Risk Prediction Scores for Mortality, Cerebrovascular, and Heart Disease Among Chinese People With Type 2 Diabetes

A literature review of quality assessment and applicability to HTA of risk prediction models of coronary heart disease in patients with diabetes

This literature review had two objectives: to identify models for predicting the risk of coronary heart diseases in patients with diabetes (DM); and to assess model quality in terms of risk of bias (RoB) and applicability for the purpose of health technology assessment (HTA). We undertook a targeted review of journal articles published in English, Dutch, Chinese, or Spanish in 5 databases from 1st January 2016 to 18th December 2022, and searched three systematic reviews for the models published after 2012. We used PROBAST (Prediction model Risk Of Bias Assessment Tool) to assess RoB, and used findings from Betts et al. 2019, which summarized recommendations and criticisms of HTA agencies on cardiovascular risk prediction models, to assess model applicability for the purpose of HTA. As a result, 71 % and 67 % models reporting C-index showed good discrimination abilities (C-index >= 0.7). Of the 26 model studies and 30 models identified, only one model study showed low RoB in all domains, and no model was fully applicable for HTA. Since the major cause of high RoB is inappropriate use of analysis method, we advise clinicians to carefully examine the model performance declared by model developers, and to trust a model if all PROBAST domains except analysis show low RoB and at least one validation study conducted in the same setting (e.g. country) is available. Moreover, since general model applicability is not informative for HTA, novel adapted tools may need to be developed.

An elevated urinary albumin-to-creatinine ratio increases the risk of incident cardia-cerebrovascular disease in individuals with type 2 diabetes

AIMS

We aimed to explore the associations between urine albumin-to-creatinine ratio (uACR) and cardia-cerebrovascular disease (CVD) in Chinese population with type 2 diabetes(T2D).

METHODS

We included 8975 participants with T2D but free of prevalent CVD (including myocardial infarction, ischemic and hemorrhagic stroke) at baseline from Kailuan study who were assessed with uACR between 2014 and 2016. The participants were divided into three groups based on their baseline uACR: normal (< 3 mg/mmol), microalbuminuria (3-30 mg/mmol), and macroalbuminuria (≥ 30 mg/mmol). Cox regression models and restricted cubic spline were used to evaluate the hazard ratios (HRs) and 95% confidence intervals (CIs) of incident CVD. The area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to see if incorporating uACR into existing models could improve performance.

RESULTS

During a median follow-up of 4.05 years, 560 participants developed first CVD event (6.24%). After adjustment for potential confounders, participants with microalbuminuria had higher risks of CVD compared with normal uACR, with HRs of 1.57(95% CI 1.04-2.37) for myocardial infarction, 1.24(95% CI 1.00-1.54) for ischemic stroke,1.62(95% CI 0.73-3.61) for hemorrhagic stroke, and 1.30(95% CI 1.07-1.57) for total CVD. The risks gradually attenuated with uACR increase, with HRs of 2.86(95% CI 1.63-5.00) for myocardial infarction, 2.46(95% CI 1.83-3.30) for ischemic stroke, 4.69(95% CI 1.72-12.78) for hemorrhagic stroke, and 2.42(95% CI 1.85-3.15) for total CVD in macroalbuminuria. The addition of uACR to established CVD risk models improved the CVD risk prediction efficacy.

CONCLUSIONS

Increasing uACR, even below the normal range, is an independent risk factor for new-onset CVD in T2D population. Furthermore, uACR could improve the risk prediction for CVD among community based T2D patients.

Precision prognostics for cardiovascular disease in Type 2 diabetes: a systematic review and meta-analysis

BACKGROUND

Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with Type 2 diabetes (T2D).

METHODS

We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies.

RESULTS

Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort.

CONCLUSIONS

Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

Effectiveness and cost-effectiveness of six GLP-1RAs for treatment of Chinese type 2 diabetes mellitus patients that inadequately controlled on metformin: a micro-simulation model

Objective

To systematically estimate and compare the effectiveness and cost-effectiveness of the glucagon-like peptide-1 receptor agonists (GLP-1RAs) approved in China and to quantify the relationship between the burden of diabetic comorbidities and glycosylated hemoglobin (HbA1c) or body mass index (BMI).

Methods

To estimate the costs (US dollars, USD) and quality-adjusted life years (QALY) for six GLP-1RAs (exenatide, loxenatide, lixisenatide, dulaglutide, semaglutide, and liraglutide) combined with metformin in the treatment of patients with type 2 diabetes mellitus (T2DM) which is inadequately controlled on metformin from the Chinese healthcare system perspective, a discrete event microsimulation cost-effectiveness model based on the Chinese Hong Kong Integrated Modeling and Evaluation (CHIME) simulation model was developed. A cohort of 30,000 Chinese patients was established, and one-way sensitivity analysis and probabilistic sensitivity analysis (PSA) with 50,000 iterations were conducted considering parameter uncertainty. Scenario analysis was conducted considering the impacts of research time limits. A network meta-analysis was conducted to compare the effects of six GLP-1RAs on HbA1c, BMI, systolic blood pressure, and diastolic blood pressure. The incremental net monetary benefit (INMB) between therapies was used to evaluate the cost-effectiveness. China's per capita GDP in 2021 was used as the willingness-to-pay threshold. A generalized linear model was used to quantify the relationship between the burden of diabetic comorbidities and HbA1c or BMI.

Results

During a lifetime, the cost for a patient ranged from USD 42,092 with loxenatide to USD 47,026 with liraglutide, while the QALY gained ranged from 12.50 with dulaglutide to 12.65 with loxenatide. Compared to exenatide, the INMB of each drug from highest to lowest were: loxenatide (USD 1,124), dulaglutide (USD -1,418), lixisenatide (USD -1,713), semaglutide (USD -4,298), and liraglutide (USD -4,672). Loxenatide was better than the other GLP-1RAs in the base-case analysis. Sensitivity and scenario analysis results were consistent with the base-case analysis. Overall, the price of GLP-1RAs most affected the results. Medications with effective control of HbA1c or BMI were associated with a significantly smaller disease burden (p < 0.05).

Conclusion

Loxenatide combined with metformin was identified as the most economical choice, while the long-term health benefits of patients taking the six GLP-1RAs are approximate.

Precision Prognostics for Cardiovascular Disease in Type 2 Diabetes: A Systematic Review and Meta-analysis

Background Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with type 2 diabetes (T2D). Methods We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies. Results Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort. Conclusions Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

Assessing Taiwan's pay-for-performance program for diabetes care: a cost-benefit net value approach

Pay-for-Performance (P4P) to better manage chronic conditions has yielded mixed results. A better understanding of the cost and benefit of P4P is needed to improve program assessment. To this end, we assessed the effect of a P4P program using a quasi-experimental intervention and control design. Two different intervention groups were used, one consisting of newly enrolled P4P patients, and another using P4P patients who have been enrolled since the beginning of the study. Patient-level data on clinical indicators, utilization and expenditures, linked with national death registry, were collected for diabetic patients at a large regional hospital in Taiwan between 2007 and 2013. Net value, defined as the value of life years gained minus the cost of care, is calculated and compared for the intervention group of P4P patients with propensity score-matched non-P4P samples. We found that Taiwan's implementation of the P4P program for diabetic care yielded positive net values, ranging from $40,084 USD to $348,717 USD, with higher net values in the continuous enrollment model. Our results suggest that the health benefits from P4P enrollment may require a sufficient time frame to manifest, so a net value approach incorporating future predicted mortality risks may be especially important for studying chronic disease management. Future research on the mechanisms by which the Taiwan P4P program helped improve outcomes could help translate our findings to other clinical contexts.

Potential health and economic impact associated with achieving risk factor control in Chinese adults with diabetes: a microsimulation modelling study

Background

The prevalence of diabetes has risen sharply in China. Improving modifiable risk factors such as glycaemia and blood pressure could substantially reduce disease burden and treatment costs to achieve a healthier China by 2030.

Methods

We used a nationally representative population-based survey of adults with diabetes in 31 provinces in mainland China to assess the prevalence of risk factor control. We adopted a microsimulation approach to estimate the impact of improved control of blood pressure and glycaemia on mortality, quality-adjusted life-years (QALYs), and healthcare cost. We applied the validated CHIME diabetes outcomes model over a 10-year time horizon. Baseline scenario of status quo was evaluated against alternative strategies based on World Health Organization and Chinese Diabetes Society guidelines.

Findings

Among 24,319 survey participants with diabetes (age 30-70), 69.1% (95% CI: 67.7-70.5) achieved optimal diabetes control (HbA1c <7% [53 mmol/mol]), 27.7% [26.1-29.3] achieved blood pressure control (<130/80 mmHg) and 20.1% (18.6-21.6) achieved both targets. Achieving 70% control rate for people with diabetes could reduce deaths before age 70 by 7.1% (5.7-8.7), reduce medical costs by 14.9% (12.3-18.0), and gain 50.4 QALYs (44.8-56.0) per 1000 people over 10 years compared to the baseline status quo. The largest health gains were for strategies including strict blood pressure control of 130/80 mmHg, particularly in rural areas.

Interpretation

Based on a nationally representative survey, few adults with diabetes in China achieved optimal control of glycaemia and blood pressure. Substantial health gains and economic savings are potentially achievable with better risk factor control especially in rural settings.

Funding

Chinese Central Government, Research Grants Council of the Hong Kong Special Administrative Region, China [27112518].

Developing a prediction model for all-cause mortality risk among patients with type 2 diabetes mellitus in Shanghai, China

BACKGROUND

All-cause mortality risk prediction models for patients with type 2 diabetes mellitus (T2DM) in mainland China have not been established. This study aimed to fill this gap.

METHODS

Based on the Shanghai Link Healthcare Database, patients diagnosed with T2DM and aged 40-99 years were identified between January 1, 2013 and December 31, 2016 and followed until December 31, 2021. All the patients were randomly allocated into training and validation sets at a 2:1 ratio. Cox proportional hazards models were used to develop the all-cause mortality risk prediction model. The model performance was evaluated by discrimination (Harrell C-index) and calibration (calibration plots).

RESULTS

A total of 399 784 patients with T2DM were eventually enrolled, with 68 318 deaths over a median follow-up of 6.93 years. The final prediction model included age, sex, heart failure, cerebrovascular disease, moderate or severe kidney disease, moderate or severe liver disease, cancer, insulin use, glycosylated hemoglobin, and high-density lipoprotein cholesterol. The model showed good discrimination and calibration in the validation sets: the mean C-index value was 0.8113 (range 0.8110-0.8115) and the predicted risks closely matched the observed risks in the calibration plots.

CONCLUSIONS

This study constructed the first 5-year all-cause mortality risk prediction model for patients with T2DM in south China, with good predictive performance.

Precision prognostics for the development of complications in diabetes

Individuals with diabetes face higher risks for macro- and microvascular complications than their non-diabetic counterparts. The concept of precision medicine in diabetes aims to optimise treatment decisions for individual patients to reduce the risk of major diabetic complications, including cardiovascular outcomes, retinopathy, nephropathy, neuropathy and overall mortality. In this context, prognostic models can be used to estimate an individual's risk for relevant complications based on individual risk profiles. This review aims to place the concept of prediction modelling into the context of precision prognostics. As opposed to identification of diabetes subsets, the development of prediction models, including the selection of predictors based on their longitudinal association with the outcome of interest and their discriminatory ability, allows estimation of an individual's absolute risk of complications. As a consequence, such models provide information about potential patient subgroups and their treatment needs. This review provides insight into the methodological issues specifically related to the development and validation of prediction models for diabetes complications. We summarise existing prediction models for macro- and microvascular complications, commonly included predictors, and examples of available validation studies. The review also discusses the potential of non-classical risk markers and omics-based predictors. Finally, it gives insight into the requirements and challenges related to the clinical applications and implementation of developed predictions models to optimise medical decision making.

Cardiovascular risk in patients with type 2 diabetes: A systematic review of prediction models

AIMS

To identify all cardiovascular disease risk prediction models developed in patients with type 2 diabetes or in the general population with diabetes as a covariate updating previous studies, describing model performance and analysing both their risk of bias and their applicability METHODS: A systematic search for predictive models of cardiovascular risk was performed in PubMed. The CHARMS and PROBAST guidelines for data extraction and for the assessment of risk of bias and applicability were followed. Google Scholar citations of the selected articles were reviewed to identify studies that conducted external validations.

RESULTS

The titles of 10,556 references were extracted to ultimately identify 19 studies with models developed in a population with diabetes and 46 studies in the general population. Within models developed in a population with diabetes, only six were classified as having a low risk of bias, 17 had a favourable assessment of applicability, 11 reported complete model information, and also 11 were externally validated.

CONCLUSIONS

There exists an overabundance of cardiovascular risk prediction models applicable to patients with diabetes, but many have a high risk of bias due to methodological shortcomings and independent validations are scarce. We recommend following the existing guidelines to facilitate their applicability.

Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong

INTRODUCTION

Patients with diabetes mellitus are risk of premature death. In this study, we developed a machine learning-driven predictive risk model for all-cause mortality among patients with type 2 diabetes mellitus using multiparametric approach with data from different domains.

RESEARCH DESIGN AND METHODS

This study used territory-wide data of patients with type 2 diabetes attending public hospitals or their associated ambulatory/outpatient facilities in Hong Kong between January 1, 2009 and December 31, 2009. The primary outcome is all-cause mortality. The association of risk variables and all-cause mortality was assessed using Cox proportional hazards models. Machine and deep learning approaches were used to improve overall survival prediction and were evaluated with fivefold cross validation method.

RESULTS

A total of 273 678 patients (mean age: 65.4±12.7 years, male: 48.2%, median follow-up: 142 (IQR=106-142) months) were included, with 91 155 deaths occurring on follow-up (33.3%; annualized mortality rate: 3.4%/year; 2.7 million patient-years). Multivariate Cox regression found the following significant predictors of all-cause mortality: age, male gender, baseline comorbidities, anemia, mean values of neutrophil-to-lymphocyte ratio, high-density lipoprotein-cholesterol, total cholesterol, triglyceride, HbA1c and fasting blood glucose (FBG), measures of variability of both HbA1c and FBG. The above parameters were incorporated into a score-based predictive risk model that had a c-statistic of 0.73 (95% CI 0.66 to 0.77), which was improved to 0.86 (0.81 to 0.90) and 0.87 (0.84 to 0.91) using random survival forests and deep survival learning models, respectively.

CONCLUSIONS

A multiparametric model incorporating variables from different domains predicted all-cause mortality accurately in type 2 diabetes mellitus. The predictive and modeling capabilities of machine/deep learning survival analysis achieved more accurate predictions.

Development and validation of the CHIME simulation model to assess lifetime health outcomes of prediabetes and type 2 diabetes in Chinese populations: A modeling study

BACKGROUND

Existing predictive outcomes models for type 2 diabetes developed and validated in historical European populations may not be applicable for East Asian populations due to differences in the epidemiology and complications. Despite the continuum of risk across the spectrum of risk factor values, existing models are typically limited to diabetes alone and ignore the progression from prediabetes to diabetes. The objective of this study is to develop and externally validate a patient-level simulation model for prediabetes and type 2 diabetes in the East Asian population for predicting lifetime health outcomes.

METHODS AND FINDINGS

We developed a health outcomes model from a population-based cohort of individuals with prediabetes or type 2 diabetes: Hong Kong Clinical Management System (CMS, 97,628 participants) from 2006 to 2017. The Chinese Hong Kong Integrated Modeling and Evaluation (CHIME) simulation model comprises of 13 risk equations to predict mortality, micro- and macrovascular complications, and development of diabetes. Risk equations were derived using parametric proportional hazard models. External validation of the CHIME model was assessed in the China Health and Retirement Longitudinal Study (CHARLS, 4,567 participants) from 2011 to 2018 for mortality, ischemic heart disease, cerebrovascular disease, renal failure, cataract, and development of diabetes; and against 80 observed endpoints from 9 published trials using 100,000 simulated individuals per trial. The CHIME model was compared to United Kingdom Prospective Diabetes Study Outcomes Model 2 (UKPDS-OM2) and Risk Equations for Complications Of type 2 Diabetes (RECODe) by assessing model discrimination (C-statistics), calibration slope/intercept, root mean square percentage error (RMSPE), and R2. CHIME risk equations had C-statistics for discrimination from 0.636 to 0.813 internally and 0.702 to 0.770 externally for diabetes participants. Calibration slopes between deciles of expected and observed risk in CMS ranged from 0.680 to 1.333 for mortality, myocardial infarction, ischemic heart disease, retinopathy, neuropathy, ulcer of the skin, cataract, renal failure, and heart failure; 0.591 for peripheral vascular disease; 1.599 for cerebrovascular disease; and 2.247 for amputation; and in CHARLS outcomes from 0.709 to 1.035. CHIME had better discrimination and calibration than UKPDS-OM2 in CMS (C-statistics 0.548 to 0.772, slopes 0.130 to 3.846) and CHARLS (C-statistics 0.514 to 0.750, slopes -0.589 to 11.411); and small improvements in discrimination and better calibration than RECODe in CMS (C-statistics 0.615 to 0.793, slopes 0.138 to 1.514). Predictive error was smaller for CHIME in CMS (RSMPE 3.53% versus 10.82% for UKPDS-OM2 and 11.16% for RECODe) and CHARLS (RSMPE 4.49% versus 14.80% for UKPDS-OM2). Calibration performance of CHIME was generally better for trials with Asian participants (RMSPE 0.48% to 3.66%) than for non-Asian trials (RMPSE 0.81% to 8.50%). Main limitations include the limited number of outcomes recorded in the CHARLS cohort, and the generalizability of simulated cohorts derived from trial participants.

CONCLUSIONS

Our study shows that the CHIME model is a new validated tool for predicting progression of diabetes and its outcomes, particularly among Chinese and East Asian populations that has been lacking thus far. The CHIME model can be used by health service planners and policy makers to develop population-level strategies, for example, setting HbA1c and lipid targets, to optimize health outcomes.

Are quality-adjusted medical prices declining for chronic disease? Evidence from diabetes care in four health systems

Improvements in medical treatment have contributed to rising health spending. Yet there is relatively little evidence on whether the spending increase is "worth it" in the sense of producing better health outcomes of commensurate value-a critical question for understanding productivity in the health sector and, as that sector grows, for deriving an accurate quality-adjusted price index for an entire economy. We analyze individual-level panel data on medical spending and health outcomes for 123,548 patients with type 2 diabetes in four health systems: Japan, The Netherlands, Hong Kong and Taiwan. Using a "cost-of-living" method that measures value based on improved survival, we find a positive net value of diabetes care: the value of improved survival outweighs the added costs of care in each of the four health systems. This finding is robust to accounting for selective survival, end-of-life spending, and a range of values for a life-year or fraction of benefits attributable to medical care. Since the estimates do not include the value from improved quality of life, they are conservative. We, therefore, conclude that the increase in medical spending for management of diabetes is offset by an increase in quality.