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. 2023 Sep;30(1):e100782.
doi: 10.1136/bmjhci-2023-100782.

Long short-term memory model identifies ARDS and in-hospital mortality in both non-COVID-19 and COVID-19 cohort

Jen-Ting Chen  1   2 , Rahil Mehrizi  3 , Boudewijn Aasman  4 , Michelle Ng Gong  2 , Parsa Mirhaji  5
Affiliations

Long short-term memory model identifies ARDS and in-hospital mortality in both non-COVID-19 and COVID-19 cohort

Jen-Ting Chen et al. BMJ Health Care Inform. 2023 Sep.

Abstract

Objective: To identify the risk of acute respiratory distress syndrome (ARDS) and in-hospital mortality using long short-term memory (LSTM) framework in a mechanically ventilated (MV) non-COVID-19 cohort and a COVID-19 cohort.

Methods: We included MV ICU patients between 2017 and 2018 and reviewed patient records for ARDS and death. Using active learning, we enriched this cohort with MV patients from 2016 to 2019 (MV non-COVID-19, n=3905). We collected a second validation cohort of hospitalised patients with COVID-19 in 2020 (COVID+, n=5672). We trained an LSTM model using 132 structured features on the MV non-COVID-19 training cohort and validated on the MV non-COVID-19 validation and COVID-19 cohorts.

Results: Applying LSTM (model score 0.9) on the MV non-COVID-19 validation cohort had a sensitivity of 86% and specificity of 57%. The model identified the risk of ARDS 10 hours before ARDS and 9.4 days before death. The sensitivity (70%) and specificity (84%) of the model on the COVID-19 cohort are lower than MV non-COVID-19 cohort. For the COVID-19 + cohort and MV COVID-19 + patients, the model identified the risk of in-hospital mortality 2.4 days and 1.54 days before death, respectively.

Discussion: Our LSTM algorithm accurately and timely identified the risk of ARDS or death in MV non-COVID-19 and COVID+ patients. By alerting the risk of ARDS or death, we can improve the implementation of evidence-based ARDS management and facilitate goals-of-care discussions in high-risk patients.

Conclusion: Using the LSTM algorithm in hospitalised patients identifies the risk of ARDS or death.

Keywords: COVID-19; Critical Care Outcomes; Decision Support Systems, Clinical; Medical Informatics.

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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
Cohort assembly and model training. ARDS, acute respiratory distress syndrome; LSTM, long short-term memory; MV, mechanically ventilated.
Figure 2
Figure 2
Model diagnostics, AUROC, PPV with sensitivity and NNE with sensitivity. AUROC, area under the receiver operating curve; MV, mechanically ventilated; NNE, number needed to evaluate; PPV, positive predictive value.
See this image and copyright information in PMC

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