Personalized Diabetes Management Using Electronic Medical Records
Personalized Diabetes Management Using Electronic Medical Records
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DOI：
10.2337/dc16-0826
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发表时间：
2017-02-01
期刊：
影响因子：
16.2
通讯作者：
Zhuo, Ying Daisy
中科院分区：
文献类型：
--
作者：
Bertsimas, Dimitris;Kallus, Nathan;Zhuo, Ying Daisy
目的目前治疗2型糖尿病的临床指南没有根据患者的具体因素进行区分。我们提出了一种数据驱动的个性化糖尿病管理算法，相对于护理标准改善了健康结果。研究设计和方法我们基于1999年至2014年波士顿医疗中心10,806名2型糖尿病患者的电子病历，模拟了13种药物治疗下的结果。对于每一次患者访问，我们使用k近邻方法分析了在替代治疗下的结果范围。选择邻居是为了最大限度地提高患者个体特征和病史的相似性，这些特征和病史最能预测健康结果。如果切换方案的预期改善超过阈值，则推荐算法指定具有最佳预测结果的方案。结果在测试集中的48,140例患者就诊中，算法的建议反映了68.2%的就诊观察到的护理标准。对于算法建议与护理标准不同的患者，算法下的治疗后糖化血红蛋白A(1c)(HbA(1c))的平均水平比护理标准低0.44+/-0.03%(4.8+/-60.3 mm o l/m ol)(P<；0.001)，从护理标准下的8.37%到我们算法下的7.93%(68.0-63.2 m ol/m ol)。我们的原型仪表板可视化了推荐算法，提供商可以使用它来为糖尿病护理提供信息并改善结果。
OBJECTIVE Current clinical guidelines formanaging type 2 diabetes do not differentiate based on patient-specific factors. We present a data-driven algorithm for personalized diabetes management that improves health outcomes relative to the standard of care.RESEARCH DESIGN AND METHODS We modeled outcomes under 13 pharmacological therapies based on electronic medical records from 1999 to 2014 for 10,806 patients with type 2 diabetes from Boston Medical Center. For each patient visit, we analyzed the range of outcomes under alternative care using a k-nearest neighbor approach. The neighbors were chosen to maximize similarity on individual patient characteristics and medical history that were most predictive of health outcomes. The recommendation algorithm prescribes the regimen with best predicted outcome if the expected improvement from switching regimens exceeds a threshold. We evaluated the effect of recommendations on matched patient outcomes from unseen data.RESULTS Among the 48,140 patient visits in the test set, the algorithm's recommendation mirrored the observed standard of care in 68.2% of visits. For patient visits in which the algorithmic recommendation differed from the standard of care, the mean posttreatment glycated hemoglobin A(1c) (HbA(1c)) under the algorithm was lower than standard of care by 0.44 +/- 0.03% (4.8 +/- 6 0.3 mmol/mol) (P < 0.001), from 8.37% under the standard of care to 7.93% under our algorithm (68.0 to 63.2 mmol/mol).CONCLUSIONS A personalized approach to diabetes management yielded substantial improvements in HbA1c outcomes relative to the standard of care. Our prototyped dashboard visualizing the recommendation algorithm can be used by providers to inform diabetes care and improve outcomes.