Abstract：
OBJECTIVE: There is currently no reliable tool for classifying dementia severity level based on administrative claims data. We aimed to develop a claims-based model to identify patients with severe dementia among a cohort of patients with dementia.
METHODS: Retrospective cohort study.
METHODS: We identified people living with dementia (PLWD) in US Medicare claims data linked with the Minimum Data Set (MDS) and Outcome and Assessment Information Set (OASIS).
METHODS: Severe dementia was defined based on cognitive and functional status data available in the MDS and OASIS. The dataset was randomly divided into training (70%) and validation (30%) sets, and a logistic regression model was developed to predict severe dementia using baseline (assessed in the prior year) features selected by generalized linear mixed models (GLMMs) with least absolute shrinkage and selection operator (LASSO) regression. We assessed model performance by area under the receiver operating characteristic curve (AUROC), area under precision-recall curve (AUPRC), and precision and recall at various cutoff points, including Youden Index. We compared the model performance with and without using Synthetic Minority Oversampling Technique (SMOTE) to reduce the imbalance of the dataset.
RESULTS: Our study cohort included 254,410 PLWD with 17,907 (7.0%) classified as having severe dementia. The AUROC of our primary model, without SMOTE, was 0.81 in the training and 0.80 in the validation set. In the validation set at the optimized Youden Index, the model had a sensitivity of 0.77 and specificity of 0.70. Using a SMOTE-balanced validation set, the model had an AUROC of 0.83, AUPRC of 0.80, sensitivity of 0.79, specificity of 0.74, positive predictive value of 0.75, and negative predictive value of 0.78 when at the optimized Youden Index.
CONCLUSIONS: Our claims-based algorithm to identify patients living with severe dementia can be useful for claims-based pharmacoepidemiologic and health services research.
METHODS: Retrospective cohort study.
METHODS: We identified people living with dementia (PLWD) in US Medicare claims data linked with the Minimum Data Set (MDS) and Outcome and Assessment Information Set (OASIS).
METHODS: Severe dementia was defined based on cognitive and functional status data available in the MDS and OASIS. The dataset was randomly divided into training (70%) and validation (30%) sets, and a logistic regression model was developed to predict severe dementia using baseline (assessed in the prior year) features selected by generalized linear mixed models (GLMMs) with least absolute shrinkage and selection operator (LASSO) regression. We assessed model performance by area under the receiver operating characteristic curve (AUROC), area under precision-recall curve (AUPRC), and precision and recall at various cutoff points, including Youden Index. We compared the model performance with and without using Synthetic Minority Oversampling Technique (SMOTE) to reduce the imbalance of the dataset.
RESULTS: Our study cohort included 254,410 PLWD with 17,907 (7.0%) classified as having severe dementia. The AUROC of our primary model, without SMOTE, was 0.81 in the training and 0.80 in the validation set. In the validation set at the optimized Youden Index, the model had a sensitivity of 0.77 and specificity of 0.70. Using a SMOTE-balanced validation set, the model had an AUROC of 0.83, AUPRC of 0.80, sensitivity of 0.79, specificity of 0.74, positive predictive value of 0.75, and negative predictive value of 0.78 when at the optimized Youden Index.
CONCLUSIONS: Our claims-based algorithm to identify patients living with severe dementia can be useful for claims-based pharmacoepidemiologic and health services research.
摘要：
目的：目前尚无可靠的工具根据行政索赔数据对痴呆严重程度进行分类。我们旨在开发一种基于索赔的模型，以在一组痴呆患者中识别严重痴呆患者。
方法：回顾性队列研究。
方法：我们在与最小数据集(MDS)和结果和评估信息集(OASIS)相关的美国医疗保险索赔数据中确定了痴呆症患者(PLWD)。
方法：根据MDS和OASIS的认知和功能状态数据定义重度痴呆。数据集随机分为训练集(70%)和验证集(30%)，使用具有最小绝对收缩率和选择算子(LASSO)回归的广义线性混合模型(GLMM)选择的基线(前一年评估)特征,开发了逻辑回归模型来预测重度痴呆.我们通过接收器工作特征曲线下面积(AUROC)评估模型性能，精确度-召回曲线下面积(AUPRC)，以及各个临界点的精确度和召回率，包括Youden指数。我们比较了使用和不使用合成少数过采样技术（SMOTE）来减少数据集的不平衡的模型性能。
结果：我们的研究队列包括254,410PLWD，其中17,907（7.0％）被归类为患有严重痴呆。我们主要模型的AUROC，没有SMOTE,在训练中为0.81，在验证集中为0.80。在优化的YoudenIndex的验证集中，该模型的敏感性为0.77,特异性为0.70.使用SMOTE平衡的验证集，在优化的Youden指数下,该模型的AUROC为0.83,AUPRC为0.80,敏感性为0.79,特异性为0.74,阳性预测值为0.75,阴性预测值为0.78.
结论：我们基于索赔的算法来识别患有严重痴呆的患者，对于基于索赔的药物流行病学和健康服务研究是有用的。
方法：回顾性队列研究。
方法：我们在与最小数据集(MDS)和结果和评估信息集(OASIS)相关的美国医疗保险索赔数据中确定了痴呆症患者(PLWD)。
方法：根据MDS和OASIS的认知和功能状态数据定义重度痴呆。数据集随机分为训练集(70%)和验证集(30%)，使用具有最小绝对收缩率和选择算子(LASSO)回归的广义线性混合模型(GLMM)选择的基线(前一年评估)特征,开发了逻辑回归模型来预测重度痴呆.我们通过接收器工作特征曲线下面积(AUROC)评估模型性能，精确度-召回曲线下面积(AUPRC)，以及各个临界点的精确度和召回率，包括Youden指数。我们比较了使用和不使用合成少数过采样技术（SMOTE）来减少数据集的不平衡的模型性能。
结果：我们的研究队列包括254,410PLWD，其中17,907（7.0％）被归类为患有严重痴呆。我们主要模型的AUROC，没有SMOTE,在训练中为0.81，在验证集中为0.80。在优化的YoudenIndex的验证集中，该模型的敏感性为0.77,特异性为0.70.使用SMOTE平衡的验证集，在优化的Youden指数下,该模型的AUROC为0.83,AUPRC为0.80,敏感性为0.79,特异性为0.74,阳性预测值为0.75,阴性预测值为0.78.
结论：我们基于索赔的算法来识别患有严重痴呆的患者，对于基于索赔的药物流行病学和健康服务研究是有用的。
