Progression to microalbuminuria in type 1 diabetes: development and validation of a prediction rule

Abstract

Aims/hypothesis: Microalbuminuria is common in type 1 diabetes and is associated with an increased risk of renal and cardiovascular disease. We aimed to develop and validate a clinical prediction rule that estimates the absolute risk of microalbuminuria.

Methods: Data from the European Diabetes Prospective Complications Study (n = 1115) were used to develop the prediction rule (development set). Multivariable logistic regression analysis was used to assess the association between potential predictors and progression to microalbuminuria within 7 years. The performance of the prediction rule was assessed with calibration and discrimination (concordance statistic [c-statistic]) measures. The rule was validated in three other diabetes studies (Pittsburgh Epidemiology of Diabetes Complications [EDC] study, Finnish Diabetic Nephropathy [FinnDiane] study and Coronary Artery Calcification in Type 1 Diabetes [CACTI] study).

Results: Of patients in the development set, 13% were microalbuminuric after 7 years. Glycosylated haemoglobin, AER, WHR, BMI and ever smoking were found to be the most important predictors. A high-risk group (n = 87 [8%]) was identified with a risk of progression to microalbuminuria of 32%. Predictions showed reasonable discriminative ability, with c-statistic of 0.71. The rule showed good calibration and discrimination in EDC, FinnDiane and CACTI (c-statistic 0.71, 0.79 and 0.79, respectively).

Conclusions/interpretation: We developed and validated a clinical prediction rule that uses relatively easily obtainable patient characteristics to predict microalbuminuria in patients with type 1 diabetes. This rule can help clinicians to decide on more frequent check-ups for patients at high risk of microalbuminuria in order to prevent long-term chronic complications.

Fig. 1

Nature of the univariable associations of the continuous predictors a HbA_1c, b AER, c WHR and d BMI with the risk of microalbuminuria. Continuous lines, restricted cubic splines with three knots; dotted lines, the chosen transformations. Transformations: a log_e, b–d linear. The deviant scaling on y-axes corresponds to the logodds or logit of microalbuminuria, which is the adequate scale to study the shape of the association

Fig. 2

Score chart to predict risk of microalbuminuria in patients with type 1 diabetes. The scores are derived from the prediction rule that contain the shrunken regression coefficients of the final model and updated intercept: , where log_e(HbA_1c) is the natural logarithm of HbA_1c, AER is expressed in μg/min, BMI is expressed in kg/m² and ever-smoking is set at 1 if true and 0 if false. . A hypothetical patient with HbA_1c of 6% (3 points), AER of 5 μg/min (2 points), WHR of 0.9 (3 points), BMI of 24 (3 points) and who has never smoked (0 points) has a sum score of 11, which corresponds to a risk of 4%