Abstract:
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.
journal_name
BMJ Openjournal_title
BMJ openauthors
Fritz BA,Chen Y,Murray-Torres TM,Gregory S,Ben Abdallah A,Kronzer A,McKinnon SL,Budelier T,Helsten DL,Wildes TS,Sharma A,Avidan MSdoi
10.1136/bmjopen-2017-020124subject
Has Abstractpub_date
2018-04-10 00:00:00pages
e020124issue
4issn
2044-6055pii
bmjopen-2017-020124journal_volume
8pub_type
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