Importance of Logical Identifiers, Names and Codes (LOINC ) in Developing in vitro
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1 Importance of Logical Identifiers, Names and Codes (LOINC ) in Developing in vitro Diagnostic (IVD) Infrastructure to Support a National Evaluation System for health Technology (NEST) Michael Waters, Ph.D. Office of In Vitro Diagnostics and Radiological Health Center for Devices and Radiological Health Food and Drug Administration June 8, 2017
2 Overall Goals Support efforts to harness non traditional data sources (e.g., data collected from clinical experience) to: support regulatory decision making, reduce burdens to the healthcare ecosystem and promote development of innovative solutions to public health challenges. 2
3 Supporting Meaningful Data Access: IVD Code Harmonization (Semantic Interoperability) Semantic interoperability for IVD tests, is essential to enabling multi source laboratory data access and advancing health information technology. The unambiguous and consistent representation of laboratory tests (and results) can drive the development of decision support tools and disease monitoring systems, provide real world evidence of safety and effectiveness and far more. Focus on the adoption of Logical Observations, Identifiers, Names and Codes (LOINC ) 3
4 Semantic Interoperability Importance 1. Laboratory consistency: Decision support/knowledge generation Public health reporting Real time epidemiology (including outbreaks) Laboratory cost savings Signal detection 2. Adverse events: Reduction in coding errors 3. FDA: Post market information (w/ Unique Device Identifiers (UDI)) Earlier IVD clearance 4. Other 4
5 Outline 1. Using Real World Data/Evidence (RWD/RWE) to support regulatory decision making for in vitro Diagnostic (IVD) devices 2. Value of a National Evaluation System for health Technology (NEST) 3. Developing IVD infrastructure to support NEST 4. Collaborating to develop valuable/sustainable infrastructure for stakeholders (Better/ Cheaper/ Faster) 5
6 Outline 1. Using Real World Data/Evidence (RWD/RWE) to support regulatory decision making for in vitro Diagnostic (IVD) devices 2. Value of a National Evaluation System for health Technology (NEST) 3. Developing IVD infrastructure to support NEST 4. Collaborating to develop valuable/sustainable infrastructure for stakeholders (Better/ Cheaper/ Faster) 6
7 What is RWD/RWE? Real World Data (RWD) Data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources * * Real World Evidence (RWE) Clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of RWD RWD bad/sketchy data RWE less evidence *Adapted from the Draft RWE Guidance: public/@fdagov meddevgen/documents/document/ucm pdf 7
8 Good Decisions Require Good Data Fit for Purpose Data must be complete, consistent, accurate, and contain all critical data elements needed to evaluate a medical device and its claims. Reliable Benefits Risks Relevant Safety Are there reasonable assurances, based on valid scientific evidence that probable benefits to health from use of the device outweigh any probable risks? [860.7(d)(1)] Effectiveness Is there reasonable assurance, based on valid scientific evidence that the use of the device in the target population will provide clinically significant results? 8 [860.7(e)(1)]
9 Outline 1. Using Real World Data/Evidence (RWD/RWE) to support regulatory decision making for in vitro Diagnostic (IVD) devices 2. Value of a National Evaluation System for health Technology (NEST) 3. Developing IVD infrastructure to support NEST 4. Collaborating to develop valuable/sustainable infrastructure for stakeholders (Better/ Cheaper/ Faster) 9
10 National Evaluation System Benefits Passive Surveillance Pre/post market balance with limited postmarket data Active surveillance to better protect patients Leverage RWE to support regulatory decisions Current National System Parallel track to clinical practice Inefficient one off studies Embedded in health care system (e.g., data collected during routine clinical care) Shared system to inform the entire ecosystem (e.g., patients, clinicians, providers, payers, FDA, CDC, device firms) 10
11 What is NEST? National Evaluation System for healthcare Technology (NEST) NEST is a strategically driven, coordinated network of voluntary partnerships to with the combined goal of generating better evidence more efficiently for medical device evaluation and regulatory decision making by leveraging RWE throughout the total product lifecycle. * * 11
12 NEST: Concept to Reality CDRH Priorities to Support NEST* 1. Award grants to support NEST development and implementation. 2. Issue draft guidance to clarify how RWE may be used to support regulatory decisions 3. Increase access to and use of realworld evidence to support regulatory decisions 4. Work with the medical device ecosystem, e.g., federal partners, health care system, manufacturers, payers and patients to build NEST. * calproductsandtobacco/cdrh/cdrhreports/ucm htm 12
13 NEST: Concept to Reality CDRH Priorities to Support NEST* 1. Award grants to support NEST development and implementation. 2. Issue draft guidance to clarify how RWE may be used to support regulatory decisions 3. Increase access to and use of realworld evidence to support regulatory decisions 4. Work with the medical device ecosystem, e.g., federal partners, health care system, manufacturers, payers and patients to build NEST. * calproductsandtobacco/cdrh/cdrhreports/ucm htm 13
14 NEST: Concept to Reality CDRH Priorities to Support NEST* 1. Award grants to support NEST development and implementation. 2. Issue draft guidance to clarify how RWE may be used to support regulatory decisions 3. Increase access to and use of realworld evidence to support regulatory decisions 4. Work with the medical device ecosystem, e.g., federal partners, health care system, manufacturers, payers and patients to build NEST. * calproductsandtobacco/cdrh/cdrhreports/ucm htm 14
15 NEST: Concept to Reality CDRH Priorities to Support NEST* 1. Award grants to support NEST development and implementation. 2. Issue draft guidance to clarify how RWE may be used to support regulatory decisions 3. Increase access to and use of realworld evidence to support regulatory decisions 4. Work with the medical device ecosystem, e.g., federal partners, health care system, manufacturers, payers and patients to build NEST. * calproductsandtobacco/cdrh/cdrhreports/ucm htm 15
16 Outline 1. Using Real World Data/Evidence (RWD/RWE) to support regulatory decision making for in vitro Diagnostic (IVD) devices 2. Value of a National Evaluation System for health Technology (NEST) 3. Developing IVD infrastructure to support NEST 4. Collaborating to develop valuable/sustainable infrastructure for stakeholders (Better/ Cheaper/ Faster) 16
17 FDA Perspective on LOINC for IVDs* 1. Any similar coding system for characterizing IVDs (e.g., LOINC) is voluntary and will not be required or reviewed by FDA (distinct from the required FDA accredited UDI system**) 2. FDA strongly encourages use of FDA recognized consensus standard structured data format to communicate IVD descriptive coding. Distribution of LOINC coding by manufacturers that suggests an unapproved/uncleared Indication for Use (i.e., off label use) could result in the device being considered adulterated and/or misbranded. Laboratories and/or other users must fulfill their obligations, including (but not limited to) any statutes, regulations, and validation procedures that must be complied with when making the results from the off label uses available to those requesting the test. 3. A 3 rd party resource for codes (and coding) could aid in harmonization efforts (e.g., Regenstrief) *See December 2 nd, 2016 FDA Industry IVD Roundtable Semantic Interoperability presentation at: 17 **
18 Digital Format for LOINC to IVD (LIVD) Clinical Laboratories Lab IT Vendors IVD Vendors IVD Industry Connectivity Consortium (IICC) Mission: Modernize connectivity between laboratory IT systems and analyzers Enable clinical laboratories to achieve more and spend less Members: Abbott Laboratories, A&T, Beckman Coulter, Beckton Dickinson, biomérieux, Data Innovations, Hitachi, IZASA SA, Orchard Software, Ortho Clinical Diagnostics, Roche Diagnostics, Samsung, Siemens Healthcare Diagnostics, Sunquest Information Systems, and Systelab Technologies SA. 18
19 Digital Format for LOINC to IVD (LIVD) Column Header Publication Version ID Manufacturer Model Equipment UID Equipment UID Type Vendor AnalyteCode Vendor Analyte Name Vendor Specimen Description Vendor Result Description Vendor Reference ID Vendor Comment LOINC Code LOINC Long Name Component Property Time System Scale Method Comments Sortable column could be used if spreadsheet form multiple manufacturers are combined into one Leave empty if no universal ID Leave empty if no additional vendor reference Leave empty if no vendor comment Leave empty if no LOINC mapping Leave empty if no LOINC mapping Leave empty if no LOINC mapping Leave empty if no LOINC mapping Leave empty if no LOINC mapping Leave empty if no LOINC mapping Leave empty if no LOINC mapping Leave empty if no LOINC mapping HL7 is creating a Data Analysis Model (DAM) for LIVD 19
20 LIVD Contributors 20
21 LIVD Contributors LAUNCHED JUNE 1 st,
22 IVD Test LOINC Code Harmonization Manufacturer Assay Assay Test Result (according to PI) LOINC Code (Vendor) LOINCs used by labs (lab name) APHL recommended LOINC M1 HIV Ag/Ab Combo Reactive Nonreactive (L1, L2) M1 HIV O Plus Reactive Nonreactive =HIV 1+O+2 Ab Fld Ql M2 M3 M4 HIV 1 Western Blot (CLIA high complexity assay) HIV 1 RNA Qualitative Assay HIV 1 DNA and RNA Qualitative Detection by PCR, Plasma Positive Negative Indeterminate Reactive Nonreactive Invalid = HIV1 RNA XXX Ql PCR = HIV1 RNA Bld Ql PCR PLT =HIV 1 proviral DNA SerPl Ql PCR (L1)
23 IVD Test LOINC Code Harmonization Manufacturer Assay Assay Test Result (according to PI) LOINC Code (Vendor) LOINCs used by labs (lab name) APHL recommended LOINC M1 HIV Ag/Ab Combo Reactive Nonreactive (L1, L2) M1 HIV O Plus Reactive Nonreactive =HIV 1+O+2 Ab Fld Ql M2 M3 M4 HIV 1 Western Blot (CLIA high complexity assay) HIV 1 RNA Qualitative Assay HIV 1 DNA and RNA Qualitative Detection by PCR, Plasma Positive Negative Indeterminate Reactive Nonreactive Invalid = HIV1 RNA XXX Ql PCR = HIV1 RNA Bld Ql PCR PLT =HIV 1 proviral DNA SerPl Ql PCR (L1)
24 LOINC Codes for Laboratories 24
25 Harmonization for Microbiology LOINC Codes Microbiology Code Harmonization Challenges/Needs: Microbiology codes (>10,700) Antimicrobial Susceptibility Test codes (>1,700) Multiple panels Open ended tests (e.g., culture) Coding for molecular assays New technology 25
26 Proposal: IVD Infectious Diseases LOINC Mapping Manual Objective: Collaborate with stakeholders in the development of an unambiguous step by step manual defining how to map LOINC to IVD devices intended to identify and evaluate infectious disease agents. Proposal: Develop a standardized document/manual to include: processes, examples of all types of Microbiology IVDs, tools for LOINC adoption in microbiology Mechanisms to solicit new LOINC codes Pilot manual clinical laboratories Coordinate with key stakeholders to attain input (Labs, Industry, EHR vendors, CDC, ONC, CMS, etc.). Post manual feedback, revision, implementation, support 26
27 Outline 1. Using Real World Data/Evidence (RWD/RWE) to support regulatory decision making for in vitro Diagnostic (IVD) devices 2. Value of a National Evaluation System for health Technology (NEST) 3. Developing IVD infrastructure to support NEST 4. Collaborating to develop valuable/sustainable infrastructure for stakeholders (Better/ Cheaper/ Faster) 27
28 Unlocking Meaningful RWE in Electronic Healthcare Records (EHRs) CDRH has a strategic priority to increase access to and use of realworld evidence to support regulatory decisions. First Steps: Harmonize the application of semantic interoperability coding standards. Identify data elements needed to leverage in a regulatory decision. Populate a structured data format with needed content. Ensure that results are directly tied to the performance of a specific device (e.g., through a Unique Device Identifier system) 28
29 Conclusions/Requests There is an unused wealth of RWE siloed in data repositories which may be leveraged in regulatory decisions which can only be unlocked if the data is harmonized. OIR is engaging in cross center and multi stakeholder efforts to assist in the adoption of semantic interoperability standards and structured data formats, beginning with LIONC. Stakeholder input is critical to realizing the benefits of these efforts; please contact us via pre submission or directly to let us know how we can help you. Questions/Comments? Contact: 29
30
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