1Department of Neurosurgery, University of Louisville, Kentucky.
2Department of Public Health Sciences, Division of Biostatistics, University of California Davis, Davis, California; and.
Neurosurg Focus. 2018 Nov 1;45(5):E10. doi: 10.3171/2018.8.FOCUS18331.
OBJECTIVEThere is increasing emphasis on patient-reported outcomes (PROs) to quantitatively evaluate quality outcomes from degenerative spine surgery. However, accurate prediction of PROs is challenging due to heterogeneity in outcome measures, patient characteristics, treatment characteristics, and methodological characteristics. The purpose of this study was to evaluate the current landscape of independently validated predictive models for PROs in elective degenerative spinal surgery with respect to study design and model generation, training, accuracy, reliability, variance, and utility.METHODSThe authors analyzed the current predictive models in PROs by performing a search of the PubMed and Ovid databases using PRISMA guidelines and a PICOS (participants, intervention, comparison, outcomes, study design) model. They assessed the common outcomes and variables used across models as well as the study design and internal validation methods.RESULTSA total of 7 articles met the inclusion criteria, including a total of 17 validated predictive models of PROs after adult degenerative spine surgery. National registry databases were used in 4 of the studies. Validation cohorts were used in 2 studies for model verification and 5 studies used other methods, including random sample bootstrapping techniques. Reported c-index values ranged from 0.47 to 0.79. Two studies report the area under the curve (0.71-0.83) and one reports a misclassification rate (9.9%). Several positive predictors, including high baseline pain intensity and disability, demonstrated high likelihood of favorable PROs.CONCLUSIONSA limited but effective cohort of validated predictive models of spine surgical outcomes had proven good predictability for PROs. Instruments with predictive accuracy can enhance shared decision-making, improve rehabilitation, and inform best practices in the setting of heterogeneous patient characteristics and surgical factors.
越来越重视患者报告的结局（PROs），以定量评估退行性脊柱手术的质量结局。然而，由于结局测量、患者特征、治疗特征和方法学特征的异质性，准确预测 PROs 具有挑战性。本研究的目的是评估目前用于选择性退行性脊柱手术的 PROs 的独立验证预测模型的现状，包括研究设计和模型生成、训练、准确性、可靠性、变异性和实用性。
作者使用 PRISMA 指南和 PICOS（参与者、干预、比较、结局、研究设计）模型，对 PubMed 和 Ovid 数据库进行了搜索，分析了 PROs 中的当前预测模型。他们评估了模型之间常用的共同结局和变量，以及研究设计和内部验证方法。
共有 7 篇文章符合纳入标准，包括总共 17 个成人退行性脊柱手术后 PROs 的验证预测模型。其中 4 项研究使用了国家登记数据库。有 2 项研究使用验证队列进行模型验证，有 5 项研究使用了其他方法，包括随机样本 bootstrap 技术。报告的 C 指数值范围为 0.47 至 0.79。有 2 项研究报告了曲线下面积（0.71-0.83），有 1 项报告了错误分类率（9.9%）。一些阳性预测因子，包括高基线疼痛强度和残疾，显示出对 PROs 的良好可能性。
有一个有限但有效的验证脊柱手术结果预测模型的队列，已证明对 PROs 具有良好的预测能力。具有预测准确性的工具可以增强共同决策，改善康复，并在患者特征和手术因素存在异质性的情况下提供最佳实践信息。
