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

Abstract

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.

Modeling Chronic Kidney Disease in Type 2 Diabetes Mellitus: A Systematic Literature Review of Models, Data Sources, and Derivation Cohorts

“…In Fig. 2, they can be seen on the edge of (or entirely disconnected from) the model-source co-occurrence network, and include the CHIME model, based on data from Hong Kong [70], the JJCEM, based on data from Japan [87], the model by Watada et al [109], based on Asian patients from the CARMELINA trial [157], and the models by Hayashino et al…”

“…There were five models [70,72,83,88,109] not connected to the main model-source network component for clinical data and four models [64,87,89,110] that were only loosely connected (Fig. 2).…”

“…[83], for Japan, and by Wu et al [110], for China, which relied exclusively (in the case of Hayashino et al) or almost exclusively (in the case of Wu et al) on country-or Asian-specific data sources instead of sources from American or European countries. The remainder of this set of models included risk equation-based IPS models, which relied on at most two sources for their clinical effectiveness data, either from large clinical management systems (as in the CHIME model [70]) or from large clinical trials (e.g., ACCORD [122] in the BRAVO model [64]) or clinical outcomes trial such as CREDENCE [29] in CREDEM-DKD [72]. With the only exception of the model by Hayashino et al, all risk equation-based IPS models either not connected or loosely connected to the main modelsource network had been published since 2018.…”

“…Node size is proportional to the indegree (normalized by the number of nodes in the network) so larger nodes indicate higher centrality. Sakthong et al's work [99] is classified as both a model and a primary data source as elicited data from experts for development of this model were subsequently used by other models Disease Management Registry [128], used for modeling by Wu et al [110], and the 42,495 patients with T2DM from the Hong Kong Health Authority Clinical Management System used by the CHIME simulation model [70]. Other large primary data sources used for modeling were a screening study from Okinawa with 78,529 patients [129], used in the Howard et al model [84], and large clinical outcomes trials such as DECLARE-TIMI 58 with 17,160 participants [111].…”

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

“…Archimedes [72,73] Archimedes model BRAVO [74] Building, relating, assessing, and validating outcomes diabetes microsimulation model Cardiff [75] The Cardiff diabetes model Caro [76,77] NA: An economic evaluation model published by Caro et al CDC [78][79][80] NA: An economic evaluation model published by the CDC diabetes cost-effectiveness group CHIME [81] Chinese Hong Kong integrated modeling and evaluation COMT [82] Chinese outcomes model for type 2 diabetes Cornerstone [83] Cornerstone diabetes simulation model DiDACT [84] The diabetes decision analysis of cost-type 2 model DMM [85] The diabetes mellitus model EAGLE [86] Economic assessment of glycemic control and long-term effects of diabetes model Eastman [33,87] NA: An economic evaluation model published by Eastman et al ECHO [88] The economic and health outcomes model of type 2 diabetes mellitus GDM [16] The global diabetes model Grima [89] NA: An economic evaluation model published by Grima et al IHE [47,90] The Swedish institute for health economics diabetes cohort model IMIB [91,92] NA: An economic evaluation model published by Palmer (Institute for Medical Informatics and Biostatistics) et al IQVIA-CORE [93] The IQVIA center for outcomes research diabetes model JADE [5] The Januvia diabetes economic model JJCEM [94] The Japan diabetes complications study/Japanese elderly diabetes intervention trial risk engine cost-effectiveness model MICADO [95,96] Modelling integrated care for diabetes based on observational data Michigan [97] The Michigan model for diabetes ODEM [98] Ontario diabetes economic model PRIME [53] PRIME type 2 diabetes model PROSIT [99] The PROSIT disease modelling community (PROSIT in Latin means "it shall be useful") RAMP-DM…”