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A comparison of alerting strategies for hemorrhage identification during prehospital emergency transport

机译：院前急诊转运过程中出血识别预警策略的比较

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Early and accurate identification of physiological abnormalities is one feature of intelligent decision support. The ideal analytic strategy for identifying pathological states would be highly sensitive and highly specific, with minimal latency. In the field of manufacturing, there are well-established analytic strategies for statistical process control, whereby aberrancies in a manufacturing process are detected by monitoring and analyzing the process output. These include simple thresholding, the sequential probability ratio test (SPRT), risk-adjusted SPRT, and the cumulative sum method. In this report, we applied these strategies to continuously monitored prehospital vital-sign data from trauma patients during their helicopter transport to level I trauma centers, seeking to determine whether one strategy would be superior. We found that different configurations of each alerting strategy yielded widely different performances in terms of sensitivity, specificity, and average time to alert. Yet, comparing the different investigational analytic strategies, we observed substantial overlap among their different configurations, without any one analytic strategy yielding distinctly superior performance. In conclusion, performance did not depend as much on the specific analytic strategy as much as the configuration of each strategy. This implies that any analytic strategy must be carefully configured to yield the optimal performance (i.e., the optimal balance between sensitivity, specificity, and latency) for a specific use case. Conversely, this also implies that an alerting strategy optimized for one use case (e.g., long prehospital transport times) may not necessarily yield performance data that are optimized for another clinical application (e.g., short prehospital transport times, intensive care units, etc.).
机译：早期和准确地识别生理异常是智能决策支持的功能之一。识别病理状态的理想分析策略应是高度敏感和高度特异性的，且延迟最小。在制造领域，存在用于统计过程控制的成熟分析策略，从而通过监视和分析过程输出来检测制造过程中的异常。其中包括简单的阈值确定，顺序概率比率检验（SPRT），风险调整后的SPRT和累积总和法。在本报告中，我们应用这些策略来连续监测创伤患者在将直升机运送到I级创伤中心期间的院前生命体征数据，以寻求确定一种策略是否更好。我们发现，每种警报策略的不同配置在敏感性，特异性和平均警报时间方面产生了截然不同的性能。但是，通过比较不同的研究分析策略，我们观察到它们的不同配置之间存在实质性的重叠，而没有任何一种分析策略能够产生明显优越的性能。总之，绩效并不像每个策略的配置那样取决于具体的分析策略。这意味着必须仔细配置任何分析策略，以针对特定用例产生最佳性能（即灵敏度，特异性和潜伏期之间的最佳平衡）。相反，这也意味着针对一个用例优化的警报策略（例如，较长的院前运输时间）可能不一定会产生针对另一种临床应用优化的性能数据（例如，较短的院前运输时间，重症监护室等）。 。

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