1Department of Neurosurgery, Johns Hopkins University School of Medicine.
2Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, Maryland; and.
J Neurosurg. 2020 May 1;134(5):1466-1471. doi: 10.3171/2020.2.JNS20181. Print 2021 May 1.
The Chiari Severity Index (CSI) and points-based algorithm of Thakar et al. are two prognostic tools that have been developed to predict the likelihood of improvement after suboccipital decompression in adult patients with Chiari malformation type I (CM-I). This study aimed to externally validate and critically evaluate these algorithms in the interest of guiding the development of improved prediction systems.
A consecutive cohort of CM-I patients undergoing suboccipital decompression between September 2006 and September 2018 were included. The CSI and Thakar point score were computed for all patients, and associations with improvement were analyzed. The ability of both prediction systems to predict improvement as measured by different Chicago Chiari Outcome Scale (CCOS) cutoffs was assessed using receiver operating curve analysis. Post hoc correlations between the algorithms and different CCOS subcomponents were also assessed.
The surgical cohort was composed of 149 adult CM-I patients, of whom 39 (26%) had a syrinx. Most patients experienced improvement after surgery (80% CCOS ≥ 13; 96% CCOS ≥ 11). The proportion of patients improving decreased with increasing CSI, but the results were not statistically significant (p = 0.246). No statistically significant difference in the mean Thakar point score was identified between improved and nonimproved patients using both CCOS cutoffs (p = 0.246 for a cutoff of 13 and p = 0.480 for a cutoff of 11). The CSI had a poor ability in identifying improved patients at a CCOS cutoff of 13 (area under the curve [AUC] 0.582) and 11 (AUC 0.646). The Thakar point score similarly had poor discrimination at a cutoff of 13 (AUC 0.467) and 11 (AUC 0.646). Neither algorithm had significant correlation with any of the CCOS subcomponents except for CSI and nonpain symptom improvement (coefficient = -0.273, p = 0.001).
Previously published algorithms failed to provide prediction value with regard to clinically meaningful improvement following suboccipital decompression in adult CM-I patients. Future models and practical scoring systems are still required to improve the decision-making process.
Chiari 严重程度指数（CSI）和 Thakar 等人的基于点数的算法是两种预测工具，旨在预测成人 Chiari 畸形 I 型（CM-I）患者行颅后窝减压术后改善的可能性。本研究旨在对这些算法进行外部验证和批判性评估，以指导改善预测系统的开发。
纳入 2006 年 9 月至 2018 年 9 月间接受颅后窝减压术的连续队列 CM-I 患者。计算所有患者的 CSI 和 Thakar 评分，并分析与改善的关联。使用受试者工作特征曲线分析评估两种预测系统在不同芝加哥 Chiari 结局量表（CCOS）截断值下预测改善的能力。还评估了算法与不同 CCOS 亚成分之间的事后相关性。
手术队列由 149 例成年 CM-I 患者组成，其中 39 例（26%）有脊髓空洞症。大多数患者术后改善（80% CCOS≥13；96% CCOS≥11）。随着 CSI 的增加，改善患者的比例降低，但结果无统计学意义（p=0.246）。使用两种 CCOS 截断值，改善和未改善患者的 Thakar 评分平均值之间无统计学差异（CCOS 截断值为 13 时 p=0.246，CCOS 截断值为 11 时 p=0.480）。CSI 在 CCOS 截断值为 13 时识别改善患者的能力较差（曲线下面积 [AUC] 0.582），在 CCOS 截断值为 11 时识别改善患者的能力较差（AUC 0.646）。Thakar 评分在 CCOS 截断值为 13 时（AUC 0.467）和 CCOS 截断值为 11 时（AUC 0.646）的区分能力也较差。除 CSI 和非疼痛症状改善外，两种算法均与 CCOS 的任何亚成分均无显著相关性（系数=-0.273，p=0.001）。
先前发表的算法未能为成人 CM-I 患者颅后窝减压术后的临床有意义的改善提供预测价值。仍需要未来的模型和实用评分系统来改善决策过程。
