Department of Anaesthesia and Perioperative Medicine, Auckland City Hospital, Auckland, New Zealand.
Data Scientist, Orion Health, Grafton, Auckland, New Zealand.
Neurosurgery. 2020 Sep 1;87(3):E313-E320. doi: 10.1093/neuros/nyaa144.
Multivariate risk prediction models individualize prediction of adverse outcomes, assisting perioperative decision-making. There are currently no models specifically designed for the neurosurgical population.
To develop and validate a neurosurgical risk prediction model, with 30-d, 1-yr, and 2-yr mortality endpoints.
We accessed information on all adults in New Zealand who underwent neurosurgery or spinal surgery between July 1, 2011, and June 30, 2016, from an administrative database. Our dataset comprised of 18 375 participants, split randomly into derivation (75%) and validation (25%) datasets. Previously established covariates tested included American Society of Anesthesiologists physical status grade (ASA-PS), surgical acuity, operative severity, cancer status, and age. Exploratory covariates included anatomical site, gender, diabetes, trauma, ethnicity, and socioeconomic status. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct 30-d, 1-yr, and 2-yr mortality models.
Our final models included 8 covariates: age, ASA-PS grade, surgical acuity, cancer status, anatomical site, diabetes, ethnicity, and trauma. The area under the receiver operating curve for the 30-d, 1-yr, and 2-yr mortality models was 0.90, 0.91, and 0.91 indicating excellent discrimination, respectively. Calibration also showed excellent performance with McFadden's pseudo R2 statistics of 0.28, 0.37, and 0.41 and calibration plot slopes of 0.93, 0.95, and 0.94, respectively. The strongest predictors of mortality were ASA-PS 4 and 5 (30 d) and cancer (1 and 2 yr).
NZRISK-NEURO is a robust multivariate calculator created specifically for neurosurgery, enabling physicians to generate data-driven individualized risk estimates, assisting shared decision-making and perioperative planning.
多变量风险预测模型可以对不良结局进行个体化预测，有助于围手术期决策。目前还没有专门为神经外科人群设计的模型。
开发和验证一个神经外科风险预测模型，以 30 天、1 年和 2 年的死亡率为终点。
我们从一个行政数据库中获取了 2011 年 7 月 1 日至 2016 年 6 月 30 日期间所有在新西兰接受神经外科或脊柱手术的成年人的信息。我们的数据集由 18375 名参与者组成，随机分为推导（75%）和验证（25%）数据集。测试的先前确定的协变量包括美国麻醉医师协会身体状况分级（ASA-PS）、手术紧迫性、手术严重程度、癌症状态和年龄。探索性协变量包括解剖部位、性别、糖尿病、创伤、种族和社会经济地位。使用最小绝对收缩和选择算子（LASSO）回归分析构建 30 天、1 年和 2 年死亡率模型。
我们的最终模型包括 8 个协变量：年龄、ASA-PS 分级、手术紧迫性、癌症状态、解剖部位、糖尿病、种族和创伤。30 天、1 年和 2 年死亡率模型的受试者工作特征曲线下面积分别为 0.90、0.91 和 0.91，表明具有出色的区分能力。校准也表现出出色的性能，McFadden 的伪 R2 统计量分别为 0.28、0.37 和 0.41，校准图斜率分别为 0.93、0.95 和 0.94。死亡率的最强预测因子是 ASA-PS 4 和 5（30 天）和癌症（1 年和 2 年）。
NZRISK-NEURO 是一个专门为神经外科创建的强大多变量计算器，使医生能够生成基于数据的个体化风险估计，帮助做出共同决策和围手术期规划。
