Anne F. Mannion, Francine Mariaux, Raluca Reitmeir, Tamas F. Fekete, Daniel Haschtmann, Markus Loibl, Dezsö Jeszenszky, Frank S. Kleinstück, François Porchet, Achim Elfering
August 2020, Volume 29, Issue 8, pp 1935 - 1952 Original Article Read Full Article 10.1007/s00586-020-06462-z
First Online: 16 June 2020
Depression, anxiety, catastrophising, and fear-avoidance beliefs are key "yellow flags" (YFs) that predict a poor outcome in back patients. Most surgeons acknowledge the importance of YFs but have difficulty assessing them due to the complexity of the instruments used for their measurement and time constraints during consultations. We performed a secondary analysis of existing questionnaire data to develop a brief tool to enable the systematic evaluation of YFs and then tested it in clinical practice.
The following questionnaire datasets were available from a total of 932 secondary/tertiary care patients (61 ± 16 years; 51% female): pain catastrophising (N = 347); ZUNG depression (N = 453); Hospital Anxiety and Depression Scale (anxiety subscale) (N = 308); fear-avoidance beliefs (N = 761). The single item that best represented the full-scale score was identified, to form the 4-item "Core Yellow Flags Index" (CYFI). 2422 patients (64 ± 16 years; 54% female) completed CYFI and a Core Outcome Measures Index (COMI) before lumbar spine surgery, and a COMI 3 and 12 months later (FU).
The item–total correlation for each item with its full-length questionnaire was: 0.77 (catastrophising), 0.67 (depression), 0.69 (anxiety), 0.68 (fear-avoidance beliefs). Cronbach's α for the CYFI was 0.79. Structural equation modelling showed CYFI uniquely explained variance (p
The 4-item CYFI proved to be a simple, practicable tool for routinely assessing key psychological attributes in spine surgery patients and made a relevant contribution in predicting postoperative outcome. CYFI's items were similar to those in the "STarT Back screening tool" used in primary care to triage patients into treatment pathways, further substantiating its validity. Wider use of CYFI may help improve the accuracy of predictive models derived using spine registry data.
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