PDF(Pubmed)
Abstract：
BACKGROUND: Identifying and managing serious spinal pathology (SSP) such as cauda equina syndrome or spinal infection in patients presenting with low back pain is challenging. Traditional red flag questioning is increasingly criticized, and previous studies show that many clinicians lack confidence in managing patients presenting with red flags. Improving decision-making and reducing the variability of care for these patients is a key priority for clinicians and researchers.
OBJECTIVE: We aimed to improve SSP identification by constructing and validating a decision support tool using a Bayesian network (BN), which is an artificial intelligence technique that combines current evidence and expert knowledge.
METHODS: A modified RAND appropriateness procedure was undertaken with 16 experts over 3 rounds, designed to elicit the variables, structure, and conditional probabilities necessary to build a causal BN. The BN predicts the likelihood of a patient with a particular presentation having an SSP. The second part of this study used an established framework to direct a 4-part validation that included comparison of the BN with consensus statements, practice guidelines, and recent research. Clinical cases were entered into the model and the results were compared with clinical judgment from spinal experts who were not involved in the elicitation. Receiver operating characteristic curves were plotted and area under the curve were calculated for accuracy statistics.
RESULTS: The RAND appropriateness procedure elicited a model including 38 variables in 3 domains: risk factors (10 variables), signs and symptoms (17 variables), and judgment factors (11 variables). Clear consensus was found in the risk factors and signs and symptoms for SSP conditions. The 4-part BN validation demonstrated good performance overall and identified areas for further development. Comparison with available clinical literature showed good overall agreement but suggested certain improvements required to, for example, 2 of the 11 judgment factors. Case analysis showed that cauda equina syndrome, space-occupying lesion/cancer, and inflammatory condition identification performed well across the validation domains. Fracture identification performed less well, but the reasons for the erroneous results are well understood. A review of the content by independent spinal experts backed up the issues with the fracture node, but the BN was otherwise deemed acceptable.
CONCLUSIONS: The RAND appropriateness procedure and validation framework were successfully implemented to develop the BN for SSP. In comparison with other expert-elicited BN studies, this work goes a step further in validating the output before attempting implementation. Using a framework for model validation, the BN showed encouraging validity and has provided avenues for further developing the outputs that demonstrated poor accuracy. This study provides the vital first step of improving our ability to predict outcomes in low back pain by first considering the problem of SSP.
UNASSIGNED: RR2-10.2196/21804.
OBJECTIVE: We aimed to improve SSP identification by constructing and validating a decision support tool using a Bayesian network (BN), which is an artificial intelligence technique that combines current evidence and expert knowledge.
METHODS: A modified RAND appropriateness procedure was undertaken with 16 experts over 3 rounds, designed to elicit the variables, structure, and conditional probabilities necessary to build a causal BN. The BN predicts the likelihood of a patient with a particular presentation having an SSP. The second part of this study used an established framework to direct a 4-part validation that included comparison of the BN with consensus statements, practice guidelines, and recent research. Clinical cases were entered into the model and the results were compared with clinical judgment from spinal experts who were not involved in the elicitation. Receiver operating characteristic curves were plotted and area under the curve were calculated for accuracy statistics.
RESULTS: The RAND appropriateness procedure elicited a model including 38 variables in 3 domains: risk factors (10 variables), signs and symptoms (17 variables), and judgment factors (11 variables). Clear consensus was found in the risk factors and signs and symptoms for SSP conditions. The 4-part BN validation demonstrated good performance overall and identified areas for further development. Comparison with available clinical literature showed good overall agreement but suggested certain improvements required to, for example, 2 of the 11 judgment factors. Case analysis showed that cauda equina syndrome, space-occupying lesion/cancer, and inflammatory condition identification performed well across the validation domains. Fracture identification performed less well, but the reasons for the erroneous results are well understood. A review of the content by independent spinal experts backed up the issues with the fracture node, but the BN was otherwise deemed acceptable.
CONCLUSIONS: The RAND appropriateness procedure and validation framework were successfully implemented to develop the BN for SSP. In comparison with other expert-elicited BN studies, this work goes a step further in validating the output before attempting implementation. Using a framework for model validation, the BN showed encouraging validity and has provided avenues for further developing the outputs that demonstrated poor accuracy. This study provides the vital first step of improving our ability to predict outcomes in low back pain by first considering the problem of SSP.
UNASSIGNED: RR2-10.2196/21804.
摘要：
背景：识别和管理严重的脊柱病理（SSP），如马尾神经综合征或脊柱感染，在出现下腰痛的患者中具有挑战性。传统的红旗提问越来越受到批评，和以前的研究表明，许多临床医生缺乏信心，在管理患者出现危险信号。改善决策并减少这些患者的护理变异性是临床医生和研究人员的关键优先事项。
目标：我们旨在通过使用贝叶斯网络（BN）构建和验证决策支持工具来改善SSP识别，这是一种结合了当前证据和专家知识的人工智能技术。
方法：对16位专家进行了3轮改进的RAND适当性程序，旨在引出变量，结构,和建立因果BN所必需的条件概率。BN预测具有特定表现的患者具有SSP的可能性。本研究的第二部分使用了一个既定的框架来指导一个四部分验证，包括比较BN与共识声明，实践指南，和最近的研究。将临床病例输入模型，并将结果与未参与激发的脊柱专家的临床判断进行比较。绘制接收器工作特性曲线，并计算曲线下面积以进行准确性统计。
结果：RAND适当性过程引出了一个模型，该模型包括3个领域的38个变量：风险因素（10个变量），体征和症状（17个变量），和判断因素(11个变量)。在SSP疾病的危险因素以及体征和症状方面发现了明确的共识。四部分BN验证总体上表现良好，并确定了进一步开发的领域。与现有临床文献的比较显示出良好的总体一致性，但建议需要进行某些改进，例如,11个判断因素中的2个。病例分析显示马尾综合征,占位性病变/癌症，和炎症状况识别在验证领域表现良好。裂缝识别效果较差，但是错误结果的原因是很清楚的。独立脊柱专家对内容的审查支持了骨折结节的问题，但国阵在其他方面被认为是可以接受的。
结论：成功实施了RAND适当性程序和验证框架，以开发用于SSP的BN。与其他专家引发的BN研究相比，这项工作在尝试实现之前进一步验证输出。使用模型验证的框架，BN显示出令人鼓舞的有效性，并为进一步开发准确性较差的输出提供了途径。这项研究提供了通过首先考虑SSP问题来提高我们预测下腰痛结果的能力的重要第一步。
■RR2-10.2196/21804。
目标：我们旨在通过使用贝叶斯网络（BN）构建和验证决策支持工具来改善SSP识别，这是一种结合了当前证据和专家知识的人工智能技术。
方法：对16位专家进行了3轮改进的RAND适当性程序，旨在引出变量，结构,和建立因果BN所必需的条件概率。BN预测具有特定表现的患者具有SSP的可能性。本研究的第二部分使用了一个既定的框架来指导一个四部分验证，包括比较BN与共识声明，实践指南，和最近的研究。将临床病例输入模型，并将结果与未参与激发的脊柱专家的临床判断进行比较。绘制接收器工作特性曲线，并计算曲线下面积以进行准确性统计。
结果：RAND适当性过程引出了一个模型，该模型包括3个领域的38个变量：风险因素（10个变量），体征和症状（17个变量），和判断因素(11个变量)。在SSP疾病的危险因素以及体征和症状方面发现了明确的共识。四部分BN验证总体上表现良好，并确定了进一步开发的领域。与现有临床文献的比较显示出良好的总体一致性，但建议需要进行某些改进，例如,11个判断因素中的2个。病例分析显示马尾综合征,占位性病变/癌症，和炎症状况识别在验证领域表现良好。裂缝识别效果较差，但是错误结果的原因是很清楚的。独立脊柱专家对内容的审查支持了骨折结节的问题，但国阵在其他方面被认为是可以接受的。
结论：成功实施了RAND适当性程序和验证框架，以开发用于SSP的BN。与其他专家引发的BN研究相比，这项工作在尝试实现之前进一步验证输出。使用模型验证的框架，BN显示出令人鼓舞的有效性，并为进一步开发准确性较差的输出提供了途径。这项研究提供了通过首先考虑SSP问题来提高我们预测下腰痛结果的能力的重要第一步。
■RR2-10.2196/21804。
