5. Extract eGFR slopes
Charlotte Boys
2023-06-16
Last updated: 2023-06-17
Checks: 7 0
Knit directory: eGFRslopes/
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Introduction
Finally, we put together a report containing the eGFR measurements, flagged episodes and eGFR slopes for each patient.
Load necessary libraries:
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ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsLoad support functions:
source("code/extractSlopes.R")Load data:
pred_long <- read.csv("output/pred_long.csv")
longitudinal_data <- read.csv("output/longitudinal_data.csv")
flagged_episodes <- read.csv("output/flagged_episodes.csv")Plot eGFR report by patient
The support function plot_by_patient() allows us to
generate a report plot showing the observed eGFR values, the fitted
slope, and the flagged AKI episodes.
Prepare the data:
predicted_values <- pred_long %>%
dplyr::select(-c("age_at_biopsy", "last_measurement_y",
"low_measurement", "upp_measurement", "endpoint")) %>%
dplyr::mutate(years_from_biopsy = start)
eGFR_values <- longitudinal_data %>%
dplyr::filter(type == "eGFR") %>%
dplyr::left_join(predicted_values, by = c("patient_id", "measurement", "years_from_biopsy"))
flagged_AKI <- flagged_episodes %>%
dplyr::filter(start >= 0, flag == "AKI and in-patient") %>%
dplyr::mutate(flag = "Meets AKI criteria\n& potential\nin-patient stay")View plot for Patient 1:
plot_by_patient(patient_str = 1,
longitudinal_data = eGFR_values,
flagged_episodes = flagged_AKI)
Create a PDF report with all eGFR slopes:
all_patients <- unique(eGFR_values$patient_id)
plots <- lapply(all_patients,
plot_by_patient,
longitudinal_data = eGFR_values,
flagged_episodes = flagged_AKI)
pdf("./output/follow_up_report_by_patient.pdf", width = 6, height = 4)
for (i in 1:length(plots)){
plot(plots[[i]])
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2 Extract table of eGFR slopes
Finally, the support function extract_egfr_table()
allows us to extract a table of the eGFR slope (ml/min/1.73m²/year) and
intercept (ml/min/1.73m²) according to the fitted joint mixed effects
model.
eGFR_slopes <- extract_egfr_table(patient_list = all_patients,
pred_eGFR = eGFR_values)knitr::kable(head(eGFR_slopes))| patient_id | slope | intercept |
|---|---|---|
| 1 | -0.00319569986566873 | 89.7425584667643 |
| 2 | -0.78319044258304 | 58.8697772721615 |
| 3 | -0.523898518827211 | 82.414218816593 |
| 4 | -1.81874084171771 | 66.1457608037737 |
| 5 | -1.00722995549735 | 77.2684360381736 |
| 6 | -0.119481484742004 | 52.342801282488 |
Save eGFR slopes:
write.csv(eGFR_slopes, "output/fitted_eGFR_slopes.csv", row.names = FALSE)
sessionInfo()R version 4.3.0 (2023-04-21)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.4
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
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time zone: Europe/Rome
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0 dplyr_1.1.2
[5] purrr_1.0.1 readr_2.1.4 tidyr_1.3.0 tibble_3.2.1
[9] ggplot2_3.4.2 tidyverse_2.0.0 workflowr_1.7.0
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[41] bslib_0.4.2 later_1.3.1 gtable_0.3.3 glue_1.6.2
[45] Rcpp_1.0.10 highr_0.10 xfun_0.39 tidyselect_1.2.0
[49] rstudioapi_0.14 knitr_1.43 farver_2.1.1 patchwork_1.1.2
[53] htmltools_0.5.5 labeling_0.4.2 rmarkdown_2.22 compiler_4.3.0
[57] getPass_0.2-2