Performance of the Diabetes Outcomes Model in the United States (DOMUS) in a
Large, Contemporary US Claims Dataset
Abstract Body (Do not enter title and authors here): Objectives: The Diabetes Outcomes Model in the US (DOMUS) was developed using 13 years (2005–2017) of data from Kaiser Permanente Northern California to project health outcomes and inform economic evaluations for patients with type 2 diabetes (T2D). However, its performance in broader, more diverse populations remains unclear. This study evaluated DOMUS using Optum Market Clarity, a large claims–electronic health record (EHR) linked dataset, following individuals with T2D in the US from 2008 to 2023. Methods: DOMUS was used to predict the 10-year cumulative incidence of macrovascular (atrial fibrillation, angina, congestive heart failure, myocardial infarction [MI], ischemic heart disease [IHD], peripheral vascular disease [PVD], and stroke) and microvascular (retinopathy, end-stage renal disease [ESRD], foot ulcer, and blindness) complications in over 70,000 participants. Model calibration (comparison of predicted vs. observed cumulative incidence) and discrimination (c-statistics) were assessed for each endpoint. For poorly calibrated outcomes, recalibration was performed by updating the model constant using a grid search. Results: Before model recalibration, DOMUS underpredicted most macrovascular complications over 10 years (absolute mean difference: 1%–9%), except for PVD (overpredicted by 6%) and stroke (accurately predicted). Among microvascular outcomes, proliferative retinopathy and ESRD were well predicted, while foot ulcer and blindness were underestimated, and non-proliferative retinopathy was overestimated. Nonetheless, DOMUS demonstrated strong discrimination (c-statistics >0.75 for all endpoints). After recalibration, the predicted 10-year cumulative incidences for all outcomes were within 1% of observed rates. Conclusions: Original DOMUS demonstrated strong discrimination across all outcomes though did not predict many outcomes well in Optum Market Clarity dataset. However, after recalibration, the model achieved accurate predictions within 1% of the observed cumulative incidence at 10 years for all outcomes. These findings highlight the importance of validating and refining risk prediction models when applied to diverse, real-world populations.
Winn, Aaron
( University of Illinois Chicago
, Chicago
, Illinois
, United States
)
Siebert, Uwes
( University for Health Sciences, Medical Informatics and Technology
, Tirol
, Austria
)
Fang, Gang
( Novo Nordisk
, Mooresville
, North Carolina
, United States
)
Xie, Lin
( Novo Nordisk
, Mooresville
, North Carolina
, United States
)
Swift, Caroline
( Novo Nordisk
, Mooresville
, North Carolina
, United States
)
Noone, Josh
( Novo Nordisk
, Mooresville
, North Carolina
, United States
)
Guevarra, Mico
( Novo Nordisk
, Mooresville
, North Carolina
, United States
)
Mehanna, Sherif
( Novo Nordisk
, Mooresville
, North Carolina
, United States
)
Laiteerapong, Neda
( University of Chicago
, Chicago
, Illinois
, United States
)
Author Disclosures:
Aaron Winn:DO have relevant financial relationships
;
Consultant:Novo Nordisk:Active (exists now)
; Consultant:CorMedix:Past (completed)
| Uwes Siebert:DO have relevant financial relationships
;
Research Funding (PI or named investigator):Zoll:Past (completed)
; Research Funding (PI or named investigator):Abiomed / J&J:Active (exists now)
| Gang Fang:No Answer
| Lin Xie:No Answer
| Caroline Swift:No Answer
| Josh Noone:No Answer
| Mico Guevarra:DO have relevant financial relationships
;
Employee:Novo Nordisk Inc:Active (exists now)
; Individual Stocks/Stock Options:Novo Nordisk Inc:Active (exists now)
| Sherif Mehanna:No Answer
| Neda Laiteerapong:DO have relevant financial relationships
;
Researcher:Novo Nordisk:Active (exists now)
