Trajectories matter: Discovery and validation of ordered EHR sequences that inform clinical risk predictions

Structured AbstractO_ST_ABSObjectiveC_ST_ABSTo test whether temporally ordered clinical sequences mined from EHRs improve the understanding of downstream adverse outcomes versus unordered bag-of-codes representations. Materials and MethodsWithin the NIH All of Us Controlled Tier, we mined frequent ordered event pairs (A[->]B) and tested for risk elevation of later adverse events (C) to create three-event (A[->]B[->]C) sequences. The algorithm used observation-period-aware indexing, a maximum 5-year A[->]B gap, and 90-day latency before follow-up. Comparators were (B without prior A)[->]C (primary), B[->]A[->]C, (A without B)[->]C, and a calendar-time baseline. Covariates were balanced with inverse probability of treatment weighting (IPTW) Weighted Aalen-Johansen estimated cumulative incidence at 1, 2, and 5 years. Discovery and confirmation were analyzed separately with global Benjamini- Hochberg false discover rate (BH-FDR). ResultsOf 633,545 persons, 432,617 met eligibility ([≥]365 observed days) and were split 70/30 into discovery and confirmation. We mined 3,066,183 trajectories from the discovery set by combining 340,687 sufficiently supported A[->]B pairs with each of 9 curated adverse third events. Due to compute constraints, we tested 20,565 of these trajectories (0.67%) for risk elevation. After discovery FDR, 234 trajectories advanced, and 39 validated for at least one time horizon in confirmation. At 5 years, the median risk ratio (RR) was 3.96 versus baseline and 2.18 versus B[->]C with no prior A. Reverse-order checks were feasible for 89.7% of hypotheses; the median (A[->]B[->]C) vs (B[->]A[->]C) RR was 1.21. DiscussionOrdered trajectories captured clinically coherent pathways where temporal order added information beyond diagnosis presence alone. ConclusionTrajectory mining and confirmation reveal actionable, risk pathways that complement conventional risk models.