• Eur Spine J · Mar 2023

    Review

    Comorbidity data collection across different spine registries: an evidence map.

    • Matthew Quigley, Esther Apos, Trieu-Anh Truong, Susannah Ahern, and Michael A Johnson.
    • School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Rd, Melbourne, VIC, 3004, Australia.
    • Eur Spine J. 2023 Mar 1; 32 (3): 753777753-777.

    IntroductionComorbidities are significant patient factors that contribute to outcomes after surgery. There is highly variable collection of this information across the literature. To help guide the systematic collection of best practice data, the Australian Spine Registry conducted an evidence map to investigate (i) what comorbidities are collected by spine registries, (ii) how they are collected and (iii) the compliance and completeness in collecting comorbidity data.MethodA literature search was performed to identify published studies of adult spine registry data reporting comorbidities. In addition, targeted questionnaires were sent to existing global spine registries to identify the maximum number of relevant results to build the evidence map.ResultsThirty-six full-text studies met the inclusion criteria. There was substantial variation in the reporting of comorbidity data; 55% of studies reported comorbidity collection, but only 25% reported the data collection method and 20% reported use of a comorbidity index. The variation in the literature was confirmed with responses from 50% of the invited registries (7/14). Of seven, three use a recognised comorbidity index and the extent and methods of comorbidity collection varied by registry.ConclusionThis evidence map identified variations in the methodology, data points and reporting of comorbidity collection in studies using spine registry data, with no consistent approach. A standardised set of comorbidities and data collection methods would encourage collaboration and data comparisons between patient cohorts and could facilitate improved patient outcomes following spine surgery by allowing data comparisons and predictive modelling of risk factors.© 2023. The Author(s).

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