A path to precision in the ICU

Abstract

Precision medicine is increasingly touted as a groundbreaking new paradigm in biomedicine. In the ICU, the complexity and ambiguity of critical illness syndromes have been identified as fundamental justifications for the adoption of a precision approach to research and practice. Inherently protean diseases states such as sepsis and acute respiratory distress syndrome have manifestations that are physiologically and anatomically diffuse, and that fluctuate over short periods of time. This leads to considerable heterogeneity among patients, and conditions in which a "one size fits all" approach to therapy can lead to widely divergent results. Current ICU therapy can thus be seen as imprecise, with the potential to realize substantial gains from the adoption of precision medicine approaches. A number of challenges still face the development and adoption of precision critical care, a transition that may occur incrementally rather than wholesale. This article describes a few concrete approaches to addressing these challenges.First, novel clinical trial designs, including registry randomized controlled trials and platform trials, suggest ways in which conventional trials can be adapted to better accommodate the physiologic heterogeneity of critical illness. Second, beyond the "omics" technologies already synonymous with precision medicine, the data-rich environment of the ICU can generate complex physiologic signatures that could fuel precision-minded research and practice. Third, the role of computing infrastructure and modern informatics methods will be central to the pursuit of precision medicine in the ICU, necessitating close collaboration with data scientists. As work toward precision critical care continues, small proof-of-concept studies may prove useful in highlighting the potential of this approach.

Keywords: Biomedical informatics; Clinical trials; Critical care; Genomics; Intensive care unit; Personalized medicine; Precision health; Precision medicine.

Fig. 1

Effect of increasing quantities of data on revealing important distinctions. Four hours of mean arterial pressure (MAP) monitoring data (simulated) are shown for three different patients. With data recorded every hour (top row), MAP trajectories appear similar between patients, with a median MAP of 70 for all cases (dashed line). With data recorded every minute (bottom row), the median MAP is still 70 for all cases but important differences between patients become evident, with Patient 1 showing a relatively stable MAP, Patient 2 showing a precipitous drop in MAP at around 16:15, and Patient 3 showing a gradually decreasing MAP. In this case, the more granular data reveal differences between physiologic trajectories that were not evident from the sparse data or the median values