Development and validation of a risk score for hospitalization for heart failure in patients with Type 2 diabetes mellitus

The association of glycaemic risk factors and diabetes duration with risk of heart failure in people with type 2 diabetes: A systematic review and meta-analysis

AIMS

To conduct a systematic review in order to better understand the association of glycaemic risk factors and diabetes duration with risk of heart failure (HF) in individuals with type 2 diabetes (T2D).

METHODS

We identified longitudinal studies investigating the association of glycaemic factors (glycated haemoglobin [HbA1c], HbA1c variability, and hypoglycaemia) and diabetes duration with HF in individuals with T2D. Hazard ratios and odds ratios were extracted and meta-analysed using a random-effects model where appropriate. Risk of bias assessment was carried out using a modified Newcastle-Ottawa Scale. Egger's test along with the trim-and-fill method were used to assess and account for publication bias.

RESULTS

Forty studies representing 4 102 589 people met the inclusion criteria. The risk of developing HF significantly increased by 15% for each percentage point increase in HbA1c, by 2% for each additional year of diabetes duration, and by 43% for having a history of severe hypoglycaemia. Additionally, variability in HbA1c levels was associated with a 20%-26% increased risk of HF for each unit increase in the metrics of variability (HbA1c standard deviation, coefficient of variation, and average successive variability). All included studies scored high in the risk of bias assessment. Egger's test suggested publication bias, with trim-and-fill analyses revealing a significant 14% increased risk of HF per percentage point increase in HbA1c.

CONCLUSIONS

Glycaemic risk factors and diabetes duration significantly contribute to the heightened risk of HF among individuals with T2D. A reduction in risk of HF is anticipated with better management of glycaemic risk factors.

Risk Prediction Scores for Type 2 Diabetes Microvascular and Cardiovascular Complications Derived and Validated With Real-world Data From 2 Provinces: The DIabeteS COmplications (DISCO) Risk Scores

OBJECTIVES

Existing tools to predict the risk of complications among people with type 2 diabetes poorly discriminate high- from low-risk patients. Our aim in this study was to develop risk prediction scores for major type 2 diabetes complications using real-world clinical care data, and to externally validate these risk scores in a different jurisdiction.

METHODS

Using health-care administrative data and electronic medical records data, risk scores were derived using data from 25,088 people with type 2 diabetes from the Canadian province of Ontario, followed between 2002 and 2017. Scores were developed for major clinically important microvascular events (treatment for retinopathy, foot ulcer, incident end-stage renal disease), cardiovascular disease events (acute myocardial infarction, heart failure, stroke, amputation), and mortality (cardiovascular, noncardiovascular, all-cause). They were then externally validated using the independent data of 11,416 people with type 2 diabetes from the province of Manitoba.

RESULTS

The 10 derived risk scores had moderate to excellent discrimination in the independent validation cohort, ranging from 0.705 to 0.977. Their calibration to predict 5-year risk was excellent across most levels of predicted risk, albeit with some displaying underestimation at the highest levels of predicted risk.

CONCLUSIONS

The DIabeteS COmplications (DISCO) risk scores for major type 2 diabetes complications were derived and externally validated using contemporary real-world clinical data. As a result, they may be more accurate than other risk prediction scores derived using randomized trial data. The use of more accurate risk scores in clinical practice will help improve personalization of clinical care for patients with type 2 diabetes.

Deep-learning-based natural-language-processing models to identify cardiovascular disease hospitalisations of patients with diabetes from routine visits' text

Writing notes is the most widespread method to report clinical events. Therefore, most of the information about the disease history of a patient remains locked behind free-form text. Natural language processing (NLP) provides a solution to automatically transform free-form text into structured data. In the present work, electronic healthcare records data of patients with diabetes were used to develop deep-learning based NLP models to automatically identify, within free-form text describing routine visits, the occurrence of hospitalisations related to cardiovascular disease (CVDs), an outcome of diabetes. Four possible time windows of increasing level of expected difficulty were considered: infinite, 24 months, 12 months, and 6 months. Model performance was evaluated by means of the area under the precision recall curve, as well as precision, recall, and F1-score after thresholding. Results showed that the proposed NLP approach was successful for both the infinite and 24-month windows, while, as expected, performance deteriorated with shorter time windows. Possible clinical applications of tools based on the proposed NLP approach include the retrospective filling of medical records with respect to a patient's CVD history for epidemiological and research purposes as well as for clinical decision making.

Prediction of complications in health economic models of type 2 diabetes: a review of methods used

AIM

Diabetes health economic (HE) models play important roles in decision making. For most HE models of diabetes 2 diabetes (T2D), the core model concerns the prediction of complications. However, reviews of HE models pay little attention to the incorporation of prediction models. The objective of the current review is to investigate how prediction models have been incorporated into HE models of T2D and to identify challenges and possible solutions.

METHODS

PubMed, Web of Science, Embase, and Cochrane were searched from January 1, 1997, to November 15, 2022, to identify published HE models for T2D. All models that participated in The Mount Hood Diabetes Simulation Modeling Database or previous challenges were manually searched. Data extraction was performed by two independent authors. Characteristics of HE models, their underlying prediction models, and methods of incorporating prediction models were investigated.

RESULTS

The scoping review identified 34 HE models, including a continuous-time object-oriented model (n = 1), discrete-time state transition models (n = 18), and discrete-time discrete event simulation models (n = 15). Published prediction models were often applied to simulate complication risks, such as the UKPDS (n = 20), Framingham (n = 7), BRAVO (n = 2), NDR (n = 2), and RECODe (n = 2). Four methods were identified to combine interdependent prediction models for different complications, including random order evaluation (n = 12), simultaneous evaluation (n = 4), the 'sunflower method' (n = 3), and pre-defined order (n = 1). The remaining studies did not consider interdependency or reported unclearly.

CONCLUSIONS

The methodology of integrating prediction models in HE models requires further attention, especially regarding how prediction models are selected, adjusted, and ordered.

Predicting and preventing heart failure in type 2 diabetes

The burden of heart failure among people with type 2 diabetes is increasing globally. People with comorbid type 2 diabetes and heart failure often have worse outcomes than those with only one of these conditions-eg, higher hospitalisation and mortality rates. Therefore, it is essential to implement optimal heart failure prevention strategies for people with type 2 diabetes. A detailed understanding of the pathophysiology underlying the occurrence of heart failure in type 2 diabetes can aid clinicians in identifying relevant risk factors and lead to early interventions that can help prevent heart failure. In this Review, we discuss the pathophysiology and risk factors of heart failure in type 2 diabetes. We also review the risk assessment tools for predicting heart failure incidence in people with type 2 diabetes as well as the data from clinical trials that have assessed the efficacy of lifestyle and pharmacological interventions. Finally, we discuss the potential challenges in implementing new management approaches and offer pragmatic recommendations to help overcome these challenges.

Evaluation of available risk scores to predict multiple cardiovascular complications for patients with type 2 diabetes mellitus using electronic health records

Aims

Various cardiovascular risk prediction models have been developed for patients with type 2 diabetes mellitus. Yet few models have been validated externally. We perform a comprehensive validation of existing risk models on a heterogeneous population of patients with type 2 diabetes using secondary analysis of electronic health record data.

Methods

Electronic health records of 47,988 patients with type 2 diabetes between 2013 and 2017 were used to validate 16 cardiovascular risk models, including 5 that had not been compared previously, to estimate the 1-year risk of various cardiovascular outcomes. Discrimination and calibration were assessed by the c-statistic and the Hosmer-Lemeshow goodness-of-fit statistic, respectively. Each model was also evaluated based on the missing measurement rate. Sub-analysis was performed to determine the impact of race on discrimination performance.

Results

There was limited discrimination (c-statistics ranged from 0.51 to 0.67) across the cardiovascular risk models. Discrimination generally improved when the model was tailored towards the individual outcome. After recalibration of the models, the Hosmer-Lemeshow statistic yielded p-values above 0.05. However, several of the models with the best discrimination relied on measurements that were often imputed (up to 39% missing).

Conclusion

No single prediction model achieved the best performance on a full range of cardiovascular endpoints. Moreover, several of the highest-scoring models relied on variables with high missingness frequencies such as HbA1c and cholesterol that necessitated data imputation and may not be as useful in practice. An open-source version of our developed Python package, cvdm, is available for comparisons using other data sources.

Cost-effectiveness of insulin degludec/insulin aspart versus biphasic insulin aspart in Chinese population with type 2 diabetes

Objective

To evaluate the long-term cost effectiveness of insulin degludec/insulin aspart (IDegAsp) vs. biphasic insulin aspart 30 (BIAsp 30) for the treatment of people with type 2 diabetes mellitus (T2DM) inadequately managed on basal insulin in China.

Methods

The CORE (the Center for Outcomes Research) Diabetes Model, which has been published and verified, was used to simulate disease progression and calculate the total direct medical costs, life years (LYs) and quality-adjusted life years (QALYs) over 30 years, from the perspective of Chinese healthcare system. The patient demographic information and clinical data needed for the model were gathered from a phase III treat-to-target clinical trial (NCT02762578) and other Chinese cohort studies. Medical costs on treating diabetes were calculated based on clinical trial and local sources. The diabetes management and complications costs were derived from published literature. A discounting rate of 5% was applied to both health and cost outcomes. And one-way and probabilistic sensitivity analyses were carried out to test the reliability of the results.

Results

Compared with BIAsp 30, treatment with IDegAsp was associated with an incremental benefit of 0.001 LYs (12.439 vs. 12.438) and 0.280 QALYs (9.522 vs. 9.242) over a 30-year time horizon, and increased CNY (Chinese Yuan) 3,888 (390,152 vs. 386,264) for total costs. IDegAsp was cost-effective vs. BIAsp 30 therapy with an incremental cost-effectiveness ratio of CNY 13,886 per QALY gained. Results were robust across a range of sensitivity analyses.

Conclusion

Compared with BIAsp 30, IDegAsp was a cost-effective treatment option for people with T2DM with inadequate glycemic management on basal insulin in China.

Prediction models of diabetes complications: a scoping review

BACKGROUND

Diabetes often places a large burden on people with diabetes (hereafter 'patients') and the society, that is, in part attributable to its complications. However, evidence from models predicting diabetes complications in patients remains unclear. With the collaboration of patient partners, we aimed to describe existing prediction models of physical and mental health complications of diabetes.

METHODS

Building on existing frameworks, we systematically searched for studies in Ovid-Medline and Embase. We included studies describing prognostic prediction models that used data from patients with pre-diabetes or any type of diabetes, published between 2000 and 2020. Independent reviewers screened articles, extracted data and narratively synthesised findings using established reporting standards.

RESULTS

Overall, 78 studies reported 260 risk prediction models of cardiovascular complications (n=42 studies), mortality (n=16), kidney complications (n=14), eye complications (n=10), hypoglycaemia (n=8), nerve complications (n=3), cancer (n=2), fracture (n=2) and dementia (n=1). Prevalent complications deemed important by patients such as amputation and mental health were poorly or not at all represented. Studies primarily analysed data from older people with type 2 diabetes (n=54), with little focus on pre-diabetes (n=0), type 1 diabetes (n=8), younger (n=1) and racialised people (n=10). Per complication, predictors vary substantially between models. Studies with details of calibration and discrimination mostly exhibited good model performance.

CONCLUSION

This rigorous knowledge synthesis provides evidence of gaps in the landscape of diabetes complication prediction models. Future studies should address unmet needs for analyses of complications n> and among patient groups currently under-represented in the literature and should consistently report relevant statistics. SCOPING REVIEW REGISTRATION: https://osf.io/fjubt/.

Clinical Prediction Models for Heart Failure Hospitalization in Type 2 Diabetes: A Systematic Review and Meta-Analysis

Background Clinical prediction models have been developed for hospitalization for heart failure in type 2 diabetes. However, a systematic evaluation of these models' performance, applicability, and clinical impact is absent. Methods and Results We searched Embase, MEDLINE, Web of Science, Google Scholar, and Tufts' clinical prediction registry through February 2021. Studies needed to report the development, validation, clinical impact, or update of a prediction model for hospitalization for heart failure in type 2 diabetes with measures of model performance and sufficient information for clinical use. Model assessment was done with the Prediction Model Risk of Bias Assessment Tool, and meta-analyses of model discrimination were performed. We included 15 model development and 3 external validation studies with data from 999 167 people with type 2 diabetes. Of the 15 models, 6 had undergone external validation and only 1 had low concern for risk of bias and applicability (Risk Equations for Complications of Type 2 Diabetes). Seven models were presented in a clinically useful manner (eg, risk score, online calculator) and 2 models were classified as the most suitable for clinical use based on study design, external validity, and point-of-care usability. These were Risk Equations for Complications of Type 2 Diabetes (meta-analyzed c-statistic, 0.76) and the Thrombolysis in Myocardial Infarction Risk Score for Heart Failure in Diabetes (meta-analyzed c-statistic, 0.78), which was the simplest model with only 5 variables. No studies reported clinical impact. Conclusions Most prediction models for hospitalization for heart failure in patients with type 2 diabetes have potential concerns with risk of bias or applicability, and uncertain external validity and clinical impact. Future research is needed to address these knowledge gaps.

The PRIME Type 2 Diabetes Model: a novel, patient-level model for estimating long-term clinical and cost outcomes in patients with type 2 diabetes mellitus

BACKGROUND AND AIMS

The growing burden of diabetes mellitus and recent progress in understanding cardiovascular outcomes for type 2 diabetes (T2D) patients continue to make the disease a priority for healthcare decision-makers around the world. Our objective was to develop a new, product-independent model capable of projecting long-term clinical and cost outcomes for populations with T2D to support health economic evaluation.

METHODS

Following a systematic literature review to identify longitudinal study data, existing T2D models and risk formulae for T2D populations, a model was developed (the PRIME Type 2 Diabetes Model [PRIME T2D Model]) in line with good practice guidelines to simulate disease progression, diabetes-related complications and mortality. The model runs as a patient-level simulation and is capable of simulating treatment algorithms and risk factor progression, and projecting the cumulative incidence of macrovascular and microvascular complications as well as hypoglycemic events. The PRIME T2D Model can report clinical outcomes, quality-adjusted life expectancy, direct and indirect costs, along with standard measures of cost-effectiveness and is capable of probabilistic sensitivity analysis. Several approaches novel to T2D modeling were utilized, such as combining risk formulae using a weighted model averaging approach that takes into account patient characteristics to evaluate complication risk.

RESULTS

Validation analyses comparing modeled outcomes with published studies demonstrated that the PRIME T2D Model projects long-term patient outcomes consistent with those reported for a number of long-term studies, including cardiovascular outcomes trials. All root mean squared deviation (RMSD) values for internal validations (against published studies used to develop the model) were 1.1% or less and all external validation RMSDs were 3.7% or less.

CONCLUSIONS

The PRIME T2D Model is a product-independent analysis tool that is available online and offers new approaches to long-standing challenges in diabetes modeling and may become a useful tool for informing healthcare policy.HIGHLIGHTSThe PRIME Type 2 Diabetes (T2D) Model is a new, product-independent simulation model.The model offers new approaches to long-standing challenges in diabetes modeling.PRIME T2D Model projects outcomes consistent with those from clinical trials.The model is designed to be a useful tool for informing healthcare policy in T2D.

Predicting morbidity by local similarities in multi-scale patient trajectories

Patient Trajectories (PTs) are a method of representing the temporal evolution of patients. They can include information from different sources and be used in socio-medical or clinical domains. PTs have generally been used to generate and study the most common trajectories in, for instance, the development of a disease. On the other hand, healthcare predictive models generally rely on static snapshots of patient information. Only a few works about prediction in healthcare have been found that use PTs, and therefore benefit from their temporal dimension. All of them, however, have used PTs created from single-source information. Therefore, the use of longitudinal multi-scale data to build PTs and use them to obtain predictions about health conditions is yet to be explored. Our hypothesis is that local similarities on small chunks of PTs can identify similar patients concerning their future morbidities. The objectives of this work are (1) to develop a methodology to identify local similarities between PTs before the occurrence of morbidities to predict these on new query individuals; and (2) to validate this methodology on risk prediction of cardiovascular diseases (CVD) occurrence in patients with diabetes. We have proposed a novel formal definition of PTs based on sequences of longitudinal multi-scale data. Moreover, a dynamic programming methodology to identify local alignments on PTs for predicting future morbidities is proposed. Both the proposed methodology for PT definition and the alignment algorithm are generic to be applied on any clinical domain. We validated this solution for predicting CVD in patients with diabetes and we achieved a precision of 0.33, a recall of 0.72 and a specificity of 0.38. Therefore, the proposed solution in the diabetes use case can result of utmost utility to secondary screening.

Predictive models for cardiovascular and kidney outcomes in patients with type 2 diabetes: systematic review and meta-analyses

OBJECTIVE

To inform a clinical practice guideline (BMJ Rapid Recommendations) considering sodium glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists for treatment of adults with type 2 diabetes, we summarised the available evidence regarding the performance of validated risk models on cardiovascular and kidney outcomes in these patients.

METHODS

We systematically searched bibliographic databases in January 2020 to identify observational studies evaluating risk models for all-cause and cardiovascular mortality, heart failure (HF) hospitalisations, end-stage kidney disease (ESKD), myocardial infarction (MI) and ischaemic stroke in ambulatory adults with type 2 diabetes. Using a random effects model, we pooled discrimination measures for each model and outcome, separately, and descriptively summarised calibration plots, when available. We used the Prediction Model Risk of Bias Assessment Tool to assess risk of bias of each included study and the Grading of Recommendations, Assessment, Development, and Evaluation approach to evaluate our certainty in the evidence.

RESULTS

Of 22 589 publications identified, 15 observational studies reporting on seven risk models proved eligible. Among the seven models with >1 validation cohort, the Risk Equations for Complications of Type 2 Diabetes (RECODe) had the best calibration in primary studies and the highest pooled discrimination measures for the following outcomes: all-cause mortality (C-statistics 0.75, 95% CI 0.70 to 0.80; high certainty), cardiovascular mortality (0.79, 95% CI 0.75 to 0.84; low certainty), ESKD (0.73, 95% CI 0.52 to 0.94; low certainty), MI (0.72, 95% CI 0.69 to 0.74; moderate certainty) and stroke (0.71, 95% CI 0.68 to 0.74; moderate certainty). This model does not, however, predict risk of HF hospitalisations.

CONCLUSION

Of available risk models, RECODe proved to have satisfactory calibration in primary validation studies and acceptable discrimination superior to other models, though with high risk of bias in most primary studies.

TRIAL REGISTRATION NUMBER

CRD42020168351.

Risk stratification tools for heart failure in the diabetes clinic

The advent of Sodium Glucose Transporter 2-inhibitors (SGLT2-i) in recent years gave endocrinologists the opportunity to actively treat and prevent heart failure (HF) in patients with type 2 diabetes (T2DM). While the relationship between T2DM and HF has been extensively reviewed, previous works focused mostly on epidemiology, pathophysiology and treatment of HF in T2DM. The aim of our work was to aid health care professionals in identifying individuals at high risk for this dreadful complication. Recent guidelines recommend to use drugs with proven cardiovascular benefits (Glucagon-like peptide-1 receptor agonists (GLP1-RA) and SGLT2-i) in patients with previous cardiovascular disease (CVD) and to prefer SGLT2-i in patients with known HF. In everyday clinical practice, the choice between these two drug classes in patients without known HF or atherosclerotic CVD is mostly arbitrary and based on the side effect profile. Recently, risk stratification tools to estimate HF incidence have been developed in order to guide treatment with a view to bring precision medicine into diabetes care. With this purpose, we provide a review of the tools able to predict HF incidence for patients in primary CVD prevention as well as risk of future hospitalizations for patients with known HF.

Circulating branched-chain amino acids and incident heart failure in type 2 diabetes: The Hong Kong Diabetes Register

AIM

Levels of branched-chain amino acids (BCAAs, namely, isoleucine, leucine, and valine) are modulated by dietary intake and metabolic/genetic factors. BCAAs are associated with insulin resistance and increased risk of type 2 diabetes (T2D). Although insulin resistance predicts heart failure (HF), the relationship between BCAAs and HF in T2D remains unknown.

METHODS

In this prospective observational study, we measured BCAAs in fasting serum samples collected at inception from 2139 T2D patients free of cardiovascular-renal diseases. The study outcome was the first hospitalization for HF.

RESULTS

During 29 103 person-years of follow-up, 115 primary events occurred (age: 54.8 ± 11.2 years, 48.2% men, median [interquartile range] diabetes duration: 5 years [1-10]). Patients with incident HF had 5.6% higher serum BCAAs than those without HF (median 639.3 [561.3-756.3] vs 605.2 [524.8-708.7] μmol/L; P = .01). Serum BCAAs had a positive linear association with incident HF (per-SD increase in logarithmically transformed BCAAs: hazard ratio [HR] 1.22 [95% CI 1.07-1.39]), adjusting for age, sex, and diabetes duration. The HR remained significant after sequential adjustment of risk factors including incident coronary heart disease (1.24, 1.09-1.41); blood pressure, low-density lipoprotein cholesterol, and baseline use of related medications (1.31, 1.14-1.50); HbA_1c , waist circumference, triglyceride, and baseline use of related medications (1.28, 1.11-1.48); albuminuria and estimated glomerular filtration rate (1.28, 1.11-1.48). The competing risk of death analyses showed similar results.

CONCLUSIONS

Circulating levels of BCAAs are independently associated with incident HF in patients with T2D. Prospective cohort analysis and randomized trials are needed to evaluate the long-term safety and efficacy of using different interventions to optimize BCAAs levels in these patients.

Health literacy and exercise-focused interventions on clinical measurements in Chinese diabetes patients: A cluster randomized controlled trial

BACKGROUND

The diabetes patients in China have low health literacy and low levels of physical activities which may result in the poor glycemic control and other clinical outcomes. This study is designed to evaluate the effectiveness of health literacy and exercise-focused interventions on clinical outcomes among Chinese patients with type 2 diabetes (T2DM).

METHODS

In this cluster randomized controlled trial, 799 T2DM patients with the most recent A1c ≥ 7·5% (58 mmol/mol, or fasting glucose level ≥10 mmol/L) were recruited from 35 clinics in 8 communities in Shanghai, China, and randomized into one standard care (control) arm and three intervention arms receiving interventions focused on health literacy, exercise or both. A1c (primary outcome), blood pressure and lipids (secondary outcomes) were measured at baseline, 3-, 6-, 12-months of intervention period and 12-months after completion of the interventions. This trial is registered with the International Standard RCT Number Register, number ISRCTN76130594.

FINDINGS

The three intervention groups had more reductions in A1c than the control group, with 0·90% reduction in the health literacy, 0·83% in the exercise and 0·54% in the comprehensive group at 12-months (p<0·001) and these improvements remained even after a 1-year follow-up period post intervention. The risk of suboptimal A1c (≥7·0% or 53 mmol/mol) was also significantly lower in three intervention groups than control group at each follow-up visit, with adjusted risk ratios (RR) ranging from 0.06 to 0.16. However, the control group has greater reductions in low-density lipoprotein (LDL) than the health literacy group from baseline to 12-months (β=0·55, p<0·0001) and from baseline to 24-months (β=0·62, p<0·0001). A higher risk of abnormal LDL was also observed for the health literacy group at 12-months [adjusted risk ratio (RR): 2·22, 95%CI: 1·11-4·44] and 24-months [adjusted risk ratio (RR): 2·37, 95%CI: 1·16-4·87] compared to the control group. No significant benefits in systolic blood pressure (SBP), diastolic blood pressure (DBP) and low-density lipoprotein (HDL) were observed from the interventions compared to the usual care.

INTERPRETATION

The health literacy and exercise interventions result in significant improvements in A1c. However, no significant benefits in blood pressure and lipids control were observed. These effective interventions may have potential of scaling up in China and other countries to help diabetes patients manage their blood glucose levels.

FUNDING

This Study was supported by the China Medical Board (CMB) Open Competition Project (No.13-159) and the Social Science Fund of China National Ministry of Education (No.14YJAZH092).

From Hong Kong Diabetes Register to JADE Program to RAMP-DM for Data-Driven Actions

In 1995, the Hong Kong Diabetes Register (HKDR) was established by a doctor-nurse team at a university-affiliated, publicly funded, hospital-based diabetes center using a structured protocol for gathering data to stratify risk, triage care, empower patients, and individualize treatment. This research-driven quality improvement program has motivated the introduction of a territory-wide diabetes risk assessment and management program provided by 18 hospital-based diabetes centers since 2000. By linking the data-rich HKDR to the territory-wide electronic medical record, risk equations were developed and validated to predict clinical outcomes. In 2007, the HKDR protocol was digitalized to establish the web-based Joint Asia Diabetes Evaluation (JADE) Program complete with risk levels and algorithms for issuance of personalized reports to reduce clinical inertia and empower self-management. Through this technologically assisted, integrated diabetes care program, we have generated big data to track secular trends, identify unmet needs, and verify interventions in a naturalistic environment. In 2009, the JADE Program was adapted to form the Risk Assessment and Management Program for Diabetes Mellitus (RAMP-DM) in the publicly funded primary care clinics, which reduced all major events by 30-60% in patients without complications. Meanwhile, a JADE-assisted assessment and empowerment program provided by a university-affiliated, self-funded, nurse-coordinated diabetes center, aimed at complementing medical care in the community, also reduced all major events by 30-50% in patients with different risk levels. By combining universal health coverage, public-private partnerships, and data-driven integrated care, the Hong Kong experience provides a possible solution than can be adapted elsewhere to make quality diabetes care accessible, affordable, and sustainable.

Meta-Analysis and Cost-Effectiveness Analysis of Insulin Glargine 100 U/mL Versus Insulin Degludec for the Treatment of Type 2 Diabetes in China

INTRODUCTION

To evaluate the efficacy and safety as well as the long-term cost-effectiveness of insulin glargine 100 U/mL (IGlar) versus insulin degludec (IDeg) for the treatment of type 2 diabetes mellitus (T2DM) from the Chinese healthcare system perspective.

METHODS

A systematic search of English and Chinese electronic databases for randomized controlled trials (RCTs) comparing IGlar with IDeg for the treatment of T2DM was performed, followed by a meta-analysis to compare the efficacy and safety of IGlar versus IDeg. The CORE Diabetes Model was used to estimate lifetime costs, quality-adjusted life years (QALYs) gained, and cost-effectiveness of IGlar versus IDeg. One-way and probabilistic sensitivity analyses were conducted to assess the underlying parameter uncertainty.

RESULTS

Six RCTs were included in the meta-analysis. The IGlar group showed a statistically significant decrease in glycated hemoglobin (HbA_1c) from baseline compared to the IDeg group (mean difference [MD] 0.08%, 95% confidence interval [CI] 0.01-0.14%, P = 0.02). Body mass index (BMI) control was numerically better in the IGlar group than in the IDeg group (MD 0.07 kg/m², 95% CI - 0.01 to 0.14 kg/m², P  = 0.08). In terms of hypoglycemia, the incidence of non-severe overall hypoglycemia was comparable between the IDeg and IGlar patient groups (P  > 0.05), while the incidence of non-severe nocturnal hypoglycemia (relative risk [RR 0.79], 95% CI 0.70-0.90, P < 0.01) and the event rates of non-severe overall (RR 0.91, 95% CI 0.85-0.97, P < 0.01) and non-severe nocturnal hypoglycemia (RR 0.91, 95% CI 0.85-0.97, P < 0.01) were lower in the IDeg group. The incidences and event rates of both severe overall and nocturnal hypoglycemia were similar for the two groups (P  > 0.05). The cost-effectiveness analysis showed that IGlar is the dominant treatment option compared with IDeg, with a lifetime savings of 1004 Chinese yuan in direct medical costs and a net gain of 0.015 QALYs per patient. Both one-way and probabilistic sensitivity analyses confirmed the robustness of the results.

CONCLUSIONS

IGlar is a cost-saving option with incremental effectiveness compared with IDeg for the treatment of T2DM in China.

FUNDING

Sanofi China.

Insulin glargine compared to neutral protamine Hagedorn (NPH) insulin in patients with type-2 diabetes uncontrolled with oral anti-diabetic agents alone in Hong Kong: a cost-effectiveness analysis

Background

International guidelines recommend using basal insulin in patients with type-2 diabetes mellitus if glycaemic target cannot be attained on non-insulin anti-diabetic drugs. Available choices of basal insulin include intermediate-acting neutral protamine Hagedorn (NPH) insulin and long-acting insulin analogues like insulin glargine U100. Despite clear advantages of glargine U100, the existing practice in Hong Kong still favours NPH insulin due to lower immediate drug costs.

Objectives

The objective of this study was to assess the cost-effectiveness of insulin glargine U100 compared to NPH insulin in patients with type-2 diabetes uncontrolled with non-insulin anti-diabetic agents alone in Hong Kong.

Methods

The IQVIA™ Core Diabetes Model (CDM) v9.0 was used to conduct the cost-effectiveness analysis of glargine U100 versus NPH. Baseline characteristics were collected from the Hong Kong Diabetes Registry. Efficacy rates were extracted from a published study comparing glargine U100 and NPH in Asia, utilities from published literature, and costs constructed using the Hong Kong Hospital Authority (HA) Gazette (public healthcare setting). The primary outcome was an incremental cost-effectiveness ratio (ICER).

Results

Insulin glargine U100 resulted in an ICER of HKD 98,663 per Quality Adjusted Life Year (QALY) gained. The incremental gains in QALY and costs were 0.217 years and HKD 21,360 respectively. Results from scenario and probabilistic sensitivity analyses were consistent with that from base case analysis.

Conclusion

Insulin glargine U100 is a cost-effective treatment for patients with type 2 diabetes compared to NPH insulin in setting in Hong Kong. This was mainly driven by the significantly lower rates of hypoglycaemia of insulin glargine U100 than NPH insulin.

Electronic supplementary material

The online version of this article (10.1186/s12962-019-0180-9) contains supplementary material, which is available to authorized users.

Development and validation of a Health Policy Model of Type 2 diabetes in Chinese setting

Aim: Due to the difference in epidemiology and outcomes between eastern and western populations with Type 2 diabetes mellitus (T2DM), an important challenge is determining how useful the outcomes from diabetes models based on western populations are for eastern patients. Consequently, the principal aim of this study was to develop and validate a Health Policy Model (Chinese Outcomes Model for T2DM [COMT]) for supporting Chinese medical and health economic studies. Methods: The model is created to simulate a series of important complications of T2DM diabetes based on the latest Risk Equations for Complications of Type 2 Diabetes, which was adjusted by adding the adjustment regulator to the linear predictor within the risk equation. The validity of the model was conducted by using a total of 171 validation outcomes from seven studies in eastern populations and ten studies in western populations. The simulation cohorts in the COMT model were generated by copying each validation study's baseline characteristics. Concordance was tested by assessing the difference between the identity (45°) line and the best-fitting regression of the scatterplots for the predicted versus observed outcomes. Results: The slope coefficients of the best-fitting regression line between the predicted and corresponding observed actual outcomes was 0.9631 and the R² was 0.8701. There were major differences between western and eastern populations. The slope and R² of predictions were 0.9473 and 0.9272 in the eastern population and 1.0566 and 0.8863 in the western population, which showed more perfect agreement with the observed values in the eastern population than the western populations. The subset of macro-vascular and micro-vascular outcomes in the eastern population showed an identical tendency (the slope coefficient was close to 1), and mortality outcomes showed a slight tendency toward overestimation (the slope coefficient was close to 0.9208). Some degree of underprediction of macro-vascular and micro-vascular end points and overprediction of mortality end point was found in the western population. Conclusion: The COMT diabetes model simulated the long-term patient outcomes observed in eastern Asian T2DM patients with prediction accuracy. This study supports the COMT as a credible tool for Chinese healthcare decision makers. Further work is necessary to incorporate new local data to improve model validity and credibility.

Development and validation of prediction models for the risks of diabetes-related hospitalization and in-hospital mortality in patients with type 2 diabetes

OBJECTIVES

Diabetes is a major cause of hospitalization and in-hospital mortality. However, a scoring system that can be used to identify diabetic patients at risk of diabetes-related hospitalization and in-hospital mortality is lacking.

METHODS

We included 32,653 patients in this retrospective cohort study. All recruited patients had type 2 diabetes, were 30-84 years of age, and were enrolled in the National Diabetes Care Management Program over the period of 2001-2003. We used the Cox proportional hazard regression model to derive risk scores. The predictive accuracy of the models was evaluated using receiver operating characteristic curves. We conducted the Hosmer-Lemeshow test to assess the agreement between predicted and observed risks.

RESULTS

Over a follow-up period of eight years, 6243 patients were hospitalized for diabetes-related events, and 2048 deaths were registered in hospital records. For the one-, three-, five-, and eight-year periods, the areas under the curve (AUC) for diabetes-related hospitalization in the validation set were 0.80, 077, 0.76, and 0.74, respectively. The corresponding values for in-hospital mortality in the validation set were 0.87, 080, 0.77, and 0.76. The goodness-of-fit test showed that the predicted and observed probabilities in the one-, three-, five-, and eight-year periods were similar for diabetes-related hospitalization and in-hospital mortality in the validation set (all p values > 0.05).

CONCLUSION

We developed models for the estimation of the risks of diabetes-related hospitalization and in-hospital mortality in patients with type 2 diabetes. The models may be used to identify diabetic patients who are at high risk for hospital admission and in-hospital mortality.

Perspective Review: Type 2 Diabetes and Readmission for Heart Failure

Heart failure is a leading cause for hospitalisation and for readmission, especially in patients over the age of 65. Diabetes is an increasingly common companion to heart failure. The presence of diabetes and its associated comorbidity increases the risk of adverse outcomes and premature mortality in patients with heart failure. In particular, patients with diabetes are more likely to be readmitted to hospital soon after discharge. This may partly reflect the greater severity of heart disease in these patients. In addition, agents that reduce the chances of readmission such as β-blockers, renin-angiotensin-aldosterone system blockers, and mineralocorticoid receptor antagonists are underutilised because of the perceived increased risks of adverse drug reactions and other limitations. In some cases, readmission to hospital is precipitated by acute decompensation of heart failure (re-exacerbation) leading to pulmonary congestion and/or refractory oedema. However, it appears that for most of the patients admitted and then discharged with a primary diagnosis of heart failure, most readmissions are not due to heart failure, but rather due to comorbidity including arrhythmia, infection, adverse drug reactions, and renal impairment/reduced hydration. All of these are more common in patients who also have diabetes, and all may be partly preventable. The many different reasons for readmission underline the critical value of multidisciplinary comprehensive care in patients admitted with heart failure, especially those with diabetes. A number of new strategies are also being developed to address this area of need, including the use of SGLT2 inhibitors, novel nonsteroidal mineralocorticoid antagonists, and neprilysin inhibitors.

A multicentre demonstration project to evaluate the effectiveness and acceptability of the web-based Joint Asia Diabetes Evaluation (JADE) programme with or without nurse support in Chinese patients with Type 2 diabetes

AIMS

To test the hypothesis that delivery of integrated care augmented by a web-based disease management programme and nurse coordinator would improve treatment target attainment and health-related behaviour.

METHODS

The web-based Joint Asia Diabetes Evaluation (JADE) and Diabetes Monitoring Database (DIAMOND) portals contain identical built-in protocols to integrate structured assessment, risk stratification, personalized reporting and decision support. The JADE portal contains an additional module to facilitate structured follow-up visits. Between January 2009 and September 2010, 3586 Chinese patients with Type 2 diabetes from six sites in China were randomized to DIAMOND (n = 1728) or JADE, plus nurse-coordinated follow-up visits (n = 1858) with comprehensive assessments at baseline and 12 months. The primary outcome was proportion of patients achieving ≥ 2 treatment targets (HbA1c < 53 mmol/mol (7%), blood pressure < 130/80 mmHg and LDL cholesterol < 2.6 mmol/l).

RESULTS

Of 3586 participants enrolled (mean age 57 years, 54% men, median disease duration 5 years), 2559 returned for repeat assessment after a median (interquartile range) follow-up of 12.5 (4.6) months. The proportion of participants attaining ≥ 2 treatment targets increased in both groups (JADE 40.6 to 50.0%; DIAMOND 38.2 to 50.8%) and there were similar absolute reductions in HbA1c [DIAMOND -8 mmol/mol vs JADE -7 mmol/mol (-0.69 vs -0.62%)] and LDL cholesterol (DIAMOND -0.32 mmol/l vs JADE -0.28 mmol/l), with no between-group difference. The JADE group was more likely to self-monitor blood glucose (50.5 vs 44.2%; P = 0.005) and had fewer defaulters (25.6 vs 32.0%; P < 0.001).

CONCLUSIONS

Integrated care augmented by information technology improved cardiometabolic control, with additional nurse contacts reducing the default rate and enhancing self-care. (Clinical trials registry no.: NCT01274364).

Type 2 Diabetes and Heart Failure: Challenges and Solutions

Increasing numbers of older patients with type 2 diabetes, and their improved survival from cardiovascular events is seeing a massive increase in patients with both diabetes and heart failure. Already, at least a third of all patients with heart failure have diabetes. This close association is partly because all the major risk factors for heart failure also cluster in patients with type 2 diabetes, including obesity, hypertension, advanced age, sleep apnoea, dyslipidaemia, anaemia, chronic kidney disease, and coronary heart disease. However, diabetes may also cause cardiac dysfunction in the absence of overt macrovascular disease, as well as complicate the response to therapy. Current management is focused on targeting modifiable risk factors for heart failure including hyperglycaemia, dyslipidaemia, hypertension, obesity and anemia. But although these are important risk markers, none of these interventions substantially prevents heart failure or improves its outcomes. Much more needs to be done to focus on this issue, including the inclusion of hospital admission for heart failure as a pre-specified component of the primary composite cardiovascular outcomes and new trials in heart failure management specifically in the context of diabetes.

Prospective Association between Diabetic Retinopathy and Cardiovascular Disease-A Systematic Review and Meta-analysis of Cohort Studies

BACKGROUND

Diabetic retinopathy (DR) is linked to increased risk of cardiovascular (CV) disease. However, the effect size of the association was not consistent. In this study, we performed a systematic review and meta-analysis of available cohort studies to determine the association between DR and CV disease, and to investigate the factors that influence the association.

METHODS

Terms related to DR and CV disease were searched from MEDLINE and EMBASE database. High-quality articles (Newcastle-Ottawa scales above 6) conducted in cohort studies reporting the association between DR and CV disease were identified. Study-specific estimates were pooled using random effects with inverse variance meta-analysis. Subgroup analysis was performed according to diabetes types. Heterogeneity of included studies was assessed using the I(2) test. The cause of the heterogeneity was examined using metaregression analyses.

RESULTS

A total of 13 studies representing 17,611 patients without CV disease at baseline were included. At follow-up, there were 1457 CV disease-related incidences. Overall, DR was associated with increased risk of CV disease (relative risk [RR]: 2.42, 95% confidence interval [CI]: 1.77-3.31) in diabetes. Specifically, the RR was 3.59 (95% CI: 1.79-7.20) for type 1 diabetes and 1.81 (95% CI: 1.47-2.23) for type 2 diabetes. Significant heterogeneity was found in studies with type 1 diabetes. Metaregression analysis showed that baseline systolic blood pressure was a key factor leading to the heterogeneity.

CONCLUSION

In conclusion, DR is significantly associated with CV disease incidence and CV disease-related mortality in diabetes. Patients with DR may need more intensive management to control future CV disease attacks.

Toward Big Data Analytics: Review of Predictive Models in Management of Diabetes and Its Complications

Diabetes is one of the top priorities in medical science and health care management, and an abundance of data and information is available on these patients. Whether data stem from statistical models or complex pattern recognition models, they may be fused into predictive models that combine patient information and prognostic outcome results. Such knowledge could be used in clinical decision support, disease surveillance, and public health management to improve patient care. Our aim was to review the literature and give an introduction to predictive models in screening for and the management of prevalent short- and long-term complications in diabetes. Predictive models have been developed for management of diabetes and its complications, and the number of publications on such models has been growing over the past decade. Often multiple logistic or a similar linear regression is used for prediction model development, possibly owing to its transparent functionality. Ultimately, for prediction models to prove useful, they must demonstrate impact, namely, their use must generate better patient outcomes. Although extensive effort has been put in to building these predictive models, there is a remarkable scarcity of impact studies.

Development and validation of risk prediction equations to estimate future risk of heart failure in patients with diabetes: a prospective cohort study

OBJECTIVE

To develop and externally validate risk prediction equations to estimate the 10-year risk of heart failure in patients with diabetes, aged 25-84 years.

DESIGN

Cohort study using routinely collected data from general practices in England between 1998 and 2014 contributing to the QResearch and Clinical Research Practice Datalink (CPRD) databases.

SETTING

We used 763 QResearch practices to develop the equations. We validated it in 254 different QResearch practices and 357 CPRD practices.

PARTICIPANTS

437,806 patients in the derivation cohort; 137,028 in the QResearch validation cohort, and 197,905 in the CPRD validation cohort.

MEASUREMENT

Incident diagnosis of heart failure recorded on the patients' linked electronic General Practitioner (GP), mortality, or hospital record. Risk factors included age, body mass index (BMI), systolic blood pressure, cholesterol/ high-density lipoprotein (HDL) ratio, glycosylated haemoglobin (HbA1c), material deprivation, ethnicity, smoking, diabetes duration, type of diabetes, atrial fibrillation, cardiovascular disease, chronic renal disease, and family history of premature coronary heart disease.

METHODS

We used Cox proportional hazards models to derive separate risk equations in men and women for evaluation at 10 years. Measures of calibration, discrimination, and sensitivity were determined in 2 external validation cohorts.

RESULTS

We identified 25,480 cases of heart failure in the derivation cohort, 8189 in the QResearch validation cohort, and 11,311 in the CPRD cohort. The equations included: age, BMI, systolic blood pressure, cholesterol/HDL ratio, HbA1c, material deprivation, ethnicity, smoking, duration and type of diabetes, atrial fibrillation, cardiovascular disease, and chronic renal disease. The equations had good performance in CPRD for women (R(2) of 41.2%; D statistic 1.71; and receiver operating characteristic curve (ROC) statistic 0.78) and men (38.7%, 1.63; and 0.77 respectively).

CONCLUSIONS

We have developed and externally validated risk prediction equations to quantify absolute risk of heart failure in men and women with diabetes. These can be used to identify patients at high risk of heart failure for prevention or assessment of the disease.

Prediction of heart failure in patients with type 2 diabetes mellitus- a systematic review and meta-analysis

BACKGROUND

Heart failure (HF) is a major cause of mortality and disability in type 2 diabetes mellitus (T2DM). This study sought to improve the assessment of HF risk in patients with T2DM-a step that would be critical for effective HF screening.

METHODS

A systematic literature search was performed on electronic databases including MEDLINE and EMBASE, using MeSH terms 'heart failure', 'risk factor', 'T2DM', 'cardiac dysfunction', 'stage B heart failure', 'incident heart failure', 'risk assessment', 'risk impact', 'risk score', 'predictor', 'prediction' and related free text terms. The search was limited to human studies in full-length publications in English language journal from 1946 to 2014. Univariable and multivariable relative risk (RR) and hazard ratio (HR) were obtained from each study.

RESULTS

Twenty-one studies (n=1111,569, including 507,637 subjects with T2DM) were included in this analysis with a follow-up ranging from 1 to 12 years. Associations between incident HF and risk variables described in ≥3 studies were reported. This association was greatest for insulin use (HR 2.48; 1.24-4.99), HbA1c 7.0-8.0% (2.41; 1.62-3.59), 5 years increase in age (1.47; 1.25-1.73), fasting glucose (1.28; 1.10-1.51 per standard deviation) and HbA1c (1.18; 1.14-1.23 each 1% increase). After adjustment for confounders, there were strong associations with coronary artery disease (1.77; 1.31, 2.39), HbA1c ≥ 10% (1.66; 1.45-1.89), insulin use (1.43; 1.14-1.79), HbA1c 9.0-10.0% (1.31; 1.14-1.50), fasting glucose (1.27; 1.10-1.47 per standard deviation) and 5 years increase in age (1.26; 1.13-1.40).

CONCLUSION

Among patients with T2DM, five common clinical variables are associated with significantly increased risk of incident HF.

Cardiovascular risk profiles in Type 2 diabetes and the impact of geographical setting

Cardiovascular (CV) disease is a leading morbidity and mortality in Type 2 diabetes (T2DM). Previous studies have shown geographic differences in the prevalence of CV and renal diseases. A literature review of longitudinal (≥5 years) studies including ≥1000 T2DM patients and reporting CV endpoints was performed to compare risk profiles. Key differences between geographies included a relatively higher prevalence of microalbuminuria in East Asian relative to North American and European patients, which in turn is an important CV risk factor. Patients from East Asia also have a relatively higher incidence of stroke and lower incidence of coronary heart disease. Overall, there are differences in CV risk in T2DM patients between different regions and that long-term studies from Africa, the Middle East and Latin America are lacking.

Refitting of the UKPDS 68 risk equations to contemporary routine clinical practice data in the UK

OBJECTIVE

Economic evaluations of new diabetes therapies rely heavily upon the UK Prospective Diabetes Study (UKPDS) equations for prediction of cardiovascular events; however, concerns persist regarding their relevance to current clinical practice and appropriate use in populations other than newly diagnosed patients. This study refits the UKPDS 68 event equations, using contemporary data describing low- and intermediate-risk patients.

RESEARCH DESIGN AND METHODS

Anonymized patient data describing demographics, risk factors and incidence of cardiovascular and microvascular events were extracted from The Health Improvement Network (THIN) database over the 10-year period from 1 January 2000 to 31 December 2009. Following multiple imputation of missing values, accelerated failure-time Weibull regression equations were refitted to produce new coefficients for each risk group. Discriminatory performance was assessed and compared with both UKPDS 68 and UKPDS 82 risk equations, and the implication of coefficient choice within an economic evaluation was assessed using the Cardiff type 2 diabetes model.

RESULTS

When applied to patient-level data, the three sets of coefficients (UKPDS, THIN low-risk and intermediate-risk) lead to fairly consistent predictions of the 5-year risk of events. Exceptions include lower predicted rates of myocardial infarction and higher rates of ischaemic heart disease, congestive heart failure and end-stage renal disease with both sets of revised THIN coefficients compared with UKPDS. Over a modelled lifetime, the coefficients derived from the low-risk data predict fewer total cardiovascular events compared with UKPDS, while those from the intermediate-risk data predict a greater number. The areas under the receiver-operating characteristic curves demonstrated a marginal improvement in the discriminatory performance of the refitted equations. The incremental cost-effectiveness ratio associated with dapagliflozin versus sulphonylurea in addition to metformin changed from £7,708 to £7,519 and £6,906 per QALY gained, using the THIN intermediate- and low-risk coefficients, respectively.

CONCLUSION

The results suggest that while the UKPDS equations perform best in newly diagnosed patients, they may overpredict the lifetime risk in this group and underpredict it in patients with more advanced diabetes. Implementation of the revised coefficients will result in different absolute numbers of predicted diabetes-related events; however, they are not expected to significantly affect the conclusions of economic modelling.

Delivery of integrated diabetes care using logistics and information technology--the Joint Asia Diabetes Evaluation (JADE) program

Diabetes is a global epidemic, and many affected individuals are undiagnosed, untreated, or uncontrolled. The silent and multi-system nature of diabetes and its complications, with complex care protocols, are often associated with omission of periodic assessments, clinical inertia, poor treatment compliance, and care fragmentation. These barriers at the system, patient, and care-provider levels have resulted in poor control of risk factors and under-usage of potentially life-saving medications such as statins and renin-angiotensin system inhibitors. However, in the clinical trial setting, use of nurses and protocol with frequent contact and regular monitoring have resulted in marked differences in event rates compared to epidemiological data collected in the real-world setting. The phenotypic heterogeneity and cognitive-psychological-behavioral needs of people with diabetes call for regular risk stratification to personalize care. Quality improvement initiatives targeted at patient education, task delegation, case management, and self-care promotion had the largest effect size in improving cardio-metabolic risk factors. The Joint Asia Diabetes Evaluation (JADE) program is an innovative care prototype that advocates a change in clinic setting and workflow, coordinated by a doctor-nurse team and augmented by a web-based portal, which incorporates care protocols and a validated risk engine to provide decision support and regular feedback. By using logistics and information technology, supported by a network of health-care professionals to provide integrated, holistic, and evidence-based care, the JADE Program aims to establish a high-quality regional diabetes database to reflect the status of diabetes care in real-world practice, confirm efficacy data, and identify unmet needs. Through collaborative efforts, we shall evaluate the feasibility, acceptability, and cost-effectiveness of this "high tech, soft touch" model to make diabetes and chronic disease care more accessible, affordable, and sustainable.

The validation of cardiovascular risk scores for patients with type 2 diabetes mellitus

OBJECTIVE

Various cardiovascular prediction models have been developed for patients with type 2 diabetes. Their predictive performance in new patients is mostly not investigated. This study aims to quantify the predictive performance of all cardiovascular prediction models developed specifically for diabetes patients.

DESIGN AND METHODS

Follow-up data of 453, 1174 and 584 type 2 diabetes patients without pre-existing cardiovascular disease (CVD) in the EPIC-NL, EPIC-Potsdam and Secondary Manifestations of ARTerial disease cohorts, respectively, were used to validate 10 prediction models to estimate risk of CVD or coronary heart disease (CHD). Discrimination was assessed by the c-statistic for time-to-event data. Calibration was assessed by calibration plots, the Hosmer-Lemeshow goodness-of-fit statistic and expected to observed ratios.

RESULTS

There was a large variation in performance of CVD and CHD scores between different cohorts. Discrimination was moderate for all 10 prediction models, with c-statistics ranging from 0.54 (95% CI 0.46 to 0.63) to 0.76 (95% CI 0.67 to 0.84). Calibration of the original models was poor. After simple recalibration to the disease incidence of the target populations, predicted and observed risks were close. Expected to observed ratios of the recalibrated models ranged from 1.06 (95% CI 0.81 to 1.40) to 1.55 (95% CI 0.95 to 2.54), mainly driven by an overestimation of risk in high-risk patients.

CONCLUSIONS

All 10 evaluated models had a comparable and moderate discriminative ability. The recalibrated, but not the original, prediction models provided accurate risk estimates. These models can assist clinicians in identifying type 2 diabetes patients who are at low or high risk of developing CVD.

The independent contribution of diabetic foot ulcer on lower extremity amputation and mortality risk

AIMS

To estimate 3-year risk for diabetic foot ulcer (DFU), lower extremity amputation (LEA) and death; determine predictive variables and assess derived models accuracy.

MATERIAL AND METHODS

Retrospective cohort study including all subjects with diabetes enrolled in our diabetic foot outpatient clinic from beginning 2002 until middle 2010. Data were collected from clinical records.

RESULTS

644 subjects with mean age of 65.1 (±11.2) and diabetes duration of 16.1 (±10.8) years. Cumulative incidence was 26.6% for DFU, 5.8% for LEA and 14.0% for death. In multivariate analysis, physical impairment, peripheral arterial disease complication history, complication count and previous DFU were associated with DFU; complication count, foot pulses and previous DFU with LEA and age, complication count and previous DFU with death. Predictive models' areas under the ROC curves ranged from 0.80 to 0.83. A simplified model including previous DFU and complication count presented high accuracy. Previous DFU was associated with all outcomes, even when adjusted for complication count, in addition to more complex models.

CONCLUSIONS

DFU seems more than a marker of complication status, having independent impact on LEA and mortality risk. Proposed models may be applicable in healthcare settings to identify patients at higher risk of DFU, LEA and death.

Impact of low-density lipoprotein cholesterol on cardiovascular outcomes in people with type 2 diabetes: a meta-analysis of prospective cohort studies

AIMS

To estimate the prospective association of low-density lipoprotein (LDL) cholesterol on cardiovascular disease (CVD) risk among people with type 2 diabetes.

METHODS

We used extensive literature searching strategies to locate prospective cohort studies that reported LDL cholesterol levels as a risk factor for cardiovascular events. We conducted meta-analytic procedures for two outcomes: incident CVD and CVD mortality.

RESULTS

A total of 16 studies were included in this analysis with a mean follow-up range of 4.8-11 years. The pooled relative risk associated with a 1mmol/L increase in LDL cholesterol in people with type 2 diabetes was 1.30 (95% confidence interval [CI], 1.19-1.43) for incident CVD, and 1.50 (95% CI, 1.25-1.80) for CVD mortality, respectively. Subgroup analyses showed that for incident CVD, the pooled relative risk was 1.28 (95% CI, 1.17-1.41) for 7 studies adjusted for blood pressure and/or glucose concentration (or insulin concentration, glycated hemoglobin) and 1.40 (95% CI, 1.05-1.86) for 3 studies that did not adjust for these variables.

CONCLUSIONS

Our study demonstrates that LDL cholesterol was associated with an increased risk for cardiovascular outcomes in people with type 2 diabetes, independent of other conventional risk factor.

Towards renewed health economic simulation of type 2 diabetes: risk equations for first and second cardiovascular events from Swedish register data

OBJECTIVE

Predicting the risk of future events is an essential part of health economic simulation models. In pursuit of this goal, the current study aims to predict the risk of developing first and second acute myocardial infarction, heart failure, non-acute ischaemic heart disease, and stroke after diagnosis in patients with type 2 diabetes, using data from the Swedish National Diabetes Register.

MATERIAL AND METHODS

Register data on 29,034 patients with type 2 diabetes were analysed over five years of follow up (baseline 2003). To develop and validate the risk equations, the sample was randomly divided into training (75%) and test (25%) subsamples. The Weibull proportional hazard model was used to estimate the coefficients of the risk equations, and these were validated in both the training and the test samples.

RESULTS

In total, 4,547 first and 2,418 second events were observed during the five years of follow up. Experiencing a first event substantially elevated the risk of subsequent events. There were heterogeneities in the effects of covariates within as well as between events; for example, while for females the hazard ratio of having a first acute myocardial infarction was 0.79 (0.70-0.90), the hazard ratio of a second was 1.21 (0.98-1.48). The hazards of second events decreased as the time since first events elapsed. The equations showed adequate calibration and discrimination (C statistics range: 0.70-0.84 in test samples).

CONCLUSION

The accuracy of health economic simulation models of type 2 diabetes can be improved by ensuring that they account for the heterogeneous effects of covariates on the risk of first and second cardiovascular events. Thus it is important to extend such models by including risk equations for second cardiovascular events.

Predicting macro- and microvascular complications in type 2 diabetes: the Japan Diabetes Complications Study/the Japanese Elderly Diabetes Intervention Trial risk engine

OBJECTIVE

To develop and validate a risk engine that calculates the risks of macro- and microvascular complications in type 2 diabetes.

RESEARCH DESIGN AND METHODS

We analyzed pooled data from two clinical trials on 1,748 Japanese type 2 diabetic patients without diabetes complications other than mild diabetic retinopathy with a median follow-up of 7.2 years. End points were coronary heart disease (CHD), stroke, noncardiovascular mortality, overt nephropathy defined by persistent proteinuria, and progression of retinopathy. We fit a multistate Cox regression model to derive an algorithm for prediction. The predictive accuracy of the calculated 5-year risks was cross-validated.

RESULTS

Sex, age, HbA1c, years after diagnosis, BMI, systolic blood pressure, non-HDL cholesterol, albumin-to-creatinine ratio, atrial fibrillation, current smoker, and leisure-time physical activity were risk factors for macro- and microvascular complications and were incorporated into the risk engine. The observed-to-predicted (O/P) ratios for each event were between 0.93 and 1.08, and Hosmer-Lemeshow tests showed no significant deviations between observed and predicted events. In contrast, the UK Prospective Diabetes Study (UKPDS) risk engine overestimated CHD risk (O/P ratios: 0.30 for CHD and 0.72 for stroke). C statistics in our Japanese patients were high for CHD, noncardiovascular mortality, and overt nephropathy (0.725, 0.696, and 0.767) but moderate for stroke and progression of retinopathy (0.636 and 0.614). By combining macro- and microvascular risks, the classification of low- and high-risk patients was improved by a net reclassification improvement of 5.7% (P = 0.02).

CONCLUSIONS

The risk engine accurately predicts macro- and microvascular complications and would provide helpful information in risk classification and health economic simulations.

Glycosylated hemoglobin in relationship to cardiovascular outcomes and death in patients with type 2 diabetes: a systematic review and meta-analysis

Background

Chronic hyperglycemia in type 2 diabetes increases the risk of microvascular events. However, there is continuing uncertainty about its effect on macrovascular outcomes and death. We conducted a meta-analysis of prospective studies to estimate the association of glycosylated hemoglobin level with the risk of all-cause mortality and cardiovascular outcomes among patients with type 2 diabetes.

Methodology/Principal Findings

We systematically searched the MEDLINE database through April 2011 by using Medical Subject Heading search terms and a standardized protocol. We included prospective cohort studies that reported data of glycosylated hemoglobin level on the risk of incident cardiovascular events and all-cause mortality. Relative risk estimates (continuous and categorical variables) were derived or abstracted from each cohort study. Twenty six studies were included in this analysis with a mean follow-up rang of 2.2–16 years. The pooled relative risk associated with a 1% increase in glycosylated hemoglobin level among patients with type 2 diabetes was 1.15 (95% CI, 1.11 to 1.20) for all-cause mortality, 1.17 (95% CI, 1.12 to 1.23) for cardiovascular disease, 1.15 (95% CI, 1.10 to 1.20) for coronary heart disease, 1.11 (95% CI, 1.05 to 1.18) for heart failure, 1.11 (95% CI, 1.06 to 1.17) for stroke, and 1.29 (95% CI, 1.18 to 1.40) for peripheral arterial disease, respectively. In addition, a positive dose-response trend existed between glycosylated hemoglobin level and cardiovascular outcomes.

Conclusions/Significance

Chronic hyperglycemia is associated with an increased risk for cardiovascular outcomes and all-cause mortality among patients with type 2 diabetes, likely independently from other conventional risk factors.

Diabetes, insulin and cancer risk

There is a consensus that both type 1 and type 2 diabetes are associated with a spectrum of cancers but the underlying mechanisms are largely unknown. On the other hand, there are ongoing debates about the risk association of insulin use with cancer. We have briefly reviewed recent related research on exploration of risk factors for cancer and pharmacoepidemiological investigations into drug use in diabetes on the risk of cancer, as well as the current understanding of metabolic pathways implicated in intermediary metabolism and cellular growth. Based on the novel findings from the Hong Kong Diabetes Registry and consistent experimental evidence, we argue that use of insulin to control hyperglycemia is unlikely to contribute to increased cancer risk and that dysregulations in the AMP-activated protein kinase pathway due to reduced insulin action and insulin resistance, the insulin-like growth factor-1 (IGF-1)-cholesterol synthesis pathway and renin-angiotensin system, presumably due to reduced insulin secretion and hyperglycemia, may play causal roles in the increased risk of cancer in diabetes. Further exploration into the possible causal relationships between abnormalities of these pathways and the risk of cancer in diabetes is warranted.

The Complexity of Vascular and Non-Vascular Complications of Diabetes: The Hong Kong Diabetes Registry

Diabetes is a complex disease characterized by chronic hyperglycemia and multiple phenotypes. In 1995, we used a doctor-nurse-clerk team and structured protocol to establish the Hong Kong Diabetes Registry in a quality improvement program. By 2009, we had accrued 2616 clinical events in 9588 Chinese type 2 diabetic patients with a follow-up duration of 6 years. The detailed phenotypes at enrollment and follow-up medications have allowed us to develop a series of risk equations to predict multiple endpoints with high sensitivity and specificity. In this prospective database, we were able to validate findings from clinical trials in real practice, confirm close links between cardiovascular and renal disease, and demonstrate the emerging importance of cancer as a leading cause of death. In addition to serving as a tool for risk stratification and quality assurance, ongoing data analysis of the registry also reveals secular changes in disease patterns and identifies unmet needs.

Diabetic nephropathy--what are the unmet needs?

In this pandemic of diabetes and obesity, Asia will have the highest number of affected people with the greatest increase in the young-to-middle aged group. Asian patients have increased risk for diabetic kidney disease which may be compounded by low grade infection, obesity and genetic factors. In these subjects, the onset of albuminuria and diabetic kidney disease causes further perturbation of metabolic milieu with increased oxidative stress, anaemia and vascular calcification which interact to markedly increase the risk of cardiovascular disease. Despite receiving optimal care to control blood pressure and metabolic risk factors as well as inhibition of the renin-angiotensin system in a clinical trial setting, there is a considerable residual risk for cardio-renal complications in patients with diabetic kidney disease. Control of obesity and low grade inflammation as well as correction of anaemia may represent areas where novel strategies can be developed and tested to curb this rising global burden of cardio-renal complications.