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- Title
Incorporating repeated measurements into prediction models in the critical care setting: a framework, systematic review and meta-analysis.
- Authors
Plate, Joost D. J.; van de Leur, Rutger R.; Leenen, Luke P. H.; Hietbrink, Falco; Peelen, Linda M.; Eijkemans, M. J. C.
- Abstract
<bold>Background: </bold>The incorporation of repeated measurements into multivariable prediction research may greatly enhance predictive performance. However, the methodological possibilities vary widely and a structured overview of the possible and utilized approaches lacks. Therefore, we [1] propose a structured framework for these approaches, [2] determine what methods are currently used to incorporate repeated measurements in prediction research in the critical care setting and, where possible, [3] assess the added discriminative value of incorporating repeated measurements.<bold>Methods: </bold>The proposed framework consists of three domains: the observation window (static or dynamic), the processing of the raw data (raw data modelling, feature extraction and reduction) and the type of modelling. A systematic review was performed to identify studies which incorporate repeated measurements to predict (e.g. mortality) in the critical care setting. The within-study difference in c-statistics between models with versus without repeated measurements were obtained and pooled in a meta-analysis.<bold>Results: </bold>From the 2618 studies found, 29 studies incorporated multiple repeated measurements. The annual number of studies with repeated measurements increased from 2.8/year (2000-2005) to 16.0/year (2016-2018). The majority of studies that incorporated repeated measurements for prediction research used a dynamic observation window, and extracted features directly from the data. Differences in c statistics ranged from - 0.048 to 0.217 in favour of models that utilize repeated measurements.<bold>Conclusions: </bold>Repeated measurements are increasingly common to predict events in the critical care domain, but their incorporation is lagging. A framework of possible approaches could aid researchers to optimize future prediction models.
- Subjects
META-analysis; CRITICAL care medicine; PREDICTION models; FEATURE extraction; DATA modeling
- Publication
BMC Medical Research Methodology, 2019, Vol 19, Issue 1, pN.PAG
- ISSN
1471-2288
- Publication type
journal article
- DOI
10.1186/s12874-019-0847-0