Best Practices on Medical Coding in MedDRA

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Best Practices for Medical Coding with MedDRA

A summary of diverse client practice

October 10, 2012 • Caroline Halsey Director of Project Management, EMEA BioPharm Systems

• Rodney Lemery, MPH, PhD Vice President, Safety and Pharmacovigilance BioPharm Systems

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• Welcome and introduction

• Issues and Decisions around Coding Practice – Who should code

– Which terms to code

– How to ensure quality

– How to optimise technology to save time

– Re-coding after new MedDRA versions

• Issues and Decisions around Analysis – Data analysis and grouping

– SMQ Definition and Use

– Signal Detection

Agenda

2 Copyright ©2012 BioPharm Systems, Inc.

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Welcome & Introductions


Rodney Lemery Vice President of Safety and Pharmacovigilance BioPharm Systems • Head of Safety/PV practice since

2007 – Expertise in managing all phases and

styles of clinical trials – Leads the team that implements,

supports, and enhances Oracle’s TMS, Argus, AERS and Empirica Products

• Extensive TMS and medical coding implementation experience – 10+ years of experience

implementing TMS and coding tools used in clinical data management and safety

Caroline Halsey Director of Project Management, EMEA BioPharm Systems

• Over 13 years of clinical operations management and data management expertise - Experience delivering validated

technology and associated training to sponsors and CROs

- Building cross-functional and international working relationships to enable the development of efficient processes

- Currently supporting enhancements to the medical coding system for a major global pharma company

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Welcome & Introductions

Safety and Pharmacovigilance Practice Services

Implementations Manage implementations of Oracle TMS, Custom medical coding system development, Argus Safety, AERS and Empirica Topics.

Integrations Build interfaces between TMS, Argus and other clinical systems.

Training Develop and/or deliver standard and custom training classes and materials for various dictionaries of interest.

Process Guidance Provide insight, advice, and solutions to specific medical/drug coding or safety related issues.



Issues and Decisions around Coding Practice

• Caroline Halsey Director of Project Management, EMEA

BioPharm Systems

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Who should code and how? Balance time and money


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Who should code?

• HCP – The hiring of health care professionals (RN, LVN/LPN,

Pharmacists, Dieticians, Public Health Practitioners, Research MDs others)

• Non-HCP with Coding Committee Review • This method is very common assuming that HCP are present

in the central coding committee


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Which terms to code

• ICH regulatory requirement – coding of reported Adverse Events and Serious Adverse Events to the

MedDRA dictionary when submitting electronic reports

• FDA – anticipated MedDRA requirement but it is not yet CFR

• Our client base (“Industry Standard” ~64 pharma, OTC, biotech and device companies) – Code at least Adverse Events

– Optionally code:

• Medical History

• Lab

• Con-Med Indications


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CRO

•Flexible resourcing

•Not experienced with coding conventions of every sponsor

•Quality: sponsor oversight/ QC via in-house team accepting codes that CRO proposes

De-centralised team in-house

(Split by TA, Phase,

Clinical vs Safety)

• If therapeutically or phase aligned may have another role as well as coding, therefore not coding experts?

• If busy with coding then likely to be busy with other responsibilities at the same time

•Quality: Less consistency between coding clinical and safety data if separate coding teams

•How implement QC checks across TA/ Phase?

Centralised in-house team (Coding all Clinical and Safety

terms)

•Code both clinical trials and safety data so should reduce SAE reconciliation issues

•Consistent coding across TAs

•Ensure sufficient people to meet pharmacovigilance and clinical trial timelines

•Quality: one person propose a code and another accept


Organisation of the Coding Team

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CRO






Clinical vs Safety)






terms)





Combination may be the ideal: sponsor accepts codes that CRO

proposes or QCs % of codes 13 Copyright ©2012 BioPharm Systems, Inc.


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CRO






Clinical vs Safety)






terms)





Combination may be the ideal: sponsor accepts codes that CRO

proposes or QCs % of codes 14 Copyright ©2012 BioPharm Systems, Inc.


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Training, training, training – Highlight new terms or changes

in each MedDRA version

Document company standards – Therapeutic area standards?

– Phase I, II, II, IV conventions?

– Cross-therapy area or cross-phase

– Coding Steering Team could improve consistency across Therapeutic Areas or Phases

Ensuring quality – Training and Documentation

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Ensuring Quality – QC and QA

• In-stream – 100% for adverse events

– Could be lower % for medical history and con med indications

• Quality Control – Four-eyes process:

• Proposer + Acceptor

• Proposer could be @ CRO and Acceptor @ sponsor

• Quality Assurance – QC of the Acceptor stage via review of listings

• Regularly review and discuss real examples

of coding solutions and errors amongst the

coding team


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Time savers - technology

• Short term expenditure on technology can lead to long term savings in human resources – Automate where possible and enable

human coders to focus on where they can add value

• Technology can include: – Autocoding based on synonym lists

– Coding distinct terms

– Automation of work assignment


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Autocoding

• Auto-encoding reported verbatim terms to previously coded data is an option within many commercial coding systems

• Can involve: – Company synonym lists that grow over time e.g. synonyms of LLT

Cardiac pain/PT Angina pectoris:

• Shooting pain in heart

• Heart pain

• Twinges in heart

– Autocoding algorithm – normalisation of text and removing erroneous characters

• E.g. “Ache/pain ongoing on heart” transforms to “Pain heart” and is then coded to “Cardiac pain”


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Code every term?

“Cold” Query

“Cold” Query

“Cold” Query


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Or code distinct terms?

“Cold” Query


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Terms out of context

• Risk with autocoding, coding distinct terms and with centralised coding team …reviewing terms out of context – “Chest pain” especially in clinical trials

• No other information is present to indicate if this is general chest pain, cardiac, GI related, respiratory related etc.

– “Chest pain” in Post Marketing • Hospitalisation report indicates an ECG performed with

indications of cardiac arrhythmias and a likely mild MI. • Best practice: manual review of autocoded adverse event

coding • Consider: BUT does context cause bias/ lead to assumptions

in coding?


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Coding Med Hist, Labs and AE terms all in one place

• Is the verbatim “ALT increased” on the AE form the same as “ALT increased” on the Lab form?

• If you coded “ALT increased” to “Alanine aminotransferase increased” in the lab data then it will code the AE verbatim the same • Is this acceptable?

• Should you query the AE term for a definitive or provisional diagnosis rather than the sign/symptom?


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Work Assignment

• Automation of work assignment enables team leaders to add value elsewhere rather than spend time assigning individual’s work

• Use technology to assign priorities per person/ role/ group

• Then adjust priorities as needed:

• Safety vs clinical terms

• One clinical trial vs others


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Coding conventions

• Most of our clients use the MSSO MedDRA Points to Consider as the premier coding convention guide

• In clinical trials, there may be protocol specific coding guidelines in the data management plan (or equivalent) – E.g. A clinical trial of a product treating “Tuberculosis”

• Verbatim terms come in as “Plaque positive”

– Term vague

• For this study only the verbatim term will be mapped to “Mycobacterium tuberculosis complex test positive”


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Recoding after each new MedDRA version

• Per MSSO: “the decision to re-code historical data is left to the discretion of each individual company” (MSSO, 2012)

• The EMA has, in some cases, required users to recode data back to 1995.

• “During the 60 day period (allowed duration for dictionary versioning), it was envisioned that organisations would review the changes in MedDRA and make what changes they (the organization) thought were necessary. Typically, this involves reviewing and re-coding LLTs that have become non-current, reviewing new direct matches to the verbatims and, if resources allow, reviewing medically “better” terms. These steps are not required but are a typical best practice of many organizations” (MSSO, 2012)


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Recoding after each new MedDRA version

Ongoing studies

• Ensures consistency of coding within a study

• Requires expenditure on technology to enable the recoding

Ongoing and locked studies

• Ensures data consistency, so beneficial when aggregating data (ISS ISE)

• To maintain record of what was originally reported need to maintain audit trails

• Requires expenditure on technology to enable the recoding

Pharmacovigilance cases

• DSUR/PSUR data table listings of LLT or PT terms will be consistent among the data (be it pre or post marketing authorisation)

• PT consistency required for reproducible SMQ usage in signal detection



Issues and Decisions around Analysis

• Rodney Lemery, MPH, PhD

Vice President, Safety and Pharmacovigilance

BioPharm Systems

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Data Retrieval and Analytical Method

1. Identify Research Questions • Partner with statistician and clinical leads to ensure a well formed and

appropriate research question given the quality and quantity of coded data

2. Develop MedDRA Search Strategy, may include: • SMQs

• SOCs with/without secondary

• Grouping terms (HLT/HLGT)

• Custom Search - Use own SMQs for targeted surveillance

3. Retrieve cases based on strategy

4. Assess data to answer research question


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• Assume that we have the AE data collected to the left. • We could ask what is the most

frequently experienced category of medical issue in our data?

• We could also ask are there any suspected or confirmed cases of “Acute pancreatitis” in our data?


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• When displayed by SOC the answer to our initial research question is “Gastrointestinal disorders” with a frequency of 9/16 = 56.25%


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• When displayed by HLGT the answer to our initial research question is “Gastrointestinal signs and symptoms” with a frequency of 5/16 = 31.25%


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• When displayed by HLT the answer to our initial research question is “Nausea and vomiting symptoms” with a frequency of 3/16 = 18.75%


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Standardised MedDRA Queries

• Groupings of relevant MedDRA terms at PT level to represent a particular area of interest, for data retrieval & analysis

• Provided by MSSO with each new MedDRA version

• Used to identify cases of interest in your data (Mozzicato, 2007)


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• Rationale behind development of SMQs (CIOMS, 2004)

• The size (~ 18 000 PTs) and complexity of MedDRA terminology carries the risk that • Different users may select differing sets of terms while trying to

retrieve cases related to the same drug safety problem

• Globally standardised search queries/analysis will give broader acceptance in signal detection methods


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• SMQs contain PT and LLT terms for the “…signs, symptoms, diagnoses, syndromes, clinical findings, laboratory and disorders, other test data, etc, related to the condition of interest…” (Mozzicato, 2007)


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• Some are hierarchical in nature:


• Others are not:

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Features of SMQs – Broad vs Narrow

• SMQ relations with dictionary terms may be identified as broad or narrow.

• Narrow terms: highly likely to represent the condition of interest

• Narrow search retrieves narrow terms

• Broad terms: help to identify all possible cases

• Including some that may prove to be of little or no interest

• A broad search retrieves broad terms

• Algorithms within SMQs are used to further define cases of suspected medical conditions

• Based on rules defined in the algorithm by the MSSO

• Processed programmatically during a search operation


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Example of MedDRA SMQ • Acute Pancreatitis SMQ Terms and Algorithm

A B C


A OR (B AND C)

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• When using the SMQ for our second research question, the answer is 5


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Use of MedDRA in Signal Detection

• In terms of spontaneous safety data reporting and signal detection methods, use of MedDRA can have a dynamic impact on the statistical results

• In our data above, let’s suppose our research question was “Do persons exposed to our product report ‘Acute Pancreatitis’ at a greater rate than those not taking our product?” • The next slides look at the ability to use the MedDRA hierarchy to

calculate a disproportionality score in an attempt to answer this question

• We will borrow from the field of epidemiology (specifically case-control studies) and calculate a type of Odds Ratio (Waller, van Puijenbroek, Egberts, & Evans, 2004)


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MedDRA Analysis “Acute Pancreatitis”

2x2 Table Person Exposed to

Product X

Background of choice (usually

persons NOT taking Product X)

Experiencing “Acute Pancreatitis” 30 (A) 10 (C)

Not Experiencing “Acute Pancreatitis” 214 (B) 234 (D)


Reporting Odds Ratio = AD

----- = 3.28

BC

Interpretation: Patients exposed to product X are 3.28 times more likely to

report “Acute Pancreatitis” than patients not exposed to

product X

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Company MedDRA Queries

• A trend we are now seeing are companies using their own SMQs not found in the MedDRA dictionary

– Company or Custom MedDRA Queries (CMQ)

– A list of targeted PT and LLTs is gathered into an over-arching clinical construct • Each PT/LLT is assigned a Broad or Narrow context and if required, complex

algorithms can also be used

• The data is then mined for cases using the CMQ in exactly the same manner as an SMQ


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Company MedDRA Queries

• Here we define a case of “Hyperglycemic Events” as the following:

– Any report from Category A

– OR

– A report from Category C with at least 2 items from Category B


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MedDRA Analysis Using CMQ “Hyperglycemic Events”

2x2 Table Person Exposed to

Product X

Background of choice (usually

persons NOT taking Product X)

Experiencing Hyperglycemic events as defined in CMQ

181 (A) 184 (C)

Not Experiencing Hyperglycemic events as defined in CMQ

63 (B) 60 (D)


Reporting Odds Ratio = AD

----- = 0.94

BC

Interpretation: Patients exposed to product X are no more likely to report

Hyperglycemic events than patients not exposed to product X

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Summary

• Quality: implement QC and QA oversight

• Cost: invest in technology/ automation where possible to allow personnel to focus on the “value add”

• Time: invest in technology

• Identify research questions and SMQ research methodology

• May include CMQs


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Ways to Engage… • For those ready

• Proof of concept or scope discussions to see if BioPharm may be able to assist you in assessing your coding or analysis methods

• For those not quite ready

• Schedule early engagement calls to provide guidance on similar projects

• For those in the midst of a process re-engineering

• Refine your approach with guidance calls to review your approach and evaluate if BioPharm’s input might be useful to your organisation


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References

• Brown, E.. (2004). Using MedDRA Implications for Risk Management. Drug Safety 2004; 27 (8)

• CIOMS. (2004). Development and Rational Use of Standardised MedDRA Queries (SMQs). WHO, Geneva, CIMS

• Mozzicato, P. (2007). Standardised MedDRA Queries Their Role in Signal Detection. Drug Safety. 30 (7): 617-619

• MSSO.. (2012). Retrieved from http://www.meddramsso.com/public_faq_meddra.asp on October 1st, 2012

• Waller, P., van Puijenbroek, E., Egberts, A., and Evans, S. (2004). The reporting odds ratio versus the proportional reporting ratio: ‘deuce’. Pharmacoepidemiology and drug safety. 13: 525–526


http://www.meddramsso.com/public_faq_meddra.asp

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Q&A

• Please feel free to enter your questions in the chat area of the screen


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• [email protected]

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