Bernstein Sean, Gilson Sarah, Zhu Mengqi, Nathan Aviva G, Cui Michael, Press Valerie G, Shah Sachin, Zarei Parmida, Laiteerapong Neda, Huang Elbert S
Rush University Medical Center, Chicago, IL, United States.
Section of General Internal Medicine, Department of Medicine, University of Chicago, Chicago, IL, United States.
JMIR Aging. 2023 Nov 9;6:e44037. doi: 10.2196/44037.
Prediction models are being increasingly used in clinical practice, with some requiring patient-reported outcomes (PROs). The optimal approach to collecting the needed inputs is unknown.
Our objective was to compare mortality prediction model inputs and scores based on electronic health record (EHR) abstraction versus patient survey.
Older patients aged ≥65 years with type 2 diabetes at an urban primary care practice in Chicago were recruited to participate in a care management trial. All participants completed a survey via an electronic portal that included items on the presence of comorbid conditions and functional status, which are needed to complete a mortality prediction model. We compared the individual data inputs and the overall model performance based on the data gathered from the survey compared to the chart review.
For individual data inputs, we found the largest differences in questions regarding functional status such as pushing/pulling, where 41.4% (31/75) of participants reported difficulties that were not captured in the chart with smaller differences for comorbid conditions. For the overall mortality score, we saw nonsignificant differences (P=.82) when comparing survey and chart-abstracted data. When allocating participants to life expectancy subgroups (<5 years, 5-10 years, >10 years), differences in survey and chart review data resulted in 20% having different subgroup assignments and, therefore, discordant glucose control recommendations.
In this small exploratory study, we found that, despite differences in data inputs regarding functional status, the overall performance of a mortality prediction model was similar when using survey and chart-abstracted data. Larger studies comparing patient survey and chart data are needed to assess whether these findings are reproduceable and clinically important.
预测模型在临床实践中的应用越来越广泛，其中一些模型需要患者报告的结局（PROs）。收集所需输入数据的最佳方法尚不清楚。
我们的目的是比较基于电子健康记录（EHR）摘要与患者调查的死亡率预测模型输入数据和得分。
招募了芝加哥一家城市初级保健机构中年龄≥65岁的2型糖尿病老年患者参与一项护理管理试验。所有参与者通过电子门户完成了一项调查，其中包括完成死亡率预测模型所需的合并症存在情况和功能状态项目。我们将基于调查收集的数据与病历审查的数据进行比较，以比较个体数据输入和整体模型性能。
对于个体数据输入，我们发现功能状态问题（如推/拉）的差异最大，41.4%（31/75）的参与者报告了病历中未记录的困难，而合并症的差异较小。对于总体死亡率得分，比较调查数据和病历摘要数据时，我们发现差异不显著（P = 0.82）。在将参与者分配到预期寿命亚组（<5年、5 - 10年、>10年）时，调查数据和病历审查数据的差异导致20%的参与者有不同的亚组分配，因此血糖控制建议不一致。
在这项小型探索性研究中，我们发现，尽管功能状态的数据输入存在差异，但使用调查数据和病历摘要数据时，死亡率预测模型的整体性能相似。需要进行更大规模的研究来比较患者调查数据和病历数据，以评估这些发现是否可重复以及在临床上是否重要。
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