Parker Scott L, Sivaganesan Ahilan, Chotai Silky, McGirt Matthew J, Asher Anthony L, Devin Clinton J
Departments of1Neurological Surgery and.
2Carolina Neurosurgery and Spine Associates, Charlotte, North Carolina.
J Neurosurg Spine. 2018 Sep;29(3):327-331. doi: 10.3171/2018.1.SPINE17505. Epub 2018 Jun 15.
OBJECTIVE Hospital readmissions lead to a significant increase in the total cost of care in patients undergoing elective spine surgery. Understanding factors associated with an increased risk of postoperative readmission could facilitate a reduction in such occurrences. The aims of this study were to develop and validate a predictive model for 90-day hospital readmission following elective spine surgery. METHODS All patients undergoing elective spine surgery for degenerative disease were enrolled in a prospective longitudinal registry. All 90-day readmissions were prospectively recorded. For predictive modeling, all covariates were selected by choosing those variables that were significantly associated with readmission and by incorporating other relevant variables based on clinical intuition and the Akaike information criterion. Eighty percent of the sample was randomly selected for model development and 20% for model validation. Multiple logistic regression analysis was performed with Bayesian model averaging (BMA) to model the odds of 90-day readmission. Goodness of fit was assessed via the C-statistic, that is, the area under the receiver operating characteristic curve (AUC), using the training data set. Discrimination (predictive performance) was assessed using the C-statistic, as applied to the 20% validation data set. RESULTS A total of 2803 consecutive patients were enrolled in the registry, and their data were analyzed for this study. Of this cohort, 227 (8.1%) patients were readmitted to the hospital (for any cause) within 90 days postoperatively. Variables significantly associated with an increased risk of readmission were as follows (OR [95% CI]): lumbar surgery 1.8 [1.1-2.8], government-issued insurance 2.0 [1.4-3.0], hypertension 2.1 [1.4-3.3], prior myocardial infarction 2.2 [1.2-3.8], diabetes 2.5 [1.7-3.7], and coagulation disorder 3.1 [1.6-5.8]. These variables, in addition to others determined a priori to be clinically relevant, comprised 32 inputs in the predictive model constructed using BMA. The AUC value for the training data set was 0.77 for model development and 0.76 for model validation. CONCLUSIONS Identification of high-risk patients is feasible with the novel predictive model presented herein. Appropriate allocation of resources to reduce the postoperative incidence of readmission may reduce the readmission rate and the associated health care costs.
目的 医院再入院导致接受择期脊柱手术患者的护理总成本显著增加。了解与术后再入院风险增加相关的因素有助于减少此类情况的发生。本研究的目的是开发并验证一个用于预测择期脊柱手术后90天内医院再入院的模型。方法 所有因退行性疾病接受择期脊柱手术的患者均纳入前瞻性纵向登记研究。前瞻性记录所有90天内的再入院情况。对于预测模型构建，通过选择那些与再入院显著相关的变量，并基于临床直觉和赤池信息准则纳入其他相关变量来选择所有协变量。随机抽取80%的样本用于模型开发，20%用于模型验证。采用贝叶斯模型平均法（BMA）进行多元逻辑回归分析，以模拟90天再入院的概率。使用训练数据集通过C统计量（即受试者工作特征曲线下面积[AUC]）评估拟合优度。使用应用于20%验证数据集的C统计量评估辨别力（预测性能）。结果 共有2803例连续患者纳入登记研究，并对其数据进行本研究分析。在该队列中，227例（8.1%）患者在术后90天内再次入院（因任何原因）。与再入院风险增加显著相关的变量如下（比值比[95%置信区间]）：腰椎手术1.8[1.1 - 2.8]，政府发放的保险2.0[1.4 - 3.0]，高血压2.1[1.4 - 3.3]，既往心肌梗死2.2[1.2 - 3.8]，糖尿病2.5[1.7 - 3.7]，以及凝血障碍3.1[1.6 - 5.8]。这些变量，以及其他事先确定为临床相关的变量，构成了使用BMA构建的预测模型中的32个输入变量。训练数据集的AUC值在模型开发时为0.77，在模型验证时为0.76。结论 使用本文提出的新型预测模型识别高危患者是可行的。合理分配资源以降低术后再入院发生率可能会降低再入院率及相关医疗保健成本。
