Ejection Fraction Endpoints

The Analysis dataset (ADaM) is created from the collected data stored in SDTM. It should have full traceability back to the input datasets. For this example we show the following endpoints:

• From CV domain
  • Percent change in ejection fraction over time
  • Decline in ejection fraction over a time period (yearly) grouped by a decline greater than 10.0%.
• From the LB domain
  • Percent change in NTproBNP over a period of time (yearly).

The SDTM example datasets from which this data is drawn are in the section "Basic CMR tests -Systolic Function" (make link) . The example datasets show visits 1 and 6 for one subject. As mentioned in the SDTM text, for the purposes of this example, the participant was enrolled in the study on 20-May-2021 and the study visits were every two months. Visit 1 represents the visit at day 1 of the study, and visit 6 represents the visit at month 12 of the study. The associated laboratory test, N-Terminal ProB-type Natriuretic Peptide, is used in conjunction with ejection fraction as a biomarker and is important for diagnosis and treatment. It is also used separately to identify the percent change over a period of time.

The analysis dataset includes treatment and demographic information drawn from the ADSL, defined in the Metadata Table below. In this example, we only show some of the required variables plus important stratification variables from ADSL. One variable mentioned body surface area (BSA), can be computed and added to ADSL. However, since the subjects are children, their body surface area changes over time. Therefore it would probably be better to have a separate dataset to capture this over time.

Questions. 

Is screening considered visit 1, or is it to include the subject into the study? SDTM team agreed to change to VISIT 1 instead of SCREENING.

Are these Real World Evidence (RWE) studies only or clinical trials?

Should both endpoints be shown in one efficacy dataset or two? I will be showing 3 datasets.

What is a good surface area to use for the child subject?

Should i use Male and 6 years? please advise.

Analysis Datasets

The table below presents the sample analysis datasets and unique parameters generated for this example. The Subject Level Analysis Dataset (ADSL)  is a necessary component in the creation process of subsequent analysis datasets for human trials. The ADSL incorporates demographics, treatment groups, study dates, and stratification variables. In this example, the ADSL is not displayed directly but serves as a source for standard ADaM variables. Additional information on this can be found in Section 2.3.1 of the ADaM Implementation Guide (ADaMIG) v1.3.

The second and third datasets follow the naming convention for ADaM by starting with the letters 'AD' representing 'Analysis Dataset'. There is flexibility in the use of the other 6 letters allowed in the dataset name, and also the Dataset description which is also the label

. In this example, the ADCVEF dataset selectively captures tests pertinent to this analysis, specifically those with CVTESTCD equal to "LVEF_C" or "RVEF_C". The relationship dataset RELREC illustrates the connections between datasets. There exists a many-to-one relationship between cardiovascular tests (CV) and laboratory data collected during the same visits. 

ADCVNTRP is a subset of LB selecting for LBTESTCD equal to "BNPPRONT". In this case, many lab draws may occur throughout the year, but we are only interested in the ones linked to a specific visit. By subsetting the Laboratory Results (LB) dataset with LBLNKID not missing, we can select laboratory data that aligns with CV data and sort using USUBJID and VISIT. If LBLNKID were not available, a windowing strategy could be employed to select the laboratory draw closest to that visit for merging with CV, computed in AVISIT.

ADCMRI is an efficacy analysis dataset with only a few records for more complex modeling of changes in ejection fraction plus the ProB-type test results. It is a Basic Structure dataset with additional variables added from ADCVEF.

Analysis Datasets

The subsequent section provides the data structures for the example ADaM datasets. In this example, columns for derived variables BASE, and CHG, PCHG and CHGCAT1 have been added fto facilitate analyses of efficacy endpoints. Specifically, example demographic data for ADSL were created for an 8 year old male child. Also, BSA at baseline (BSABASE), an example of a custom variable, was added from ADSL. The ADaM standard only suggests variable order, so the demographics and treatment variables are shown at the right of the dataset.

Example 1 

This example dataset shows the findings and additional analysis variables associated with :

• Left Ventricular Ejection Fraction, Calculated (%)
• Right Ventricular Ejection Fraction, Calculated (%)
• N-Terminal ProB-type Natriuretic Peptide (pg/mL)

The dataset above can also be set up to provide multivariate analyses by taking the lab tests of interest, transposing and merging with the ADCVNTPP dataset. In that case, the records from LB are not needed as a row.

The variables transposed and merged to the rows created from CV are:

Example Analysis Results Metadata (ARM) Tables

This table may be included in the submission package for regulatory agencies. It describes the analyses conducted for the study (usually limited to primary and secondary  endpoints) with details on data and analysis procedures used. The first example uses the dataset ADCVNTPR, selecting for the parameter "N-Terminal ProB-type Natriuretic Peptide (IU/L)" .