Cervical sarcopenia and frailty as complementary predictors of postoperative outcomes after odontoid fracture fixation: a retrospective cohort study in the United States

Article information

Asian Spine J. 2026;20(4):687-697
Publication date (electronic) : 2026 August 5
doi : https://doi.org/10.31616/asj.2026.0268
Department of Orthopaedics, University of Maryland Medical Center, Baltimore, MD, USA
Corresponding author: Steven Ludwig, Department of Orthopaedics, University of Maryland Medical Center, 110 South Paca Street, Suite 300, Baltimore, MD 21201, USA, Tel: +1-410-683-4101, Fax: +1-410-328-0534, E-mail: umdorthospine@gmail.com
Received 2026 March 31; Revised 2026 April 29; Accepted 2026 May 24.

Abstract

Study Design

Retrospective cohort study.

Purpose

To compare three cervical sarcopenia metrics and frailty as preoperative predictors of unfavorable postoperative outcomes following surgical fixation of odontoid fractures.

Overview of Literature

Sarcopenia is increasingly recognized as an indicator of physiologic vulnerability in spine surgery and thoracolumbar trauma. However, its prognostic significance in odontoid fractures remains unclear, and no previous study has directly compared cervical sarcopenia measurement sites in this population.

Methods

Adult patients who underwent surgical fixation of traumatic odontoid fractures at a single level I trauma center were retrospectively reviewed. Sarcopenia was quantified on perioperative computed tomography using sternocleidomastoid, prevertebral, and paraspinal cross-sectional area normalized to the C3 vertebral body area. Frailty was assessed using the 5-factor modified frailty index (mFI-5), with hypertension status confirmed through a review of prescribed medications. Associations with postoperative outcomes were evaluated using threshold-based analyses, intercorrelation testing, subgroup analysis of combined low-sarcopenia/high-frailty patients, and an exploratory Combined Sarcopenia-Frailty Score.

Results

This study included 61 patients (mean age, 61.7 years; 50.8% female). The three sarcopenia metrics were only modestly intercorrelated (Spearman rho, 0.34–0.47), with 46.7%–53.3% overlap among patients in the most sarcopenic quartiles. No individual sarcopenia or mFI-5 threshold independently predicted postoperative outcomes. However, patients in the most sarcopenic quartile on any cervical metric with an mFI-5 ≥2 (n=11) experienced higher rates of mortality, urinary tract infection, pneumonia, and nonunion. In exploratory receiver operating characteristic analysis, the Combined Sarcopenia-Frailty Score exhibited moderate discrimination for significant adverse events, with an area under the curve of 0.73 and an optimal threshold of ≥9.

Conclusions

Cervical sarcopenia and frailty were not independently associated with adverse outcomes following odontoid fracture fixation. However, their combination identified patients at substantially higher risk for mortality, complications, and nonunion, supporting a potential role for combined preoperative risk stratification (Level of Evidence: level III).

Graphical Abstract

Introduction

Among older adults, odontoid fractures are among the most clinically consequential cervical spine injuries, accounting for approximately 10%–15% of all cervical fractures and occurring more frequently than any other cervical spine fracture type in those older than 65 years [1,2]. With population aging, their incidence and related health care burden continue to increase. In the United States, hospitalization rates for C2 fracture in adults older than 84 years rose more than threefold, from 3.18 to 9.77 per 10,000 persons per year between 2000 and 2010 [3]. Despite advances in operative and nonoperative management, outcomes in older adults remain highly variable, with substantial morbidity and mortality not fully accounted for by fracture pattern or treatment strategy alone [4,5]. Consequently, increasing attention has been directed toward patient-specific factors that may better reflect physiologic vulnerability in this population.

Frailty and sarcopenia have emerged as complementary yet distinct markers of reduced physiologic reserve that may help explain variability in spine surgery outcomes beyond age alone [6,7]. In adult spine surgery and thoracolumbar trauma, both have been linked to poorer postoperative outcomes. Recent trauma literature suggests that frailty may be a stronger predictor of mortality than sarcopenia and may act synergistically with low muscle mass in particularly vulnerable patients [810]. This question is particularly pertinent in odontoid fracture management, where frailty has been shown to predict higher mortality independent of treatment approach in older adults with type II injuries [11]. However, the role of sarcopenia in this context is unclear, partly due to lack of standardized methods for measuring cervical sarcopenia. Although C3-based muscle assessments have been validated as practical surrogates for whole-body skeletal muscle mass on cervical imaging, prior work suggests that cervical cross-sectional area (CSA) may capture distinct biologic characteristics based on the muscle group studied and may not exclusively represent muscle quality [1214].

Accordingly, the utility of cervical sarcopenia in preoperative risk assessment after odontoid fracture fixation remains uncertain. It is unclear whether different C3-based muscle measurements reflect the same underlying biologic vulnerability, whether any single cervical sarcopenia metric predicts postoperative risk comparably to frailty, or whether combining sarcopenia and frailty better identifies high-risk patients. Therefore, this study aimed to (1) compare three cervical sarcopenia metrics with frailty as predictors of unfavorable postoperative outcomes following surgical fixation of odontoid fractures; (2) characterize the intercorrelation and patient overlap among these cervical sarcopenia measures; and (3) determine whether combining cervical sarcopenia and frailty improves the identification of patients at elevated risk for postoperative complications.

Materials and Methods

Study design

This retrospective cohort study was conducted at a single urban academic quaternary level I trauma center. The study was conducted following the Strengthening the Reporting of Observational Studies in Epidemiology guidelines [15] and was granted Institutional Review Board exemption (HP-00108743). Adult patients aged ≥18 years who underwent surgery for traumatic odontoid fractures between January 2015 and December 2023 were identified from the departmental surgical billing database using Current Procedural Terminology codes. All surgeries were performed by one of four fellowship-trained orthopedic spine surgeons. Patients were excluded if perioperative cervical computed tomography (CT) imaging was unavailable or if postoperative follow-up was shorter than 30 days, except in cases of mortality within that period.

Data source and collection

Demographic, clinical, surgical, and postoperative data were collected through manual review of the electronic medical record, including inpatient and outpatient documentation. Recorded variables included demographics, comorbidities, surgical characteristics, and postoperative outcomes. Charlson comorbidity index and American Society of Anesthesiologists classification were obtained from preoperative anesthesia records. Polytraumatic injuries were categorized by involved body region, including head, thoracic, abdominal, pelvic, and extremity injuries.

Sarcopenia measurement

Cervical sarcopenia was evaluated using perioperative cervical CT imaging. Axial CT images at the C3 vertebral level were analyzed within the Picture Archiving and Communication Software (PACS, eUnity; Client Outlook Inc., Waterloo, ON, Canada) to quantify CSAs of the C3 vertebral body and bilateral cervical muscle groups. This methodology was based on prior studies demonstrating that C3-level muscle measurements can serve as a practical surrogate for skeletal muscle mass on cervical imaging [12,13]. Bilateral CSA measurements were obtained for the sternocleidomastoid (SCM), prevertebral, and paraspinal muscle groups at the C3 level.

Vertebral body area (VBA)-based normalization was selected to provide an anatomic adjustment for local patient size using measurements obtained from the same axial CT image, as both cervical muscle CSA and C3 VBA were measured at the same level. This approach was intended to generate cervical region-specific morphometric ratios for cohort-relative risk stratification rather than diagnose sarcopenia using consensus whole-body criteria. Height-normalized skeletal muscle index was not used because the primary aim was to compare cervical muscle measurements obtained from routine trauma CT imaging at the anatomical region of interest.

Frailty measurement

Frailty was graded using the 5-factor modified frailty index (mFI-5) [7], which includes diabetes mellitus, congestive heart failure, hypertension requiring medication, chronic obstructive pulmonary disease or pneumonia, and preoperative dependent functional status. Hypertension status was verified through a review of preoperative medication reconciliation records. Antihypertensive therapy was identified by medication class, including angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, calcium channel blockers, beta-blockers, thiazide and loop diuretics, centrally acting agents, and direct vasodilators. The mFI-5 was derived for each patient using these verified preoperative comorbidity data.

Outcomes

Postoperative outcomes of interest included overall mortality, any complication, non-home discharge, extended length of stay, revision surgery, motor deficit, sensory deficit, urinary tract infection, pneumonia, deep vein thrombosis, pulmonary embolism, wound infection, cerebrospinal fluid leak, nonunion, and fusion failure. Extended length of stay was defined as hospitalization exceeding the cohort median of 6.5 days.

Group assignment

For each sarcopenia metric, values were stratified into quartiles relative to the study sample. Patients in the first quartile exhibited the highest ratio values, reflecting the greatest muscle bulk, whereas those in the fourth quartile had the lowest ratio values and were considered the most sarcopenic. The mFI-5 was analyzed as an ordinal variable ranging from 0 to 5. An mFI-5 threshold of ≥2 was used to identify patients with increased frailty burden, defined as the presence of at least two mFI-5 deficits. This threshold was selected because it is clinically interpretable, has been used in prior spine literature to identify frail or high-frailty patients, and minimizes sparse subgroup comparisons at higher mFI-5 values within this cohort [16,17].

Statistical analysis

Categorical outcomes were compared between threshold-defined groups using Pearson χ2 tests or Fisher exact tests when expected cell counts were small. Threshold analyses compared patients in the lowest quartile of each sarcopenia metric with the remainder of the cohort and patients with an mFI-5 ≥2 with those below this threshold. Spearman rank correlation was used to evaluate intercorrelation among the three cervical sarcopenia metrics, and overlap among the lowest-quartile sarcopenia groups was assessed descriptively. Mann-Whitney U tests were performed to compare the ability of each continuous or ordinal predictor to distinguish between patients with and without each outcome, with effect sizes reported as r.

An exploratory subgroup analysis was performed to evaluate outcomes in patients who were both in the lowest quartile of at least one of the three sarcopenia metrics and had an mFI-5 ≥2. A Combined Sarcopenia-Frailty Score was subsequently constructed as a hypothesis-generating cumulative burden metric. Each sarcopenia metric was assigned a quartile rank from 1 to 4, with 1 representing the least sarcopenic quartile and 4 representing the most sarcopenic quartile. The three quartile ranks were summed to generate a cervical sarcopenia burden component ranging from 3 to 12, and the mFI-5 score was then added directly to yield a total score ranging from 3 to 17, with higher values indicating greater combined physiologic vulnerability. The three cervical muscle metrics were summed because they demonstrated only modest intercorrelation and partially overlapping lowest-quartile groups, suggesting that they may capture related but distinct aspects of cervical muscle morphology. An unweighted ordinal approach was selected because the cohort size and number of adverse events were limited, making regression-derived weighting susceptible to overfitting. Receiver operating characteristic analysis was performed to evaluate the discriminatory performance of this combined score for the exploratory adverse event composite. Area under the curve (AUC) values with 95% bootstrap confidence intervals (CIs) based on 2,000 iterations were calculated, and the optimal score threshold was identified using the Youden index. All analyses were conducted in Python ver. 3.12 (Python Software Foundation, Wilmington, DE, USA) with SciPy ver. 1.14 (SciPy Community, Austin, TX, USA), with statistical significance defined as p<0.05.

Results

Population characteristics

Sixty-one patients met the inclusion criteria. The cohort had a mean age of 61.7±12.4 years, and 31 patients (50.8%) were female. Most patients were White (73.8%), followed by Black patients (16.4%). The mean body mass index was 27.3±7.2 kg/m2, and 13 patients (21.3%) were current smokers. Polytrauma was observed in 40 patients (65.6%), most commonly affecting the head (57.4%) and extremities (31.1%). One patient (1.6%) sustained a spinal cord injury. Patients underwent instrumentation across a mean of 1.6±1 spinal levels, and fusion procedures were performed in 56 patients (91.8%). The mean hospital length of stay was 9±10.8 days, and the mean follow-up duration was 17±21.1 months (Table 1).

Population characteristics

Predictors of interest

The SCM/VBA values ranged from 0.94 to 5.64, with a median of 2.49. Prevertebral/VBA values ranged from 0.59 to 3.33, with a median of 1.60, while paraspinal/VBA values ranged from 7.81 to 29.89, with a median of 14.30. Quartile assignment distributed 15–16 patients per quartile for each sarcopenia metric. The mFI-5 distribution was 0 in 22 patients (36.1%), 1 in 23 (37.7%), 2 in 12 (19.7%), 3 in 3 (4.9%), and 4 in 1 (1.6%), with a median score of 1. Overall, 16 patients (26.2%) demonstrated an mFI-5 score ≥2 (Table 2).

Predictors of interest in study sample

Intercorrelation of sarcopenia metrics

The 3 cervical sarcopenia metrics demonstrated only modest intercorrelation. Spearman correlation coefficients were 0.440 for SCM/VBA versus prevertebral/VBA (p<0.001), 0.468 for SCM/VBA versus paraspinal/VBA (p<0.001), and 0.335 for prevertebral/VBA versus paraspinal/VBA (p=0.008) (Table 3). Overlap between the most sarcopenic quartile groups ranged from 46.7% to 53.3%, suggesting that each metric identified only a partially overlapping subset of patients classified as sarcopenic (Table 4).

Intercorrelation of cervical sarcopenia metrics: Spearman correlation between sarcopenia metrics

Intercorrelation of cervical sarcopenia metrics: overlap of lowest quartile (Q4) groups

Threshold analysis

In threshold-based analyses, neither individual sarcopenia thresholds—defined by the most sarcopenic quartile (Q4)—nor an mFI-5 threshold of ≥2 was significantly associated with any assessed postoperative outcome (Table 5). The overall mortality rate was 3.3% (2/61), with both deaths occurring beyond 90 days postoperatively. A nonsignificant trend toward higher pneumonia rates was observed among patients with an mFI-5 ≥2 (18.8% vs. 1.8%, p=0.052).

Observed adverse events and univariate analysis of predictor thresholdsa)

Combined low-sarcopenia/high-frailty subgroup

Eleven patients (18%) demonstrated both severe cervical sarcopenia, defined by placement in the lowest quartile of at least one cervical metric and an mFI-5 ≥2. Compared with the remaining 50 patients, this subgroup demonstrated significantly greater rates of mortality (2/11 [18.2%] vs. 0/50 [0%], p=0.030), urinary tract infection (4/11 [36.4%] vs. 5/50 [10%], p=0.047), pneumonia (3/11 [27.3%] vs. 1/50 [2.0%], p=0.016), and nonunion (2/11 [18.2%] vs. 0/50 [0%], p=0.030). This subgroup accounted for all deaths and nonunions in the cohort, along with three of the four pneumonia cases and four of the nine urinary tract infections (Table 6).

Subanalysis of combined groups

Comparison of predictor discrimination

When analyzed as continuous or ordinal variables, the predictors demonstrated limited ability to discriminate most postoperative outcomes. Effect sizes were consistently small for any complication, non-home discharge, prolonged hospital stay, and fusion failure (r=0.00–0.20). Among the sarcopenia measures, prevertebral/VBA showed the strongest discrimination for mortality (p=0.022, r=0.29), followed by mFI-5 (p=0.014, r=0.32). In contrast, SCM/VBA (p=0.113) and paraspinal/VBA (p=0.280) were not significantly associated with mortality status (Table 7).

Comparison of predictive discrimination (Mann-Whitney U)

Combined Sarcopenia-Frailty Score

The Combined Sarcopenia-Frailty Score ranged from 4 to 15, with a median value of 8. In exploratory receiver operating characteristic analysis for the composite outcome of mortality, urinary tract infection, pneumonia, or nonunion—comprising 11 unique patients with at least one composite event—the score yielded an AUC of 0.73 (95% CI, 0.52–0.90). The Youden-optimal cutoff was ≥9, corresponding to a sensitivity of 73% and a specificity of 68% (J=0.41). Overall, 24 patients (39.3%) met this threshold, with event rates of 33% among patients with scores ≥9 compared with 8% among those with scores <9 (Fig. 1).

Fig. 1

Combined Sarcopenia-Frailty Score for predicting significant adverse events (mortality, urinary tract infection, pneumonia, nonunion) after surgical fixation of odontoid fractures. (A) Receiver operating characteristic curve. The Combined Score equals the sum of three cervical sarcopenia quartile ranks (Q1=most muscle to Q4=most sarcopenic; summed range, 3–12) plus the mFI-5 (range, 0–5), with each component weighted equally. A score of 9 can be reached, for example, by Q3 on all three sarcopenia metrics with no comorbidities (3+3+3+0=9), or by Q4 on one metric and Q2 on the other two with one mFI-5 comorbidity (4+2+2+1=9). The Youden optimal threshold of 9 or greater yielded 73% sensitivity and 68% specificity (J=0.41). Area under the curve (AUC)=0.73 (95% bootstrap confidence interval [CI], 0.52–0.90). (B) Score distribution by outcome, shown as a box plot with a jittered scatter overlay; each dot represents an individual patient. Patients scoring 9 or greater had a 33% event rate compared with 8% for those below the threshold.

Discussion

In the present cohort, neither individual cervical sarcopenia metrics nor the mFI-5 consistently predicted postoperative complications, non-home discharge, or prolonged hospitalization following odontoid fracture fixation. However, the modest intercorrelation among these measures should be interpreted cautiously. Because the analysis relied solely on CT-derived CSA measurements, it remains unclear whether the limited overlap among SCM, prevertebral, and paraspinal measurements reflects true regional biological heterogeneity, differences in local muscle degeneration, or the intrinsic limitations of CSA-based assessment alone. CSA measurements do not distinguish contractile muscle tissue from fatty infiltration or other qualitative degenerative changes, which may differ among cervical muscle groups. Thus, these metrics should be interpreted as noninterchangeable cervical morphometric parameters rather than definitive representations of distinct sarcopenia phenotypes. These findings suggest that isolated muscle size measurements and brief frailty indices may incompletely characterize postoperative risk in this population. This relatively more modest standalone signal is not entirely discordant with the broader spine-related literature, where the prognostic utility of sarcopenia and frailty has varied across surgical populations and appears influenced by clinical context, case acuity, and measures used to define these constructs [8,18]. For example, a 2024 meta-analysis including 24 studies and 243,453 patients reported that sarcopenia was associated with increased overall adverse events after spine surgery, although these relationships remained heterogeneous across studies [19]. By contrast, prior odontoid-specific frailty work and higher-acuity spine cohorts suggest that physiologic reserve remains clinically relevant but may not be adequately captured by a single cervical muscle metric or brief frailty assessment alone [911,20]. Collectively, these findings support the concept that postoperative vulnerability following odontoid fracture fixation is likely multifactorial and may be incompletely characterized when sarcopenia or frailty are assessed independently.

Not all cervical sarcopenia metrics appeared to reflect the same underlying risk profile in this cohort. The three C3-based measurements demonstrated modest correlation (rs=0.34–0.47), and their lowest-quartile groups overlapped by only 47%–53%, suggesting that SCM, prevertebral, and paraspinal measurements frequently identified different subsets of at-risk patients. Clinically, this suggests that cervical muscle loss in odontoid fracture patients may not occur uniformly and that different muscle groups may reflect distinct anatomic or biologic domains of vulnerability. This interpretation aligns with prior cervical imaging studies demonstrating that C3-based muscle assessment is a practical surrogate for systemic skeletal muscle mass, but that cervical muscle morphology remains heterogeneous across regions [12,13]. Other cervical studies have likewise revealed that muscle CSA alone does not fully reflect muscle quality or degeneration, particularly in the setting of fatty infiltration [14]. These findings suggest that no single cervical muscle group can be assumed to represent global cervical sarcopenia in this population.

The strongest prognostic signal in this study emerged when sarcopenia and frailty were evaluated in combination rather than separately. Although patients exhibiting both low cervical muscle mass on any metric and an mFI-5 ≥2 comprised a relatively small subgroup, they accounted for all the deaths and nonunions in the cohort and experienced substantially higher rates of major medical complications. A similar pattern was noted with the exploratory Combined Sarcopenia-Frailty Score, where a threshold of 9 or greater produced an AUC of 0.73 and identified a subgroup with a 33% rate of significant adverse events, compared with 8% below this threshold. Clinically, these findings suggest that postoperative risk after odontoid fracture fixation may be most evident when reduced muscle reserve and systemic frailty coexist, rather than when either is assessed independently. This interpretation is consistent with emerging spine trauma-related literature suggesting that frailty may outperform sarcopenia as an isolated mortality predictor, while an mFI-5 threshold of 2 or greater may synergistically interact with sarcopenia to identify patients with particularly limited physiologic reserve [10]. However, the Combined Sarcopenia-Frailty Score should be interpreted cautiously. Because the score incorporates quartile rankings from three cervical muscle groups and one mFI-5 score, it is inherently weighted toward imaging-based sarcopenia burden rather than being equally balanced between sarcopenia and frailty. This weighting was not intended to suggest that cervical muscle measurements are more clinically important than systemic frailty but rather represented an exploratory attempt to integrate multicompartment cervical muscle loss with a brief frailty assessment. In addition, because the three muscle measurements were only modestly intercorrelated, the score may incorporate partially overlapping information across metrics. Therefore, the Combined Sarcopenia-Frailty Score should be considered a hypothesis-generating risk-stratification tool that requires external validation and comparison with alternative weighting models before clinical implementation.

This study should be interpreted considering several limitations. First, the retrospective single-institution design introduces potential selection bias and limits generalizability, especially since it only includes surgically treated odontoid fractures with accessible perioperative cervical CT imaging. Second, the cohort size was modest, and several outcomes of interest were infrequent, limiting statistical power and increasing the risk of type II error when evaluating individual sarcopenia metrics or frailty in isolation. Third, cervical sarcopenia was measured using CT-based CSA normalized to the C3 VBA. While this method provides a practical same-image adjustment for local skeletal size on routine cervical trauma CT, it should be regarded as an exploratory cervical morphometric surrogate rather than a consensus diagnostic definition of sarcopenia. Major sarcopenia definitions incorporate muscle strength, physical performance, and height-normalized muscle mass indices; therefore, VBA-normalized CSA may limit comparison with established skeletal muscle index thresholds and may not fully account for variation in sex, ethnicity, or overall body habitus [21,22]. In addition, CSA-based measurements do not capture muscle quality, fatty infiltration, strength, or functional capacity [23,24]. This limitation is particularly relevant to the observed modest intercorrelation among the three cervical muscle metrics, as CSA alone cannot determine whether differences between muscle groups reflect true biological heterogeneity or unmeasured variation in muscle quality on CT. Future studies incorporating magnetic resonance imaging-based muscle quality assessment, CT attenuation, fatty infiltration grading, or functional measures may better clarify whether multicompartment cervical muscle assessment provides prognostic value beyond simple CSA measurement. Fourth, the sarcopenia quartiles and combined score threshold were derived from this cohort and should therefore be viewed as exploratory rather than definitive clinical cutoffs. The Combined Sarcopenia-Frailty Score is intrinsically weighted toward imaging-based sarcopenia burden, and the inclusion of partially correlated cervical muscle metrics may introduce overlapping information. Hence, this score should not be interpreted as a validated prediction tool without external validation and comparison with alternative normalization and weighting approaches. Finally, although the hypertension status for the mFI-5 was independently confirmed through a review of medication records to improve accuracy, frailty was still assessed using a brief administrative index rather than a more comprehensive multidomain frailty assessment. Larger multicenter studies are needed to corroborate these findings, determine whether cervical muscle quality adds incremental prognostic value, and determine whether combined sarcopenia-frailty screening can be implemented as a practical preoperative risk-stratification tool.

Conclusions

Standalone cervical sarcopenia metrics and mFI-5 were not independently associated with adverse postoperative outcomes after odontoid fracture fixation. However, because the three cervical sarcopenia measurements revealed partially distinct at-risk patients and had only a modest intercorrelation, they could not be used interchangeably. In contrast, the coexistence of low cervical muscle mass and frailty identified a subgroup of patients with elevated rates of mortality, urinary tract infection, pneumonia, and nonunion. An exploratory Combined Sarcopenia-Frailty Score threshold ≥9 may offer a framework for combining multicompartment cervical muscle burden with systemic frailty; however, its internally derived cutoff and imaging-weighted structure require external validation before clinical use.

Key Points

  • No individual cervical sarcopenia parameter or the modified frailty index (mFI-5) alone was associated with adverse outcomes after surgical fixation of odontoid fractures.

  • Three C3-level cervical sarcopenia metrics (sternocleidomastoid, prevertebral, and paraspinal cross-sectional area normalized to vertebral body area) were only modestly intercorrelated, with under 54% overlap between the lowest-quartile groups.

  • Patients with concurrent low cervical muscle mass and an mFI-5 of ≥2 demonstrated significantly higher rates of mortality, pneumonia, urinary tract infection, and nonunion.

  • An exploratory Combined Sarcopenia-Frailty Score yielded an area under the curve of 0.73 for predicting significant adverse events, with an optimal threshold of ≥9.

  • Postoperative risk after odontoid fracture fixation may be more effectively identified through combined sarcopenia-frailty assessment rather than isolated measures.

Notes

Conflict of Interest

The following potential competing interests and funding sources have been declared by the authors below: Julio Jauregui (Children: editorial or governing board), Daniel Cavanaugh (Alphatec Spine: paid consultant, stock or stock options), Eugene Koh (Alphatec Spine: stock or stock options), and Steven Ludwig (AAOS: board or committee member; Alphatec Spine: IP royalties, stock or stock options; American Board of Orthopaedic Surgery Inc.: board or committee member; American Orthopaedic Association: board or committee member; AO Spine North America Spine Fellowship Support: research support; ASIP, ISD: stock or stock options; Atlas Spine: IP royalties; Baxter: research support; Cervical Spine Research Society: board or committee member; Contemporary Spine Surgery: editorial or governing board; DePuy, A Johnson & Johnson Company: IP royalties; LSRS: board or committee member; MDC: stock or stock options; Nuvasive: IP royalties, paid consultant, paid presenter or speaker, stock or stock, options; OMEGA: research support; Smiss: board or committee member; Stryker: IP royalties; The Spine Journal: editorial or governing board). No other potential conflict of interest relevant to this article was reported.

Author Contributions

Conceptualization: SP, HP, SL, JJ. Data curation: SP, HP, RS, BA, SV, MS, AC, HaP, EH, UZ, RC. Formal analysis: SP, HP, RS, JJ. Funding acquisition: SL, JJ. Methodology: SP, SL, JJ. Project administration: SL, JJ. Visualization: SP, RS. Writing–original draft: SP, HP, RS, BA, SV. Writing–review & editing: MS, AC, HaP, EH, UZ, RC, AP, LB, DC, EK, SL, JJ. Final approval of the manuscript: all authors.

References

1. Mathkour M, Cardona JJ, Chaiyamoon A, et al. Classifications of odontoid process fractures: a systematic review and proposal of a new simplified classification system based on embryology. Cureus 2022;14:e32520. https://doi.org/10.7759/cureus.32520.
2. Nourbakhsh A, Hanson ZC. Odontoid fractures: a standard review of current concepts and treatment recommendations. J Am Acad Orthop Surg 2022;30:e561–72. https://doi.org/10.5435/JAAOS-D-21-00165.
3. Daniels AH, Arthur M, Esmende SM, Vigneswaran H, Palumbo MA. Incidence and cost of treating axis fractures in the United States from 2000 to 2010. Spine (Phila Pa 1976) 2014;39:1498–505. https://doi.org/10.1097/BRS.0000000000000417.
4. Schroeder GD, Kepler CK, Kurd MF, et al. A systematic review of the treatment of geriatric Type II odontoid fractures. Neurosurgery 2015;77(Suppl 4):S6–14. https://doi.org/10.1227/NEU.0000000000000942.
5. Graffeo CS, Perry A, Puffer RC, et al. Deadly falls: operative versus nonoperative management of Type II odontoid process fracture in octogenarians. J Neurosurg Spine 2017;26:4–9. https://doi.org/10.3171/2016.3.SPINE151202.
6. Flexman AM, Street J, Charest-Morin R. The impact of frailty and sarcopenia on patient outcomes after complex spine surgery. Curr Opin Anaesthesiol 2019;32:609–15. https://doi.org/10.1097/ACO.0000000000000759.
7. Subramaniam S, Aalberg JJ, Soriano RP, Divino CM. New 5-factor modified frailty index using American College of Surgeons NSQIP data. J Am Coll Surg 2018;226:173–81. https://doi.org/10.1016/j.jamcollsurg.2017.11.005.
8. Moskven E, Bourassa-Moreau E, Charest-Morin R, Flexman A, Street J. The impact of frailty and sarcopenia on postoperative outcomes in adult spine surgery: a systematic review of the literature. Spine J 2018;18:2354–69. https://doi.org/10.1016/j.spinee.2018.07.008.
9. Hirase T, Haghshenas V, Bratescu R, et al. Sarcopenia predicts perioperative adverse events following complex revision surgery for the thoracolumbar spine. Spine J 2021;21:1001–9. https://doi.org/10.1016/j.spinee.2021.02.001.
10. Shear BM, Chiu AK, Stombler A, et al. Comparison of sarcopenia with frailty and area deprivation index for predicting postoperative mortality and complications in thoracolumbar trauma. Clin Spine Surg 2026;39:E54–62. https://doi.org/10.1097/BSD.0000000000001812.
11. Carlstrom LP, Helal A, Perry A, Lakomkin N, Graffeo CS, Clarke MJ. Too frail is to fail: frailty portends poor outcomes in the elderly with type II odontoid fractures independent of management strategy. J Clin Neurosci 2021;93:48–53. https://doi.org/10.1016/j.jocn.2021.08.027.
12. Ufuk F, Herek D, Yuksel D. Diagnosis of sarcopenia in head and neck computed tomography: cervical muscle mass as a strong indicator of sarcopenia. Clin Exp Otorhinolaryngol 2019;12:317–24. https://doi.org/10.21053/ceo.2018.01613.
13. Bril SI, Chargi N, Wendrich AW, et al. Validation of skeletal muscle mass assessment at the level of the third cervical vertebra in patients with head and neck cancer. Oral Oncol 2021;123:105617. https://doi.org/10.1016/j.oraloncology.2021.105617.
14. Pinter ZW, Wagner S, Fredericks D, et al. Cervical paraspinal muscle fatty degeneration is not associated with muscle cross-sectional area: qualitative assessment is preferable for cervical sarcopenia. Clin Orthop Relat Res 2021;479:726–32. https://doi.org/10.1097/CORR.0000000000001621.
15. von Elm E, Altman DG, Egger M, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ 2007;335:806–8. https://doi.org/10.1136/bmj.39335.541782.AD.
16. Bowers CA, Varela S, Conlon M, et al. Comparison of the risk analysis index and the modified 5-factor frailty index in predicting 30-day morbidity and mortality after spine surgery. J Neurosurg Spine 2023;39:136–45. https://doi.org/10.3171/2023.2.SPINE221019.
17. Camino-Willhuber G, Choi J, Holc F, et al. Utility of the modified 5-items frailty index to predict complications and mortality after elective cervical, thoracic and lumbar posterior spine fusion surgery: multicentric analysis from ACS-NSQIP database. Global Spine J 2024;14:839–45. https://doi.org/10.1177/21925682221124101.
18. Charest-Morin R, Street J, Zhang H, et al. Frailty and sarcopenia do not predict adverse events in an elderly population undergoing non-complex primary elective surgery for degenerative conditions of the lumbar spine. Spine J 2018;18:245–54. https://doi.org/10.1016/j.spinee.2017.07.003.
19. Luo M, Mei Z, Tang S, et al. The impact of sarcopenia on the incidence of postoperative outcomes following spine surgery: systematic review and meta-analysis. PLoS One 2024;19:e0302291. https://doi.org/10.1371/journal.pone.0302291.
20. Bourassa-Moreau E, Versteeg A, Moskven E, et al. Sarcopenia, but not frailty, predicts early mortality and adverse events after emergent surgery for metastatic disease of the spine. Spine J 2020;20:22–31. https://doi.org/10.1016/j.spinee.2019.08.012.
21. Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing 2019;48:16–31. https://doi.org/10.1093/ageing/afy169.
22. Chen LK, Woo J, Assantachai P, et al. Asian Working Group for Sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J Am Med Dir Assoc 2020;21:300–7. https://doi.org/10.1016/j.jamda.2019.12.012.
23. Fortin M, Dobrescu O, Courtemanche M, et al. Association between paraspinal muscle morphology, clinical symptoms, and functional status in patients with degenerative cervical myelopathy. Spine (Phila Pa 1976) 2017;42:232–9. https://doi.org/10.1097/BRS.0000000000001704.
24. Fortin M, Wilk N, Dobrescu O, Martel P, Santaguida C, Weber MH. Relationship between cervical muscle morphology evaluated by MRI, cervical muscle strength and functional outcomes in patients with degenerative cervical myelopathy. Musculoskelet Sci Pract 2018;38:1–7. https://doi.org/10.1016/j.msksp.2018.07.003.

Article information Continued

Fig. 1

Combined Sarcopenia-Frailty Score for predicting significant adverse events (mortality, urinary tract infection, pneumonia, nonunion) after surgical fixation of odontoid fractures. (A) Receiver operating characteristic curve. The Combined Score equals the sum of three cervical sarcopenia quartile ranks (Q1=most muscle to Q4=most sarcopenic; summed range, 3–12) plus the mFI-5 (range, 0–5), with each component weighted equally. A score of 9 can be reached, for example, by Q3 on all three sarcopenia metrics with no comorbidities (3+3+3+0=9), or by Q4 on one metric and Q2 on the other two with one mFI-5 comorbidity (4+2+2+1=9). The Youden optimal threshold of 9 or greater yielded 73% sensitivity and 68% specificity (J=0.41). Area under the curve (AUC)=0.73 (95% bootstrap confidence interval [CI], 0.52–0.90). (B) Score distribution by outcome, shown as a box plot with a jittered scatter overlay; each dot represents an individual patient. Patients scoring 9 or greater had a 33% event rate compared with 8% for those below the threshold.

Table 1

Population characteristics

Characteristic Value
Total patients 61
Age (yr) 61.7±12.4
Sex
 Male 30 (49.2)
 Female 31 (50.8)
Race
 White 45 (73.8)
 Black 10 (16.4)
 Hispanic 3 (4.9)
 Other 3 (4.9)
Body mass index (kg/m2) 27.3±7.2
Fusion
 Yes 56 (91.8)
 No 5 (8.2)
Smoking history
 Current 13 (21.3)
 Former 18 (29.5)
 Never 30 (49.2)
Polytrauma
 None (isolated spine) 21 (34.4)
 Any 40 (65.6)
 Head 35 (57.4)
 Thoracic 6 (9.8)
 Abdominal 11 (18.0)
 Pelvic 3 (4.9)
 Extremity 19 (31.1)
Spinal cord injury
 Any 1 (1.6)
Levels instrumented
 Average 1.6±1.0
 1–2 55 (90.2)
 3–4 4 (6.6)
 5+ 2 (3.3)
Length of stays (day) 9.0±10.8
Follow-up (mo) 17.0±21.1

Values are presented as mean±standard deviation or number (%).

Table 2

Predictors of interest in study sample

Predictor No. of patients (%) Median (range)
SCM/VBA quartile
 First quartile 16 (3.18–5.64)
 Second quartile 15 (2.49–3.16)
 Third quartile 15 (1.96–2.49)
 Fourth quartile 15 (0.94–1.96)
 Median value 2.49
Prevertebral/VBA quartile
 First quartile 16 (1.98–3.33)
 Second quartile 15 (1.60–1.98)
 Third quartile 15 (1.22–1.59)
 Fourth quartile 15 (0.59–1.21)
 Median value 1.60
Paraspinal/VBA quartile
 First quartile 16 (17.03–29.89)
 Second quartile 15 (14.30–16.87)
 Third quartile 15 (12.19–14.26)
 Fourth quartile 15 (7.81–12.11)
 Median value 14.30
mFI–5 (recalculated)a)
 0 22 (36.1)
 1 23 (37.7)
 2 12 (19.7)
 3 3 (4.9)
 4 1 (1.6)
 5 0 (0.0)
 Median 1

Cervical muscle CSA/C3 VBA. Quartiles divided relative to study sample.

SCM, sternocleidomastoid; VBA, vertebral body area; mFI-5, 5-factor modified frailty index; CSA, cross-sectional area.

a)

mFI-5 recalculated with hypertension status derived from medication list review (see Methods).

Table 3

Intercorrelation of cervical sarcopenia metrics: Spearman correlation between sarcopenia metrics

SCM/VBA Prevertebral/VBA Paraspinal/VBA mFI-5
SCM/VBA - 0.440 (<0.001) 0.468 (<0.001) −0.232 (0.075)
Prevertebral/VBA - 0.335 (0.008) −0.216 (0.095)
Paraspinal/VBA - −0.104 (0.426)
mFI-5 -

Values are presented as r (p-value).

SCM, sternocleidomastoid; VBA, vertebral body area; mFI-5, 5-factor modified frailty index.

Table 4

Intercorrelation of cervical sarcopenia metrics: overlap of lowest quartile (Q4) groups

SCM/VBA Q4 (n=15) Prevertebral/VBA Q4 (n=15) Paraspinal/VBA Q4 (n=15)
SCM/VBA Q4 15 (100.0) 8 (53.3) 7 (46.7)
Prevertebral/VBA Q4 8 (53.3) 15 (100.0) 7 (46.7)
Paraspinal/VBA Q4 7 (46.7) 7 (46.7) 15 (100.0)

Values are presented as number (%).

SCM, sternocleidomastoid; VBA, vertebral body area.

Table 5

Observed adverse events and univariate analysis of predictor thresholdsa)

Adverse event Total Lowest SCM/VBA Q4 (n=15) p-value Lowest prevertebral/VBA Q4 (n=15) p-value Lowest paraspinal/VBA Q4 (n=15) p-value mFI-5 of 2+ (n=16) p-value
Overall mortality 2 (3.3) 1 (6.7) 0.434 2 (13.3) 0.057 1 (6.7) 0.434 2 (12.5) 0.066
Any complication 16 (26.2) 3 (20.0) 0.738 4 (26.7) 1.000 5 (33.3) 0.702 5 (31.2) 0.841
Non-home discharge 30 (49.2) 7 (46.7) 1.000 7 (46.7) 1.000 7 (46.7) 1.000 7 (43.8) 0.830
Extended LOS 28 (45.9) 7 (46.7) 1.000 6 (40.0) 0.818 7 (46.7) 1.000 8 (50.0) 0.928
Revision 1 (1.6) 0 (0.0) 1.000 1 (6.7) 0.246 1 (6.7) 0.246 1 (6.2) 0.262
Motor deficit 7 (11.5) 2 (13.3) 1.000 2 (13.3) 1.000 2 (13.3) 1.000 3 (18.8) 0.365
Sensory deficit 5 (8.2) 1 (6.7) 1.000 2 (13.3) 0.589 0 (0.0) 0.321 1 (6.2) 1.000
UTI 9 (14.8) 3 (20.0) 0.676 2 (13.3) 1.000 3 (20.0) 0.676 4 (25.0) 0.224
Pneumonia 4 (6.6) 2 (13.3) 0.251 1 (6.7) 1.000 1 (6.7) 1.000 3 (18.8) 0.052
DVT 3 (4.9) 1 (6.7) 1.000 0 (0.0) 0.569 1 (6.7) 1.000 1 (6.2) 1.000
PE 3 (4.9) 0 (0.0) 0.569 1 (6.7) 1.000 1 (6.7) 1.000 0 (0.0) 0.560
Wound infection 0 (0.0) 0 (0.0) 1.000 0 (0.0) 1.000 0 (0.0) 1.000 0 (0.0) 1.000
CSF leak 1 (1.6) 0 (0.0) 1.000 0 (0.0) 1.000 0 (0.0) 1.000 0 (0.0) 1.000
Nonunion 2 (3.3) 1 (6.7) 0.434 2 (13.3) 0.057 2 (13.3) 0.057 2 (12.5) 0.066
Fusion failure 6 (9.8) 2 (13.3) 0.630 1 (6.7) 1.000 2 (13.3) 0.630 2 (12.5) 0.648

Values are presented as number (%) unless otherwise stated. A p-value <0.05 is statistically significant.

SCM, sternocleidomastoid; VBA, vertebral body area; mFI-5, 5-factor modified frailty index; LOS, length of stay; UTI, urinary tract infection; DVT, deep vein thrombosis; PE, pulmonary embolism; CSF, cerebrospinal fluid leak.

a)

Thresholds defined as the most hypothesized “at-risk” quartile.

Table 6

Subanalysis of combined groups

Adverse event Others (n=50) Lowest sarcopenia Q4a) + mFI-5 2+ (n=11) p-value
Overall mortality 0 (0.0) 2 (18.2) 0.030
Any complication 11 (22.0) 5 (45.5) 0.222
Non-home discharge 24 (48.0) 6 (54.5) 0.952
Extended LOS 24 (48.0) 4 (36.4) 0.526
Revision 0 (0.0) 1 (9.1) 0.180
Motor deficit 4 (8.0) 3 (27.3) 0.103
Sensory deficit 4 (8.0) 1 (9.1) 1.000
UTI 5 (10.0) 4 (36.4) 0.047
Pneumonia 1 (2.0) 3 (27.3) 0.016
DVT 2 (4.0) 1 (9.1) 0.455
PE 3 (6.0) 0 (0.0) 1.000
Wound infection 0 (0.0) 0 (0.0) 1.000
CSF leak 1 (2.0) 0 (0.0) 1.000
Nonunion 0 (0.0) 2 (18.2) 0.030
Fusion failure 5 (10.0) 1 (9.1) 1.000

Values are presented as number (%) unless otherwise stated. Bolded values represent p-values below 0.05.

mFI-5, 5-factor modified frailty index; LOS, length of stay; UTI, urinary tract infection; DVT, deep vein thrombosis; PE, pulmonary embolism; CSF, cerebrospinal fluid leak; SCM, sternocleidomastoid; VBA, vertebral body area.

a)

Lowest sarcopenia quartile defined as fourth quartile on any of SCM/VBA, prevertebral/VBA, or paraspinal/VBA.

Table 7

Comparison of predictive discrimination (Mann-Whitney U)

Outcome SCM/VBA Prevertebral/VBA Paraspinal/VBA mFI-5
Any complication 0.657 (0.06) 0.700 (0.05) 0.941 (0.01) 0.493 (0.09)
Non-home discharge 0.549 (0.08) 0.994 (0.00) 0.264 (0.14) 0.686 (0.05)
Extended LOS 0.270 (0.14) 0.268 (0.14) 0.558 (0.08) 0.113 (0.20)
Overall mortality 0.113 (0.20) 0.022 (0.29) 0.280 (0.14) 0.014 (0.32)
Fusion failure 0.555 (0.08) 0.804 (0.03) 0.427 (0.10) 0.868 (0.02)

Values are presented as p-value (r). Bolded values represent p<0.05. Effect size r: small (0.10), medium (0.30), and large (0.50).

SCM, sternocleidomastoid; VBA, vertebral body area; mFI-5, 5-factor modified frailty index; LOS, length of stay.