Electronic Health Records (EHRs) consists of patient information such as demographics, medications, laboratory test results, diagnosis codes and procedures. Mining EHRs could lead to improvement in patient healthcare management as EHRs contain detailed information related to disease prognosis for large patient populations. We hypothesize that a patient's condition does not deteriorate at random, the trajectories, sequences in which diseases appear in a patient, are determined by a finite number of underlying disease mechanisms. In this work, we exploit this idea by predicting a patient's risk of mortality in the context of the metabolic syndrome by assessing which of many available trajectories a patient is following and progression along this trajectory. Implementing this idea required innovative enhancements both for the study design and also for the fitting algorithm. We propose a forensic-style study design, which aligns patients on last follow-up and measures time backwards. We modify the time-dependent covariate Cox proportional hazards model to better capture coefficients of covariate that follow a particular temporal sequence, such as trajectories. Knowledge extracted from such analysis can lead to personalized treatments, thereby forming the basis for future trajectory-centered guidelines.