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on the progression and reversion of MCI，especially there version of MCI to normal cognition. Objective To construct a multi-
class classification model of MCI outcomes （reversion，stabilization and progression） and to explore the possible associated
factors of these outcomes. Methods Data were collected from the National Alzheimer's Disease Coordinating Center Unified Data
Set，involving 397 patients who were initially diagnosed with MCI during 2005—2019 with at least two follow-ups and complete
clinical and follow-up data. Patients' demographic information，physical examination，disease history，and measurement
results by assessment scales in MCI were selected for analysis. Boruta was used for feature selection. Random forest was used for
supporting the classification of MCI outcomes. A stepwise multinomial logistic regression was used to explore the associated factors
of different MCI outcomes. Results Of the 397 cases，124（31.23%） reverted to normal cognition，77（44.58%） were in
stable condition，and 96 （24.18%） progressed to dementia. In the multi-classification task，the accuracy of direct three-
class classification was 67.58%. In multiple binary classifications，the accuracies of reversion and stabilization，and reversion
and progression were above 90%. Among the hierarchical three-class classification，the accuracy based on stabilization →
reversion → progression was 84.38%. Stepwise multinomial Logistic regression analysis showed that age ≥ 80 years 〔OR=0.260，
95%CI（0.117，0.574）〕，self-reported cognitive impairment 〔OR=0.295，95%CI（0.139，0.623）〕，and decreased
Clinical Dementia Rating （CDR） scores〔OR=0.220，95%CI（0.092，0.525）〕 were associated with decreased risk of MCI
progressing to dementia，while history of stroke 〔OR=2.896，95%CI（1.370，6.122）〕，fecal incontinence 〔OR=6.556，
95%CI（1.787，24.047）〕，and higher Functional Activities Questionnaire score〔OR=1.048，95%CI（1.003，1.095）〕
were associated with increased risk of MCI progressing to dementia. Decreased probability of reversion from MCI to normal
cognition was related to age ≥ 80 years 〔OR=0.289，95%CI（0.091，0.914）〕，obesity 〔OR=0.236，95%CI（0.075，
0.740）〕，self-reported cognitive impairment 〔OR=0.289，95%CI（0.111，0.757）〕，and higher CDR scores 〔OR=0.015，
95%CI（0.003，0.089）〕，while increased probability of reversion from MCI to normal cognition was related to higher MMSE
score 〔OR=1.708，95%CI（1.428，2.043）〕 and higher numbers of correct naming of animals 〔OR=1.139，95%CI（1.046，
1.240）〕. Conclusion The outcome of MCI patients is affected by multiple factors. Advanced age，obesity，history of stroke，
fecal incontinence，self-reported cognitive impairment and reduced functional activity were important factors influencing MCI
progression and reversion.
【Key words】 Cognitive dysfunction；Cognition；Reversion；Alzheimer disease；Outcome；Muti-classification；
Root cause analysis

作为正常老化和痴呆之间的中间状态，轻度认知 本研究创新之处：
障碍（mild cognitive impairment，MCI）人群的认知轨 关于轻度认知障碍（MCI）的转归研究，绝大多
迹高度可变，其转归一直受到学界的高度重视。多项 数将 MCI 稳定及进展作为 MCI 转归的参考，并未专
研究表明，每年有 10%~15% 的 MCI 患者进展为痴呆， 门讨论 MCI 逆转的转归。本研究基于机器学习和多
近 24% 的 MCI 患者逆转为认知正常（normal cognition， 分类 Logistic 回归方法，探究 MCI 患者三种不同转归
NC） ［1］ 。鲜少有关于 MCI 人群的双向转归，尤其是 （逆转、稳定及进展）的影响因素及随机森林的分类
MCI 逆转为 NC 的研究。因此，本研究基于美国公共数 性能，为 MCI 患者的健康管理干预和预防性护理提
据库——国家阿尔茨海默病症协调中心（NACC），寻 供理论指导。
找与MCI患者不同转归的重要影响因素，对其早期预防、
管理和干预有重要意义。 面知情同意书，且所有参与该研究的机构已得到当地机
1 资料与方法 构审查委员会批准。
1.1 一般资料 本研究所使用的真实数据来自 NACC 本研究筛选了 2005—2019 年初次诊断为 MCI，至
维护的统一数据集 UDS（Uniform Data Set）。该研究 少随访 2 次及以上且数据无缺失的对象，最终纳入 397
队列始于 2005 年，对美国老龄研究所的受试者进行 例 MCI 患者，所有入选研究对象均由神经心理专业评
前瞻性、标准化和纵向临床评估，约间隔 1 年随访 测人员按照要求采用统一制式量表进行评测，评测时应
1 次，至今已随访 15 年，在每位受试者每年的随访 用标准化语言，并排除具有视力、听力、书写能力障碍
中，由临床医生填写 16 份数据收集表，涵盖受试者 不能完成量表评测的患者。
人口统计学、神经系统检查、临床诊断等资料，其致 1.2 评估工具 基本信息包括年龄、性别、受教育年限、
力于为痴呆的治疗和预防提供途径 ［2］ 。NACC 详细信 婚姻状况、独立能力、惯用手、吸烟（总吸烟量超过
息、参与者纳入与排除标准以及完整的研究方案详见 100 支）、佩戴眼镜、佩戴助听器、自我报告认知障碍
https：//naccdata.org/。所有受试者在进入研究时签署书 和他人报告认知障碍。身体检查包括身高、体质量、心率、