Abstract
Laboratories contemplating either the addition of new molecular tests or modifying methods approved by the Food and Drug Administration for human papillomavirus testing should be aware of a variety of procedural, performance, and regulatory issues surrounding such activity. Diagnostic medical laboratory testing in the United States is regulated by the Centers for Medicare and Medicaid Services, an agency formerly known as the Health Care Finance Administration. The regulatory vehicle of the Centers for Medicare and Medicaid Services is manifested in the Clinical Laboratory Improvement Amendments (CLIA). The CLIA program has put into place specific regulations for laboratory quality control, which includes specific recommendations for method validation. Regulations that must be followed regarding personnel, quality control, quality assurance, method validation, and proficiency testing depend on the complexity category of the individual test. All molecular diagnostic tests, including those for human papillomavirus, are considered high complexity. The Centers for Medicare and Medicaid Services retains the authority to allow private, national accreditation organizations to “deem” that a laboratory is compliant with CLIA '88 requirements. Accreditation organizations, such as the Joint Commission for Accreditation of Hospitals, the Commission on Office Laboratory Accreditation, and the College of American Pathologists (CAP), as well as several state medical laboratory–accrediting agencies, possess the authority to deem laboratories as “CLIA-approved.” The CAP, through its Laboratory Accreditation Program, has promoted standards for laboratory performance and method validation. In general, guidelines set forth in the CAP Laboratory Accreditation Program checklists specify that all clinical laboratory testing must essentially meet those requirements defined for high-complexity testing under CLIA '88, including test validation standards, reportable/reference ranges, performance criteria, and proficiency testing.
Data comparisons and uncertainty: a roadmap for gaining in competence and improving the reliability of results