Last updated: 2023-06-17
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Knit directory: eGFRslopes/
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We flag AKI episodes from serum creatinine measurements.
Load necessary libraries:
library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.2 ✔ readr 2.1.4
✔ forcats 1.0.0 ✔ stringr 1.5.0
✔ ggplot2 3.4.2 ✔ tibble 3.2.1
✔ lubridate 1.9.2 ✔ tidyr 1.3.0
✔ purrr 1.0.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(zoo)
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numericLoad support functions:
source("code/flagAKI.R")Read in the synthetic longitudinal data:
longitudinal_data <- read.csv("data/simulated_longitudinal_data.csv")For each SCr measurement, we test if one of the following criteria are met [@hapca2021relationship]:
SCr \(\geq\) 1.5$ times higher than median of all creatinine measures in the 8-365 days before index (day the measurement was taken)
SCr \(\geq\) 1.5 times higher than the lowest creatinine in the 7 days before or post-index
SCr \(>\) 26 umol/L higher than the lowest creatinine in the 2 days before or post-index
SCr \(\geq\) 1.5 times higher than median of all creatinine measures in the 8-365 days post-index
The functions in which these tests are implemented can be found in
the script code/flagAKI.R.
scr_values <- testAKIcriteria(longitudinal_data)Testing criterion 1...Testing criterion 2...Testing criterion 3...Testing criterion 4...Done!knitr::kable(head(scr_values))| patient_id | years_from_biopsy | measurement | type | units | days_from_biopsy | AKIcrit1 | AKIcrit2 | AKIcrit3 | AKIcrit4 |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.0054757 | 107.02514 | SCr | umol/L | 2 | NA | FALSE | FALSE | FALSE |
| 1 | 0.0191650 | 107.14072 | SCr | umol/L | 7 | NA | FALSE | FALSE | FALSE |
| 1 | 0.0273785 | 101.41421 | SCr | umol/L | 10 | FALSE | FALSE | FALSE | FALSE |
| 1 | 0.0301164 | 106.18664 | SCr | umol/L | 11 | FALSE | FALSE | FALSE | FALSE |
| 1 | 0.7091034 | 95.95474 | SCr | umol/L | 259 | FALSE | FALSE | FALSE | NA |
| 1 | 0.9637235 | 95.74575 | SCr | umol/L | 352 | FALSE | FALSE | FALSE | NA |
We identify “episodes of care”, that is, periods where multiple observations are made in a short space of time. This will allow us to later identify potential hospital admissions or in-patient events.
We define an “episode of care” by linking together any observation
made within seven days of another into one group of observations. To
give a concrete example: observations made on days 1, 8, and 14
post-biopsy belong to one “episode of care” because they all occur
within seven days of one another. If the next observation were made on
day 22 (eight days after day 14), then it would belong in a separate
episode of care. The code below assigns a unique numeric
group_id to each observation by which its episode of care
can be identified.
scr_episodes <- scr_values %>%
dplyr::group_by(patient_id) %>%
dplyr::mutate(lag_day = abs(days_from_biopsy - lag(days_from_biopsy)) <= 7,
lead_day = abs(days_from_biopsy - lead(days_from_biopsy)) <= 7) %>%
dplyr::mutate_at(vars(lag_day, lead_day), ~ ifelse(., ., NA))%>%
dplyr::mutate(episode_of_care = ifelse(is.na(lead_day), NA, data.table::rleid(lead_day))) %>%
dplyr::mutate(episode_of_care = ifelse(!is.na(lag_day), zoo::na.locf(episode_of_care, na.rm = FALSE), episode_of_care)) %>%
dplyr::mutate(group_id = coalesce(
case_when(is.na(episode_of_care) ~ nrow(.) + row_number()), # number of rows in df plus row number ensures unique ID
episode_of_care)) %>%
dplyr::ungroup()Now we have our observations grouped into episodes of care, we use
the above AKI criteria to flag for potential AKI events. Specifically,
an episode of care is given the flag AKI if and only if one
or more observations belonging to the episode meets one or more of the
AKI criteria. We also take note of the number of serum creatinine
observations within each episode, which we will use to flag for
potential in-patient events (hospital admissions). Specifically, if one
episode of care contains five or more observations we give it the
in-patient flag.
summarised_episodes <- scr_episodes %>%
dplyr::group_by(patient_id, group_id) %>%
dplyr::summarise(
AKI = any(AKIcrit1, AKIcrit2, AKIcrit3, AKIcrit4, na.rm = TRUE),
n_obs = n(),
start = min(days_from_biopsy),
end = max(days_from_biopsy))`summarise()` has grouped output by 'patient_id'. You can override using the
`.groups` argument.flagged_episodes <- summarised_episodes %>%
dplyr::filter(AKI | n_obs >= 5) %>%
dplyr::mutate(in_patient = ifelse(n_obs >= 5, TRUE, FALSE)) %>%
dplyr::mutate(flag = ifelse(AKI & in_patient, "AKI and in-patient",
ifelse(AKI, "AKI", "in-patient"))) %>%
dplyr::mutate(flag = as.factor(flag)) %>%
dplyr::select(start, end, patient_id, n_obs, flag, group_id)knitr::kable(head(flagged_episodes))| start | end | patient_id | n_obs | flag | group_id |
|---|---|---|---|---|---|
| 1 | 5 | 2 | 4 | AKI | 1 |
| 2759 | 2759 | 2 | 1 | AKI | 2945 |
| 3893 | 3893 | 2 | 1 | AKI | 2954 |
| 6192 | 6192 | 2 | 1 | AKI | 2980 |
| 6234 | 6234 | 2 | 1 | AKI | 2982 |
| 0 | 13 | 3 | 5 | in-patient | 1 |
Save the flagged episodes, so that if desired we can remove the outlying observations from the data when calculating the long-term eGFR slopes:
write.csv(flagged_episodes, "output/flagged_episodes.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:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Rome
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] zoo_1.8-12 lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[5] dplyr_1.1.2 purrr_1.0.1 readr_2.1.4 tidyr_1.3.0
[9] tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] sass_0.4.6 utf8_1.2.3 generics_0.1.3 lattice_0.21-8
[5] stringi_1.7.12 hms_1.1.3 digest_0.6.31 magrittr_2.0.3
[9] timechange_0.2.0 evaluate_0.21 grid_4.3.0 fastmap_1.1.1
[13] rprojroot_2.0.3 jsonlite_1.8.5 processx_3.8.1 whisker_0.4.1
[17] ps_1.7.5 promises_1.2.0.1 httr_1.4.6 fansi_1.0.4
[21] scales_1.2.1 jquerylib_0.1.4 cli_3.6.1 rlang_1.1.1
[25] munsell_0.5.0 withr_2.5.0 cachem_1.0.8 yaml_2.3.7
[29] tools_4.3.0 tzdb_0.4.0 colorspace_2.1-0 httpuv_1.6.11
[33] vctrs_0.6.2 R6_2.5.1 lifecycle_1.0.3 git2r_0.32.0
[37] fs_1.6.2 pkgconfig_2.0.3 callr_3.7.3 pillar_1.9.0
[41] bslib_0.4.2 later_1.3.1 gtable_0.3.3 data.table_1.14.8
[45] glue_1.6.2 Rcpp_1.0.10 xfun_0.39 tidyselect_1.2.0
[49] rstudioapi_0.14 knitr_1.43 htmltools_0.5.5 rmarkdown_2.22
[53] compiler_4.3.0 getPass_0.2-2