Takahashi Paul Y, Ryu Euijung, Olson Janet E, Winkler Erin M, Hathcock Matthew A, Gupta Ruchi, Sloan Jeff A, Pathak Jyotishman, Bielinski Suzette J, Cerhan James R
Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN, USA ; Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA.
Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Int J Gen Med. 2015 Aug 11;8:247-54. doi: 10.2147/IJGM.S85473. eCollection 2015.
Hospital risk stratification models using electronic health records (EHRs) often use age and comorbid health burden. Our primary aim was to determine if quality of life or health behaviors captured in an EHR-linked biobank can predict future risk of hospitalization.
Participants in the Mayo Clinic Biobank completed self-administered questionnaires at enrollment that included quality of life and health behaviors. Participants enrolled as of December 31, 2010 were followed for one year to ascertain hospitalization. Data on comorbidities and hospitalization were derived from the Mayo Clinic EHR. Hazard ratios (HR) and 95% confidence interval (CI) were used, adjusted for age and sex. We used gradient boosting machines models to integrate multiple factors. Different models were compared using C-statistic.
Of the 8,927 eligible Mayo Clinic Biobank participants, 834 (9.3%) were hospitalized. Self-perceived health status and alcohol use had the strongest associations with risk of hospitalization. Compared to participants with excellent self-perceived health, those reporting poor/fair health had higher risk of hospitalization (HR =3.66, 95% CI 2.74-4.88). Alcohol use was inversely associated with hospitalization (HR =0.57 95% CI 0.45-0.72). The gradient boosting machines model estimated self-perceived health as the most influential factor (relative influence =16%). The predictive ability of the model based on comorbidities was slightly higher than the one based on the self-perceived health (C-statistic =0.67 vs 0.65).
This study demonstrates that self-perceived health may be an important piece of information to add to the EHR. It may be another method to determine hospitalization risk.
使用电子健康记录（EHR）的医院风险分层模型通常采用年龄和合并症健康负担。我们的主要目的是确定与EHR关联的生物样本库中记录的生活质量或健康行为能否预测未来的住院风险。
梅奥诊所生物样本库的参与者在入组时完成了自我管理的问卷，内容包括生活质量和健康行为。对截至2010年12月31日入组的参与者进行了为期一年的随访以确定是否住院。合并症和住院数据来自梅奥诊所的EHR。使用风险比（HR）和95%置信区间（CI），并对年龄和性别进行了调整。我们使用梯度提升机模型整合多个因素。使用C统计量比较不同模型。
在8927名符合条件的梅奥诊所生物样本库参与者中，834人（9.3%）住院。自我感知健康状况和饮酒与住院风险的关联最强。与自我感知健康状况极佳的参与者相比，报告健康状况差/一般的参与者住院风险更高（HR = 3.66，95% CI 2.74 - 4.88）。饮酒与住院呈负相关（HR = 0.57，95% CI 0.45 - 0.72）。梯度提升机模型将自我感知健康状况估计为最具影响力的因素（相对影响力 = 16%）。基于合并症的模型的预测能力略高于基于自我感知健康状况的模型（C统计量 = 0.67对0.65）。
本研究表明，自我感知健康状况可能是要添加到EHR中的一项重要信息。它可能是确定住院风险的另一种方法。
