Preoperative Dermatomal Somatosensory Evoked Potentials in Risk Prediction of Postoperative Neurological Deficit After Thoracic Spine Surgery: A Retrospective Cohort Study

Preoperative Dermatomal Somatosensory Evoked Potentials in Risk Prediction of Postoperative Neurological Deficit After Thoracic Spine Surgery: A Retrospective Cohort Study | 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 Preoperative Dermatomal Somatosensory Evoked Potentials in Risk Prediction of Postoperative Neurological Deficit After Thoracic Spine Surgery: A Retrospective Cohort Study Yongjie Zhang, Yuan Liu, Lixuan Wang, Yuchen Wang, Jialiang Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9038212/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Background Postoperative neurological deficit represents one of the most serious complications following thoracic spine surgery. Traditional risk assessment primarily relies on clinical and imaging variables, whereas the predictive value of electrophysiological indicators in preoperative risk stratification remains insufficiently explored. Dermatomal somatosensory evoked potentials (DSEP) directly reflect the integrity of sensory conduction pathways and may provide functional information beyond conventional structural imaging. This study aimed to develop and internally validate a clinically applicable prediction model integrating electrophysiological parameters. Methods A total of 508 patients who underwent thoracic decompression surgery were retrospectively included. Collected variables comprised age, preoperative Japanese Orthopaedic Association (JOA) score, number of compressed levels, T2-weighted signal changes, and DSEP parameters including latency, amplitude, and number of abnormal segments. Multivariable logistic regression models were constructed, including a clinical model, a clinical–imaging model, a clinical–electrophysiological model, and a combined model. Internal validation was performed using stratified five-fold cross-validation. Model discrimination was assessed using receiver operating characteristic (ROC) curves and the area under the curve (AUC). Ninety-five percent confidence intervals were estimated using bootstrap resampling. Results Among the 508 patients, 107 (21.1%) developed postoperative neurological deficit. Multivariable analysis identified age (OR = 1.03), preoperative JOA score (OR = 0.67), number of compressed levels (OR = 1.84), and maximal N1 latency (OR = 1.13) as independent predictors. In cross-validated model evaluation, the clinical model demonstrated limited discriminative ability (AUC = 0.58, 95% CI 0.519–0.64). The clinical–imaging model showed modest improvement (AUC = 0.625, 95% CI 0.561–0.683). Incorporation of electrophysiological parameters substantially improved prediction performance, with the clinical–electrophysiological model achieving an AUC of 0.736 (95% CI 0.688–0.789). The combined model integrating clinical, imaging, and electrophysiological variables showed the highest overall performance (AUC = 0.742, 95% CI 0.689–0.792). Conclusions Preoperative DSEP parameters, particularly maximal N1 latency, significantly improve prediction of postoperative neurological deficit after thoracic spine surgery. Integration of electrophysiological and clinical variables may enhance perioperative risk stratification and support individualized surgical decision-making. Thoracic spine spinal cord compression functional reserve dermatomal somatosensory evoked potentials risk prediction Figures Figure 1 Figure 2 Figure 3 Background Thoracic spinal cord compression is a relatively uncommon spinal disorder, yet the uncertainty of neurological recovery following surgical treatment is significantly greater than that observed in cervical pathology[ 1 ]. Although decompressive surgery aims to relieve mechanical compression of the spinal cord and create conditions favorable for neurological recovery, clinical practice frequently reveals paradoxical findings: some patients with radiologically complete decompression experience limited neurological improvement, whereas others with similar degrees of preoperative compression demonstrate markedly different postoperative recovery trajectories[ 2 , 3 ]. These observations strongly suggest that postoperative neurological outcomes are not determined solely by the extent of mechanical decompression and may involve more complex pathophysiological mechanisms[ 4 ]. Currently, preoperative assessment primarily depends on imaging findings and clinical functional scores. Conventional imaging modalities such as magnetic resonance imaging (MRI) mainly depict morphological structural alterations rather than functional integrity of neural conduction pathways[ 5 , 6 ]. T2-weighted intramedullary hyperintensity is often interpreted as a marker of spinal cord edema, gliosis, or cystic degeneration; however, its association with prognosis remains controversial[ 7 ]. Multiple studies have demonstrated that the relationship between T2 signal change and neurological recovery is inconsistent. Some patients with pronounced signal alterations achieve favorable postoperative recovery, whereas others with minimal imaging abnormalities show poor outcomes. This “imaging–clinical paradox” highlights the limitation of purely morphological assessment, as imaging findings represent static structural alterations and fail to reflect the dynamic functional state of axonal conduction within the spinal cord[ 8 ]. Neurophysiological assessment provides an important functional complement to imaging evaluation. In the pathological process of spinal cord compression, abnormalities in neural conduction frequently precede irreversible structural damage. Somatosensory evoked potentials (SEP), which assess dorsal column (gracile and cuneate fasciculi) sensory conduction, theoretically offer early warning value[ 9 , 10 ]. However, conventional mixed-nerve SSEP recordings represent composite potentials conducted through long ascending pathways to the cortex, and waveform characteristics and latency are influenced by multiple individual factors. These confounders include height-related physiological variation, peripheral neuropathy secondary to metabolic disorders, and coexisting lumbar or cervical stenosis, all of which may independently alter SSEP results[ 3 , 11 , 12 ]. Even under intraoperative monitoring conditions using self-controlled comparisons, interpretation may be influenced by anesthetic regimen, core temperature, mean arterial pressure, and surgical manipulation[ 13 ]. Therefore, although SEP is widely applied in intraoperative monitoring, its sensitivity and specificity as an independent preoperative predictor of postoperative functional recovery remain limited. Given the limitations of traditional imaging and electrophysiological indicators in predicting prognosis of thoracic spinal cord compression, this study proposed integration of dermatomal somatosensory evoked potentials (DSEP) with clinical variables to construct a multidimensional prediction model[ 9 , 14 ]. Compared with SSEP, DSEP records conduction from specific dermatomes or peripheral nerves, allowing more precise localization and evaluation of segmental nerve root and spinal cord function, and potentially more sensitively reflecting the impact of localized compression on neural conduction pathways. The objectives of this study were: (1) to systematically evaluate the association between preoperative DSEP characteristics and postoperative neurological outcomes; (2) to identify independent risk factors through multivariable analysis incorporating clinical variables; (3) to develop and internally validate a prognostic prediction model and assess its clinical applicability for preoperative risk stratification. We hypothesized that DSEP-derived functional conduction parameters would compensate for the inability of imaging to evaluate functional impairment, thereby more accurately identifying patients most likely to benefit from surgical intervention. Methods Study Design and Population This was a single-center retrospective cohort study. Consecutive patients who underwent thoracic decompression surgery at our institution were included. Inclusion criteria were:1.Radiologically confirmed thoracic spinal cord compression; 2.Standard posterior or combined decompression surgery; 3.Completion of preoperative DSEP examination; 4.Availability of complete perioperative follow-up data. Exclusion criteria were:1.History of prior spinal cord injury;2.Severe peripheral neuropathy or metabolic neurological disorders;3.Incomplete data or loss to follow-up. All patients provided written informed consent for electrophysiological examination. The study protocol was approved by the institutional ethics committee and conducted in accordance with the Declaration of Helsinki. DSEP Examination All electrophysiological examinations were performed preoperatively by the same neurophysiological team to ensure methodological consistency and reproducibility. Testing was conducted in a sound-attenuated room with ambient temperature maintained at 24–26°C. Patients were positioned supine comfortably. The total examination duration was approximately 1–2 hours. Stimulation electrodes were placed bilaterally along the midaxillary line corresponding to thoracic dermatomes:T2: sternal angle level,T4: nipple level,T6: xiphoid level,T8: costal arch level,T10: umbilical level,T12: midpoint between umbilicus and pubic symphysis Surface electrodes were used. Stimulation intensity was set at 2–3 times sensory threshold, with a stimulation frequency of 3 Hz. Signals were recorded using a Nihon Kohden electromyography system. Subdermal monopolar needle electrodes were positioned according to the international 10–20 system. The active recording electrode was placed at Cz′ (2 cm posterior to Cz), and the reference electrode at Fz. Each stimulation site was averaged over 100 trials and repeated twice to ensure waveform stability and reproducibility. Standardized quality control procedures were applied throughout recording. Measured parameters included: N1 latency P1 latency Peak-to-peak amplitude Number of abnormal segments Abnormality was defined by prolonged latency, reduced amplitude, or waveform absence. Clinical and Imaging Data Collection Collected variables included age, preoperative JOA score, number of compressed levels, operative time, and presence of T2-weighted signal changes on MRI. Imaging was independently evaluated by two spine surgeons; discrepancies were resolved by consensus. Outcome Definition The primary outcome was postoperative neurological deficit, defined as new neurological deterioration or significant worsening compared with preoperative status occurring within 3 months after surgery, including sensory or motor decline, confirmed by neurological examination and JOA score assessment. Model Development and Internal Validation Multivariable logistic regression models were constructed: Clinical model, Clinical + imaging model, Clinical + electrophysiological model, Combined model Internal validation was performed using stratified five-fold cross-validation. In each fold, predicted probabilities were generated and aggregated to compute cross-validated AUC. Ninety-five percent confidence intervals for AUC were estimated using 1,000 bootstrap resamples. Calibration was evaluated using quantile-based calibration curves with ten equal-frequency bins. Decision curve analysis was conducted across threshold probabilities ranging from 0.01 to 0.99. All statistical analyses were performed using Python (version 3.12). Results 1. Baseline Characteristics and Perioperative Differences A total of 508 patients undergoing thoracic decompression surgery were included, of whom 107 (21.1%) developed postoperative neurological deficit. There was no significant difference in age distribution between groups (59.00 [50.00–67.00] vs 59.00 [52.50–65.50], P = 0.838), suggesting limited discriminative value of age in univariable comparison. However, operative time was significantly longer in patients who developed neurological deterioration (203.00 [188.00–225.00] minutes vs 196.00 [174.00–219.00] minutes, P = 0.005), indicating that increased surgical complexity may be associated with adverse neurological outcomes. Electrophysiological parameters differed markedly between groups. Patients with postoperative deficit exhibited significantly higher DSEP risk scores (2.00 [2.00–3.00] vs 1.00 [0.00–2.00], P < 0.001) and a greater number of abnormal segments (3.00 [2.00–3.00] vs 1.00 [1.00–2.00], P < 0.001). These differences were consistent and robust, supporting a relationship between the severity of electrophysiological abnormalities and postoperative neurological status (Table 1 ). Table 1 Baseline characteristics according to neurological outcome Variable No deficit Deficit P value Demographics Age (years) 59.00 [50.00, 67.00] 59.00 [52.50, 65.50] 0.838 Surgical characteristics Operative time (min) 196.00 [174.00, 219.00] 203.00 [188.00, 225.00] 0.005 Electrophysiological parameters DSEP risk score 1.00 [0.00, 2.00] 2.00 [2.00, 3.00] < 0.001 Number of abnormal segments 1.00 [1.00, 2.00] 3.00 [2.00, 3.00] < 0.001 2. Comparative Discriminative Performance of Prediction Models Stratified five-fold cross-validation was performed to evaluate the discriminative performance of the four prediction models. The clinical model demonstrated limited predictive ability, with a cross-validated AUC of 0.58. Incorporation of imaging variables resulted in a modest improvement, yielding an AUC of 0.63 for the clinical–imaging model. Notably, the addition of electrophysiological variables substantially enhanced model discrimination. The clinical–electrophysiological model achieved a higher AUC of 0.74, indicating that DSEP-derived functional parameters provide meaningful incremental predictive value beyond conventional clinical and imaging factors. The combined model integrating clinical, imaging, and electrophysiological variables achieved the highest overall discriminative performance, with an AUC of 0.74. These findings suggest that electrophysiological indicators play an important role in improving risk prediction for postoperative neurological deficit. Detailed model performance, including cross-validated AUC values and confidence intervals, is presented in Table 2 . The ROC curves comparing the predictive performance of the four models are shown in Fig. 1 . Table 2 Predictive performance of different models Model Features (n) AUC (95% CI) Variables Clinical 4 0.58 (0.519–0.64) age, JOA_preop, num_levels_compressed, op_time_min Clinical–Imaging 5 0.625 (0.561–0.683) age, JOA_preop, num_levels_compressed, op_time_min, signal_change_T2 Clinical–Electrophysiology 7 0.736 (0.688–0.789) age, JOA_preop, num_levels_compressed, op_time_min, dsep_max_n1_latency, dsep_min_amplitude, dsep_abnormal_segments_count Combined 8 0.742 (0.689–0.792) age, JOA_preop, num_levels_compressed, op_time_min, signal_change_T2, dsep_max_n1_latency, dsep_min_amplitude, dsep_abnormal_segments_count 3. Calibration and Clinical Utility Assessment Calibration curves revealed acceptable agreement between predicted probabilities and observed event rates for the electrophysiological and combined models across most risk intervals. In contrast, the clinical-only model demonstrated a tendency toward overestimation in higher-risk strata. Decision curve analysis further showed that models incorporating electrophysiological variables yielded greater net benefit across a wide range of threshold probabilities (0.01–0.99). Within clinically relevant intermediate thresholds, the clinical–electrophysiological and combined models consistently outperformed alternative strategies, indicating potential advantages in guiding perioperative risk stratification (Figs. 2 – 3 ). 4. Independent Predictors of Postoperative Neurological Deficit Multivariable logistic regression analysis identified age (OR = 1.03, 95%CI 1.01–1.05, P = 0.001), preoperative JOA score (OR = 0.67, 95%CI 0.58–0.75, P < 0.001), number of compressed levels (OR = 1.84, 95%CI 1.16–2.90, P = 0.008), and maximal N1 latency (OR = 1.13, 95%CI 1.02–1.25, P = 0.012) as independent predictors of postoperative neurological deficit. Preoperative JOA score exhibited a protective association, whereas increasing number of compressed levels and prolonged N1 latency were associated with elevated risk. Operative time (P = 0.453), T2-weighted signal change (P = 0.215), minimal amplitude (P = 0.864), and number of abnormal segments (P = 0.076) did not retain statistical significance in the multivariable model, suggesting possible confounding or collinearity effects (Table 3 ). Table 3 Multivariable logistic regression analysis Variable OR CI_low CI_high P_value age 1.03 1.01 1.05 0.001 JOA_preop 0.67 0.58 0.75 < 0.001 num_levels_compressed 1.84 1.16 2.9 0.008 op_time_min 0.99 0.98 1.00 0.453 signal_change_T2 1.41 0.81 2.45 0.215 dsep_max_n1_latency 1.13 1.02 1.25 0.012 dsep_min_amplitude 1.18 0.17 8.17 0.864 dsep_abnormal_segments_count 1.24 0.97 1.59 0.076 Discussion This study systematically evaluated the prognostic value of dermatomal somatosensory evoked potentials (DSEP) in predicting postoperative neurological deficit following thoracic decompression surgery in a cohort of 508 patients. The principal findings are as follows: First, the incidence of postoperative neurological deficit was 21.1%, which is consistent with previously reported complication rates in thoracic spine surgery. Second, multivariable analysis identified age, preoperative JOA score, number of compressed levels, and maximal N1 latency as independent predictors, whereas T2-weighted MRI signal change and P1-related parameters were not independently associated with outcome. Third, predictive models incorporating electrophysiological parameters demonstrated superior discriminative performance compared with clinical-only or clinical–imaging models. Fourth, decision curve analysis confirmed greater net clinical benefit for models including DSEP parameters across clinically relevant decision thresholds. In the present study, prolonged maximal N1 latency emerged as an independent risk factor for postoperative neurological deficit (OR = 1.13 per 1 ms increase). This finding has a clear neurophysiological foundation. The N1 component originates from postsynaptic activity within the dorsal column–medial lemniscal pathway, which mediates fine touch and proprioceptive information[ 15 , 16 ]. Previous electrophysiological studies have demonstrated that early SEP components are closely linked to dorsal column conduction integrity[ 17 ]. Prolonged latency reflects delayed axonal conduction, often associated with demyelination or disruption of nodal architecture[ 18 – 20 ]. In chronic spinal cord compression, sustained mechanical stress may induce myelin sheath injury, nodal disorganization, and conduction slowing. Once structural damage progresses to axonal degeneration, functional recovery may remain incomplete despite adequate surgical decompression[ 21 ]. Therefore, preoperative prolongation of N1 latency may represent reduced spinal cord functional reserve and partial irreversible injury, thereby increasing the likelihood of postoperative neurological deterioration. Notably, P1 latency-related variables did not enter the final multivariable model. This observation is also physiologically plausible. P1 represents an early cortical positive component primarily generated in the primary somatosensory cortex. Unlike N1, which reflects dorsal column conduction integrity, cortical components are influenced not only by ascending pathway integrity but also by cortical excitability, attention state, vigilance level, and anesthetic effects. Such state-dependent variability may reduce the independent predictive contribution of P1 in multivariable modeling[ 16 , 17 , 20 ]. These findings suggest that spinal conduction–related parameters provide more stable and clinically relevant prognostic information in thoracic spinal cord compression. The present study demonstrated that T2-weighted intramedullary hyperintensity was not independently associated with postoperative neurological deficit after adjustment for confounding variables (P = 0.215). This finding aligns with prior studies reporting inconsistent prognostic significance of T2 signal changes. The pathophysiological basis of T2 hyperintensity is heterogeneous, including vasogenic edema, gliosis, cystic change, or micro-necrosis[ 22 , 23 ]. Edema-related signal changes may be reversible following decompression, whereas gliotic or necrotic alterations represent irreversible structural damage. Conventional MRI lacks the ability to distinguish these pathological substrates reliably. This limitation explains the frequently observed “imaging–clinical paradox,” wherein pronounced signal changes may coexist with favorable recovery and minimal signal alteration may precede poor outcome[ 24 ]. Our findings reinforce the concept that morphological assessment alone is insufficient for accurate preoperative risk stratification. Functional evaluation is necessary to capture the dynamic conduction reserve of the spinal cord. Interpretation of Model Performance The clinical–electrophysiological model demonstrated substantially improved discriminative ability (AUC = 0.736) compared with the clinical model (AUC = 0.58) and the clinical–imaging model (AUC = 0.625). The combined model integrating clinical, imaging, and electrophysiological variables achieved the highest overall performance (AUC = 0.742). These findings indicate that electrophysiological parameters provide meaningful incremental predictive value beyond conventional clinical and imaging variables. Interestingly, although imaging variables slightly improved prediction compared with the clinical model alone, their contribution was modest relative to electrophysiological parameters. This suggests that functional conduction abnormalities detected by DSEP may better capture the underlying neural impairment associated with thoracic spinal cord compression.From a methodological perspective, this finding suggests that adding variables lacking independent predictive value may introduce noise and reduce generalizability. In the present study, T2 hyperintensity did not demonstrate independent association with outcome. Inclusion of weak predictors may lead to subtle overfitting effects, particularly when event numbers are limited. Although the total sample size was 508, the number of outcome events was 107, which only modestly satisfies the commonly cited rule-of-thumb of 10–15 events per predictor variable in logistic regression modeling. The relatively wide 95% confidence intervals of AUC estimates reflect both statistical uncertainty and the multifactorial nature of postoperative neurological deterioration. Future studies may benefit from incorporating additional functional or structural biomarkers, such as diffusion tensor imaging parameters reflecting white matter integrity, motor evoked potentials assessing corticospinal tract function, or biochemical markers of neural injury. The findings of this study have several practical implications. First, preoperative DSEP examination may serve as an important adjunct in routine assessment of patients undergoing thoracic decompression surgery. Patients with significantly prolonged N1 latency, particularly beyond laboratory-defined upper limits, may represent a subgroup with reduced functional reserve and elevated risk of postoperative deterioration. Second, for high-risk patients, intensified intraoperative neuromonitoring strategies and hemodynamic optimization may be warranted. Third, early postoperative rehabilitation planning may be particularly important in patients demonstrating preoperative conduction impairment. Integration of electrophysiological assessment into preoperative risk stratification frameworks may facilitate individualized surgical counseling and perioperative management. This study has several limitations. First, the retrospective single-center design introduces potential selection bias and limits generalizability. External validation in independent multicenter cohorts is required. Second, follow-up duration was limited to 3 months postoperatively, and long-term neurological recovery was not assessed. Third, only sensory pathway function (DSEP) was evaluated. Motor pathway assessment using motor evoked potentials (MEP) was not incorporated. Combined evaluation of sensory and motor conduction may provide a more comprehensive representation of spinal cord functional status. Fourth, DSEP examination requires technical expertise and standardized methodology, which may limit widespread adoption in resource-limited settings. Conclusion In this retrospective cohort of 508 patients undergoing thoracic decompression surgery, preoperative dermatomal somatosensory evoked potential parameters—particularly maximal N1 latency—were identified as independent predictors of postoperative neurological deficit. Predictive models incorporating electrophysiological variables demonstrated superior discrimination and clinical net benefit compared with models based solely on clinical or imaging parameters. These findings support the integration of functional neurophysiological assessment into preoperative risk stratification frameworks for thoracic spinal cord compression, thereby enabling more precise identification of high-risk patients and optimization of perioperative management strategies. Declarations Ethics approval：The study was approved by the Ethics Committee of Xi’an Honghui Hospital, Xi’an Jiaotong University and conducted in accordance with the Declaration of Helsinki. Consent to participate：Written informed consent was obtained from all participants. Consent for publication：Not applicable. Clinical trial number: Not applicable. Data availability: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Funding: This work was supported by the Xi’an Science and Technology Bureau Medical Research Program (Grant No. 2023YXYJ0038). Competing interests: The authors declare that they have no competing interests. Author Contributions Y.Z.: Conceptualization, Methodology, Investigation, Formal analysis, Writing – Original Draft. Y.L.: Investigation, Validation, Data Curation, Writing – Review & Editing. L.W.: Resources, Investigation. Y.W.: Software, Validation. J.L.: Visualization, Data Curation. Y.Y.: Supervision, Project administration, Funding acquisition, Writing – Review & Editing. References Gader G, Gharbi MA, Kharrat MA, Harbaoui A, Zammel I. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Apr, 2026 Reviews received at journal 29 Mar, 2026 Reviews received at journal 22 Mar, 2026 Reviews received at journal 17 Mar, 2026 Reviews received at journal 15 Mar, 2026 Reviewers agreed at journal 14 Mar, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers invited by journal 12 Mar, 2026 Editor assigned by journal 08 Mar, 2026 Submission checks completed at journal 08 Mar, 2026 First submitted to journal 05 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-9038212","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":606366554,"identity":"4e34f168-9f83-47cc-b710-956753966751","order_by":0,"name":"Yongjie Zhang","email":"","orcid":"","institution":"Xi’an Honghui Hospital, Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yongjie","middleName":"","lastName":"Zhang","suffix":""},{"id":606366555,"identity":"8272c7bd-15bb-4514-9709-e6d1085377a8","order_by":1,"name":"Yuan Liu","email":"","orcid":"","institution":"Xi’an Honghui Hospital, Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Liu","suffix":""},{"id":606366556,"identity":"be204f7e-b989-4b8c-8a6b-33b42642e705","order_by":2,"name":"Lixuan Wang","email":"","orcid":"","institution":"Xi’an Honghui Hospital, Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Lixuan","middleName":"","lastName":"Wang","suffix":""},{"id":606366557,"identity":"fe5f7bd6-e1f1-4cd6-9b53-0a37b7173ff8","order_by":3,"name":"Yuchen Wang","email":"","orcid":"","institution":"Xi’an Honghui Hospital, Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yuchen","middleName":"","lastName":"Wang","suffix":""},{"id":606366558,"identity":"c88f4f7a-4278-4f16-8b45-900edaf9f1b3","order_by":4,"name":"Jialiang Li","email":"","orcid":"","institution":"Xi’an Honghui Hospital, Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Jialiang","middleName":"","lastName":"Li","suffix":""},{"id":606366559,"identity":"9509a8b8-53f8-4c0f-a688-e119562053c3","order_by":5,"name":"Yang Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBACfvmDjQ8+VNjw8LM3EKlFcgbzYcMZZ9LkJHsOEKnF4AZbmjRv22FjgxsJxLrsdo+BBFBL4syZjzfeYKixiSaog3HOGQMDiXPpif3SacUWDMfSchsIaWFmyDFIMCizTpw5O8dMgrHhMGEtbEAtBxLYmBM33DxDpBYeibTEhgNtzkDv8xCpRYLn8GHGBnAgA/2SQIxf7I83tv/+A47KwxtvfKixIawFGRhIJJCiHKKFVB2jYBSMglEwMgAAZMFD3pVgeQ8AAAAASUVORK5CYII=","orcid":"","institution":"Xi’an Honghui Hospital, Xi’an Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Yang","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2026-03-05 08:57:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9038212/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9038212/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104874255,"identity":"f4816783-155e-4ec2-b807-53dc3198792d","added_by":"auto","created_at":"2026-03-18 08:29:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99200,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves comparing the predictive performance of four models for postoperative neurological deficit. The clinical–electrophysiological model demonstrated substantially improved discriminative ability compared with the clinical and clinical–imaging models, while the combined model showed the highest overall performance.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9038212/v1/1e1289583c4708e0615d52f5.png"},{"id":104874211,"identity":"05f9816a-b228-4955-b612-4d557fa97b81","added_by":"auto","created_at":"2026-03-18 08:29:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":92813,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves of the four predictive models. The combined and electrophysiological models showed acceptable agreement between predicted probabilities and observed event rates across most risk strata.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9038212/v1/7ae9eb9d36814422dc30af43.png"},{"id":104874156,"identity":"1df6626c-8a65-45cc-ad29-0ce2b5cb5a8f","added_by":"auto","created_at":"2026-03-18 08:29:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":113781,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis comparing the net clinical benefit of different models across a range of threshold probabilities. The combined model provided greater net benefit within clinically relevant decision thresholds.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9038212/v1/fb078b55d8f854b40d34d146.png"},{"id":104874284,"identity":"d82dfa10-9e7f-495a-a7d0-eb1684a82747","added_by":"auto","created_at":"2026-03-18 08:29:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":803157,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9038212/v1/bf7cfc8a-19e6-49b6-8d58-99a3065e6dfd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Preoperative Dermatomal Somatosensory Evoked Potentials in Risk Prediction of Postoperative Neurological Deficit After Thoracic Spine Surgery: A Retrospective Cohort Study","fulltext":[{"header":"Background","content":"\u003cp\u003eThoracic spinal cord compression is a relatively uncommon spinal disorder, yet the uncertainty of neurological recovery following surgical treatment is significantly greater than that observed in cervical pathology[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although decompressive surgery aims to relieve mechanical compression of the spinal cord and create conditions favorable for neurological recovery, clinical practice frequently reveals paradoxical findings: some patients with radiologically complete decompression experience limited neurological improvement, whereas others with similar degrees of preoperative compression demonstrate markedly different postoperative recovery trajectories[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These observations strongly suggest that postoperative neurological outcomes are not determined solely by the extent of mechanical decompression and may involve more complex pathophysiological mechanisms[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, preoperative assessment primarily depends on imaging findings and clinical functional scores. Conventional imaging modalities such as magnetic resonance imaging (MRI) mainly depict morphological structural alterations rather than functional integrity of neural conduction pathways[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. T2-weighted intramedullary hyperintensity is often interpreted as a marker of spinal cord edema, gliosis, or cystic degeneration; however, its association with prognosis remains controversial[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Multiple studies have demonstrated that the relationship between T2 signal change and neurological recovery is inconsistent. Some patients with pronounced signal alterations achieve favorable postoperative recovery, whereas others with minimal imaging abnormalities show poor outcomes. This \u0026ldquo;imaging\u0026ndash;clinical paradox\u0026rdquo; highlights the limitation of purely morphological assessment, as imaging findings represent static structural alterations and fail to reflect the dynamic functional state of axonal conduction within the spinal cord[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNeurophysiological assessment provides an important functional complement to imaging evaluation. In the pathological process of spinal cord compression, abnormalities in neural conduction frequently precede irreversible structural damage. Somatosensory evoked potentials (SEP), which assess dorsal column (gracile and cuneate fasciculi) sensory conduction, theoretically offer early warning value[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, conventional mixed-nerve SSEP recordings represent composite potentials conducted through long ascending pathways to the cortex, and waveform characteristics and latency are influenced by multiple individual factors. These confounders include height-related physiological variation, peripheral neuropathy secondary to metabolic disorders, and coexisting lumbar or cervical stenosis, all of which may independently alter SSEP results[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Even under intraoperative monitoring conditions using self-controlled comparisons, interpretation may be influenced by anesthetic regimen, core temperature, mean arterial pressure, and surgical manipulation[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, although SEP is widely applied in intraoperative monitoring, its sensitivity and specificity as an independent preoperative predictor of postoperative functional recovery remain limited.\u003c/p\u003e \u003cp\u003eGiven the limitations of traditional imaging and electrophysiological indicators in predicting prognosis of thoracic spinal cord compression, this study proposed integration of dermatomal somatosensory evoked potentials (DSEP) with clinical variables to construct a multidimensional prediction model[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Compared with SSEP, DSEP records conduction from specific dermatomes or peripheral nerves, allowing more precise localization and evaluation of segmental nerve root and spinal cord function, and potentially more sensitively reflecting the impact of localized compression on neural conduction pathways.\u003c/p\u003e \u003cp\u003eThe objectives of this study were:\u003c/p\u003e \u003cp\u003e(1) to systematically evaluate the association between preoperative DSEP characteristics and postoperative neurological outcomes;\u003c/p\u003e \u003cp\u003e(2) to identify independent risk factors through multivariable analysis incorporating clinical variables;\u003c/p\u003e \u003cp\u003e(3) to develop and internally validate a prognostic prediction model and assess its clinical applicability for preoperative risk stratification.\u003c/p\u003e \u003cp\u003eWe hypothesized that DSEP-derived functional conduction parameters would compensate for the inability of imaging to evaluate functional impairment, thereby more accurately identifying patients most likely to benefit from surgical intervention.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003eThis was a single-center retrospective cohort study. Consecutive patients who underwent thoracic decompression surgery at our institution were included.\u003c/p\u003e \u003cp\u003eInclusion criteria were:1.Radiologically confirmed thoracic spinal cord compression; 2.Standard posterior or combined decompression surgery; 3.Completion of preoperative DSEP examination; 4.Availability of complete perioperative follow-up data.\u003c/p\u003e \u003cp\u003eExclusion criteria were:1.History of prior spinal cord injury;2.Severe peripheral neuropathy or metabolic neurological disorders;3.Incomplete data or loss to follow-up.\u003c/p\u003e \u003cp\u003eAll patients provided written informed consent for electrophysiological examination. The study protocol was approved by the institutional ethics committee and conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDSEP Examination\u003c/h3\u003e\n\u003cp\u003eAll electrophysiological examinations were performed preoperatively by the same neurophysiological team to ensure methodological consistency and reproducibility. Testing was conducted in a sound-attenuated room with ambient temperature maintained at 24\u0026ndash;26\u0026deg;C. Patients were positioned supine comfortably. The total examination duration was approximately 1\u0026ndash;2 hours.\u003c/p\u003e \u003cp\u003eStimulation electrodes were placed bilaterally along the midaxillary line corresponding to thoracic dermatomes:T2: sternal angle level,T4: nipple level,T6: xiphoid level,T8: costal arch level,T10: umbilical level,T12: midpoint between umbilicus and pubic symphysis\u003c/p\u003e \u003cp\u003eSurface electrodes were used. Stimulation intensity was set at 2\u0026ndash;3 times sensory threshold, with a stimulation frequency of 3 Hz.\u003c/p\u003e \u003cp\u003eSignals were recorded using a Nihon Kohden electromyography system. Subdermal monopolar needle electrodes were positioned according to the international 10\u0026ndash;20 system. The active recording electrode was placed at Cz\u0026prime; (2 cm posterior to Cz), and the reference electrode at Fz.\u003c/p\u003e \u003cp\u003eEach stimulation site was averaged over 100 trials and repeated twice to ensure waveform stability and reproducibility. Standardized quality control procedures were applied throughout recording.\u003c/p\u003e \u003cp\u003eMeasured parameters included:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eN1 latency\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eP1 latency\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePeak-to-peak amplitude\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNumber of abnormal segments\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAbnormality was defined by prolonged latency, reduced amplitude, or waveform absence.\u003c/p\u003e\n\u003ch3\u003eClinical and Imaging Data Collection\u003c/h3\u003e\n\u003cp\u003eCollected variables included age, preoperative JOA score, number of compressed levels, operative time, and presence of T2-weighted signal changes on MRI. Imaging was independently evaluated by two spine surgeons; discrepancies were resolved by consensus.\u003c/p\u003e\n\u003ch3\u003eOutcome Definition\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was postoperative neurological deficit, defined as new neurological deterioration or significant worsening compared with preoperative status occurring within 3 months after surgery, including sensory or motor decline, confirmed by neurological examination and JOA score assessment.\u003c/p\u003e\n\u003ch3\u003eModel Development and Internal Validation\u003c/h3\u003e\n\u003cp\u003eMultivariable logistic regression models were constructed: Clinical model, Clinical\u0026thinsp;+\u0026thinsp;imaging model, Clinical\u0026thinsp;+\u0026thinsp;electrophysiological model, Combined model\u003c/p\u003e \u003cp\u003eInternal validation was performed using stratified five-fold cross-validation. In each fold, predicted probabilities were generated and aggregated to compute cross-validated AUC.\u003c/p\u003e \u003cp\u003eNinety-five percent confidence intervals for AUC were estimated using 1,000 bootstrap resamples.\u003c/p\u003e \u003cp\u003eCalibration was evaluated using quantile-based calibration curves with ten equal-frequency bins.\u003c/p\u003e \u003cp\u003eDecision curve analysis was conducted across threshold probabilities ranging from 0.01 to 0.99.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using Python (version 3.12).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e1. Baseline Characteristics and Perioperative Differences\u003c/p\u003e \u003cp\u003eA total of 508 patients undergoing thoracic decompression surgery were included, of whom 107 (21.1%) developed postoperative neurological deficit. There was no significant difference in age distribution between groups (59.00 [50.00\u0026ndash;67.00] vs 59.00 [52.50\u0026ndash;65.50], P\u0026thinsp;=\u0026thinsp;0.838), suggesting limited discriminative value of age in univariable comparison. However, operative time was significantly longer in patients who developed neurological deterioration (203.00 [188.00\u0026ndash;225.00] minutes vs 196.00 [174.00\u0026ndash;219.00] minutes, P\u0026thinsp;=\u0026thinsp;0.005), indicating that increased surgical complexity may be associated with adverse neurological outcomes.\u003c/p\u003e \u003cp\u003eElectrophysiological parameters differed markedly between groups. Patients with postoperative deficit exhibited significantly higher DSEP risk scores (2.00 [2.00\u0026ndash;3.00] vs 1.00 [0.00\u0026ndash;2.00], P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a greater number of abnormal segments (3.00 [2.00\u0026ndash;3.00] vs 1.00 [1.00\u0026ndash;2.00], P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These differences were consistent and robust, supporting a relationship between the severity of electrophysiological abnormalities and postoperative neurological status (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eBaseline characteristics according to neurological outcome\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \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\u003eNo deficit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeficit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\u003eDemographics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.00 [50.00, 67.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.00 [52.50, 65.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperative time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e196.00 [174.00, 219.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e203.00 [188.00, 225.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectrophysiological parameters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSEP risk score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 [0.00, 2.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.00 [2.00, 3.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eNumber of abnormal segments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 [1.00, 2.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00 [2.00, 3.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003e2. Comparative Discriminative Performance of Prediction Models\u003c/p\u003e \u003cp\u003eStratified five-fold cross-validation was performed to evaluate the discriminative performance of the four prediction models. The clinical model demonstrated limited predictive ability, with a cross-validated AUC of 0.58. Incorporation of imaging variables resulted in a modest improvement, yielding an AUC of 0.63 for the clinical\u0026ndash;imaging model.\u003c/p\u003e \u003cp\u003eNotably, the addition of electrophysiological variables substantially enhanced model discrimination. The clinical\u0026ndash;electrophysiological model achieved a higher AUC of 0.74, indicating that DSEP-derived functional parameters provide meaningful incremental predictive value beyond conventional clinical and imaging factors. The combined model integrating clinical, imaging, and electrophysiological variables achieved the highest overall discriminative performance, with an AUC of 0.74.\u003c/p\u003e \u003cp\u003eThese findings suggest that electrophysiological indicators play an important role in improving risk prediction for postoperative neurological deficit. Detailed model performance, including cross-validated AUC values and confidence intervals, is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The ROC curves comparing the predictive performance of the four models are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003ePredictive performance of different models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFeatures (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58 (0.519\u0026ndash;0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eage, JOA_preop, num_levels_compressed, op_time_min\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical\u0026ndash;Imaging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.625 (0.561\u0026ndash;0.683)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eage, JOA_preop, num_levels_compressed, op_time_min, signal_change_T2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical\u0026ndash;Electrophysiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.736 (0.688\u0026ndash;0.789)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eage, JOA_preop, num_levels_compressed, op_time_min, dsep_max_n1_latency, dsep_min_amplitude, dsep_abnormal_segments_count\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.742 (0.689\u0026ndash;0.792)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eage, JOA_preop, num_levels_compressed, op_time_min, signal_change_T2, dsep_max_n1_latency, dsep_min_amplitude, dsep_abnormal_segments_count\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\u003e \u003c/p\u003e \u003cp\u003e3. Calibration and Clinical Utility Assessment\u003c/p\u003e \u003cp\u003eCalibration curves revealed acceptable agreement between predicted probabilities and observed event rates for the electrophysiological and combined models across most risk intervals. In contrast, the clinical-only model demonstrated a tendency toward overestimation in higher-risk strata.\u003c/p\u003e \u003cp\u003eDecision curve analysis further showed that models incorporating electrophysiological variables yielded greater net benefit across a wide range of threshold probabilities (0.01\u0026ndash;0.99). Within clinically relevant intermediate thresholds, the clinical\u0026ndash;electrophysiological and combined models consistently outperformed alternative strategies, indicating potential advantages in guiding perioperative risk stratification (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e4. Independent Predictors of Postoperative Neurological Deficit\u003c/p\u003e \u003cp\u003eMultivariable logistic regression analysis identified age (OR\u0026thinsp;=\u0026thinsp;1.03, 95%CI 1.01\u0026ndash;1.05, P\u0026thinsp;=\u0026thinsp;0.001), preoperative JOA score (OR\u0026thinsp;=\u0026thinsp;0.67, 95%CI 0.58\u0026ndash;0.75, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), number of compressed levels (OR\u0026thinsp;=\u0026thinsp;1.84, 95%CI 1.16\u0026ndash;2.90, P\u0026thinsp;=\u0026thinsp;0.008), and maximal N1 latency (OR\u0026thinsp;=\u0026thinsp;1.13, 95%CI 1.02\u0026ndash;1.25, P\u0026thinsp;=\u0026thinsp;0.012) as independent predictors of postoperative neurological deficit.\u003c/p\u003e \u003cp\u003ePreoperative JOA score exhibited a protective association, whereas increasing number of compressed levels and prolonged N1 latency were associated with elevated risk. Operative time (P\u0026thinsp;=\u0026thinsp;0.453), T2-weighted signal change (P\u0026thinsp;=\u0026thinsp;0.215), minimal amplitude (P\u0026thinsp;=\u0026thinsp;0.864), and number of abnormal segments (P\u0026thinsp;=\u0026thinsp;0.076) did not retain statistical significance in the multivariable model, suggesting possible confounding or collinearity effects (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\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\u003eMultivariable logistic regression analysis\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=\"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 \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\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI_low\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI_high\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJOA_preop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003enum_levels_compressed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eop_time_min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esignal_change_T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edsep_max_n1_latency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edsep_min_amplitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edsep_abnormal_segments_count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study systematically evaluated the prognostic value of dermatomal somatosensory evoked potentials (DSEP) in predicting postoperative neurological deficit following thoracic decompression surgery in a cohort of 508 patients. The principal findings are as follows:\u003c/p\u003e \u003cp\u003eFirst, the incidence of postoperative neurological deficit was 21.1%, which is consistent with previously reported complication rates in thoracic spine surgery. Second, multivariable analysis identified age, preoperative JOA score, number of compressed levels, and maximal N1 latency as independent predictors, whereas T2-weighted MRI signal change and P1-related parameters were not independently associated with outcome. Third, predictive models incorporating electrophysiological parameters demonstrated superior discriminative performance compared with clinical-only or clinical\u0026ndash;imaging models. Fourth, decision curve analysis confirmed greater net clinical benefit for models including DSEP parameters across clinically relevant decision thresholds.\u003c/p\u003e \u003cp\u003eIn the present study, prolonged maximal N1 latency emerged as an independent risk factor for postoperative neurological deficit (OR\u0026thinsp;=\u0026thinsp;1.13 per 1 ms increase). This finding has a clear neurophysiological foundation.\u003c/p\u003e \u003cp\u003eThe N1 component originates from postsynaptic activity within the dorsal column\u0026ndash;medial lemniscal pathway, which mediates fine touch and proprioceptive information[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Previous electrophysiological studies have demonstrated that early SEP components are closely linked to dorsal column conduction integrity[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Prolonged latency reflects delayed axonal conduction, often associated with demyelination or disruption of nodal architecture[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn chronic spinal cord compression, sustained mechanical stress may induce myelin sheath injury, nodal disorganization, and conduction slowing. Once structural damage progresses to axonal degeneration, functional recovery may remain incomplete despite adequate surgical decompression[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, preoperative prolongation of N1 latency may represent reduced spinal cord functional reserve and partial irreversible injury, thereby increasing the likelihood of postoperative neurological deterioration.\u003c/p\u003e \u003cp\u003eNotably, P1 latency-related variables did not enter the final multivariable model. This observation is also physiologically plausible. P1 represents an early cortical positive component primarily generated in the primary somatosensory cortex. Unlike N1, which reflects dorsal column conduction integrity, cortical components are influenced not only by ascending pathway integrity but also by cortical excitability, attention state, vigilance level, and anesthetic effects. Such state-dependent variability may reduce the independent predictive contribution of P1 in multivariable modeling[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These findings suggest that spinal conduction\u0026ndash;related parameters provide more stable and clinically relevant prognostic information in thoracic spinal cord compression.\u003c/p\u003e \u003cp\u003eThe present study demonstrated that T2-weighted intramedullary hyperintensity was not independently associated with postoperative neurological deficit after adjustment for confounding variables (P\u0026thinsp;=\u0026thinsp;0.215). This finding aligns with prior studies reporting inconsistent prognostic significance of T2 signal changes.\u003c/p\u003e \u003cp\u003eThe pathophysiological basis of T2 hyperintensity is heterogeneous, including vasogenic edema, gliosis, cystic change, or micro-necrosis[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Edema-related signal changes may be reversible following decompression, whereas gliotic or necrotic alterations represent irreversible structural damage. Conventional MRI lacks the ability to distinguish these pathological substrates reliably. This limitation explains the frequently observed \u0026ldquo;imaging\u0026ndash;clinical paradox,\u0026rdquo; wherein pronounced signal changes may coexist with favorable recovery and minimal signal alteration may precede poor outcome[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur findings reinforce the concept that morphological assessment alone is insufficient for accurate preoperative risk stratification. Functional evaluation is necessary to capture the dynamic conduction reserve of the spinal cord.\u003c/p\u003e \u003cp\u003eInterpretation of Model Performance\u003c/p\u003e \u003cp\u003eThe clinical\u0026ndash;electrophysiological model demonstrated substantially improved discriminative ability (AUC\u0026thinsp;=\u0026thinsp;0.736) compared with the clinical model (AUC\u0026thinsp;=\u0026thinsp;0.58) and the clinical\u0026ndash;imaging model (AUC\u0026thinsp;=\u0026thinsp;0.625). The combined model integrating clinical, imaging, and electrophysiological variables achieved the highest overall performance (AUC\u0026thinsp;=\u0026thinsp;0.742). These findings indicate that electrophysiological parameters provide meaningful incremental predictive value beyond conventional clinical and imaging variables.\u003c/p\u003e \u003cp\u003eInterestingly, although imaging variables slightly improved prediction compared with the clinical model alone, their contribution was modest relative to electrophysiological parameters. This suggests that functional conduction abnormalities detected by DSEP may better capture the underlying neural impairment associated with thoracic spinal cord compression.From a methodological perspective, this finding suggests that adding variables lacking independent predictive value may introduce noise and reduce generalizability. In the present study, T2 hyperintensity did not demonstrate independent association with outcome. Inclusion of weak predictors may lead to subtle overfitting effects, particularly when event numbers are limited.\u003c/p\u003e \u003cp\u003eAlthough the total sample size was 508, the number of outcome events was 107, which only modestly satisfies the commonly cited rule-of-thumb of 10\u0026ndash;15 events per predictor variable in logistic regression modeling. The relatively wide 95% confidence intervals of AUC estimates reflect both statistical uncertainty and the multifactorial nature of postoperative neurological deterioration.\u003c/p\u003e \u003cp\u003eFuture studies may benefit from incorporating additional functional or structural biomarkers, such as diffusion tensor imaging parameters reflecting white matter integrity, motor evoked potentials assessing corticospinal tract function, or biochemical markers of neural injury.\u003c/p\u003e \u003cp\u003eThe findings of this study have several practical implications. First, preoperative DSEP examination may serve as an important adjunct in routine assessment of patients undergoing thoracic decompression surgery. Patients with significantly prolonged N1 latency, particularly beyond laboratory-defined upper limits, may represent a subgroup with reduced functional reserve and elevated risk of postoperative deterioration. Second, for high-risk patients, intensified intraoperative neuromonitoring strategies and hemodynamic optimization may be warranted. Third, early postoperative rehabilitation planning may be particularly important in patients demonstrating preoperative conduction impairment. Integration of electrophysiological assessment into preoperative risk stratification frameworks may facilitate individualized surgical counseling and perioperative management.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the retrospective single-center design introduces potential selection bias and limits generalizability. External validation in independent multicenter cohorts is required. Second, follow-up duration was limited to 3 months postoperatively, and long-term neurological recovery was not assessed. Third, only sensory pathway function (DSEP) was evaluated. Motor pathway assessment using motor evoked potentials (MEP) was not incorporated. Combined evaluation of sensory and motor conduction may provide a more comprehensive representation of spinal cord functional status. Fourth, DSEP examination requires technical expertise and standardized methodology, which may limit widespread adoption in resource-limited settings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this retrospective cohort of 508 patients undergoing thoracic decompression surgery, preoperative dermatomal somatosensory evoked potential parameters\u0026mdash;particularly maximal N1 latency\u0026mdash;were identified as independent predictors of postoperative neurological deficit. Predictive models incorporating electrophysiological variables demonstrated superior discrimination and clinical net benefit compared with models based solely on clinical or imaging parameters. These findings support the integration of functional neurophysiological assessment into preoperative risk stratification frameworks for thoracic spinal cord compression, thereby enabling more precise identification of high-risk patients and optimization of perioperative management strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval：The study was approved by the Ethics Committee of Xi’an Honghui Hospital, Xi’an Jiaotong University and conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eConsent to participate：Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003eConsent for publication：Not applicable.\u003c/p\u003e\n\u003cp\u003eClinical trial number:\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003eData availability:\u0026nbsp;The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eFunding:\u0026nbsp;This work was supported by the Xi’an Science and Technology Bureau Medical Research Program (Grant No. 2023YXYJ0038).\u003c/p\u003e\n\u003cp\u003eCompeting interests:\u0026nbsp;The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eY.Z.: Conceptualization, Methodology, Investigation, Formal analysis, Writing – Original Draft.\u003c/p\u003e\n\u003cp\u003eY.L.: Investigation, Validation, Data Curation, Writing – Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eL.W.: Resources, Investigation.\u003c/p\u003e\n\u003cp\u003eY.W.: Software, Validation.\u003c/p\u003e\n\u003cp\u003eJ.L.: Visualization, Data Curation.\u003c/p\u003e\n\u003cp\u003eY.Y.: Supervision, Project administration, Funding acquisition, Writing – Review \u0026amp; Editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGader G, Gharbi MA, Kharrat MA, Harbaoui A, Zammel I. Solitary thoracic spine osteochondroma: a rare cause for spinal cord compression. Spinal Cord Ser Cases. 2024;10(1):63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacki M, Lo SF, Bydon M, Kaloostian P, Bydon A. Post-surgical thoracic pseudomeningocele causing spinal cord compression. J Clin Neurosci. 2014;21(3):367\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNardone R, Holler Y, Brigo F, Frey VN, Lochner P, Leis S, Golaszewski S, Trinka E. The contribution of neurophysiology in the diagnosis and management of cervical spondylotic myelopathy: a review. Spinal Cord. 2016;54(10):756\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTom MC, Komatineni S, Wang C, de Almeida RAA, Ghia AJ, Beckham TH, Perni S, McAleer MF, Swanson T, Yeboa DN, et al. Spinal laser interstitial thermal therapy and radiotherapy for thoracic metastatic epidural spinal cord compression. J Neurooncol. 2024;170(2):289\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiammona G, Giuffrida S, Greco S, Grassi C, Le Pira F. Magnetic resonance imaging in cervical spinal cord compression. Arq Neuropsiquiatr. 1993;51(3):407\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKerkovsky M, Bednarik J, Dusek L, Sprlakova-Pukova A, Urbanek I, Mechl M, Valek V, Kadanka Z. Magnetic resonance diffusion tensor imaging in patients with cervical spondylotic spinal cord compression: correlations between clinical and electrophysiological findings. Spine (Phila Pa 1976). 2012;37(1):48\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim YH, Ha KY, Park HY, Cho CH, Kim HC, Heo Y, Kim SI. Simple and Reliable Magnetic Resonance Imaging Parameter to Predict Postoperative Ambulatory Function in Patients With Metastatic Epidural Spinal Cord Compression. Global Spine J. 2023;13(2):479\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaufer I, Zuckerman SL, Bird JE, Bilsky MH, Lazary A, Quraishi NA, Fehlings MG, Sciubba DM, Shin JH, Mesfin A, et al. Predicting Neurologic Recovery after Surgery in Patients with Deficits Secondary to MESCC: Systematic Review. Spine (Phila Pa 1976). 2016;41(Suppl 20):S224\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDikmen PY, Oge AE. Diagnostic use of dermatomal somatosensory-evoked potentials in spinal disorders: Case series. J Spinal Cord Med. 2013;36(6):672\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoncarevic N, Tiric-Campara M, Mulabegovic N. Somatosensory evoked cerebral potentials (SSEP) in multiple sclerosis. Med Arh. 2008;62(2):80\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinto V, Liebsch M. Technical note: pre-positioning lower limb SSEP during semi-sitting positioning in posterior fossa surgery- does it matter? J Clin Monit Comput. 2023;37(6):1627\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosenthal ES. The utility of EEG, SSEP, and other neurophysiologic tools to guide neurocritical care. Neurotherapeutics. 2012;9(1):24\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Li J, Yuan Y, Wang Y, Huang D, Qi H. The application value of intraoperative neurophysiological monitoring in cervical spinal canal stenosis decompression surgery. Spine J. 2025;25(9):2026\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu B, Xue L, Jiang M, Qin D, Gao G, Zhang H. Evaluating somatosensory evoked potentials in predicting treatment outcomes for thoracolumbar spinal compression fractures using closed reduction and over-extension techniques. Am J Transl Res. 2024;16(7):3026\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAminoff MJ, Goodin DS, Barbaro NM, Weinstein PR, Rosenblum ML. Dermatomal somatosensory evoked potentials in unilateral lumbosacral radiculopathy. Ann Neurol. 1985;17(2):171\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBruyns-Haylett M, Luo J, Kennerley AJ, Harris S, Boorman L, Milne E, Vautrelle N, Hayashi Y, Whalley BJ, Jones M, et al. The neurogenesis of P1 and N1: A concurrent EEG/LFP study. NeuroImage. 2017;146:575\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCruccu G, Aminoff MJ, Curio G, Guerit JM, Kakigi R, Mauguiere F, Rossini PM, Treede RD, Garcia-Larrea L. Recommendations for the clinical use of somatosensory-evoked potentials. Clin Neurophysiol. 2008;119(8):1705\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJo NG, Ko MH, Won YH, Park SH, Kim GW, Seo JH. Diagnostic Implication and Clinical Relevance of Dermatomal Somatosensory Evoked Potentials in Patients with Radiculopathy: A Retrospective Study. \u003cem\u003ePain Res Manag\u003c/em\u003e 2021, 2021:8850281.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorioka T, Shima F, Kato M, Fukui M. Direct recording of somatosensory evoked potentials in the vicinity of the dorsal column nuclei in man: their generator mechanisms and contribution to the scalp far-field potentials. Electroencephalogr Clin Neurophysiol. 1991;80(3):215\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeterson NN, Schroeder CE, Arezzo JC. Neural generators of early cortical somatosensory evoked potentials in the awake monkey. Electroencephalogr Clin Neurophysiol. 1995;96(3):248\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlimp JC, Rubner DE, Snowden ML, Stolov WC. Dermatomal somatosensory evoked potentials: cervical, thoracic, and lumbosacral levels. Electroencephalogr Clin Neurophysiol. 1992;84(1):55\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun H, Wang W. Predictors of Surgical Outcome in Cervical Spondylotic Myelopathy: MR Features Based on Axial Images Should Be Used in Combination with Other Parameters. Radiology. 2016;279(3):978\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakahashi M, Sakamoto Y, Miyawaki M, Bussaka H. Increased MR signal intensity secondary to chronic cervical cord compression. Neuroradiology. 1987;29(6):550\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYukawa Y, Kato F, Ito K, Horie Y, Hida T, Machino M, Ito ZY, Matsuyama Y. Postoperative changes in spinal cord signal intensity in patients with cervical compression myelopathy: comparison between preoperative and postoperative magnetic resonance images. J Neurosurg Spine. 2008;8(6):524\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-orthopaedic-surgery-and-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"josr","sideBox":"Learn more about [Journal of Orthopaedic Surgery and Research](http://josr-online.biomedcentral.com)","snPcode":"13018","submissionUrl":"https://submission.nature.com/new-submission/13018/3","title":"Journal of Orthopaedic Surgery and Research","twitterHandle":"@MSKmedBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Thoracic spine, spinal cord compression, functional reserve, dermatomal somatosensory evoked potentials, risk prediction","lastPublishedDoi":"10.21203/rs.3.rs-9038212/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9038212/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePostoperative neurological deficit represents one of the most serious complications following thoracic spine surgery. Traditional risk assessment primarily relies on clinical and imaging variables, whereas the predictive value of electrophysiological indicators in preoperative risk stratification remains insufficiently explored. Dermatomal somatosensory evoked potentials (DSEP) directly reflect the integrity of sensory conduction pathways and may provide functional information beyond conventional structural imaging. This study aimed to develop and internally validate a clinically applicable prediction model integrating electrophysiological parameters.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 508 patients who underwent thoracic decompression surgery were retrospectively included. Collected variables comprised age, preoperative Japanese Orthopaedic Association (JOA) score, number of compressed levels, T2-weighted signal changes, and DSEP parameters including latency, amplitude, and number of abnormal segments. Multivariable logistic regression models were constructed, including a clinical model, a clinical\u0026ndash;imaging model, a clinical\u0026ndash;electrophysiological model, and a combined model. Internal validation was performed using stratified five-fold cross-validation. Model discrimination was assessed using receiver operating characteristic (ROC) curves and the area under the curve (AUC). Ninety-five percent confidence intervals were estimated using bootstrap resampling.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 508 patients, 107 (21.1%) developed postoperative neurological deficit. Multivariable analysis identified age (OR\u0026thinsp;=\u0026thinsp;1.03), preoperative JOA score (OR\u0026thinsp;=\u0026thinsp;0.67), number of compressed levels (OR\u0026thinsp;=\u0026thinsp;1.84), and maximal N1 latency (OR\u0026thinsp;=\u0026thinsp;1.13) as independent predictors. In cross-validated model evaluation, the clinical model demonstrated limited discriminative ability (AUC\u0026thinsp;=\u0026thinsp;0.58, 95% CI 0.519\u0026ndash;0.64). The clinical\u0026ndash;imaging model showed modest improvement (AUC\u0026thinsp;=\u0026thinsp;0.625, 95% CI 0.561\u0026ndash;0.683). Incorporation of electrophysiological parameters substantially improved prediction performance, with the clinical\u0026ndash;electrophysiological model achieving an AUC of 0.736 (95% CI 0.688\u0026ndash;0.789). The combined model integrating clinical, imaging, and electrophysiological variables showed the highest overall performance (AUC\u0026thinsp;=\u0026thinsp;0.742, 95% CI 0.689\u0026ndash;0.792).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePreoperative DSEP parameters, particularly maximal N1 latency, significantly improve prediction of postoperative neurological deficit after thoracic spine surgery. Integration of electrophysiological and clinical variables may enhance perioperative risk stratification and support individualized surgical decision-making.\u003c/p\u003e","manuscriptTitle":"Preoperative Dermatomal Somatosensory Evoked Potentials in Risk Prediction of Postoperative Neurological Deficit After Thoracic Spine Surgery: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 08:27:07","doi":"10.21203/rs.3.rs-9038212/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-02T00:56:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-29T17:06:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-22T17:29:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-17T07:28:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-15T14:01:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269343314507171910518660617784985055337","date":"2026-03-14T06:09:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228802654626502283175168893203259881335","date":"2026-03-13T07:35:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209755811326175828038799959108110075083","date":"2026-03-12T21:51:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251402387886359660105898622790103196917","date":"2026-03-12T16:03:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8993603828890945260528316697657016881","date":"2026-03-12T16:03:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-12T16:01:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-09T02:57:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-09T02:56:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Orthopaedic Surgery and Research","date":"2026-03-05T08:49:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-orthopaedic-surgery-and-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"josr","sideBox":"Learn more about [Journal of Orthopaedic Surgery and Research](http://josr-online.biomedcentral.com)","snPcode":"13018","submissionUrl":"https://submission.nature.com/new-submission/13018/3","title":"Journal of Orthopaedic Surgery and Research","twitterHandle":"@MSKmedBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e81ca5b3-a98a-4b1d-b237-1e8dd3b974aa","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T14:38:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 08:27:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9038212","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9038212","identity":"rs-9038212","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}