Data set with 2000 blood culture isolates of septic patients

An anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found in septic patients in 4 different hospitals in the Netherlands, between 2001 and 2017. It is true, genuine data. This data.frame can be used to practice AMR analysis. For examples, press F1.

septic_patients

Format

A data.frame with 2,000 observations and 49 variables:

date

date of receipt at the laboratory

hospital_id

ID of the hospital, from A to D

ward_icu

logical to determine if ward is an intensive care unit

ward_clinical

logical to determine if ward is a regular clinical ward

ward_outpatient

logical to determine if ward is an outpatient clinic

age

age of the patient

gender

gender of the patient

patient_id

ID of the patient, first 10 characters of an SHA hash containing irretrievable information

mo

ID of microorganism created with as.mo, see also microorganisms

peni:rifa

40 different antibiotics with class rsi (see as.rsi); these column names occur in antibiotics data set and can be translated with abname

Examples

# ----------- #
# PREPARATION #
# ----------- #

# Save this example data set to an object, so we can edit it:
my_data <- septic_patients

# load the dplyr package to make data science A LOT easier
library(dplyr)

# Add first isolates to our data set:
my_data <- my_data %>%
  mutate(first_isolates = first_isolate(my_data, "date", "patient_id", "mo"))
#> => Found 1,315 first isolates (65.8% of total)

# -------- #
# ANALYSIS #
# -------- #

# 1. Get the amoxicillin resistance percentages (p)
#     and numbers (n) of E. coli, divided by hospital:

my_data %>%
  filter(mo == guess_mo("E. coli"),
         first_isolates == TRUE) %>%
  group_by(hospital_id) %>%
  summarise(n = n_rsi(amox),
            p = portion_IR(amox))
#> Warning: Introducing NA: only 19 results available (minimum set to 30).
#> # A tibble: 4 x 3
#>   hospital_id     n      p
#>   <fct>       <int>  <dbl>
#> 1 A              19 NA    
#> 2 B              65  0.477
#> 3 C              35  0.543
#> 4 D              93  0.495


# 2. Get the amoxicillin/clavulanic acid resistance
#    percentages of E. coli, trend over the years:

my_data %>%
  filter(mo == guess_mo("E. coli"),
         first_isolates == TRUE) %>%
  group_by(year = format(date, "%Y")) %>%
  summarise(n = n_rsi(amcl),
            p = portion_IR(amcl, minimum = 20))
#> Warning: Introducing NA: only 13 results available (minimum set to 20).
#> Warning: Introducing NA: only 14 results available (minimum set to 20).
#> Warning: Introducing NA: only 13 results available (minimum set to 20).
#> Warning: Introducing NA: only 15 results available (minimum set to 20).
#> Warning: Introducing NA: only 16 results available (minimum set to 20).
#> Warning: Introducing NA: only 17 results available (minimum set to 20).
#> Warning: Introducing NA: only 17 results available (minimum set to 20).
#> Warning: Introducing NA: only 18 results available (minimum set to 20).
#> Warning: Introducing NA: only 13 results available (minimum set to 20).
#> Warning: Introducing NA: only 10 results available (minimum set to 20).
#> Warning: Introducing NA: only 13 results available (minimum set to 20).
#> Warning: Introducing NA: only 13 results available (minimum set to 20).
#> # A tibble: 16 x 3
#>    year      n       p
#>    <chr> <int>   <dbl>
#>  1 2002     13 NA     
#>  2 2003     14 NA     
#>  3 2004     13 NA     
#>  4 2005     15 NA     
#>  5 2006     16 NA     
#>  6 2007     17 NA     
#>  7 2008     17 NA     
#>  8 2009     18 NA     
#>  9 2010     13 NA     
#> 10 2011     21  0.0952
#> 11 2012     10 NA     
#> 12 2013     13 NA     
#> 13 2014     20  0.2   
#> 14 2015     13 NA     
#> 15 2016     21  0.190 
#> 16 2017     20  0.4