R/calculate_mgfr_relm.R
In JMLuther/tabletools: Helpful Tools to Make html Tables
Defines functions
calculate_mgfr_relm
Documented in
calculate_mgfr_relm
#' Relmipirazin measured GFR with 2-compartment model (early and late sampling)
#'
#' This method requires multiple early (alpha phase, typically <120 min) and
#' late (beta phase) sample measurements of relmipirazin to determine GFR (glomerular
#' filtration rate). This function uses nonlinear modeling via the `nls()`
#' function to fit the 2-compartment model of relmipirazin kinetics using the formula \eqn{C=A*e^(- \alpha * t) +
#' B*e^(- \beta * t) }. The general method and provided examples are
#' modified from \href{https://pubmed.ncbi.nlm.nih.gov/16612328/}{Schwartz et al.
#' Kidney Int. 2006.}. Results are returned as a non-indexed value for GFR (`mgfr_2c`) and also indexed to BSA of 1.73m2 if height and weight are provided (`mgfr_2c_bsa`).
#' The summary provides estimates of kinetic parameters (k10, k21, k12) which can be used in ODE models, but is not implemented here.
#'
#' @param time A vector of time values (minutes)
#' @param relmipirazin_conc A vector of relmipirazin plasma measurements (ug/mL)
#' @param relm_inj_wt Relmipirazin injection Weight by syringe weight determination
#' (Pre-Post weight difference; recommended method) in grams
#' @param relm_inj_vol Relmipirazin injection Volume by syringe volume injected (Not
#' preferred; use if weights not available)
#' @param height Patient Height, cm
#' @param weight Patient Weight, kg
#' @param height_units Height units, if not in cm
#' @param weight_units Weight units, if not in kg
#' @param relm_units relmipirazin concentration units, defaults to `ng/mL`
#' @param id Study identifier (optional, passed to plot title)
#' @param time_units Time units, defaults to `min`
#' @param output Desired output, defaults to `summary` of model. Alternatively
#' can specify `gfr`, `gfr_bsa`, `fit`, or `plot`
#'
#' `summary` results include the named variables:
#' * `mgfr_2c` The measured GFR (mL/min)
#' * `mgfr_2c_bsa` The measured GFR (mL/min/1.73 m2) indexed to body surface area (BSA calculated by DuBois equation)
#' * `relmipirazin_m` Relmipirazin mass (ucg) injected. Determined either by user-provided syringe weight or volume injected.
#' * `relmipirazin_0` Relmipirazin concentration at t=0 calculated by A+B (theoretical, not measured)
#' * `relmipirazin_vd` Relmipirazin Volume of distribution estimated by Dose/relmipirazin[t0]
#' * `relm_auc` Relmipirazin calculated AUC from t=0 to Infinity, estimated by A/a + B/b
#' * `A` Model parameter A
#' * `a` Model parameter a, or \alpha
#' * `B` Model parameter B
#' * `b` Model parameter b, or \beta
#' * `model_r2` Model Pseudo-R2, calculated as the linear regression R2 for predicted~observed values
#' * `k10` Relmipirazin elimination rate constant from central compartment (1/min)
#' * `k21` Relmipirazin transfer rate constant from peripheral to central compartment (1/min)
#' * `k12` Relmipirazin transfer rate constant from central to peripheral compartment (1/min)
#'
#' `gfr` returns a single `mgfr_2c` value, the measured GFR (mL/min).
#' `gfr_bsa` returns a single `mgfr_2c_bsa` value, the measured GFR
#' (mL/min/1.73 m2) indexed to body surface area (BSA calculated by DuBois
#' equation).
#'
#' `fit` returns the non-linear regression model fit object.
#'
#' `plot` returns a base-R plot of observed and predicted regression curve vs
#' time, and summary measures in the legend.
#'
#' @return Desired output with either a data.frame of results (`summary`), a
#' single value of BSA adjusted GFR (`gfr`), an `nls object` for model fit
#' (`fit`), or a plot with observed values, model fit curve, and summary
#' results in the figure legend (`plot`)
#' @md
#'
#' @examples
#' calculate_mgfr_relm(df_rel_a$time, df_rel_a$relmapirazin_ng_ml, height = 1.6002, weight = 71.698, output="plot")
#' calculate_mgfr_relm(df_rel_a$time, df_rel_a$relmapirazin_ng_ml, height = 1.6002, weight = 71.698, output="plot", nls_weights = FALSE)
#'
#' # ODE MODEL FOR COMPARISON;
#' # Example only to verify ODE parameter estimates, not required for GFR calculation
#' # This model uses solution from the nls model to get the kinetic parameters
#' library(deSolve)
#' m2c <- function(Time, State, Pars) {
#' with(as.list(c(State, Pars)), {
#' drelmipirazin <- -k10*relmipirazin - k12*relmipirazin + k21*C2 # Central Conc
#' dC2 <- k12*relmipirazin - k21*C2 # Peripheral Conc
#' return(list(c(drelmipirazin, dC2)))
#' })
#' }
#' # get parameters from nls model
#' relm_nlfit <- calculate_mgfr_2c(dat$time, dat$relmipirazin_ug_ml, height = 1.67, weight = 70, relm_inj_vol = 5)
#' pars <- c(k10 = relm_nlfit$k10, k21 = relm_nlfit$k21, k12 = relm_nlfit$k12)
#' # plug into the ODE model solution
#' yini <- c(relmipirazin = relm_nlfit$A+relm_nlfit$B, C2=0)
#' times <- seq(0, 360, by = 1)
#' out <- ode(yini, times, m2c, pars)
#' out_df <- as.data.frame(out)
#' # Plot to verfiy kinetic parameters vs nls model
#' calculate_mgfr_2c(dat$time, dat$relmipirazin_ug_ml, height = 1.67, weight = 70, relm_inj_vol = 5, output="plot")
#' lines(out_df$time, out_df$relmipirazin, lty=2)
calculate_mgfr_relm <- function(time, relmipirazin_conc,
# omnipaque_v=300,
# relm_inj_vol=5.0,
# relm_inj_wt=NULL,
relm_units="ng/mL", time_units="min",
id=NULL,
height=NA, height_units = "m",
weight=NA, weight_units = "kg",
nls_weights = TRUE,
output="summary"
){
# Subject calcs
weight = tabletools::convert_weight_to_kg(weight, weight_units)
bsa = ifelse(is.na(height) || is.na(weight), NA,
calculate_bsa(weight, height, height_units = height_units, method = "DuBois"))
# if (!is.null(relm_inj_wt) & !is.null(relm_inj_vol)) stop("If relmipirazin injection weight is provided, assign `relm_inj_vol=NULL` and exact relmipirazin mass will be calculated.")
# if (is.null(relm_inj_wt) & is.null(relm_inj_vol)) stop("missing injection details; must provide `relm_inj_wt` or `relm_inj_vol`")
# relmipirazin_m = relm_inj_vol*relmipirazin_mg_ml*1000 # mass mcg relmipirazin injected
relmiparazin_m = 1.5*weight * 1000 # ucg
# Data: get time, relmipirazin data
time_min = convert_time_to_min(time, time_units)
relmipirazin_valid = ifelse(tolower(relm_units) %in% c("ng/ml", "mcg/L", "ug/L"), T, NA)
relmipirazin = relmipirazin_conc[relmipirazin_valid]
wt <- 1/relmipirazin^2
A_start = relmipirazin[1]*0.7
B_start = relmipirazin[1]*0.3
# fit model to data
nonlin2c <- function(t, A, a, B, b) { A*(exp(-a*t)) + B*(exp(-b*t)) }
fit = nls(relmipirazin ~ nonlin2c(time_min, A, a, B, b),
data=data.frame(time_min, relmipirazin, wt),
start=list(A = A_start,
B = B_start,
a =0.03,
b =0.004),
weights = if(nls_weights) wt else NULL)
A = coef(fit)[["A"]]
a = coef(fit)[["a"]]
B = coef(fit)[["B"]]
b = coef(fit)[["b"]]
model_r2 = summary(lm(relmipirazin ~ predict(fit)))[["r.squared"]] # this is a pseudo-r2
AUC_inf <- A/a + B/b # AUC_inf (ng*min/ml)) # ug*min/L
mgfr_2c = relmipirazin_m/AUC_inf *1000 # Dose/AUC_inf = ucg / (ucg*min/L)*10^3mL/L; mL/min (not indexed to BSA)
mgfr_2c_bsa = ifelse(!is.na(bsa), mgfr_2c*1.73/bsa, NA) # indexed to BSA
relmipirazin_vd = relmipirazin_m/(A+B) # volume of distribution, L
# ODE micro parameters:
k10 = relmipirazin_m/relmipirazin_vd/AUC_inf/1000 # 1/min
k21 = a*b/k10
k12 = a+b - k10 - k21
if (output == "summary") {
res = data.frame("mgfr_2c_relm" = mgfr_2c, # mL/min
"mgfr_2c_relm_bsa" = mgfr_2c_bsa, # mL/min/1.73m2
"relmipirazin_m" = relmipirazin_m, # ucg
"relmipirazin_0" = A+B, # ng/mL
"relmipirazin_vd" = relmipirazin_vd,# L
"relm_auc" = AUC_inf, # ng*min/mL or ucg*min/L
"A" = A,
"a" = a,
"B" = B,
"b" = b,
"model_r2" = model_r2, # this is a pseudo-r2
"k10" = k10, # 1/min
"k21" = k21, # 1/min
"k12" = k12) # 1/min
return(res)}
if (output == "gfr") return(mgfr_2c) # mGFR adjusted to 1.73m2 BSA
if (output == "gfr_bsa") return(mgfr_2c_bsa) # mGFR adjusted to 1.73m2 BSA
if (output == "fit") return(fit)
if (output == "plot") {
plot_nls_fit <- function(model, time, relmipirazin) {
# Main plot:
time_range = 0:max(time)
plot(time, relmipirazin, pch=21, xlab = "Time after injection (minutes)", ylab="Relmipirazin (ng/mL)",
log="y", col="red")
lines(predict(model, list(time_min=0:max(time))), col="blue", lty=2)
title(main = ifelse(!is.null(id), # add subtitle with subject identifier
paste0("Study: ",id, "\nRelmipirazin measured GFR, 2-Compartment model"),
"Relmipirazin measured GFR, 2-Compartment model"), adj = 0)
# model summary values:
A = coef(model)[["A"]] |> round(1)
a = coef(model)[["a"]] |> round(4)
B = coef(model)[["B"]] |> round(1)
b = coef(model)[["b"]] |> round(4)
mgfr_2c = mgfr_2c |> round(1)
mgfr_2c_bsa = mgfr_2c_bsa |> round(1)
AUC_inf = AUC_inf |> round(1)
model_r2 = model_r2 |> round(4)
relmipirazin_vd = relmipirazin_vd |> round(2)
legend("topright", adj=0.02, cex = 0.9,
legend=c(bquote(C[Relm.]==.(A)*e^(-.(a)*t)+.(B)*e^(-.(b)*t)),
bquote(GFR==.(mgfr_2c)~mL/min),
bquote(GFR[bsa-adj]==.(mgfr_2c_bsa)~mL/min/1.73~m^2),
bquote(AUC[inf]==.(AUC_inf)~ug~min/L),
bquote(Vd==.(relmipirazin_vd)~L),
bquote(pseudo-R^2==.(model_r2))) )
}
plot_nls_fit(fit, time_min, relmipirazin)}
}
JMLuther/tabletools documentation built on April 14, 2025, 3:09 a.m.
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Defines functions calculate_mgfr_relm
Documented in calculate_mgfr_relm
#' Relmipirazin measured GFR with 2-compartment model (early and late sampling)
#'
#' This method requires multiple early (alpha phase, typically <120 min) and
#' late (beta phase) sample measurements of relmipirazin to determine GFR (glomerular
#' filtration rate). This function uses nonlinear modeling via the `nls()`
#' function to fit the 2-compartment model of relmipirazin kinetics using the formula \eqn{C=A*e^(- \alpha * t) +
#' B*e^(- \beta * t) }. The general method and provided examples are
#' modified from \href{https://pubmed.ncbi.nlm.nih.gov/16612328/}{Schwartz et al.
#' Kidney Int. 2006.}. Results are returned as a non-indexed value for GFR (`mgfr_2c`) and also indexed to BSA of 1.73m2 if height and weight are provided (`mgfr_2c_bsa`).
#' The summary provides estimates of kinetic parameters (k10, k21, k12) which can be used in ODE models, but is not implemented here.
#'
#' @param time A vector of time values (minutes)
#' @param relmipirazin_conc A vector of relmipirazin plasma measurements (ug/mL)
#' @param relm_inj_wt Relmipirazin injection Weight by syringe weight determination
#' (Pre-Post weight difference; recommended method) in grams
#' @param relm_inj_vol Relmipirazin injection Volume by syringe volume injected (Not
#' preferred; use if weights not available)
#' @param height Patient Height, cm
#' @param weight Patient Weight, kg
#' @param height_units Height units, if not in cm
#' @param weight_units Weight units, if not in kg
#' @param relm_units relmipirazin concentration units, defaults to `ng/mL`
#' @param id Study identifier (optional, passed to plot title)
#' @param time_units Time units, defaults to `min`
#' @param output Desired output, defaults to `summary` of model. Alternatively
#' can specify `gfr`, `gfr_bsa`, `fit`, or `plot`
#'
#' `summary` results include the named variables:
#' * `mgfr_2c` The measured GFR (mL/min)
#' * `mgfr_2c_bsa` The measured GFR (mL/min/1.73 m2) indexed to body surface area (BSA calculated by DuBois equation)
#' * `relmipirazin_m` Relmipirazin mass (ucg) injected. Determined either by user-provided syringe weight or volume injected.
#' * `relmipirazin_0` Relmipirazin concentration at t=0 calculated by A+B (theoretical, not measured)
#' * `relmipirazin_vd` Relmipirazin Volume of distribution estimated by Dose/relmipirazin[t0]
#' * `relm_auc` Relmipirazin calculated AUC from t=0 to Infinity, estimated by A/a + B/b
#' * `A` Model parameter A
#' * `a` Model parameter a, or \alpha
#' * `B` Model parameter B
#' * `b` Model parameter b, or \beta
#' * `model_r2` Model Pseudo-R2, calculated as the linear regression R2 for predicted~observed values
#' * `k10` Relmipirazin elimination rate constant from central compartment (1/min)
#' * `k21` Relmipirazin transfer rate constant from peripheral to central compartment (1/min)
#' * `k12` Relmipirazin transfer rate constant from central to peripheral compartment (1/min)
#'
#' `gfr` returns a single `mgfr_2c` value, the measured GFR (mL/min).
#' `gfr_bsa` returns a single `mgfr_2c_bsa` value, the measured GFR
#' (mL/min/1.73 m2) indexed to body surface area (BSA calculated by DuBois
#' equation).
#'
#' `fit` returns the non-linear regression model fit object.
#'
#' `plot` returns a base-R plot of observed and predicted regression curve vs
#' time, and summary measures in the legend.
#'
#' @return Desired output with either a data.frame of results (`summary`), a
#' single value of BSA adjusted GFR (`gfr`), an `nls object` for model fit
#' (`fit`), or a plot with observed values, model fit curve, and summary
#' results in the figure legend (`plot`)
#' @md
#'
#' @examples
#' calculate_mgfr_relm(df_rel_a$time, df_rel_a$relmapirazin_ng_ml, height = 1.6002, weight = 71.698, output="plot")
#' calculate_mgfr_relm(df_rel_a$time, df_rel_a$relmapirazin_ng_ml, height = 1.6002, weight = 71.698, output="plot", nls_weights = FALSE)
#'
#' # ODE MODEL FOR COMPARISON;
#' # Example only to verify ODE parameter estimates, not required for GFR calculation
#' # This model uses solution from the nls model to get the kinetic parameters
#' library(deSolve)
#' m2c <- function(Time, State, Pars) {
#' with(as.list(c(State, Pars)), {
#' drelmipirazin <- -k10*relmipirazin - k12*relmipirazin + k21*C2 # Central Conc
#' dC2 <- k12*relmipirazin - k21*C2 # Peripheral Conc
#' return(list(c(drelmipirazin, dC2)))
#' })
#' }
#' # get parameters from nls model
#' relm_nlfit <- calculate_mgfr_2c(dat$time, dat$relmipirazin_ug_ml, height = 1.67, weight = 70, relm_inj_vol = 5)
#' pars <- c(k10 = relm_nlfit$k10, k21 = relm_nlfit$k21, k12 = relm_nlfit$k12)
#' # plug into the ODE model solution
#' yini <- c(relmipirazin = relm_nlfit$A+relm_nlfit$B, C2=0)
#' times <- seq(0, 360, by = 1)
#' out <- ode(yini, times, m2c, pars)
#' out_df <- as.data.frame(out)
#' # Plot to verfiy kinetic parameters vs nls model
#' calculate_mgfr_2c(dat$time, dat$relmipirazin_ug_ml, height = 1.67, weight = 70, relm_inj_vol = 5, output="plot")
#' lines(out_df$time, out_df$relmipirazin, lty=2)
calculate_mgfr_relm <- function(time, relmipirazin_conc,
# omnipaque_v=300,
# relm_inj_vol=5.0,
# relm_inj_wt=NULL,
relm_units="ng/mL", time_units="min",
id=NULL,
height=NA, height_units = "m",
weight=NA, weight_units = "kg",
nls_weights = TRUE,
output="summary"
){
# Subject calcs
weight = tabletools::convert_weight_to_kg(weight, weight_units)
bsa = ifelse(is.na(height) || is.na(weight), NA,
calculate_bsa(weight, height, height_units = height_units, method = "DuBois"))
# if (!is.null(relm_inj_wt) & !is.null(relm_inj_vol)) stop("If relmipirazin injection weight is provided, assign `relm_inj_vol=NULL` and exact relmipirazin mass will be calculated.")
# if (is.null(relm_inj_wt) & is.null(relm_inj_vol)) stop("missing injection details; must provide `relm_inj_wt` or `relm_inj_vol`")
# relmipirazin_m = relm_inj_vol*relmipirazin_mg_ml*1000 # mass mcg relmipirazin injected
relmiparazin_m = 1.5*weight * 1000 # ucg
# Data: get time, relmipirazin data
time_min = convert_time_to_min(time, time_units)
relmipirazin_valid = ifelse(tolower(relm_units) %in% c("ng/ml", "mcg/L", "ug/L"), T, NA)
relmipirazin = relmipirazin_conc[relmipirazin_valid]
wt <- 1/relmipirazin^2
A_start = relmipirazin[1]*0.7
B_start = relmipirazin[1]*0.3
# fit model to data
nonlin2c <- function(t, A, a, B, b) { A*(exp(-a*t)) + B*(exp(-b*t)) }
fit = nls(relmipirazin ~ nonlin2c(time_min, A, a, B, b),
data=data.frame(time_min, relmipirazin, wt),
start=list(A = A_start,
B = B_start,
a =0.03,
b =0.004),
weights = if(nls_weights) wt else NULL)
A = coef(fit)[["A"]]
a = coef(fit)[["a"]]
B = coef(fit)[["B"]]
b = coef(fit)[["b"]]
model_r2 = summary(lm(relmipirazin ~ predict(fit)))[["r.squared"]] # this is a pseudo-r2
AUC_inf <- A/a + B/b # AUC_inf (ng*min/ml)) # ug*min/L
mgfr_2c = relmipirazin_m/AUC_inf *1000 # Dose/AUC_inf = ucg / (ucg*min/L)*10^3mL/L; mL/min (not indexed to BSA)
mgfr_2c_bsa = ifelse(!is.na(bsa), mgfr_2c*1.73/bsa, NA) # indexed to BSA
relmipirazin_vd = relmipirazin_m/(A+B) # volume of distribution, L
# ODE micro parameters:
k10 = relmipirazin_m/relmipirazin_vd/AUC_inf/1000 # 1/min
k21 = a*b/k10
k12 = a+b - k10 - k21
if (output == "summary") {
res = data.frame("mgfr_2c_relm" = mgfr_2c, # mL/min
"mgfr_2c_relm_bsa" = mgfr_2c_bsa, # mL/min/1.73m2
"relmipirazin_m" = relmipirazin_m, # ucg
"relmipirazin_0" = A+B, # ng/mL
"relmipirazin_vd" = relmipirazin_vd,# L
"relm_auc" = AUC_inf, # ng*min/mL or ucg*min/L
"A" = A,
"a" = a,
"B" = B,
"b" = b,
"model_r2" = model_r2, # this is a pseudo-r2
"k10" = k10, # 1/min
"k21" = k21, # 1/min
"k12" = k12) # 1/min
return(res)}
if (output == "gfr") return(mgfr_2c) # mGFR adjusted to 1.73m2 BSA
if (output == "gfr_bsa") return(mgfr_2c_bsa) # mGFR adjusted to 1.73m2 BSA
if (output == "fit") return(fit)
if (output == "plot") {
plot_nls_fit <- function(model, time, relmipirazin) {
# Main plot:
time_range = 0:max(time)
plot(time, relmipirazin, pch=21, xlab = "Time after injection (minutes)", ylab="Relmipirazin (ng/mL)",
log="y", col="red")
lines(predict(model, list(time_min=0:max(time))), col="blue", lty=2)
title(main = ifelse(!is.null(id), # add subtitle with subject identifier
paste0("Study: ",id, "\nRelmipirazin measured GFR, 2-Compartment model"),
"Relmipirazin measured GFR, 2-Compartment model"), adj = 0)
# model summary values:
A = coef(model)[["A"]] |> round(1)
a = coef(model)[["a"]] |> round(4)
B = coef(model)[["B"]] |> round(1)
b = coef(model)[["b"]] |> round(4)
mgfr_2c = mgfr_2c |> round(1)
mgfr_2c_bsa = mgfr_2c_bsa |> round(1)
AUC_inf = AUC_inf |> round(1)
model_r2 = model_r2 |> round(4)
relmipirazin_vd = relmipirazin_vd |> round(2)
legend("topright", adj=0.02, cex = 0.9,
legend=c(bquote(C[Relm.]==.(A)*e^(-.(a)*t)+.(B)*e^(-.(b)*t)),
bquote(GFR==.(mgfr_2c)~mL/min),
bquote(GFR[bsa-adj]==.(mgfr_2c_bsa)~mL/min/1.73~m^2),
bquote(AUC[inf]==.(AUC_inf)~ug~min/L),
bquote(Vd==.(relmipirazin_vd)~L),
bquote(pseudo-R^2==.(model_r2))) )
}
plot_nls_fit(fit, time_min, relmipirazin)}
}
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