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Exploring flexible polynomial regression as a method to align routine clinical outcomes with daily data capture through remote technologies

Filipow, Nicole; Main, Eleanor; Tanriver, Gizem; Raywood, Emma; Davies, Gwyneth; Douglas, Helen; Laverty, Aidan; (2023) Exploring flexible polynomial regression as a method to align routine clinical outcomes with daily data capture through remote technologies. BMC Medical Research Methodology , 23 , Article 114. 10.1186/s12874-023-01942-4. Green open access

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Abstract

BACKGROUND: Clinical outcomes are normally captured less frequently than data from remote technologies, leaving a disparity in volumes of data from these different sources. To align these data, flexible polynomial regression was investigated to estimate personalised trends for a continuous outcome over time. METHODS: Using electronic health records, flexible polynomial regression models inclusive of a 1st up to a 4th order were calculated to predict forced expiratory volume in 1 s (FEV1) over time in children with cystic fibrosis. The model with the lowest AIC for each individual was selected as the best fit. The optimal parameters for using flexible polynomials were investigated by comparing the measured FEV1 values to the values given by the individualised polynomial. RESULTS: There were 8,549 FEV1 measurements from 267 individuals. For individuals with > 15 measurements (n = 178), the polynomial predictions worked well; however, with < 15 measurements (n = 89), the polynomial models were conditional on the number of measurements and time between measurements. The method was validated using BMI in the same population of children. CONCLUSION: Flexible polynomials can be used to extrapolate clinical outcome measures at frequent time intervals to align with daily data captured through remote technologies.

Type: Article
Title: Exploring flexible polynomial regression as a method to align routine clinical outcomes with daily data capture through remote technologies
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1186/s12874-023-01942-4
Publisher version: https://doi.org/10.1186/s12874-023-01942-4
Language: English
Additional information: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Keywords: Chronic disease, Clinical outcomes, Missing data, Polynomial regression, Remote patient monitoring
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Infection, Immunity and Inflammation Dept
URI: https://discovery.ucl.ac.uk/id/eprint/10170005
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