Abstract：
Intramedullary spinal cord tumors are challenging to resect, and their postoperative neurological outcomes are often difficult to predict, with few studies assessing this outcome.
We reviewed the medical records of all patients surgically treated for Intramedullary spinal cord tumors at our multisite tertiary care institution (Mayo Clinic Arizona, Mayo Clinic Florida, Mayo Clinic Rochester) between June 2002 and May 2020. Variables that were significant in the univariate analyses were included in a multivariate logistic regression. \"MissForest\" operating on the Random Forest algorithm, was used for data imputation, and K-prototype was used for data clustering. Heatmaps were added to show correlations between postoperative neurological deficit and all other included variables. Shapley Additive exPlanations were implemented to understand each feature\'s importance.
Our query resulted in 315 patients, with 160 meeting the inclusion criteria. There were 53 patients with astrocytoma, 66 with ependymoma, and 41 with hemangioblastoma. The mean age (standard deviation) was 42.3 (17.5), and 48.1% of patients were women (n = 77/160). Multivariate analysis revealed that pathologic grade >3 (OR = 1.55; CI = [0.67, 3.58], P = 0.046 predicted a new neurological deficit. Random Forest algorithm (supervised machine learning) found age, use of neuromonitoring, histology of the tumor, performing a midline myelotomy, and tumor location to be the most important predictors of new postoperative neurological deficits.
Tumor grade/histology, age, use of neuromonitoring, and myelotomy type appeared to be most predictive of postoperative neurological deficits. These results can be used to better inform patients of perioperative risk.
We reviewed the medical records of all patients surgically treated for Intramedullary spinal cord tumors at our multisite tertiary care institution (Mayo Clinic Arizona, Mayo Clinic Florida, Mayo Clinic Rochester) between June 2002 and May 2020. Variables that were significant in the univariate analyses were included in a multivariate logistic regression. \"MissForest\" operating on the Random Forest algorithm, was used for data imputation, and K-prototype was used for data clustering. Heatmaps were added to show correlations between postoperative neurological deficit and all other included variables. Shapley Additive exPlanations were implemented to understand each feature\'s importance.
Our query resulted in 315 patients, with 160 meeting the inclusion criteria. There were 53 patients with astrocytoma, 66 with ependymoma, and 41 with hemangioblastoma. The mean age (standard deviation) was 42.3 (17.5), and 48.1% of patients were women (n = 77/160). Multivariate analysis revealed that pathologic grade >3 (OR = 1.55; CI = [0.67, 3.58], P = 0.046 predicted a new neurological deficit. Random Forest algorithm (supervised machine learning) found age, use of neuromonitoring, histology of the tumor, performing a midline myelotomy, and tumor location to be the most important predictors of new postoperative neurological deficits.
Tumor grade/histology, age, use of neuromonitoring, and myelotomy type appeared to be most predictive of postoperative neurological deficits. These results can be used to better inform patients of perioperative risk.
摘要：
背景：髓内脊髓肿瘤（IMSCT）的切除具有挑战性，他们术后的神经结果往往很难预测，很少有研究评估这一结果。
方法：我们回顾了在我们的多中心三级护理机构（亚利桑那州梅奥诊所，佛罗里达梅奥诊所,梅奥诊所罗切斯特)，2002年6月至2020年5月。在单变量分析中显著的变量包括在多变量逻辑回归中。“MissForest”在随机森林(RF)算法上运行，用于数据填补，并使用K原型进行数据聚类。添加热图以显示术后神经功能缺损与所有其他纳入变量之间的相关性。实施SHAP（Shapley加法扩张）以了解每个功能的重要性。
结果：我们的查询结果为315名患者，160人符合纳入标准。有53例星形细胞瘤患者，66室管膜瘤，41例血管母细胞瘤.平均年龄(标准差)为42.3(17.5)，48.1%的患者为女性(n=77/160)。多因素分析显示病理分级>3（OR=1.55；CI=[0.67，3.58]，p=0.046预测新的神经缺陷。随机森林算法（监督机器学习）发现年龄，使用神经监测，肿瘤的组织学，做中线骨髓切开术,肿瘤位置是术后新的神经功能缺损的最重要预测因素。
结论：肿瘤分级/组织学，年龄,使用神经监测，和骨髓切开术类型似乎最有预测术后神经功能缺损。这些结果可用于更好地告知患者围手术期风险。
方法：我们回顾了在我们的多中心三级护理机构（亚利桑那州梅奥诊所，佛罗里达梅奥诊所,梅奥诊所罗切斯特)，2002年6月至2020年5月。在单变量分析中显著的变量包括在多变量逻辑回归中。“MissForest”在随机森林(RF)算法上运行，用于数据填补，并使用K原型进行数据聚类。添加热图以显示术后神经功能缺损与所有其他纳入变量之间的相关性。实施SHAP（Shapley加法扩张）以了解每个功能的重要性。
结果：我们的查询结果为315名患者，160人符合纳入标准。有53例星形细胞瘤患者，66室管膜瘤，41例血管母细胞瘤.平均年龄(标准差)为42.3(17.5)，48.1%的患者为女性(n=77/160)。多因素分析显示病理分级>3（OR=1.55；CI=[0.67，3.58]，p=0.046预测新的神经缺陷。随机森林算法（监督机器学习）发现年龄，使用神经监测，肿瘤的组织学，做中线骨髓切开术,肿瘤位置是术后新的神经功能缺损的最重要预测因素。
结论：肿瘤分级/组织学，年龄,使用神经监测，和骨髓切开术类型似乎最有预测术后神经功能缺损。这些结果可用于更好地告知患者围手术期风险。
