Selection of memory clinic patients for CSF biomarker assessment can be restricted to a quarter of cases by using computerized decision support, without compromising diagnostic accuracy

借助计算机化决策支持，在不影响诊断准确性的前提下，记忆门诊患者中需进行脑脊液生物标志物检测的病例比例可缩减至四分之一。

Clicks: 346
ID: 266929
2020
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Abstract
Introduction An accurate and timely diagnosis for Alzheimer’s disease (AD) is important, both for care and research. The current diagnostic criteria allow the use of CSF biomarkers to provide pathophysiological support for the diagnosis of AD. How these criteria should be operationalized by clinicians is unclear. Tools that guide in selecting patients in which CSF biomarkers have clinical utility are needed. We evaluated computerized decision support to select patients for CSF biomarker determination. Methods We included 535 subjects (139 controls, 286 Alzheimer’s disease dementia, 82 frontotemporal dementia and 28 vascular dementia) from three clinical cohorts. Positive (AD like) and negative (normal) CSF biomarker profiles were simulated to estimate whether knowledge of CSF biomarkers would impact (confidence in) diagnosis. We applied these simulated CSF values and combined them with demographic, neuropsychology and MRI data to initiate CSF testing (computerized decision support approach). We compared proportion of CSF measurements and patients diagnosed with sufficient confidence (probability of correct class ≥0.80) based on an algorithm with scenarios without CSF (only neuropsychology, MRI and APOE), CSF according to the appropriate use criteria (AUC) and CSF for all patients. Results The computerized decision support approach recommended CSF testing in 140 (26%) patients, which yielded a diagnosis with sufficient confidence in 379 (71%) of all patients. This approach was more efficient than CSF in none (0% CSF, 308 (58%) diagnosed), CSF selected based on AUC (295 (55%) CSF, 350 (65%) diagnosed) or CSF in all (100% CSF, 348 (65%) diagnosed). Conclusions We used a computerized decision support with simulated CSF results in controls and patients with different types of dementia. This approach can support clinicians in making a balanced decision in ordering additional biomarker testing. Computer-supported prediction restricts CSF testing to only 26% of cases, without compromising diagnostic accuracy.
引言 对阿尔茨海默病（Alzheimer’s disease, AD）进行准确及时的诊断，对于临床照护和科学研究均至关重要。当前的诊断标准允许利用脑脊液（cerebrospinal fluid, CSF）生物标志物为AD的诊断提供病理生理学依据。然而，临床医生应如何在实践中具体实施这些标准尚不明确。因此，亟需能够指导筛选出CSF生物标志物具有临床应用价值的患者的工具。本研究评估了利用计算机化决策支持系统筛选患者进行CSF生物标志物检测的可行性。 方法 本研究纳入了来自三个临床队列的535名受试者（包括139名对照者、286名阿尔茨海默病痴呆患者、82名额颞叶痴呆患者以及28名血管性痴呆患者）。通过模拟阳性（AD样）和阴性（正常）CSF生物标志物特征谱，以评估获知CSF生物标志物信息是否会影响诊断（及诊断信心）。我们将这些模拟的CSF数值与人口统计学、神经心理学及MRI数据相结合，以启动CSF检测（即计算机化决策支持方法）。我们将基于算法得出的CSF检测比例以及具有充分诊断信心（正确分类概率≥0.80）的患者比例，与以下三种场景进行了对比：不进行CSF检测（仅包含神经心理学、MRI和APOE）、依据适宜使用标准（appropriate use criteria, AUC）进行CSF检测，以及对所有患者进行CSF检测。 结果 计算机化决策支持方法推荐对140名（26%）患者进行CSF检测，最终使全体患者中有379名（71%）获得了具有充分信心的诊断。该方法的效率高于完全不进行CSF检测（CSF检测率0%，确诊308名 [58%]）、基于AUC筛选CSF检测（CSF检测率295名 [55%]，确诊350名 [65%]）以及全员进行CSF检测（CSF检测率100%，确诊348名 [65%]）。 结论 我们在对照者和不同类型痴呆患者中应用了结合模拟CSF结果的计算机化决策支持系统。该方法可协助临床医生在是否开展额外的生物标志物检测时做出权衡合理的决策。计算机辅助预测将CSF检测限制在仅26%的病例中，且未对诊断准确性造成不利影响。
Reference Key
rhodius-meester2020plosselection Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hanneke F. M. Rhodius-Meester;Ingrid S. van Maurik;Juha Koikkalainen;Antti Tolonen;Kristian S. Frederiksen;Steen G. Hasselbalch;Hilkka Soininen;Sanna-Kaisa Herukka;Anne M. Remes;Charlotte E. Teunissen;Frederik Barkhof;Yolande A. L. Pijnenburg;Philip Scheltens;Jyrki Lötjönen;Wiesje M. van der Flier;
Journal PloS one
Year 2020
DOI
10.1371/journal.pone.0226784
URL
http://dx.plos.org/10.1371/journal.pone.0226784
Keywords
Magnetic resonance imaging biomarkers alzheimer's disease cerebrospinal fluid frontotemporal dementia neuropsychology vascular dementia diagnostic medicine

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