Accurately identifying cirrhosis and its complications to create the novel statewide Indiana digital cirrhosis registry

Administrative datasets are important for cirrhosis research but limited by suboptimal cirrhosis identification. We developed and validated algorithms to accurately identify cirrhosis and its complications in real-world, statewide dataset. From 2017 to 2020 Indiana Patient Care Network data, 15,636 records were grouped by combinations of code and lab criteria (group A: cirrhosis codes, B: FIB-4/APRI criteria, C: cirrhosis complication codes, D: code/lab for liver disease). Diagnoses were confirmed by chart review in 4.5% of 15,636 records. Positive predictive values (PPV) were calculated for various algorithms which were externally validated in hepatology clinic (n = 1,039) and emergency department-based cirrhosis cohorts (n = 2,124). Charts meeting criteria for group A and at least one other group (“AX”, e.g., ABC) had an overall PPV of 86%. Highest PPVs were seen in ACD and ABCD and confirmed during external validation: 88% and 97% (hepatology cohort), 79% and 93% (ED cohort). Without complication codes, ABD showed strong PPVs: 86%(internal), 92%(hepatology), 72%(ED). ICD-10-based definitions alone were suboptimal for complications: ascites (57%), hepatic encephalopathy (HE:55%). PPV for HE was improved with addition of medications but remained < 80%. Taken together, we provide algorithms to identify both compensated and decompensated cirrhosis in real-world data. Using the “AX” algorithm, we created the statewide Indiana Digital Cirrhosis Cohort to support future research across cirrhosis stages.