Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Vector autoregression : Useful in rare diseases?—Predicting organ response patterns in a rare case of secondary AA amyloidosis

Ihne-Schubert, Sandra M. LU ; Kircher, Malte ; Werner, Rudolf A. ; Lapa, Constantin ; Einsele, Hermann ; Geier, Andreas and Schubert, Torben LU (2023) In PLoS ONE 18(8 August).
Abstract

Background Statistical analyses of clinical data are a cornerstone in understanding pathomechanisms of disorders. In rare disorders, cross-sectional datasets of sufficient size are usually not available. Taking AA amyloidosis as an example of a life-threatening rare disorder resulting from of uncontrolled chronic inflammation, we propose techniques from time series analysis to predict organ response to treatment. The advantage of time-series analysis is that it solely relies on temporal variation and therefore allows analyzing organ response to treatment even when the cross-sectional dimension is small. Methods The joint temporal interdependence of inflammatory activity and organ response was modelled multivariately using vector... (More)

Background Statistical analyses of clinical data are a cornerstone in understanding pathomechanisms of disorders. In rare disorders, cross-sectional datasets of sufficient size are usually not available. Taking AA amyloidosis as an example of a life-threatening rare disorder resulting from of uncontrolled chronic inflammation, we propose techniques from time series analysis to predict organ response to treatment. The advantage of time-series analysis is that it solely relies on temporal variation and therefore allows analyzing organ response to treatment even when the cross-sectional dimension is small. Methods The joint temporal interdependence of inflammatory activity and organ response was modelled multivariately using vector autoregression (VAR) based on a unique 4.5 year spanning data set of routine laboratory, imaging data (e.g., 18F-Florbetaben-PET/CT) and functional investigations of a 68-year-old patient with multi-organ involvement of AA amyloidosis due to ongoing inflammatory activity of a malignant paraganglioma in stable disease for >20 years and excellent response to tocilizumab). Results VAR analysis showed that alterations in inflammatory activity forecasted alkaline phosphatase (AP). AP levels, but not inflammatory activity at the previous measurement time point predicted proteinuria. Conclusion We demonstrate the feasibility and value of time series analysis for obtaining clinically reliable information when the rarity of a disease prevents conventional prognostic modelling approaches. We illustrate the comparative utility of blood, functional and imaging markers to monitor the development and regression of AA amyloidosis.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
PLoS ONE
volume
18
issue
8 August
article number
e0289921
publisher
Public Library of Science (PLoS)
external identifiers
  • pmid:37561769
  • scopus:85167675103
ISSN
1932-6203
DOI
10.1371/journal.pone.0289921
language
English
LU publication?
yes
id
e9e42671-19d4-416e-bb29-343d58300907
date added to LUP
2023-11-01 11:24:07
date last changed
2024-04-19 03:23:36
@article{e9e42671-19d4-416e-bb29-343d58300907,
  abstract     = {{<p>Background Statistical analyses of clinical data are a cornerstone in understanding pathomechanisms of disorders. In rare disorders, cross-sectional datasets of sufficient size are usually not available. Taking AA amyloidosis as an example of a life-threatening rare disorder resulting from of uncontrolled chronic inflammation, we propose techniques from time series analysis to predict organ response to treatment. The advantage of time-series analysis is that it solely relies on temporal variation and therefore allows analyzing organ response to treatment even when the cross-sectional dimension is small. Methods The joint temporal interdependence of inflammatory activity and organ response was modelled multivariately using vector autoregression (VAR) based on a unique 4.5 year spanning data set of routine laboratory, imaging data (e.g., 18F-Florbetaben-PET/CT) and functional investigations of a 68-year-old patient with multi-organ involvement of AA amyloidosis due to ongoing inflammatory activity of a malignant paraganglioma in stable disease for &gt;20 years and excellent response to tocilizumab). Results VAR analysis showed that alterations in inflammatory activity forecasted alkaline phosphatase (AP). AP levels, but not inflammatory activity at the previous measurement time point predicted proteinuria. Conclusion We demonstrate the feasibility and value of time series analysis for obtaining clinically reliable information when the rarity of a disease prevents conventional prognostic modelling approaches. We illustrate the comparative utility of blood, functional and imaging markers to monitor the development and regression of AA amyloidosis.</p>}},
  author       = {{Ihne-Schubert, Sandra M. and Kircher, Malte and Werner, Rudolf A. and Lapa, Constantin and Einsele, Hermann and Geier, Andreas and Schubert, Torben}},
  issn         = {{1932-6203}},
  language     = {{eng}},
  number       = {{8 August}},
  publisher    = {{Public Library of Science (PLoS)}},
  series       = {{PLoS ONE}},
  title        = {{Vector autoregression : Useful in rare diseases?—Predicting organ response patterns in a rare case of secondary AA amyloidosis}},
  url          = {{http://dx.doi.org/10.1371/journal.pone.0289921}},
  doi          = {{10.1371/journal.pone.0289921}},
  volume       = {{18}},
  year         = {{2023}},
}