A1 Refereed original research article in a scientific journal
Identifying classes with different disability profiles among patients undergoing lumbar discectomy: a latent class analysis
Authors: Saltychev, Mikhail; Koivunen, Konsta; Pernaa, Katri; Juhola, Juhani
Publisher: Ovid Technologies (Wolters Kluwer Health)
Publication year: 2026
Journal: International Journal of Rehabilitation Research
ISSN: 0342-5282
eISSN: 1473-5660
DOI: https://doi.org/10.1097/MRR.0000000000000707
Publication's open availability at the time of reporting: No Open Access
Publication channel's open availability : Partially Open Access publication channel
Web address : https://doi.org/10.1097/mrr.0000000000000707
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/523653831
Self-archived copy's version: Final draft
The objective of this retrospective register-based study among 534 patients undergoing lumbar discectomy was to identify larger classes with different functional profiles. Main statistical method was latent class analysis. The mean age was 46.0 (17.8) years. Based on the distribution of 10 domains of functioning included in Oswestry Disability Index, four latent classes were identified: 'no or mild disability' (26% of the sample); 'moderate restrictions in sitting and traveling' (26%); 'moderate restrictions in standing and lifting and severe pain' (19%); and 'severe disability in most of the domains' (30%). Female sex (49 vs. 37%), obesity (48 vs. 28%), older age (63 vs.42%), greater pain severity (73-83 vs. 59-65%), and longer preoperative pain (27 vs. 20%) were associated with a higher likelihood of classification into a severe disability class. The results show that the functional difficulties of patients undergoing lumbar discectomy are not evenly distributed in the same way across individuals and are not simply a matter of variation at the individual level. It seems that there are larger classes whose disability domains are distributed in a different way than other classes. Belonging to a particular class can potentially be predicted by descriptive factors such as sex, age, pain intensity and pain history. This information can help identify risks that predict particularly severe disability and recognize the most common functional limitations that may be overlooked if all the patients are treated as a homogeneous group and the severity of disability is described by only one composite score.
