{"paper_id":"0cea12c3-2a4c-4fca-b4c9-6e31eee7a6b5","body_text":"Prognostic Value Of The SF-36 Questionnaire In The Surgical Treatment Of Degenerative Spinal Stenosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic Value Of The SF-36 Questionnaire In The Surgical Treatment Of Degenerative Spinal Stenosis Rositsa Krasteva, Stephan Stanchev, Kiril Panayotov This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8691004/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: The aim of the study was to evaluate the prognostic value of health-related quality of life (HRQoL), measured with the SF-36 (Short Form Health Survey-36) questionnaire, for the success of surgical treatment of degenerative spinal stenosis. Methods 108 patients (mean age 73.2 years, 61.1% male) were included. The success of surgical treatment was defined by the minimal clinically important difference (MCID): a decrease in ODI by 1.5 points, a decrease in VAS by 1 point, or an increase in the total SF-36 score by 15 points. Paired-sample t-test, Spearman correlation analysis, and ROC analysis were used to assess the prognostic value. Results A statistically significant improvement was reported in all parameters after surgery (p < 0.001 for all). The mean ODI index decreased from 3.15 to 2.19, the VAS score from 2.51 to 1.60, and the SF-36 increased from 49.11 to 69.02 points. ROC analysis demonstrated excellent predictive ability of the baseline SF-36 for determining surgical success (AUC = 0.951). Conclusion Preoperative quality of life, measured by the SF-36, is a strong and independent predictor of the success of surgical treatment of degenerative spinal stenosis. Degenerative spinal stenosis SF-36 Prognostic factors Health-related quality of life (HRQoL) Surgical treatment Figures Figure 1 1. Introduction Degenerative spinal stenosis (DSS) is one of the most common diseases leading to surgical intervention in the elderly (12). Despite the high efficacy of decompressive surgery in relieving symptoms, selecting patients who will derive maximum benefit from surgical treatment remains a challenge. Clinical scales such as the visual analogue scale (VAS) for pain and the Oswestry Disability Index (ODI) (7), as well as imaging classifications such as Schizas’s (21) classification, are routinely used, but although classifications such as Schizas’s (21) are widely used, the lack of a clear correlation between morphological severity and functional status necessitates the inclusion of patient-oriented indicators, including patient-reported outcomes, PROMs (3, 10). Health-related quality of life (HRQoL), measured with common questionnaires such as the SF-36, can provide a more comprehensive assessment of a patient's well-being and potential for recovery. HRQoL is increasingly being used as an independent predictor of surgical success in degenerative spinal stenosis (15, 16), and the use of HRQoL instruments such as the SF-36 allows for a more objective prediction of postoperative recovery beyond pure neurological decompression (2). The aim of the present study was to investigate the prognostic value of the preoperative SF-36 in assessing the success of surgical treatment of DSS. 2. Methods 2.1. Study population: The study was prospective and included a total of 108 patients undergoing surgical treatment for degenerative spinal stenosis. Patients with severe somatic comorbidities, dementia, and a history of psychiatric illness were excluded from the sample. The demographic profile of the patients was a mean age of 73.2 years (Standard Deviation = 5.3) and a range of 61 to 83 years. The gender distribution included 66 men (61.1%) and 42 women (38.9%). 2.2. Measurement instruments and assessment: Patients were assessed twice: before surgery (preoperative measurement, t1) and 6 months after surgery (postoperative measurement, t2). The following instruments were used: • Pain intensity: Assessed using a 3-point ordinal VAS scale for mild, moderate, and severe pain. • Degree of disability: Assessed using a 5-point ordinal ODI scale. • Health-related quality of life (HRQoL): Assessed using the total (sum) score of the SF-36 questionnaire. • Anatomical severity of stenosis: Classified according to Schizas grade (A, B, C, D) based on preoperative magnetic resonance imaging. For the purpose of ROC analysis, surgical \"success\" (Outcome) was defined as achieving a minimal clinically important difference (MCID): a decrease in ODI of at least 1.5 points, a decrease in VAS of at least 1 point, or an increase in the total SF-36 score of at least 15 points (5, 19). Defining success by the minimal clinically important difference (MCID) is a standard in modern spinal surgery to objectify subjective improvement (8). Patients who achieved this threshold were classified as \"positive\" cases (N = 61) and the rest as \"negative\" (N = 47). 2.3. Statistical analysis: Statistical analysis was performed using the SPSS 26 software package. Descriptive statistics (means, standard deviations, frequencies), paired-sample t-tests for comparing t1 and t2 measurements, Spearman's correlation analysis (Spearman's Rho) for assessing the relationship between ordinal scales (Schizas, VAS, ODI), and ROC analysis for determining the predictive power and optimal cut-off point of the SF-36 were used. Although the VAS and ODI scales are technically ordinal, for the purposes of this analysis their numerical values ​​were treated as interval data, an approach that is widely accepted and used in clinical research on spinal pathology (12). The minimal clinically important difference (MCID) was applied to define therapeutic success, in view of the need to distinguish the mathematical significance of the results from their real clinical value for the patient and to establish the proportion of subjects with a tangible improvement in quality of life and functional status (6). 3. Results 3.1. Distribution of anatomical severity according to the Schizas scale: The distribution of patients according to the degree of spinal stenosis according to the Schizas scale is as follows ( Table 1 . ) : Table 1 Distribution of patients according to the degree of stenosis according to the Shizas classification. The majority of patients were classified as grade C, indicating a predominant sample with severe anatomical stenosis. Schizas Degree Frequency Percentage A 1 0.9% B 27 25.0% C 68 63.0% D 12 11.1% Total 108 100.0% 3.2. Effectiveness of surgical treatment: The paired-samples t-test revealed a statistically significant improvement in all clinical parameters after surgery (p < 0.001 for all measurements), (Table 2 . ) : Table 2 Comparative analysis of functional status, pain and quality of life indicators before and after treatment Mean value before (t1) Mean value after (t2) Mean difference t-value p-value ODI 3.15 2.19 0.96 11.135 < 0.001 VAS 2.51 1.60 0.91 12.691 < 0.001 SF-36 49.11 69.02 19.91 -10.975 < 0.001 3.3. Correlation analysis between clinical and anatomical parameters: Spearman's correlation analysis (Spearman's Rho) provides key data on the relationships between variables, ( Table 3 . ) : Table 3 Correlation analysis between preoperative parameters and final therapeutic outcome Variable Correlation (r) with SF-36 (t1) Sig. (p-value) Correlation (r) with Outcome Sig. (p-value) Shizas 0.102 0.294 (Insignificant) -0.287 0.003 VAS1 -0.075 0.437 (Insignificant) -0.027 0.778 (Insignificant) ODI1 -0.122 0.210 (Insignificant) -0.129 0.184 (Insignificant) SF-36 (t1) 1.000 - -0.544 < 0.001 A lack of statistically significant correlation was reported between the anatomical Schizas grade and the baseline subjective parameters (SF-36, VAS, ODI), highlighting the presence of a clinical-radiological dissociation in the sample. This dissociation has been widely described in the literature as one of the main challenges in the diagnosis of DSS (4, 26). 3.4. ROC Analysis and Prognostic Value of SF-36: ROC analysis to assess the prognostic value of SF-36 for surgical success showed exceptional results ( Fig. 1 . ) : • Area under the curve (AUC): 0.951 (95% CI). • Optimal cut-off value (Cut-off): Approximately 0.652 (probability of success), with a sensitivity of 86.9% and a specificity of 89.4%. The resulting area under the curve (AUC = 0.951) defines the preoperative SF-36 as an excellent predictor of final success, which is consistent with the latest prognostic models in spinal surgery (11, 22). 4. Discussion The present study confirms both the effectiveness of surgical treatment of degenerative spinal stenosis in a typical geriatric population and the exceptional prognostic value of preoperative assessment of quality of life using the SF-36 questionnaire. The significant improvement in the parameters of our sample (mean age 73.2 years) corresponds with recent studies showing that advanced age in itself is not a contraindication to excellent functional outcome (1). 4.1. Efficacy and clinical improvement: The results of the paired t-test clearly demonstrate a significant clinical and functional improvement in all measured parameters (VAS, ODI, SF-36) after surgery (p < 0.001). The mean increase in the total SF-36 score of nearly 20 points represents a large clinically significant difference, highlighting the benefits of timely surgical treatment in properly selected patients. 4.2. The phenomenon of clinical-radiological dissociation: One of the most important conclusions from the Spearman correlation analysis is the lack of a statistically significant relationship between the anatomical severity of the stenosis (according to Schizas) and the patient's preoperative subjective state: HRQoL (SF-36), pain (VAS) or disability (ODI). This result confirms the well-known phenomenon of clinical-radiological dissociation in the literature. It suggests that the severity assessed by imaging studies is not a direct and reliable indicator of the intensity of pain or functional limitations from which the patient suffers. The lack of a relationship between the Schizas grade and the baseline quality of life confirms the conclusions of Jensen et al. (2022) that morphological changes are only part of the clinical picture and should not be the only criterion for surgical intervention (13). Our data support the idea that basing indications solely on imaging studies carries a risk of diagnostic errors, whereas the use of PROMs such as the SF-36, for example, provides more precise patient selection (25). Therefore, basing indications for surgery solely on MRI or CT findings may lead to non-operative treatment or unnecessary surgery in certain groups of patients. 4.3. SF-36 as a leading prognostic tool: In contrast to anatomical and other clinical scales, the preoperative SF-36 was the strongest predictor of the final success of surgery (Spearman's r = -0.544, p < 0.001). The exceptional area under the ROC curve (AUC = 0.951) further reinforces this finding. The high prognostic value of the baseline SF-36 is consistent with the data of Khor et al. (2018), who highlight the accuracy of preoperative scores in predicting functional recovery in spinal surgery (17). This high discriminative ability means that the SF-36 can serve as a reliable screening tool for stratifying patients before making a decision to undergo surgery. The defined optimal threshold can be used in clinical practice to identify those patients who have the greatest potential for a favorable outcome. An interesting aspect is also the weak correlation between preoperative and postoperative SF-36 scores (r = 0.074), indicating that patients with a severe baseline condition have a significant potential for recovery, which is consistent with the observations of Sanders et al. (2024) that low baseline HRQoL should not be a criterion for refusing surgical treatment (20). It turns out that even patients with extremely low baseline quality of life (often excluded from surgery due to concerns about poor outcome) have great potential for significant improvement and should not be denied surgical treatment solely based on their severe baseline status. 4.4. Study limitations: A major limitation of the present study is the use of parametric statistical methods (t-test and Pearson correlation) on ordinal variables (VAS and ODI). However, the large sample size (N = 108) and the fact that the results of the nonparametric Spearman analysis confirmed the main conclusions about the relationship with the outcome suggest that the results of the parametric tests are reliable and clinically valid. The use of parametric methods on ordinal data is considered acceptable and reliable for samples with a size of more than 100 cases, as they have sufficient statistical power (18, 24). “The definition of ‘success’ by MCID (6, 8) provides an objective and reproducible criterion for outcome assessment. 5. Conclusion In conclusion, our study conclusively demonstrates that surgical treatment of degenerative spinal stenosis leads to significant improvement in quality of life, pain and disability in the affected geriatric population. The most important finding is that the preoperative total score of the SF-36 is an extremely strong and reliable predictor of surgical success, outperforming traditional indicators such as VAS, ODI and anatomical criteria of Schizas. The established clinical-radiological dissociation suggests that the assessment of anatomical stenosis alone is not sufficient for clinical decision-making. These results support the inclusion of the standardized assessment of quality of life (HRQoL) by the SF-36 in the routine preoperative process. The defined optimal threshold can be used for more accurate stratification of patients, allowing for optimization of surgical outcomes and avoiding exclusion of patients with severe baseline status who have great potential for improvement. Declarations Е thics declarations The conduct of this study was approved by the regional ethics committee at the Multi-profile Hospital for Active Treatment MEDIKA Ruse. All participants completed the informed consent forms. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Data availability The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request Funding No external funding has been received for conducting the study A cknowledgments Not applicable. Author Contribution R.K. wrote the main manuscript text and prepare figures, S.S and K.P. conducted patient surveys with questionnaires. All authors reviewed the manuscript. References Ammendola, M., et al. (2025). Surgical Outcomes in Octogenarians with Lumbar Spinal Stenosis: A Prospective SF-36 and ODI Analysis. European Spine Journal, 34 (2), 450-458. Boden, S. D., Davis, D. O., Dina, T. S., Patronas, N. J., & Wiesel, S. W. (1990). Abnormal magnetic-resonance scans of the lumbar spine in asymptomatic subjects. A prospective investigation. J Bone Joint Surg Am, 72 (3), 403-408. Broda, A., Sanford, Z., Turcotte, J., Patton, C. (2020). Development of a Risk Prediction Model With Improved Clinical Utility in Elective Cervical and Lumbar Spine Surgery. Spine, 45 (9), E542-E551. doi.org Chung, A. S., Copay, A. G., Olmscheid, N., Campbell, D., Walker, J. B., & Chutkan, N. (2017). Minimum Clinically Important Difference: Current Trends in the Spine Literature. Spine, 42 (14), 1096-1105. doi.org Ciancarelli, I., Martino Cinnera, A., Ricci, A., Iosa, M., Cerasa, A., Calabrò, R. S., & Morone, G. (2025). Preoperative Health Status and Clinical Predictors of Health-Related Quality of Life Improvement After Lumbar Spinal Stenosis Surgery: A Longitudinal Study. Journal of Clinical Medicine, 14 (13), 4391. https://doi.org/10.3390/jcm14134391 Çiftci İnceoğlu, S., Ayyıldız, A., Şahin, B., Özcan, S., İnceoğlu, A., Ayyıldız, H., & Kuran, B. (2026). Association Between Radiological Stenosis Level and Patient-Reported Outcomes in Patients with Lumbar Spinal Stenosis: A Cross-Sectional Study. Medicina, 62 (1), 29. https://doi.org/10.3390/medicina62010029 Copay, A. G., Subach, B. R., Glassman, S. D., Polly, D. W., Jr, & Schuler, T. C. (2007). Understanding the minimum clinically important difference: a review of concepts and methods. Spine J, 7 (5), 541-546. doi.org Fairbank, J. C., & Pynsent, P. B. (2000). The Oswestry Disability Index. Spine (Phila Pa 1976), 25 (22), 2940-2952. doi.org Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143 (1), 29-36. doi.org He, A. Y., et al. (2023). The lack of correlation between radiologic stenosis and clinical symptoms: A systematic review of the Schizas classification. The Spine Journal, 23 (12), 1785-1798. Jensen, M. P., Turner, J. A., Romano, J. M., & Fisher, L. D. (1999). Comparative reliability and validity of chronic pain intensity measures. Pain, 83 (2), 157-162. doi.org Jensen, R. K., et al. (2022). Is there a relationship between lumbar spinal stenosis appearance on MRI and clinical symptom severity? A systematic review. Chiropractic & Manual Therapies, 30 (1), 1-15. Katz, J. N., Zimmerman, Z. E., Mass, H., & Makhni, M. C. (2022). Diagnosis and Management of Lumbar Spinal Stenosis: A Review. JAMA, 327 (17), 1688-1699. doi.org Khor, S., Lavallee, D., Cizik, A. M., et al. (2018). Development and Validation of a Prediction Model for Pain and Functional Outcomes After Lumbar Spine Surgery. JAMA Surg, 153 (7), 634-642. doi.org Ko, S., & Choi, W. (2022). Usefulness of preoperative Short Form-36 Mental Component Score as a prognostic factor in patients who underwent decompression surgery for degenerative lumbar spinal stenosis. Medicine (Baltimore), 101 (39), e30231. doi.org Minetama, M., Kawakami, M., Teraguchi, M., Matsuo, S., Enyo, Y., Nakagawa, M., Yamamoto, Y., Nakatani, T., Sakon, N., Nagata, W., & Nakagawa, Y. (2022). MRI grading of spinal stenosis is not associated with the severity of low back pain in patients with lumbar spinal stenosis. BMC Musculoskelet Disord, 23 (1), 857. doi.org Mullan, A., et al. (2025). Parametric vs. Non-parametric analysis of Patient Reported Outcome Measures in Spine Surgery. Journal of Clinical Epidemiology, 178 , 112-120. Parker, S. L., Mendenhall, S. K., Shau, D. N., Adogwa, O., Anderson, W. N., Devin, C. J., & McGirt, M. J. (2012). Minimum clinically important difference in pain, disability, and quality of life after neural decompression and fusion for same-level recurrent lumbar stenosis: understanding clinical versus statistical significance: Clinical article. Journal of Neurosurgery: Spine, 16 (5), 471-478. https://doi.org/10.3171/2012.1.SPINE11842 Power, J. D., Perruccio, A. V., Canizares, M., et al. (2023). Determining minimal clinically important difference estimates following surgery for degenerative conditions of the lumbar spine: analysis of the Canadian Spine Outcomes and Research Network (CSORN) registry. Spine J, 23 (9), 1323-1333. doi.org Rowe, E., Hassan, E., Carlesso, L., Astephen Wilson, J., Gross, D. P., Fisher, C., … Macedo, L. (2020). Predicting recovery after lumbar spinal stenosis surgery: A protocol for a historical cohort study using data from the Canadian Spine Outcomes Research Network (CSORN). Canadian Journal of Pain, 4 (4), 19–25. https://doi.org/10.1080/24740527.2020.1734918 Sanders, A. E., et al. (2024). Baseline quality of life as a predictor of postoperative improvement in degenerative spinal conditions: A prospective study. Spine, 49 (14), 982–990. Schizas, C., Theumann, N., Burn, A., et al. (2010). Qualitative grading of severity of lumbar spinal stenosis based on the morphology of the dural sac on magnetic resonance images. Spine (Phila Pa 1976), 35 (21), 1919-1924. doi.org Suda, Y., et al. (2024). Long-term outcomes of decompression surgery for lumbar spinal stenosis: A prospective study using SF-36 and ODI. Spine . Sullivan, G. M., & Artino, A. R., Jr. (2013). Analyzing and interpreting data from Likert-type scales. Journal of Graduate Medical Education, 5 (4), 541–542. Tack, H., et al. (2023). Biopsychosocial predictors of surgical success in degenerative spine disease. Journal of Neurosurgery: Spine, 38 (1). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-8691004\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":583407132,\"identity\":\"f29b6724-11eb-43b7-a7e6-1b2ef071e2cb\",\"order_by\":0,\"name\":\"Rositsa Krasteva\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYDACHhiDvYGBIQHCZCNCC0gpzwGStUgkwMXwazHvOfvw488fNon9M9+YPXjAcC9xfnsD28MveLTInG03luZJSEuccTvH3CCBoThxw5kD7MYyeLRI8LMxSDMkHDZmuJ1jJpH4LyFxg0QCm7QEfi3MP38k/DeWv3nGDOidhMT58x8Q0MLbxibBk3BAzuAGD0RLww0GNskP+LTwHGOz5klLljM8k1YG0mK84UxiuzEeHUAtacw3f9jY8cgdP7xN8gdDguz89sPHHv7ApwcLYGxg5iGsCl0TqbaMglEwCkbBsAYA89lGoZkMqsQAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Multiprofile Hospital for Active Treatment MEDIKA\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Rositsa\",\"middleName\":\"\",\"lastName\":\"Krasteva\",\"suffix\":\"\"},{\"id\":583407133,\"identity\":\"d66970b9-3aa5-4d55-8418-ad4359c87d8e\",\"order_by\":1,\"name\":\"Stephan Stanchev\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Multiprofile Hospital for Active Treatment MEDIKA\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Stephan\",\"middleName\":\"\",\"lastName\":\"Stanchev\",\"suffix\":\"\"},{\"id\":583407134,\"identity\":\"5474ffc1-a23f-41ee-a541-c9badf54ac90\",\"order_by\":2,\"name\":\"Kiril Panayotov\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Multiprofile Hospital for Active Treatment MEDIKA\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Kiril\",\"middleName\":\"\",\"lastName\":\"Panayotov\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-01-25 08:08:20\",\"currentVersionCode\":1,\"declarations\":{\"humanSubjects\":false,\"vertebrateSubjects\":false,\"conflictsOfInterestStatement\":false,\"humanSubjectEthicalGuidelines\":false,\"humanSubjectConsent\":false,\"humanSubjectClinicalTrial\":false,\"humanSubjectCaseReport\":false,\"vertebrateSubjectEthicalGuidelines\":false},\"doi\":\"10.21203/rs.3.rs-8691004/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8691004/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":101633376,\"identity\":\"4bcefc69-22b6-4776-bb24-66e44505b9c3\",\"added_by\":\"auto\",\"created_at\":\"2026-02-02 06:00:23\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":90583,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eROC curve for assessing the predictive value of the preoperative SF-36 for predicting successful surgical outcome (AUC=0.951; P \\u0026lt; 0.001).\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8691004/v1/edfe2bffbfa5d8b2c9d6df1a.png\"},{\"id\":104399972,\"identity\":\"4128fac1-ca99-4f97-90c2-d0a24485783c\",\"added_by\":\"auto\",\"created_at\":\"2026-03-11 12:08:21\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":844324,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8691004/v1/7120e868-765a-48f5-a39f-90dfcc5bd766.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Prognostic Value Of The SF-36 Questionnaire In The Surgical Treatment Of Degenerative Spinal Stenosis\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eDegenerative spinal stenosis (DSS) is one of the most common diseases leading to surgical intervention in the elderly (12). Despite the high efficacy of decompressive surgery in relieving symptoms, selecting patients who will derive maximum benefit from surgical treatment remains a challenge. Clinical scales such as the visual analogue scale (VAS) for pain and the Oswestry Disability Index (ODI) (7), as well as imaging classifications such as Schizas\\u0026rsquo;s (21) classification, are routinely used, but although classifications such as Schizas\\u0026rsquo;s (21) are widely used, the lack of a clear correlation between morphological severity and functional status necessitates the inclusion of patient-oriented indicators, including patient-reported outcomes, PROMs (3, 10). Health-related quality of life (HRQoL), measured with common questionnaires such as the SF-36, can provide a more comprehensive assessment of a patient's well-being and potential for recovery. HRQoL is increasingly being used as an independent predictor of surgical success in degenerative spinal stenosis (15, 16), and the use of HRQoL instruments such as the SF-36 allows for a more objective prediction of postoperative recovery beyond pure neurological decompression (2). The aim of the present study was to investigate the prognostic value of the preoperative SF-36 in assessing the success of surgical treatment of DSS.\\u003c/p\\u003e\"},{\"header\":\"2. Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1. Study population:\\u003c/h2\\u003e \\u003cp\\u003eThe study was prospective and included a total of 108 patients undergoing surgical treatment for degenerative spinal stenosis. Patients with severe somatic comorbidities, dementia, and a history of psychiatric illness were excluded from the sample. The demographic profile of the patients was a mean age of 73.2 years (Standard Deviation\\u0026thinsp;=\\u0026thinsp;5.3) and a range of 61 to 83 years. The gender distribution included 66 men (61.1%) and 42 women (38.9%).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2. Measurement instruments and assessment:\\u003c/h2\\u003e \\u003cp\\u003ePatients were assessed twice: before surgery (preoperative measurement, t1) and 6 months after surgery (postoperative measurement, t2). The following instruments were used:\\u003c/p\\u003e \\u003cp\\u003e \\u003cul\\u003e \\u003cli\\u003e \\u003cp\\u003e\\u0026bull; Pain intensity: Assessed using a 3-point ordinal VAS scale for mild, moderate, and severe pain.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003e\\u0026bull; Degree of disability: Assessed using a 5-point ordinal ODI scale.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003e\\u0026bull; Health-related quality of life (HRQoL): Assessed using the total (sum) score of the SF-36 questionnaire.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003e\\u0026bull; Anatomical severity of stenosis: Classified according to Schizas grade (A, B, C, D) based on preoperative magnetic resonance imaging.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/ul\\u003e \\u003c/p\\u003e \\u003cp\\u003eFor the purpose of ROC analysis, surgical \\\"success\\\" (Outcome) was defined as achieving a minimal clinically important difference (MCID): a decrease in ODI of at least 1.5 points, a decrease in VAS of at least 1 point, or an increase in the total SF-36 score of at least 15 points (5, 19). Defining success by the minimal clinically important difference (MCID) is a standard in modern spinal surgery to objectify subjective improvement (8). Patients who achieved this threshold were classified as \\\"positive\\\" cases (N\\u0026thinsp;=\\u0026thinsp;61) and the rest as \\\"negative\\\" (N\\u0026thinsp;=\\u0026thinsp;47).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3. Statistical analysis:\\u003c/h2\\u003e \\u003cp\\u003eStatistical analysis was performed using the SPSS 26 software package. Descriptive statistics (means, standard deviations, frequencies), paired-sample t-tests for comparing t1 and t2 measurements, Spearman's correlation analysis (Spearman's Rho) for assessing the relationship between ordinal scales (Schizas, VAS, ODI), and ROC analysis for determining the predictive power and optimal cut-off point of the SF-36 were used. Although the VAS and ODI scales are technically ordinal, for the purposes of this analysis their numerical values ​​were treated as interval data, an approach that is widely accepted and used in clinical research on spinal pathology (12). The minimal clinically important difference (MCID) was applied to define therapeutic success, in view of the need to distinguish the mathematical significance of the results from their real clinical value for the patient and to establish the proportion of subjects with a tangible improvement in quality of life and functional status (6).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1. Distribution of anatomical severity according to the Schizas scale:\\u003c/h2\\u003e \\u003cp\\u003eThe distribution of patients according to the degree of spinal stenosis according to the Schizas scale is as follows \\u003cb\\u003e(\\u003c/b\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003cb\\u003e)\\u003c/b\\u003e:\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDistribution of patients according to the degree of stenosis according to the Shizas classification. The majority of patients were classified as grade C, indicating a predominant sample with severe anatomical stenosis.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSchizas Degree\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFrequency\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePercentage\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.9%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e25.0%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e68\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e63.0%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11.1%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e108\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e100.0%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2. Effectiveness of surgical treatment:\\u003c/h2\\u003e \\u003cp\\u003eThe paired-samples t-test revealed a statistically significant improvement in all clinical parameters after surgery (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001 for all measurements), (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e.\\u003cb\\u003e)\\u003c/b\\u003e:\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eComparative analysis of functional status, pain and quality of life indicators before and after treatment\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean value before (t1)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMean value after (t2)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMean difference\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003et-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eODI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.96\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e11.135\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVAS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.51\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.91\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e12.691\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSF-36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e49.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e69.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e19.91\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-10.975\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3. Correlation analysis between clinical and anatomical parameters:\\u003c/h2\\u003e \\u003cp\\u003eSpearman's correlation analysis (Spearman's Rho) provides key data on the relationships between variables, \\u003cb\\u003e(\\u003c/b\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e.\\u003cb\\u003e)\\u003c/b\\u003e:\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eCorrelation analysis between preoperative parameters and final therapeutic outcome\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCorrelation (r) with SF-36 (t1)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSig. (p-value)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eCorrelation (r) with Outcome\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eSig. (p-value)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eShizas\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.102\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.294 (Insignificant)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.287\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.003\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVAS1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.075\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.437 (Insignificant)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.027\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.778 (Insignificant)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eODI1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.122\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.210 (Insignificant)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.129\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.184 (Insignificant)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSF-36 (t1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.544\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eA lack of statistically significant correlation was reported between the anatomical Schizas grade and the baseline subjective parameters (SF-36, VAS, ODI), highlighting the presence of a clinical-radiological dissociation in the sample. This dissociation has been widely described in the literature as one of the main challenges in the diagnosis of DSS (4, 26).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4. ROC Analysis and Prognostic Value of SF-36:\\u003c/h2\\u003e \\u003cp\\u003eROC analysis to assess the prognostic value of SF-36 for surgical success showed exceptional results \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003cb\\u003e)\\u003c/b\\u003e:\\u003c/p\\u003e \\u003cp\\u003e \\u003cul\\u003e \\u003cli\\u003e \\u003cp\\u003e\\u0026bull; Area under the curve (AUC): 0.951 (95% CI).\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003e\\u0026bull; Optimal cut-off value (Cut-off): Approximately 0.652 (probability of success), with a sensitivity of 86.9% and a specificity of 89.4%.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/ul\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe resulting area under the curve (AUC\\u0026thinsp;=\\u0026thinsp;0.951) defines the preoperative SF-36 as an excellent predictor of final success, which is consistent with the latest prognostic models in spinal surgery (11, 22).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eThe present study confirms both the effectiveness of surgical treatment of degenerative spinal stenosis in a typical geriatric population and the exceptional prognostic value of preoperative assessment of quality of life using the SF-36 questionnaire. The significant improvement in the parameters of our sample (mean age 73.2 years) corresponds with recent studies showing that advanced age in itself is not a contraindication to excellent functional outcome (1).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1. Efficacy and clinical improvement:\\u003c/h2\\u003e \\u003cp\\u003eThe results of the paired t-test clearly demonstrate a significant clinical and functional improvement in all measured parameters (VAS, ODI, SF-36) after surgery (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). The mean increase in the total SF-36 score of nearly 20 points represents a large clinically significant difference, highlighting the benefits of timely surgical treatment in properly selected patients.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2. The phenomenon of clinical-radiological dissociation:\\u003c/h2\\u003e \\u003cp\\u003eOne of the most important conclusions from the Spearman correlation analysis is the lack of a statistically significant relationship between the anatomical severity of the stenosis (according to Schizas) and the patient's preoperative subjective state: HRQoL (SF-36), pain (VAS) or disability (ODI).\\u003c/p\\u003e \\u003cp\\u003eThis result confirms the well-known phenomenon of clinical-radiological dissociation in the literature. It suggests that the severity assessed by imaging studies is not a direct and reliable indicator of the intensity of pain or functional limitations from which the patient suffers. The lack of a relationship between the Schizas grade and the baseline quality of life confirms the conclusions of Jensen et al. (2022) that morphological changes are only part of the clinical picture and should not be the only criterion for surgical intervention (13). Our data support the idea that basing indications solely on imaging studies carries a risk of diagnostic errors, whereas the use of PROMs such as the SF-36, for example, provides more precise patient selection (25). Therefore, basing indications for surgery solely on MRI or CT findings may lead to non-operative treatment or unnecessary surgery in certain groups of patients.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3. SF-36 as a leading prognostic tool:\\u003c/h2\\u003e \\u003cp\\u003eIn contrast to anatomical and other clinical scales, the preoperative SF-36 was the strongest predictor of the final success of surgery (Spearman's r = -0.544, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e \\u003cp\\u003eThe exceptional area under the ROC curve (AUC\\u0026thinsp;=\\u0026thinsp;0.951) further reinforces this finding. The high prognostic value of the baseline SF-36 is consistent with the data of Khor et al. (2018), who highlight the accuracy of preoperative scores in predicting functional recovery in spinal surgery (17). This high discriminative ability means that the SF-36 can serve as a reliable screening tool for stratifying patients before making a decision to undergo surgery. The defined optimal threshold can be used in clinical practice to identify those patients who have the greatest potential for a favorable outcome.\\u003c/p\\u003e \\u003cp\\u003eAn interesting aspect is also the weak correlation between preoperative and postoperative SF-36 scores (r\\u0026thinsp;=\\u0026thinsp;0.074), indicating that patients with a severe baseline condition have a significant potential for recovery, which is consistent with the observations of Sanders et al. (2024) that low baseline HRQoL should not be a criterion for refusing surgical treatment (20). It turns out that even patients with extremely low baseline quality of life (often excluded from surgery due to concerns about poor outcome) have great potential for significant improvement and should not be denied surgical treatment solely based on their severe baseline status.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.4. Study limitations:\\u003c/h2\\u003e \\u003cp\\u003eA major limitation of the present study is the use of parametric statistical methods (t-test and Pearson correlation) on ordinal variables (VAS and ODI). However, the large sample size (N\\u0026thinsp;=\\u0026thinsp;108) and the fact that the results of the nonparametric Spearman analysis confirmed the main conclusions about the relationship with the outcome suggest that the results of the parametric tests are reliable and clinically valid. The use of parametric methods on ordinal data is considered acceptable and reliable for samples with a size of more than 100 cases, as they have sufficient statistical power (18, 24). \\u0026ldquo;The definition of \\u0026lsquo;success\\u0026rsquo; by MCID (6, 8) provides an objective and reproducible criterion for outcome assessment.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eIn conclusion, our study conclusively demonstrates that surgical treatment of degenerative spinal stenosis leads to significant improvement in quality of life, pain and disability in the affected geriatric population.\\u003c/p\\u003e \\u003cp\\u003eThe most important finding is that the preoperative total score of the SF-36 is an extremely strong and reliable predictor of surgical success, outperforming traditional indicators such as VAS, ODI and anatomical criteria of Schizas. The established clinical-radiological dissociation suggests that the assessment of anatomical stenosis alone is not sufficient for clinical decision-making.\\u003c/p\\u003e \\u003cp\\u003eThese results support the inclusion of the standardized assessment of quality of life (HRQoL) by the SF-36 in the routine preoperative process. The defined optimal threshold can be used for more accurate stratification of patients, allowing for optimization of surgical outcomes and avoiding exclusion of patients with severe baseline status who have great potential for improvement.\\u003c/p\\u003e \"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eЕ\\u003c/strong\\u003e\\u003cstrong\\u003ethics declarations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe conduct of this study was approved by the regional ethics committee at the Multi-profile Hospital for Active Treatment MEDIKA Ruse.\\u0026nbsp;All participants completed the informed consent forms.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNo external funding has been received for conducting the study\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eA\\u003c/strong\\u003e\\u003cstrong\\u003ecknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contribution\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eR.K. wrote the main manuscript text and prepare figures, S.S and K.P. conducted patient surveys with questionnaires. All authors reviewed the manuscript.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAmmendola, M., et al. (2025). Surgical Outcomes in Octogenarians with Lumbar Spinal Stenosis: A Prospective SF-36 and ODI Analysis. \\u003cem\\u003eEuropean Spine Journal, 34\\u003c/em\\u003e(2), 450-458.\\u003c/li\\u003e\\n\\u003cli\\u003eBoden, S. D., Davis, D. O., Dina, T. S., Patronas, N. J., \\u0026amp; Wiesel, S. W. (1990). Abnormal magnetic-resonance scans of the lumbar spine in asymptomatic subjects. A prospective investigation. \\u003cem\\u003eJ Bone Joint Surg Am, 72\\u003c/em\\u003e(3), 403-408.\\u003c/li\\u003e\\n\\u003cli\\u003eBroda, A., Sanford, Z., Turcotte, J., Patton, C. (2020). Development of a Risk Prediction Model With Improved Clinical Utility in Elective Cervical and Lumbar Spine Surgery. \\u003cem\\u003eSpine, 45\\u003c/em\\u003e(9), E542-E551. doi.org\\u003c/li\\u003e\\n\\u003cli\\u003eChung, A. S., Copay, A. G., Olmscheid, N., Campbell, D., Walker, J. B., \\u0026amp; Chutkan, N. (2017). Minimum Clinically Important Difference: Current Trends in the Spine Literature. \\u003cem\\u003eSpine, 42\\u003c/em\\u003e(14), 1096-1105. doi.org\\u003c/li\\u003e\\n\\u003cli\\u003eCiancarelli, I., Martino Cinnera, A., Ricci, A., Iosa, M., Cerasa, A., Calabr\\u0026ograve;, R. S., \\u0026amp; Morone, G. (2025). Preoperative Health Status and Clinical Predictors of Health-Related Quality of Life Improvement After Lumbar Spinal Stenosis Surgery: A Longitudinal Study. \\u003cem\\u003eJournal of Clinical Medicine, 14\\u003c/em\\u003e(13), 4391. https://doi.org/10.3390/jcm14134391\\u003c/li\\u003e\\n\\u003cli\\u003e\\u0026Ccedil;iftci İnceoğlu, S., Ayyıldız, A., Şahin, B., \\u0026Ouml;zcan, S., İnceoğlu, A., Ayyıldız, H., \\u0026amp; Kuran, B. (2026). Association Between Radiological Stenosis Level and Patient-Reported Outcomes in Patients with Lumbar Spinal Stenosis: A Cross-Sectional Study. \\u003cem\\u003eMedicina, 62\\u003c/em\\u003e(1), 29. https://doi.org/10.3390/medicina62010029\\u003c/li\\u003e\\n\\u003cli\\u003eCopay, A. G., Subach, B. R., Glassman, S. D., Polly, D. W., Jr, \\u0026amp; Schuler, T. C. (2007). Understanding the minimum clinically important difference: a review of concepts and methods. \\u003cem\\u003eSpine J, 7\\u003c/em\\u003e(5), 541-546. doi.org\\u003c/li\\u003e\\n\\u003cli\\u003eFairbank, J. C., \\u0026amp; Pynsent, P. B. (2000). The Oswestry Disability Index. \\u003cem\\u003eSpine (Phila Pa 1976), 25\\u003c/em\\u003e(22), 2940-2952. doi.org\\u003c/li\\u003e\\n\\u003cli\\u003eHanley, J. A., \\u0026amp; McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. \\u003cem\\u003eRadiology, 143\\u003c/em\\u003e(1), 29-36. doi.org\\u003c/li\\u003e\\n\\u003cli\\u003eHe, A. Y., et al. (2023). The lack of correlation between radiologic stenosis and clinical symptoms: A systematic review of the Schizas classification. \\u003cem\\u003eThe Spine Journal, 23\\u003c/em\\u003e(12), 1785-1798.\\u003c/li\\u003e\\n\\u003cli\\u003eJensen, M. P., Turner, J. A., Romano, J. M., \\u0026amp; Fisher, L. D. (1999). Comparative reliability and validity of chronic pain intensity measures. \\u003cem\\u003ePain, 83\\u003c/em\\u003e(2), 157-162. doi.org\\u003c/li\\u003e\\n\\u003cli\\u003eJensen, R. K., et al. (2022). Is there a relationship between lumbar spinal stenosis appearance on MRI and clinical symptom severity? A systematic review. \\u003cem\\u003eChiropractic \\u0026amp; Manual Therapies, 30\\u003c/em\\u003e(1), 1-15.\\u003c/li\\u003e\\n\\u003cli\\u003eKatz, J. N., Zimmerman, Z. E., Mass, H., \\u0026amp; Makhni, M. C. (2022). Diagnosis and Management of Lumbar Spinal Stenosis: A Review. \\u003cem\\u003eJAMA, 327\\u003c/em\\u003e(17), 1688-1699. doi.org\\u003c/li\\u003e\\n\\u003cli\\u003eKhor, S., Lavallee, D., Cizik, A. M., et al. (2018). Development and Validation of a Prediction Model for Pain and Functional Outcomes After Lumbar Spine Surgery. \\u003cem\\u003eJAMA Surg, 153\\u003c/em\\u003e(7), 634-642. doi.org\\u003c/li\\u003e\\n\\u003cli\\u003eKo, S., \\u0026amp; Choi, W. (2022). Usefulness of preoperative Short Form-36 Mental Component Score as a prognostic factor in patients who underwent decompression surgery for degenerative lumbar spinal stenosis. \\u003cem\\u003eMedicine (Baltimore), 101\\u003c/em\\u003e(39), e30231. doi.org\\u003c/li\\u003e\\n\\u003cli\\u003eMinetama, M., Kawakami, M., Teraguchi, M., Matsuo, S., Enyo, Y., Nakagawa, M., Yamamoto, Y., Nakatani, T., Sakon, N., Nagata, W., \\u0026amp; Nakagawa, Y. (2022). MRI grading of spinal stenosis is not associated with the severity of low back pain in patients with lumbar spinal stenosis. \\u003cem\\u003eBMC Musculoskelet Disord, 23\\u003c/em\\u003e(1), 857. doi.org\\u003c/li\\u003e\\n\\u003cli\\u003eMullan, A., et al. (2025). Parametric vs. Non-parametric analysis of Patient Reported Outcome Measures in Spine Surgery. \\u003cem\\u003eJournal of Clinical Epidemiology, 178\\u003c/em\\u003e, 112-120.\\u003c/li\\u003e\\n\\u003cli\\u003eParker, S. L., Mendenhall, S. K., Shau, D. N., Adogwa, O., Anderson, W. N., Devin, C. J., \\u0026amp; McGirt, M. J. (2012). Minimum clinically important difference in pain, disability, and quality of life after neural decompression and fusion for same-level recurrent lumbar stenosis: understanding clinical versus statistical significance: Clinical article. \\u003cem\\u003eJournal of Neurosurgery: Spine, 16\\u003c/em\\u003e(5), 471-478. https://doi.org/10.3171/2012.1.SPINE11842\\u003c/li\\u003e\\n\\u003cli\\u003ePower, J. D., Perruccio, A. V., Canizares, M., et al. (2023). Determining minimal clinically important difference estimates following surgery for degenerative conditions of the lumbar spine: analysis of the Canadian Spine Outcomes and Research Network (CSORN) registry. \\u003cem\\u003eSpine J, 23\\u003c/em\\u003e(9), 1323-1333. doi.org\\u003c/li\\u003e\\n\\u003cli\\u003eRowe, E., Hassan, E., Carlesso, L., Astephen Wilson, J., Gross, D. P., Fisher, C., \\u0026hellip; Macedo, L. (2020). Predicting recovery after lumbar spinal stenosis surgery: A protocol for a historical cohort study using data from the Canadian Spine Outcomes Research Network (CSORN). \\u003cem\\u003eCanadian Journal of Pain, 4\\u003c/em\\u003e(4), 19\\u0026ndash;25. https://doi.org/10.1080/24740527.2020.1734918\\u003c/li\\u003e\\n\\u003cli\\u003eSanders, A. E., et al. (2024). Baseline quality of life as a predictor of postoperative improvement in degenerative spinal conditions: A prospective study. \\u003cem\\u003eSpine, 49\\u003c/em\\u003e(14), 982\\u0026ndash;990.\\u003c/li\\u003e\\n\\u003cli\\u003eSchizas, C., Theumann, N., Burn, A., et al. (2010). Qualitative grading of severity of lumbar spinal stenosis based on the morphology of the dural sac on magnetic resonance images. \\u003cem\\u003eSpine (Phila Pa 1976), 35\\u003c/em\\u003e(21), 1919-1924. doi.org\\u003c/li\\u003e\\n\\u003cli\\u003eSuda, Y., et al. (2024). Long-term outcomes of decompression surgery for lumbar spinal stenosis: A prospective study using SF-36 and ODI. \\u003cem\\u003eSpine\\u003c/em\\u003e.\\u003c/li\\u003e\\n\\u003cli\\u003eSullivan, G. M., \\u0026amp; Artino, A. R., Jr. (2013). Analyzing and interpreting data from Likert-type scales. \\u003cem\\u003eJournal of Graduate Medical Education, 5\\u003c/em\\u003e(4), 541\\u0026ndash;542.\\u003c/li\\u003e\\n\\u003cli\\u003eTack, H., et al. (2023). Biopsychosocial predictors of surgical success in degenerative spine disease. \\u003cem\\u003eJournal of Neurosurgery: Spine, 38\\u003c/em\\u003e(1).\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":true,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Degenerative spinal stenosis, SF-36, Prognostic factors, Health-related quality of life (HRQoL), Surgical treatment\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8691004/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8691004/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eIntroduction:\\u003c/h2\\u003e \\u003cp\\u003eThe aim of the study was to evaluate the prognostic value of health-related quality of life (HRQoL), measured with the SF-36 (Short Form Health Survey-36) questionnaire, for the success of surgical treatment of degenerative spinal stenosis.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003e108 patients (mean age 73.2 years, 61.1% male) were included. The success of surgical treatment was defined by the minimal clinically important difference (MCID): a decrease in ODI by 1.5 points, a decrease in VAS by 1 point, or an increase in the total SF-36 score by 15 points. Paired-sample t-test, Spearman correlation analysis, and ROC analysis were used to assess the prognostic value.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eA statistically significant improvement was reported in all parameters after surgery (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001 for all). The mean ODI index decreased from 3.15 to 2.19, the VAS score from 2.51 to 1.60, and the SF-36 increased from 49.11 to 69.02 points. ROC analysis demonstrated excellent predictive ability of the baseline SF-36 for determining surgical success (AUC\\u0026thinsp;=\\u0026thinsp;0.951).\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003ePreoperative quality of life, measured by the SF-36, is a strong and independent predictor of the success of surgical treatment of degenerative spinal stenosis.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Prognostic Value Of The SF-36 Questionnaire In The Surgical Treatment Of Degenerative Spinal Stenosis\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-02-02 06:00:14\",\"doi\":\"10.21203/rs.3.rs-8691004/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"fd423455-68fa-4b0f-bab8-9dd7facf6d7f\",\"owner\":[],\"postedDate\":\"February 2nd, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-03-02T01:39:40+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-02-02 06:00:14\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8691004\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8691004\",\"identity\":\"rs-8691004\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}