reporting-adverse-events

# Reporting adverse events into FAERS / ICH E2B(R3) A pharmacovigilance case starts as free-text narrative ("68 yo on warfarin developed GI bleed, hospitalized"). To make it reportable you must structure it into the **ICH E2B(R3)** data elements that the FDA's **FAERS** (and EMA's EudraVigilance) expect: a **suspect drug**, one or more **reactions** coded to **MedDRA** Preferred Terms, **seriousness** criteria, and a **reaction outcome**. OpenMed extracts the drug and condition spans on-device; this skill turns those spans plus the narrative into the E2B(R3) skeleton. The reaction coding step needs **MedDRA**, which is **licensed by the MSSO and user-supplied** — it is never bundled with OpenMed and must be loaded from the user's own subscription. ## When to use - A narrative names a drug and an adverse reaction and you need an ICSR (Individual Case Safety Report) shell with the right E2B(R3) fields. - You must classify **seriousness** (E2B sections C.1.7 / E.i.3) — death, life-threatening, hospitalization/prolongation, disability, congenital anomaly, or "other medically important condition". - You need to characterize each drug as **suspect / concomitant / interacting** (the `drugcharacterization` axis FAERS uses). - You are pre-filling a 3500A / FAERS electronic submission or staging cases for a safety database. This skill produces a **structured draft for human safety review** — it does not file reports or perform causality assessment autonomously. ## Quick start ```python import openmed narrative = ( "68-year-old patient on warfarin 5 mg daily developed a gastrointestinal " "hemorrhage and was hospitalized. Warfarin was discontinued; the patient " "recovered." ) # 1) Extract drug spans (Pharmaceutical category) on-device. drugs = openmed.analyze_text( narrative, model_name="pharma_detection_superclinical", output_format="dict", )["entities"] # 2) Extract condition / reaction spans (Disease category). conditions = openmed.analyze_text( narrative, model_name="disease_detection_superclinical", output_format="dict", )["entities"] # 3) Assemble an E2B(R3)-shaped ICSR skeleton (reaction PTs filled later via MedDRA). icsr = { "patient": {"age": None, "sex": None}, # from de-identified demographics "drugs": [ { "name": e["text"], "drugcharacterization": 1, # 1=suspect 2=concomitant 3=interacting "action": None, # e.g. drug withdrawn / dose reduced } for e in drugs ], "reactions": [ { "verbatim": e["text"], # narrative term, pre-MedDRA "meddra_pt": None, # coded with user's MedDRA dict "outcome": None, # E2B reaction outcome code } for e in conditions ], "seriousness": { "serious": None, "death": False, "lifeThreatening": False, "hospitalization": True, "disability": False, "congenitalAnomaly": False, "otherMedicallyImportant": False, }, } ``` ## E2B(R3) seriousness and outcome value sets Seriousness is a set of boolean criteria (E2B E.i.3.2). A case is **serious** if *any* criterion is true: | Criterion | E2B element | FAERS field | | --- | --- | --- | | Death | E.i.3.2a | `seriousnessdeath` | | Life-threatening | E.i.3.2b | `seriousnesslifethreatening` | | Hospitalization / prolonged | E.i.3.2c | `seriousnesshospitalization` | | Disability / incapacity | E.i.3.2d | `seriousnessdisabling` | | Congenital anomaly | E.i.3.2e | `seriousnesscongenitalanomali` | | Other medically important | E.i.3.2f | `seriousnessother` | Reaction outcome (E2B E.i.7) is a coded value: `1` recovered/resolved, `2` recovering/resolving, `3` not recovered/not resolved, `4` recovered with sequelae, `5` fatal, `6` unknown. Drug characterization (E2B G.k.1): `1` suspect, `2` concomitant, `3` interacting. ## Workflow 1. **De-identify first.** Run `openmed.deidentify(narrative, policy=...)` and work from `result.deidentified_text`. Patient name, MRN, and dates must be removed/shifted before the case leaves your environment. 2. **Extract drugs and reactions** with the two `analyze_text` calls above. Keep each entity's `start`/`end` offsets for traceability. 3. **Characterize each drug** as suspect (`1`), concomitant (`2`), or interacting (`3`). The drug that temporally precedes the reaction and was acted upon (withdrawn/reduced) is usually the suspect. 4. **Code reactions to MedDRA.** Map each verbatim reaction term to a MedDRA **Preferred Term (PT)** and its System Organ Class using the user's licensed MedDRA dictionary (see "Edge cases"). Never invent PTs. 5. **Determine seriousness.** Scan the narrative for the six criteria; set `serious=True` if any is met. "Hospitalized", "admitted", "ICU" → C.1.7c. 6. **Assign reaction outcome** from the value set above. 7. **Hand the structured draft to a qualified safety reviewer** for causality (e.g. WHO-UMC or Naranjo), expectedness, and final submission. ## Hand-off to / from OpenMed OpenMed's `analyze_text` returns a `dict`; `result["entities"]` is a list whose items carry `text`, `label`, `confidence`, `start`, `end`. Consume them: - **From** `extracting-clinical-entities`: Pharmaceutical entities → `icsr["drugs"]`; Disease entities → `icsr["reactions"]`. Keep offsets so each E2B field is traceable to the source span. - **From** `normalizing-rxnorm`: optionally attach an RxCUI to each suspect drug for product identification (E2B G.k.2.2) before coding. - **De-identify** with `deidentifying-clinical-text` (`openmed.deidentify`) **before** the case is exported or transmitted to any safety database. - **To** `detecting-pv-signals`: aggregated, coded cases feed disproportionality analysis. **To** `querying-openfda-labels`: confirm the reaction is/ isn't a labeled event (expectedness). ## Edge cases & gotchas - **MedDRA is licensed — never bundle it.** MedDRA is distributed by the MSSO under subscription; OpenMed ships none of it. Load PTs/LLTs from the user's own MedDRA release (the version is itself a reportable field, E2B C.1.x). Verbatim reaction text stays in the case until a coder maps it. - **One reaction term ≠ one PT.** "GI bleed" maps to the PT *Gastrointestinal haemorrhage*; keep the verbatim term alongside the coded PT for the audit trail. Multi-word reactions span several OpenMed tokens — reassemble by offset. - **Suspect vs concomitant matters.** Disproportionality and labeling decisions hinge on `drugcharacterization`. Do not default every drug to suspect. - **Seriousness is OR, not a severity scale.** A mild rash that caused hospitalization is *serious*; a severe headache that resolved at home may not be. Classify by the six regulatory criteria, not by clinical severity words. - **Causality is out of scope here.** This skill structures the case; it does not assert the drug caused the event. Leave causality to the reviewer. - **Local-first.** NER and de-identification run on-device. Only de-identified, structured case data should reach an external safety database, and only under the appropriate regulatory agreement. ## Standards & references - FDA FAERS overview: https://www.fda.gov/drugs/surveillance/fda-adverse-event-reporting-system-faers - ICH E2B(R3) ICSR implementation guide: https://www.ich.org/page/efficacy-guidelines (E2B(R3)) - FDA E2B(R3) regional implementation: https://www.fda.gov/industry/fda-data-standards-advisory-board/ich-e2br3-individual-case-safety-report-icsr - MedDRA (licensed, user-supplied): https://www.meddra.org/ - FDA MedWatch 3500A reporting: https://www.fda.gov/safety/medical-product-safety-information/medwatch-fda-safety-information-and-adverse-event-reporting-program