Package 'admiralneuro'

Description

An updated ADaM ADAPET dataset using NV, AG, SUPPNV, ADSL

Usage

admiralneuro_adapet

Format

An object of class tbl_df (inherits from tbl, data.frame) with 83 rows and 50 columns.

See Also

Other datasets: admiralneuro_adtpet, adsl_neuro

Description

An updated ADaM ADTPET dataset using NV, AG, SUPPNV, ADSL

Usage

admiralneuro_adtpet

Format

An object of class tbl_df (inherits from tbl, data.frame) with 34 rows and 47 columns.

See Also

Other datasets: admiralneuro_adapet, adsl_neuro

Description

An updated ADaM ADSL dataset with Alzheimer's Disease patients

Usage

adsl_neuro

Format

An object of class tbl_df (inherits from tbl, data.frame) with 15 rows and 36 columns.

See Also

Other datasets: admiralneuro_adapet, admiralneuro_adtpet

Description

Computes the Centiloid value based on an amyloid Positron Emission Tomography (PET) scan radioactive tracer, Standardized Uptake Value Ratio (SUVR) value, pipeline, and reference region. Also allows for custom formula parameters.

Usage

compute_centiloid(
  tracer,
  pipeline,
  ref_region,
  suvr,
  custom_slope = NULL,
  custom_intercept = NULL
)

Arguments

Details

The Centiloid scale is a standardized quantitative measure for amyloid PET imaging that allows comparison between different tracers and analysis methods. This function converts SUVR values to the Centiloid scale based on published conversion equations for specific tracer, pipeline, and reference region combinations.

Centiloid is calculated as:

$Centiloid = slope \times SUVR + intercept$

where slope and intercept are formula parameters. If custom_slope and custom_intercept are not specified, this function uses pre-defined slope and intercept based on the user's selections of tracer, pipeline, and reference region.

The combinations of tracer, pipeline and reference region in the table below are supported. The columns "slope" and "intercept" then show the values of the slope and intercept that compute_centiloid() will use to calculate the centiloid value in each case.

The equations used for the conversions are based on the following references:

¹ Navitsky, et al. (2018). doi:10.1016/j.jalz.2018.06.1353 ² Sims, et al. (2024). doi:10.1001/jama.2023.13239 ³ Royse, et al. (2021). doi:10.1186/s13195-021-00836-1

Alternatively, the user can override the pre-selection by specifying both custom_slope and custom_intercept instead. When custom_slope and custom_intercept are specified, the function ignores tracer, pipeline and ref_region for calculation purposes. However, this function always requires specification of tracer, pipeline, and ref_region parameters, even when using custom slope and intercept values. This design choice ensures that users remain cognizant of the imaging context and analysis methodology when computing Centiloid values.

For additional Centiloid transformation formulas, see: Iaccarino, L. et al. (2025). doi:10.1016/j.nicl.2025.103765

If a matching combination of tracer, pipeline, and reference region is not specified and both custom_slope and custom_intercept are not specified, the function aborts with an error.

Value

A numeric Centiloid value.

Examples

Using standard parameters

Computes Centiloid with predefined slope/intercept for supported tracer/pipeline/ref_region combinations:

compute_centiloid(
  tracer = "18F-Florbetapir",
  pipeline = "AVID FBP SUVR PIPELINE",
  ref_region = "Whole Cerebellum",
  suvr = 1.25
)
#> [1] 51.5775

Using custom parameters

Computes Centiloid by overriding slope/intercept using custom values:

compute_centiloid(
  tracer = "MyTracer",
  pipeline = "MyPipeline",
  ref_region = "MyRegion",
  suvr = 1.25,
  custom_slope = 193,
  custom_intercept = -187
)
#> [1] 54.25

See Also

Other com_bds_findings: compute_upsit_percentile()

Description

This function computes the UPSIT (University of Pennsylvania Smell Identification Test) percentile for a given subject based on their age, sex, and UPSIT raw score. The percentile is determined by looking up the corresponding value in a normative reference table.

Usage

compute_upsit_percentile(sex, age, upsit_score)

Arguments

Details

The function uses an internal lookup table (upsit_lookup) that contains normative percentile data stratified by sex, age range, and UPSIT score. The lookup table is based on published normative data.

Age ranges in the lookup table include:

• 50-54 years

• 55-59 years

• 60-64 years

• 65-69 years

• 70-74 years

• 75-79 years

• 80+ years (no upper limit)

The function is designed to work efficiently in vectorized operations within data processing pipelines (e.g., with dplyr::mutate())

Value

A numeric percentile value. The UPSIT percentile value corresponding to the input parameters. Returns NA_real_ if no matching entry is found in the lookup table.

Examples

Look up for male percentile

A 52 years old male with upsit raw score of 25

compute_upsit_percentile(sex = "M", age = 52, upsit_score = 25)
#> [1] 5

Look up for female percentile

A 81 years old female with upsit raw score of 30

compute_upsit_percentile(sex = "F", age = 81, upsit_score = 30)
#> [1] 39

Returns NA

Minimal age is 50 and score of 0 and 40, return NA if no match found

compute_upsit_percentile(sex = "M", age = 45, upsit_score = 25)
#> [1] NA

References

Brumm MC, et. al., Updated Percentiles for the University of Pennsylvania Smell Identification Test in Adults 50 Years of Age and Older. doi:10.1212/WNL.0000000000201463

See Also

Other com_bds_findings: compute_centiloid()