Summarise patient characteristics

Introduction

In this example we’re going to summarise the characteristics of individuals with an ankle sprain, ankle fracture, forearm fracture, or a hip fracture using the Eunomia synthetic database.

We’ll begin by creating our condition study cohorts with the generateConceptCohortSet function from CDMConnector.

library(omock)
library(CDMConnector)
library(dplyr, warn.conflicts = FALSE)
library(ggplot2)
library(CodelistGenerator)
library(PatientProfiles)
library(CohortCharacteristics)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

cdm <- generateConceptCohortSet(
  cdm = cdm,
  name = "injuries",
  conceptSet = list(
    "ankle_sprain" = 81151,
    "ankle_fracture" = 4059173,
    "forearm_fracture" = 4278672,
    "hip_fracture" = 4230399
  ),
  end = "event_end_date",
  limit = "all"
)
settings(cdm$injuries)
#> # A tibble: 4 × 6
#>   cohort_definition_id cohort_name    limit prior_observation future_observation
#>                  <int> <chr>          <chr>             <dbl>              <dbl>
#> 1                    1 ankle_sprain   all                   0                  0
#> 2                    2 ankle_fracture all                   0                  0
#> 3                    3 forearm_fract… all                   0                  0
#> 4                    4 hip_fracture   all                   0                  0
#> # ℹ 1 more variable: end <chr>
cohortCount(cdm$injuries)
#> # A tibble: 4 × 3
#>   cohort_definition_id number_records number_subjects
#>                  <int>          <int>           <int>
#> 1                    1           1915            1357
#> 2                    2            464             427
#> 3                    3            569             510
#> 4                    4            138             132

Summarising study cohorts

Now we’ve created our cohorts, we can obtain a summary of the characteristics in the patients included in these cohorts. We’ll create two different age group in below example: under 50 and 50+.

chars <- cdm$injuries |>
  summariseCharacteristics(ageGroup = list(c(0, 49), c(50, Inf)))
chars |>
  glimpse()
#> Rows: 220
#> Columns: 13
#> $ result_id        <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ cdm_name         <chr> "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBleed"…
#> $ group_name       <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_…
#> $ group_level      <chr> "ankle_sprain", "ankle_sprain", "ankle_sprain", "ankl…
#> $ strata_name      <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ strata_level     <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ variable_name    <chr> "Number records", "Number subjects", "Cohort start da…
#> $ variable_level   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ estimate_name    <chr> "count", "count", "min", "q25", "median", "q75", "max…
#> $ estimate_type    <chr> "integer", "integer", "date", "date", "date", "date",…
#> $ estimate_value   <chr> "1915", "1357", "1912-02-25", "1968-06-15", "1982-11-…
#> $ additional_name  <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ additional_level <chr> "overall", "overall", "overall", "overall", "overall"…

Now we have generated the results, we can create a nice table in gt format to display the results using tableCharacteristics function. By default it returns summary statistics for, number records, number subjects, age, cohort_start_date, cohort_end_date, prior_observations, future_observations and days in cohort. Days in cohort is defined as the number of days between the cohort start and end dates, inclusive of both boundaries (i.e., cohort_end_date – cohort_start_date + 1). This represents the total duration an individual in the cohort. In contrast, future observation time is defined as the time from the cohort start date to the end of the individual’s observation period (observation_period_end_date – cohort_start_date). Prior observation works similar to future observation but look at observations before cohort_start_date, hence defined as (cohort_start_date - observation_period_start_date). The difference in definitions arises because days in cohort captures a duration within the cohort, while future observation time measures a remaining time window for potential follow-up beyond cohort entry.

tableCharacteristics(chars)
CDM name
GiBleed
Variable name Variable level Estimate name
Cohort name
ankle_sprain ankle_fracture forearm_fracture hip_fracture
Number records – N 1,915 464 569 138
Number subjects – N 1,357 427 510 132
Cohort start date – Median [Q25 - Q75] 1982-11-09 [1968-06-15 - 1999-04-13] 1981-01-15 [1965-03-11 - 1997-08-03] 1981-07-24 [1967-03-05 - 2000-12-16] 1996-09-17 [1977-09-20 - 2010-06-22]
Range 1912-02-25 to 2019-05-30 1911-09-07 to 2019-06-23 1917-08-16 to 2019-06-26 1927-12-14 to 2019-05-08
Cohort end date – Median [Q25 - Q75] 1982-12-10 [1968-07-06 - 1999-05-09] 1981-02-28 [1965-04-11 - 1997-10-12] 1981-08-23 [1967-04-10 - 2001-02-27] 1996-11-16 [1977-12-04 - 2010-07-22]
Range 1912-03-10 to 2019-05-30 1911-12-06 to 2019-06-24 1917-11-14 to 2019-06-26 1928-03-13 to 2019-06-07
Age – Median [Q25 - Q75] 21 [9 - 41] 16 [9 - 43] 17 [9 - 46] 40 [13 - 66]
Mean (SD) 26.63 (21.03) 27.38 (24.70) 28.69 (25.97) 40.06 (28.82)
Range 0 to 105 0 to 107 0 to 106 1 to 108
Age group 0 to 49 N (%) 1,587 (82.87%) 367 (79.09%) 440 (77.33%) 87 (63.04%)
50 or above N (%) 328 (17.13%) 97 (20.91%) 129 (22.67%) 51 (36.96%)
Sex Female N (%) 954 (49.82%) 238 (51.29%) 286 (50.26%) 74 (53.62%)
Male N (%) 961 (50.18%) 226 (48.71%) 283 (49.74%) 64 (46.38%)
Prior observation – Median [Q25 - Q75] 7,833 [3,628 - 15,147] 6,030 [3,360 - 16,032] 6,289 [3,390 - 16,847] 14,522 [4,801 - 24,401]
Mean (SD) 9,918.17 (7,672.74) 10,196.57 (9,011.31) 10,670.43 (9,480.30) 14,821.73 (10,521.89)
Range 299 to 38,429 299 to 39,430 299 to 38,943 390 to 39,792
Future observation – Median [Q25 - Q75] 12,868 [6,860 - 18,078] 13,748 [6,878 - 19,331] 13,165 [5,988 - 18,548] 7,798 [2,874 - 14,913]
Mean (SD) 12,865.11 (7,543.50) 13,470.92 (8,215.96) 12,913.27 (7,929.17) 9,167.33 (7,160.81)
Range 0 to 38,403 1 to 39,051 0 to 36,654 0 to 29,045
Days in cohort – Median [Q25 - Q75] 22 [15 - 29] 61 [31 - 91] 61 [31 - 91] 61 [31 - 91]
Mean (SD) 25.02 (8.00) 61.65 (25.38) 62.16 (25.32) 59.26 (24.79)
Range 1 to 37 2 to 92 1 to 91 1 to 91
Days to next record – Median [Q25 - Q75] 6,082 [2,640 - 10,540] 5,100 [2,430 - 11,474] 5,760 [1,922 - 15,606] 7,430 [2,355 - 15,966]
Mean (SD) 7,457.56 (5,937.15) 7,784.46 (7,686.21) 9,217.83 (8,826.17) 9,037.50 (8,138.51)
Range 300 to 29,145 330 to 28,365 360 to 34,482 690 to 19,200

We can also use the plotCharacteristics function to display the results in a plot. The plotCharacteristics function can only take in one variable. So you will need to filter the results to the variable you want to create a plot for beforehand.

chars |>
  filter(variable_name == "Age") |>
  plotCharacteristics(
    plotType = "boxplot",
    colour = "cohort_name",
    facet = c("cdm_name")
  )

Stratified summaries

We can also generate summaries that are stratified by some variable of interest. In this example we added an age group variable to our cohort table and then created the stratification for age group in our results.

chars <- cdm$injuries |>
  addAge(ageGroup = list(
    c(0, 49),
    c(50, Inf)
  )) |>
  summariseCharacteristics(strata = list("age_group"))

Again we used the tableCharacteristics function to display the results in gt table format.

tableCharacteristics(chars,
  groupColumn = "age_group"
)
CDM name
GiBleed
Variable name Variable level Estimate name
Cohort name
ankle_sprain ankle_fracture forearm_fracture hip_fracture
overall
Number records – N 1,915 464 569 138
Number subjects – N 1,357 427 510 132
Cohort start date – Median [Q25 - Q75] 1982-11-09 [1968-06-15 - 1999-04-13] 1981-01-15 [1965-03-11 - 1997-08-03] 1981-07-24 [1967-03-05 - 2000-12-16] 1996-09-17 [1977-09-20 - 2010-06-22]
Range 1912-02-25 to 2019-05-30 1911-09-07 to 2019-06-23 1917-08-16 to 2019-06-26 1927-12-14 to 2019-05-08
Cohort end date – Median [Q25 - Q75] 1982-12-10 [1968-07-06 - 1999-05-09] 1981-02-28 [1965-04-11 - 1997-10-12] 1981-08-23 [1967-04-10 - 2001-02-27] 1996-11-16 [1977-12-04 - 2010-07-22]
Range 1912-03-10 to 2019-05-30 1911-12-06 to 2019-06-24 1917-11-14 to 2019-06-26 1928-03-13 to 2019-06-07
Age – Median [Q25 - Q75] 21 [9 - 41] 16 [9 - 43] 17 [9 - 46] 40 [13 - 66]
Mean (SD) 26.63 (21.03) 27.38 (24.70) 28.69 (25.97) 40.06 (28.82)
Range 0 to 105 0 to 107 0 to 106 1 to 108
Sex Female N (%) 954 (49.82%) 238 (51.29%) 286 (50.26%) 74 (53.62%)
Male N (%) 961 (50.18%) 226 (48.71%) 283 (49.74%) 64 (46.38%)
Prior observation – Median [Q25 - Q75] 7,833 [3,628 - 15,147] 6,030 [3,360 - 16,032] 6,289 [3,390 - 16,847] 14,522 [4,801 - 24,401]
Mean (SD) 9,918.17 (7,672.74) 10,196.57 (9,011.31) 10,670.43 (9,480.30) 14,821.73 (10,521.89)
Range 299 to 38,429 299 to 39,430 299 to 38,943 390 to 39,792
Future observation – Median [Q25 - Q75] 12,868 [6,860 - 18,078] 13,748 [6,878 - 19,331] 13,165 [5,988 - 18,548] 7,798 [2,874 - 14,913]
Mean (SD) 12,865.11 (7,543.50) 13,470.92 (8,215.96) 12,913.27 (7,929.17) 9,167.33 (7,160.81)
Range 0 to 38,403 1 to 39,051 0 to 36,654 0 to 29,045
Days in cohort – Median [Q25 - Q75] 22 [15 - 29] 61 [31 - 91] 61 [31 - 91] 61 [31 - 91]
Mean (SD) 25.02 (8.00) 61.65 (25.38) 62.16 (25.32) 59.26 (24.79)
Range 1 to 37 2 to 92 1 to 91 1 to 91
Days to next record – Median [Q25 - Q75] 6,082 [2,640 - 10,540] 5,100 [2,430 - 11,474] 5,760 [1,922 - 15,606] 7,430 [2,355 - 15,966]
Mean (SD) 7,457.56 (5,937.15) 7,784.46 (7,686.21) 9,217.83 (8,826.17) 9,037.50 (8,138.51)
Range 300 to 29,145 330 to 28,365 360 to 34,482 690 to 19,200
0 to 49
Number records – N 1,587 367 440 87
Number subjects – N 1,211 341 411 86
Cohort start date – Median [Q25 - Q75] 1978-07-08 [1965-08-07 - 1992-05-07] 1974-08-26 [1960-08-21 - 1988-07-30] 1974-12-23 [1964-05-04 - 1988-03-09] 1983-05-29 [1973-07-30 - 1997-03-20]
Range 1912-02-25 to 2019-05-06 1911-09-07 to 2018-10-12 1917-08-16 to 2019-06-26 1927-12-14 to 2019-01-09
Cohort end date – Median [Q25 - Q75] 1978-08-05 [1965-09-01 - 1992-05-28] 1974-10-25 [1960-10-20 - 1988-10-09] 1975-02-06 [1964-06-11 - 1988-05-07] 1983-08-27 [1973-08-29 - 1997-05-19]
Range 1912-03-10 to 2019-05-06 1911-12-06 to 2018-11-11 1917-11-14 to 2019-06-26 1928-03-13 to 2019-04-09
Age – Median [Q25 - Q75] 16 [7 - 31] 13 [7 - 25] 13 [7 - 23] 15 [9 - 34]
Mean (SD) 19.32 (13.95) 16.49 (12.90) 16.48 (12.87) 21.15 (15.27)
Range 0 to 49 0 to 49 0 to 49 1 to 49
Sex Female N (%) 791 (49.84%) 190 (51.77%) 213 (48.41%) 41 (47.13%)
Male N (%) 796 (50.16%) 177 (48.23%) 227 (51.59%) 46 (52.87%)
Prior observation – Median [Q25 - Q75] 5,970 [2,910 - 11,512] 4,941 [2,640 - 9,266] 4,814 [2,662 - 8,680] 5,838 [3,510 - 12,728]
Mean (SD) 7,249.25 (5,084.37) 6,221.68 (4,697.60) 6,212.80 (4,686.12) 7,920.29 (5,584.42)
Range 299 to 18,243 299 to 18,105 299 to 18,158 390 to 18,086
Future observation – Median [Q25 - Q75] 14,582 [9,510 - 19,018] 15,936 [10,900 - 20,859] 15,833 [11,020 - 19,580] 12,667 [7,957 - 16,282]
Mean (SD) 14,564.63 (6,955.73) 15,980.16 (7,193.49) 15,495.41 (6,973.47) 12,656.62 (6,557.62)
Range 0 to 38,403 30 to 39,051 0 to 36,654 162 to 29,045
Days in cohort – Median [Q25 - Q75] 22 [15 - 29] 61 [31 - 91] 61 [31 - 91] 61 [31 - 91]
Mean (SD) 25.06 (7.88) 61.01 (25.37) 63.18 (25.35) 63.41 (23.87)
Range 1 to 37 31 to 91 1 to 91 31 to 91
Days to next record – Median [Q25 - Q75] 6,225 [2,758 - 10,942] 5,100 [2,430 - 12,824] 8,702 [3,098 - 16,603] 17,465 [9,078 - 18,332]
Mean (SD) 7,618.24 (6,033.81) 8,245.36 (7,985.71) 10,818.35 (9,256.74) 12,451.67 (10,222.78)
Range 300 to 29,145 630 to 28,365 660 to 34,482 690 to 19,200
50 or above
Number records – N 328 97 129 51
Number subjects – N 292 93 116 48
Cohort start date – Median [Q25 - Q75] 2008-10-08 [1997-01-11 - 2014-03-06] 2009-07-25 [1999-01-22 - 2015-04-07] 2008-12-20 [2000-10-17 - 2014-09-23] 2010-09-19 [2005-05-10 - 2016-01-10]
Range 1961-02-11 to 2019-05-30 1970-06-04 to 2019-06-23 1961-07-16 to 2019-06-12 1982-01-17 to 2019-05-08
Cohort end date – Median [Q25 - Q75] 2008-10-30 [1997-02-13 - 2014-03-25] 2009-09-23 [1999-04-22 - 2015-06-03] 2009-01-19 [2000-12-09 - 2014-12-22] 2010-10-19 [2005-06-24 - 2016-03-26]
Range 1961-02-25 to 2019-05-30 1970-07-04 to 2019-06-24 1961-08-15 to 2019-06-13 1982-04-17 to 2019-06-07
Age – Median [Q25 - Q75] 59 [53 - 67] 68 [60 - 75] 69 [61 - 78] 71 [62 - 82]
Mean (SD) 62.00 (11.40) 68.59 (11.77) 70.33 (12.90) 72.31 (13.84)
Range 50 to 105 50 to 107 50 to 106 51 to 108
Sex Female N (%) 163 (49.70%) 48 (49.48%) 73 (56.59%) 33 (64.71%)
Male N (%) 165 (50.30%) 49 (50.52%) 56 (43.41%) 18 (35.29%)
Prior observation – Median [Q25 - Q75] 21,747 [19,421 - 24,795] 25,114 [22,188 - 27,715] 25,445 [22,496 - 28,815] 25,964 [22,994 - 30,277]
Mean (SD) 22,831.56 (4,167.50) 25,235.61 (4,310.11) 25,874.71 (4,714.82) 26,594.78 (5,045.12)
Range 18,264 to 38,429 18,354 to 39,430 18,379 to 38,943 18,899 to 39,792
Future observation – Median [Q25 - Q75] 3,494 [1,722 - 6,684] 2,909 [1,173 - 5,608] 3,335 [1,316 - 5,988] 2,808 [914 - 4,672]
Mean (SD) 4,642.15 (4,070.72) 3,977.22 (3,624.08) 4,105.97 (3,334.07) 3,215.02 (3,035.15)
Range 0 to 19,780 1 to 17,814 1 to 16,492 0 to 13,595
Days in cohort – Median [Q25 - Q75] 22 [15 - 29] 61 [31 - 91] 61 [31 - 91] 61 [31 - 61]
Mean (SD) 24.82 (8.58) 64.10 (25.37) 58.69 (25.01) 52.18 (24.95)
Range 1 to 36 2 to 92 2 to 91 1 to 91
Days to next record – Median [Q25 - Q75] 4,130 [2,205 - 8,048] 4,545 [2,985 - 5,542] 2,624 [990 - 5,451] 3,390 [2,700 - 7,430]
Mean (SD) 5,127.69 (3,614.35) 3,982.00 (2,662.27) 3,554.46 (3,260.29) 5,623.33 (5,110.16)
Range 630 to 13,260 330 to 6,508 360 to 9,660 2,010 to 11,470

Then plotted age stratified prior observation time.

chars |>
  filter(variable_name == "Prior observation") |>
  plotCharacteristics(
    plotType = "boxplot",
    colour = "cohort_name",
    facet = c("age_group")
  ) +
  coord_flip()

Summaries including presence in other cohorts

We explored whether patients had any exposure to a list of selected medications (acetaminophen, morphine, warfarin)

medsCs <- getDrugIngredientCodes(
  cdm = cdm,
  name = c("acetaminophen", "morphine", "warfarin")
)
cdm <- generateConceptCohortSet(
  cdm = cdm,
  name = "meds",
  conceptSet = medsCs,
  end = "event_end_date",
  limit = "all",
  overwrite = TRUE
)

We can use the intersects arguement inside the function to get this information.

chars <- cdm$injuries |>
  summariseCharacteristics(cohortIntersectFlag = list(
    "Medications prior to index date" = list(
      targetCohortTable = "meds",
      window = c(-Inf, -1)
    ),
    "Medications on index date" = list(
      targetCohortTable = "meds",
      window = c(0, 0)
    )
  ))

To view the summary table

tableCharacteristics(chars)
CDM name
GiBleed
Variable name Variable level Estimate name
Cohort name
ankle_sprain ankle_fracture forearm_fracture hip_fracture
Number records – N 1,915 464 569 138
Number subjects – N 1,357 427 510 132
Cohort start date – Median [Q25 - Q75] 1982-11-09 [1968-06-15 - 1999-04-13] 1981-01-15 [1965-03-11 - 1997-08-03] 1981-07-24 [1967-03-05 - 2000-12-16] 1996-09-17 [1977-09-20 - 2010-06-22]
Range 1912-02-25 to 2019-05-30 1911-09-07 to 2019-06-23 1917-08-16 to 2019-06-26 1927-12-14 to 2019-05-08
Cohort end date – Median [Q25 - Q75] 1982-12-10 [1968-07-06 - 1999-05-09] 1981-02-28 [1965-04-11 - 1997-10-12] 1981-08-23 [1967-04-10 - 2001-02-27] 1996-11-16 [1977-12-04 - 2010-07-22]
Range 1912-03-10 to 2019-05-30 1911-12-06 to 2019-06-24 1917-11-14 to 2019-06-26 1928-03-13 to 2019-06-07
Age – Median [Q25 - Q75] 21 [9 - 41] 16 [9 - 43] 17 [9 - 46] 40 [13 - 66]
Mean (SD) 26.63 (21.03) 27.38 (24.70) 28.69 (25.97) 40.06 (28.82)
Range 0 to 105 0 to 107 0 to 106 1 to 108
Sex Female N (%) 954 (49.82%) 238 (51.29%) 286 (50.26%) 74 (53.62%)
Male N (%) 961 (50.18%) 226 (48.71%) 283 (49.74%) 64 (46.38%)
Prior observation – Median [Q25 - Q75] 7,833 [3,628 - 15,147] 6,030 [3,360 - 16,032] 6,289 [3,390 - 16,847] 14,522 [4,801 - 24,401]
Mean (SD) 9,918.17 (7,672.74) 10,196.57 (9,011.31) 10,670.43 (9,480.30) 14,821.73 (10,521.89)
Range 299 to 38,429 299 to 39,430 299 to 38,943 390 to 39,792
Future observation – Median [Q25 - Q75] 12,868 [6,860 - 18,078] 13,748 [6,878 - 19,331] 13,165 [5,988 - 18,548] 7,798 [2,874 - 14,913]
Mean (SD) 12,865.11 (7,543.50) 13,470.92 (8,215.96) 12,913.27 (7,929.17) 9,167.33 (7,160.81)
Range 0 to 38,403 1 to 39,051 0 to 36,654 0 to 29,045
Days in cohort – Median [Q25 - Q75] 22 [15 - 29] 61 [31 - 91] 61 [31 - 91] 61 [31 - 91]
Mean (SD) 25.02 (8.00) 61.65 (25.38) 62.16 (25.32) 59.26 (24.79)
Range 1 to 37 2 to 92 1 to 91 1 to 91
Days to next record – Median [Q25 - Q75] 6,082 [2,640 - 10,540] 5,100 [2,430 - 11,474] 5,760 [1,922 - 15,606] 7,430 [2,355 - 15,966]
Mean (SD) 7,457.56 (5,937.15) 7,784.46 (7,686.21) 9,217.83 (8,826.17) 9,037.50 (8,138.51)
Range 300 to 29,145 330 to 28,365 360 to 34,482 690 to 19,200
Medications prior to index date 11289 warfarin N (%) 12 (0.63%) 8 (1.72%) 11 (1.93%) 4 (2.90%)
7052 morphine N (%) 15 (0.78%) 1 (0.22%) 2 (0.35%) 2 (1.45%)
161 acetaminophen N (%) 1,530 (79.90%) 357 (76.94%) 447 (78.56%) 119 (86.23%)
Medications on index date 161 acetaminophen N (%) 773 (40.37%) 240 (51.72%) 264 (46.40%) 90 (65.22%)
11289 warfarin N (%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%)
7052 morphine N (%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%)

To visualise the exposure of these drugs in a bar plot.

plot_data <- chars |>
  filter(
    variable_name == "Medications prior to index date",
    estimate_name == "percentage"
  )

plot_data |>
  plotCharacteristics(
    plotType = "barplot",
    colour = "variable_level",
    facet = c("cdm_name", "cohort_name")
  ) +
  scale_x_discrete(limits = rev(sort(unique(plot_data$variable_level)))) +
  coord_flip() +
  ggtitle("Medication use prior to index date")

Summaries Using Concept Sets Directly

Instead of creating cohorts, we could have directly used our concept sets for medications when characterising our study cohorts.

chars <- cdm$injuries |>
  summariseCharacteristics(conceptIntersectFlag = list(
    "Medications prior to index date" = list(
      conceptSet = medsCs,
      window = c(-Inf, -1)
    ),
    "Medications on index date" = list(
      conceptSet = medsCs,
      window = c(0, 0)
    )
  ))

Although, like here, concept sets can lead to the same result as using cohorts it is important to note this will not always be the case. This is because the creation of cohorts will have involved the collapsing of overlapping records as well as imposing certain requirements, such as only including records that were observed during an an ongoing observation period. Meanwhile, when working with concept sets we will instead be working directly with record-level data.

tableCharacteristics(chars)
CDM name
GiBleed
Variable name Variable level Estimate name
Cohort name
ankle_sprain ankle_fracture forearm_fracture hip_fracture
Number records – N 1,915 464 569 138
Number subjects – N 1,357 427 510 132
Cohort start date – Median [Q25 - Q75] 1982-11-09 [1968-06-15 - 1999-04-13] 1981-01-15 [1965-03-11 - 1997-08-03] 1981-07-24 [1967-03-05 - 2000-12-16] 1996-09-17 [1977-09-20 - 2010-06-22]
Range 1912-02-25 to 2019-05-30 1911-09-07 to 2019-06-23 1917-08-16 to 2019-06-26 1927-12-14 to 2019-05-08
Cohort end date – Median [Q25 - Q75] 1982-12-10 [1968-07-06 - 1999-05-09] 1981-02-28 [1965-04-11 - 1997-10-12] 1981-08-23 [1967-04-10 - 2001-02-27] 1996-11-16 [1977-12-04 - 2010-07-22]
Range 1912-03-10 to 2019-05-30 1911-12-06 to 2019-06-24 1917-11-14 to 2019-06-26 1928-03-13 to 2019-06-07
Age – Median [Q25 - Q75] 21 [9 - 41] 16 [9 - 43] 17 [9 - 46] 40 [13 - 66]
Mean (SD) 26.63 (21.03) 27.38 (24.70) 28.69 (25.97) 40.06 (28.82)
Range 0 to 105 0 to 107 0 to 106 1 to 108
Sex Female N (%) 954 (49.82%) 238 (51.29%) 286 (50.26%) 74 (53.62%)
Male N (%) 961 (50.18%) 226 (48.71%) 283 (49.74%) 64 (46.38%)
Prior observation – Median [Q25 - Q75] 7,833 [3,628 - 15,147] 6,030 [3,360 - 16,032] 6,289 [3,390 - 16,847] 14,522 [4,801 - 24,401]
Mean (SD) 9,918.17 (7,672.74) 10,196.57 (9,011.31) 10,670.43 (9,480.30) 14,821.73 (10,521.89)
Range 299 to 38,429 299 to 39,430 299 to 38,943 390 to 39,792
Future observation – Median [Q25 - Q75] 12,868 [6,860 - 18,078] 13,748 [6,878 - 19,331] 13,165 [5,988 - 18,548] 7,798 [2,874 - 14,913]
Mean (SD) 12,865.11 (7,543.50) 13,470.92 (8,215.96) 12,913.27 (7,929.17) 9,167.33 (7,160.81)
Range 0 to 38,403 1 to 39,051 0 to 36,654 0 to 29,045
Days in cohort – Median [Q25 - Q75] 22 [15 - 29] 61 [31 - 91] 61 [31 - 91] 61 [31 - 91]
Mean (SD) 25.02 (8.00) 61.65 (25.38) 62.16 (25.32) 59.26 (24.79)
Range 1 to 37 2 to 92 1 to 91 1 to 91
Days to next record – Median [Q25 - Q75] 6,082 [2,640 - 10,540] 5,100 [2,430 - 11,474] 5,760 [1,922 - 15,606] 7,430 [2,355 - 15,966]
Mean (SD) 7,457.56 (5,937.15) 7,784.46 (7,686.21) 9,217.83 (8,826.17) 9,037.50 (8,138.51)
Range 300 to 29,145 330 to 28,365 360 to 34,482 690 to 19,200
Medications prior to index date 161 acetaminophen N (%) 1,530 (79.90%) 357 (76.94%) 447 (78.56%) 119 (86.23%)
7052 morphine N (%) 15 (0.78%) 1 (0.22%) 2 (0.35%) 2 (1.45%)
11289 warfarin N (%) 12 (0.63%) 8 (1.72%) 11 (1.93%) 4 (2.90%)
Medications on index date 161 acetaminophen N (%) 773 (40.37%) 240 (51.72%) 264 (46.40%) 90 (65.22%)
11289 warfarin N (%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%)
7052 morphine N (%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%)

Summaries using clinical tables

More generally, we can also include summaries of the patients’ presence in other clinical tables of the OMOP CDM. For example, here we add a count of visit occurrences

chars <- cdm$injuries |>
  summariseCharacteristics(
    tableIntersectCount = list(
      "Visits in the year prior" = list(
        tableName = "visit_occurrence",
        window = c(-365, -1)
      )
    ),
    tableIntersectFlag = list(
      "Any drug exposure in the year prior" = list(
        tableName = "drug_exposure",
        window = c(-365, -1)
      ),
      "Any procedure in the year prior" = list(
        tableName = "procedure_occurrence",
        window = c(-365, -1)
      )
    )
  )
tableCharacteristics(chars)
CDM name
GiBleed
Variable name Variable level Estimate name
Cohort name
ankle_sprain ankle_fracture forearm_fracture hip_fracture
Number records – N 1,915 464 569 138
Number subjects – N 1,357 427 510 132
Cohort start date – Median [Q25 - Q75] 1982-11-09 [1968-06-15 - 1999-04-13] 1981-01-15 [1965-03-11 - 1997-08-03] 1981-07-24 [1967-03-05 - 2000-12-16] 1996-09-17 [1977-09-20 - 2010-06-22]
Range 1912-02-25 to 2019-05-30 1911-09-07 to 2019-06-23 1917-08-16 to 2019-06-26 1927-12-14 to 2019-05-08
Cohort end date – Median [Q25 - Q75] 1982-12-10 [1968-07-06 - 1999-05-09] 1981-02-28 [1965-04-11 - 1997-10-12] 1981-08-23 [1967-04-10 - 2001-02-27] 1996-11-16 [1977-12-04 - 2010-07-22]
Range 1912-03-10 to 2019-05-30 1911-12-06 to 2019-06-24 1917-11-14 to 2019-06-26 1928-03-13 to 2019-06-07
Age – Median [Q25 - Q75] 21 [9 - 41] 16 [9 - 43] 17 [9 - 46] 40 [13 - 66]
Mean (SD) 26.63 (21.03) 27.38 (24.70) 28.69 (25.97) 40.06 (28.82)
Range 0 to 105 0 to 107 0 to 106 1 to 108
Sex Female N (%) 954 (49.82%) 238 (51.29%) 286 (50.26%) 74 (53.62%)
Male N (%) 961 (50.18%) 226 (48.71%) 283 (49.74%) 64 (46.38%)
Prior observation – Median [Q25 - Q75] 7,833 [3,628 - 15,147] 6,030 [3,360 - 16,032] 6,289 [3,390 - 16,847] 14,522 [4,801 - 24,401]
Mean (SD) 9,918.17 (7,672.74) 10,196.57 (9,011.31) 10,670.43 (9,480.30) 14,821.73 (10,521.89)
Range 299 to 38,429 299 to 39,430 299 to 38,943 390 to 39,792
Future observation – Median [Q25 - Q75] 12,868 [6,860 - 18,078] 13,748 [6,878 - 19,331] 13,165 [5,988 - 18,548] 7,798 [2,874 - 14,913]
Mean (SD) 12,865.11 (7,543.50) 13,470.92 (8,215.96) 12,913.27 (7,929.17) 9,167.33 (7,160.81)
Range 0 to 38,403 1 to 39,051 0 to 36,654 0 to 29,045
Days in cohort – Median [Q25 - Q75] 22 [15 - 29] 61 [31 - 91] 61 [31 - 91] 61 [31 - 91]
Mean (SD) 25.02 (8.00) 61.65 (25.38) 62.16 (25.32) 59.26 (24.79)
Range 1 to 37 2 to 92 1 to 91 1 to 91
Days to next record – Median [Q25 - Q75] 6,082 [2,640 - 10,540] 5,100 [2,430 - 11,474] 5,760 [1,922 - 15,606] 7,430 [2,355 - 15,966]
Mean (SD) 7,457.56 (5,937.15) 7,784.46 (7,686.21) 9,217.83 (8,826.17) 9,037.50 (8,138.51)
Range 300 to 29,145 330 to 28,365 360 to 34,482 690 to 19,200
Any drug exposure in the year prior – N (%) 597 (31.17%) 149 (32.11%) 171 (30.05%) 41 (29.71%)
Any procedure in the year prior – N (%) 123 (6.42%) 26 (5.60%) 37 (6.50%) 15 (10.87%)
Visits in the year prior – Median [Q25 - Q75] 0.00 [0.00 - 0.00] 0.00 [0.00 - 0.00] 0.00 [0.00 - 0.00] 0.00 [0.00 - 0.00]
Mean (SD) 0.00 (0.06) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00)
Range 0.00 to 1.00 0.00 to 0.00 0.00 to 0.00 0.00 to 0.00