Copy of Open-ended Findings vs Pre-specified Findings Modeling in IS

User Inquiry:

"I wanted to revisit this discussion on ANA pattern reporting. We also deal with testing labs where they pre-coordinate the pattern result into the test name and perform a call of whether the pattern is observed or not. For example, the 'test' the lab performs evaluates each staining pattern as listed below:

Then, the result for each of these tests is 'POSITIVE' or 'NEGATIVE', affirming presence or identity of the specific pattern in question. " 

In cases like these and the study is submitting under SDTMIG v3.4, should we simply add each of these patterns as sponsor-extended ISTEST/TESTCD terms? Or, would it be acceptable that these remain in LB (MI is also another possible consideration...)?

Our Recommendations and Rationales:

For modeling Open-ended Findings Question vs. Prespecified Findings Question, analogy can be drawn from the MB domain modeling principles. For modeling pre-specified findings vs open-ended findings in MB, the approach has been:

• When asking an open-ended “what bug do you have in the culture” question,  MBTEST = Microbial Organism Identification; MBORRES = Name of the Microorg (from the Microorg codelist).
• When asking for a pre-specified finding “do you have this particular named bug in the culture” type question, MBTEST = Name of the specific Microorg, MBORRES = positive/negative, present/absent.

The MB example is to illustrate that the result of an open-ended findings question becomes the test or a test qualifier for the pre-specified findings question. This modeling principle applies consistently in IS as well.

Therefore for the pre-specified ANA Stain Pattern tests,

• ISTEST = Autoantibody; ISBDAGNT = Nuclear Autoantigens; ISTSTDTL = Centromere Pattern/ Cytoplasmic Pattern/or other specific ANA Patterns; ISOORES = Positive/Negative. In this case, the ANA pattern results are mapped to ISTSTDTL, because they are “pre-specified” findings, and you are asking the question “do you observe this ANA pattern?”
• The IS example 10 in the SDTMIG 3.4 where ISTSTDTL = Staining Pattern, essentially serves to asks the “open-ended” question of “what ANA pattern do you observe?” - the specific ANA patterns are then treated as results.
• Additionally, having the values of “Staining Pattern”, “Centromere Pattern/ Cytoplasmic Pattern/or other ANA Patterns” mapped to IS Test Detail, is also the consistent use of this variable.

Open-ended findings modeling: What antinuclear antibody staining pattern(s) do you observe in the specimen? (This is the IS Example 10 in the SDTMIG v3.4)

Con: This approach would only allow people to report the positive findings - ANA patterns that have been observed and reported as results. Patterns that are not observed will not be reported.

Supporting LOINC codes:

1. https://loinc.org/14722-3/ Presence
2. https://loinc.org/29953-7/ Titer

3. https://loinc.org/14611-8/ Pattern

   Example: IS Example 1

   is.xpt

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   Row	STUDYID	DOMAIN	USUBJID	ISSEQ	ISREFID	ISGRPID	ISTESTCD	ISTEST	ISBDAGNT	ISTSTDTL	ISTSTOPO	ISORRES	ISORRESU	ISSTRESC	ISSTRESN	ISSTRESU	ISSPEC	ISMETHOD	VISITNUM	VISIT	ISDTC
   1	XYZ	IS	XYZ1234	1	19283746	1	ATAB	Autoantibody	NUCLEAR AUTOANTIGENS		SCREEN	POSITIVE		POSITIVE			SERUM	FLUORESCENT IMMUNOASSAY	1	SCREENING	2018-06-20
   2	XYZ	IS	XYZ1234	2	19283746	1a	ATAB	Autoantibody	NUCLEAR AUTOANTIGENS		QUANTIFY	1:340		340	340	titer	SERUM	FLUORESCENT IMMUNOASSAY	1	SCREENING	2018-06-20
   3	XYZ	IS	XYZ1234	3	19283746	1a	ATAB	Autoantibody	NUCLEAR AUTOANTIGENS	STAINING PATTERN		CYTOPLASMIC, SPECKLED PATTERN		CYTOPLASMIC, SPECKLED PATTERN			SERUM	FLUORESCENT IMMUNOASSAY	1	SCREENING	2018-06-20
   4	XYZ	IS	XYZ1234	4	19283746	1b	ATAB	Autoantibody	NUCLEAR AUTOANTIGENS		QUANTIFY	1:170		170	170	titer	SERUM	FLUORESCENT IMMUNOASSAY	1	SCREENING	2018-06-20
   5	XYZ	IS	XYZ1234	5	19283746	1b	ATAB	Autoantibody	NUCLEAR AUTOANTIGENS	STAINING PATTERN		NUCLEAR, NUCLEOLAR PATTERN		NUCLEAR, NUCLEOLAR PATTERN			SERUM	FLUORESCENT IMMUNOASSAY	1	SCREENING	2018-06-20
   6	XYZ	IS	XYZ1007	1	19283746	1	ATAB	Autoantibody	NUCLEAR AUTOANTIGENS		SCREEN	NEGATIVE		NEGATIVE			SERUM	FLUORESCENT IMMUNOASSAY	1	SCREENING	2018-06-20

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Pre-specified findings modeling: Do you observe nuclear centromere ANA pattern? Do you observe cytoplasmic speckled ANA pattern? etc.

Pro:

1. This approach allows the users to report both the "specific, pre-specified" positive and negative patterns.
2. This is also almost a 1:1 mapping to LOINC codes.
3. Consistent modeling approach across specimen-based domains.

Supporting LOINC Codes:

• https://loinc.org/53994-0/ Presence

• https://loinc.org/96921-2/ Titer

  Example: IS Example 2

  is.xpt

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  is.xpt

  Row	STUDYID	DOMAIN	USUBJID	ISSEQ	ISREFID	ISGRPID	ISTESTCD	ISTEST	ISBDAGNT	ISTSTDTL	ISTSTOPO	ISORRES	ISORRESU	ISSTRESC	ISSTRESN	ISSTRESU	ISSPEC	ISMETHOD	VISITNUM	VISIT	ISDTC
  1	XYZ	IS	XYZ1234	1	19283746	1	ATAB	Autoantibody	NUCLEAR AUTOANTIGENS	CYTOPLASMIC, SPECKLED PATTERN	SCREEN	POSITIVE		POSITIVE			SERUM	FLUORESCENT IMMUNOASSAY	1	SCREENING	2018-06-20
  2	XYZ	IS	XYZ1234	2	19283746	1	ATAB	Autoantibody	NUCLEAR AUTOANTIGENS	CYTOPLASMIC, SPECKLED PATTERN	QUANTIFY	1:170		170	170	titer	SERUM	FLUORESCENT IMMUNOASSAY	1	SCREENING	2018-06-20
  3	XYZ	IS	XYZ1234	1	19283746	2	ATAB	Autoantibody	NUCLEAR AUTOANTIGENS	NUCLEAR, NUCLEOLAR PATTERN	SCREEN	POSITIVE		POSITIVE			SERUM	FLUORESCENT IMMUNOASSAY	1	SCREENING	2018-06-20
  4	XYZ	IS	XYZ1234	2	19283746	2	ATAB	Autoantibody	NUCLEAR AUTOANTIGENS	NUCLEAR, NUCLEOLAR PATTERN	QUANTIFY	1:340		340	340	titer	SERUM	FLUORESCENT IMMUNOASSAY	1	SCREENING	2018-06-20
  5	XYZ	IS	XYZ1234	1	19283746		ATAB	Autoantibody	NUCLEAR AUTOANTIGENS	NUCLEAR, CENTROMERE PATTERN	SCREEN	NEGATIVE		NEGATIVE			SERUM	FLUORESCENT IMMUNOASSAY	1	SCREENING	2018-06-20

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Decisions and Additional Notes/Action Items:

• Team is happy with both approaches, this "reciprocal" modeling approach provides flexibility and allows sponsors to accommodate different data collection schemas. It is also highly consistent with LOINC, both example have LOINC codes corroboration.
• Jordan already added this one to the SDTMIG v4.0.