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Cell Tissue Bank. 2014 Mar;15(1):75-84. doi: 10.1007/s10561-013-9376-y. Epub 2013 Jul 4.

Assessment of tissue allograft safety monitoring with administrative healthcare databases: a pilot project using Medicare data.

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Division of Epidemiology (DE), Office of Biostatistics and Epidemiology (OBE), Center for Biologics Evaluation and Research (CBER), Food and Drug Administration, 1401 Rockville Pike WOC I 441 S, Rockville, MD, 20852, USA.


Assess whether Medicare data are useful for monitoring tissue allograft safety and utilization. We used health care claims (billing) data from 2007 for 35 million fee-for-service Medicare beneficiaries, a predominantly elderly population. Using search terms for transplant-related procedures, we generated lists of ICD-9-CM and CPT(®) codes and assessed the frequency of selected allograft procedures. Step 1 used inpatient data and ICD-9-CM procedure codes. Step 2 added non-institutional provider (e.g., physician) claims, outpatient institutional claims, and CPT codes. We assembled preliminary lists of diagnosis codes for infections after selected allograft procedures. Many ICD-9-CM codes were ambiguous as to whether the procedure involved an allograft. Among 1.3 million persons with a procedure ascertained using the list of ICD-9-CM codes, only 1,886 claims clearly involved an allograft. CPT codes enabled better ascertainment of some allograft procedures (over 17,000 persons had corneal transplants and over 2,700 had allograft skin transplants). For spinal fusion procedures, CPT codes improved specificity for allografts; of nearly 100,000 patients with ICD-9-CM codes for spinal fusions, more than 34,000 had CPT codes indicating allograft use. Monitoring infrequent events (infections) after infrequent exposures (tissue allografts) requires large study populations. A strength of the large Medicare databases is the substantial number of certain allograft procedures. Limitations include lack of clinical detail and donor information. Medicare data can potentially augment passive reporting systems and may be useful for monitoring tissue allograft safety and utilization where codes clearly identify allograft use and coding algorithms can effectively screen for infections.

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