Details

Autor(en) / Beteiligte

• Lapointe-Shaw, Lauren
• Georgie, Firass
• Carlone, David
• Cerocchi, Orlando
• Chung, Hannah
• Dewit, Yvonne
• Feld, Jordan J
• Holder, Laura
• Kwong, Jeffrey C
• Sander, Beate
• Flemming, Jennifer A
• Kanda, Tatsuo

Titel

Identifying cirrhosis, decompensated cirrhosis and hepatocellular carcinoma in health administrative data: A validation study

Ist Teil von

• PloS one, 2018-08, Vol.13 (8), p.e0201120-e0201120

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2018

Link zum Volltext

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• To evaluate screening and treatment strategies, large-scale real-world data on liver disease-related outcomes are needed. We sought to validate health administrative data for identification of cirrhosis, decompensated cirrhosis and hepatocellular carcinoma among patients with known liver disease. Primary patient data were abstracted from patients of the Toronto Center for Liver Disease with viral hepatitis (2006-2014), and all patients with liver disease from the Kingston Health Sciences Centre Hepatology Clinic (2013). We linked clinical information to health administrative data and tested a range of coding algorithms against the clinical reference standard. A total of 6,714 patients had primary chart data abstracted. A single physician visit code for cirrhosis was sensitive (98-99%), and a single hospital diagnostic code for cirrhosis was specific (91-96%). The most sensitive algorithm for decompensated cirrhosis was one cirrhosis code with any of: a hospital diagnostic code, death code, or procedure code for decompensation (range 88-99% across groups). The most specific was one cirrhosis code and one hospital diagnostic code (range 89-98% across groups). Two physician visit codes or a single hospital diagnostic code, death code, or procedure code combined with a code for cirrhosis were sensitive and specific for hepatocellular carcinoma (sensitivity 94-96%, specificity 93-98%). These sensitive and specific algorithms can be used to define patient cohorts or detect clinical outcomes using health administrative data. Our results will facilitate research into the adequacy of screening and treatment for patients with chronic viral hepatitis or other liver diseases.