Nothing
PEDALFAST Data
In pedalfast.data: PEDALFAST Data
# ############################################################################ #
# **IMPORTANT NOTE**
#
# If you are reading this comment in a .Rmd file **DO NOT EDIT THE FILE** The
# vignette was written in the source package under the
# vignette-spinners/datasets.R file. Any changes to this vignette need to be
# made in that file.
#
# Edits made to the vignettes/datasets.Rmd file will not be preserved during the
# build process for the package.
#
# **IMPORTANT NOTE**
# ############################################################################ #
options(qwraps2_markup = "markdown")
knitr::opts_chunk$set(collapse = TRUE)
Introduction
The PEDALFAST (PEDiatric vALidation oF vAriableS in
TBI) project was a prospective cohort study conducted at multiple American
College of Surgeons freestanding level I Pediatric Trauma Centers. The cohort
consists of patients under 18 years of age who were admitted to the intensive
care unit (ICU) with an acute traumatic brain injury (TBI) diagnosis and
Glasgow Coma Scale (GCS) score not exceeding 12 or a neurosurgical procedure
(intracranial pressure [ICP] monitor, external ventricular drain [EVD],
craniotomy, or craniectomy) within the first 24 hours of admission.
This data set was used for several publications:
- @Bennett2017icp
- @Bennett2017codes
- @Bennett2016fss
Funded by NICHD grant number R03HD094912 we retroactively mapped the data
collected by the PEDALFAST project the Federal Interagency Traumatic Brain
Injury Research (FITBIR) data standard. The R data
package
r qwraps2::Rpkg(pedalfast.data)
provides the data submitted to FITBIR as both raw files
and in ready to use R data sets.
The PEDALFAST study data were collected and managed using REDCap electronic
data capture tools hosted at the University of Colorado Denver.
[@harris2009research] REDCap (Research Electronic Data Capture) is a secure,
web-based application designed to support data capture for research studies,
providing 1) an intuitive interface for validated data entry; 2) audit trails
for tracking data manipulation and export procedures; 3) automated export
procedures for seamless data downloads to common statistical packages; and 4)
procedures for importing data from external sources.
This vignette documents the provided data set and other utilities of this
package.
Provided Data Sets
The
r qwraps2::Rpkg(pedalfast.data)
package provides the following data objects:
data(package = "pedalfast.data")$results[, c("Item", "Title")]
Each of these objects will be described in detail in the following sections.
The provided data sets are data.frames. Examples for working with the
provided data sets will be done using base R, the
tidyverse, and
data.table. Click the
following buttons to have the different data paradigms displayed or not while
reading this vignette.
PEDALFAST Data
The data collected during the PEDALFAST study has been provided in two
data.frames so the end user may opt into using another paradigm such as
r qwraps2::CRANpkg(data.table)
or the tidyverse. The following will focus on
use of base R methods only.
Reproduction of the examples in this vignette will require the following
namespaces.
library(pedalfast.data)
Load the provided data sets into the active session via data as follows.
data(pedalfast, package = "pedalfast.data")
data(pedalfast_metadata, package = "pedalfast.data")
str(pedalfast, max.level = 0)
str(pedalfast_metadata, max.level = 0)
The
r qwraps2::backtick(pedalfast)
is a data frame with each row reporting the collected data for one
subject, and each column being a unique variable. The
r qwraps2::backtick(pedalfast_metadata)
data frame is a selection of columns from the data dictionary provided by a
REDCap export of the project. In the following you will find examples of
specific utilities provided in this package to make formatting the data
easier.
Let's look at the first three columns of pedalfast, and the first three rows
of pedalfast_metadata.
head(pedalfast[, 1:3])
pedalfast_metadata[1:3, ]
The first column of
r qwraps2::backtick(pedalfast)
is the studyid, and the first row of
r qwraps2::backtick(pedalfast_metadata)
is the documentation for the studyid. Similarly, the
second column of
r qwraps2::backtick(pedalfast)
and second row of
r qwraps2::backtick(pedalfast_metadata)
are for the age of the patient. The first notable change in is in the third row of the
r qwraps2::backtick(pedalfast_metadata)
where the indicator for
r pedalfast_metadata[3, "variable"]
is documented including the mapping from integer to English:
r pedalfast_metadata[3, "values"]
The rest of this section of the vignette provides details on each of the
variables in the data set and provides some examples for data use.
Study ID
The PEDALFAST data was collected at multiple sites. The study id provided is
a patient specific random number between 100 and 999 with no mapping to the
sites. That is, you should not be able to determine which site provided a
specific row of data.
knitr::kable(subset(pedalfast_metadata, variable == "studyid"))
str(pedalfast$studyid)
Age
Age of the patient is reported in days.
knitr::kable(subset(pedalfast_metadata, variable == "age"))
summary(pedalfast$age) # in days
summary(pedalfast$age / 365.25) # in years
The PEDALFAST data has been submitted to the Federal Interagency Traumatic
Brain Injury Research (FITBIR) Informatics System.
As part of that submission age of the patient was to be reported as the floor
of the patients age in years with the exception of those under one year of
age. For those under one year of age the reported value was to be the
truncated three decimal age in years. For example, a patient more than one
month but less than two months would have a reported age of 0.083 (1/12), a 8
month old would have a reported age of 0.666 (8/12). Note the truncation of
the decimal. If you require the same rounding scheme we have provided a
function in this package
r qwraps2::backtick(round_age)
to provide the rounding with the truncation. The function will return age as
a character by default, a numeric value will be returned when specified.
fitbir_ages <-
data.frame(age = pedalfast$age / 365.25,
char = round_age(pedalfast$age / 365.25),
num = round_age(pedalfast$age / 365.25, type = "numeric"))
plot(fitbir_ages$age, fitbir_ages$num, xlab = "Age (years)", ylab = "FITBIR Age (Years)")
Female/Male
The variable female is an indicator for sex/gender. The category of
female/male was made by the attending physicians or reported by the
patient/caregivers. This variable was not determined by sex chromosomes
genotyping. The intent was to report sex but gender, the social constructed
identify of sex, might be more appropriate.
knitr::kable(subset(pedalfast_metadata, variable == "female"))
sum(pedalfast$female)
mean(pedalfast$female)
Injury
Three variables related to injury. The source of information for the injury
and the injury mechanism (injurymech) are both categorical variables with
known values and are presented as character vectors in the pedalfast
data.frame. The time from injury to admission (injurytoadmit) is reported in
days, if the date of injury was known.
inj_vars <- c("sourceinj", "injurytoadmit", "injurymech")
knitr::kable(subset(pedalfast_metadata, variable %in% inj_vars))
summary(pedalfast[, inj_vars])
The injurymech is a character vector by default so the end user may build a
factor as needed.
table(pedalfast$injurymech, useNA = "always")
Emergency Department
Several variables were collected in both the emergency department (ED) and
the intensive care unit (ICU). The following are the notes for the variables
collected in the ED.
GCS
The Glasgow Coma Score was assessed in one or both of the Emergency
Department (ED) and the ICU. There are several variables noted here for GCS
with the suffix 'ed' which are also reported later from the ICU with the
suffix 'icu'.
knitr::kable(subset(pedalfast_metadata, grepl("^gcs.*ed$", variable)))
summary(pedalfast[, grep("^gcs.*ed$", names(pedalfast))])
GCS for the eye, verbal, and motor can be used as both numeric
values (as reported in the pedalfast data.frame) or as a categorical
variable. The
r qwraps2::Rpkg(pedalfast.data)
package provides functions for quickly mapping from the numeric values to a
factor for gcs. The functions
r qwraps2::backtick(gcs_as_integer)
and
r qwraps2::backtick(gcs_as_factor)
While GCS is a common assessment, the specific language used may vary. By
providing these functions we are able to report the exact language used on
the assessment.
Lower numeric values of GCS correspond to lower neurological functioning.
To illustrate this consider, mapping the integer values 1 through 6 to the
labels for the GCS scales:
knitr::kable(
data.frame(integers = 1:6,
eye = gcs_as_factor(1:6, scale = "eye"),
motor = gcs_as_factor(1:6, scale = "motor"),
verbal = gcs_as_factor(1:6, scale = "verbal"))
)
By default, the mapping of the integer values to factor levels will map the
the integer value of 1 to level 1. The argument
r qwraps2::backtick(highest_first)
will reverse the order of the levels. This option has been provided to help
make setting a logical reference level for modeling. For example, say we
want to estimate hospital length of stay by the motor GCS score.
gcs_example_data <-
data.frame(los = pedalfast$hosplos,
motor_int = pedalfast$gcsmotored,
motor_f1 = gcs_as_factor(pedalfast$gcseyeed, scale = "eye"),
motor_f2 = gcs_as_factor(pedalfast$gcseyeed, scale = "eye", highest_first = TRUE))
head(gcs_example_data)
Just looking at the summary of the example data set shows the order of the
factor is different
summary(gcs_example_data)
Thus, simple regression models will use either "no response" or "spontaneous"
as the reference level. Pick the one you want to use.
summary(lm(los ~ motor_int, data = gcs_example_data))$coef
summary(lm(los ~ motor_f1, data = gcs_example_data))$coef
summary(lm(los ~ motor_f2, data = gcs_example_data))$coef
The total GCS score is provided.
summary(pedalfast$gcsed)
Be careful with factors. Recall that factors are numeric vectors with the
first level mapped to the value one, regardless if that is logical or not.
Thus:
identical(pedalfast$gcsed,
pedalfast$gcseyeed + pedalfast$gcsmotored + pedalfast$gcsverbaled)
identical(pedalfast$gcsed,
as.integer(gcs_as_factor(pedalfast$gcseyeed, "eye")) +
as.integer(gcs_as_factor(pedalfast$gcsmotored, "motor")) +
as.integer(gcs_as_factor(pedalfast$gcsverbaled, "verbal")))
identical(pedalfast$gcsed,
as.integer(gcs_as_factor(pedalfast$gcseyeed, "eye", highest_first = TRUE)) +
as.integer(gcs_as_factor(pedalfast$gcsmotored, "motor", highest_first = TRUE)) +
as.integer(gcs_as_factor(pedalfast$gcsverbaled, "verbal", highest_first = TRUE)))
Disposition
Disposition form the emergency department:
knitr::kable(subset(pedalfast_metadata, variable == "eddisposition"))
table(pedalfast$eddisposition, useNA = "always")
Imaging
If the patient had CT imaging the information is provided in one of the
variables prefixed by "ct" with the exception of the time from admission to
ct.
knitr::kable(subset(pedalfast_metadata, grepl("^(admitto)*ct", variable)))
head(pedalfast[, grepl("^(admitto)*ct", names(pedalfast))])
summary(pedalfast[, grepl("^(admitto)*ct", names(pedalfast))])
ICU
There are several variables groups from the ICU.
Source of information
knitr::kable(subset(pedalfast_metadata, variable == "sourceicu"))
table(pedalfast$sourceicu, useNA = "always")
Pupil Reactivity
knitr::kable(subset(pedalfast_metadata, variable == "puplrcticu"))
table(pedalfast$puplrcticu, useNA = "always")
GCS
GCS in the ICU are similar variables as where noted in the emergency
department. Variable names are appended by "icu" for the values attended in
the ICU
knitr::kable(subset(pedalfast_metadata, grepl("^gcs.*icu$", variable)))
summary(pedalfast[, grepl("^gcs.*icu$", names(pedalfast))])
Discharge and Readmission to the ICU
The variable admitttoicudc1 is the number of days from admission to discharge
from the ICU. If the patient had a readmission to the ICU then the first
readmission (second overall admission) occurred admittoicuadmit2 days from
admission. The duration of the first readmission (second overall admission)
would be the difference between admitttoicudc2 and admittoicuadmit2.
knitr::kable(subset(pedalfast_metadata, grepl("^admittoicu", variable)))
summary(pedalfast[, grepl("^admittoicu", names(pedalfast))])
Mechanical Ventilation
knitr::kable(subset(pedalfast_metadata, variable %in% c("ventyn", "admittoint", "admittoext")))
summary(pedalfast[, c("ventyn", "admittoint", "admittoext")])
Intracranial pressure (ICP) monitors
knitr::kable(subset(pedalfast_metadata, grepl("^(admitto)*icp.+\\d", variable)))
summary(pedalfast[pedalfast$icpyn1 == 1, grepl("^(admitto)*icp.+\\d", names(pedalfast))])
ICP monitor types:
table(pedalfast$icptype1)
Invasive Vascular Catheters
knitr::kable(subset(pedalfast_metadata, grepl("^(admitto)*cath.+\\d", variable)))
First catheter
summary(
subset(pedalfast,
!is.na(cathtype1),
select = grep("cath.*1$", names(pedalfast)))
)
table(pedalfast$cathtype1)
Second catheter
summary(
subset(pedalfast,
!is.na(cathtype2),
select = grep("cath.*2$", names(pedalfast)))
)
table(pedalfast$cathtype2)
Third catheter
summary(
subset(pedalfast,
!is.na(cathtype3),
select = grep("cath.*3$", names(pedalfast)))
)
table(pedalfast$cathtype3)
Fourth catheter
summary(
subset(pedalfast,
!is.na(cathtype4),
select = grep("cath.*4$", names(pedalfast)))
)
table(pedalfast$cathtype4)
Tracheostomy
knitr::kable(subset(pedalfast_metadata, variable %in% c("newtrachyn", "admittotrach")))
summary(pedalfast[pedalfast$newtrachyn == 1, c("newtrachyn", "admittotrach")])
Gastrostomy
knitr::kable(subset(pedalfast_metadata, variable %in% c("newgastyn", "admittogast")))
summary(pedalfast[pedalfast$newgastyn == 1, c("newgastyn", "admittogast")])
Decompressive craniectomy
knitr::kable(subset(pedalfast_metadata, variable %in% c("decomcranyn", "admittocrani")))
summary(pedalfast[pedalfast$decomcranyn == 1, c("decomcranyn", "admittocrani")])
Lumbar Drain
knitr::kable(subset(pedalfast_metadata, variable %in% c("lmbrdrainyn", "admittolmbdrain")))
summary(pedalfast[pedalfast$lmbrdrainyn == 1, c("lmbrdrainyn", "admittolmbdrain")])
Epidural Hematoma Evacuated
knitr::kable(subset(pedalfast_metadata, variable %in% c("epihemyn", "admittoedhevac")))
summary(pedalfast[pedalfast$epihemyn == 1, c("epihemyn", "admittoedhevac")])
Subdural Hematoma Evacuated
knitr::kable(subset(pedalfast_metadata, variable %in% c("subhemyn", "admittosdhevac")))
summary(pedalfast[pedalfast$subhemyn == 1, c("subhemyn", "admittosdhevac")])
Medications
knitr::kable(subset(pedalfast_metadata, grepl("^rx", variable)))
summary(pedalfast[, grepl("^rx", names(pedalfast))])
TPN
knitr::kable(subset(pedalfast_metadata, variable %in% c("tpnyn", "admittotpn")))
summary(pedalfast[pedalfast$tpnyn == 1, c("tpnyn", "admittotpn")])
Enteral Nutrition
knitr::kable(subset(pedalfast_metadata, variable %in% c("entnutyn", "admittoentnut")))
summary(pedalfast[pedalfast$entnutyn == 1, c("entnutyn", "admittoentnut")])
Hospital LOS and Disposition
knitr::kable(subset(pedalfast_metadata, variable %in% c("hosplos", "hospdisposition")))
summary(pedalfast[, c("hosplos", "hospdisposition")])
table(pedalfast$hospdisposition, useNA = "always")
Cardiac Arrest
Indicator and location for cardiac arrest.
knitr::kable(subset(pedalfast_metadata, grepl("^cardiac", variable)))
summary(pedalfast[, grepl("^cardiac", names(pedalfast))])
Functional Status Scale
knitr::kable(subset(pedalfast_metadata, grepl("fss", variable)))
summary(pedalfast[, grepl("fss", names(pedalfast))])
Mapping PEDALFAST to FITBIR
The PEDALFAST data sets have been organized, submitted, and
published via the Federal Interagency Traumatic Brain Injury
Research (FITBIR) Informatics System.
Funding
The mapping of PEDALFAST data to FITBIR and the construction and release of
this R package was funded in part by NIH grant R03HD094912.
The PEDALFAST project was funded in part by NICHD grant number K23HD074620.
References
Try the pedalfast.data package in your browser
Any scripts or data that you put into this service are public.
pedalfast.data documentation built on Nov. 11, 2023, 1:07 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# ############################################################################ # # **IMPORTANT NOTE** # # If you are reading this comment in a .Rmd file **DO NOT EDIT THE FILE** The # vignette was written in the source package under the # vignette-spinners/datasets.R file. Any changes to this vignette need to be # made in that file. # # Edits made to the vignettes/datasets.Rmd file will not be preserved during the # build process for the package. # # **IMPORTANT NOTE** # ############################################################################ #
options(qwraps2_markup = "markdown") knitr::opts_chunk$set(collapse = TRUE)
Introduction
The PEDALFAST (PEDiatric vALidation oF vAriableS in TBI) project was a prospective cohort study conducted at multiple American College of Surgeons freestanding level I Pediatric Trauma Centers. The cohort consists of patients under 18 years of age who were admitted to the intensive care unit (ICU) with an acute traumatic brain injury (TBI) diagnosis and Glasgow Coma Scale (GCS) score not exceeding 12 or a neurosurgical procedure (intracranial pressure [ICP] monitor, external ventricular drain [EVD], craniotomy, or craniectomy) within the first 24 hours of admission.
This data set was used for several publications:
- @Bennett2017icp
- @Bennett2017codes
- @Bennett2016fss
Funded by NICHD grant number R03HD094912 we retroactively mapped the data
collected by the PEDALFAST project the Federal Interagency Traumatic Brain
Injury Research (FITBIR) data standard. The R data
package
r qwraps2::Rpkg(pedalfast.data)
provides the data submitted to FITBIR as both raw files
and in ready to use R data sets.
The PEDALFAST study data were collected and managed using REDCap electronic data capture tools hosted at the University of Colorado Denver. [@harris2009research] REDCap (Research Electronic Data Capture) is a secure, web-based application designed to support data capture for research studies, providing 1) an intuitive interface for validated data entry; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for importing data from external sources.
This vignette documents the provided data set and other utilities of this package.
Provided Data Sets
The
r qwraps2::Rpkg(pedalfast.data)
package provides the following data objects:
data(package = "pedalfast.data")$results[, c("Item", "Title")]
Each of these objects will be described in detail in the following sections.
The provided data sets are data.frames. Examples for working with the provided data sets will be done using base R, the tidyverse, and data.table. Click the following buttons to have the different data paradigms displayed or not while reading this vignette.
PEDALFAST Data
The data collected during the PEDALFAST study has been provided in two
data.frames so the end user may opt into using another paradigm such as
r qwraps2::CRANpkg(data.table)
or the tidyverse. The following will focus on
use of base R methods only.
Reproduction of the examples in this vignette will require the following namespaces.
library(pedalfast.data)
Load the provided data sets into the active session via data as follows.
data(pedalfast, package = "pedalfast.data") data(pedalfast_metadata, package = "pedalfast.data") str(pedalfast, max.level = 0) str(pedalfast_metadata, max.level = 0)
The
r qwraps2::backtick(pedalfast)
is a data frame with each row reporting the collected data for one
subject, and each column being a unique variable. The
r qwraps2::backtick(pedalfast_metadata)
data frame is a selection of columns from the data dictionary provided by a
REDCap export of the project. In the following you will find examples of
specific utilities provided in this package to make formatting the data
easier.
Let's look at the first three columns of pedalfast, and the first three rows of pedalfast_metadata.
head(pedalfast[, 1:3]) pedalfast_metadata[1:3, ]
The first column of
r qwraps2::backtick(pedalfast)
is the studyid, and the first row of
r qwraps2::backtick(pedalfast_metadata)
is the documentation for the studyid. Similarly, the
second column of
r qwraps2::backtick(pedalfast)
and second row of
r qwraps2::backtick(pedalfast_metadata)
are for the age of the patient. The first notable change in is in the third row of the
r qwraps2::backtick(pedalfast_metadata)
where the indicator for
r pedalfast_metadata[3, "variable"]
is documented including the mapping from integer to English:
r pedalfast_metadata[3, "values"]
The rest of this section of the vignette provides details on each of the variables in the data set and provides some examples for data use.
Study ID
The PEDALFAST data was collected at multiple sites. The study id provided is a patient specific random number between 100 and 999 with no mapping to the sites. That is, you should not be able to determine which site provided a specific row of data.
knitr::kable(subset(pedalfast_metadata, variable == "studyid")) str(pedalfast$studyid)
Age
Age of the patient is reported in days.
knitr::kable(subset(pedalfast_metadata, variable == "age")) summary(pedalfast$age) # in days summary(pedalfast$age / 365.25) # in years
The PEDALFAST data has been submitted to the Federal Interagency Traumatic
Brain Injury Research (FITBIR) Informatics System.
As part of that submission age of the patient was to be reported as the floor
of the patients age in years with the exception of those under one year of
age. For those under one year of age the reported value was to be the
truncated three decimal age in years. For example, a patient more than one
month but less than two months would have a reported age of 0.083 (1/12), a 8
month old would have a reported age of 0.666 (8/12). Note the truncation of
the decimal. If you require the same rounding scheme we have provided a
function in this package
r qwraps2::backtick(round_age)
to provide the rounding with the truncation. The function will return age as
a character by default, a numeric value will be returned when specified.
fitbir_ages <- data.frame(age = pedalfast$age / 365.25, char = round_age(pedalfast$age / 365.25), num = round_age(pedalfast$age / 365.25, type = "numeric")) plot(fitbir_ages$age, fitbir_ages$num, xlab = "Age (years)", ylab = "FITBIR Age (Years)")
Female/Male
The variable female is an indicator for sex/gender. The category of female/male was made by the attending physicians or reported by the patient/caregivers. This variable was not determined by sex chromosomes genotyping. The intent was to report sex but gender, the social constructed identify of sex, might be more appropriate.
knitr::kable(subset(pedalfast_metadata, variable == "female")) sum(pedalfast$female) mean(pedalfast$female)
Injury
Three variables related to injury. The source of information for the injury and the injury mechanism (injurymech) are both categorical variables with known values and are presented as character vectors in the pedalfast data.frame. The time from injury to admission (injurytoadmit) is reported in days, if the date of injury was known.
inj_vars <- c("sourceinj", "injurytoadmit", "injurymech") knitr::kable(subset(pedalfast_metadata, variable %in% inj_vars)) summary(pedalfast[, inj_vars])
The injurymech is a character vector by default so the end user may build a factor as needed.
table(pedalfast$injurymech, useNA = "always")
Emergency Department
Several variables were collected in both the emergency department (ED) and the intensive care unit (ICU). The following are the notes for the variables collected in the ED.
GCS
The Glasgow Coma Score was assessed in one or both of the Emergency Department (ED) and the ICU. There are several variables noted here for GCS with the suffix 'ed' which are also reported later from the ICU with the suffix 'icu'.
knitr::kable(subset(pedalfast_metadata, grepl("^gcs.*ed$", variable))) summary(pedalfast[, grep("^gcs.*ed$", names(pedalfast))])
GCS for the eye, verbal, and motor can be used as both numeric
values (as reported in the pedalfast data.frame) or as a categorical
variable. The
r qwraps2::Rpkg(pedalfast.data)
package provides functions for quickly mapping from the numeric values to a
factor for gcs. The functions
r qwraps2::backtick(gcs_as_integer)
and
r qwraps2::backtick(gcs_as_factor)
While GCS is a common assessment, the specific language used may vary. By providing these functions we are able to report the exact language used on the assessment.
Lower numeric values of GCS correspond to lower neurological functioning. To illustrate this consider, mapping the integer values 1 through 6 to the labels for the GCS scales:
knitr::kable( data.frame(integers = 1:6, eye = gcs_as_factor(1:6, scale = "eye"), motor = gcs_as_factor(1:6, scale = "motor"), verbal = gcs_as_factor(1:6, scale = "verbal")) )
By default, the mapping of the integer values to factor levels will map the
the integer value of 1 to level 1. The argument
r qwraps2::backtick(highest_first)
will reverse the order of the levels. This option has been provided to help
make setting a logical reference level for modeling. For example, say we
want to estimate hospital length of stay by the motor GCS score.
gcs_example_data <- data.frame(los = pedalfast$hosplos, motor_int = pedalfast$gcsmotored, motor_f1 = gcs_as_factor(pedalfast$gcseyeed, scale = "eye"), motor_f2 = gcs_as_factor(pedalfast$gcseyeed, scale = "eye", highest_first = TRUE)) head(gcs_example_data)
Just looking at the summary of the example data set shows the order of the factor is different
summary(gcs_example_data)
Thus, simple regression models will use either "no response" or "spontaneous" as the reference level. Pick the one you want to use.
summary(lm(los ~ motor_int, data = gcs_example_data))$coef summary(lm(los ~ motor_f1, data = gcs_example_data))$coef summary(lm(los ~ motor_f2, data = gcs_example_data))$coef
The total GCS score is provided.
summary(pedalfast$gcsed)
Be careful with factors. Recall that factors are numeric vectors with the first level mapped to the value one, regardless if that is logical or not. Thus:
identical(pedalfast$gcsed, pedalfast$gcseyeed + pedalfast$gcsmotored + pedalfast$gcsverbaled) identical(pedalfast$gcsed, as.integer(gcs_as_factor(pedalfast$gcseyeed, "eye")) + as.integer(gcs_as_factor(pedalfast$gcsmotored, "motor")) + as.integer(gcs_as_factor(pedalfast$gcsverbaled, "verbal"))) identical(pedalfast$gcsed, as.integer(gcs_as_factor(pedalfast$gcseyeed, "eye", highest_first = TRUE)) + as.integer(gcs_as_factor(pedalfast$gcsmotored, "motor", highest_first = TRUE)) + as.integer(gcs_as_factor(pedalfast$gcsverbaled, "verbal", highest_first = TRUE)))
Disposition
Disposition form the emergency department:
knitr::kable(subset(pedalfast_metadata, variable == "eddisposition")) table(pedalfast$eddisposition, useNA = "always")
Imaging
If the patient had CT imaging the information is provided in one of the variables prefixed by "ct" with the exception of the time from admission to ct.
knitr::kable(subset(pedalfast_metadata, grepl("^(admitto)*ct", variable))) head(pedalfast[, grepl("^(admitto)*ct", names(pedalfast))]) summary(pedalfast[, grepl("^(admitto)*ct", names(pedalfast))])
ICU
There are several variables groups from the ICU.
Source of information
knitr::kable(subset(pedalfast_metadata, variable == "sourceicu")) table(pedalfast$sourceicu, useNA = "always")
Pupil Reactivity
knitr::kable(subset(pedalfast_metadata, variable == "puplrcticu")) table(pedalfast$puplrcticu, useNA = "always")
GCS
GCS in the ICU are similar variables as where noted in the emergency department. Variable names are appended by "icu" for the values attended in the ICU
knitr::kable(subset(pedalfast_metadata, grepl("^gcs.*icu$", variable))) summary(pedalfast[, grepl("^gcs.*icu$", names(pedalfast))])
Discharge and Readmission to the ICU
The variable admitttoicudc1 is the number of days from admission to discharge from the ICU. If the patient had a readmission to the ICU then the first readmission (second overall admission) occurred admittoicuadmit2 days from admission. The duration of the first readmission (second overall admission) would be the difference between admitttoicudc2 and admittoicuadmit2.
knitr::kable(subset(pedalfast_metadata, grepl("^admittoicu", variable))) summary(pedalfast[, grepl("^admittoicu", names(pedalfast))])
Mechanical Ventilation
knitr::kable(subset(pedalfast_metadata, variable %in% c("ventyn", "admittoint", "admittoext"))) summary(pedalfast[, c("ventyn", "admittoint", "admittoext")])
Intracranial pressure (ICP) monitors
knitr::kable(subset(pedalfast_metadata, grepl("^(admitto)*icp.+\\d", variable))) summary(pedalfast[pedalfast$icpyn1 == 1, grepl("^(admitto)*icp.+\\d", names(pedalfast))])
ICP monitor types:
table(pedalfast$icptype1)
Invasive Vascular Catheters
knitr::kable(subset(pedalfast_metadata, grepl("^(admitto)*cath.+\\d", variable)))
First catheter
summary( subset(pedalfast, !is.na(cathtype1), select = grep("cath.*1$", names(pedalfast))) ) table(pedalfast$cathtype1)
Second catheter
summary( subset(pedalfast, !is.na(cathtype2), select = grep("cath.*2$", names(pedalfast))) ) table(pedalfast$cathtype2)
Third catheter
summary( subset(pedalfast, !is.na(cathtype3), select = grep("cath.*3$", names(pedalfast))) ) table(pedalfast$cathtype3)
Fourth catheter
summary( subset(pedalfast, !is.na(cathtype4), select = grep("cath.*4$", names(pedalfast))) ) table(pedalfast$cathtype4)
Tracheostomy
knitr::kable(subset(pedalfast_metadata, variable %in% c("newtrachyn", "admittotrach"))) summary(pedalfast[pedalfast$newtrachyn == 1, c("newtrachyn", "admittotrach")])
Gastrostomy
knitr::kable(subset(pedalfast_metadata, variable %in% c("newgastyn", "admittogast"))) summary(pedalfast[pedalfast$newgastyn == 1, c("newgastyn", "admittogast")])
Decompressive craniectomy
knitr::kable(subset(pedalfast_metadata, variable %in% c("decomcranyn", "admittocrani"))) summary(pedalfast[pedalfast$decomcranyn == 1, c("decomcranyn", "admittocrani")])
Lumbar Drain
knitr::kable(subset(pedalfast_metadata, variable %in% c("lmbrdrainyn", "admittolmbdrain"))) summary(pedalfast[pedalfast$lmbrdrainyn == 1, c("lmbrdrainyn", "admittolmbdrain")])
Epidural Hematoma Evacuated
knitr::kable(subset(pedalfast_metadata, variable %in% c("epihemyn", "admittoedhevac"))) summary(pedalfast[pedalfast$epihemyn == 1, c("epihemyn", "admittoedhevac")])
Subdural Hematoma Evacuated
knitr::kable(subset(pedalfast_metadata, variable %in% c("subhemyn", "admittosdhevac"))) summary(pedalfast[pedalfast$subhemyn == 1, c("subhemyn", "admittosdhevac")])
Medications
knitr::kable(subset(pedalfast_metadata, grepl("^rx", variable))) summary(pedalfast[, grepl("^rx", names(pedalfast))])
TPN
knitr::kable(subset(pedalfast_metadata, variable %in% c("tpnyn", "admittotpn"))) summary(pedalfast[pedalfast$tpnyn == 1, c("tpnyn", "admittotpn")])
Enteral Nutrition
knitr::kable(subset(pedalfast_metadata, variable %in% c("entnutyn", "admittoentnut"))) summary(pedalfast[pedalfast$entnutyn == 1, c("entnutyn", "admittoentnut")])
Hospital LOS and Disposition
knitr::kable(subset(pedalfast_metadata, variable %in% c("hosplos", "hospdisposition"))) summary(pedalfast[, c("hosplos", "hospdisposition")]) table(pedalfast$hospdisposition, useNA = "always")
Cardiac Arrest
Indicator and location for cardiac arrest.
knitr::kable(subset(pedalfast_metadata, grepl("^cardiac", variable))) summary(pedalfast[, grepl("^cardiac", names(pedalfast))])
Functional Status Scale
knitr::kable(subset(pedalfast_metadata, grepl("fss", variable))) summary(pedalfast[, grepl("fss", names(pedalfast))])
Mapping PEDALFAST to FITBIR
The PEDALFAST data sets have been organized, submitted, and published via the Federal Interagency Traumatic Brain Injury Research (FITBIR) Informatics System.
Funding
The mapping of PEDALFAST data to FITBIR and the construction and release of this R package was funded in part by NIH grant R03HD094912.
The PEDALFAST project was funded in part by NICHD grant number K23HD074620.
References
Try the pedalfast.data package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.