Departures from the protocol during conduct of a clinical trial: a pattern from the data record consistent with a learning curve.

OBJECTIVE: Recognition of learning curves in medical skill acquisition has enhanced patient safety through improved training techniques. Clinical trials research has not been similarly scrutinised. The VALsartan In Acute myocardial iNfarcTion, a large multinational, pragmatic, randomised, double-blind, multicentre trial, was retrospectively evaluated for evidence of research conduct consistent with a performance "learning curve". DESIGN: Records provided protocol departure (deviations/violations) and documentation query data. For each site, analysis included patient order (eg, first, second), recruitment rate and first enrollment relative to study start date. SETTING: Computerised data from a trial coordinated by an academic research organisation collaborating with 10 academic and 2 commercial research organisations and an industry sponsor. Interventions 931 sites enrolled 14,703 patients. Departures were restricted to the first year. Exclusions included patient's death or loss to follow-up within 12 months and subjects enrolled 80th or higher at a site. Departures were assessed for variance with higher patient rank, more frequent recruitment and later start date. METHODS AND RESULTS: 12,367 patients at 931 sites were analysed. Departures were more common for patients enrolled earlier at a site (p<0.0001). For example, compared with the 30th patient, the first had 47% more departures. Departures were also more common with slower enrollment and site start closer to the trial start date (p<0.0001). Similar patterns existed for queries. CONCLUSIONS: Research performance improved during the VALsartan In Acute myocardial iNfarcTion consistent with a "learning curve". Although effects were not related to a change in outcome (mortality), learning curves in clinical research may have important safety, ethical, research quality and economic implications for trial conduct.

Duke Scholars

Author Jeffrey Marc Taekman Anesthesiology, Neuroanesthesia

Author Mark Stafford-Smith Anesthesiology, Cardiothoracic

Author Mark Franklin Newman Anesthesiology, Cardiothoracic