Wahba, A.J. orcid.org/0000-0001-5746-0129, Phillips, N., Mathew, R.K. orcid.org/0000-0002-2609-9876 et al. (3 more authors) (2023) Benchmarking short-term postoperative mortality across neurosurgery units: is hospital administrative data good enough for risk-adjustment? Acta Neurochirurgica, 165 (7). pp. 1695-1706. ISSN 0001-6268
Abstract
Background Surgical mortality indicators should be risk-adjusted when evaluating the performance of organisations. This study evaluated the performance of risk-adjustment models that used English hospital administrative data for 30-day mortality after neurosurgery.
Methods This retrospective cohort study used Hospital Episode Statistics (HES) data from 1 April 2013 to 31 March 2018. Organisational-level 30-day mortality was calculated for selected subspecialties (neuro-oncology, neurovascular and trauma neurosurgery) and the overall cohort. Risk adjustment models were developed using multivariable logistic regression and incorporated various patient variables: age, sex, admission method, social deprivation, comorbidity and frailty indices. Performance was assessed in terms of discrimination and calibration.
Results The cohort included 49,044 patients. Overall, 30-day mortality rate was 4.9%, with unadjusted organisational rates ranging from 3.2 to 9.3%. The variables in the best performing models varied for the subspecialties; for trauma neurosurgery, a model that included deprivation and frailty had the best calibration, while for neuro-oncology a model with these variables plus comorbidity performed best. For neurovascular surgery, a simple model of age, sex and admission method performed best. Levels of discrimination varied for the subspecialties (range: 0.583 for trauma and 0.740 for neurovascular). The models were generally well calibrated. Application of the models to the organisation figures produced an average (median) absolute change in mortality of 0.33% (interquartile range (IQR) 0.15–0.72) for the overall cohort model. Median changes for the subspecialty models were 0.29% (neuro-oncology, IQR 0.15–0.42), 0.40% (neurovascular, IQR 0.24–0.78) and 0.49% (trauma neurosurgery, IQR 0.23–1.68).
Conclusions Reasonable risk-adjustment models for 30-day mortality after neurosurgery procedures were possible using variables from HES, although the models for trauma neurosurgery performed less well. Including a measure of frailty often improved model performance.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and 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/. |
Keywords: | Risk-adjustment; Mortality; Neurosurgery; Neurovascular; Neuro-oncology; Hospital administrative data |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Medical Research (LIMR) > Division of Gastroenterology and Surgery The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) |
Funding Information: | Funder Grant number Candlelighters Not Known Yorkshire's Brain Tumour Charity was Brain Tumour Res & Support ax Yorks Not Known |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 Sep 2023 14:10 |
Last Modified: | 13 Sep 2023 14:10 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/s00701-023-05623-5 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:203299 |