{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T00:08:39Z","timestamp":1777334919010,"version":"3.51.4"},"reference-count":42,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T00:00:00Z","timestamp":1691625600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"\nBackground<\/jats:title>\nStatistical 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.<\/jats:p>\n<\/jats:sec>\n\nMethods<\/jats:title>\nThe 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).<\/jats:p>\n<\/jats:sec>\n\nResults<\/jats:title>\nVAR 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.<\/jats:p>\n<\/jats:sec>\n\nConclusion<\/jats:title>\nWe 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.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0289921","type":"journal-article","created":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T17:45:18Z","timestamp":1691689518000},"page":"e0289921","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Vector autoregression: Useful in rare diseases?\u2014Predicting organ response patterns in a rare case of secondary AA amyloidosis"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8540-7122","authenticated-orcid":true,"given":"Sandra 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