• Rabia Bashir, Didi Surian, and Adam G Dunn.
• Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales 2109, Australia. Electronic address: rabia.bashir@students.mq.edu.au.
• J Clin Epidemiol. 2019 Jun 1; 110: 42-49.

ObjectivesTo determine which systematic review characteristics are needed to estimate the risk of conclusion change in systematic review updates.Study Design And SettingWe applied classification trees (a machine learning method) to model the risk of conclusion change in systematic review updates, using pairs of systematic reviews and their updates as samples. The classifiers were constructed using a set of features extracted from systematic reviews and the relevant trials added in published updates. Model performance was measured by recall, precision, and area under the receiver operating characteristic curve (AUC).ResultsWe identified 63 pairs of systematic reviews and updates, of which 20 (32%) exhibited a change in conclusion in their updates. A classifier using information about new trials exhibited the highest performance (AUC: 0.71; recall: 0.75; precision: 0.43) compared to a classifier that used fewer features (AUC: 0.65; recall: 0.75; precision: 0.39).ConclusionWhen estimating the risk of conclusion change in systematic review updates, information about the sizes of trials that will be added in an update are most useful. Future tools aimed at signaling conclusion change risks would benefit from complementary tools that automate screening of relevant trials.Copyright © 2019 Elsevier Inc. All rights reserved.

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