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使用机器学习衍生的身体成分分析评估与转移性脊柱肿瘤手术相关的虚弱、死亡率和并发症。

Evaluating frailty, mortality, and complications associated with metastatic spine tumor surgery using machine learning-derived body composition analysis.

作者信息

Massaad Elie, Bridge Christopher P, Kiapour Ali, Fourman Mitchell S, Duvall Julia B, Connolly Ian D, Hadzipasic Muhamed, Shankar Ganesh M, Andriole Katherine P, Rosenthal Michael, Schoenfeld Andrew J, Bilsky Mark H, Shin John H

机构信息

1Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston.

2Massachusetts General Hospital and Brigham and Women's Hospital Center for Clinical Data Science, Harvard Medical School, Boston.

出版信息

J Neurosurg Spine. 2022 Feb 25;37(2):263-273. doi: 10.3171/2022.1.SPINE211284. Print 2022 Aug 1.

Abstract

OBJECTIVE

Cancer patients with spinal metastases may undergo surgery without clear assessments of prognosis, thereby impacting the optimal palliative strategy. Because the morbidity of surgery may adversely impact recovery and initiation of adjuvant therapies, evaluation of risk factors associated with mortality risk and complications is critical. Evaluation of body composition of cancer patients as a surrogate for frailty is an emerging area of study for improving preoperative risk stratification.

METHODS

To examine the associations of muscle characteristics and adiposity with postoperative complications, length of stay, and mortality in patients with spinal metastases, the authors designed an observational study of 484 cancer patients who received surgical treatment for spinal metastases between 2010 and 2019. Sarcopenia, muscle radiodensity, visceral adiposity, and subcutaneous adiposity were assessed on routinely available 3-month preoperative CT images by using a validated deep learning methodology. The authors used k-means clustering analysis to identify patients with similar body composition characteristics. Regression models were used to examine the associations of sarcopenia, frailty, and clusters with the outcomes of interest.

RESULTS

Of 484 patients enrolled, 303 had evaluable CT data on muscle and adiposity (mean age 62.00 ± 11.91 years; 57.8% male). The authors identified 2 clusters with significantly different body composition characteristics and mortality risks after spine metastases surgery. Patients in cluster 2 (high-risk cluster) had lower muscle mass index (mean ± SD 41.16 ± 7.99 vs 50.13 ± 10.45 cm2/m2), lower subcutaneous fat area (147.62 ± 57.80 vs 289.83 ± 109.31 cm2), lower visceral fat area (82.28 ± 48.96 vs 239.26 ± 98.40 cm2), higher muscle radiodensity (35.67 ± 9.94 vs 31.13 ± 9.07 Hounsfield units [HU]), and significantly higher risk of 1-year mortality (adjusted HR 1.45, 95% CI 1.05-2.01, p = 0.02) than individuals in cluster 1 (low-risk cluster). Decreased muscle mass, muscle radiodensity, and adiposity were not associated with a higher rate of complications after surgery. Prolonged length of stay (> 7 days) was associated with low muscle radiodensity (mean 30.87 vs 35.23 HU, 95% CI 1.98-6.73, p < 0.001).

CONCLUSIONS

Body composition analysis shows promise for better risk stratification of patients with spinal metastases under consideration for surgery. Those with lower muscle mass and subcutaneous and visceral adiposity are at greater risk for inferior outcomes.

摘要

目的

患有脊柱转移瘤的癌症患者可能在未对预后进行明确评估的情况下接受手术,从而影响最佳姑息治疗策略。由于手术的发病率可能对恢复和辅助治疗的启动产生不利影响,因此评估与死亡风险和并发症相关的危险因素至关重要。将癌症患者的身体成分评估作为虚弱的替代指标是改善术前风险分层的一个新兴研究领域。

方法

为了研究肌肉特征和肥胖与脊柱转移瘤患者术后并发症、住院时间和死亡率之间的关联,作者设计了一项观察性研究,纳入了2010年至2019年间接受脊柱转移瘤手术治疗的484例癌症患者。通过使用经过验证的深度学习方法,在术前3个月常规获取的CT图像上评估肌肉减少症、肌肉放射密度、内脏脂肪和皮下脂肪。作者使用k均值聚类分析来识别具有相似身体成分特征的患者。回归模型用于研究肌肉减少症、虚弱和聚类与感兴趣的结局之间的关联。

结果

在纳入的484例患者中,303例有关于肌肉和肥胖的可评估CT数据(平均年龄62.00±11.91岁;57.8%为男性)。作者识别出2个聚类,其身体成分特征和脊柱转移瘤手术后的死亡风险显著不同。聚类2(高风险聚类)中的患者肌肉质量指数较低(平均值±标准差41.16±7.99 vs 50.13±10.45 cm2/m2),皮下脂肪面积较低(147.62±57.80 vs 289.83±109.31 cm2),内脏脂肪面积较低(82.28±48.96 vs 239.26±98.40 cm2),肌肉放射密度较高(35.67±9.94 vs 31.13±9.07亨氏单位[HU]),与聚类1(低风险聚类)中的个体相比,1年死亡率风险显著更高(调整后HR 1.45,95%CI 1.05 - 2.01,p = 0.02)。肌肉质量、肌肉放射密度和肥胖的降低与术后并发症发生率较高无关。住院时间延长(>7天)与低肌肉放射密度相关(平均值30.87 vs 35.23 HU,95%CI 1.98 - 6.73,p < 0.001)。

结论

身体成分分析有望为考虑手术的脊柱转移瘤患者进行更好的风险分层。肌肉质量以及皮下和内脏脂肪较低的患者预后较差的风险更大。

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