DOI: 10.1097/ccm.0000000000007384 ISSN: 0090-3493

Association of Acute Respiratory Distress Syndrome Radiographic Findings Identified by Machine Learning and Outcomes of Patients Receiving Invasive Mechanical Ventilation

Yingchao Zhong, Mark Nuppnau, Negar Farzaneh, Sardar Ansari, Michael W. Sjoding
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Objectives:

To determine whether predictions from a deep learning model that identifies acute respiratory distress syndrome (ARDS) findings on chest radiographs (CXRs) can also predict clinical outcomes beyond traditional indicators of hypoxemia and illness severity.

Design:

Retrospective cohort study.

Setting:

Single academic medical center.

Intervention:

None.

Patients:

Adult patients with CXRs performed at the time of invasive mechanical ventilation initiation.

Methods and Main Results:

We applied a deep learning model that was previously trained to identify bilateral airspace opacities consistent with ARDS to CXRs of 6591 patients receiving invasive mechanical ventilation. The resulting ARDS-CXR scores were evaluated for their ability to discriminate risk of 28-day mortality and their association with 28-day mortality and time to extubation after adjusting for a patient’s Pa

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2 /F
io
2 or Acute Physiology and Chronic Health Evaluation-IV. The median ARDS-CXR score was –2.09, corresponding to an ARDS probability of 15%, and 1089 (17%) patients had a positive score, corresponding to an ARDS probability above 50%. Twenty-eight-day mortality was 35.1% among patients with a positive score and 13.5% among patients with a negative score. ARDS-CXR scores below the ARDS threshold also stratified risk, with mortality increasing from 12.8% at a score of –2 to 19.9% at –1 and 27.5% at 0. The ARDS-CXR score area under the receiver operating characteristic (AUROC) curve was 0.68 (95% CI, 0.66–0.70) for predicting 28-day mortality, whereas the Pa
o
2 /F
io
2 AUROC was 0.64 (95% CI, 0.62–0.66). After adjusting for either the patient’s Pa
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2 /F
io
2 or Acute Physiology and Chronic Health Evaluation-IV, a 1-point increase in the ARDS-CXR score was significantly associated with higher 28-day mortality or prolonged time to extubation.

Conclusions:

A deep learning–derived ARDS-CXR score quantifying radiographic findings of ARDS independently predicted mortality and ventilator outcomes beyond hypoxemia and illness severity. Algorithmically derived markers of lung injury severity from chest imaging could be used to support clinical care decisions, predictive enrichment in research studies, or incorporated into a future ARDS definition.

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