Predicting medical complications after spine surgery: a validated model using a prospective surgical registry.
Predicting medical complications after spine surgery: a validated model using a prospective surgical registry.
复制标题
DOI：
10.1016/j.spinee.2013.10.043
复制
发表时间：
2014-02-01
期刊：
影响因子：
--
通讯作者：
Chapman JR
中科院分区：
文献类型：
--
作者：
Lee MJ;Cizik AM;Hamilton D;Chapman JR
脊柱手术后发生术后医疗并发症的可能性和可能性无疑在外科医生和患者的决策中发挥着重要作用。虽然先前的研究已经确定了相对风险和优势比的值来量化风险因素，但在咨询手术选择时，这些值可能很难转化为患者。理想情况下，预测医疗并发症的绝对风险的模型，而不是相对风险或优势比的值，将极大地促进脊柱手术安全性的讨论。到目前为止，还没有专门预测医疗并发症风险的风险分层模型。这项研究的目的是建立和验证脊柱手术中和术后医疗并发症风险的预测模型。使用记录了广泛的人口学、外科手术和并发症数据的前瞻性外科脊柱登记进行统计分析。检查的结果是医学并发症，这些并发症是事先明确定义的。这一分析是对我们之前发表的报告的统计分析的继续。使用前瞻性收集的超过1,476名患者的外科登记，这些患者具有广泛的人口学、合并症、手术和术后2年的详细并发症记录，我们先前确定了几个医疗并发症的危险因素。使用这些对数二项回归分析的贝塔系数，我们创建了一个模型来预测脊柱手术后医疗并发症的发生。我们将数据分成两个子集，用于模型的内部验证和交叉验证。我们创建了两个预测模型：一个预测任何医疗并发症的发生，另一个预测重大医疗并发症的发生。任何医疗并发症的最终预测模型的接受者操作员曲线特征为0.76，被认为是一个公平的衡量标准。任何重大医疗并发症的最终预测模型都有0.81的受试者操作曲线特征，被认为是一个很好的衡量标准。最终的模型已经上传到SpineSage.com上供使用。我们提出了一个预测脊柱手术后医疗并发症的有效模型。这个模型的价值在于，它根据患者的共病情况和手术的侵袭性，为用户提供脊柱手术后并发症的绝对百分比可能性。患者更有可能理解绝对百分比，而不是相对风险和可信区间值。像这样的模式在咨询患者和提高脊柱手术的安全性方面至关重要。此外，像这样的工具可能非常有用，特别是在医疗保健趋势向按绩效付费、质量指标和风险调整方面。为了方便这个模型的使用，我们创建了一个网站(SpineSage.com)，用户可以在网站上输入患者数据，以确定脊柱手术后出现医疗并发症的可能性。
The possibility and likelihood of a postoperative medical complication after spine surgery undoubtedly play a major role in the decision making of the surgeon and patient alike. Although prior study has determined relative risk and odds ratio values to quantify risk factors, these values may be difficult to translate to the patient during counseling of surgical options. Ideally, a model that predicts absolute risk of medical complication, rather than relative risk or odds ratio values, would greatly enhance the discussion of safety of spine surgery. To date, there is no risk stratification model that specifically predicts the risk of medical complication. The purpose of this study was to create and validate a predictive model for the risk of medical complication during and after spine surgery. Statistical analysis using a prospective surgical spine registry that recorded extensive demographic, surgical, and complication data. Outcomes examined are medical complications that were specifically defined a priori. This analysis is a continuation of statistical analysis of our previously published report. Using a prospectively collected surgical registry of more than 1,476 patients with extensive demographic, comorbidity, surgical, and complication detail recorded for 2 years after surgery, we previously identified several risk factor for medical complications. Using the beta coefficients from those log binomial regression analyses, we created a model to predict the occurrence of medical complication after spine surgery. We split our data into two subsets for internal and cross-validation of our model. We created two predictive models: one predicting the occurrence of any medical complication and the other predicting the occurrence of a major medical complication. The final predictive model for any medical complications had a receiver operator curve characteristic of 0.76, considered to be a fair measure. The final predictive model for any major medical complications had receiver operator curve characteristic of 0.81, considered to be a good measure. The final model has been uploaded for use on SpineSage.com. We present a validated model for predicting medical complications after spine surgery. The value in this model is that it gives the user an absolute percent likelihood of complication after spine surgery based on the patient’s comorbidity profile and invasiveness of surgery. Patients are far more likely to understand an absolute percentage, rather than relative risk and confidence interval values. A model such as this is of paramount importance in counseling patients and enhancing the safety of spine surgery. In addition, a tool such as this can be of great use particularly as health care trends toward pay-for-performance, quality metrics, and risk adjustment. To facilitate the use of this model, we have created a website (SpineSage.com) where users can enter in patient data to determine likelihood of medical complications after spine surgery.
登录
查看更多内容
影响因子：
2.3
作者：
Mirza, Sohail K.;Deyo, Richard A.;Goodkin, Robert
通讯作者：
Goodkin, Robert
影响因子：
--
作者：
Thoren, Emma;Hellgren, Laila;Stahle, Elisabeth
通讯作者：
Stahle, Elisabeth
影响因子：
5.3
作者：
Cizik, Amy M.;Lee, Michael J.;Mirza, Sohail K.
通讯作者：
Mirza, Sohail K.
影响因子：
4.5
作者：
Lee, Michael J.;Konodi, Mark A.;Cizik, Amy M.;Bransford, Richard J.;Bellabarba, Carlo;Chapman, Jens R.
通讯作者：
Chapman, Jens R.
影响因子：
3
作者：
Lee MJ;Konodi MA;Cizik AM;Weinreich MA;Bransford RJ;Bellabarba C;Chapman J
通讯作者：
Chapman J