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PatientProfiles

CRAN status codecov.io R-CMD-check Lifecycle:stable metacran downloads metacran downloads

Package overview

PatientProfiles contains functions for adding characteristics to OMOP CDM tables containing patient level data (e.g. condition occurrence, drug exposure, and so on) and OMOP CDM cohort tables. The characteristics that can be added include an individual´s sex, age, and days of prior observation Time varying characteristics, such as age, can be estimated relative to any date in the corresponding table. In addition, PatientProfiles also provides functionality for identifying intersections between a cohort table and OMOP CDM tables containing patient level data or other cohort tables.

Package installation

You can install the latest version of PatientProfiles like so:

install.packages("PatientProfiles")

Citation 

citation("PatientProfiles")
#> 
#> To cite package 'PatientProfiles' in publications use:
#> 
#>   Catala M, Guo Y, Du M, Lopez-Guell K, Burn E, Mercade-Besora N
#>   (????). _PatientProfiles: Identify Characteristics of Patients in the
#>   OMOP Common Data Model_. R package version 1.2.1,
#>   <https://darwin-eu.github.io/PatientProfiles/>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {PatientProfiles: Identify Characteristics of Patients in the OMOP Common Data Model},
#>     author = {Marti Catala and Yuchen Guo and Mike Du and Kim Lopez-Guell and Edward Burn and Nuria Mercade-Besora},
#>     note = {R package version 1.2.1},
#>     url = {https://darwin-eu.github.io/PatientProfiles/},
#>   }

Example usage

Create a reference to data in the OMOP CDM format

The PatientProfiles package is designed to work with data in the OMOP CDM format, so our first step is to create a reference to the data using the CDMConnector package.

Creating a connection to a Postgres database would for example look like:

con <- DBI::dbConnect(
  RPostgres::Postgres(),
  dbname = Sys.getenv("CDM5_POSTGRESQL_DBNAME"),
  host = Sys.getenv("CDM5_POSTGRESQL_HOST"),
  user = Sys.getenv("CDM5_POSTGRESQL_USER"),
  password = Sys.getenv("CDM5_POSTGRESQL_PASSWORD")
)

cdm <- cdm_from_con(
  con,
  cdm_schema = Sys.getenv("CDM5_POSTGRESQL_CDM_SCHEMA"),
  write_schema = Sys.getenv("CDM5_POSTGRESQL_RESULT_SCHEMA")
)

To see how you would create a reference to your database please consult the CDMConnector package documentation. For this example though we’ll work with simulated data, and we’ll generate an example cdm reference like so:

cdm <- mockPatientProfiles(numberIndividuals = 1000)

Adding individuals´ characteristics

Adding characteristics to patient-level data

Say we wanted to get individuals´sex and age at condition start date for records in the condition occurrence table. We can use the addAge and addSex functions to do this:

cdm$condition_occurrence %>%
  glimpse()
#> Rows: ??
#> Columns: 6
#> Database: DuckDB v1.1.1 [martics@Windows 10 x64:R 4.2.1/:memory:]
#> $ person_id                 <int> 778, 530, 421, 858, 144, 579, 646, 185, 174,…
#> $ condition_start_date      <date> 1912-06-09, 1990-01-17, 1946-11-29, 1953-11…
#> $ condition_end_date        <date> 1922-03-03, 1991-11-16, 1949-05-22, 1965-10…
#> $ condition_occurrence_id   <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 1…
#> $ condition_concept_id      <int> 2, 5, 5, 5, 7, 7, 10, 4, 10, 5, 6, 7, 2, 4, …
#> $ condition_type_concept_id <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…

cdm$condition_occurrence <- cdm$condition_occurrence %>%
  addAge(indexDate = "condition_start_date") %>%
  addSex()

cdm$condition_occurrence %>%
  glimpse()
#> Rows: ??
#> Columns: 8
#> Database: DuckDB v1.1.1 [martics@Windows 10 x64:R 4.2.1/:memory:]
#> $ person_id                 <int> 778, 530, 858, 144, 579, 185, 174, 426, 9, 5…
#> $ condition_start_date      <date> 1912-06-09, 1990-01-17, 1953-11-10, 1966-05…
#> $ condition_end_date        <date> 1922-03-03, 1991-11-16, 1965-10-24, 1966-07…
#> $ condition_occurrence_id   <int> 1, 2, 4, 5, 6, 8, 9, 10, 11, 13, 14, 15, 17,…
#> $ condition_concept_id      <int> 2, 5, 5, 7, 7, 4, 10, 5, 6, 2, 4, 1, 6, 5, 6…
#> $ condition_type_concept_id <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ age                       <int> 5, 31, 22, 16, 50, 0, 16, 28, 20, 10, 34, 7,…
#> $ sex                       <chr> "Male", "Male", "Male", "Female", "Male", "M…

We could, for example, then limit our data to only males aged between 18 and 65

cdm$condition_occurrence %>%
  filter(age >= 18 & age <= 65) %>%
  filter(sex == "Male")
#> # Source:   SQL [?? x 8]
#> # Database: DuckDB v1.1.1 [martics@Windows 10 x64:R 4.2.1/:memory:]
#>    person_id condition_start_date condition_end_date condition_occurrence_id
#>        <int> <date>               <date>                               <int>
#>  1       530 1990-01-17           1991-11-16                               2
#>  2       858 1953-11-10           1965-10-24                               4
#>  3       579 1996-12-07           1998-01-13                               6
#>  4       426 1991-03-14           1997-04-27                              10
#>  5         9 1966-12-27           1969-06-02                              11
#>  6        32 2000-06-03           2008-07-17                              17
#>  7         9 1973-12-31           1987-05-25                              23
#>  8       649 2000-08-22           2008-02-22                              33
#>  9       284 1931-11-30           1939-01-08                              49
#> 10       707 1977-02-11           1978-10-10                              54
#> # ℹ more rows
#> # ℹ 4 more variables: condition_concept_id <int>,
#> #   condition_type_concept_id <int>, age <int>, sex <chr>

Adding characteristics of a cohort

As with other tables in the OMOP CDM, we can work in a similar way with cohort tables. For example, say we have the below cohort table

cdm$cohort1 %>%
  glimpse()
#> Rows: ??
#> Columns: 4
#> Database: DuckDB v1.1.1 [martics@Windows 10 x64:R 4.2.1/:memory:]
#> $ cohort_definition_id <int> 1, 1, 2, 1, 2, 3, 1, 3, 2, 3, 1, 2, 1, 2, 1, 2, 2…
#> $ subject_id           <int> 93, 783, 571, 218, 462, 103, 15, 404, 602, 333, 1…
#> $ cohort_start_date    <date> 1970-07-10, 1943-10-13, 1933-05-15, 1934-06-18, …
#> $ cohort_end_date      <date> 1984-02-26, 1947-11-11, 1933-06-11, 1949-09-17, …

We can add age, age groups, sex, and days of prior observation to a cohort like so

cdm$cohort1 <- cdm$cohort1 %>%
  addAge(
    indexDate = "cohort_start_date",
    ageGroup = list(c(0, 18), c(19, 65), c(66, 100))
  ) %>%
  addSex() %>%
  addPriorObservation()

cdm$cohort1 %>%
  glimpse()
#> Rows: ??
#> Columns: 8
#> Database: DuckDB v1.1.1 [martics@Windows 10 x64:R 4.2.1/:memory:]
#> $ cohort_definition_id <int> 1, 2, 2, 2, 1, 3, 2, 2, 3, 1, 1, 2, 1, 2, 2, 2, 3…
#> $ subject_id           <int> 2, 4, 5, 7, 8, 9, 11, 13, 14, 15, 17, 18, 22, 24,…
#> $ cohort_start_date    <date> 1931-10-24, 1975-04-04, 1978-03-13, 1914-07-03, …
#> $ cohort_end_date      <date> 1953-08-30, 1987-07-31, 1988-01-13, 1925-06-15, …
#> $ age                  <int> 10, 0, 3, 0, 12, 5, 21, 14, 8, 16, 0, 10, 1, 0, 8…
#> $ age_group            <chr> "0 to 18", "0 to 18", "0 to 18", "0 to 18", "0 to…
#> $ sex                  <chr> "Male", "Male", "Female", "Female", "Male", "Male…
#> $ prior_observation    <int> 3948, 93, 1167, 183, 4705, 2100, 7942, 5238, 3203…

We could use this information to subset the cohort. For example limiting to those with at least 365 days of prior observation available before their cohort start date like so

cdm$cohort1 %>%
  filter(prior_observation >= 365)
#> # Source:   SQL [?? x 8]
#> # Database: DuckDB v1.1.1 [martics@Windows 10 x64:R 4.2.1/:memory:]
#>    cohort_definition_id subject_id cohort_start_date cohort_end_date   age
#>                   <int>      <int> <date>            <date>          <int>
#>  1                    1          2 1931-10-24        1953-08-30         10
#>  2                    2          5 1978-03-13        1988-01-13          3
#>  3                    1          8 1933-11-19        1935-05-11         12
#>  4                    3          9 1951-10-02        1966-05-19          5
#>  5                    2         11 1987-09-30        1991-02-25         21
#>  6                    2         13 1934-05-05        1937-09-21         14
#>  7                    3         14 1988-10-08        1990-12-31          8
#>  8                    1         15 1920-05-19        1924-07-11         16
#>  9                    2         18 1973-11-17        1978-07-31         10
#> 10                    1         22 1938-09-28        1956-07-31          1
#> # ℹ more rows
#> # ℹ 3 more variables: age_group <chr>, sex <chr>, prior_observation <int>

Cohort intersections

Detect the presence of another cohort in a certain window

We can use addCohortIntersectFlag to add a flag for the presence (or not) of a cohort in a certain window.

cdm$cohort1 %>%
  glimpse()
#> Rows: ??
#> Columns: 4
#> Database: DuckDB v1.1.1 [martics@Windows 10 x64:R 4.2.1/:memory:]
#> $ cohort_definition_id <int> 3, 1, 2, 2, 3, 2, 2, 2, 1, 3
#> $ subject_id           <int> 10, 4, 1, 5, 3, 7, 9, 6, 2, 8
#> $ cohort_start_date    <date> 1974-02-03, 1918-10-07, 1967-07-21, 1951-05-19, 1…
#> $ cohort_end_date      <date> 1974-12-26, 1920-03-27, 1967-12-09, 1953-09-30, 1…

cdm$cohort1 <- cdm$cohort1 %>%
  addCohortIntersectFlag(
    targetCohortTable = "cohort2",
    window = c(-Inf, -1)
  )

cdm$cohort1 %>%
  glimpse()
#> Rows: ??
#> Columns: 7
#> Database: DuckDB v1.1.1 [martics@Windows 10 x64:R 4.2.1/:memory:]
#> $ cohort_definition_id <int> 3, 2, 2, 2, 2, 1, 3, 2, 1, 3
#> $ subject_id           <int> 10, 1, 5, 7, 9, 4, 3, 6, 2, 8
#> $ cohort_start_date    <date> 1974-02-03, 1967-07-21, 1951-05-19, 1934-06-19, 1…
#> $ cohort_end_date      <date> 1974-12-26, 1967-12-09, 1953-09-30, 1938-06-10, 1…
#> $ cohort_1_minf_to_m1  <dbl> 0, 1, 0, 0, 1, 0, 0, 0, 0, 0
#> $ cohort_2_minf_to_m1  <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0
#> $ cohort_3_minf_to_m1  <dbl> 0, 0, 1, 1, 0, 0, 0, 0, 0, 0

Count appearances of a certain cohort in a certain window

If we wanted the number of appearances, we could instead use the addCohortIntersectCount function

cdm$cohort1 %>%
  glimpse()
#> Rows: ??
#> Columns: 4
#> Database: DuckDB v1.1.1 [martics@Windows 10 x64:R 4.2.1/:memory:]
#> $ cohort_definition_id <int> 2, 1, 3, 2, 1, 1, 2, 3, 2, 1
#> $ subject_id           <int> 4, 1, 7, 2, 3, 5, 10, 6, 9, 8
#> $ cohort_start_date    <date> 1961-04-27, 1997-02-08, 1956-03-09, 1980-07-04, 1…
#> $ cohort_end_date      <date> 1970-08-24, 2011-04-10, 1956-12-19, 1989-02-23, 1…

cdm$cohort1 <- cdm$cohort1 %>%
  addCohortIntersectCount(
    targetCohortTable = "cohort2",
    targetCohortId = 1,
    window = list("short_term" = c(1, 30), "mid_term" = c(31, 180))
  )

cdm$cohort1 %>%
  glimpse()
#> Rows: ??
#> Columns: 6
#> Database: DuckDB v1.1.1 [martics@Windows 10 x64:R 4.2.1/:memory:]
#> $ cohort_definition_id <int> 2, 2, 1, 3, 2, 1, 1, 2, 3, 1
#> $ subject_id           <int> 9, 4, 1, 7, 2, 3, 5, 10, 6, 8
#> $ cohort_start_date    <date> 1922-08-27, 1961-04-27, 1997-02-08, 1956-03-09, 1…
#> $ cohort_end_date      <date> 1928-06-22, 1970-08-24, 2011-04-10, 1956-12-19, 1…
#> $ cohort_1_mid_term    <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0
#> $ cohort_1_short_term  <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0

Add a column with the first/last event in a certain window

Say we wanted the date at which an individual was in another cohort then we can use the addCohortIntersectDate function. As there might be multiple records for the other cohort, we can also choose the first or the last appearance in that cohort.

First occurrence:

cdm$cohort1 %>%
  glimpse()
#> Rows: ??
#> Columns: 4
#> Database: DuckDB v1.1.1 [martics@Windows 10 x64:R 4.2.1/:memory:]
#> $ cohort_definition_id <int> 3, 3, 2, 1, 1, 1, 3, 1, 3, 2
#> $ subject_id           <int> 9, 4, 6, 8, 1, 5, 10, 7, 2, 3
#> $ cohort_start_date    <date> 1972-05-17, 1936-05-20, 1954-03-09, 1935-10-23, 2…
#> $ cohort_end_date      <date> 1988-05-11, 1945-08-14, 1966-11-09, 1937-04-23, 2…

cdm$cohort1 <- cdm$cohort1 %>%
  addCohortIntersectDate(
    targetCohortTable = "cohort2",
    targetCohortId = 1,
    order = "first",
    window = c(-Inf, Inf)
  )

cdm$cohort1 %>%
  glimpse()
#> Rows: ??
#> Columns: 5
#> Database: DuckDB v1.1.1 [martics@Windows 10 x64:R 4.2.1/:memory:]
#> $ cohort_definition_id <int> 3, 2, 1, 2, 3, 1, 1, 3, 1, 3
#> $ subject_id           <int> 9, 6, 8, 3, 4, 1, 5, 10, 7, 2
#> $ cohort_start_date    <date> 1972-05-17, 1954-03-09, 1935-10-23, 1938-12-02, 1…
#> $ cohort_end_date      <date> 1988-05-11, 1966-11-09, 1937-04-23, 1941-11-19, 1…
#> $ cohort_1_minf_to_inf <date> 1980-05-05, 1967-03-31, 1916-11-14, 1919-01-09, …

Last occurrence:

cdm$cohort1 %>%
  glimpse()
#> Rows: ??
#> Columns: 4
#> Database: DuckDB v1.1.1 [martics@Windows 10 x64:R 4.2.1/:memory:]
#> $ cohort_definition_id <int> 2, 3, 3, 2, 3, 2, 2, 1, 2, 3
#> $ subject_id           <int> 4, 10, 7, 1, 2, 9, 6, 3, 8, 5
#> $ cohort_start_date    <date> 1921-12-06, 1989-08-06, 1992-06-11, 1981-10-14, 1…
#> $ cohort_end_date      <date> 1928-08-03, 1995-01-24, 1997-09-11, 1982-11-12, 1…

cdm$cohort1 <- cdm$cohort1 %>%
  addCohortIntersectDate(
    targetCohortTable = "cohort2",
    targetCohortId = 1,
    order = "last",
    window = c(-Inf, Inf)
  )

cdm$cohort1 %>%
  glimpse()
#> Rows: ??
#> Columns: 5
#> Database: DuckDB v1.1.1 [martics@Windows 10 x64:R 4.2.1/:memory:]
#> $ cohort_definition_id <int> 2, 2, 1, 2, 3, 3, 2, 3, 2, 3
#> $ subject_id           <int> 9, 6, 3, 4, 10, 7, 1, 2, 8, 5
#> $ cohort_start_date    <date> 1969-11-14, 1938-04-30, 1983-05-27, 1921-12-06, 1…
#> $ cohort_end_date      <date> 1973-09-22, 1939-02-01, 1993-11-26, 1928-08-03, 1…
#> $ cohort_1_minf_to_inf <date> 1971-02-23, 1942-11-21, 1971-04-30, NA, NA, NA, …

Add the number of days instead of the date

Instead of returning a date, we could return the days to the intersection by using addCohortIntersectDays

cdm$cohort1 %>%
  glimpse()
#> Rows: ??
#> Columns: 4
#> Database: DuckDB v1.1.1 [martics@Windows 10 x64:R 4.2.1/:memory:]
#> $ cohort_definition_id <int> 3, 2, 2, 1, 1, 3, 1, 1, 2, 1
#> $ subject_id           <int> 1, 9, 5, 7, 4, 3, 2, 10, 6, 8
#> $ cohort_start_date    <date> 1966-12-06, 1955-09-03, 1963-11-24, 1995-11-30, 1…
#> $ cohort_end_date      <date> 1990-01-06, 1977-12-24, 1978-05-27, 1996-07-27, 2…

cdm$cohort1 <- cdm$cohort1 %>%
  addCohortIntersectDays(
    targetCohortTable = "cohort2",
    targetCohortId = 1,
    order = "last",
    window = c(-Inf, Inf)
  )

cdm$cohort1 %>%
  glimpse()
#> Rows: ??
#> Columns: 5
#> Database: DuckDB v1.1.1 [martics@Windows 10 x64:R 4.2.1/:memory:]
#> $ cohort_definition_id <int> 2, 1, 1, 3, 2, 1, 1, 3, 1, 2
#> $ subject_id           <int> 5, 2, 8, 1, 9, 7, 4, 3, 10, 6
#> $ cohort_start_date    <date> 1963-11-24, 1913-08-23, 1982-09-08, 1966-12-06, 1…
#> $ cohort_end_date      <date> 1978-05-27, 1914-08-12, 1991-06-28, 1990-01-06, 1…
#> $ cohort_1_minf_to_inf <dbl> 8809, 4412, -2029, NA, NA, NA, NA, NA, NA, NA

Combine multiple cohort intersects

If we want to combine multiple cohort intersects we can concatenate the operations using the pipe operator:

cdm$cohort1 %>%
  glimpse()
#> Rows: ??
#> Columns: 4
#> Database: DuckDB v1.1.1 [martics@Windows 10 x64:R 4.2.1/:memory:]
#> $ cohort_definition_id <int> 2, 1, 2, 3, 2, 3, 3, 2, 2, 3
#> $ subject_id           <int> 2, 4, 3, 7, 10, 1, 6, 8, 9, 5
#> $ cohort_start_date    <date> 1945-02-11, 1955-06-28, 1957-11-19, 1986-03-17, 1…
#> $ cohort_end_date      <date> 1980-06-10, 1962-08-23, 1958-05-22, 1992-08-12, 1…

cdm$cohort1 <- cdm$cohort1 %>%
  addCohortIntersectDate(
    targetCohortTable = "cohort2",
    targetCohortId = 1,
    order = "last",
    window = c(-Inf, Inf)
  ) %>%
  addCohortIntersectCount(
    targetCohortTable = "cohort2",
    targetCohortId = 1,
    window = c(-Inf, Inf)
  )

cdm$cohort1 %>%
  glimpse()
#> Rows: ??
#> Columns: 5
#> Database: DuckDB v1.1.1 [martics@Windows 10 x64:R 4.2.1/:memory:]
#> $ cohort_definition_id <int> 2, 3, 2, 1, 2, 3, 2, 3, 3, 2
#> $ subject_id           <int> 8, 5, 2, 4, 3, 7, 10, 1, 6, 9
#> $ cohort_start_date    <date> 1972-02-17, 1944-11-17, 1945-02-11, 1955-06-28, 1…
#> $ cohort_end_date      <date> 1987-10-18, 1947-12-20, 1980-06-10, 1962-08-23, 1…
#> $ cohort_1_minf_to_inf <dbl> 1, 1, 0, 0, 0, 0, 0, 0, 0, 0