BACKGROUND Multivariate risk prediction models individualize prediction of adverse outcomes, assisting perioperative decision-making. There are currently no models specifically designed for the neurosurgical population. OBJECTIVE To develop and validate a neurosurgical risk prediction model, with 30-d, 1-yr, and 2-yr mortality endpoints. METHODS 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. RESULTS 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). CONCLUSION 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.

中文翻译：

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神经外科围手术期死亡率多变量预测模型的开发和验证：新西兰神经外科风险工具 (NZRISK-NEURO)

背景多变量风险预测模型对不良结果进行个体化预测，有助于围手术期决策。目前没有专门为神经外科人群设计的模型。目的 开发和验证具有 30 天、1 年和 2 年死亡率终点的神经外科风险预测模型。方法 我们从行政数据库中访问了新西兰所有在 2011 年 7 月 1 日至 2016 年 6 月 30 日期间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 18 375 名参与者组成，随机分为派生 (75%) 和验证 (25%) 数据集。先前建立的协变量测试包括美国麻醉医师协会身体状况等级 (ASA-PS)、手术敏锐度、手术严重程度、癌症状况和年龄。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。最小绝对收缩和选择算子 (LASSO) 回归分析用于构建 30 天、1 年和 2 年死亡率模型。结果 我们的最终模型包括 8 个协变量：年龄、ASA-PS 等级、手术敏锐度、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曲线下面积分别为 0.90、0.91 和 0.91，表明区分度非常高。McFadden 的伪 R2 统计值为 0.28、0.37 和 0.41，校准曲线斜率分别为 0.93、0.95 和 0.94，校准也显示出出色的性能。死亡率的最强预测因子是 ASA-PS 4 和 5（30 天）和癌症（1 和 2 年）。