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Using electronic health record collected clinical variables to predict medical intensive care unit mortality

View Article: PubMed Central - PubMed

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.

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.

No MeSH data available.


Patient distribution across AutoTriage score for survivors and non-survivors. The vertical line represents an AutoTriage score of −2.
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fig3: Patient distribution across AutoTriage score for survivors and non-survivors. The vertical line represents an AutoTriage score of −2.

Mentions: In Fig. 3 we represent the distribution of patients across the AutoTriage score for survivors and non-survivors. The vertical line represents the threshold of AutoTriage = −2, which represents a sensitivity of 80% and a specificity of 81%. Adjusting the threshold to a lower value will flag additional patients at risk but increase the number of false positive alarms.


Using electronic health record collected clinical variables to predict medical intensive care unit mortality
Patient distribution across AutoTriage score for survivors and non-survivors. The vertical line represents an AutoTriage score of −2.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5037117&req=5

fig3: Patient distribution across AutoTriage score for survivors and non-survivors. The vertical line represents an AutoTriage score of −2.
Mentions: In Fig. 3 we represent the distribution of patients across the AutoTriage score for survivors and non-survivors. The vertical line represents the threshold of AutoTriage = −2, which represents a sensitivity of 80% and a specificity of 81%. Adjusting the threshold to a lower value will flag additional patients at risk but increase the number of false positive alarms.

View Article: PubMed Central - PubMed

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.

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.

No MeSH data available.