calculate_mgfr_2c: Iohexol measured GFR with 2-compartment model (early and late...
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calculate_mgfr_2c

R Documentation

Iohexol measured GFR with 2-compartment model (early and late sampling)

Description

This method requires multiple early (alpha phase, typically <120 min) and late (beta phase) sample measurements of iohexol to determine GFR (glomerular filtration rate). This function uses nonlinear modeling via Levenberg-Marquardt method in the gsl_nsl package and the gslnls() function to fit the 2-compartment model of iohexol kinetics. The NLLS methods use the formula C=A \cdot exp^{- \alpha \cdot t} + B*e^{- \beta \cdot t} and weighted by 1/Iohexol^2. Initial estimates for early (A, a) and late (B, b) parameters used for NLLS estimation are obtained using standard "Slope-Intercept" methods for linear regresion of log(Iohexol)~time for two lines for the early and late portions. Results for this model can be obtained using the nls_v="SI" option. Use of the gsl_nsl package allows for better support of parameter constraints (constrained to positive values) and other methods (e.g. predict methods using fit object) compared to the base nls() function. The general method and provided examples are modified from Schwartz et al. Kidney Int. 2006.. Further discussion and details regarding methods of fitting the 2-compartment models can be found at Pottel et al. 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 (see example in compare_mgfr_summary), but is not implemented here.

Usage

calculate_mgfr_2c(
  time,
  iohexol_conc,
  omnipaque_v = 300,
  ioh_inj_vol = 5,
  ioh_inj_wt = NULL,
  ioh_units = "ug/mL",
  time_units = "min",
  id = NULL,
  height = NA,
  height_units = "m",
  weight = NA,
  weight_units = "kg",
  nls_weights = TRUE,
  nls_v = "gslnls",
  t_late = 120,
  t_early = 100,
  legend_cex = 1,
  output = "summary"
)

Arguments

`time`	A vector of time values (minutes)
`iohexol_conc`	A vector of Iohexol plasma measurements (ug/mL)
`omnipaque_v`	Omnipaque version (eg 300 = Omnipaque 300 recommended). Other versions commonly available include 300 and 350 (USA) and 140, 280, 240 (other areas)
`ioh_inj_vol`	Iohexol injection Volume by syringe volume injected (Not preferred; use if weights not available)
`ioh_inj_wt`	Iohexol injection Weight by syringe weight determination (Pre-Post weight difference; recommnded method) in grams
`ioh_units`	Iohexol concentration units, defaults to `ug/mL`
`time_units`	Time units, defaults to `min`
`id`	Study identifier (optional, passed to plot title)
`height`	Patient Height, m
`height_units`	Height units, if not in m
`weight`	Patient Weight, kg
`weight_units`	Weight units, if not in kg
`nls_weights`	Use weights in nls model (`TRUE` or `FALSE`), defaults to `TRUE`. Uses 1/iohexol^2 as weights, which more heavily weight lower concentrations obtained at later time points
`nls_v`	Model estimation method (defaults to `gslnls`). Can also use `SI`, `base`, or `MSP`. If `MSP` is selected, only "late" time points will be used and "early" time points will be discarded. If no early timepoints are available, method defaults to `MSP`.
`t_late`	First Time point (minutes) to use in the "Late" elimination phase, defaults to `120`
`t_early`	Last Time point (minutes) to use in the "Early" distribution phase, defaults to `100`
`legend_cex`	Magnification factor for plot text, default 1.0 (100%)
`output`	Desired output, defaults to `summary` of model. Alternatively can specify `gfr`, `gfr_bsa`, `fit`, or `plot` The `mgfr_method` describes the method for estimation, which can have the following options: `SI` Slope-Intercept method obtained by performing two linear regressions of log(iohexol) vs time for early and late time points. Obtained by setting `nls_v = "SI"` `modified-SI` SI method with shared time-point(s) between early and late periods (ie overlapping `t_early` = `t_late`). Obtained by setting, for example `⁠nls_v = "SI", t_early=120, t_late=120⁠` or another option with overlapping times. `NLLS-gslnls-weighted` (Default method) NLS fit using the `gslnls()` function from the `gsl_nls` package. Obtained by setting `⁠nls_v = "gslnls", nls_weights=T⁠` `NLLS-base-weighted` NLS fit using the `nls()` function from the base `stats` package. Obtained by setting `⁠nls_v = "base", nls_weights=T⁠`. Similar to `gslnls` method, except that parameter constraints cannot be set with `nls()`, and so implausible estimates (negative for a, b) may be obtained. `NLLS-gslnls-unweighted` NLS fit using the `gslnls()` function from the `gsl_nls` package. Obtained by setting `⁠nls_v = "gslnls", nls_weights=F⁠` `NLLS-base-unweighted` NLS fit using the `nls()` function from the base `stats` package. Obtained by setting `⁠nls_v = "base", nls_weights=F⁠`. This method weights heavily to early time points (higher concentration). Also, parameter constraints cannot be set with `nls()`, and so implausible estimates (negative for a, b) may be obtained. `MSP-BM` The Multiple Sample Protocol (MSP), or Multiple Late Sample (MLS) estimate using 1-compartment analysis of Late time points only (>120 minutes post injection). Adjustment made using the Brochner-Mortenson correction equation to correct for unmeasured early time points. `SS-Jacobbson` Single Sample estimate of GFR For `output = "summary"` results returned includes the named variables: `mgfr_method` Method used for GFR calculation (see above). Results should be similar. `mgfr_2c` 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) `iohexol_m` Iohexol mass (ucg) injected. Determined either by user-provided syringe weight or volume injected. `iohexol_0` Iohexol concentration at t=0 calculated by A+B (theoretical, not measured) `iohexol_vd` Iohexol Volume of distribution estimated by Dose/Iohexolt0 `ioh_auc` Iohexol 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 `ssr` Sum of squared residuals `sse` Sum of squared residuals for early time points `ssl` Sum of squared residuals for late time points `k10` Iohexol elimination rate constant from central compartment (1/min) `k21` Iohexol transfer rate constant from peripheral to central compartment (1/min) `k12` Iohexol transfer rate constant from central to peripheral compartment (1/min) `n_early` Number of early time points measured and used in model `n_late` Number of late time points measured and used in model `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.

Value

Desired output with either a data.frame of results (summary), a single value of BSA adjusted GFR (gfr or gfr_bsa), an ⁠nls object⁠ for model fit (fit), or a plot with observed values, model fit curve, and summary results in the figure legend (plot)

Examples

library(tabletools)
# PUBLISHED IOHEXOL DATA ----
# data available in package by calling
## ├ Schwartz Data ----
# Data extracted from Schwartz Fig1 https://pubmed.ncbi.nlm.nih.gov/16612328/
# Iohexol 5mL IV injection (Omnipaque 300, 5mL ~3235mg Iohexol)
# sampling at 10, 20, 30, 60, 120, 240, 300, 360 min
# time (minutes)
# Iohexol (ug/ml)
# age, height, weight not known for the example
dat_schwartz

## ├ Pottel data ----
# from Supplemental document in https://pubmed.ncbi.nlm.nih.gov/33952185/
dat10
dat17

## ├ Tondel data ----
# full example data provided by Tondel in Table 2: https://pubmed.ncbi.nlm.nih.gov/29134449/
dat_tondel

# Comparison of Output options
calculate_mgfr_2c(dat_schwartz$time, dat_schwartz$iohexol_ug_ml, height = 1.67, weight = 70, ioh_inj_vol = 5) # Default
calculate_mgfr_2c(dat_schwartz$time, dat_schwartz$iohexol_ug_ml, height = 1.67, weight = 70, ioh_inj_vol = 5, output="gfr")
calculate_mgfr_2c(dat_schwartz$time, dat_schwartz$iohexol_ug_ml, height = 1.67, weight = 70, ioh_inj_vol = 5, output="gfr_bsa")
calculate_mgfr_2c(dat_schwartz$time, dat_schwartz$iohexol_ug_ml, height = 1.67, weight = 70, ioh_inj_vol = 5, output="fit")
calculate_mgfr_2c(dat_schwartz$time, dat_schwartz$iohexol_ug_ml, height = 1.67, weight = 70, ioh_inj_vol = 5, output="fit", nls_v="base")
calculate_mgfr_2c(dat_schwartz$time, dat_schwartz$iohexol_ug_ml, height = 1.67, weight = 70, ioh_inj_vol = 5, output="fit", nls_v="SI") # two fits

# Plot options: ideally pass the ID information
calculate_mgfr_2c(dat_schwartz$time, dat_schwartz$iohexol_ug_ml, height = 1.67, weight = 70, ioh_inj_vol = 5, output="plot", id="Name-IDnumber-Date")
calculate_mgfr_2c(dat_schwartz$time, dat_schwartz$iohexol_ug_ml, height = 1.67, weight = 70, ioh_inj_vol = 5, output="plot")
calculate_mgfr_2c(dat_schwartz$time, dat_schwartz$iohexol_ug_ml, height = 1.67, weight = 70, ioh_inj_vol = 5, output="plot", nls_v = "SI") # fit not found
calculate_mgfr_2c(dat_schwartz$time, dat_schwartz$iohexol_ug_ml, height = 1.67, weight = 70, ioh_inj_vol = 5, output="plot", nls_v = "SI", t_early = 120, t_late = 120)
calculate_mgfr_2c(dat_schwartz$time, dat_schwartz$iohexol_ug_ml, height = 1.67, weight = 70, ioh_inj_vol = 5, output="plot", nls_v = "gslnls")
calculate_mgfr_2c(dat_schwartz$time, dat_schwartz$iohexol_ug_ml, height = 1.67, weight = 70, ioh_inj_vol = 5, output="plot", nls_v = "base")
calculate_mgfr_2c(dat_schwartz$time, dat_schwartz$iohexol_ug_ml, height = 1.67, weight = 70, ioh_inj_vol = 5, output="plot", nls_v = "gslnls", nls_weights = F)
calculate_mgfr_2c(dat_schwartz$time, dat_schwartz$iohexol_ug_ml, height = 1.67, weight = 70, ioh_inj_vol = 5, output="plot", nls_v = "base", nls_weights = F)
calculate_mgfr_2c(dat_schwartz$time, dat_schwartz$iohexol_ug_ml, height = 1.67, weight = 70, ioh_inj_vol = 5, output="plot", legend_cex = 1.5)

# examples with fewer time points
dat_5p <- dat_schwartz[dat_schwartz$time %in% c(10, 20, 30, 120, 300), ]
calculate_mgfr_2c(dat_5p$time, dat_5p$iohexol_ug_ml, height = 1.67, weight = 70, ioh_inj_vol = 5, output="plot")

dat_5p <- dat_schwartz[dat_schwartz$time %in% c(10, 30, 60, 120, 300), ]
calculate_mgfr_2c(dat_5p$time, dat_5p$iohexol_ug_ml, height = 1.67, weight = 70, ioh_inj_vol = 5, output="plot")

dat_7p <- dat_schwartz[dat_schwartz$time %in% c(10, 20, 30, 60, 240, 300, 360), ]
calculate_mgfr_2c(dat_7p$time, dat_7p$iohexol_ug_ml, height = 1.67, weight = 70, ioh_inj_vol = 5, output="plot")

# if early time points not present, defaults to MSP-BM estimation (using `calculate_mgfr_msp` with warning)
dat_ebert
calculate_mgfr_2c(dat_ebert$time, dat_ebert$iohexol, height = 1.68, weight=87, ioh_inj_vol = 5.06)
calculate_mgfr_2c(dat_ebert$time, dat_ebert$iohexol, height = 1.68, weight=87, ioh_inj_vol = 5.06, output="plot", leg)
calculate_mgfr_msp(dat_ebert$time, dat_ebert$iohexol, height = 1.68, weight=87, ioh_inj_vol = 5.06)

JMLuther/tabletools documentation built on April 14, 2025, 3:09 a.m.