BACKGROUND: A novel data warehouse based on automated retrieval from an institutional health care information system (HIS) was made available to be compared with a traditional prospectively maintained surgical database. METHODS: A newly established institutional data warehouse at a single-institution academic medical center autopopulated by HIS was queried for International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) diagnosis codes for pancreatic neoplasm. Patients with ICD-9-CM diagnosis codes for pancreatic neoplasm were captured. A parallel query was performed using a prospective database populated by manual entry. Duplicated patients and those unique to either data set were identified. All patients were manually reviewed to determine the accuracy of diagnosis. RESULTS: A total of 1107 patients were identified from the HIS-linked data set with pancreatic neoplasm from 1999-2009. Of these, 254 (22.9%) patients were also captured by the surgical database, whereas 853 (77.1%) patients were only in the HIS-linked data set. Manual review of the HIS-only group demonstrated that 45.0% of patients were without identifiable pancreatic pathology, suggesting erroneous capture, whereas 36.3% of patients were consistent with pancreatic neoplasm and 18.7% with other pancreatic pathology. Of the 394 patients identified by the surgical database, 254 (64.5%) patients were captured by HIS, whereas 140 (35.5%) patients were not. Manual review of patients only captured by the surgical database demonstrated 85.9% with pancreatic neoplasm and 14.1% with other pancreatic pathology. Finally, review of the 254 patient overlap demonstrated that 80.3% of patients had pancreatic neoplasm and 19.7% had other pancreatic pathology. CONCLUSIONS: These results suggest that cautious interpretation of administrative data rely only on ICD-9-CM diagnosis codes and clinical correlation through previously validated mechanisms.

OBJECTIVE: We evaluated the positive predictive values (PPVs) of specific criteria based upon International Classification of Diseases, 9th revision (ICD-9-CM) codes documented in health plan administrative databases for identification of cases of serious myopathy and rhabdomyolysis. STUDY DESIGN AND SETTING: We conducted a retrospective study among patients enrolled in 11 geographically dispersed managed care organizations. Cohorts of new users of specific statins and fibrates were identified by selecting patients with an initial dispensing of the drug during the period 1 January 1998 to 30 June 2001. Potential cases of serious myopathy or rhabdomyolysis were identified using specific criteria based upon ICD-9-CM codes suggesting a muscle disorder or acute renal failure. RESULTS: A total of 194 hospitalizations meeting the criteria for chart review selection were identified among 206,732 new users of statins and 15,485 new users of fibrates. Overall, 31 cases of serious, clinically important myopathy or rhabdomyolysis (18%) were confirmed through chart review. Of these, 26 (84%) had a claim including codes for myoglobinuria (ICD-9-CM 791.3) or other disorders of muscle, ligament, and fascia (ICD-9-CM 728.89). A PPV of 74% (26 of 35 patients meeting criteria) was found for a composite definition that included (1) a primary or secondary discharge code for myoglobinuria, (2) a primary code for "other disorders of muscle," or (3) a secondary code for "other disorders of muscle" accompanied by a claim for a CK test within 7 days of hospitalization or a discharge code for acute renal failure. CONCLUSION: For rare adverse events such as serious myopathy or rhabdomyolysis, large population-based databases that include diagnosis and laboratory test claims data can facilitate epidemiologic research.