1 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:

  • Bennett, DeWitt, Greene, et al. (2017)
  • Bennett, DeWitt, Dixon, et al. (2017)
  • Bennett et al. (2016)

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 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. (Harris et al. 2009) 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.

2 Provided Data Sets

The pedalfast.data package provides the following data objects:

data(package = "pedalfast.data")$results[, c("Item", "Title")]
##      Item                 Title               
## [1,] "pedalfast"          "PEDALFAST Data"    
## [2,] "pedalfast_metadata" "PEDALFAST Metadata"

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.


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 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.


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)
## 'data.frame':    388 obs. of  103 variables:
str(pedalfast_metadata, max.level = 0)
## 'data.frame':    103 obs. of  3 variables:

The pedalfast is a data frame with each row reporting the collected data for one subject, and each column being a unique variable. The 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])
##   studyid  age female
## 1     102 1179      0
## 2     103   90      0
## 3     110 1164      1
## 4     112 1413      1
## 5     114  233      0
## 6     116 5791      0
pedalfast_metadata[1:3, ]
##   variable                        description         values
## 1  studyid               PEDALFAST Patient ID           <NA>
## 2      age Age, in days, at time of admission           <NA>
## 3   female             Is the patient female? 0, no | 1, yes

The first column of pedalfast is the studyid, and the first row of pedalfast_metadata is the documentation for the studyid. Similarly, the second column of pedalfast and second row of pedalfast_metadata are for the age of the patient. The first notable change in is in the third row of the pedalfast_metadata where the indicator for female is documented including the mapping from integer to English: 0, no | 1, yes

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.

3.1 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"))
variable description values
studyid PEDALFAST Patient ID NA
##  int [1:388] 102 103 110 112 114 116 120 122 123 124 ...

3.2 Age

Age of the patient is reported in days.

knitr::kable(subset(pedalfast_metadata, variable == "age"))
variable description values
2 age Age, in days, at time of admission NA
summary(pedalfast$age)          # in days
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0   679.5  2508.5  2699.3  4635.5  6501.0
summary(pedalfast$age / 365.25) # in years
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   1.860   6.868   7.390  12.691  17.799

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 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)")

3.3 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"))
variable description values
3 female Is the patient female? 0, no | 1, yes
## [1] 149
## [1] 0.3840206

3.4 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))
variable description values
4 sourceinj Source of Injury Information NA
5 injurytoadmit Days from injury, if known, to admission. NA
6 injurymech Injury mechanism 1, traffic | 2, fall | 3, known or suspected abuse | 4, self-harm | 9, other
summary(pedalfast[, inj_vars])
##   sourceinj         injurytoadmit      injurymech       
##  Length:388         Min.   :  0.000   Length:388        
##  Class :character   1st Qu.:  0.000   Class :character  
##  Mode  :character   Median :  0.000   Mode  :character  
##                     Mean   :  1.415                     
##                     3rd Qu.:  0.000                     
##                     Max.   :366.000                     
##                     NA's   :41

The injurymech is a character vector by default so the end user may build a factor as needed.

table(pedalfast$injurymech, useNA = "always")
##                     Fall Known or suspected abuse                    Other 
##                       72                       91                       77 
##                Self-harm                  Traffic                     <NA> 
##                        6                      142                        0

3.5 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.

3.5.1 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$", variable)))
variable description values
7 gcsyned Was a GCS obtained in the ED? 0, no | 1, yes
8 gcseyeed ED GCS Eye 4, spontaneous | 3, to speech | 2, to pain only | 1, no response
9 gcsverbaled ED GCS Verbal 5, oriented, appropriate or coos and babbles | 4, confused or irritable cries | 3, inappropriate words or cries to pain | 2, incomprehensible sounds or moans to pain | 1, no response
10 gcsmotored ED GCS Motor 6, obeys commands | 5, localizes pain or withdraws to touch | 4, withdraws from painful stimuli | 3, abnormal flexion to pain | 2, abnormal extension to pain | 1, no response/flaccid
11 gcsed ED GCS Total [gcseyeed]+[gcsverbaled]+[gcsmotored]
12 gcsetted Was the patient intubated at the time of their ED GCS assessment? 0, no | 1, yes
13 gcsseded Was the patient sedated at the time of their ED GCS assessment? 0, no | 1, yes
14 gcspared Was the patient chemically paralyzed at the time of their ED GCS assessment? 0, no | 1, yes
15 gcseyeobed Were the patient’s eyes obscured by injury, swelling, or bandage at the time of their ED GCS assessment? 0, no | 1, yes
summary(pedalfast[, grep("^gcs.*ed$", names(pedalfast))])
##     gcsyned          gcseyeed      gcsverbaled      gcsmotored   
##  Min.   :0.0000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.0000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000  
##  Median :1.0000   Median :1.000   Median :1.000   Median :4.000  
##  Mean   :0.9835   Mean   :1.677   Mean   :1.595   Mean   :3.326  
##  3rd Qu.:1.0000   3rd Qu.:2.000   3rd Qu.:1.000   3rd Qu.:5.000  
##  Max.   :1.0000   Max.   :4.000   Max.   :5.000   Max.   :6.000  
##  NA's   :24       NA's   :20      NA's   :20      NA's   :20     
##      gcsed           gcsetted         gcsseded         gcspared     
##  Min.   : 3.000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.: 3.000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000  
##  Median : 6.000   Median :1.0000   Median :1.0000   Median :0.0000  
##  Mean   : 6.598   Mean   :0.7371   Mean   :0.6158   Mean   :0.1355  
##  3rd Qu.: 9.000   3rd Qu.:1.0000   3rd Qu.:1.0000   3rd Qu.:0.0000  
##  Max.   :15.000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
##  NA's   :20       NA's   :19       NA's   :21       NA's   :19      
##    gcseyeobed     
##  Min.   :0.00000  
##  1st Qu.:0.00000  
##  Median :0.00000  
##  Mean   :0.04905  
##  3rd Qu.:0.00000  
##  Max.   :1.00000  
##  NA's   :21

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 pedalfast.data package provides functions for quickly mapping from the numeric values to a factor for gcs. The functions gcs_as_integer and 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:

             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"))
integers eye motor verbal
1 No response No response/flaccid No response
2 To pain only Abnormal extension to pain Incomprehensible sounds or moans to pain
3 To speech Abnormal flexion to pain Inappropriate words or cries to pain
4 Spontaneous Withdraws from painful stimuli Confused or irritable cries
5 NA Localizes pain or withdraws to touch Oriented, appropriate or coos and babbles
6 NA Obeys commands NA

By default, the mapping of the integer values to factor levels will map the the integer value of 1 to level 1. The argument 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))

##   los motor_int     motor_f1     motor_f2
## 1  22         4  No response  No response
## 2  24         2  No response  No response
## 3   9         4 To pain only To pain only
## 4   6         5  Spontaneous  Spontaneous
## 5  40         6  No response  No response
## 6  36         5  No response  No response

Just looking at the summary of the example data set shows the order of the factor is different

##       los           motor_int             motor_f1           motor_f2  
##  Min.   :  0.00   Min.   :1.000   No response :262   Spontaneous : 63  
##  1st Qu.:  4.00   1st Qu.:1.000