Construction of a dynamic prediction model for diabetic retinopathy risk in adults with adult-onset type 1 diabetes: a real-world longitudinal cohort study.

OBJECTIVE

To construct and validate a dynamic prediction model for diabetic retinopathy (DR) risk in adults with adult-onset type 1 diabetes mellitus (T1DM).

METHODS

A longitudinal cohort of 572 adult-onset T1DM patients from a tertiary hospital in Tianjin, China (2014-2023) was analyzed. LASSO regression and multivariable Cox regression with time-varying covariates (duration and HbA1c) were used to identify independent predictors of DR and to construct a nomogram. Model performance was assessed using the concordance index (C-index), area under the receiver operating characteristic curve (AUC), Brier score, calibration curves, Hosmer-Lemeshow test, and decision curve analysis (DCA). Internal validation was performed by bootstrap resampling (1000 replicates).

RESULTS

Multivariable Cox regression identified diabetic peripheral vascular disease, diabetic kidney disease, metabolic bone disease, dynamic disease duration, time-varying HbA1c, body mass index (BMI), and gender as independent predictors of DR. The dynamic model had a C-index of 0.704 (corrected: 0.681), with 1-, 2-, and 3-year AUCs of 0.704, 0.713, and 0.732, respectively. Brier scores were all <0.1. Calibration was acceptable at 2 and 3 years; the 1-year calibration slope was elevated (1.895). Hosmer-Lemeshow test P-values were >0.05 at all time points. The dynamic model outperformed a traditional static model in both discrimination and calibration. DCA showed favorable net clinical benefit for 2- and 3-year predictions.

CONCLUSION

This study demonstrates the feasibility of dynamic DR risk assessment in adult-onset T1DM. The proposed nomogram may serve as an auxiliary tool for predicting 2- to 3-year DR risk, supporting nurses in risk stratification and individualized screening guidance.