Calculate Body Surface Area (BSA). Body Surface Area is a measure of body fat based on height and weight
References
Gehan EA, George SL. Estimation of human body surface area from height and weight. Cancer Chemother Rep. 1970 Aug;54(4):225-35. PMID: 5527019
Examples
library(dplyr)
dmcognigen_cov %>%
mutate(BSA = calculate_bsa(htcm = HTCM, wtkg = WTKG))
#> Formula to calculate BSA:
#> BSA [m^2] = ((HTCM [cm]) ^ 0.42246 * (WTKG [kg]) ^ 0.51456) * 0.0235
#> # A tibble: 254 × 53
#> DOMAIN STUDYID USUBJID ID RACEN RACEC SEXF SEXFC HTCM WTKG AST ASTULN
#> <chr> <chr> <chr> <dbl> <dbl> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 DM CDISCP… 01-701… 10101 1 Whit… 1 Fema… 147. 54.4 40 34
#> 2 DM CDISCP… 01-701… 10102 1 Whit… 0 Male 163. 80.3 21 36
#> 3 DM CDISCP… 01-701… 10103 1 Whit… 0 Male 178. 99.3 24 36
#> 4 DM CDISCP… 01-701… 10104 1 Whit… 0 Male 175. 88.4 20 36
#> 5 DM CDISCP… 01-701… 10105 1 Whit… 1 Fema… 155. 62.6 23 34
#> 6 DM CDISCP… 01-701… 10106 1 Whit… 1 Fema… 149. 67.1 25 34
#> 7 DM CDISCP… 01-701… 10108 1 Whit… 0 Male 169. 78.0 19 36
#> 8 DM CDISCP… 01-701… 10109 1 Whit… 1 Fema… 158. 59.9 28 34
#> 9 DM CDISCP… 01-701… 10110 1 Whit… 0 Male 182. 78.9 26 36
#> 10 DM CDISCP… 01-701… 10111 1 Whit… 0 Male 180. 71.2 15 36
#> # ℹ 244 more rows
#> # ℹ 41 more variables: SCR <dbl>, SCRULN <dbl>, TBIL <dbl>, TBILULN <dbl>,
#> # ASTCAT <dbl>, BMI <dbl>, BSA <dbl>, IBW <dbl>, CRCL <dbl>, CRCLP <dbl>,
#> # EGFR <dbl>, EGFRSCHW <dbl>, IBWCHILD <dbl>, LBM <dbl>, TBILCAT <dbl>,
#> # RFCAT <dbl>, RFCATC <chr>, NCILIV <dbl>, NCILIVC <chr>, SUBJID <chr>,
#> # RFSTDTC <chr>, RFENDTC <chr>, RFXSTDTC <chr>, RFXENDTC <chr>,
#> # RFICDTC <chr>, RFPENDTC <chr>, DTHDTC <chr>, DTHFL <chr>, SITEID <chr>, …
# Below will also work if the dataset contains expected variables
dmcognigen_cov %>%
mutate(BSA = calculate_bsa())
#> HTCM variable found and used for the htcm argument.
#> WTKG variable found and used for the wtkg argument.
#> Formula to calculate BSA:
#> BSA [m^2] = ((HTCM [cm]) ^ 0.42246 * (WTKG [kg]) ^ 0.51456) * 0.0235
#> # A tibble: 254 × 53
#> DOMAIN STUDYID USUBJID ID RACEN RACEC SEXF SEXFC HTCM WTKG AST ASTULN
#> <chr> <chr> <chr> <dbl> <dbl> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 DM CDISCP… 01-701… 10101 1 Whit… 1 Fema… 147. 54.4 40 34
#> 2 DM CDISCP… 01-701… 10102 1 Whit… 0 Male 163. 80.3 21 36
#> 3 DM CDISCP… 01-701… 10103 1 Whit… 0 Male 178. 99.3 24 36
#> 4 DM CDISCP… 01-701… 10104 1 Whit… 0 Male 175. 88.4 20 36
#> 5 DM CDISCP… 01-701… 10105 1 Whit… 1 Fema… 155. 62.6 23 34
#> 6 DM CDISCP… 01-701… 10106 1 Whit… 1 Fema… 149. 67.1 25 34
#> 7 DM CDISCP… 01-701… 10108 1 Whit… 0 Male 169. 78.0 19 36
#> 8 DM CDISCP… 01-701… 10109 1 Whit… 1 Fema… 158. 59.9 28 34
#> 9 DM CDISCP… 01-701… 10110 1 Whit… 0 Male 182. 78.9 26 36
#> 10 DM CDISCP… 01-701… 10111 1 Whit… 0 Male 180. 71.2 15 36
#> # ℹ 244 more rows
#> # ℹ 41 more variables: SCR <dbl>, SCRULN <dbl>, TBIL <dbl>, TBILULN <dbl>,
#> # ASTCAT <dbl>, BMI <dbl>, BSA <dbl>, IBW <dbl>, CRCL <dbl>, CRCLP <dbl>,
#> # EGFR <dbl>, EGFRSCHW <dbl>, IBWCHILD <dbl>, LBM <dbl>, TBILCAT <dbl>,
#> # RFCAT <dbl>, RFCATC <chr>, NCILIV <dbl>, NCILIVC <chr>, SUBJID <chr>,
#> # RFSTDTC <chr>, RFENDTC <chr>, RFXSTDTC <chr>, RFXENDTC <chr>,
#> # RFICDTC <chr>, RFPENDTC <chr>, DTHDTC <chr>, DTHFL <chr>, SITEID <chr>, …