Abstract

Purpose: We examined whether diabetic polyneuropathy (DPN) and diabetic foot ulcers in type 2 diabetes can be accurately identified using International Classification of Diseases, 10th revision discharge diagnosis codes, surgery codes, and drug prescription codes.Methods: We identified all type 2 diabetes patients in the Central Denmark region, 2009–2016, who had ≥1 primary/secondary diagnosis code of “diabetes with neurological complication” (E10.4-E14.4), “diabetic polyneuropathy” (G63.2), or “polyneuropathy, unspecified” (G62.9). Patients with potential painful DPN and non-painful DPN were identified based on prescription history for serotonin–norepinephrine reuptake inhibitors, tricyclic antidepressants, or gabapentinoids. Likewise, type 2 diabetes patients with potential foot ulcers were identified based on diagnosis or surgery codes. We used medical record review as the reference standard and calculated positive predictive values (PPVs).Results: Of 53 randomly selected patients with potential painful DPN, 38 were classified as having DPN when validated against medical records; of these, 18 also had neuropathic pain, yielding a PPV of 72% (95% CI: 58–83%) for DPN and 34% (95% CI: 22–48%) for painful DPN. Likewise, among 54 randomly selected patients with potential non-painful DPN, 30 had DPN based on medical record data; of these, 27 had non-painful DPN, yielding PPVs of 56% (95% CI: 41–69%) and 50% (95% CI: 36–64%), respectively. Secondary E-chapter codes often denoted stroke or mononeuropathies, rather than DPN. Excluding secondary E-chapter codes from the algorithm increased the PPV for DPN to 78% (95% CI: 63–89%) for the painful DPN cohort and to 74% (95% CI: 56–87%) for the non-painful DPN cohort. Of 53 randomly selected patients with potential diabetic foot ulcer, only 18 diagnoses were confirmed; PPV=34% (95% CI: 22–48%).Conclusion: G-chapter and primary E-chapter diagnosis codes can detect type 2 diabetes patients with hospital-diagnosed DPN, and may be useful in epidemiological research. In contrast, our diabetic foot ulcer algorithm did not perform well.

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