Early Warning Systems: The Neglected Importance of Timing

Detection systems face the limitation that they lack the capability to identify a state before its occurrence. Prediction systems are more likely to be actionable, as they provide more lead time for intervention, but accurate prediction models are also more difficult to develop. With a predictive system, an additional dimension of timing becomes important: the time horizon for prediction. Prediction models may be trained to recognize a state within a specific time frame (eg, 6, 12, or 24 hours), and test characteristics, including PPV, may vary with the window.¹⁸ A given PPV (of eventual development of sepsis) is compatible with varying time windows and thus again lacks important information on performance.

The timing relative to clinical time zero remains important for prediction. For a predictive EWS, the graph in the figure may be expected to shift to the left. Models with good performance will occasionally send an alert after time zero. For a prediction system with a time horizon of six hours, it is more useful to have alerts occur a mean time of four hours prior to time zero than four minutes prior.

Improving the clinical utility of EWSs requires better measurement of timing. Researchers should incorporate timing into system development, and operational leaders should be cognizant of timing during implementation. Specific steps should include devising better strategies to estimate the relationship of state recognition to clinical time zero and developing methods to discount recognition when it occurs too late to be actionable.

Disclosures

Dr. Rolnick is a consultant to Tuple Health, Inc. and was previously a part-time employee of Acumen, LLC. Dr. Weissman has nothing to disclose.