Patient-specific prompts in the cholesterol management of renal transplant outpatients: results and analysis of underperformance

Abstract

Background: Renal transplant recipients have an increased risk of cardiovascular disease compared with age- and gender-matched controls. It is recommended that "high-risk" patients are treated with hydroxymethylglutaryl CoA reductase inhibitors to reduce cholesterol levels.

Method: We evaluated the effect of a computer-based decision support algorithm in delivering patient-specific prompts to manage cholesterol in renal transplant outpatients. Data were analyzed retrospectively for a 2-year period with attention to changes in cholesterol levels, prescribing patterns of statins, and causes of underperformance.

Results: At baseline, 36.7% of patients achieved a total serum cholesterol level less than 5.0 mmol/L, compared with 67.2% at 2 years, with mean values of 5.6+/-0.1 mmol/L and 4.8+/-0.1 mmol/L (P<0.0001). At baseline, 24% of the patients were receiving statin therapy, increasing to 61% at 2 years. There were no significant changes in creatinine phosphokinase, trough cyclosporine levels, or total cyclosporine dose. Alkaline phosphatase levels increased (166.1+/-3.6-184.6+/-6.1 mmol/L, P=0.009), but remained within the normal clinical range; creatinine clearance increased (58.6+/-1.0-61.0+/-1.2 mL/min, P=0.05). For patients followed concurrently in two units without the algorithm, serum cholesterol measurements decreased from 5.57 mmol/L and 5.34 mmol/L to 5.31 mmol/L and 5.27 mmol/L, respectively (P=0.05), both higher than that achieved contemporaneously at St. James's. Underperformance depended less on medical noncompliance than with systematic features of the methodology and patient preference/collaboration with treatment.

Conclusions: The introduction of the algorithm coincided with a significant reduction in cholesterol levels, an increase in the number of patients receiving appropriate therapy, and no serious adverse effects. Our results illustrate the positive effect of computer-generated prompts and decision support software.