Northwestern University Feinberg School of Medicine, Department of Neurological Surgery, 676 N. St. Clair Street, Suite 2210, Chicago, IL, 60611, USA.
Northwestern University Feinberg School of Medicine, Department of Orthopaedic Surgery, 676 N. St. Clair Street, Suite 1350, Chicago, IL, 60611, USA.
Clin Neurol Neurosurg. 2020 May;192:105718. doi: 10.1016/j.clineuro.2020.105718. Epub 2020 Feb 3.
Machine Learning and Artificial Intelligence (AI) are rapidly growing in capability and increasingly applied to model outcomes and complications within medicine. In spinal surgery, post-operative surgical site infections (SSIs) are a rare, yet morbid complication. This paper applied AI to predict SSIs after posterior spinal fusions.
4046 posterior spinal fusions were identified at a single academic center. A Deep Neural Network DNN classification model was trained using 35 unique input variables The model was trained and tested using cross-validation, in which the data were randomly partitioned into training n = 3034 and testing n = 1012 datasets. Stepwise multivariate regression was further used to identify actual model weights based on predictions from our trained model.
The overall rate of infection was 1.5 %. The mean area under the curve (AUC), representing the accuracy of the model, across all 300 iterations was 0.775 (95 % CI [0.767,0.782]) with a median AUC of 0.787. The positive predictive value (PPV), representing how well the model predicted SSI when a patient had SSI, over all predictions was 92.56 % with a negative predictive value (NPV), representing how well the model predicted absence of SSI when a patient did not have SSI, of 98.45 %. In analyzing relative model weights, the five highest weighted variables were Congestive Heart Failure, Chronic Pulmonary Failure, Hemiplegia/Paraplegia, Multilevel Fusion and Cerebrovascular Disease respectively. Notable factors that were protective against infection were ICU Admission, Increasing Charlson Comorbidity Score, Race (White), and being male. Minimally invasive surgery (MIS) was also determined to be mildly protective.
Machine learning and artificial intelligence are relevant and impressive tools that should be employed in the clinical decision making for patients. The variables with the largest model weights were primarily comorbidity related with the exception of multilevel fusion. Further study is needed, however, in order to draw any definitive conclusions.
机器学习和人工智能（AI）在能力上迅速发展，并越来越多地应用于医学中的模型结果和并发症。在脊柱外科中，术后手术部位感染（SSI）是一种罕见但严重的并发症。本文应用 AI 预测后路脊柱融合术后 SSI。
在一家学术中心确定了 4046 例后路脊柱融合术。使用 35 个独特的输入变量训练深度神经网络（DNN）分类模型。该模型使用交叉验证进行训练和测试，其中数据随机分为训练 n=3034 和测试 n=1012 数据集。进一步使用逐步多元回归根据我们训练的模型的预测结果确定实际模型权重。
总的感染率为 1.5%。所有 300 次迭代的平均曲线下面积（AUC），代表模型的准确性，均为 0.775（95%CI[0.767,0.782]），中位数 AUC 为 0.787。阳性预测值（PPV），代表当患者发生 SSI 时模型对 SSI 的预测能力，所有预测的 PPV 为 92.56%，阴性预测值（NPV），代表当患者未发生 SSI 时模型对 SSI 不存在的预测能力，为 98.45%。在分析相对模型权重时，权重最高的五个变量分别是充血性心力衰竭、慢性肺衰竭、偏瘫/截瘫、多节段融合和脑血管疾病。感染保护因素是 ICU 入院、增加 Charlson 合并症评分、种族（白人）和男性。微创外科（MIS）也被确定为轻度保护因素。
机器学习和人工智能是相关且令人印象深刻的工具，应在患者的临床决策中使用。权重最大的变量主要与合并症有关，多节段融合除外。然而，需要进一步研究才能得出任何明确的结论。
