Yehya, Nadir; Peters, Mark J; (2025) Current Consent Models Perpetuate Ignorance in Pediatric Critical Care. Pediatric Critical Care Medicine 10.1097/PCC.0000000000003730. (In press).

Text Peters_PCCM trials editorial submitted.pdf Access restricted to UCL open access staff until 15 March 2026. Download (251kB)

Abstract

Clinical trials are poorly integrated into clinical practice (1,2). Furthermore, as currently structured, they are expensive, inefficient, and provide answers that offer limited value for everyday clinical decision-making (1). The present system is designed primarily to ensure patient safety when investigating new unapproved products. It is unclear how best to transform this into a system capable of improving patient outcomes in a safe, equitable, cost-efficient, and ethical manner. These problems are exacerbated in critical care, and doubly so in pediatric critical care. Lengthy consent forms are routinely presented for prospective signed consent to overwhelmed parents and caregivers within minutes of being told that their child is at risk of dying. As investigators, we expect that these documents are read, adequately understood (3,4), risks and benefits considered sufficiently by non-trained people to render an informed decision, and documented. This seems unreasonable. Indeed, this system seems primed instead to guarantee slow accrual rates, non-representative enrollment with under-representation of severely ill and disadvantaged children (5,6), and uninformative (not truly negative, but simply uninterpretable) trials (7). For example, U.S. studies have demonstrated lower rates of both approach and consent for Black, Hispanic, and non-English speaking families (5,6,8). Trial populations therefore may differ substantially from the cohort at risks, thereby introducing bias into trial results. Overall, the process is suboptimal and drives many practitioners to simply guess the best interventions for an individual patient. In an ideal system, each such decision should be part of a systematic inquiry from which we can reduce uncertainty

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