Background Artificial intelligence models constitute specific uses of analysis results and, therefore, necessitate evaluation of analytical performance specifications (APS) for this context specifically. The Model of End-stage Liver Disease (MELD) is a clinical prediction model based on measurements of bilirubin, creatinine, and the international normalized ratio (INR). This study evaluates the propagation of error through the MELD, to inform choice of APS for the MELD input variables. Methods A total of 6093 consecutive MELD scores and underlying analysis results were retrospectively collected. “Desirable analytical variation” based on biological variation as well as current local analytical variation was simulated onto the data set as well as onto a constructed data set, representing a worst-case scenario. Resulting changes in MELD score and risk classification were calculated. Results Biological variation-based APS in the worst-case scenario resulted in 3.26% of scores changing by ≥1 MELD point. In the patient-derived data set, the same variation resulted in 0.92% of samples changing by ≥1 MELD point, and 5.5% of samples changing risk category. Local analytical performance resulted in lower reclassification rates. Conclusions Error propagation through MELD is complex and includes population-dependent mechanisms. Biological variation-derived APS were acceptable for all uses of the MELD score. Other combinations of APS can yield equally acceptable results. This analysis exemplifies how error propagation through artificial intelligence models can become highly complex. This complexity will necessitate that both model suppliers and clinical laboratories address analytical performance specifications for the specific use case, as these may differ from performance specifications for traditional use of the analyses.

中文翻译：

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输入变量的分析性能规范：终末期肝病模型的研究


背景人工智能模型构成了分析结果的特定用途，因此需要专门针对这种情况评估分析性能规范（APS）。终末期肝病模型 (MELD) 是基于胆红素、肌酐和国际标准化比值 (INR) 测量的临床预测模型。本研究评估了 MELD 中的误差传播，以告知 MELD 输入变量的 APS 选择。方法回顾性收集6093例连续MELD评分及基础分析结果。基于生物变异以及当前局部分析变异的“理想分析变异”被模拟到数据集以及构建的数据集上，代表最坏的情况。计算了 MELD 评分和风险分类的最终变化。结果 在最坏的情况下，基于生物变异的 APS 导致 3.26% 的分数变化≥1 MELD 分。在源自患者的数据集中，相同的变化导致 0.92% 的样本改变 ≥1 MELD 点，5.5% 的样本改变风险类别。本地分析性能导致重新分类率较低。结论 通过 MELD 的错误传播很复杂，并且包括依赖于群体的机制。生物变异衍生的 APS 对于 MELD 评分的所有用途都是可接受的。 APS 的其他组合也可以产生同样可接受的结果。该分析举例说明了通过人工智能模型的错误传播如何变得高度复杂。 这种复杂性要求模型供应商和临床实验室都针对特定用例制定分析性能规范，因为这些规范可能与传统分析使用的性能规范不同。