Abstract：
OBJECTIVE: There is limited consensus regarding management of spinal epidural abscesses (SEAs), particularly in patients without neurologic deficits. Several models have been created to predict failure of medical management in patients with SEA. We evaluate the external validity of 5 predictive models in an independent cohort of patients with SEA.
METHODS: One hundred seventy-six patients with SEA between 2010 and 2019 at our institution were identified, and variables relevant to each predictive model were collected. Published prediction models were used to assign probability of medical management failure to each patient. Predicted probabilities of medical failure and actual patient outcomes were used to create receiver operating characteristic (ROC) curves, with the area under the receiver operating characteristic curve used to quantify a model\'s discriminative ability. Calibration curves were plotted using predicted probabilities and actual outcomes. The Spiegelhalter z-test was used to determine adequate model calibration.
RESULTS: One model (Kim et al) demonstrated good discriminative ability and adequate model calibration in our cohort (ROC = 0.831, P value = 0.83). Parameters included in the model were age >65, diabetes, methicillin-resistant Staphylococcus aureus infection, and neurologic impairment. Four additional models did not perform well for discrimination or calibration metrics (Patel et al, ROC = 0.580, P ≤ 0.0001; Shah et al, ROC = 0.653, P ≤ 0.0001; Baum et al, ROC = 0.498, P ≤ 0.0001; Page et al, ROC = 0.534, P ≤ 0.0001).
CONCLUSIONS: Only 1 published predictive model demonstrated acceptable discrimination and calibration in our cohort, suggesting limited generalizability of the evaluated models. Multi-institutional data may facilitate the development of widely applicable models to predict medical management failure in patients with SEA.
METHODS: One hundred seventy-six patients with SEA between 2010 and 2019 at our institution were identified, and variables relevant to each predictive model were collected. Published prediction models were used to assign probability of medical management failure to each patient. Predicted probabilities of medical failure and actual patient outcomes were used to create receiver operating characteristic (ROC) curves, with the area under the receiver operating characteristic curve used to quantify a model\'s discriminative ability. Calibration curves were plotted using predicted probabilities and actual outcomes. The Spiegelhalter z-test was used to determine adequate model calibration.
RESULTS: One model (Kim et al) demonstrated good discriminative ability and adequate model calibration in our cohort (ROC = 0.831, P value = 0.83). Parameters included in the model were age >65, diabetes, methicillin-resistant Staphylococcus aureus infection, and neurologic impairment. Four additional models did not perform well for discrimination or calibration metrics (Patel et al, ROC = 0.580, P ≤ 0.0001; Shah et al, ROC = 0.653, P ≤ 0.0001; Baum et al, ROC = 0.498, P ≤ 0.0001; Page et al, ROC = 0.534, P ≤ 0.0001).
CONCLUSIONS: Only 1 published predictive model demonstrated acceptable discrimination and calibration in our cohort, suggesting limited generalizability of the evaluated models. Multi-institutional data may facilitate the development of widely applicable models to predict medical management failure in patients with SEA.
摘要：
目的：关于脊柱硬膜外脓肿（SEA）的治疗，特别是在没有神经缺陷的患者中。已经创建了几种模型来预测SEA患者的医疗管理失败。我们在一个独立的SEA患者队列中评估了五个预测模型的外部有效性。
方法：确定了2010年至2019年在我们机构患有SEA的176例患者，并收集与每个预测模型相关的变量。已发布的预测模型用于为每位患者分配医疗管理失败的概率。使用预测的医疗失败概率和实际患者结果来创建受试者工作特征(ROC)曲线,ROC曲线下面积(AUROC)用于量化模型的辨别能力。使用预测的概率和实际结果绘制校准曲线。SpiegelhalterZ检验用于确定适当的模型校准。
结果：一个模型（Kim等人。)在我们的队列中表现出良好的判别能力和足够的模型校准(ROC=0.831，p值=0.83)。模型中包含的参数是年龄>65，糖尿病，MRSA感染,和神经损伤。另外四个模型在辨别或校准指标方面表现不佳(Patel等人。,ROC=0.580,p=<0.0001;Shah等人。,ROC=0.653,p=<0.0001;Baum等人。,ROC=0.498,p=<0.0001;Page等人。,ROC=0.534,p=<0.0001)。
结论：在我们的队列中，只有一个已发表的预测模型显示出可接受的区分和校准，表明评估模型的泛化性有限。多机构数据可能有助于开发广泛适用的模型来预测SEA患者的医疗管理失败。
方法：确定了2010年至2019年在我们机构患有SEA的176例患者，并收集与每个预测模型相关的变量。已发布的预测模型用于为每位患者分配医疗管理失败的概率。使用预测的医疗失败概率和实际患者结果来创建受试者工作特征(ROC)曲线,ROC曲线下面积(AUROC)用于量化模型的辨别能力。使用预测的概率和实际结果绘制校准曲线。SpiegelhalterZ检验用于确定适当的模型校准。
结果：一个模型（Kim等人。)在我们的队列中表现出良好的判别能力和足够的模型校准(ROC=0.831，p值=0.83)。模型中包含的参数是年龄>65，糖尿病，MRSA感染,和神经损伤。另外四个模型在辨别或校准指标方面表现不佳(Patel等人。,ROC=0.580,p=<0.0001;Shah等人。,ROC=0.653,p=<0.0001;Baum等人。,ROC=0.498,p=<0.0001;Page等人。,ROC=0.534,p=<0.0001)。
结论：在我们的队列中，只有一个已发表的预测模型显示出可接受的区分和校准，表明评估模型的泛化性有限。多机构数据可能有助于开发广泛适用的模型来预测SEA患者的医疗管理失败。
