Risk Factors and Incidence of Unplanned Hospital Readmission After Surgery for Spinal Metastases: A Systematic Review and Meta-Analysis

Risk Factors and Incidence of Unplanned Hospital Readmission After Surgery for Spinal Metastases: A Systematic Review and Meta-Analysis | 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 Risk Factors and Incidence of Unplanned Hospital Readmission After Surgery for Spinal Metastases: A Systematic Review and Meta-Analysis Ying-Ching Li, Cheng-Yu Li, Sheng-Han Huang, Hong-Kai Wang, Kuan-Hung Chen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9284951/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 Purpose No prior meta-analysis has quantified unplanned readmission after surgery for spinal metastases. This study aimed to estimate the pooled 30-day readmission incidence and to identify independently associated risk factors. Methods PubMed, Embase, Cochrane CENTRAL, Scopus, and Web of Science were systematically searched from January 2010 to March 2026. Studies reporting readmission after open surgery for spinal metastases in adults were included. Pooled incidence was estimated using Freeman-Tukey double arcsine transformation with DerSimonian-Laird random-effects modelling; restricted maximum likelihood (REML) estimation was used as sensitivity analysis. Risk factors were synthesised narratively or pooled where feasible. Quality was assessed using the Newcastle-Ottawa Scale; evidence certainty using GRADE. Results Fourteen studies (8,132 patients for incidence pooling) met inclusion criteria. Seven studies (8,132 patients) contributed to incidence pooling. The pooled 30-day readmission incidence was 16.0% (95% CI: 12.2–20.3%; I² = 93.2%). Single-centre studies (k = 5) showed 13.9% (I² = 0%), whereas database studies (k = 2) showed 20.4% (p for subgroup difference < 0.001). Prior spinal radiation was the only poolable risk factor (pooled effect 1.79; 95% CI: 1.21–2.63; k = 2). Comorbidity burden consistently increased risk across four studies (OR 1.25–2.54) with dose–response. Other significant factors included diabetes, prolonged operative time, postoperative complications, and lung primary tumour. The pooled 90-day incidence was 31.2% (95% CI: 26.9–35.6%; k = 7). Evidence certainty was very low (GRADE). Conclusions Approximately one in six patients is readmitted within 30 days of spinal metastasis surgery, a rate substantially exceeding that of general spine surgery. Comorbidity burden, prior spinal radiation, and poor functional status are the most consistently identified risk factors. These findings provide an evidence base for targeted preoperative risk stratification and structured discharge planning in this vulnerable population. spinal metastases readmission risk factors meta-analysis systematic review spine surgery Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Spinal metastases affect up to 40% of cancer patients, representing the most common site of skeletal metastatic disease [ 1 , 2 ]. Surgical intervention plays a critical role in managing neurological compromise, mechanical instability, and intractable pain in patients with metastatic epidural spinal cord compression [ 3 , 4 ]. As systemic oncological therapies have improved, the number of patients undergoing surgery for spinal metastases has increased substantially [ 5 ]. Unplanned hospital readmission is an increasingly recognised indicator of postoperative morbidity, healthcare quality, and resource utilisation [ 6 ]. In the general spine surgery population, 30-day readmission rates range from 4% to 14% [ 7 ]. However, patients with spinal metastases represent a uniquely vulnerable population characterised by advanced systemic disease, immunosuppression, poor nutritional status, and limited physiological reserve. Individual studies have reported 30-day readmission rates ranging from 10% to 24% [ 8 – 14 ], yet the reported risk factor profiles remain heterogeneous and no consensus exists on which patient-level or treatment-related variables most reliably predict readmission. Despite the clinical significance of readmission in this population, no prior systematic review or meta-analysis has comprehensively synthesised the incidence of or risk factors for readmission specifically among patients undergoing open surgery for spinal metastases. The objective of this study was to (1) estimate the pooled incidence of 30-day unplanned readmission and (2) identify independently associated risk factors. METHODS Protocol and registration This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement [ 15 ] and the Meta-analysis of Observational Studies in Epidemiology (MOOSE) checklist [ 16 ]. The protocol was registered with the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY; registration number: INPLASY202630120). Eligibility criteria Studies were eligible if they (1) enrolled adults aged 18 years or older undergoing open surgery (decompression, stabilisation, or corpectomy) for spinal metastases; (2) reported unplanned hospital readmission as a primary or secondary outcome; and (3) were observational cohort or case-control studies published from January 2010 onward in English. Studies were excluded if they exclusively evaluated vertebroplasty or kyphoplasty, enrolled exclusively primary spinal tumour populations without extractable metastatic subgroup data, were conference abstracts, or included fewer than 10 patients with metastatic disease. Information sources and search strategy A comprehensive search was conducted across five electronic databases: PubMed/MEDLINE, Embase, Cochrane CENTRAL, Scopus, and Web of Science. The search strategy combined controlled vocabulary (MeSH and Emtree terms) and free-text synonyms organised into two concept blocks: (A) spinal metastases/metastatic spinal cord compression and (B) hospital readmission. Database-specific syntax was adapted for each platform. The complete search strategies are provided in Supplementary Appendix A. Study selection and data extraction Records were imported into EndNote and deduplicated. Title and abstract screening, followed by full-text eligibility assessment, was performed independently by two reviewers, with disagreements resolved by consensus. Studies with potentially overlapping cohorts were identified and adjudicated based on institution, study period, and sample size. When cohort overlap was confirmed, the study with the larger sample or more comprehensive readmission analysis was retained. Data were extracted into a standardised spreadsheet including study characteristics, patient demographics, surgical details, readmission definitions and rates, causes of readmission, and multivariate-adjusted risk factor effect sizes (odds ratios, hazard ratios, or adjusted odds ratios) with 95% confidence intervals. Risk of bias assessment Methodological quality was assessed using the Newcastle-Ottawa Scale (NOS) for cohort studies [ 17 ]. Studies scoring 7 or above (of 9) were classified as good quality, 5 to 6 as fair, and below 5 as poor. Statistical analysis The primary analysis estimated the pooled 30-day readmission incidence using the Freeman-Tukey double arcsine transformation [ 18 ] to stabilise proportions. A DerSimonian-Laird random-effects model was applied [ 19 ]. Between-study heterogeneity was assessed using the Cochran Q statistic, I-squared, and tau-squared [ 31 ]. The Hartung-Knapp-Sidik-Jonkman (HKSJ) adjustment was applied as a sensitivity analysis [ 20 ]. A restricted maximum likelihood (REML) estimator was additionally employed as a sensitivity analysis for the between-study variance component. A 95% prediction interval was calculated. Pre-specified subgroup analysis compared single-centre cohort studies with national database studies. Exploratory mixed-effects meta-regression was performed using publication year and log-transformed sample size as pre-specified covariates. Leave-one-out sensitivity analysis assessed the influence of individual studies. A funnel plot was generated (Fig. 5 ), though formal publication bias testing was not conducted given the limited number of studies (k < 10), consistent with Cochrane Handbook recommendations [ 32 ]. For risk factor analysis, inverse-variance random-effects meta-analysis was performed where two or more studies reported the same risk factor with comparable effect measures. Remaining risk factors were synthesised narratively. As a secondary analysis, pooled 90-day readmission incidence was estimated where extractable data were available. Evidence certainty was assessed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) framework [ 21 ]. All analyses were performed using Python 3.x (NumPy, SciPy, Matplotlib). RESULTS Study selection The systematic search identified 656 records across five databases (PubMed 88, Embase 315, Cochrane CENTRAL 3, Scopus 182, Web of Science 68). After deduplication (316 removed), 296 records underwent title and abstract screening, of which 25 were sought for full-text retrieval. Three reports could not be retrieved. Twenty-two reports were assessed for full-text eligibility. Eight were excluded: wrong outcome with no readmission data (n = 2), wrong population (n = 1), wrong exposure framework (n = 1), mixed primary and metastatic cohort without extractable metastatic subgroup (n = 2), and overlapping cohort or subset (n = 2). Fourteen studies were included in the qualitative synthesis (Fig. 1 ). The complete list of excluded studies with reasons is provided in Supplementary Appendix B. Study characteristics The 14 included studies comprised 8 single-centre retrospective cohorts, 4 United States Nationwide Readmissions Database (NRD) studies, 1 Korean national database study, and 1 European single-centre study. Publication years ranged from 2014 to 2025. Eleven studies originated from the United States, with one each from Singapore, South Korea, and Germany. Metastatic cohort sizes ranged from 30 to 13,974 patients. The most common surgical approach was posterior decompression with or without instrumentation. Detailed study characteristics are presented in Table 1 . Table 1 Characteristics of included studies Study Year Data source Country N (met) Mean age 30d rate 90d rate NOS Key MV risk factors Schairer 2014 2014 Single (UCSF) USA 164 57 ± 13 16.8% — 9 Op time, DM, obesity Lau 2016 2016 Single (UCSF) USA 118 — — 11.9% 8 LOS, lung mets Abu-Bonsrah 2017 2017 Single (JHU) USA 159 — 13.8% 47.2%* 9 Age > 60 HR1.68; prior RT HR1.71 Elsamadicy 2018 2018 Single (Duke) USA 30 56 ± 14 ~ 10% — 5 Descriptive Kumar N 2021 2021 Single (NUH) SG 272 59 ± 13 17.2% 31.1% 9 CCI > 7 OR2.54; lung OR3.09 Elsamadicy 2021 2021 NRD 2013–15 USA 4423 — 24.1% 13.3%† 8 HTN OR1.45; renal OR1.53 Patel 2021 2021 Single (JHU) USA 345 59 ± 12 12.2% — 9 CCI/pt OR1.25; RT OR1.96 Shahrestani 2021 2021 NRD 2016–17 USA ~ 1005 62 13–15% ~ 40% 8 Frailty (NS) Elsamadicy 2022 2022 NRD 2016–18 USA 4346 — ML — 8 HFRS frailty (ML) Chanbour 2023 2023 Single (Vandy) USA 357 — — 21.9%‡ 9 Postop comp OR1.38 Larkin 2023 2023 Single (NW) USA 263 — Reported — 7 Age, sex, SES factors Noh 2024 2024 Korean HIRA Korea 13974 — — Reported 8 RT OR1.92; CCI OR1.55–2.47 Pojskic 2024 2024 Single (Marburg) Germany 175 67 med RFS — 8 ECOG, fracture, tumour Tsai 2025 2025 NRD 2016–20 Taiwan§ 2706 64 17.5% 34.3% 8 VTE aOR2.06; CCI ≥ 4 CCI Charlson Comorbidity Index; DM diabetes mellitus; HFRS Hospital Frailty Risk Score; HTN hypertension; LOS length of stay; ML machine learning; NOS Newcastle-Ottawa Scale; NW Northwestern; RT radiotherapy; SG Singapore; Vandy Vanderbilt. *1-year rate. †31–90d additional. ‡3-month rate. §Analysed US NRD data; first author affiliated with Taiwan institution. Risk of bias Thirteen of 14 studies (92.9%) were rated as good quality on the NOS (score ≥ 7/9; median 8). One study (Elsamadicy 2018, n = 30) was rated fair quality (score 5) due to the absence of a defined comparator group and limited adjustment for confounders. The most common limitation was inability to assess follow-up adequacy in administrative database studies (Fig. 3 ). Detailed NOS assessments are provided in Supplementary Appendix C. Pooled 30-day readmission incidence Seven studies with extractable numerator and denominator data (8,132 patients; 1,665 readmission events) contributed to incidence pooling. The pooled 30-day unplanned readmission incidence was 16.0% (95% CI: 12.2–20.3%) with considerable heterogeneity (I² = 93.2%; Q = 88.85; df = 6; p < 0.001; τ² = 0.017). The HKSJ-adjusted 95% confidence interval was 11.3–21.4%. The REML sensitivity analysis yielded a comparable pooled estimate of 15.9% (95% CI: 11.9–20.4%), confirming the robustness of the primary DerSimonian-Laird result. The 95% prediction interval ranged from 5.3% to 30.8%, indicating substantial variability in readmission rates across clinical settings (Fig. 2 ). Subgroup and sensitivity analyses Pre-specified subgroup analysis by data source revealed a statistically significant and clinically meaningful difference between single-centre and national database studies (test for subgroup difference: Q = 29.68, df = 1, p < 0.001). Single-centre studies (k = 5; N = 1,003) yielded a pooled incidence of 13.9% (95% CI: 11.8–16.2%) with no detected heterogeneity (I² = 0%), whereas national database studies (k = 2; N = 7,129) yielded 20.4% (95% CI: 13.7–28.0%) with extreme heterogeneity (I² = 98.2%; Fig. 4 ). Leave-one-out analysis demonstrated that removal of Elsamadicy 2021 (the largest database study, n = 4,423, rate = 24.1%) reduced the overall I² from 93.2% to 29.6% and shifted the pooled estimate to 15.1% (95% CI: 13.2–17.1%). All other individual study exclusions yielded pooled estimates between 15.1% and 16.9%, confirming the robustness and stability of the overall estimate. Meta-regression identified publication year (β = 0.042; p = 0.011) and log-transformed sample size (β = 0.184; p < 0.001) as significant moderators, though these findings must be interpreted with caution given the limited number of studies (k = 7), the ecological nature of the analysis, and the elevated risk of false-positive findings inherent to meta-regression with few studies. Pooled 90-day readmission incidence. Seven studies with extractable 90-day readmission data (22,198 patients; 8,465 readmission events) contributed to the secondary analysis. The pooled 90-day unplanned readmission incidence was 31.2% (95% CI: 26.9–35.6%) with considerable heterogeneity (I² = 96.5%; Q = 173.5, df = 6, p < 0.001; τ² = 0.0138). The HKSJ-adjusted 95% confidence interval was 21.2–42.1%. The 95% prediction interval ranged from 17.9% to 46.2%. Subgroup analysis by data source showed that single-centre studies (k = 3; Lau 2016, Kumar 2021, Chanbour 2023) yielded a pooled 90-day incidence of 19.8% (95% CI: 11.3–30.2%; I² = 86.0%), whereas national database studies (k = 4; Elsamadicy 2021, Shahrestani 2021, Noh 2024, Tsai 2025) yielded 38.1% (95% CI: 33.9–42.4%; I² = 92.8%; p for subgroup difference < 0.001). The nearly twofold increase from the 30-day (16.0%) to 90-day (31.2%) estimate indicates that a substantial proportion of readmissions occur between days 31 and 90, highlighting the importance of extended post-discharge surveillance in this population (Supplementary Fig. S1 ). Risk factor synthesis (Fig. 6 ) Prior spinal radiation. Prior spinal radiation was the only risk factor amenable to formal meta-analytic pooling. Two studies reported multivariate-adjusted effect sizes: Abu-Bonsrah 2017 (HR 1.68; 95% CI: 1.05–2.69) and Patel 2021 (OR 1.96; 95% CI: 0.98–3.90). The pooled effect was 1.79 (95% CI: 1.21–2.63; p = 0.003; I² = 0%), suggesting an approximately 80% increased hazard of readmission. Noh 2024 further supported this association (OR 1.92; 95% CI: 1.72–2.14 for radiotherapy vs surgery). Comorbidity burden. Four studies consistently identified elevated Charlson Comorbidity Index (CCI) scores as a significant risk factor for readmission, although differing operationalisations across studies precluded formal pooling. Patel 2021 reported OR 1.25 per CCI point (95% CI: 1.03–1.52), Kumar 2021 OR 2.54 for CCI > 7 (95% CI: 1.22–5.27), and Noh 2024 demonstrated a dose–response relationship (CCI = 2: OR 1.55; CCI = 3: OR 2.47). Individual comorbidities including hypertension (OR 1.45) and renal failure (OR 1.53) were also independently significant in Elsamadicy 2021. Functional status. Poor preoperative functional status was associated with increased readmission risk. ECOG > 1 showed a borderline association in Kumar 2021 (OR 2.23; 95% CI: 0.98–5.11; p = 0.057) and was associated with worse clinical outcomes in Pojskic 2024. Normal preoperative ambulation was protective in Schairer 2014 (HR 0.49; 95% CI: 0.27–0.90). Operative and postoperative factors. Operative time exceeding 10 hours was strongly associated with readmission in Schairer 2014 (HR 3.64; 95% CI: 1.89–7.00). Postoperative complications independently predicted 3-month readmission in Chanbour 2023 (OR 1.38; 95% CI: 1.25–1.52), and readmission was in turn associated with reduced overall survival (HR 1.75; 95% CI: 1.28–2.39). Tumour-specific and metabolic factors. Lung cancer as the primary tumour was independently significant in Kumar 2021 (OR 3.09; 95% CI: 1.19–8.03). Venous thromboembolism predicted 30-day readmission in Tsai 2025 (aOR 2.06; 95% CI: 1.33–3.19). Diabetes (HR 7.15; 95% CI: 2.57–19.85) and obesity (HR 5.08; 95% CI: 1.29–20.00) were significant in Schairer 2014. Certainty of evidence The overall certainty of evidence was rated as very low (GRADE) for all outcomes (Table 2 ). Starting from a baseline of low certainty (observational studies), the pooled incidence was downgraded one level for serious inconsistency (I² = 93.2%), and the pooled prior radiation effect was downgraded for imprecision (k = 2; optimal information size not met). Table 2 GRADE Summary of Findings Outcome k N RoB Incon. Indir. Imprec. Pub. bias GRADE Result (95% CI) 30-day readmission incidence 7 8132 NS S (− 1) NS NS NS ⊕○○○ Very low 16.0% (12.2–20.3%) Prior RT → readmission 2 504 NS NS NS S (− 1) NS ⊕○○○ Very low 1.79 (1.21–2.63) CCI → readmission (narrative) 4 ~ 13k NS NS S (− 1) NS NS ⊕○○○ Very low OR 1.25–2.54 90-day readmission incidence 7 ~ 22k NS S (− 1) NS NS NS ⊕○○○ Very low 31.2% (26.9–35.6%) NS not serious (no downgrade); S serious (− 1 level). ⊕ point awarded; ○ point not awarded. RoB risk of bias; Incon. inconsistency; Indir. indirectness; Imprec. imprecision. DISCUSSION This study represents, to our knowledge, the first systematic review and meta-analysis to comprehensively synthesise evidence on unplanned readmission following surgery for spinal metastases, identifying a pooled 30-day readmission incidence of 16.0% (95% CI: 12.2–20.3%).This rate is two to four times higher than the 4–14% reported in general spine surgery populations [ 7 ], underscoring the heightened medical complexity and vulnerability of patients with metastatic spinal disease. The 95% prediction interval (5.3–30.8%) reflects the wide variability that clinicians should anticipate across practice settings. A key methodological finding was the marked and statistically significant difference in readmission rates between single-centre cohort studies (13.9%; I² = 0%) and national database studies (20.4%; I² = 98.2%).National databases such as the NRD capture readmissions to any facility within the healthcare system, whereas single-centre studies inherently undercount readmissions occurring at outside institutions, a limitation particularly relevant for oncological patients who frequently receive care across multiple centres. Additionally, administrative databases may apply broader coding definitions. These findings parallel observations in other surgical specialties where database-derived readmission rates consistently exceed institutional rates [ 22 ]. Comorbidity burden emerged as the most consistently identified risk factor. Four studies using different CCI operationalisations all demonstrated increased readmission risk, with a dose–response relationship in Noh 2024 (CCI = 2: OR 1.55; CCI = 3: OR 2.47). This convergent evidence from methodologically diverse studies strengthens the rationale for systematic comorbidity-based preoperative risk stratification. Frailty indices examined by Shahrestani 2021 (JHACG) and Elsamadicy 2022 (HFRS) represent emerging complementary tools that may enhance predictive accuracy. Prior spinal radiation was the only formally poolable risk factor (1.79; 95% CI: 1.21–2.63; I² = 0%), potentially reflecting impaired wound healing and soft-tissue vascularity in previously irradiated surgical fields. This finding has practical relevance for surgical timing and wound management protocols. The optimal interval between radiation and surgery remains an active area of investigation [ 23 ]. The finding that readmission was associated with reduced overall survival in Chanbour 2023 (HR 1.75; 95% CI: 1.28–2.39) suggests that unplanned readmission may serve as a sentinel event reflecting underlying disease trajectory and overall physiological decline. This association has implications for goals-of-care discussions and integration of palliative care services. Our secondary analysis revealed that the pooled 90-day readmission incidence (31.2%) was nearly double the 30-day rate (16.0%), indicating that approximately half of all readmissions within three months occur after the initial 30-day window. This finding has important implications for post-discharge surveillance strategies. While most readmission research in spine surgery focuses on the 30-day endpoint, our data suggest that the 31–90 day interval represents an equally critical period for patients with spinal metastases, likely reflecting delayed manifestations of disease progression, radiation-related complications, and systemic oncological deterioration. These results support extending structured follow-up programmes beyond the conventional 30-day window for this population. Strengths and limitations Strengths of this review include the comprehensive five-database search, strict exclusion of mixed primary and metastatic cohorts to ensure population specificity, use of appropriate Freeman-Tukey transformation for proportion pooling, and transparent exploration of heterogeneity through pre-specified subgroup analysis. Several limitations must be acknowledged. First, all included studies were retrospective, limiting evidence certainty to very low (GRADE). Second, heterogeneity was considerable (I² = 93.2%), though largely explained by data source. Third, most risk factors could not be formally pooled due to different operationalisations. Fourth, potential cohort overlap between the UCSF studies (Schairer 2014, Lau 2016) and between NRD studies with partially overlapping years was identified but could not be fully resolved. Fifth, the small number of studies (k = 7 for incidence) limited meta-regression power and precluded formal publication bias assessment. Sixth, the exclusion of non-English publications and conference abstracts may have introduced selection bias. Implications for practice and research These findings support the development of preoperative risk stratification tools incorporating comorbidity indices, ECOG performance status, and radiation history to identify patients at elevated readmission risk and enable targeted preventive interventions. Such interventions might include enhanced discharge planning, transitional care programmes, extended postoperative monitoring, and coordinated communication with oncology teams. Future prospective, multi-centre studies should adopt standardised readmission definitions, report both 30-day and 90-day rates, distinguish between surgical, medical, and disease-progression causes of readmission, and evaluate the effectiveness of targeted readmission-reduction interventions. CONCLUSIONS Approximately one in six patients (16.0%; 95% CI: 12.2–20.3%) experiences unplanned hospital readmission within 30 days of surgery for spinal metastases, a rate that is substantially higher than that observed in general spine surgery.Single-centre studies yield a more homogeneous and likely conservative estimate of approximately 14%. The 90-day readmission rate (31.2%) is nearly double the 30-day rate, underscoring the need for extended post-discharge surveillance. Comorbidity burden, prior spinal radiation, poor functional status, and postoperative complications are the most consistently identified risk factors. The overall certainty of evidence remains very low (GRADE), underscoring an urgent need for prospective multi-centre studies with standardised outcome definitions to establish robust readmission reduction strategies for this vulnerable oncological population. Declarations Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Conflicts of Interest: The authors declare no conflicts of interest. Ethics Statement: This study is a systematic review and meta-analysis of previously published studies and did not involve direct interaction with human subjects. Institutional review board approval was not required. Data Availability: All data generated during this study are included in the published article and its supplementary materials. The analysis code is available from the corresponding author upon reasonable request. INPLASY Registration: INPLASY202630120 Author Contribution Y.C Li: Conceptualization, Methodology, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administrationC.Y Li: Investigation, Data curation, Writing – review & editingS.H Huang: Investigation, Data curation, Writing – review & editingH.K Wang: Investigation, Writing – review & editingK.H Chen: Investigation, Writing – review & editingY.J Lu: Supervision, Writing – review & editing References Sciubba DM, Petteys RJ, Dekutoski MB et al (2010) Diagnosis and management of metastatic spine disease. J Neurosurg Spine 13:94–108 Laufer I, Rubin DG, Lis E et al (2013) The NOMS framework: approach to the treatment of spinal metastatic tumors. 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Supplementary Files SupFigS1ForestPlot90d.png Supplementary Fig. S1 Forest plot of pooled 90-day unplanned readmission incidence (random-effects model, Freeman-Tukey double arcsine transformation). Seven studies contributed to the analysis. Diamond represents pooled estimate. SupplementaryAppendixCNOS.xlsx SupplementaryAppendixBExcluded.xlsx SupplementaryPRISMAChecklist.xlsx SupplementaryAppendixDRiskFactors.xlsx Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. 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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-9284951","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619145661,"identity":"ec3d2d26-275f-4a1a-ba17-3177df5de9d6","order_by":0,"name":"Ying-Ching Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYBACxgYeBmYos/HBhx82YMYBYrRIMDAwHzac2ZMGFsOrhYEBroUtTZqH7TBYDK8W5vbeY48L2+7U8UufMTbg4Tlvt7b9MNCWGptonA7rOZduPLPtmYRkX47hAwmL28nbziQCtRxLy23ApWVGjpk0b9thCYMzPMYGBjy3k80OALUwNhwmrMX+DI+ZRALbuWSz8w+J1GLAw5YmcYDtgJ3ZDUK29Jwxk+Y5d1hyxhlgIDf2JCeY3QDakoDHL4btPUAtZYf5+XsYGx//+WFnb3Y+/eGDDzU2uLWgSySCBRJwKAcBeXQBezyKR8EoGAWjYIQCAPGYYS90lsTCAAAAAElFTkSuQmCC","orcid":"","institution":"Linkou Chang Gung Memorial Hospital","correspondingAuthor":true,"prefix":"","firstName":"Ying-Ching","middleName":"","lastName":"Li","suffix":""},{"id":619145662,"identity":"cf1f6e84-788e-4ab7-926d-7a8df7bbb3b7","order_by":1,"name":"Cheng-Yu Li","email":"","orcid":"","institution":"Linkou Chang Gung Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Cheng-Yu","middleName":"","lastName":"Li","suffix":""},{"id":619145663,"identity":"7975fc7e-696c-4324-ae9c-54712f3c4084","order_by":2,"name":"Sheng-Han Huang","email":"","orcid":"","institution":"Linkou Chang Gung Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sheng-Han","middleName":"","lastName":"Huang","suffix":""},{"id":619145664,"identity":"e5e6fe4c-4b4b-4f9c-a431-43d381f93264","order_by":3,"name":"Hong-Kai Wang","email":"","orcid":"","institution":"Linkou Chang Gung Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hong-Kai","middleName":"","lastName":"Wang","suffix":""},{"id":619145665,"identity":"95529bdc-1da9-4975-8ee9-ff49fd031d19","order_by":4,"name":"Kuan-Hung Chen","email":"","orcid":"","institution":"Linkou Chang Gung Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kuan-Hung","middleName":"","lastName":"Chen","suffix":""},{"id":619145666,"identity":"7c68c6da-aa59-4973-a514-503633eb5563","order_by":5,"name":"Yu-Jen Lu","email":"","orcid":"","institution":"Linkou Chang Gung Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yu-Jen","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2026-04-01 00:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9284951/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9284951/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106870918,"identity":"691b3a0a-602d-4525-99db-ace10826cac5","added_by":"auto","created_at":"2026-04-14 09:43:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":164852,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA 2020 flow diagram. From 656 identified records, 14 studies were included after deduplication, screening, and full-text assessment.\u003c/p\u003e","description":"","filename":"Fig1PRISMAFlowDiagram.png","url":"https://assets-eu.researchsquare.com/files/rs-9284951/v1/f2c7d8c447c4ed221102bda6.png"},{"id":106870960,"identity":"a27e7d31-c4b2-427d-96de-50e615260580","added_by":"auto","created_at":"2026-04-14 09:44:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132759,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of pooled 30-day unplanned readmission incidence (random-effects model, Freeman-Tukey double arcsine transformation). Diamond represents pooled estimate; dashed red line represents 95% prediction interval.\u003c/p\u003e","description":"","filename":"Fig2ForestPlot30dIncidence.png","url":"https://assets-eu.researchsquare.com/files/rs-9284951/v1/b72cdd28db2e11038b116d26.png"},{"id":106870966,"identity":"2f88d519-5ddd-44af-9cb2-009f0c3c552c","added_by":"auto","created_at":"2026-04-14 09:44:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":247589,"visible":true,"origin":"","legend":"\u003cp\u003eNewcastle-Ottawa Scale risk of bias traffic light plot for 14 included cohort studies. Green = star awarded; amber = not awarded.\u003c/p\u003e","description":"","filename":"Fig3NOSTrafficLight.png","url":"https://assets-eu.researchsquare.com/files/rs-9284951/v1/600e60eec07376c44fdb4f0d.png"},{"id":106870879,"identity":"594bcc4e-c634-4363-8e0e-92ec2adcef66","added_by":"auto","created_at":"2026-04-14 09:43:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":124564,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup forest plot comparing single-centre cohort studies (k = 5; I² = 0%) versus national database studies (k = 2). Test for subgroup difference: p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig4SubgroupForestPlot.png","url":"https://assets-eu.researchsquare.com/files/rs-9284951/v1/3f6fc8b5fec5c1e503a90051.png"},{"id":106870917,"identity":"ee23e20f-f39a-47b0-8dfc-d4ac588c050b","added_by":"auto","created_at":"2026-04-14 09:43:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":135636,"visible":true,"origin":"","legend":"\u003cp\u003eFunnel plot of 30-day readmission incidence (k = 7). Standard error plotted against incidence; dashed lines represent pseudo 95% confidence limits around the pooled estimate (16.0%). Formal publication bias tests (Egger, Begg) were not conducted given the limited number of studies (k \u0026lt; 10). Visual inspection does not suggest gross asymmetry.\u003c/p\u003e","description":"","filename":"Fig5FunnelPlot.png","url":"https://assets-eu.researchsquare.com/files/rs-9284951/v1/24c003d76a1c94fad1ea716a.png"},{"id":106870951,"identity":"c58a1492-d2d8-4c90-b831-21e41896b39e","added_by":"auto","created_at":"2026-04-14 09:44:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":192180,"visible":true,"origin":"","legend":"\u003cp\u003eRisk factors for readmission after spinal metastasis surgery. Multivariate-adjusted effect sizes (odds ratios, hazard ratios, or adjusted odds ratios with 95% confidence intervals) from individual studies, grouped by factor category. Asterisks denote statistically significant associations. Effect sizes are displayed on a logarithmic scale; values to the left of 1 indicate protective factors, values to the right indicate risk factors.\u003c/p\u003e","description":"","filename":"Fig6RiskFactors.png","url":"https://assets-eu.researchsquare.com/files/rs-9284951/v1/5a1f420bfe05b2f3ae08b8ec.png"},{"id":108490809,"identity":"fcea74f1-f44d-4325-a88d-f606bee3fc53","added_by":"auto","created_at":"2026-05-05 09:48:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1333086,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9284951/v1/1f1987a0-e282-46c0-9875-81dc942661a6.pdf"},{"id":106870963,"identity":"90010dc3-3656-412c-88d5-452225d5f4bc","added_by":"auto","created_at":"2026-04-14 09:44:07","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":113353,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Fig. S1 \u003c/strong\u003eForest plot of pooled 90-day unplanned readmission incidence (random-effects model, Freeman-Tukey double arcsine transformation). Seven studies contributed to the analysis. Diamond represents pooled estimate.\u003c/p\u003e","description":"","filename":"SupFigS1ForestPlot90d.png","url":"https://assets-eu.researchsquare.com/files/rs-9284951/v1/7abae35da5715550803ab98c.png"},{"id":106870893,"identity":"c6c84fa6-2e19-4bd0-be87-39901e477651","added_by":"auto","created_at":"2026-04-14 09:43:49","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14210,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryAppendixCNOS.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9284951/v1/614eea051f7e8a9c1b6bf6f0.xlsx"},{"id":106870915,"identity":"5c8994e7-49b9-4243-a94d-6b8c02fcdb0a","added_by":"auto","created_at":"2026-04-14 09:43:55","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":5907,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryAppendixBExcluded.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9284951/v1/b8192be3c4baccb7fc81d53b.xlsx"},{"id":106870914,"identity":"292fc2ca-98dc-4b57-8ac5-91bdd9f07dcf","added_by":"auto","created_at":"2026-04-14 09:43:55","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":7873,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryPRISMAChecklist.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9284951/v1/551623f842cbf3a1102cef8f.xlsx"},{"id":106870891,"identity":"b7247a29-6974-4bc3-92ec-1cad3d91ce2f","added_by":"auto","created_at":"2026-04-14 09:43:49","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":8786,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryAppendixDRiskFactors.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9284951/v1/b3f63f65d9c8067fd41c91fb.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk Factors and Incidence of Unplanned Hospital Readmission After Surgery for Spinal Metastases: A Systematic Review and Meta-Analysis","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSpinal metastases affect up to 40% of cancer patients, representing the most common site of skeletal metastatic disease [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Surgical intervention plays a critical role in managing neurological compromise, mechanical instability, and intractable pain in patients with metastatic epidural spinal cord compression [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As systemic oncological therapies have improved, the number of patients undergoing surgery for spinal metastases has increased substantially [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnplanned hospital readmission is an increasingly recognised indicator of postoperative morbidity, healthcare quality, and resource utilisation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In the general spine surgery population, 30-day readmission rates range from 4% to 14% [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, patients with spinal metastases represent a uniquely vulnerable population characterised by advanced systemic disease, immunosuppression, poor nutritional status, and limited physiological reserve. Individual studies have reported 30-day readmission rates ranging from 10% to 24% [\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12 CR13\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], yet the reported risk factor profiles remain heterogeneous and no consensus exists on which patient-level or treatment-related variables most reliably predict readmission.\u003c/p\u003e \u003cp\u003eDespite the clinical significance of readmission in this population, no prior systematic review or meta-analysis has comprehensively synthesised the incidence of or risk factors for readmission specifically among patients undergoing open surgery for spinal metastases. The objective of this study was to (1) estimate the pooled incidence of 30-day unplanned readmission and (2) identify independently associated risk factors.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eProtocol and registration\u003c/h2\u003e \u003cp\u003eThis review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and the Meta-analysis of Observational Studies in Epidemiology (MOOSE) checklist [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The protocol was registered with the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY; registration number: INPLASY202630120).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEligibility criteria\u003c/h3\u003e\n\u003cp\u003eStudies were eligible if they (1) enrolled adults aged 18 years or older undergoing open surgery (decompression, stabilisation, or corpectomy) for spinal metastases; (2) reported unplanned hospital readmission as a primary or secondary outcome; and (3) were observational cohort or case-control studies published from January 2010 onward in English. Studies were excluded if they exclusively evaluated vertebroplasty or kyphoplasty, enrolled exclusively primary spinal tumour populations without extractable metastatic subgroup data, were conference abstracts, or included fewer than 10 patients with metastatic disease.\u003c/p\u003e\n\u003ch3\u003eInformation sources and search strategy\u003c/h3\u003e\n\u003cp\u003eA comprehensive search was conducted across five electronic databases: PubMed/MEDLINE, Embase, Cochrane CENTRAL, Scopus, and Web of Science. The search strategy combined controlled vocabulary (MeSH and Emtree terms) and free-text synonyms organised into two concept blocks: (A) spinal metastases/metastatic spinal cord compression and (B) hospital readmission. Database-specific syntax was adapted for each platform. The complete search strategies are provided in Supplementary Appendix A.\u003c/p\u003e\n\u003ch3\u003eStudy selection and data extraction\u003c/h3\u003e\n\u003cp\u003eRecords were imported into EndNote and deduplicated. Title and abstract screening, followed by full-text eligibility assessment, was performed independently by two reviewers, with disagreements resolved by consensus. Studies with potentially overlapping cohorts were identified and adjudicated based on institution, study period, and sample size. When cohort overlap was confirmed, the study with the larger sample or more comprehensive readmission analysis was retained. Data were extracted into a standardised spreadsheet including study characteristics, patient demographics, surgical details, readmission definitions and rates, causes of readmission, and multivariate-adjusted risk factor effect sizes (odds ratios, hazard ratios, or adjusted odds ratios) with 95% confidence intervals.\u003c/p\u003e\n\u003ch3\u003eRisk of bias assessment\u003c/h3\u003e\n\u003cp\u003eMethodological quality was assessed using the Newcastle-Ottawa Scale (NOS) for cohort studies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Studies scoring 7 or above (of 9) were classified as good quality, 5 to 6 as fair, and below 5 as poor.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe primary analysis estimated the pooled 30-day readmission incidence using the Freeman-Tukey double arcsine transformation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] to stabilise proportions. A DerSimonian-Laird random-effects model was applied [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Between-study heterogeneity was assessed using the Cochran Q statistic, I-squared, and tau-squared [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The Hartung-Knapp-Sidik-Jonkman (HKSJ) adjustment was applied as a sensitivity analysis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A restricted maximum likelihood (REML) estimator was additionally employed as a sensitivity analysis for the between-study variance component. A 95% prediction interval was calculated.\u003c/p\u003e \u003cp\u003ePre-specified subgroup analysis compared single-centre cohort studies with national database studies. Exploratory mixed-effects meta-regression was performed using publication year and log-transformed sample size as pre-specified covariates. Leave-one-out sensitivity analysis assessed the influence of individual studies. A funnel plot was generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e5\u003c/span\u003e), though formal publication bias testing was not conducted given the limited number of studies (k\u0026thinsp;\u0026lt;\u0026thinsp;10), consistent with Cochrane Handbook recommendations [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor risk factor analysis, inverse-variance random-effects meta-analysis was performed where two or more studies reported the same risk factor with comparable effect measures. Remaining risk factors were synthesised narratively. As a secondary analysis, pooled 90-day readmission incidence was estimated where extractable data were available. Evidence certainty was assessed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) framework [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. All analyses were performed using Python 3.x (NumPy, SciPy, Matplotlib).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy selection\u003c/h2\u003e \u003cp\u003eThe systematic search identified 656 records across five databases (PubMed 88, Embase 315, Cochrane CENTRAL 3, Scopus 182, Web of Science 68). After deduplication (316 removed), 296 records underwent title and abstract screening, of which 25 were sought for full-text retrieval. Three reports could not be retrieved. Twenty-two reports were assessed for full-text eligibility. Eight were excluded: wrong outcome with no readmission data (n\u0026thinsp;=\u0026thinsp;2), wrong population (n\u0026thinsp;=\u0026thinsp;1), wrong exposure framework (n\u0026thinsp;=\u0026thinsp;1), mixed primary and metastatic cohort without extractable metastatic subgroup (n\u0026thinsp;=\u0026thinsp;2), and overlapping cohort or subset (n\u0026thinsp;=\u0026thinsp;2). Fourteen studies were included in the qualitative synthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The complete list of excluded studies with reasons is provided in Supplementary Appendix B.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy characteristics\u003c/h2\u003e \u003cp\u003eThe 14 included studies comprised 8 single-centre retrospective cohorts, 4 United States Nationwide Readmissions Database (NRD) studies, 1 Korean national database study, and 1 European single-centre study. Publication years ranged from 2014 to 2025. Eleven studies originated from the United States, with one each from Singapore, South Korea, and Germany. Metastatic cohort sizes ranged from 30 to 13,974 patients. The most common surgical approach was posterior decompression with or without instrumentation. Detailed study characteristics are presented in 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\u003eCharacteristics of included studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN (met)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean age\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30d rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90d rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNOS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eKey MV risk factors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchairer 2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle (UCSF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOp time, DM, obesity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLau 2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle (UCSF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLOS, lung mets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbu-Bonsrah 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle (JHU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47.2%*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;60 HR1.68; prior RT HR1.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElsamadicy 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle (Duke)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e~\u0026thinsp;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDescriptive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKumar N 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle (NUH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCCI\u0026thinsp;\u0026gt;\u0026thinsp;7 OR2.54; lung OR3.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElsamadicy 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNRD 2013\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.3%\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHTN OR1.45; renal OR1.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatel 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle (JHU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCCI/pt OR1.25; RT OR1.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShahrestani 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNRD 2016\u0026ndash;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;1005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u0026ndash;15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e~\u0026thinsp;40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFrailty (NS)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElsamadicy 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNRD 2016\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHFRS frailty (ML)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChanbour 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle (Vandy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21.9%\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePostop comp OR1.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarkin 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle (NW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eReported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAge, sex, SES factors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNoh 2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKorean HIRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKorea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eReported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRT OR1.92; CCI OR1.55\u0026ndash;2.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePojskic 2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle (Marburg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67 med\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eECOG, fracture, tumour\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTsai 2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNRD 2016\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTaiwan\u0026sect;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eVTE aOR2.06; CCI\u0026thinsp;\u0026ge;\u0026thinsp;4\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 \u003cem\u003eCCI Charlson Comorbidity Index; DM diabetes mellitus; HFRS Hospital Frailty Risk Score; HTN hypertension; LOS length of stay; ML machine learning; NOS Newcastle-Ottawa Scale; NW Northwestern; RT radiotherapy; SG Singapore; Vandy Vanderbilt. *1-year rate. \u0026dagger;31\u0026ndash;90d additional. \u0026Dagger;3-month rate. \u0026sect;Analysed US NRD data; first author affiliated with Taiwan institution.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRisk of bias\u003c/h2\u003e \u003cp\u003eThirteen of 14 studies (92.9%) were rated as good quality on the NOS (score\u0026thinsp;\u0026ge;\u0026thinsp;7/9; median 8). One study (Elsamadicy 2018, n\u0026thinsp;=\u0026thinsp;30) was rated fair quality (score 5) due to the absence of a defined comparator group and limited adjustment for confounders. The most common limitation was inability to assess follow-up adequacy in administrative database studies (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Detailed NOS assessments are provided in Supplementary Appendix C.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePooled 30-day readmission incidence\u003c/h2\u003e \u003cp\u003eSeven studies with extractable numerator and denominator data (8,132 patients; 1,665 readmission events) contributed to incidence pooling. The pooled 30-day unplanned readmission incidence was 16.0% (95% CI: 12.2\u0026ndash;20.3%) with considerable heterogeneity (I\u0026sup2; = 93.2%; Q\u0026thinsp;=\u0026thinsp;88.85; df\u0026thinsp;=\u0026thinsp;6; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; τ\u0026sup2; = 0.017). The HKSJ-adjusted 95% confidence interval was 11.3\u0026ndash;21.4%. The REML sensitivity analysis yielded a comparable pooled estimate of 15.9% (95% CI: 11.9\u0026ndash;20.4%), confirming the robustness of the primary DerSimonian-Laird result. The 95% prediction interval ranged from 5.3% to 30.8%, indicating substantial variability in readmission rates across clinical settings (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup and sensitivity analyses\u003c/h2\u003e \u003cp\u003ePre-specified subgroup analysis by data source revealed a statistically significant and clinically meaningful difference between single-centre and national database studies (test for subgroup difference: Q\u0026thinsp;=\u0026thinsp;29.68, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Single-centre studies (k\u0026thinsp;=\u0026thinsp;5; N\u0026thinsp;=\u0026thinsp;1,003) yielded a pooled incidence of 13.9% (95% CI: 11.8\u0026ndash;16.2%) with no detected heterogeneity (I\u0026sup2; = 0%), whereas national database studies (k\u0026thinsp;=\u0026thinsp;2; N\u0026thinsp;=\u0026thinsp;7,129) yielded 20.4% (95% CI: 13.7\u0026ndash;28.0%) with extreme heterogeneity (I\u0026sup2; = 98.2%; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLeave-one-out analysis demonstrated that removal of Elsamadicy 2021 (the largest database study, n\u0026thinsp;=\u0026thinsp;4,423, rate\u0026thinsp;=\u0026thinsp;24.1%) reduced the overall I\u0026sup2; from 93.2% to 29.6% and shifted the pooled estimate to 15.1% (95% CI: 13.2\u0026ndash;17.1%). All other individual study exclusions yielded pooled estimates between 15.1% and 16.9%, confirming the robustness and stability of the overall estimate.\u003c/p\u003e \u003cp\u003eMeta-regression identified publication year (β\u0026thinsp;=\u0026thinsp;0.042; p\u0026thinsp;=\u0026thinsp;0.011) and log-transformed sample size (β\u0026thinsp;=\u0026thinsp;0.184; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as significant moderators, though these findings must be interpreted with caution given the limited number of studies (k\u0026thinsp;=\u0026thinsp;7), the ecological nature of the analysis, and the elevated risk of false-positive findings inherent to meta-regression with few studies.\u003c/p\u003e \u003cp\u003ePooled 90-day readmission incidence. Seven studies with extractable 90-day readmission data (22,198 patients; 8,465 readmission events) contributed to the secondary analysis. The pooled 90-day unplanned readmission incidence was 31.2% (95% CI: 26.9\u0026ndash;35.6%) with considerable heterogeneity (I\u0026sup2; = 96.5%; Q\u0026thinsp;=\u0026thinsp;173.5, df\u0026thinsp;=\u0026thinsp;6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; τ\u0026sup2; = 0.0138). The HKSJ-adjusted 95% confidence interval was 21.2\u0026ndash;42.1%. The 95% prediction interval ranged from 17.9% to 46.2%. Subgroup analysis by data source showed that single-centre studies (k\u0026thinsp;=\u0026thinsp;3; Lau 2016, Kumar 2021, Chanbour 2023) yielded a pooled 90-day incidence of 19.8% (95% CI: 11.3\u0026ndash;30.2%; I\u0026sup2; = 86.0%), whereas national database studies (k\u0026thinsp;=\u0026thinsp;4; Elsamadicy 2021, Shahrestani 2021, Noh 2024, Tsai 2025) yielded 38.1% (95% CI: 33.9\u0026ndash;42.4%; I\u0026sup2; = 92.8%; p for subgroup difference\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The nearly twofold increase from the 30-day (16.0%) to 90-day (31.2%) estimate indicates that a substantial proportion of readmissions occur between days 31 and 90, highlighting the importance of extended post-discharge surveillance in this population (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRisk factor synthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePrior spinal radiation.\u003c/b\u003e Prior spinal radiation was the only risk factor amenable to formal meta-analytic pooling. Two studies reported multivariate-adjusted effect sizes: Abu-Bonsrah 2017 (HR 1.68; 95% CI: 1.05\u0026ndash;2.69) and Patel 2021 (OR 1.96; 95% CI: 0.98\u0026ndash;3.90). The pooled effect was 1.79 (95% CI: 1.21\u0026ndash;2.63; p\u0026thinsp;=\u0026thinsp;0.003; I\u0026sup2; = 0%), suggesting an approximately 80% increased hazard of readmission. Noh 2024 further supported this association (OR 1.92; 95% CI: 1.72\u0026ndash;2.14 for radiotherapy vs surgery).\u003c/p\u003e \u003cp\u003e \u003cb\u003eComorbidity burden.\u003c/b\u003e Four studies consistently identified elevated Charlson Comorbidity Index (CCI) scores as a significant risk factor for readmission, although differing operationalisations across studies precluded formal pooling. Patel 2021 reported OR 1.25 per CCI point (95% CI: 1.03\u0026ndash;1.52), Kumar 2021 OR 2.54 for CCI\u0026thinsp;\u0026gt;\u0026thinsp;7 (95% CI: 1.22\u0026ndash;5.27), and Noh 2024 demonstrated a dose\u0026ndash;response relationship (CCI\u0026thinsp;=\u0026thinsp;2: OR 1.55; CCI\u0026thinsp;=\u0026thinsp;3: OR 2.47). Individual comorbidities including hypertension (OR 1.45) and renal failure (OR 1.53) were also independently significant in Elsamadicy 2021.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFunctional status.\u003c/b\u003e Poor preoperative functional status was associated with increased readmission risk. ECOG\u0026thinsp;\u0026gt;\u0026thinsp;1 showed a borderline association in Kumar 2021 (OR 2.23; 95% CI: 0.98\u0026ndash;5.11; p\u0026thinsp;=\u0026thinsp;0.057) and was associated with worse clinical outcomes in Pojskic 2024. Normal preoperative ambulation was protective in Schairer 2014 (HR 0.49; 95% CI: 0.27\u0026ndash;0.90).\u003c/p\u003e \u003cp\u003e \u003cb\u003eOperative and postoperative factors.\u003c/b\u003e Operative time exceeding 10 hours was strongly associated with readmission in Schairer 2014 (HR 3.64; 95% CI: 1.89\u0026ndash;7.00). Postoperative complications independently predicted 3-month readmission in Chanbour 2023 (OR 1.38; 95% CI: 1.25\u0026ndash;1.52), and readmission was in turn associated with reduced overall survival (HR 1.75; 95% CI: 1.28\u0026ndash;2.39).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTumour-specific and metabolic factors.\u003c/b\u003e Lung cancer as the primary tumour was independently significant in Kumar 2021 (OR 3.09; 95% CI: 1.19\u0026ndash;8.03). Venous thromboembolism predicted 30-day readmission in Tsai 2025 (aOR 2.06; 95% CI: 1.33\u0026ndash;3.19). Diabetes (HR 7.15; 95% CI: 2.57\u0026ndash;19.85) and obesity (HR 5.08; 95% CI: 1.29\u0026ndash;20.00) were significant in Schairer 2014.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCertainty of evidence\u003c/h2\u003e \u003cp\u003eThe overall certainty of evidence was rated as very low (GRADE) for all outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Starting from a baseline of low certainty (observational studies), the pooled incidence was downgraded one level for serious inconsistency (I\u0026sup2; = 93.2%), and the pooled prior radiation effect was downgraded for imprecision (k\u0026thinsp;=\u0026thinsp;2; optimal information size not met).\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\u003eGRADE Summary of Findings\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ek\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRoB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncon.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIndir.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eImprec.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePub. bias\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGRADE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eResult (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day readmission incidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS (\u0026minus;\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026oplus;○○○ Very low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.0% (12.2\u0026ndash;20.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior RT \u0026rarr; readmission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS (\u0026minus;\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026oplus;○○○ Very low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.79 (1.21\u0026ndash;2.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI \u0026rarr; readmission (narrative)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;13k\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS (\u0026minus;\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026oplus;○○○ Very low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOR 1.25\u0026ndash;2.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90-day readmission incidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;22k\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS (\u0026minus;\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026oplus;○○○ Very low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e31.2% (26.9\u0026ndash;35.6%)\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 \u003cem\u003eNS not serious (no downgrade); S serious (\u0026minus;\u0026thinsp;1 level). \u0026oplus; point awarded; ○ point not awarded. RoB risk of bias; Incon. inconsistency; Indir. indirectness; Imprec. imprecision.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study represents, to our knowledge, the first systematic review and meta-analysis to comprehensively synthesise evidence on unplanned readmission following surgery for spinal metastases, identifying a pooled 30-day readmission incidence of 16.0% (95% CI: 12.2\u0026ndash;20.3%).This rate is two to four times higher than the 4\u0026ndash;14% reported in general spine surgery populations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], underscoring the heightened medical complexity and vulnerability of patients with metastatic spinal disease. The 95% prediction interval (5.3\u0026ndash;30.8%) reflects the wide variability that clinicians should anticipate across practice settings.\u003c/p\u003e \u003cp\u003eA key methodological finding was the marked and statistically significant difference in readmission rates between single-centre cohort studies (13.9%; I\u0026sup2; = 0%) and national database studies (20.4%; I\u0026sup2; = 98.2%).National databases such as the NRD capture readmissions to any facility within the healthcare system, whereas single-centre studies inherently undercount readmissions occurring at outside institutions, a limitation particularly relevant for oncological patients who frequently receive care across multiple centres. Additionally, administrative databases may apply broader coding definitions. These findings parallel observations in other surgical specialties where database-derived readmission rates consistently exceed institutional rates [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eComorbidity burden emerged as the most consistently identified risk factor. Four studies using different CCI operationalisations all demonstrated increased readmission risk, with a dose\u0026ndash;response relationship in Noh 2024 (CCI\u0026thinsp;=\u0026thinsp;2: OR 1.55; CCI\u0026thinsp;=\u0026thinsp;3: OR 2.47). This convergent evidence from methodologically diverse studies strengthens the rationale for systematic comorbidity-based preoperative risk stratification. Frailty indices examined by Shahrestani 2021 (JHACG) and Elsamadicy 2022 (HFRS) represent emerging complementary tools that may enhance predictive accuracy.\u003c/p\u003e \u003cp\u003ePrior spinal radiation was the only formally poolable risk factor (1.79; 95% CI: 1.21\u0026ndash;2.63; I\u0026sup2; = 0%), potentially reflecting impaired wound healing and soft-tissue vascularity in previously irradiated surgical fields. This finding has practical relevance for surgical timing and wound management protocols. The optimal interval between radiation and surgery remains an active area of investigation [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe finding that readmission was associated with reduced overall survival in Chanbour 2023 (HR 1.75; 95% CI: 1.28\u0026ndash;2.39) suggests that unplanned readmission may serve as a sentinel event reflecting underlying disease trajectory and overall physiological decline. This association has implications for goals-of-care discussions and integration of palliative care services.\u003c/p\u003e \u003cp\u003eOur secondary analysis revealed that the pooled 90-day readmission incidence (31.2%) was nearly double the 30-day rate (16.0%), indicating that approximately half of all readmissions within three months occur after the initial 30-day window. This finding has important implications for post-discharge surveillance strategies. While most readmission research in spine surgery focuses on the 30-day endpoint, our data suggest that the 31\u0026ndash;90 day interval represents an equally critical period for patients with spinal metastases, likely reflecting delayed manifestations of disease progression, radiation-related complications, and systemic oncological deterioration. These results support extending structured follow-up programmes beyond the conventional 30-day window for this population.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eStrengths of this review include the comprehensive five-database search, strict exclusion of mixed primary and metastatic cohorts to ensure population specificity, use of appropriate Freeman-Tukey transformation for proportion pooling, and transparent exploration of heterogeneity through pre-specified subgroup analysis.\u003c/p\u003e \u003cp\u003eSeveral limitations must be acknowledged. First, all included studies were retrospective, limiting evidence certainty to very low (GRADE). Second, heterogeneity was considerable (I\u0026sup2; = 93.2%), though largely explained by data source. Third, most risk factors could not be formally pooled due to different operationalisations. Fourth, potential cohort overlap between the UCSF studies (Schairer 2014, Lau 2016) and between NRD studies with partially overlapping years was identified but could not be fully resolved. Fifth, the small number of studies (k\u0026thinsp;=\u0026thinsp;7 for incidence) limited meta-regression power and precluded formal publication bias assessment. Sixth, the exclusion of non-English publications and conference abstracts may have introduced selection bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eImplications for practice and research\u003c/h2\u003e \u003cp\u003eThese findings support the development of preoperative risk stratification tools incorporating comorbidity indices, ECOG performance status, and radiation history to identify patients at elevated readmission risk and enable targeted preventive interventions. Such interventions might include enhanced discharge planning, transitional care programmes, extended postoperative monitoring, and coordinated communication with oncology teams. Future prospective, multi-centre studies should adopt standardised readmission definitions, report both 30-day and 90-day rates, distinguish between surgical, medical, and disease-progression causes of readmission, and evaluate the effectiveness of targeted readmission-reduction interventions.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eApproximately one in six patients (16.0%; 95% CI: 12.2\u0026ndash;20.3%) experiences unplanned hospital readmission within 30 days of surgery for spinal metastases, a rate that is substantially higher than that observed in general spine surgery.Single-centre studies yield a more homogeneous and likely conservative estimate of approximately 14%. The 90-day readmission rate (31.2%) is nearly double the 30-day rate, underscoring the need for extended post-discharge surveillance. Comorbidity burden, prior spinal radiation, poor functional status, and postoperative complications are the most consistently identified risk factors. The overall certainty of evidence remains very low (GRADE), underscoring an urgent need for prospective multi-centre studies with standardised outcome definitions to establish robust readmission reduction strategies for this vulnerable oncological population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement:\u0026nbsp;\u003c/strong\u003eThis study is a systematic review and meta-analysis of previously published studies and did not involve direct interaction with human subjects. Institutional review board approval was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eAll data generated during this study are included in the published article and its supplementary materials. The analysis code is available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eINPLASY Registration:\u0026nbsp;\u003c/strong\u003eINPLASY202630120\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY.C Li: Conceptualization, Methodology, Formal analysis, Data curation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Supervision, Project administrationC.Y Li: Investigation, Data curation, Writing \u0026ndash; review \u0026amp; editingS.H Huang: Investigation, Data curation, Writing \u0026ndash; review \u0026amp; editingH.K Wang: Investigation, Writing \u0026ndash; review \u0026amp; editingK.H Chen: Investigation, Writing \u0026ndash; review \u0026amp; editingY.J Lu: Supervision, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSciubba DM, Petteys RJ, Dekutoski MB et al (2010) Diagnosis and management of metastatic spine disease. J Neurosurg Spine 13:94\u0026ndash;108\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaufer I, Rubin DG, Lis E et al (2013) The NOMS framework: approach to the treatment of spinal metastatic tumors. Oncologist 18:744\u0026ndash;751\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatchell RA, Tibbs PA, Regine WF et al (2005) Direct decompressive surgical resection in the treatment of spinal cord compression caused by metastatic cancer: a randomised trial. Lancet 366:643\u0026ndash;648\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFehlings MG, Nater A, Tetreault L et al (2016) Survival and clinical outcomes in surgically treated patients with metastatic epidural spinal cord compression. J Clin Oncol 34:268\u0026ndash;276\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshihara H, Yoneoka D (2014) Trends in the surgical treatment for spinal metastasis and the in-hospital patient outcomes in the United States from 2000 to 2009. Spine J 14:1844\u0026ndash;1849\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJencks SF, Williams MV, Coleman EA (2009) Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med 360:1418\u0026ndash;1428\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHills J, Sivaganesan A, Khan I et al (2018) Causes and timing of unplanned 90-day readmissions following spine surgery. Spine 43:991\u0026ndash;998\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchairer WW, Carrer A, Sing DC et al (2014) Hospital readmission rates after surgical treatment of primary and metastatic tumors of the spine. Spine 39:1801\u0026ndash;1808\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbu-Bonsrah N, Goodwin CR, De la Garza-Ramos R et al (2017) Readmissions after surgical resection of metastatic tumors of the spine at a single institution. World Neurosurg 101:695\u0026ndash;701\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElsamadicy AA, Adogwa O, Lubkin DT et al (2018) Thirty-day complication and readmission rates associated with resection of metastatic spinal tumors. J Spine Surg 4:304\u0026ndash;310\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar N, Thomas A, Madhu S et al (2021) Analysis of unplanned hospital readmissions up to 2-years after metastatic spine tumour surgery. Eur Spine J 30:2887\u0026ndash;2895\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElsamadicy AA, Koo AB, David WB et al (2021) Thirty- and 90-day readmissions after spinal surgery for spine metastases: a national trend analysis of 4423 patients. Spine 46:828\u0026ndash;835\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel J, Pennington Z, Hersh AM et al (2021) Drivers of readmission and reoperation after surgery for vertebral column metastases. World Neurosurg 154:e806\u0026ndash;e814\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChanbour H, Chen JW, Gangavarapu LS et al (2023) Unplanned readmission is associated with decreased overall survival and performance after metastatic spine surgery. Spine 48:653\u0026ndash;663\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePage MJ, McKenzie JE, Bossuyt PM et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:n71\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStroup DF, Berlin JA, Morton SC et al (2000) Meta-analysis of observational studies in epidemiology: a proposal for reporting. JAMA 283:2008\u0026ndash;2012\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWells GA, Shea B, O\u0026rsquo;Connell D et al (2014) The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Ottawa Hospital Research Institute, Ottawa\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreeman MF, Tukey JW (1950) Transformations related to the angular and the square root. Ann Math Stat 21:607\u0026ndash;611\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDerSimonian R, Laird N (1986) Meta-analysis in clinical trials. Control Clin Trials 7:177\u0026ndash;188\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHartung J, Knapp G (2001) A refined method for the meta-analysis of controlled clinical trials with binary outcome. Stat Med 20:3875\u0026ndash;3889\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuyatt GH, Oxman AD, Vist GE et al (2008) GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ 336:924\u0026ndash;926\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsai TC, Joynt KE, Orav EJ et al (2013) Variation in surgical-readmission rates and quality of hospital care. N Engl J Med 369:1134\u0026ndash;1142\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar N, Madhu S, Bohra H et al (2020) Is there an optimal timing between radiotherapy and surgery to reduce wound complications in metastatic spine disease? A systematic review. Eur Spine J 29:2387\u0026ndash;2398\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShahrestani S, Ballatori AM, Chen XT et al (2021) Analysis of readmission rates and causes following lumbar spinal fusion for metastatic disease. World Neurosurg 155:e574\u0026ndash;e585\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElsamadicy AA, Freedman IG, Koo AB et al (2022) Modified-frailty index does not independently predict complications following surgery for spinal metastases. Global Spine J 12:1082\u0026ndash;1091\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLarkin CJ, Zakaria HM, Harrop JS et al (2023) Sociodemographic factors and spinal metastases outcomes. Clin Neurol Neurosurg 224:107549\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoh SH, Ha Y, Shin DA et al (2024) Risk factors for 90-day readmission among patients with metastatic spine tumors in South Korea. World Neurosurg 190:e207\u0026ndash;e219\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePojskić M, Sa\u0026szlig; B, Bopp MHA et al (2024) Determinants of overall and readmission-free survival in patients with metastatic epidural spinal cord compression. Cancers 16:4248\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsai PJ et al (2025) Venous thromboembolism and readmission after surgery for spinal metastases. Circ J (in press)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLau D, Chan AK, Theologis AA et al (2016) Costs and readmission rates for the resection of primary and metastatic spinal tumors. J Neurosurg Spine 25:366\u0026ndash;378\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiggins JPT, Thompson SG, Deeks JJ et al (2003) Measuring inconsistency in meta-analyses. BMJ 327:557\u0026ndash;560\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiggins JPT, Thomas J, Chandler J et al (eds) (2023) Cochrane Handbook for Systematic Reviews of Interventions, version 6.4. Cochrane\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuksanapruksa P, Buchowski JM, Zebala LP et al (2017) Perioperative complications of spinal metastases surgery. Clin Spine Surg 30:4\u0026ndash;13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHussain AK, Cheung ZB, Vig KS et al (2019) Hypoalbuminemia as an independent risk factor for perioperative complications following surgical decompression of spinal metastases. Global Spine J 9:321\u0026ndash;330\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarhade AV, Vasudeva VS, Dasenbrock HH et al (2016) Thirty-day readmission and reoperation after surgery for spinal tumors. Neurosurg Focus 41:E5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWan X, Wang W, Liu J et al (2014) Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol 14:135\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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":"spinal metastases, readmission, risk factors, meta-analysis, systematic review, spine surgery","lastPublishedDoi":"10.21203/rs.3.rs-9284951/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9284951/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eNo prior meta-analysis has quantified unplanned readmission after surgery for spinal metastases. This study aimed to estimate the pooled 30-day readmission incidence and to identify independently associated risk factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePubMed, Embase, Cochrane CENTRAL, Scopus, and Web of Science were systematically searched from January 2010 to March 2026. Studies reporting readmission after open surgery for spinal metastases in adults were included. Pooled incidence was estimated using Freeman-Tukey double arcsine transformation with DerSimonian-Laird random-effects modelling; restricted maximum likelihood (REML) estimation was used as sensitivity analysis. Risk factors were synthesised narratively or pooled where feasible. Quality was assessed using the Newcastle-Ottawa Scale; evidence certainty using GRADE.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFourteen studies (8,132 patients for incidence pooling) met inclusion criteria. Seven studies (8,132 patients) contributed to incidence pooling. The pooled 30-day readmission incidence was 16.0% (95% CI: 12.2\u0026ndash;20.3%; I\u0026sup2; = 93.2%). Single-centre studies (k\u0026thinsp;=\u0026thinsp;5) showed 13.9% (I\u0026sup2; = 0%), whereas database studies (k\u0026thinsp;=\u0026thinsp;2) showed 20.4% (p for subgroup difference\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Prior spinal radiation was the only poolable risk factor (pooled effect 1.79; 95% CI: 1.21\u0026ndash;2.63; k\u0026thinsp;=\u0026thinsp;2). Comorbidity burden consistently increased risk across four studies (OR 1.25\u0026ndash;2.54) with dose\u0026ndash;response. Other significant factors included diabetes, prolonged operative time, postoperative complications, and lung primary tumour. The pooled 90-day incidence was 31.2% (95% CI: 26.9\u0026ndash;35.6%; k\u0026thinsp;=\u0026thinsp;7). Evidence certainty was very low (GRADE).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eApproximately one in six patients is readmitted within 30 days of spinal metastasis surgery, a rate substantially exceeding that of general spine surgery. Comorbidity burden, prior spinal radiation, and poor functional status are the most consistently identified risk factors. These findings provide an evidence base for targeted preoperative risk stratification and structured discharge planning in this vulnerable population.\u003c/p\u003e","manuscriptTitle":"Risk Factors and Incidence of Unplanned Hospital Readmission After Surgery for Spinal Metastases: A Systematic Review and Meta-Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 09:41:28","doi":"10.21203/rs.3.rs-9284951/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"02a45b7e-2a17-4106-b7c2-a52ee09105d2","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T08:53:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 09:41:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9284951","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9284951","identity":"rs-9284951","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}