PURPOSE:To assess how different diagnostic decision aids perform in terms of sensitivity, specificity, and harm. METHODS:Four diagnostic decision aids were compared, as applied to a simulated patient population: a findings-based algorithm following a linear or branched pathway, a serial threshold-based strategy, and a parallel threshold-based strategy. Headache in immune-compromised HIV patients in a developing country was used as an example. Diagnoses included cryptococcal meningitis, cerebral toxoplasmosis, tuberculous meningitis, bacterial meningitis, and malaria. Data were derived from literature and expert opinion. Diagnostic strategies' validity was assessed in terms of sensitivity, specificity, and harm related to mortality and morbidity. Sensitivity analyses and Monte Carlo simulation were performed. RESULTS:The parallel threshold-based approach led to a sensitivity of 92% and a specificity of 65%. Sensitivities of the serial threshold-based approach and the branched and linear algorithms were 47%, 47%, and 74%, respectively, and the specificities were 85%, 95%, and 96%. The parallel threshold-based approach resulted in the least harm, with the serial threshold-based approach, the branched algorithm, and the linear algorithm being associated with 1.56-, 1.44-, and 1.17-times higher harm, respectively. Findings were corroborated by sensitivity and Monte Carlo analyses. CONCLUSION:A threshold-based diagnostic approach is designed to find the optimal trade-off that minimizes expected harm, enhancing sensitivity and lowering specificity when appropriate, as in the given example of a symptom pointing to several life-threatening diseases. Findings-based algorithms, in contrast, solely consider clinical observations. A parallel workup, as opposed to a serial workup, additionally allows for all potential diseases to be reviewed, further reducing false negatives. The parallel threshold-based approach might, however, not be as good in other disease settings.

译文

目的:评估在敏感性,特异性和危害性方面不同的诊断决策辅助措施的表现。
方法:比较了四种诊断决策辅助方法,将其应用于模拟患者人群:遵循线性或分支途径的基于发现的算法,基于串行阈值的策略和基于并行阈值的策略。以发展中国家免疫受损的艾滋病毒患者的头痛为例。诊断包括隐球菌性脑膜炎,脑弓形体病,结核性脑膜炎,细菌性脑膜炎和疟疾。数据来自文献和专家意见。根据敏感性,特异性以及与死亡率和发病率相关的危害,评估了诊断策略的有效性。进行了敏感性分析和蒙特卡洛模拟。
结果:基于平行阈值的方法导致敏感性为92%,特异性为65%。基于串行阈值的方法,分支算法和线性算法的灵敏度分别为47%,47%和74%,特异性为85%,95%和96%。基于并行阈值的方法造成的危害最小,而基于串行阈值的方法,分支算法和线性算法分别造成的伤害分别高1.56倍,1.44倍和1.17倍。敏感性和蒙特卡洛分析证实了这一发现。
结论:基于阈值的诊断方法旨在找到最佳折衷方案,以便在适当的情况下最大程度地减少预期的伤害,增强敏感性并降低特异性,如在给定的症状示例中指出的,这些症状可能会危及生命。相比之下,基于结果的算法仅考虑临床观察结果。与串行检查相反,并行检查另外还允许检查所有潜在疾病,从而进一步减少假阴性。但是,基于并行阈值的方法在其他疾病环境中可能不那么理想。

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