Comparison of predictive models for the selection of high-complexity patients
Comparison of predictive models for the selection of high-complexity patients
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DOI：
10.1016/j.gaceta.2017.06.003
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发表时间：
2019-01-01
期刊：
影响因子：
1.9
通讯作者：
Sanchez-Janariz, Hilda
中科院分区：
文献类型：
--
作者：
Estupinan-Ramirez, Marcos;Tristancho-Ajamil, Rita;Sanchez-Janariz, Hilda
目的：比较临床风险组（CRG）与校正的死亡率组（AMG）复杂性权重的一致性。以确定哪一个是最好的预测病人入院。为了优化用于选择0.5%的患者的更高的复杂性，将包括在一个干预protocol.Method的方法：横断面分析研究在18个加那利群岛的健康领域，385,049公民参加，使用社会人口学变量从健康卡;诊断和使用从初级卫生保健电子记录（PCHR）和基本最低限度的医院数据集获得的卫生保健资源; PCHR中记录的功能状态，以及通过电子处方系统开具的药物。根据这些数据估计了地层之间的相关性。评估了每个分层因子预测患者入院的能力，并构建了预测优化模型。结果：权重复杂性分层因子之间的一致性很强（rho = 0.735），复杂性类别之间的相关性中等（加权kappa = 0.515）。AMG复杂性权重预测患者入院的效果优于CRG（AUC：0.696 [0.695-0.697] vs 0.692 [0.691-0.693]）。其他预测变量被添加到AMG的重量，获得最佳的AUC（0.708 [0.707-0.708]）的模型组成的AMG，性别，年龄，Pfeiffer和Barthel量表，再入院和数量的规定therapeutic groups.Conclusions：强烈的一致性之间的分层，和较高的预测能力，从AMG的入院，这可以通过增加其他方面。(C)2017年SESPAS。由Elsevier Espana出版，S.L.U.
Objective: To compare the concordance of complexity weights between Clinical Risk Groups (CRG) and Adjusted Morbidity Groups (AMG). To determine which one is the best predictor of patient admission. To optimise the method used to select the 0.5% of patients of higher complexity that will be included in an intervention protocol.Method: Cross-sectional analytical study in 18 Canary Island health areas, 385,049 citizens were enrolled, using sociodemographic variables from health cards; diagnoses and use of healthcare resources obtained from primary health care electronic records (PCHR) and the basic minimum set of hospital data; the functional status recorded in the PCHR, and the drugs prescribed through the electronic prescription system. The correlation between stratifiers was estimated from these data. The ability of each stratifier to predict patient admissions was evaluated and prediction optimisation models were constructed.Results: Concordance between weights complexity stratifiers was strong (rho = 0.735) and the correlation between categories of complexity was moderate (weighted kappa = 0.515). AMG complexity weight predicts better patient admission than CRG (AUC: 0.696 [0.695-0.697] versus 0.692 [0.691-0.693]). Other predictive variables were added to the AMG weight, obtaining the best AUC (0.708 [0.707-0.708]) the model composed by AMG, sex, age, Pfeiffer and Barthel scales, re-admissions and number of prescribed therapeutic groups.Conclusions: strong concordance was found between stratifiers, and higher predictive capacity for admission from AMG, which can be increased by adding other dimensions. (C) 2017 SESPAS. Published by Elsevier Espana, S.L.U.