Adams Roy, Leoutsakos Jeannie-Marie, Nowrangi Milap A, Oh Esther S, Rosenberg Paul B, Skolariki Konstantina, Yasar Sevil, Zandi Peter P, Lyketsos Constantine G
Department of Psychiatry and Behavioral Sciences Johns Hopkins University School of Medicine Baltimore Maryland USA.
Department of Computer Science Johns Hopkins University Baltimore Maryland USA.
Alzheimers Dement (N Y). 2025 Apr 24;11(2):e70070. doi: 10.1002/trc2.70070. eCollection 2025 Apr-Jun.
Dementia progression is heterogeneous and predicting who will decline quickly remains an open problem. Most work in this area has focused on volunteer-based cohorts, which are subject to recruitment biases. Instead, we examine predictors of rate of cognitive decline in cognitive assessment scores using electronic health record (EHR) data from a US memory clinic.
Data include patients with their first memory clinic visit (baseline) between January 1, 2014 and May 31, 2024. We used a discrete-time model to identify significant predictors of baseline and 6 month change in Mini-Mental State Examination (MMSE) scores (Montreal Cognitive Assessment scores were converted to MMSE equivalents for analysis). Inverse probability weighting was used to account for selection and censoring biases and values were adjusted for multiple comparisons.
The cohort included 9583 patients, of which 7113 had a baseline cognitive assessment. Average MMSE at baseline was 23.2. Variables associated with lower baseline MMSE included female sex, non-White race, Medicaid enrollment, baseline dementia diagnosis, and cholinesterase inhibitor prescription, while higher scores were associated with prior diagnoses of chronic pain or fatigue. Quicker post-baseline decline was associated with older age, dementia diagnoses, and cholinesterase inhibitor prescription, while slower decline was associated with a higher number of total prescriptions, distance from home to clinic, and Social Deprivation Index. Notably, rate of decline was not associated with mild cognitive impairment, other non-dementia cognitive impairment, or any of the comorbidities considered.
While several significant predictors were identified, the lack of associations with broad categories of comorbidities and social determinants of health suggest that finer grained predictors may be needed. Additionally, the finding that cholinesterase inhibitor prescriptions predicted quicker decline merits additional investigation in real-world samples. The model developed in this work may serve as a first step toward an EHR-based progression risk tool.
In a memory clinic setting, faster decline in Mini-Mental State Examination scores was associated with age, dementia diagnosis, and cholinesterase inhibitor or memantine prescription.Slower decline was associated with the patient's total number of prescriptions.Neither race nor ethnicity were associated with rate of decline, nor were baseline mild cognitive impairment, other non-dementia cognitive impairment, diabetes, hypertension, obesity, depression, anxiety, chronic pain/fatigue, or hearing loss.
痴呆症的进展具有异质性,预测谁会快速衰退仍是一个悬而未决的问题。该领域的大多数研究都集中在基于志愿者的队列上,而这些队列存在招募偏差。相反,我们使用美国一家记忆诊所的电子健康记录(EHR)数据,研究认知评估分数中认知衰退率的预测因素。
数据包括2014年1月1日至2024年5月31日期间首次到记忆诊所就诊(基线)的患者。我们使用离散时间模型来确定简易精神状态检查表(MMSE)分数在基线和6个月变化的显著预测因素(蒙特利尔认知评估分数已转换为MMSE等效分数用于分析)。采用逆概率加权法来处理选择和审查偏差,并对 值进行多重比较调整。
该队列包括9583名患者,其中7113名患者进行了基线认知评估。基线时的平均MMSE为23.2。与较低基线MMSE相关的变量包括女性、非白人种族、医疗补助登记、基线痴呆症诊断和胆碱酯酶抑制剂处方,而较高分数与先前诊断的慢性疼痛或疲劳有关。基线后衰退较快与年龄较大、痴呆症诊断和胆碱酯酶抑制剂处方有关,而衰退较慢与总处方数量较多、家到诊所的距离以及社会剥夺指数有关。值得注意的是,衰退率与轻度认知障碍、其他非痴呆性认知障碍或任何所考虑的合并症均无关。
虽然确定了几个显著的预测因素,但缺乏与广泛的合并症类别和健康社会决定因素的关联表明,可能需要更精细的预测因素。此外,胆碱酯酶抑制剂处方预测衰退更快这一发现值得在真实世界样本中进行进一步研究。本研究中开发的模型可能是迈向基于EHR的进展风险工具的第一步。
在记忆诊所环境中,简易精神状态检查表分数下降较快与年龄、痴呆症诊断以及胆碱酯酶抑制剂或美金刚处方有关。下降较慢与患者的总处方数量有关。种族和族裔均与衰退率无关,基线轻度认知障碍、其他非痴呆性认知障碍、糖尿病、高血压、肥胖、抑郁、焦虑、慢性疼痛/疲劳或听力损失也与衰退率无关。