ArticleJournal of spine surgery (Hong Kong)2019
Development and validation of risk-adjustment models for elective, single-level posterior lumbar spinal fusions.
Article in Journal of spine surgery (Hong Kong), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances.Neurospine · 2024Article
- Prediction of outcome after spinal surgery-using The Dialogue Support based on the Swedish national quality register.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2022Article
- Epidemiological Relevance of Elevated Preoperative Patient Health Questionnaire-9 Scores on Clinical Improvement Following Lumbar Decompression.International journal of spine surgery · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere is a paucity of literature examining the development and subsequent validation of risk-adjustment models that inform the trade-off between adequate risk-adjustment and data collection burden. We aimed to evaluate patient risk stratification by surgeons with the development and validation of risk-adjustment models for elective, single-level, posterior lumbar spinal fusions (PLSFs).
methodsPatients undergoing PLSF from 2011-2014 were identified in the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP). The derivation cohort included patients from 2011-2013, while the validation cohort included patients from 2014. Outcomes of interest were severe adverse events (SAEs) and unplanned readmission. Bivariate analysis of risk factors followed by a stepwise logistic regression model was used. Limited risk-adjustment models were created and analyzed by sequentially adding variables until the full model was reached.
resultsA total of 7,192 and 4,182 patients were included in our derivation and validation cohorts, respectively. Full model performance was similar for the derivation and validation cohorts in both 30-day SAEs (C-statistic =0.66
conclusionsRisk-adjustment models for PLSF demonstrated acceptable calibration and discrimination using variables commonly found in health records and demonstrated only a limited set of variables were required to achieve an appropriate level of risk prediction.
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