Abstract
Physicians could greatly benefit from automated diagnosis and prognosis tools to help address information overload and decision fatigue. Intensive care physicians stand to benefit greatly from such tools as they are at particularly high risk for those factors. Acute Respiratory Distress Syndrome (ARDS) is a life-threatening condition affecting >10% of critical care patients and has a mortality rate over 40%. However, recognition rates for ARDS have been shown to be low (30-70%) in clinical settings. In this work, we present a reproducible computational pipeline that automatically adjudicates ARDS on retrospective datasets of mechanically ventilated adult patients. This pipeline automates the steps outlined by the Berlin Definition through implementation of natural language processing tools and classification algorithms. First, we used labeled chest imaging reports from two different hospitals over three different time periods to train an XGBoost model to detect bilateral infiltrates, and a subset of attending physician notes from one hospital labeled for the most common ARDS risk factor (pneumonia) to train another XGBoost model to detect a pneumonia diagnosis. Both models achieve high performance when tested on out-of-bag samples-an area under the receiver operating characteristic curve (AUROC) of 0.88 for adjudicating chest imaging reports, and an AUROC of 0.86 for detecting pneumonia on attending physician notes. Next, we integrate the models and validate the entire pipeline on a fourth cohort from a third hospital (MIMIC-III) and find a sensitivity of 93.5% - an extraordinary improvement over the 22.6% ARDS recognition rate reported for these encounters - along with a false positive rate of 18.8%. We conclude that our reproducible, automated diagnostic pipeline exhibits promising ARDS retrospective adjudication performance, thus providing a valuable resource for physicians aiming to enhance ARDS diagnosis and treatment strategies. We surmise that real-time integration of the pipeline with EHR systems has the potential to aid clinical practice by facilitating the recognition of ARDS cases at scale.
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