Lazarova Sophia, Grigorova Denitsa, Petrova-Antonova Dessislava
GATE Institute, Sofia University "St. Kliment Ohridski", 1504 Sofia, Bulgaria.
Institute of Neurobiology, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria.
Brain Sci. 2023 Jul 29;13(8):1139. doi: 10.3390/brainsci13081139.
Alzheimer's disease is an incurable disorder that accounts for up to 70% of all dementia cases. While the prevalence of Alzheimer's disease and other types of dementia has increased by more than 160% in the last 30 years, the rates of undetected cases remain critically high. The present work aims to address the underdetection of Alzheimer's disease by proposing four logistic regression models that can be used as a foundation for community-based screening tools that do not require the participation of medical professionals. Our models make use of individual clock drawing errors as well as complementary patient data that is highly available and easily collectible. All models were controlled for age, education, and gender. The discriminative ability of the models was evaluated by area under the receiver operating characteristic curve (AUC), the Hosmer-Lemeshow test, and calibration plots were used to assess calibration. Finally, decision curve analysis was used to quantify clinical utility. We found that among 10 possible CDT errors, only 3 were informative for the detection of Alzheimer's disease. Our base regression model, containing only control variables and clock drawing errors, produced an AUC of 0.825. The other three models were built as extensions of the base model with the step-wise addition of three groups of complementary data, namely cognitive features (semantic fluency score), genetic predisposition (family history of dementia), and cardio-vascular features (BMI, blood pressure). The addition of verbal fluency scores significantly improved the AUC compared to the base model (0.91 AUC). However, further additions did not make a notable difference in discriminatory power. All models showed good calibration. In terms of clinical utility, the derived models scored similarly and greatly outperformed the base model. Our results suggest that the combination of clock symmetry and clock time errors plus verbal fluency scores may be a suitable candidate for developing accessible screening tools for Alzheimer's disease. However, future work should validate our findings in larger and more diverse datasets.
阿尔茨海默病是一种无法治愈的疾病，占所有痴呆病例的70%。在过去30年中，阿尔茨海默病和其他类型痴呆症的患病率增加了160%以上，但未被检测出的病例比例仍然极高。本研究旨在通过提出四个逻辑回归模型来解决阿尔茨海默病检测不足的问题，这些模型可作为基于社区的筛查工具的基础，无需医学专业人员参与。我们的模型利用个体时钟绘图误差以及易于获取和收集的补充患者数据。所有模型均对年龄、教育程度和性别进行了控制。通过受试者工作特征曲线下面积（AUC）评估模型的判别能力，使用Hosmer-Lemeshow检验，并使用校准图评估校准情况。最后，使用决策曲线分析来量化临床效用。我们发现，在10种可能的时钟绘图误差中，只有3种对阿尔茨海默病的检测具有信息价值。我们的基础回归模型仅包含控制变量和时钟绘图误差，其AUC为0.825。其他三个模型是在基础模型的基础上逐步添加三组补充数据构建而成，即认知特征（语义流畅性得分）、遗传易感性（痴呆家族史）和心血管特征（体重指数、血压）。与基础模型相比，添加言语流畅性得分显著提高了AUC（AUC为0.91）。然而，进一步添加数据在判别能力上并没有显著差异。所有模型均显示出良好的校准。在临床效用方面，推导模型得分相似，且大大优于基础模型。我们的结果表明，时钟对称性和时钟时间误差加上言语流畅性得分的组合可能是开发阿尔茨海默病便捷筛查工具的合适选择。然而，未来的工作应在更大、更多样化的数据集中验证我们的发现。
