| 1. Features/strengths |
Institution‐level data Detailed health records (electronic and paper), consisting of patient demographics and clinical characteristics (e.g., height/weight, genetics, allergy/family/social/history, organ functions, and disease status), inclusive details of concomitant diseases, medications, and prescriptions Full assessment of each individual patient is feasible
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Population‐based data Opportunity for decade‐long follow‐up period Efficient data analysis possible because of its structured secondary database nature Sample size large enough to perform subgroup analyses
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Nation‐level data ADRs reported voluntarily across healthcare professionals, pharmaceutical delegates, and the public The voluminous reports of longitudinal nature facilitate the extraction of knowledge from rare signals
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| 2. Challenges and limitations |
Laborious data collection: individual health records (electronic and paper) review or assessment is essential Transformation of unstructured data (verbatim—e.g., admission/progress/nursing notes, and diagnostic reports) into structured/schematized ones might be necessary Generalizability is confined by limited study duration, setting/site, target cohort, and single institution (tertiary care)
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Inadequate clinical information Unavailable laboratory and body image data Lack of a patient's genetics, socioeconomic status, health habits, and lifestyle Drug prescriptions and dispensing records may not fully reflect the real‐life drug adherence of the patients
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Possible underreporting, biased reporting or misclassification, incomplete or missing data, stimulated reporting (by regulatory actions or publicity), and duplicate reporting (e.g., from healthcare institutes and the pharmaceutical industry) Absence of information on population exposure, patients’ clinical details, and confirming rechallenge data
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| 3. Data sectors utilized |
Patient demographic data Comprehensive medical records (outpatient, emergency department, inpatient) Precise laboratory data files Complete pharmacy (hospital) dispensing datasets
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Patient registration data sets Medical data sets (outpatients, emergency department, inpatients) Pharmacy data sets (hospital, community)
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Constructed data: origin of the report, patient demographics, prescription and comedication details, and ADR type and seriousness Verbatim: specifics on clinical presentations, comorbid medical conditions, liver biochemistry values, and ADR manifestations and consequences
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| 4. Drug‐associated covariates identified |
Nephrotoxic polypharmacy: significant association between four‐nephrotoxic polypharmacy and contrast medium–induced nephropathy among inpatients undergoing contrast‐enhanced computed tomography Interacting drugs: drug‐related factors (amiodarone cumulative dose, interacting drugs) are significant predictors of amiodarone‐associated acute liver injury
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Dose intensity (usage duration, exposure frequency) of bisphosphonates: inversely associated with esophageal cancer risk Cumulative doses of poststroke statin exposure: inversely associated with PSE risk Comedications: potential predictors of or protectors against PSE identified
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Risk of amiodarone‐related liver injury are associated with:
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