چکیده (انگلیسی):

Background: The presence of Alcohol Use Disorder (AUD) complicates the medical conditions of patients
and increases the difficulty of detecting and predicting the onset of septic shock for patients in the ICU.
Methods: We have developed a high-performance sepsis prediction algorithm, InSight, which outperforms
existing methods for AUD patient populations. InSight analyses a combination of singlets,
doublets, and triplets of clinical measurements over time to generate a septic shock risk score. AUD
patients obtained from the MIMIC III database were used in this retrospective study to train InSight and
compare performance with the Modified Early Warning Score (MEWS), the Simplified Acute Physiology
Score (SAPS II), and the Systemic Inflammatory Response Syndrome (SIRS) for septic shock prediction
and detection.
Results: From 4-fold cross validation, InSight performs particularly well on diagnostic odds ratio and
demonstrates a relatively high Area Under the Receiver Operating Characteristic (AUROC) metric. Four
hours prior to onset, InSight had an average AUROC of 0.815, and at the time of onset, InSight had an
average AUROC value of 0.965. When applied to patient populations where AUD may complicate prediction
methods of sepsis, InSight outperforms existing diagnostic tools.
Conclusions: Analysis of the higher order correlations and trends between relevant clinical measurements
using the InSight algorithm leads to more accurate detection and prediction of septic shock, even
in cases where diagnosis may be confounded by AUD.