Detecting hazardous intensive care patient episodes using real-time mortality models
Author(s)Hug, Caleb W. (Caleb Wayne)
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
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The modern intensive care unit (ICU) has become a complex, expensive, data-intensive environment. Caregivers maintain an overall assessment of their patients based on important observations and trends. If an advanced monitoring system could also reliably provide a systemic interpretation of a patient's observations it could help caregivers interpret these data more rapidly and perhaps more accurately. In this thesis I use retrospective analysis of mixed medical/surgical intensive care patients to develop predictive models. Logistic regression is applied to 7048 development patients with several hundred candidate variables. These candidate variables range from simple vitals to long term trends and baseline deviations. Final models are selected by backward elimination on top cross-validated variables and validated on 3018 additional patients. The real-time acuity score (RAS) that I develop demonstrates strong discrimination ability for patient mortality, with an ROC area (AUC) of 0.880. The final model includes a number of variables known to be associated with mortality, but also computationally intensive variables absent in other severity scores. In addition to RAS, I also develop secondary outcome models that perform well at predicting pressor weaning (AUC=0.825), intraaortic balloon pump removal (AUC=0.816), the onset of septic shock (AUC=0.843), and acute kidney injury (AUC=0.742). Real-time mortality prediction is a feasible way to provide continuous risk assessment for ICU patients. RAS offers similar discrimination ability when compared to models computed once per day, based on aggregate data over that day.(cont.) Moreover, RAS mortality predictions are better at discrimination than a customized SAPS II score (Day 3 AUC=0.878 vs AUC=0.849, p < 0.05). The secondary outcome models also provide interesting insights into patient responses to care and patient risk profiles. While models trained for specifically recognizing secondary outcomes consistently outperform the RAS model at their specific tasks, RAS provides useful baseline risk estimates throughout these events and in some cases offers a notable level of predictive utility.
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 229-237).
DepartmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.