Tag: factor analysis

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Are there distinct patterns of clinical deficits in cervical deformity? A discriminant analysis of health-related quality of life measures

J Neurosurg Spine 44:242–252, 2026

This clinical study analyzes preoperative health-related quality of life (HRQOL) measures in 134 adults with cervical deformity (CD) to identify distinct clinical deficit patterns and their association with radiographic morphotypes. Using factor and cluster analyses on NDI, mJOA, and SWAL-QOL items, four patient clusters emerged: sleep/fatigue, low neck disability, dysphagia with severe neck disability, and myelopathy.

These HRQOL-derived clusters correlated with specific radiographic types among severe deformities: cervicothoracic, focal, and flat-neck morphotypes. The findings support integrating patient-reported clinical dimensions into CD classification systems to guide tailored surgical planning and outcome assessment.

Distinct HRQOL Patterns: Four distinct patterns of health-related quality of life (HRQOL) deficits were identified in patients with cervical deformity: sleep/fatigue impairment, low neck disability, severe dysphagia and neck disability, and predominant myelopathy.

Cluster Analysis: Principal component and cluster analyses using NDI, mJOA, and SWAL-QOL questionnaires grouped patients into four homogeneous outcome clusters, each reflecting a unique constellation of clinical deficits.

Radiographic Associations: Each HRQOL deficit pattern was significantly associated with specific cervical deformity morphotypes: severe dysphagia/neck disability with cervicothoracic deformity, myelopathy with focal deformity, and sleep/fatigue or low disability with flat neck deformity.

Clinical Implications: Recognizing these HRQOL patterns may inform tailored management strategies, surgical planning, and prognosis for different cervical deformity subtypes.

Measurement Tools: The study utilized validated patient-reported outcome measures: Neck Disability Index (NDI), modified Japanese Orthopaedic Association (mJOA) scale, and Swallowing Quality of Life (SWAL-QOL) questionnaire.

Radiographic Parameters: Comprehensive imaging assessments included cervical and spinopelvic alignment measures, such as cervical SVA, T1 slope, C2–7 lordosis, and T1S-CL mismatch.

Demographic Factors: No significant differences in age, sex, BMI, or most comorbidities were found across clusters, except for higher rates of depression and prior spine surgery in those with the most severe clinical deficits.

Classification Framework: Findings support integrating HRQOL measures with radiographic parameters for a more comprehensive, patient-centered cervical deformity classification system.

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A novel weighted scoring system for estimating the risk of rapid growth in untreated intracranial meningiomas

J Neurosurg 127:971–980, 2017

Advances in neuroimaging techniques have led to the increased detection of asymptomatic intracranial meningiomas (IMs). Despite several studies on the natural history of IMs, a comprehensive evaluation method for estimating the growth potential of these tumors, based on the relative weight of each risk factor, has not been developed. The aim of this study was to develop a weighted scoring system that estimates the risk of rapid tumor growth to aid treatment decision making.

METHODS The authors performed a retrospective analysis of 232 patients with presumed IM who had been prospectively followed up in the absence of treatment from 1997 to 2013. Tumor volume was measured by imaging at each follow-up visit, and the growth rate was determined by regression analysis. Predictors of rapid tumor growth (defined as ≥ 2 cm3/year) were identified using a logistic regression model; each factor was awarded a score based on its own coefficient value. The probability (P) of rapid tumor growth was estimated using the following formula:

[Eq. 1]

RESULTS Fifty-nine tumors (25.4%) showed rapid growth. Tumor size (OR per cm3 1.07, p = 0.000), absence of calcification (OR 3.87, p = 0.004), peritumoral edema (OR 2.74, p = 0.025), and hyperintense or isointense signal on T2- weighted MRI (OR 3.76, p = 0.049) were predictors of tumor growth rate. In the Asan Intracranial Meningioma Scoring System (AIMSS), tumor size was categorized into 3 groups of < 2.5 cm, ≥ 2.5 to < 4.0 cm, and ≥ 4.0 cm in diameter and awarded a score of 0, 3, and 6, respectively; the parameters of calcification and peritumoral edema were categorized into 2 groups based on their presence or absence and given a score of 0 or 2 and 1 or 0, respectively; and the signal on T2-weighted MRI was categorized into 2 groups of hypointense and hyperintense/isointense and given a score of 0 or 2, respectively. The risk of rapid tumor growth was estimated to be < 10% when the total score was 0–2, 10%–50% when the total score was 3–6, and ≥ 50% when the total score was 7–11 (Hosmer-Lemeshow goodness-of-fit test, p = 0.9958). The area under the receiver operating characteristic curve was 0.86.

CONCLUSIONS The authors suggest a weighted scoring system (AIMSS) that predicts the specific probability of rapid tumor growth for patients with untreated IM. This scoring system will aid treatment decision making in clinical settings by screening out patients at high risk for rapid tumor growth.

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