An international initiative to create a collaborative for pharmacovigilance in hospice and palliative care clinical...

Medication registration currently requires evidence of safety and efficacy from adequately powered phase 3 studies. Pharmacovigilance (phase 4 studies, postmarketing data, adverse drug reaction reporting) provide data on more widespread and longer term use. Historically, voluntary reporting systems for pharmacovigilance have had low reporting rates, relying on ad hoc reporting and retrospective chart reviews, or prospective registries have often been limited to specific drugs or clinical conditions. Furthermore, these data are often irrelevant in hospice and palliative care due to the timeliness of which such data become available and the unique characteristics of our population and prescribing: compounding comorbidities, progressive organ failure, accumulation of symptom-specific medications, tendency to attribute toxicity to disease progression, use of old, off-patent medications, and incorporation of evolving evidence. There is a need for prospective, systematic pharmacovigilance in hospice and palliative care.

Here we describe an international, Web-based, 128-bit secure initiative to collect pharmacovigilance data documenting net clinical benefit and safety of common medications. The intention is for a diverse and large group of clinical units to record data prospectively on a small deidentified consecutive cohort of patients started on the medication of interest. A new medication would be studied every 3 months. Three key time points (different for each medication) will be assessed for each patient, collecting easily codefiable data at baseline, a point at which clinical benefit should be experienced, and a point at which short- to medium-term toxicities may occur. Toxicities can additionally be recorded at any time they occur. Data collection will take a maximum of 10 minutes per patient.

The intention is to create an efficient, relevant system to improve hospice and palliative care with maximally generalizable results.