Exploring data reduction strategies in the analysis of continuous pressure imaging technology
Open Access
- 1 March 2023
- journal article
- research article
- Published by Springer Science and Business Media LLC in BMC Medical Research Methodology
- Vol. 23 (1), 1-10
- https://doi.org/10.1186/s12874-023-01875-y
Abstract
Science is becoming increasingly data intensive as digital innovations bring new capacity for continuous data generation and storage. This progress also brings challenges, as many scientific initiatives are challenged by the shear volumes of data produced. Here we present a case study of a data intensive randomized clinical trial assessing the utility of continuous pressure imaging (CPI) for reducing pressure injuries. To explore an approach to reducing the amount of CPI data required for analyses to a manageable size without loss of critical information using a nested subset of pressure data. Data from four enrolled study participants excluded from the analytical phase of the study were used to develop an approach to data reduction. A two-step data strategy was used. First, raw data were sampled at different frequencies (5, 30, 60, 120, and 240 s) to identify optimal measurement frequency. Second, similarity between adjacent frames was evaluated using correlation coefficients to identify position changes of enrolled study participants. Data strategy performance was evaluated through visual inspection using heat maps and time series plots. A sampling frequency of every 60 s provided reasonable representation of changes in interface pressure over time. This approach translated to using only 1.7% of the collected data in analyses. In the second step it was found that 160 frames within 24 h represented the pressure states of study participants. In total, only 480 frames from the 72 h of collected data would be needed for analyses without loss of information. Only ~ 0.2% of the raw data collected would be required for assessment of the primary trial outcome. Data reduction is an important component of big data analytics. Our two-step strategy markedly reduced the amount of data required for analyses without loss of information. This data reduction strategy, if validated, could be used in other CPI and other settings where large amounts of both temporal and spatial data must be analysed.Keywords
Funding Information
- Alberta Innovates - Health Solutions (20130152, 20130152, 20130152, 20130152, 20130152, 20130152, 20130152, 20130152, 20130152)
This publication has 20 references indexed in Scilit:
- Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost PatientsHealth Affairs, 2014
- Visual Feedback of Continuous Bedside Pressure Mapping to Optimize Effective Patient RepositioningAdvances in Wound Care, 2014
- Big data analytics in healthcare: promise and potentialHealth Information Science and Systems, 2014
- Accuracy and Reliability of Continuous Glucose Monitoring Systems: A Head-to-Head ComparisonDiabetes Technology & Therapeutics, 2013
- The effect of continuous pressure monitoring on strategic shifting of medical inpatients at risk for PUsJournal of Wound Care, 2012
- Pressure ulcers: Back to the basicsIndian Journal of Plastic Surgery, 2012
- Measurement of wood shrinkage in jack pine using three dimensional digital image correlation (DIC)Holzforschung, 2012
- Continuous monitoring of interface pressure distribution in intensive care patients for pressure ulcer preventionJournal of Advanced Nursing, 2009
- New technique for real-time interface pressure analysis: Getting more out of large image data setsJournal of Rehabilitation Research and Development, 2008
- Quantitative analysis of mRNA amplification by in vitro transcriptionNucleic Acids Research, 2001