PURPOSE: To evaluate self-referral from the Internet for genetic diagnosis of several rare inherited kidney diseases. METHODS: Retrospective study from 1996 to 2017 analyzing data from an academic referral center specializing in autosomal dominant tubulointerstitial kidney disease (ADTKD). Individuals were referred by academic health-care providers (HCPs) nonacademic HCPs, or directly by patients/families. RESULTS: Over 21 years, there were 665 referrals, with 176 (27%) directly from families, 269 (40%) from academic HCPs, and 220 (33%) from nonacademic HCPs. Forty-two (24%) direct family referrals had positive genetic testing versus 73 (27%) families from academic HCPs and 55 (25%) from nonacademic HCPs (P = 0.72). Ninety-nine percent of direct family contacts were white and resided in zip code locations with a mean median income of $77,316 ± 34,014 versus US median income $49,445. CONCLUSION: Undiagnosed families with Internet access bypassed their physicians and established direct contact with an academic center specializing in inherited kidney disease to achieve a diagnosis. Twenty-five percent of all families diagnosed with ADTKD were the result of direct family referral and would otherwise have been undiagnosed. If patients suspect a rare disorder that is undiagnosed by their physicians, actively pursuing self-diagnosis using the Internet can be successful. Centers interested in rare disorders should consider improving direct access to families.

• Keywords
• autosomal dominant tubulointerstitial kidney disease, internet, mucin-1, rare disease, uromodulin,
• MeSH
• Adult MeSH
• Genetic Testing MeSH
• Internet MeSH
• Referral and Consultation classification   statistics & numerical data   MeSH
• Middle Aged MeSH
• Humans MeSH
• Kidney Diseases diagnosis   genetics   MeSH
• Retrospective Studies MeSH
• Rare Diseases diagnosis   genetics   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH