Abstract:
Background
Clinical decision support systems are used to help predict patient stability and mortality in the Intensive Care Unit (ICU). Accurate patient information can assist clinicians with patient management and in allocating finite resources. However, systems currently in common use have limited predictive value in the clinical setting. The increasing availability of Electronic Health Records (EHR) provides an opportunity to use medical information for more accurate patient stability and mortality prediction in the ICU.
Objective
Develop and evaluate an algorithm which more accurately predicts patient mortality in the ICU, using the correlations between widely available clinical variables from the EHR.
Methods
We have developed an algorithm, AutoTriage, which uses eight common clinical variables from the EHR to assign patient mortality risk scores. Each clinical variable produces a subscore, and combinations of two or three discretized clinical variables also produce subscores. A combination of weighted subscores produces the overall score. We validated the performance of this algorithm in a retrospective study on the MIMIC III medical ICU dataset.
Results
AutoTriage 12 h mortality prediction yields an Area Under Receiver Operating Characteristic value of 0.88 (95% confidence interval 0.86 to 0.88). At a sensitivity of 80%, AutoTriage maintains a specificity of 81% with a diagnostic odds ratio of 16.26.
Keywords:
Clinical decision support systems, Mortality prediction, Electronic health records, Medical informatics
Citation: Jacob Calvert, Qingqing Mao, Jana L. Hoffman, Melissa Jay, Thomas Desautels, Hamid Mohamadlou, Uli Chettipally, Ritankar Das Using Electronic Health Record Collected Clinical Variables To Predict Medical Intensive Care Unit Mortality DOI: http://dx.doi.org/10.1016/j.amsu.2016.09.002
Published Online: September 06, 2016, Accepted: September 4, 2016, Received in revised form: September 2, 2016, Received: May 19, 2016
Copyright: © 2016 The Authors. Published by Elsevier Ltd on behalf of IJS Publishing Group Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Conclusions
Through the multidimensional analysis of the correlations between eight common clinical variables, AutoTriage provides an improvement in the specificity and sensitivity of patient mortality prediction over existing prediction methods.
