Using machine learning techniques to develop forecasting algorithms for postoperative complications: protocol for a retrospective study
Open accessAbstract
Introduction: Mortality and morbidity following surgery are pressing public health concerns in the USA. Traditional prediction models for postoperative adverse outcomes demonstrate good discrimination at the population level, but the ability to forecast an individual patient’s trajectory in real time remains poor. We propose to apply machine learning techniques to perioperative time-series data to develop algorithms for predicting adverse perioperative outcomes. Methods and analysis: This study will include all adult patients who had surgery at our tertiary care hospital over a 4-year period. Patient history, laboratory values, minute-by-minute intraoperative vital signs and medications administered will be extracted from the electronic medical record. Outcomes will include in-hospital mortality, postoperative acute kidney injury and postoperative respiratory failure. Forecasting algorithms for each of these outcomes will be constructed using density-based logistic regression after employing a Nadaraya-Watson kernel density estimator. Time-series variables will be analysed using first and second-order feature extraction, shapelet methods and convolutional neural networks. The algorithms will be validated through measurement of precision and recall. Ethics and dissemination: This study has been approved by the Human Research Protection Office at Washington University in St Louis. The successful development of these forecasting algorithms will allow perioperative healthcare clinicians to predict more accurately an individual patient’s risk for specific adverse perioperative outcomes in real time. Knowledge of a patient’s dynamic risk profile may allow clinicians to make targeted changes in the care plan that will alter the patient’s outcome trajectory. This hypothesis will be tested in a future randomised controlled trial.
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Although various research projects are focusing on CDSS in the ICU, translation into clinical routine is lagging far behind.
“Although various research projects are focusing on CDSS in the ICU, translation into the clinical routine is lagging far behind [15,46-49].”
From Clinical requirements of future patient monitoring in the intensive care unit: qualitative study (2019) · cites this paper as [46]
Models for real-time prediction have been trained in clinical contexts such as circulatory failure in critical care and post-operative complications.
“Models for real-time prediction have been trained in clinical contexts such as circulatory failure in critical care24 and post-operative complications25,26.”
From Machine learning for real-time aggregated prediction of hospital admission for emergency patients (2022) · cites this paper as [25]
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Matar, D. Y., Matar, A. Y., Nimbalkar, A., Saad, C. G., Lee, J. J., Huang, W., Ren, T., Knoedler, S. A., Ghanad, I., Orgill, D. P., et al. (2026) · JAMA Otolaryngology–Head & Neck Surgery · doi:10.1001/jamaoto.2026.1453
- Authors
- Fritz, B. A., Chen, Y., Murray-Torres, T. M., Gregory, S., Ben Abdallah, A., Kronzer, A., McKinnon, S. L., Budelier, T., Helsten, D. L., Wildes, T. S., Sharma, A., & Avidan, M. S.
- Affiliations
- Washington University in St. Louis (US)
- Department of Anesthesiology, Washington University in St Louis, St Louis, Missouri, USA
- Corresponding author
- Fritz, Bradley A
- ORCID
- Venue
- BMJ Open
- Publisher
- BMJ
- Published
- 2018-04
- Volume
- 8
- Issue
- 4
- Pages
- e020124
- ISSN
- 2044-6055 (ISSN-L)
- PMC
- PMC5898287
- OpenAlex
- W2797650827
- Cited by
- 42
- References
- 56
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 6 · 2023: 3 · 2022: 5 · 2021: 6 · 2020: 5 · 2019: 10 · 2018: 4
- FWCI
- 3.52
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc
- Locations
- BMJ Open (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Digital Commons@Becker (Washington University School of Medicine) (submittedVersion, cc-by-nc)
- Funders
- Agency for Healthcare Research and Quality
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac, Anesthesia and Surgical Outcomes
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- postoperative complications, machine learning forecasting, in-hospital mortality, postoperative acute kidney injury, postoperative respiratory failure, electronic health records, time series analysis, convolutional neural networks
- MeSH terms
- Machine Learning, Adult, Algorithms, Humans, Missouri, Postoperative Complications, Retrospective Studies
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