3) Identification of novel candidate genes by specific scoring approaches; (4) Interrogation of disease-relevant genes and pathways by transcriptional profiling and epigenome mapping techniques; and (5) Investigation of microbial community alterations in patients with T2D. In this work we review these advances in application of next-generation sequencing methods for elucidation of T2D pathogenesis, as well as progress and challenges in implementation of this new knowledge about T2D genetics in diagnosis, prevention, and treatment of the disease.

Association of a Combined Measure of Adherence and Treatment Intensity With Cardiovascular Outcomes in Patients With Atherosclerosis or Other Cardiovascular Risk Factors Treated With Statins and/or Ezetimibe

IMPORTANCE

Both adherence and treatment intensity can alter the effectiveness of lipid-lowering therapy in routine clinical practice.

OBJECTIVE

To evaluate the association of adherence and treatment intensity with cardiovascular outcomes in patients with documented cardiovascular disease (CVD), type 2 diabetes without CVD or chronic kidney disease (CKD), and CKD without CVD.

DESIGN, SETTING, AND PARTICIPANTS

Retrospective cohort study using the Clinical Practice Research Datalink from January 2010 through February 2016. United Kingdom primary care was the setting. Participants were newly treated patients who received their first statin and/or ezetimibe prescription between January 1, 2010, and December 31, 2013, plus an additional prescription for statins and/or ezetimibe during the following year.

EXPOSURES

Adherence was assessed annually using the proportion of days covered, with adherent defined as a proportion of days covered of 80% or higher. Treatment intensity was classified according to guidelines based on the expected percentage of low-density lipoprotein cholesterol (LDL-C) reduction as low (<30% reduction), moderate (30% to <50% reduction), or high (≥50% reduction). Adherence and treatment intensity were multiplied to create a combined measure, reflecting treatment intensity after accounting for adherence.

MAIN OUTCOMES AND MEASURES

Composite end point of cardiovascular death or hospitalization for myocardial infarction, unstable angina, ischemic stroke, heart failure, or revascularization. Hazard ratios (HRs) were estimated against patients not treated for 1 year or longer.

RESULTS

Among a total of 29 797 newly treated patients, there were 16 701, 12 422, and 674 patients with documented CVD, type 2 diabetes without CVD or CKD, and CKD without CVD, respectively; mean (SD) ages were 68.3 (13.2), 59.3 (12.4), and 67.3 (15.1) years, and male proportions were 60.6%, 55.0%, and 47.0%. In the documented CVD cohort, patients receiving high-intensity therapy were more likely to be adherent over time (84.1% in year 1 and 72.3% in year 6) than patients receiving low-intensity therapy (57.4% in year 1 and 48.4% in year 6). Using a combined measure of adherence and treatment intensity, a graded association was observed with both LDL-C reduction and CVD outcomes: each 10% increase in the combined measure was associated with a 10% lower risk (HR, 0.90; 95% CI, 0.86-0.94). Adherent patients receiving a high-intensity regimen had the lowest risk (HR, 0.60; 95% CI, 0.54-0.68) vs patients untreated for 1 year or longer. Findings in the other 2 cohorts were similar.

CONCLUSIONS AND RELEVANCE

Results of this study demonstrate that the lowest cardiovascular risk was observed among adherent patients receiving high-intensity therapy, and the highest cardiovascular risk was observed among nonadherent patients receiving low-intensity therapy. Strategies that improve adherence and greater use of intensive therapies could substantially improve cardiovascular risk.

Filmed Monologue Vignettes: a novel method for investigating how clinicians document consultations in electronic health records

Introduction and Objectives

The accuracy of conclusions from research based on Electronic Healthcare Records (EHRs) is highly dependent on the correct selection of descriptors (codes) by users, but few methods exist for examining quality and drivers of documentation. We aimed to evaluate the feasibility and acceptability of filmed vignette monologues as a resource-light method of assessing and comparing how different EHR users record the same clinical scenario.

Methods

Six short monologues portraying simulated patients presenting allergic conditions to their General Practitioners were filmed head-on then electronically distributed for the study; no researcher was present during data collection. The method was assessed by participant uptake, reported ease of completion by participants, compliance with instructions, the receipt of interpretable data by researchers, and participant perceptions of vignette quality, realism and information content.

Results

Twenty-two participants completed the study, reporting only minor difficulties. 132 screenshots were returned electronically, enabling analysis of codes, free text and EHR features. Participants assigned a quality rating of 7.7/10 (range 2-10) to the vignettes and rated the extent to which vignettes reflected real-life at 93% (range 86-100%). Between 1 and 2 hours were required to complete the task. Full compliance with instructions varied between participants, but was largely successful.

Conclusions

Filmed monologues are a reproducible, standardized method, which require relatively few resources, yet allow clear assessment of clinicians' and EHRs systems' impact on documentation. The novel nature of this method necessitates clear instructions, so participants can fully complete the study without face-to-face researcher supervision.

Differences in the subgingival microbial population of chronic periodontitis in subjects with and without type 2 diabetes mellitus-a systematic review

OBJECTIVES

The purpose of this systematic review was to evaluate the available evidence in the literature in regard to the subgingival microbial population of chronic periodontitis in subjects with type 2 diabetes mellitus (T2DM+PD) compared to non-diabetic subjects (NDM+PD).

MATERIALS AND METHODS

A literature search was conducted at Ovid MEDLINE and EMBASE database from 1980 to 2016, supplemented by hand searching as needed. Studies presenting with at least one of the primary outcomes (presence of any subgingival microorganisms, proportion and/or the amount of any subgingival plaque bacteria in T2DM+PD versus NDM+PD) were included. Screening, data extraction and quality assessment were conducted independently and in duplicate.

RESULTS

From 611 citations, 19 full-text papers were screened and 11 articles were included for critical appraisal by both reviewers. Some evidence of a difference in the microbial profile between chronic PD subjects with and without T2DM was identified. The strength of evidence is strongest in Tannerella forthysia (T .forsythia) which was reported to be less frequent in the diabetic (T2DM+PD) group in five of the studies, followed by a weaker strength of evidence for other periodontal pathogens such as Porphyromonas gingivalis (P. gingivalis) and Aggregatibacter actinomycetemcomitans (A. actinomycetemcomitans), which were also found less frequent in the diabetic (T2DM+PD) group .

CONCLUSION

Only few studies have compared T2DM+PD with NDM+PD. It is therefore strongly recommended that further studies which include four distinct groups of participants (NDM+PD, T2DM+PD, NDM+NPD, T2DM+NPD) instead of using intra-subject comparisons between healthy and diseased sites of the same subjects.

CLINICAL RELEVANCE

Differences in bacterial populations of T2DM+PD in comparison to NDM+PD subjects may indicate the need of different protocols for the treatment of the diabetic patients with periodontal disease.

Changes in body mass index and incidence of diabetes: A longitudinal study of Alberta's Tomorrow Project Cohort

Although obesity is a known risk factor for diabetes, the impact of body mass index (BMI) changes over time, especially BMI reduction, on diabetes development is less than clear. The objective of this study is to characterize the association between BMI changes over time and incidence of diabetes in a cohort of adults in Alberta. From 2000 to 2008, Alberta's Tomorrow Project (ATP) enrolled participants aged 35-69 to a population-based prospective cohort study. BMI was calculated from self-reported height and weight; change in BMI (∆BMI) was calculated as the difference between baseline and follow-up measurements. Diabetes cases were identified using the Canadian National Diabetes Surveillance System algorithm applied to linked administrative data (2000-2015). Multivariable Cox regression was used to examine the association between ∆BMI and incidence of diabetes. In a subset of the ATP cohort (n=19,164), 1168 incident cases of diabetes were identified during 198,853person-years of follow-up. Overall, BMI increase was associated with increased risk and BMI reduction was associated with reduced risk of diabetes. Particularly, compared to minimal BMI change (±5%), moderate (5%-10%) reduction in BMI was associated with 34% (95% CI: 12%-51%) reduction in risk of diabetes in participants with obesity; whereas 10% or greater increase in BMI was associated with an increased risk of diabetes of 64% or more in participants with overweight and obesity; in participants with normal and underweight, BMI changes was not apparently associated with risk of diabetes. Public health programs promoting weight loss, even at a moderate extent, would reduce risk of diabetes.

Data Mining Approach to Identify Disease Cohorts from Primary Care Electronic Medical Records: A Case of Diabetes Mellitus

Background:

Identification of diseased patients from primary care based electronic medical records (EMRs) has methodological challenges that may impact epidemiologic inferences.

Objective:

To compare deterministic clinically guided selection algorithms with probabilistic machine learning (ML) methodologies for their ability to identify patients with type 2 diabetes mellitus (T2DM) from large population based EMRs from nationally representative primary care database.

Methods:

Four cohorts of patients with T2DM were defined by deterministic approach based on disease codes. The database was mined for a set of best predictors of T2DM and the performance of six ML algorithms were compared based on cross-validated true positive rate, true negative rate, and area under receiver operating characteristic curve.

Results:

In the database of 11,018,025 research suitable individuals, 379 657 (3.4%) were coded to have T2DM. Logistic Regression classifier was selected as best ML algorithm and resulted in a cohort of 383,330 patients with potential T2DM. Eighty-three percent (83%) of this cohort had a T2DM code, and 16% of the patients with T2DM code were not included in this ML cohort. Of those in the ML cohort without disease code, 52% had at least one measure of elevated glucose level and 22% had received at least one prescription for antidiabetic medication.

Conclusion:

Deterministic cohort selection based on disease coding potentially introduces significant mis-classification problem. ML techniques allow testing for potential disease predictors, and under meaningful data input, are able to identify diseased cohorts in a holistic way.

An Exploratory Case Study to Understand Primary Care Users and Their Data Quality Tradeoffs

Primary care data is an important part of the evolving healthcare ecosystem. Generally, users in primary care are expected to provide excellent patient care and record high-quality data. In practice, users must balance sets of priorities regarding care and data. The goal of this study was to understand data quality tradeoffs between timeliness, validity, completeness, and use among primary care users. As a case study, data quality measures and metrics are developed through a focus group session with managers. After calculating and extracting measurements of data quality from six years of historic data, each measure was modeled with logit binomial regression to show correlations, characterize tradeoffs, and investigate data quality interactions. Measures and correlations for completeness, use, and timeliness were calculated for 196,967 patient encounters. Based on the analysis, there was a positive relationship between validity and completeness, and a negative relationship between timeliness and use. Use of data and reductions in entry delay were positively associated with completeness and validity. Our results suggest that if users are not provided with sufficient time to record data as part of their regular workflow, they will prioritize spending available time with patients. As a measurement of a primary care system's effectiveness, the negative correlation between use and timeliness points to a self-reinforcing relationship that provides users with little external value. In the future, additional data can be generated from comparable organizations to test several new hypotheses about primary care users.

Rationale and protocol for the After Diabetes Diagnosis REsearch Support System (ADDRESS): an incident and high risk type 1 diabetes UK cohort study

INTRODUCTION

Type 1 diabetes is heterogeneous in its presentation and progression. Variations in clinical presentation between children and adults, and with ethnic group warrant further study in the UK to improve understanding of this heterogeneity. Early interventions to limit beta cell damage in type 1 diabetes are undergoing evaluation, but recruitment is challenging. The protocol presented describes recruitment of people with clinician-assigned, new-onset type 1 diabetes to understand the variation in their manner of clinical presentation, to facilitate recruitment into intervention studies and to create an open-access resource of data and biological samples for future type 1 diabetes research.

METHODS AND ANALYSIS

Using the National Institute for Health Research Clinical Research Network, patients >5 years of age diagnosed clinically with type 1 diabetes (and their siblings) are recruited within 6 months of diagnosis. Participants agree to have their clinical, laboratory and demographic data stored on a secure database, for their clinical progress to be monitored using information held by NHS Digital, and to be contacted about additional research, in particular immunotherapy and other interventions. An optional blood sample is taken for islet autoantibody measurement and storage of blood and DNA for future analyses. Data will be analysed statistically to describe the presentation of incident type 1 diabetes in a contemporary UK population.

ETHICS AND DISSEMINATION

Ethical approval was obtained from the independent NHS Research Ethics Service. Results will be presented at national and international meetings and submitted for publication to peer-reviewed journals.

Incidence of diabetic ketoacidosis among patients with type 2 diabetes mellitus treated with SGLT2 inhibitors and other antihyperglycemic agents

AIMS

To estimate and compare incidence of diabetes ketoacidosis (DKA) among patients with type 2 diabetes who are newly treated with SGLT2 inhibitors (SGLT2i) versus non-SGLT2i antihyperglycemic agents (AHAs) in actual clinical practice.

METHODS

A new-user cohort study design using a large insurance claims database in the US. DKA incidence was compared between new users of SGLT2i and new users of non-SGLT2i AHAs pair-matched on exposure propensity scores (EPS) using Cox regression models.

RESULTS

Overall, crude incidence rates (95% CI) per 1000 patient-years for DKA were 1.69 (1.22-2.30) and 1.83 (1.58-2.10) among new users of SGLT2i (n=34,442) and non-SGLT2i AHAs (n=126,703). These rates more than doubled among patients with prior insulin prescriptions but decreased by more than half in analyses that excluded potential autoimmune diabetes (PAD). The hazard ratio (95% CI) for DKA comparing new users of SGLT2i to new users of non-SGLT2i AHAs was 1.91 (0.94-4.11) (p=0.09) among the 30,196 EPS-matched pairs overall, and 1.13 (0.43-3.00) (p=0.81) among the 27,515 EPS-matched pairs that excluded PAD.

CONCLUSIONS

This was the first observational study that compared DKA risk between new users of SGLT2i and non-SGLT2i AHAs among patients with type 2 diabetes, and overall no statistically significant difference was detected.

Quality of recording of diabetes in the UK: how does the GP's method of coding clinical data affect incidence estimates? Cross-sectional study using the CPRD database

OBJECTIVE

To assess the effect of coding quality on estimates of the incidence of diabetes in the UK between 1995 and 2014.

DESIGN

A cross-sectional analysis examining diabetes coding from 1995 to 2014 and how the choice of codes (diagnosis codes vs codes which suggest diagnosis) and quality of coding affect estimated incidence.

SETTING

Routine primary care data from 684 practices contributing to the UK Clinical Practice Research Datalink (data contributed from Vision (INPS) practices).

MAIN OUTCOME MEASURE

Incidence rates of diabetes and how they are affected by (1) GP coding and (2) excluding 'poor' quality practices with at least 10% incident patients inaccurately coded between 2004 and 2014.

RESULTS

Incidence rates and accuracy of coding varied widely between practices and the trends differed according to selected category of code. If diagnosis codes were used, the incidence of type 2 increased sharply until 2004 (when the UK Quality Outcomes Framework was introduced), and then flattened off, until 2009, after which they decreased. If non-diagnosis codes were included, the numbers continued to increase until 2012. Although coding quality improved over time, 15% of the 666 practices that contributed data between 2004 and 2014 were labelled 'poor' quality. When these practices were dropped from the analyses, the downward trend in the incidence of type 2 after 2009 became less marked and incidence rates were higher.

CONCLUSIONS

In contrast to some previous reports, diabetes incidence (based on diagnostic codes) appears not to have increased since 2004 in the UK. Choice of codes can make a significant difference to incidence estimates, as can quality of recording. Codes and data quality should be checked when assessing incidence rates using GP data.

Lower Frequency of HLA-DRB1 Type 1 Diabetes Risk Alleles in Pediatric Patients with MODY

Objective

The aim of this study was to determine the frequency of susceptible HLA-DRB1 alleles for type 1 diabetes in a cohort of pediatric patients with a confirmed genetic diagnosis of MODY.

Materials and Methods

160 families with a proband diagnosed with type 1 diabetes and 74 families with a molecular diagnosis of MODY (61 GCK-MODY and 13 HNF1A-MODY) were categorized at high definition for HLA-DRB1 locus. According to the presence or absence of the susceptible HLA-DRB1 alleles for type 1 diabetes, we considered three different HLA-DRB1 genotypes: 0 risk alleles (no DR3 no DR4); 1 risk allele (DR3 or DR4); 2 risk alleles (DR3 and/or DR4).

Results

Compared with type 1 diabetes, patients with MODY carried higher frequency of 0 risk alleles, OR 22.7 (95% CI: 10.7–48.6) and lower frequency of 1 or 2 risk alleles, OR 0.53 (95% CI: 0.29–0.96) and OR 0.06 (95% CI: 0.02–0.18), respectively.

Conclusions

The frequency of HLA-DRB1 risk alleles for type 1 diabetes is significantly lower in patients with MODY. In children and adolescents with diabetes, the presence of 2 risk alleles (DR3 and/or DR4) reduces the probability of MODY diagnosis, whereas the lack of risk alleles increases it. Therefore, we might consider that HLA-DRB1 provides additional information for the selection of patients with high probability of monogenic diabetes.

Validity of hospital discharge records to identify pregestational diabetes in an Italian population

AIMS

In recent years, the prevalence of pregestational diabetes (PGDM) and the concern about the possibility of adverse pregnancy outcomes in affected women have been increasing. Routinely collected health data represent a timely and cost-efficient approach in PGDM epidemiological research. This study aims to evaluate the reliability of hospital discharge (HD) coding to identify a population-based cohort of pregnant women with PGDM and to assess trends in prevalence in two provinces of Northern Italy.

METHODS

We selected all deliveries occurred in the period 1997-2010 with ICD-9-CM codes for PGDM in HD record and we matched up to 5 controls from mothers without diabetes. We used Diabetes Registers (DRs) as the gold standard for validation analysis.

RESULTS

We selected 3800 women, 653 with diabetes and 3147 without diabetes. The agreement between HD records and DRs was 90.7%, with K=0.58. We detected 350 false positives and only 1 false negative. Sensitivity was 99.3%, specificity 90.0%, positive predictive value 46.4% and negative predictive value 99.9%. Of the false positives, 48.6% had gestational diabetes and 2.3% impaired glucose tolerance. After the validation process, PGDM prevalence decreased from 4.4 to 2.0 per 1000 deliveries.

CONCLUSIONS

Our results show that HD facilitate detection of almost all PGDM cases, but they also include a large number of false positives, mainly due to gestational diabetes. This misclassification causes a large overestimation of PGMD prevalence. Our findings require accuracy evaluation of ICD-9-CM codes, before they can be widely applied to epidemiological research and public health surveillance related to PGDM.

Is diabetes mellitus correctly registered and classified in primary care? A population-based study in Catalonia, Spain

OBJECTIVE

To assess the prevalence of miscoding, misclassification, misdiagnosis and under-registration of diabetes mellitus (DM) in primary health care in Catalonia (Spain), and to explore use of automated algorithms to identify them.

METHODS

In this cross-sectional, retrospective study using an anonymized electronic general practice database, data were collected from patients or users with a diabetes-related code or from patients with no DM or prediabetes code but treated with antidiabetic drugs (unregistered DM). Decision algorithms were designed to classify the true diagnosis of type 1 DM (T1DM), type 2 DM (T2DM), and undetermined DM (UDM), and to classify unregistered DM patients treated with antidiabetic drugs.

RESULTS

Data were collected from a total of 376,278 subjects with a DM ICD-10 code, and from 8707 patients with no DM or prediabetes code but treated with antidiabetic drugs. After application of the algorithms, 13.9% of patients with T1DM were identified as misclassified, and were probably T2DM; 80.9% of patients with UDM were reclassified as T2DM, and 19.1% of them were misdiagnosed as DM when they probably had prediabetes. The overall prevalence of miscoding (multiple codes or UDM) was 2.2%. Finally, 55.2% of subjects with unregistered DM were classified as prediabetes, 35.7% as T2DM, 8.5% as UDM treated with insulin, and 0.6% as T1DM.

CONCLUSIONS

The prevalence of inappropriate codification or classification and under-registration of DM is relevant in primary care. Implementation of algorithms could automatically flag cases that need review and would substantially decrease the risk of inappropriate registration or coding.

Practical Classification Guidelines for Diabetes in patients treated with insulin: a cross-sectional study of the accuracy of diabetes diagnosis

Background

Differentiating between type 1 and type 2 diabetes is fundamental to ensuring appropriate management of patients, but can be challenging, especially when treating with insulin. The 2010 UK Practical Classification Guidelines for Diabetes were developed to help make the differentiation.

Aim

To assess diagnostic accuracy of the UK guidelines against ‘gold standard’ definitions of type 1 and type 2 diabetes based on measured C-peptide levels.

Design and setting

In total, 601 adults with insulin-treated diabetes and diabetes duration ≥5 years were recruited in Devon, Northamptonshire, and Leicestershire.

Method

Baseline information and home urine sample were collected. Urinary C-peptide creatinine ratio (UCPCR) measures endogenous insulin production. Gold standard type 1 diabetes was defined as continuous insulin treatment within 3 years of diagnosis and absolute insulin deficiency (UCPCR<0.2 nmol/mmol ≥5 years post-diagnosis); all others classed as having type 2 diabetes. Diagnostic performance of the clinical criteria was assessed and other criteria explored using receiver operating characteristic (ROC) curves.

Results

UK guidelines correctly classified 86% of participants. Most misclassifications occurred in patients classed as having type 1 diabetes who had significant endogenous insulin levels (57 out of 601; 9%); most in those diagnosed ≥35 years and treated with insulin from diagnosis, where 37 out of 66 (56%) were misclassified. Time to insulin and age at diagnosis performed best in predicting long-term endogenous insulin production (ROC AUC = 0.904 and 0.871); BMI was a less strong predictor of diabetes type (AUC = 0.824).

Conclusion

Current UK guidelines provide a pragmatic clinical approach to classification reflecting long-term endogenous insulin production; caution is needed in older patients commencing insulin from diagnosis, where misclassification rates are increased.

Interaction between Mean Arterial Pressure and HbA1c in Prediction of Cardiovascular Disease Hospitalisation: A Population-Based Case-Control Study

Objective. To explore the relationship between mean arterial pressure (MAP), HbA1c, and cardiovascular (CV) hospitalisation risk in type 2 diabetes. Design. Population-based case-control study. Settings. Primary and secondary care level in Cambridgeshire, United Kingdom. Participants. 588 patients with type 2 diabetes from 18 English general practices recording a CV hospitalisation in 2009-2011 were included. Risk-set sampling was used to select 2920 gender, age, and practice matched control type 2 diabetes patients. Main Outcome Measure. Conditional logistic regression was used to explore further dose-response relationships between MAP, HbA1c, and CV hospitalisation risk. Results. The relationship between MAP and CV hospitalisation was nonlinear (P < 0.001 for linearity test). The MAP associated with the lowest CV hospitalisation risk was 97 (95% CI: 93-101) mmHg. An interaction between MAP and HbA1c for increased risk of cardiovascular hospitalisation was observed among those with HbA1c < 7% (53 mmol/mol) and MAP < 97 mmHg. Conclusions. In type 2 diabetes, MAP is a good predictor of CV hospitalisation risk. CV hospitalisation is lowest with a MAP between 93 and 101 mmHg. CV hospitalisation was particularly high among those with both a low MAP and a lower HbA1c.

Assessment of Newly Proposed Clinical Criteria to Identify HNF1A MODY in Patients with an Initial Diagnosis of Type 1 or Type 2 Diabetes Mellitus

The most common form of maturity-onset diabetes of the young (MODY) is caused by mutations in the hepatocyte nuclear factor 1A (HNF1A) gene. However, most HNF1A mutation-carriers are initially misdiagnosed with type 1 (T1DM) or type 2 (T2DM) diabetes mellitus; hence, they often receive nonoptimal treatment. The aim of our study was to test newly proposed clinical criteria for the identification of HNF1A MODY in patients with a diagnosis of T1DM or T2DM. To achieve this, the following criteria to preselect patients for screening were used: for T1DM: TDIR (total daily insulin requirement) > 0.3 IU of insulin/kg and the percentage of basal insulin > 30% of TDIR; for T2DM: sulphonylurea- (SU-) based oral treatment (monotherapy or combined with Metformin) > 15 years and BMI < 30 kg/m(2). We reviewed the clinical data of 140 patients with T1DM and 524 clinically diagnosed with T2DM. On the basis of these criteria, we found a HNF1A mutation in 1 out of 2 individuals with a diagnosis of T1DM and 1 out of 11 selected individuals with a diagnosis of T2DM. We believe that the simplicity of the proposed criteria might prove useful in clinical practice, as an alternative to more time-consuming classical diagnostic techniques.

A genetic diagnosis of maturity-onset diabetes of the young (MODY): experiences of patients and family members

AIMS

Genetic testing for maturity-onset diabetes of the young (MODY) facilitates a correct diagnosis, enabling treatment optimization and allowing monitoring of asymptomatic family members. To date, the majority of people with MODY remain undiagnosed. To identify patients' needs and areas for improving care, this study explores the experiences of patients and family members who have been genetically tested for MODY.

METHODS

Fourteen semi-structured interviews with patients and the parents of patients, and symptomatic and asymptomatic family members were conducted. Atlas.ti was used for thematic analysis.

RESULTS

Most people with MODY were initially misdiagnosed with Type 1 or Type 2 diabetes; they had been seeking for the correct diagnosis for a long time. Reasons for having a genetic test included reassurance, removing the uncertainty of developing diabetes (in asymptomatic family members) and informing relatives. Reasons against testing were the fear of genetic discrimination and not having symptoms. Often a positive genetic test result did not come as a surprise. Both patients and family members were satisfied with the decision to get tested because it enabled them to adjust their lifestyle and treatment accordingly. All participants experienced a lack of knowledge of MODY among healthcare professionals, in their social environment and in patient organizations. Additionally, problems with the reimbursement of medical expenses were reported.

CONCLUSIONS

Patients and family members are generally positive about genetic testing for MODY. More education of healthcare professionals and attention on the part of diabetes organizations is needed to increase awareness and optimize care and support for people with MODY.

Diabetes mellitus: The epidemic of the century

The epidemic nature of diabetes mellitus in different regions is reviewed. The Middle East and North Africa region has the highest prevalence of diabetes in adults (10.9%) whereas, the Western Pacific region has the highest number of adults diagnosed with diabetes and has countries with the highest prevalence of diabetes (37.5%). Different classes of diabetes mellitus, type 1, type 2, gestational diabetes and other types of diabetes mellitus are compared in terms of diagnostic criteria, etiology and genetics. The molecular genetics of diabetes received extensive attention in recent years by many prominent investigators and research groups in the biomedical field. A large array of mutations and single nucleotide polymorphisms in genes that play a role in the various steps and pathways involved in glucose metabolism and the development, control and function of pancreatic cells at various levels are reviewed. The major advances in the molecular understanding of diabetes in relation to the different types of diabetes in comparison to the previous understanding in this field are briefly reviewed here. Despite the accumulation of extensive data at the molecular and cellular levels, the mechanism of diabetes development and complications are still not fully understood. Definitely, more extensive research is needed in this field that will eventually reflect on the ultimate objective to improve diagnoses, therapy and minimize the chance of chronic complications development.

Genetics of Type 2 Diabetes and Clinical Utility

A large proportion of heritability of type 2 diabetes (T2D) has been attributed to inherent genetics. Recent genetic studies, especially genome-wide association studies (GWAS), have identified a multitude of variants associated with T2D. It is thus reasonable to question if these findings may be utilized in a clinical setting. Here we briefly review the identification of risk loci for T2D and discuss recent efforts and propose future work to utilize these loci in clinical setting-for the identification of individuals who are at particularly high risks of developing T2D and for the stratification of specific health-care approaches for those who would benefit most from such interventions.

Initiation of treatment for incident diabetes: evidence from the electronic health records in an ambulatory care setting

OBJECTIVE

We examined patterns and predictors of initiation of treatment for incident diabetes in an ambulatory care setting in the US.

METHODS

Data were extracted from electronic health records (EHR) for active patients ≥ 35 years in a multispecialty, multiclinic ambulatory care organization with 1000 providers. New onset type 2 diabetes and subsequent treatment were identified using lab, diagnosis, medication prescription, and service use data. Time from the first evidence of diabetes until initial treatment, either medication or education/counseling, was examined using a Kaplan-Meier hazards curve. Potential predictors of initial treatment were examined using multinomial logistic models accounting for physician random effects.

RESULTS

Of 2258 patients with incident diabetes, 55% received either medication or education/counseling (20% received both) during the first year. Of the treated patients, 68% received a treatment within the first four weeks, and 13% after initial 16 weeks. Strong positive predictors (P < 0.01) of combined treatment were younger age, higher fasting glucose at diagnosis, obesity, and visits with an endocrinologist.

CONCLUSIONS

Among insured patients who have a primary care provider in a multispecialty health care system, incident diabetes is treated only half the time. Improved algorithms for identifying incident diabetes from the EHR and team approach for monitoring may help treatment initiation.

Making decisions about medications in critically ill children: a survey of Canadian pediatric critical care clinicians

OBJECTIVE

Changing clinician practice in pediatric critical care is often difficult. Tailored knowledge translation interventions may be more effective than other types of interventions. To inform the design of tailored interventions, the primary objective of this survey was to describe the importance of specific factors that influence physicians and pharmacists when they make decisions about medications in critically ill children.

DESIGN

In this postal survey, respondents used 7-point scales to rate the importance of specific factors that influence their decisions in the following scenarios: corticosteroids for shock, intensive insulin therapy, stress ulcer prophylaxis, surfactant for acute respiratory distress syndrome, and sedation interruption. We used generalized estimating equations to examine the association between the importance of specific factors influencing decision making and the scenario and respondents' practice, views, and demographics.

SETTING

Canadian PICUs.

PARTICIPANTS

One hundred and seventeen physicians and pharmacists practicing in 18 PICUs.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The response rate was 61%. The three factors reported to most strongly influence clinician decision making overall were: severity of illness (mean [SD] 5.8 [1.8]), physiologic rationale (5.2 [1.3]), and adverse effects (5.1 [1.9]). Factors least likely to influence decision making were drug costs (2.0 [1.5]), unit policies (2.9 [1.9]), and non-critical care randomized controlled trials (3.1 [1.9]). The relative importance of 8 of the 10 factors varied significantly among the five scenarios: only randomized controlled trials in critically ill children and other clinical research did not vary. Clinician characteristics associated with the greatest difference in importance ratings were: frequent use of the intervention in that scenario (seven factors), profession (five factors), and respondents' assessment of the quality of evidence (five factors).

CONCLUSIONS

The relative importance of many factors that clinicians consider when making decisions about medications varies by demographics, and depends on the clinical problem. This variability should be considered in quality improvement and knowledge translation interventions in this setting.

The Genetic Profile from HLA and Non-HLA Loci Allows Identification of Atypical Type 2 Diabetes Patients

The complex diagnosis and treatment of diabetes highlight the need for markers to define how to monitor patients correctly during the course of their disease. Different studies demonstrate the existence of patients who cannot be clearly classified. We have previously shown that it is possible to differentiate "atypical diabetic patients" based on genotyping the HLA. In this work we show that the analysis of non-HLA related to type 1 diabetes in the INS-VNTR, SNP rs689, and rs3842753 improves the identification of these patients. We genotyped 913 individuals comprising controls from the general population and "classic" and "atypical" diabetic patients. We compared the distribution of these loci and analyzed linkage disequilibrium. The haplotype was in LD for all the SNPs that were evaluated. Regarding their association with the disease, the haplotype IAC was associated with type 1 (odds 2.60, 1.82-3.72, CI 95%) and "atypical diabetes" (odds 1.50, 1.01-2.23, CI 95%), whereas we did not observe an association with type 2 diabetes. Therefore, our results confirm that atypical diabetes is a different entity of the disease where the patient presents with a genetic background of T1D and a T2D phenotype, findings that are likely to be relevant for patient diagnosis and management in the clinic.

HLA alleles may serve as a tool to discriminate atypical type 2 diabetic patients

AIM: To investigate whether the presence of human leukocyte antigen (HLA) marker could add new information to discriminated atypical diabetic type 2 patients.

METHODS: We analyzed 199 patients initially diagnosed as type 2 diabetes who are treated in special care diabetes clinics (3^rd level). This population was classified in “atypical” (sample A) and “classic” (sample B) according to HLA typing. We consider “classic patient” when has absence of type 1 diabetes associated HLA alleles and no difficulties in their diagnosis and treatments. By the other hand, we considered “atypical patient” when show type 1 diabetes associated HLA alleles and difficulties in their diagnosis and treatments. The standard protocol Asociacion Latinoamericana de Diabetes 2006 was used for patients follow up. To analyze differences between both populations in paraclinical parameters we used unpaired t tests and contingence tables. Bivariate and multivariate analyses were carried out using the SPSS software program. In all studies we assume differences statistically significant, with a P-value < 0.05 corrected and 95%CI.

RESULTS: The typing HLA in the “atypical” populations show that 92.47% patients presented at list one type 1 diabetes associated HLA alleles (DQB1*0201-0302 and DR 3-4) and 7.53% had two of its. The results showed for categorical variables (family history, presence or absence of hypertension and/or dyslipidemia, reason for initial consultation) the only difference found was at dyslipidemia (OR = 0.45, 0.243 < OD < 0.822 (P < 0.001). In relation to continuous variables we found significant differences between atypical vs classic only in cholesterol (5.07 ± 1.1 vs 5.56 ± 1.5, P < 0.05), high density lipoproteins (1.23 ± 0.3 vs 1.33 ± 0.3, P < 0.05) and low density lipoproteins (2.86 ± 0.9 vs 3.38 ± 1.7, P < 0.01). None of the variables had discriminating power when logistic regression was done.

CONCLUSION: We propose an algorithm including HLA genotyping as a tool to discriminate atypical patients, complementing international treatment guidelines for complex patients.

The effect of diabetes mellitus on costs and length of stay in patients with peripheral arterial disease undergoing vascular surgery

OBJECTIVE

To determine the impact of diabetes mellitus (DM) and other comorbidities on length of stay (LOS) and costs in patients with peripheral arterial disease (PAD) admitted to a vascular surgical unit.

METHODS

A retrospective study was conducted between January 2011 and July 2012 at a tertiary referral hospital in Sydney. Demographic, laboratory, and operative data were obtained from the Australasian Vascular Audit database and hospital diagnostic-related group (DRG) reports. Patients with confirmed PAD with or without DM requiring hospital admission for a diagnosis of claudication, rest pain, ulcer/gangrene, and infection that required lower limb surgical intervention were included. Associations between LOS, surgical procedure, and DRG were explored.

RESULTS

Five hundred and sixty-eight admissions (492 patients) were identified: 292 admissions with PAD and 276 admissions with PAD in conjunction with DM (PADDM). Mean LOS for patients with PAD was 10 ± 13.7 days compared with 15 ± 18.2 days for PADDM (p < .01; 95% confidence interval 2.7-8.0). LOS and costs were greatest in patients with PADDM undergoing major amputation (37 ± 13.7 days; US$42,236; p < .01). Analysis of variance indicated that the best predictors of LOS were the presence of DM, bypass surgery, amputation, chronic kidney disease (CKD) stage V, infection, and emergency admission. Over 18 months, the estimated total inpatient costs associated with lower limb intervention for PAD with and without DM amounted to US$7,598,597. People with DM incurred greater inpatient costs, averaging US$1,912 more per episode of admission and a total of US$528,029 over 18 months.

CONCLUSION

The impact of diabetes as a comorbid condition in patients with PAD is significant, both clinically and economically. Factors that predict increased LOS in patients with PAD are DM, bypass surgery, amputation, CKD stage V, infection, and emergency admission.

Prevalence and characteristics in coding, classification and diagnosis of diabetes in primary care

INTRODUCTION

Approximately 366 million people worldwide live with diabetes and this figure is expected to rise. Among the correct diagnosis, there will be errors in the diagnosis, classification and coding, resulting in adverse health and financial implications.

AIM

To determine the prevalence and characteristics of diagnostic errors in people with diabetes managed in primary care settings.

METHODS

We conducted a cross-sectional study in nine general practices in Leicester, UK, from May to August 2011, using a validated electronic toolkit. Searches identified cases with potential errors which were manually checked for accuracy.

RESULTS

There were 54 088 patients and 2434 (4.5%) diagnosed with diabetes. Out of 316 people identified with potential errors with the toolkit, 180 (57%) had confirmed errors after manually reviewing the records, resulting in an error prevalence of 7.4%. Correctly coded people on registers had significantly greater glycated haemoglobin (HbA1c) reductions. There were no significant differences between patients with and without errors in their HbA1C, body mass index, age and size of practice. There was also no significant association of the errors with pay-for-performance initiatives; however, those patients not on disease register had worse glycaemic control.

CONCLUSIONS

A high prevalence of diabetic diagnostic errors was confirmed using medication, biochemical and demographic data. Larger studies are needed to more accurately assess the scale of this problem. Automation of these processes might be possible, which would allow searches to be even more user friendly.

Identifying patients with diabetes and the earliest date of diagnosis in real time: an electronic health record case-finding algorithm

BACKGROUND

Effective population management of patients with diabetes requires timely recognition. Current case-finding algorithms can accurately detect patients with diabetes, but lack real-time identification. We sought to develop and validate an automated, real-time diabetes case-finding algorithm to identify patients with diabetes at the earliest possible date.

METHODS

The source population included 160,872 unique patients from a large public hospital system between January 2009 and April 2011. A diabetes case-finding algorithm was iteratively derived using chart review and subsequently validated (n = 343) in a stratified random sample of patients, using data extracted from the electronic health records (EHR). A point-based algorithm using encounter diagnoses, clinical history, pharmacy data, and laboratory results was used to identify diabetes cases. The date when accumulated points reached a specified threshold equated to the diagnosis date. Physician chart review served as the gold standard.

RESULTS

The electronic model had a sensitivity of 97%, specificity of 90%, positive predictive value of 90%, and negative predictive value of 96% for the identification of patients with diabetes. The kappa score for agreement between the model and physician for the diagnosis date allowing for a 3-month delay was 0.97, where 78.4% of cases had exact agreement on the precise date.

CONCLUSIONS

A diabetes case-finding algorithm using data exclusively extracted from a comprehensive EHR can accurately identify patients with diabetes at the earliest possible date within a healthcare system. The real-time capability may enable proactive disease management.

Stress-strain analysis of jejunal contractility in response to flow and ramp distension in type 2 diabetic GK rats: effect of carbachol stimulation

Investigation of intestinal motility in a genetic model of GK rats abandons the possible neurotoxic effect of streptozotocin in streptozotocin-induced diabetic model. Seven GK male rats (GK group) and nine normal Wistar rats (Normal group) were used in the study. The motility experiments were carried out in an organ bath containing physiological Krebs solution. Before and after 10(-5)M carbachol application, the pressure and diameter changes of jejunum were obtained in relation to (1) basic contraction, (2) flow-induced contraction with different outlet resistance pressures and (3) contractions induced by ramp distension. The frequency and amplitude of contractions were analyzed from pressure-diameter curves. Distension-induced contraction thresholds and maximum contraction amplitude of basic and flow-induced contractions were calculated in terms of stress and strain. (1) The contraction amplitude increased to the peak value in less than 10s after adding carbachol. More than two peaks were observed in the GK group. (2) Carbachol decreased the pressure and stress threshold and Young's modulus in the GK group (P<0.01). (3) Carbachol increased the maximum pressure and stress of flow-induced contractions at most outlet pressure levels in both two groups (P<0.001). Furthermore, the flow-induced contractions were significantly bigger at low outlet pressure levels in GK group (P<0.05 and P<0.01). (4) The contraction frequency, the strain threshold and the maximum contraction strain did not differ between the two groups (P>0.05) and between before and after carbachol application (P>0.05). In GK diabetic rats, the jejunal contractility was hypersensitive to flow and distension stimulation after carbachol application.

Diabetes and cardiovascular events in women with polycystic ovary syndrome: a 20-year retrospective cohort study

OBJECTIVE

Women with polycystic ovary syndrome (PCOS) are potentially at increased risk of cardiovascular (CV) diseases due to well-established risk factors, including insulin resistance, obesity and type 2 diabetes mellitus (T2DM). However, data showing excess CV events in this population are still lacking. We investigated the incidence and prevalence of CV events in a cohort of women with PCOS.

DESIGN

Retrospective cohort study (total follow-up >12,000 person-years).

SETTING

Leicester, Leicestershire and Rutland (Total Female population of 434,859), UK.

PARTICIPANTS

Two thousand three hundred and one women with PCOS (mean age = 29.6 years) attending a speciality clinic in Leicestershire, UK.

MAIN OUTCOMES MEASURES

T2DM, myocardial infarction (MI), angina, heart failure (HF), stroke and CV-related death.

RESULTS

Incidence of T2DM, MI, angina, HF, stroke and CV death was respectively 3.6, 0.8, 1.0, 0.3, 0.0 and 0.4 per 1000 person-years. At the end of follow-up, the prevalence of MI in the age groups 45-54, 55-64 and >65 years was 1.9%, 6.0% and 27.3% and of angina was 2.6%, 6.0% and 27.3%, respectively. Age-group-specific odds ratios for the prevalence of MI and angina compared to the local female population ranged between 2.6 (95% CI: 1.0-6.3) and 12.9 (CI: 3.4-48.6) with the highest ratio being for MI in the group >65 years old. Age, history of hypertension and smoking had significant correlations with CV outcomes in the PCOS patients.

CONCLUSION

We have shown a high incidence and age-group-specific prevalence of T2DM, MI and angina in the women with PCOS, with over a quarter having had MI or angina in those >65 years. These findings should be considered in the treatment strategies and long-term planning for women with PCOS.

Morpho-mechanical intestinal remodeling in type 2 diabetic GK rats--is it related to advanced glycation end product formation?

Little is known about the mechanisms for the biomechanical remodeling in diabetes. The histomorphology, passive biomechanical properties and expression of advanced glycation end product (N epsilon-(carboxymethyl) lysine, AGE) and its receptor (RAGE) were studied in jejunal segments from 8 GK diabetic rats (GK group) and 10 age-matched normal rats (Normal group). The mechanical test was done by using a ramp distension of fluid into the jejunal segments in vitro. Circumferential stress and strain were computed from the length, diameter and pressure data and from the zero-stress state geometry. AGE and RAGE were detected by immunohistochemistry staining. Linear regression analysis was done to study association between the glucose level and AGE/RAGE expression with the histomorphometric and biomechanical parameters. The blood glucose level, the jejunal weight per length, wall thickness, wall area and layer thickness significantly increased in the GK group compared with the Normal group (P<0.05, P<0.01 and P<0.001). The opening angle and absolute values of residual strain decreased whereas the circumferential stiffness of the jejunal wall increased in the GK group (P<0.05 and P<0.01). Furthermore, stronger AGE expression in the villi and crypt and RAGE expression in the villi were found in the GK group (P<0.05 and P<0.01). Most histomorphometric and biomechanical changes were associated with blood glucose level and AGE/RAGE expression. In conclusion, histomorphometric and biomechanical remodeling occurred in type 2 diabetic GK rats. The increasing blood glucose level and the increased AGE/RAGE expression were associated with the remodeling, indicating a causal relationship.

Role of regulatory micro RNAs in type 2 diabetes mellitus-related inflammation

Micro RNAs (miRNAs) are small, non-coding RNAs with the function of post-transcriptional gene expression regulation. Micro RNAs may function in networks, forming a complex relationship with diseases. Alterations of specific miRNA levels have significant correlation with diseases of divergent origin, such as diabetes. Type 2 diabetes mellitus (T2DM) has an increasing worldwide epidemic with serious complications. However, T2DM is a chronic process, and from early metabolic alterations to manifest complications decades may pass, during which our diagnostic arsenal is limited. Micro RNAs may thus serve as novel diagnostic tools as well as therapeutic targets in pre-diabetes. Recent Fundings: Micro RNAs (miRNAs) involved in inflammatory processes contributing to the development of type 2 diabetes mellitus (T2DM) published mostly in the past 2 years. MiRNAs are involved in such early diabetic processes as non-alcoholic steatohepatitis (NASH) and inflammation of the visceral adipose tissue. Evidence is emerging regarding the continuous spectrum between type 1 diabetes (T1DM) and T2DM being just 2 endpoints of the same disease with different genetic background. Thus, miRNA regulation of autoimmune components in T2DM may shed new light on pathogenesis. Finally, the involvement of miRNAs in inflammation as a key driving force of diabetic complications is also summarized.

CONCLUSION

Inflammation is emerging as a central pathophysiological process in the development of T2DM. Visceral adipose tissue inflammation and non-alcoholic steatohepatitis together with insulitis are probably the first events leading to a complex metabolic disorder. These early events may be diagnosed or even influenced through our increasing knowledge about the involvement of post-transcriptional gene regulation by miRNAs.

Evaluating tools to support a new practical classification of diabetes: excellent control may represent misdiagnosis and omission from disease registers is associated with worse control

AIMS

To conduct a service evaluation of usability and utility on-line clinical audit tools developed as part of a UK Classification of Diabetes project to improve the categorisation and ultimately management of diabetes.

METHOD

We conducted the evaluation in eight volunteer computerised practices all achieving maximum pay-for-performance (P4P) indicators for diabetes; two allowed direct observation and videotaping of the process of running the on-line audit. We also reported the utility of the searches and the national levels of uptake.

RESULTS

Once launched 4235 unique visitors accessed the download pages in the first 3 months. We had feedback about problems from 10 practices, 7 were human error. Clinical audit naive staff ran the audits satisfactorily. However, they would prefer more explanation and more user-familiar tools built into their practice computerised medical record system. They wanted the people misdiagnosed and misclassified flagged and to be convinced miscoding mattered. People with T2DM misclassified as T1DM tended to be older (mean 62 vs. 47 years old). People misdiagnosed as having T2DM have apparently 'excellent' glycaemic control mean HbA1c 5.3% (34 mmol/mol) vs. 7.2% (55 mmol/mol) (p<0.001). People with vague codes not included in the P4P register (miscoded) have worse glycaemic control [HbA1c 8.1% (65 mmol/mol) SEM=0.42 vs.7.0% (53mmol/mol) SEM=0.11, p=0.006].

CONCLUSIONS

There was scope to improve diabetes management in practice achieving quality targets. Apparently 'excellent' glycaemic control may imply misdiagnosis, while miscoding is associated with worse control. On-line clinical audit toolkits provide a rapid method of dissemination and should be added to the armamentarium of quality improvement interventions.

Miscoding, misclassification and misdiagnosis of diabetes in primary care

AIMS

To determine the effectiveness of self-audit tools designed to detect miscoding, misclassification and misdiagnosis of diabetes in primary care.

METHODS

We developed six searches to identify people with diabetes with potential classification errors. The search results were automatically ranked from most to least likely to have an underlying problem. Eight practices with a combined population of 72,000 and diabetes prevalence 2.9% (n = 2340) completed audit forms to verify whether additional information within the patients' medical record confirmed or refuted the problems identified.

RESULTS

The searches identified 347 records, mean 42 per practice. Pre-audit 20% (n = 69) had Type 1 diabetes, 70% (n = 241) had Type 2 diabetes, 9% (n = 30) had vague codes that were hard to classify, 2% (n = 6) were not coded and one person was labelled as having gestational diabetes. Of records, 39.2% (n = 136) had important errors: 10% (n = 35) had coding errors; 12.1% (42) were misclassified; and 17.0% (59) misdiagnosed as having diabetes. Thirty-two per cent (n = 22) of people with Type 2 diabetes (n = 69) were misclassified as having Type 1 diabetes; 20% (n = 48) of people with Type 2 diabetes (n = 241) did not have diabetes; of the 30 patients with vague diagnostic terms, 50% had Type 2 diabetes, 20% had Type 1 diabetes and 20% did not have diabetes. Examples of misdiagnosis were found in all practices, misclassification in seven and miscoding in six.

CONCLUSIONS

Volunteer practices successfully used these self-audit tools. Approximately 40% of patients identified by computer searches (5.8% of people with diabetes) had errors; misdiagnosis is commonest, misclassification may affect treatment options and miscoding in omission from disease registers and the potential for reduced quality of care.

Risk models and scores for type 2 diabetes: systematic review

OBJECTIVE

To evaluate current risk models and scores for type 2 diabetes and inform selection and implementation of these in practice.

DESIGN

Systematic review using standard (quantitative) and realist (mainly qualitative) methodology. Inclusion criteria Papers in any language describing the development or external validation, or both, of models and scores to predict the risk of an adult developing type 2 diabetes.

DATA SOURCES

Medline, PreMedline, Embase, and Cochrane databases were searched. Included studies were citation tracked in Google Scholar to identify follow-on studies of usability or impact.

DATA EXTRACTION

Data were extracted on statistical properties of models, details of internal or external validation, and use of risk scores beyond the studies that developed them. Quantitative data were tabulated to compare model components and statistical properties. Qualitative data were analysed thematically to identify mechanisms by which use of the risk model or score might improve patient outcomes.

RESULTS

8864 titles were scanned, 115 full text papers considered, and 43 papers included in the final sample. These described the prospective development or validation, or both, of 145 risk prediction models and scores, 94 of which were studied in detail here. They had been tested on 6.88 million participants followed for up to 28 years. Heterogeneity of primary studies precluded meta-analysis. Some but not all risk models or scores had robust statistical properties (for example, good discrimination and calibration) and had been externally validated on a different population. Genetic markers added nothing to models over clinical and sociodemographic factors. Most authors described their score as "simple" or "easily implemented," although few were specific about the intended users and under what circumstances. Ten mechanisms were identified by which measuring diabetes risk might improve outcomes. Follow-on studies that applied a risk score as part of an intervention aimed at reducing actual risk in people were sparse.

CONCLUSION

Much work has been done to develop diabetes risk models and scores, but most are rarely used because they require tests not routinely available or they were developed without a specific user or clear use in mind. Encouragingly, recent research has begun to tackle usability and the impact of diabetes risk scores. Two promising areas for further research are interventions that prompt lay people to check their own diabetes risk and use of risk scores on population datasets to identify high risk "hotspots" for targeted public health interventions.

Increased length of inpatient stay and poor clinical coding: audit of patients with diabetes

Objectives

People with diabetes stay in hospital for longer than those without diabetes for similar conditions. Clinical coding is poor across all specialties. Inpatients with diabetes often have unrecognized foot problems. We wanted to look at the relationships between these factors.

Design

A single day audit, looking at the prevalence of diabetes in all adult inpatients. Also looking at their feet to find out how many were high-risk or had existing problems.

Setting

A 998-bed university teaching hospital.

Participants

All adult inpatients.

Main outcome measures

(a) To see if patients with diabetes and foot problems were in hospital for longer than the national average length of stay compared with national data; (b) to see if there were people in hospital with acute foot problems who were not known to the specialist diabetic foot team; and (c) to assess the accuracy of clinical coding.

Results

We identified 110 people with diabetes. However, discharge coding data for inpatients on that day showed 119 people with diabetes. Length of stay (LOS) was substantially higher for those with diabetes compared to those without (± SD) at 22.39 (22.26) days, vs. 11.68 (6.46) (P < 0.001). Finally, clinical coding was poor with some people who had been identified as having diabetes on the audit, who were not coded as such on discharge.

Conclusion

Clinical coding – which is dependent on discharge summaries – poorly reflects diagnoses. Additionally, length of stay is significantly longer than previous estimates. The discrepancy between coding and diagnosis needs addressing by increasing the levels of awareness and education of coders and physicians. We suggest that our data be used by healthcare planners when deciding on future tariffs.

A method of identifying and correcting miscoding, misclassification and misdiagnosis in diabetes: a pilot and validation study of routinely collected data

AIMS

Incorrect classification, diagnosis and coding of the type of diabetes may have implications for patient management and limit our ability to measure quality. The aim of the study was to measure the accuracy of diabetes diagnostic data and explore the scope for identifying errors.

METHOD

We used two sets of anonymized routinely collected computer data: the pilot used Cutting out Needless Deaths Using Information Technology (CONDUIT) study data (n = 221 958), which we then validated using 100 practices from the Quality Improvement in Chronic Kidney Disease (QICKD) study (n = 760,588). We searched for contradictory diagnostic codes and also compatibility with prescription, demographic and laboratory test data. We classified errors as: misclassified-incorrect type of diabetes; misdiagnosed-where there was no evidence of diabetes; or miscoded-cases where it was difficult to infer the type of diabetes.

RESULTS

The standardized prevalence of diabetes was 5.0 and 4.0% in the CONDUIT and the QICKD data, respectively: 13.1% (n = 930) of CONDUIT and 14.8% (n = 4363) QICKD are incorrectly coded; 10.3% (n = 96) in CONDUIT and 26.2% (n = 1143) in QICKD are misclassified; nearly all of these cases are people classified with Type 1 diabetes who should be classified as Type 2. Approximately 5% of T2DM in both samples have no objective evidence to support a diagnosis of diabetes. Miscoding was present in approximately 7.8% of the CONDUIT and 6.1% of QICKD diabetes records.

CONCLUSIONS

The prevalence of miscoding, misclassification and misdiagnosis of diabetes is high and there is substantial scope for further improvement in diagnosis and data quality. Algorithms which identify likely misdiagnosis, misclassification and miscoding could be used to flag cases for review.