@gowthamrao , I made an error in the 'New users of IL-23 inhibitors' cohorts (ID 1042 and 1057 in the Phenotype Library). The conceptset is supposed to contain drugs that are IL-23 inhibitors. I incorrectly included ustekinumab (which is an IL12/23 inhibitor) and incorrectly omitted guselkumab (which is an IL23 inhibitor, alongside risankizumab and tildrakizumab). How can we correct these errors?

Hi, I am not sure if this was changed in newer versions but wanted to mention that for phenotypes 551 and 676, they are defined by non-standard concepts, and thus in the corresponding sql,
JOIN #Codesets cs on (co.condition_concept_id = cs.concept_id and cs.codeset_id = 0)
this most likely won't grab any patients since the non-standard concept ids will be in their co.condition_source_concept_id if I understand correctly. Thanks!

Identify the cohort definition
Cohort 441202003: Anaphylactic reaction by Walsh (Mini-sentinial validated definition)

Describe the issue
This cohort definition seems to capture many more events than I would expect. I apologize for the lack of detail but have very limited clinical background to understand what the problem is. I do think that this cohort definition should be reviewed by experts though.

Screenshots
I cannot share any screenshots unfortunately but can tell you that I believe the problem has to do with the third part of the cohort definition.

Initial Event Cohort
People having any of the following:
A visit occurrence of Outpatient visit
Having all of the following criteria:
Having any of the following criteria:
at least 1 occurrences of a drug era of Diphenhydramine hydrochloride5
where event starts between 0 days Before and 0 days After index start date
or at least 1 occurrences of a condition occurrence of Bronchospasm3
where event starts between all days Before and all days After index start date
or at least 1 occurrences of a condition occurrence of Stridor11
where event starts between all days Before and all days After index start date
And having all of the following criteria:
at least 1 occurrences of a drug era of Epinephrine6
where event starts between 0 days Before and 0 days After index start date
and at least 1 occurrences of a condition occurrence of Hypotension7
where event starts between 0 days Before and 0 days After index start date
and at least 1 occurrences of a procedure of Cardiopulmonary resuscition4
where event starts between 0 days Before and 0 days After index start date
And having all of the following criteria:
at least 1 occurrences of a condition occurrence of Allergy unspecified1
where event starts between 0 days Before and 0 days After index start date

Additional context
Just want to flag this cohort for a second look by the experts.

The cohort name of 1219 is "Acute Bronchitis", but this should be "Hyperlipidemia":

cohort <- getPlCohortDefinitionSet(1219)
cohort$cohortName
[1] "Acute Bronchitis"

writeLines(cohort$json)
{
	"cdmVersionRange" : ">=5.0.0",
	"PrimaryCriteria" : {
		"CriteriaList" : [
			{
				"ConditionOccurrence" : {
					"CodesetId" : 0,
					"ConditionTypeExclude" : false
				}
			}
		],
		"ObservationWindow" : {
			"PriorDays" : 0,
			"PostDays" : 0
		},
		"PrimaryCriteriaLimit" : {
			"Type" : "First"
		}
	},
	"ConceptSets" : [
		{
			"id" : 0,
			"name" : "Hyperlipidemia",
			"expression" : {
				"items" : [
					{
						"concept" : {
							"CONCEPT_ID" : 432867,
							"CONCEPT_NAME" : "Hyperlipidemia",
							"STANDARD_CONCEPT" : "S",
							"STANDARD_CONCEPT_CAPTION" : "Standard",
							"INVALID_REASON" : "V",
							"INVALID_REASON_CAPTION" : "Valid",
							"CONCEPT_CODE" : "55822004",
							"DOMAIN_ID" : "Condition",
							"VOCABULARY_ID" : "SNOMED",
							"CONCEPT_CLASS_ID" : "Clinical Finding"
						},
						"isExcluded" : false,
						"includeDescendants" : true,
						"includeMapped" : false
					},
					{
						"concept" : {
							"CONCEPT_ID" : 437530,
							"CONCEPT_NAME" : "Disorder of lipid metabolism",
							"STANDARD_CONCEPT" : "S",
							"STANDARD_CONCEPT_CAPTION" : "Standard",
							"INVALID_REASON" : "V",
							"INVALID_REASON_CAPTION" : "Valid",
							"CONCEPT_CODE" : "267431006",
							"DOMAIN_ID" : "Condition",
							"VOCABULARY_ID" : "SNOMED",
							"CONCEPT_CLASS_ID" : "Clinical Finding"
						},
						"isExcluded" : false,
						"includeDescendants" : false,
						"includeMapped" : false
					}
				]
			}
		}
	],
	"QualifiedLimit" : {
		"Type" : "First"
	},
	"ExpressionLimit" : {
		"Type" : "First"
	},
	"InclusionRules" : [],
	"CensoringCriteria" : [],
	"CollapseSettings" : {
		"CollapseType" : "ERA",
		"EraPad" : 0
	},
	"CensorWindow" : {}
}

The documentation refers to https://dash.ohdsi.org/phenotype-explorer but this is not loading for me. Is this indeed not working anymore?

Idea is posted in Atlas github issue here OHDSI/Atlas#2940

Going forward, I see a potential issue occuring when phenotypes update and different end users working with old phenotypes that may have been updated. Implementing a form of publish subscriber model for this would be ideal. For example, following the WebSub protocol would be one approach.

In the websub model, 'webhooks' are callbacks that run when updates are published to 'topics' that users intentionally follow. In the case of OHDSI network collaborators this would be implementing tools that automatically run updates to generated cohorts across sites incrementally. This could also provide a model for keeping track of changes in librarians specific phenotype's of interest providing them with updates when related cohorts are changed. They could then plan actions to update cohort diagnostics or results across different network sites.

I don't have a concrete idea of how to implement this exactly, but I do have the use case of wanting to regenerate cohorts when the PhenotypeLibrary definition is updated. Designing a clear, consistent, way to do this would be beneficial.

We noticed that the app is not working anymore:

https://data.ohdsi.org/PhenotypeLibrary/

Can someone look into that?

Identify the cohort definition
Looking at Malignant Melanoma of Skin. C1 Incident Cohort, but I believe this applies to most if not all Incident vs Prevalent cohorts.

Describe the issue
The Description does not show the 365 day prior observation period requirement. But the JSON code does. (The JSON appears correct to me (in terms of my understanding of incident and the fact that the count is lower on C1 than C2.)

Screenshots

Additional context
The incident and prevalent descriptions need to be corrected (ie switched), potentially for every C1 and C2 in the library.

I had to hunt around to find these URLs and I think adding them to the GitHub ABOUT would be helpful.

• https://data.ohdsi.org/PhenotypeLibrary/
• https://atlas-phenotype.ohdsi.org/

The second link I do not have access to. Maybe if it is only admins, but if not, instructions on how to get access would be helpful too.

An issue for Cohort ID 228 - [P] Type 2 Diabetes Mellitus indexed on diagnosis, treatment or lab results

The concept set "Hemoglobin A1c (HbA1c) measurements" should also include the following CONCEPT_ID:
37393623 - HbA1c level (Diabetes Control and Complications Trial aligned)

The most common unit is percent (at least in my local DB that uses it)

A Create Date and an Update Date on the Cohort Definitions would be helpful. Before the holidays I was working with a few definitions and now it looks like there are new ones in the library. These dates would help me know how much might be out of sync.

Error Description

No idea what is happening with the PhenotypeLibrary website, but I just tried accessing it this morning and got this error page:

Error Details

Here is the traceback:

su: ignoring --preserve-environment, it's mutually exclusive with --login
Connecting using PostgreSQL driver
Error in value[[3L]](cond) : 
  Error reading from results.database: org.postgresql.util.PSQLException: ERROR: permission denied for table database
Calls: runApp ... tryCatch -> tryCatchList -> tryCatchOne -> <Anonymous>
Execution halted

Additional Details

I tested on the Vivaldi browser:

Vivaldi | 4.2.2406.52 (Stable channel) (x86_64)
-- | --
Revision | 15706f801ff5b5ab0b97af359fe9b12707e81807
OS | macOS Version 10.15.7 (Build 19H1417)
JavaScript | V8 9.3.345.19
User Agent | Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.99 Safari/537.36
Command Line | /Applications/Vivaldi.app/Contents/MacOS/Vivaldi --flag-switches-begin --flag-switches-end --disable-smooth-scrolling --save-page-as-mhtml
Executable Path | /Applications/Vivaldi.app/Contents/MacOS/Vivaldi
Profile Path | /Users/jzelko3/Library/Application Support/Vivaldi/Default
Linker | ld

And Safari Browser:

Version 14.1.2 (15611.3.10.1.7, 15611)

Hello,

I was trying to use the T2DM_broad.json file in my Atlas instance to generate the cohort. But when I copy the contents of the file from github and put it in my Atlas Instance, the file doesn't load and I don't see any concept sets or cohort definition being generated. For ex, when I copy the json file the screen looks like below and even after I click on "Reload", I don't see any updates in concept tab of Atlas. Can help with this please. May I kindly check with you on what's the right way to do this?

instead of pod.PERSON_ID = e.PERSON_ID used pod.PERSON_ID = pod.EVENT_ID

cteOvul2StartDates (PERSON_ID, EVENT_ID, EPISODE_START_DATE, CATEGORY, DATE_RANK) as
(
select PERSON_ID, EVENT_ID, EPISODE_START_DATE, CATEGORY, 4 as DATE_RANK
from
(
select e.PERSON_ID, e.EVENT_ID as EVENT_ID, dateadd(d,(-14) + 1, p.EVENT_DATE) as EPISODE_START_DATE, p.Category,
row_number() over (partition by e.person_id, e.event_id order by p.EVENT_DATE) as rn
from cteOutcomeEvents e
JOIN @target_database_schema.@tablestem_term_durations lb on e.Category = lb.CATEGORY
JOIN ctePriorOutcomeDates pod on pod.PERSON_ID = pod.EVENT_ID and pod.EVENT_ID = e.EVENT_ID
JOIN #pregnancy_events p on e.PERSON_ID = p.PERSON_ID
where p.CATEGORY = 'OVUL2'
and dateadd(d,(-14) + 1, p.EVENT_DATE) between
case when dateadd(d, lb.retry , pod.PRIOR_OUTCOME_DATE) > dateadd(d, -1* lb.MAX_TERM, e.EVENT_DATE) then dateadd(d, lb.retry , pod.PRIOR_OUTCOME_DATE)
else dateadd(d, -1* lb.MAX_TERM, e.EVENT_DATE) end
and dateadd(d, -1* lb.MIN_TERM, e.EVENT_DATE)
) Q
where rn=1
)

R check reveals some issues in the code that need to be fixed:

* checking R code for possible problems ... NOTE
getPhenotypeLog: no visible binding for global variable ‘cohortId’
getPhenotypeLog: no visible binding for global variable ‘addedVersion’
getPhenotypeLog: no visible binding for global variable ‘addedDate’
getPhenotypeLog: no visible binding for global variable
  ‘deprecatedVersion’
getPhenotypeLog: no visible binding for global variable
  ‘deprecatedDate’
getPhenotypeLog: no visible binding for global variable
  ‘updatedVersion’
getPhenotypeLog: no visible binding for global variable ‘updatedDate’
getPhenotypeLog: no visible binding for global variable ‘notes’
getPlCohortDefinitionSet: no visible binding for global variable
  ‘cohortId’
updatePhenotypeLog: no visible binding for global variable
  ‘createdDate’
updatePhenotypeLog: no visible binding for global variable
  ‘modifiedDate’
updatePhenotypeLog: no visible binding for global variable ‘name’
updatePhenotypeLog: no visible binding for global variable
  ‘description’
updatePhenotypeLog: no visible binding for global variable ‘cohortId’
updatePhenotypeLog: no visible binding for global variable ‘cohortName’
updatePhenotypeLog: no visible binding for global variable ‘addedDate’
updatePhenotypeLog: no visible binding for global variable
  ‘updatedDate’
updatePhenotypeLog: no visible binding for global variable ‘notes’
updatePhenotypeLog: no visible binding for global variable ‘getResults’
updatePhenotypeLog: no visible binding for global variable
  ‘addedVersion’
updatePhenotypeLog: no visible binding for global variable
  ‘deprecatedDate’
updatePhenotypeLog: no visible binding for global variable
  ‘deprecatedVersion’
updatePhenotypeLog: no visible binding for global variable
  ‘updatedVersion’
Undefined global functions or variables:
  addedDate addedVersion cohortId cohortName createdDate deprecatedDate
  deprecatedVersion description getResults modifiedDate name notes
  updatedDate updatedVersion

I think these are all caused by not using .data$ or quotes in dplyr calls. For example, this

cohorts <- listPhenotypes() %>%
    filter(cohortId %in% cohortIds)

should be

cohorts <- listPhenotypes() %>%
    filter(.data$cohortId %in% cohortIds)

Of course, nowadays select and rename need quotes instead of .data$.

Are the json files in the Phenotype library supposed to be valid UTF-8?
If not what encoding is used?

library(PhenotypeLibrary)

listPhenotypes()$cohortId |>
  getPlCohortDefinitionSet() |>
  dplyr::filter(!validUTF8(json)) 
#> # A tibble: 5 × 4
#>   cohortId cohortName                                              json    sql  
#>      <dbl> <chr>                                                   <chr>   <chr>
#> 1        6 [P] Fever (3Pe, 30Era)                                  "{\n\t… "CRE…
#> 2       16 [P] Exposure to SARS-Cov 2 and coronavirus (7Pe, 30Era) "{\n\t… "CRE…
#> 3       29 [W] Autoimmune condition (FP)                           "{\n\t… "CRE…
#> 4       64 [P] Flu-like symptoms (3P, 30Era)                       "{\n\t… "CRE…
#> 5       73 [W] Pregnancy (270P, 0Era)                              "{\n\t… "CRE…

^{Created on 2023-06-20 with reprex v2.0.2}

sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.2.2 (2022-10-31)
#>  os       macOS Big Sur ... 10.16
#>  system   x86_64, darwin17.0
#>  ui       X11
#>  language (EN)
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       Europe/London
#>  date     2023-06-20
#>  pandoc   3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package          * version date (UTC) lib source
#>  backports          1.4.1   2021-12-13 [1] CRAN (R 4.2.0)
#>  bit                4.0.5   2022-11-15 [1] CRAN (R 4.2.0)
#>  bit64              4.0.5   2020-08-30 [1] CRAN (R 4.2.0)
#>  checkmate          2.2.0   2023-04-27 [1] CRAN (R 4.2.0)
#>  cli                3.6.1   2023-03-23 [1] CRAN (R 4.2.0)
#>  crayon             1.5.2   2022-09-29 [1] CRAN (R 4.2.0)
#>  digest             0.6.31  2022-12-11 [1] CRAN (R 4.2.0)
#>  dplyr              1.1.2   2023-04-20 [1] CRAN (R 4.2.0)
#>  evaluate           0.21    2023-05-05 [1] CRAN (R 4.2.0)
#>  fansi              1.0.4   2023-01-22 [1] CRAN (R 4.2.0)
#>  fastmap            1.1.1   2023-02-24 [1] CRAN (R 4.2.0)
#>  fs                 1.6.2   2023-04-25 [1] CRAN (R 4.2.0)
#>  generics           0.1.3   2022-07-05 [1] CRAN (R 4.2.0)
#>  glue               1.6.2   2022-02-24 [1] CRAN (R 4.2.0)
#>  hms                1.1.3   2023-03-21 [1] CRAN (R 4.2.0)
#>  htmltools          0.5.5   2023-03-23 [1] CRAN (R 4.2.0)
#>  knitr              1.43    2023-05-25 [1] CRAN (R 4.2.0)
#>  lifecycle          1.0.3   2022-10-07 [1] CRAN (R 4.2.0)
#>  magrittr           2.0.3   2022-03-30 [1] CRAN (R 4.2.0)
#>  PhenotypeLibrary * 3.2.0   2023-06-20 [1] local
#>  pillar             1.9.0   2023-03-22 [1] CRAN (R 4.2.0)
#>  pkgconfig          2.0.3   2019-09-22 [1] CRAN (R 4.2.0)
#>  purrr              1.0.1   2023-01-10 [1] CRAN (R 4.2.0)
#>  R.cache            0.16.0  2022-07-21 [1] CRAN (R 4.2.0)
#>  R.methodsS3        1.8.2   2022-06-13 [1] CRAN (R 4.2.0)
#>  R.oo               1.25.0  2022-06-12 [1] CRAN (R 4.2.0)
#>  R.utils            2.12.2  2022-11-11 [1] CRAN (R 4.2.0)
#>  R6                 2.5.1   2021-08-19 [1] CRAN (R 4.2.0)
#>  readr              2.1.4   2023-02-10 [1] CRAN (R 4.2.0)
#>  reprex             2.0.2   2022-08-17 [1] CRAN (R 4.2.0)
#>  rlang              1.1.1   2023-04-28 [1] CRAN (R 4.2.0)
#>  rmarkdown          2.22    2023-06-01 [1] CRAN (R 4.2.0)
#>  rstudioapi         0.14    2022-08-22 [1] CRAN (R 4.2.0)
#>  sessioninfo        1.2.2   2021-12-06 [1] CRAN (R 4.2.0)
#>  styler             1.10.1  2023-06-05 [1] CRAN (R 4.2.0)
#>  tibble             3.2.1   2023-03-20 [1] CRAN (R 4.2.0)
#>  tidyselect         1.2.0   2022-10-10 [1] CRAN (R 4.2.0)
#>  tzdb               0.4.0   2023-05-12 [1] CRAN (R 4.2.0)
#>  utf8               1.2.3   2023-01-31 [1] CRAN (R 4.2.0)
#>  vctrs              0.6.2   2023-04-19 [1] CRAN (R 4.2.0)
#>  vroom              1.6.3   2023-04-28 [1] CRAN (R 4.2.0)
#>  withr              2.5.0   2022-03-03 [1] CRAN (R 4.2.0)
#>  xfun               0.39    2023-04-20 [1] CRAN (R 4.2.0)
#>  yaml               2.3.7   2023-01-23 [1] CRAN (R 4.2.0)
#> 
#>  [1] /Library/Frameworks/R.framework/Versions/4.2/Resources/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────

When installing this package I get a number of warnings like this. Is non-portability of long file names a potential concern?

 Warning in utils::tar(filepath, pkgname, compression = compression, compression_level = 9L,  :
     storing paths of more than 100 bytes is not portable:
     'PhenotypeLibrary/inst/197320000/literature/Acute kidney injury or Acute renal failure syndrome literature review.xlsx'

Your package is almost ready for inclusion in HADES! I did find some issues that need resolving first:

1. There are no unit test. Please add some. Since the package only has 2 functions, it should be easy to achieve 100% coverage ;-)
2. The package manual (PDF) and website are out of date. At the next release, don't forget to run these lines in PackageMaintenance.R before pushing to main.
3. The set of issue labels does not correspond to those recommended in HADES. You don't have to copy them exactly, but right now the issue labels are a bit of a mess.
4. Please clean up your issues (I think all can be closed).

Ideally, there should also be a vignette or maybe just some example code in the README demonstrating how to use the package, for example in combination with CohortDiagnostics, to instantiate cohorts in the Library.

Not a requirement for HADES, but I think many people will want to know

• Which phenotypes are included in the library, and how to find out if they're any good for a new study someone wants to design.
• By what process the Phenotype Library came about. What quality checks have been applied, etc.
• Where to find the Cohort Diagnostics of these phenotypes.
• How one can contribute new phenotypes.

This does not have to be documented in this repo, but pointers to websites where this is documented would be helpful.

Identify the cohort definition
46271022005 | Chronic kidney disease prevalent cohort: earliest CKD with evidence CKD 90d post-index, 2+ dialysis events 90d apart, 2+ kayexlate drug exposure 90d apart, or evidence of AV Fistula

Describe the issue
The CKD definition says it exclude Stage 1 & 2 but has:
3185897 | Chronic kidney disease stage II

Currently this concept is not used (Nebraska Lexicon), according to PHEOBE. But if you are doing clean up it would better non paper.

Additional context
Low Priority

Might I ask that extra/PhenotypeDescription.csv get moved into inst so that its contents may be used for other (dependent) packages? Pretty please.

I would like to propose ensuring that all json and sql files in the Phenotype library contain Unicode encoded characters. Preferably all characters would be valid UTF-8. Ensuring that all characters are valid UTF8 will make files easier to read into R. See https://rdrr.io/rforge/stringi/man/stringi-encoding.html for some background.

1. I propose converting any non-utf8 encoded characters to their utf8 counterparts (e.g. "Sjögren's syndrome")
2. Adding a test that would check to make sure all text files contain only valid utf8 characters
3. Adding a dataframe/tibble to the package that would contain one row per cohort with metadata, json (as a list column), and sql (as a list column) so that loading the package in R would provide instant access to all phenotype and cohort data the package provides.

Currently there are a number of files that contain non-utf8 (ansi) characters.

# verify that all text is valid utf-8
library(dplyr)
phenotypeIds <- list.files("inst") %>%
  stringr::str_subset("\\d+")

jsonFiles <- purrr::map(phenotypeIds, ~paste0(glue::glue("inst/{.}/") , list.files(glue::glue("inst/{.}")))) %>%
  unlist() %>%
  stringr::str_subset("\\.json")

df <- tibble::tibble(jsonFiles) %>%
  mutate(isutf8 = purrr::map(jsonFiles, ~stringi::stri_enc_isutf8(readr::read_lines_raw(.)))) %>%
  mutate(bad_lines = purrr::map_chr(isutf8, ~paste(which(!.), collapse = ","))) %>%
  filter(bad_lines != "") %>%
  mutate(notutf8 = paste("file:", jsonFiles, "lines:", bad_lines)) %>%
  select(notutf8)

if(nrow(df) != 0){
  message(paste0(c("The following lines contain non-utf8 characters", df$notutf8), collapse = "\n"))
}

#> The following lines contain non-utf8 characters
#> file: inst/254443000/254443001.json lines: 24,30
#> file: inst/254443000/254443002.json lines: 24,30
#> file: inst/254443000/254443003.json lines: 30
#> file: inst/254443000/254443004.json lines: 64
#> file: inst/378419000/378419003.json lines: 966,1227
#> file: inst/4098597000/4098597001.json lines: 24,30
#> file: inst/4098597000/4098597002.json lines: 24,30
#> file: inst/4101602000/4101602001.json lines: 24,30
#> file: inst/4101602000/4101602002.json lines: 24,30
#> file: inst/4101602000/4101602003.json lines: 23,29
#> file: inst/4101602000/4101602004.json lines: 73
#> file: inst/4137275000/4137275003.json lines: 23
#> file: inst/4164770000/4164770001.json lines: 24,30
#> file: inst/4164770000/4164770002.json lines: 24,30
#> file: inst/4164770000/4164770003.json lines: 63
#> file: inst/4266367000/4266367003.json lines: 173,929
#> file: inst/436642000/436642004.json lines: 81
#> file: inst/437663000/437663003.json lines: 129,885

Suuuuper thanks for working on this!

I want to contribute, but hate to step on others' toes. To that end, could you add a contribution file? Here's an example from GitHub (the organization, not the platform 😄).

I'm happy to have my hand at it, if you like.

Thanks!!