Painful diabetic peripheral neuropathy in a managed care setting: patient identification, prevalence estimates, and pharmacy utilization patterns.

The objectives of this study were to validate an algorithm for identifying patients with painful diabetic peripheral neuropathy (pDPN) and demonstrate its practical applications. Using the Kaiser Permanente Colorado Diabetes Registry, an algorithm was developed with selected ICD-9 diagnosis codes combined with automated pharmacy data for medications prescribed for pDPN symptoms. Medical records were reviewed to confirm pDPN presence and to inform algorithm refinement. Prevalence was estimated with a numerator of members with diabetes who had inclusion but no exclusion codes in 2003 (Method 1) and with a numerator of diabetes patients with inclusion codes between 1998 and 2003 who had no subsequent exclusion codes and who remained members in 2003 (Method 2); the denominator was all members with diabetes in 2003. Medication utilization was compared between patients with and without pDPN. A total of 19,577 members with diabetes were identified; 2612 met initial inclusion criteria. Medical record review (n = 298) demonstrated sensitivity of 94%, specificity of 55%, and positive predictive value (PPV) of 64%. Inclusion criteria were modified and pharmacy data eliminated. The revised algorithm identified 1754 additional patients meeting inclusion criteria. Medical record review (n = 190) demonstrated sensitivity of 99%, specificity of 49%, and PPV of 79%. Using the validated algorithm, pDPN prevalence was 113 (Method 1) and 208 (Method 2) per 1000 persons with diabetes. Significant differences were observed in medication prescriptions between patients with and without pDPN. Estimated pDPN prevalence among persons with diabetes was 11%-21% and pDPN patients had greater utilization of selected medications than those without pDPN. Identifying patients with pDPN is a fundamental step for improving disease management and understanding the economic impact of pDPN.