Cognitive Neuroscience Division, Department of Neurology, Columbia University College of Physicians and Surgeons, New York, NY, USA.
J Alzheimers Dis. 2014;38(3):661-8. doi: 10.3233/JAD-131142.
The ability to predict the length of time to death and institutionalization has strong implications for Alzheimer's disease patients and caregivers, health policy, economics, and the design of intervention studies.
To develop and validate a prediction algorithm that uses data from a single visit to estimate time to important disease endpoints for individual Alzheimer's disease patients.
Two separate study cohorts (Predictors 1, N = 252; Predictors 2, N = 254), all initially with mild Alzheimer's disease, were followed for 10 years at three research centers with semiannual assessments that included cognition, functional capacity, and medical, psychiatric, and neurologic information. The prediction algorithm was based on a longitudinal Grade of Membership model developed using the complete series of semiannually-collected Predictors 1 data. The algorithm was validated on the Predictors 2 data using data only from the initial assessment to predict separate survival curves for three outcomes.
For each of the three outcome measures, the predicted survival curves fell well within the 95% confidence intervals of the observed survival curves. Patients were also divided into quintiles for each endpoint to assess the calibration of the algorithm for extreme patient profiles. In all cases, the actual and predicted survival curves were statistically equivalent. Predictive accuracy was maintained even when key baseline variables were excluded, demonstrating the high resilience of the algorithm to missing data.
The new prediction algorithm accurately predicts time to death, institutionalization, and need for full-time care in individual Alzheimer's disease patients; it can be readily adapted to predict other important disease endpoints. The algorithm will serve an unmet clinical, research, and public health need.
预测死亡和住院时间的能力对阿尔茨海默病患者和护理人员、医疗政策、经济学以及干预研究设计具有重要意义。
开发和验证一种预测算法，该算法使用单次就诊的数据来估计个体阿尔茨海默病患者重要疾病终点的时间。
两个独立的研究队列（Predictors 1，N=252；Predictors 2，N=254），最初均为轻度阿尔茨海默病患者，在三个研究中心进行了 10 年的随访，每半年进行一次评估，包括认知、功能能力以及医疗、精神和神经信息。预测算法基于使用完整的 Predictors 1 数据系列开发的纵向成员隶属度模型。该算法在 Predictors 2 数据上进行了验证，仅使用初始评估时的数据来预测三个结局的单独生存曲线。
对于三个结局衡量指标中的每一个，预测的生存曲线均在观察到的生存曲线的 95%置信区间内。还为每个终点将患者分为五组，以评估算法对极端患者特征的校准情况。在所有情况下，实际和预测的生存曲线在统计学上都是等效的。即使排除了关键的基线变量，预测准确性也得以维持，这表明该算法对缺失数据具有很高的弹性。
新的预测算法可以准确预测个体阿尔茨海默病患者的死亡、住院和需要全职护理的时间；它可以很容易地适应预测其他重要的疾病终点。该算法将满足未满足的临床、研究和公共卫生需求。
