Computational gene mapping to analyze continuous automated physiologic monitoring data in neuro-trauma intensive care

We asked whether the advanced machine learning applications used in microarray gene profiling could assess critical thresholds in the massive databases generated by continuous electronic physiologic vital signs (VS) monitoring in the neuro-trauma intensive care unit.

We used Class Prediction Analysis to predict binary outcomes (life/death, good/bad Extended Glasgow Outcome Score, etc.) based on data accrued within 12, 24, 48, and 72 hours after admission to the neuro-trauma intensive care unit. Univariate analyses selected "features," discriminator VS segments or "genes," in each individual's data set. Prediction models using these selected features were then constructed using six different statistical modeling techniques to predict outcome for other individuals in the sample cohort based on the selected features of each individual then cross-validated with a leave-one-out method.

We gleaned complete sets of 588 VS monitoring segment features for each of four periods and outcomes from 52 of 60 patients with severe traumatic brain injury who met study inclusion criteria. Overall, intracranial pressures and blood pressures over time (e.g., intracranial pressure >20 mm Hg for 20 minutes) provided the best discrimination for outcomes. Modeling performed best in the first 12 hours of care and for mortality. The mean number of selected features included 76 predicting 14-day hospital stay in that period, 11 predicting mortality, and 4 predicting 3-month Extended Glasgow Outcome Score. Four of the six techniques constructed models that correctly identified mortality by 12 hours 75% of the time or higher.

Our results suggest that valid prediction models after severe traumatic brain injury can be constructed using gene mapping techniques to analyze large data sets from conventional electronic monitoring data, but that this methodology needs validation in larger data sets, and that additional unstructured learning techniques may also prove useful.