Research in context
Evidence before this study
Artificial intelligence-augmented care is an emerging field. Consequently, the existing literature is relatively sparse. We searched MEDLINE and arXiv for the term ((“real-time prediction”) OR (“deep learning”) OR (“real-time scoring”) OR (“machine learning”) OR (“artificial intelligence”)) AND ((intensive OR critical) care) with no language restrictions or date limitations. We retrieved 510 MEDLINE results and 252 arXiv results, 72 of which were relevant original studies. The relevant prior evidence included 18 articles investigating real-time prediction approaches. None of these articles used a deep learning methodology. Most of the articles described the prediction of sepsis and mortality, using often curated or open datasets such as the MIMIC-III dataset. All studies described a specific approach predicting a single outcome. At the time of writing, prediction of sepsis in real time is the topic with most available evidence.
Added value of this study
We developed deep learning models to predict severe complications following cardiothoracic surgery. These models used uncurated clinical datasets to predict three endpoints. By contrast with standard clinical risk scores, our approach was not based on the average patient but used cohort data to inform predictions. This approach yields higher accuracy for each individual patient. The selected clinical variables reflect the range of routinely collected information at intensive care units for all postoperative patients, removing the need for any additional manual data collection or annotation. The deep learning methods we implemented achieved superior predictive power and timeliness compared with three standard-of-care baselines.
Implications of all the available evidence
A real-time complication prediction system based on deep learning outperforms the selected standard-of-care baselines in timeliness and accuracy, even when acting on a real, uncurated data stream. We are currently deploying our system in our intensive care unit and will do a trial to confirm the results prospectively to enable its use in the clinical routine.