A Cluster-Based Method for the Preoperative Identification of Lumbar Degenerative Patients having Higher Risk for Poor Global Outcome
| Autor: | Peter Endre Eltes, Istvan Klemencsics, Arpad Bozsodi, Aron Lazary, Péter Varga |
|---|---|
| Rok vydání: | 2015 |
| Předmět: | |
| Zdroj: | Global Spine Journal. 5:s-0035 |
| ISSN: | 2192-5690 2192-5682 |
| DOI: | 10.1055/s-0035-1554264 |
| Popis: | Introduction Surgery for lumbar disc degeneration has been shown to yield poor results in 10 to 45% of the patients. Evidence-based clinical tools for preoperative risk estimation would be very important to identify the “at-risk” patients. Several factors referring on the patients' symptoms, psychosocial issues, and medical history were previously published as significant individual risk factors on outcome, but there is low number of reports on integrative models with advanced statistics. The aim of this study was to identify significant outcome predictors and clinically different subgroups in patients surgically treated because of lumbar degenerative disc disease. Patients and Methods A total of 750 consecutive patients indicated for one- or two-level lumbar decompression/microdiscectomy or fusion surgery (TLIF) were recruited into the study. Patients with previous lumbar surgery or nondisc degeneration-related phenotype were not included into the analysis. Patients completed a questionnaire booklet containing the Oswestry disability index (ODI), visual analogue scales on low back (LBP), and leg pain (LP), Zung depression scale (ZDS), and the Modified Somatic Perception Questionnaire (MSPQ) before the surgery and 2 years after the surgery. Five-point Likert scale on self-reported outcome of the surgery was classified into two categories (“good” and “poor” outcome) and effect of preoperative parameters on that was determined applying uni- and multivariate logistic regression models. Two-step agglomerative cluster analysis was used to determine the best fit cluster structure of the cohort entering the significant outcome predictors into the model. Multivariate analysis of variance (MANOVA) and Fischer test were used to analyze the clinical clusters and their canonical discriminant functions. Chi-square tests were applied to investigate the clinical outcome in the different clusters. Results Four different preoperative parameters; number of spinal levels treated, severity of low back pain, duration of pain, and distress (calculated from ZDS and MSPQ scores) were proved to be significant predictors on surgical outcome in the final multivariate regression model ( p Conclusion Preoperative parameters of the patients with lumbar disc degeneration have got significant influence on the midterm surgical outcome. Outcome predictors determine different patient clusters with different risk for poor outcome. Advanced statistical approaches are advised to use in the future for patient stratification and risk estimation. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|