first_isolate: Determine first (weighted) isolates

Description Usage Arguments Details Value Key antibiotics Read more on our website! Source See Also Examples

View source: R/first_isolate.R

Description

Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.

Usage

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
first_isolate(tbl, col_date = NULL, col_patient_id = NULL,
  col_mo = NULL, col_testcode = NULL, col_specimen = NULL,
  col_icu = NULL, col_keyantibiotics = NULL, episode_days = 365,
  testcodes_exclude = NULL, icu_exclude = FALSE,
  specimen_group = NULL, type = "keyantibiotics", ignore_I = TRUE,
  points_threshold = 2, info = TRUE, ...)

filter_first_isolate(tbl, col_date = NULL, col_patient_id = NULL,
  col_mo = NULL, ...)

filter_first_weighted_isolate(tbl, col_date = NULL,
  col_patient_id = NULL, col_mo = NULL, col_keyantibiotics = NULL,
  ...)

Arguments

tbl

a data.frame containing isolates.

col_date

column name of the result date (or date that is was received on the lab), defaults to the first column of with a date class

col_patient_id

column name of the unique IDs of the patients, defaults to the first column that starts with 'patient' or 'patid' (case insensitive)

col_mo

column name of the unique IDs of the microorganisms (see mo), defaults to the first column of class mo. Values will be coerced using as.mo.

col_testcode

column name of the test codes. Use col_testcode = NULL to not exclude certain test codes (like test codes for screening). In that case testcodes_exclude will be ignored.

col_specimen

column name of the specimen type or group

col_icu

column name of the logicals (TRUE/FALSE) whether a ward or department is an Intensive Care Unit (ICU)

col_keyantibiotics

column name of the key antibiotics to determine first weighted isolates, see key_antibiotics. Defaults to the first column that starts with 'key' followed by 'ab' or 'antibiotics' (case insensitive). Use col_keyantibiotics = FALSE to prevent this.

episode_days

episode in days after which a genus/species combination will be determined as 'first isolate' again

testcodes_exclude

character vector with test codes that should be excluded (case-insensitive)

icu_exclude

logical whether ICU isolates should be excluded (rows with value TRUE in column col_icu)

specimen_group

value in column col_specimen to filter on

type

type to determine weighed isolates; can be "keyantibiotics" or "points", see Details

ignore_I

logical to determine whether antibiotic interpretations with "I" will be ignored when type = "keyantibiotics", see Details

points_threshold

points until the comparison of key antibiotics will lead to inclusion of an isolate when type = "points", see Details

info

print progress

...

parameters passed on to the first_isolate function

Details

WHY THIS IS SO IMPORTANT
To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode [1]. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all S. aureus isolates would be overestimated, because you included this MRSA more than once. It would be selection bias.

The function filter_first_isolate is essentially equal to:

1
2
3
4
 tbl %>%
   mutate(only_firsts = first_isolate(tbl, ...)) %>%
   filter(only_firsts == TRUE) %>%
   select(-only_firsts)

The function filter_first_weighted_isolate is essentially equal to:

1
2
3
4
5
6
 tbl %>%
   mutate(keyab = key_antibiotics(.)) %>%
   mutate(only_weighted_firsts = first_isolate(tbl,
                                               col_keyantibiotics = "keyab", ...)) %>%
   filter(only_weighted_firsts == TRUE) %>%
   select(-only_weighted_firsts)

Value

Logical vector

Key antibiotics

There are two ways to determine whether isolates can be included as first weighted isolates which will give generally the same results:

1. Using type = "keyantibiotics" and parameter ignore_I
Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With ignore_I = FALSE, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2.

2. Using type = "points" and parameter points_threshold
A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds points_threshold, an isolate will be (re)selected as a first weighted isolate.

Read more on our website!


On our website https://msberends.gitlab.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.

Source

Methodology of this function is based on: M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition, 2014, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.

See Also

key_antibiotics

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
# septic_patients is a dataset available in the AMR package. It is true, genuine data.
?septic_patients

library(dplyr)
# Filter on first isolates:
septic_patients %>%
  mutate(first_isolate = first_isolate(.,
                                       col_date = "date",
                                       col_patient_id = "patient_id",
                                       col_mo = "mo")) %>%
  filter(first_isolate == TRUE)

# Which can be shortened to:
septic_patients %>%
  filter_first_isolate()
# or for first weighted isolates:
septic_patients %>%
  filter_first_weighted_isolate()

# Now let's see if first isolates matter:
A <- septic_patients %>%
  group_by(hospital_id) %>%
  summarise(count = n_rsi(gent),            # gentamicin availability
            resistance = portion_IR(gent))  # gentamicin resistance

B <- septic_patients %>%
  filter_first_weighted_isolate() %>%       # the 1st isolate filter
  group_by(hospital_id) %>%
  summarise(count = n_rsi(gent),            # gentamicin availability
            resistance = portion_IR(gent))  # gentamicin resistance

# Have a look at A and B.
# B is more reliable because every isolate is only counted once.
# Gentamicin resitance in hospital D appears to be 5.4% higher than
# when you (erroneously) would have used all isolates!


## OTHER EXAMPLES:

## Not run: 

# set key antibiotics to a new variable
tbl$keyab <- key_antibiotics(tbl)

tbl$first_isolate <-
  first_isolate(tbl)

tbl$first_isolate_weighed <-
  first_isolate(tbl,
                col_keyantibiotics = 'keyab')

tbl$first_blood_isolate <-
  first_isolate(tbl,
                specimen_group = 'Blood')

tbl$first_blood_isolate_weighed <-
  first_isolate(tbl,
                specimen_group = 'Blood',
                col_keyantibiotics = 'keyab')

tbl$first_urine_isolate <-
  first_isolate(tbl,
                specimen_group = 'Urine')

tbl$first_urine_isolate_weighed <-
  first_isolate(tbl,
                specimen_group = 'Urine',
                col_keyantibiotics = 'keyab')

tbl$first_resp_isolate <-
  first_isolate(tbl,
                specimen_group = 'Respiratory')

tbl$first_resp_isolate_weighed <-
  first_isolate(tbl,
                specimen_group = 'Respiratory',
                col_keyantibiotics = 'keyab')

## End(Not run)

msberends/AMR documentation built on Feb. 15, 2019, 3:43 a.m.