Feedback loops in intensive care unit prognostic models: an under-recognised threat to clinical validity

Abstract

Prognostic models developed for use in the intensive care unit (ICU) can inform treatment decisions and improve patient care. However, despite extensive research, few models have contributed to improved patient-centred outcomes. A major limitation is that the influence of treatment interventions on patient outcomes during model development and validation is often overlooked. Upon implementation, prognostic models can affect clinical interventions, creating feedback loops that alter the relationship between predictors and observed patient outcomes. This alteration caused by model-mediated intervention is known as model drift. Positive feedback loops reinforce initial prognoses, leading to self-fulfilling prophecies, whereas negative feedback loops obscure the efficacy of successful interventions by rendering them as apparent model inaccuracies. To mitigate these issues, prognostic models for use in ICUs should account for treatment effects and the causal relationships among predictions, interventions, and outcomes. Thus, collaboration among data scientists, epidemiologists, clinical researchers, and implementation scientists is required to ensure that prognostic models enhance patient care without causing inadvertent harm.