Institute of Data Science, National University of Singapore, 3 Research Link, #04-06, Singapore, 117602, Singapore.
Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
Sci Rep. 2022 Dec 3;12(1):20910. doi: 10.1038/s41598-022-24494-x.
Type-2 diabetes mellitus (T2DM) is a medical condition in which oral medications avail to patients to curb their hyperglycaemia after failed dietary therapy. However, individual responses to the prescribed pharmacotherapy may differ due to their clinical profiles, comorbidities, lifestyles and medical adherence. One approach is to identify similar patients within the same community to predict their likely response to the prescribed diabetes medications. This study aims to present an evidence-based diabetes medication recommendation system (DMRS) underpinned by patient similarity analytics. The DMRS was developed using 10-year electronic health records of 54,933 adult patients with T2DM from six primary care clinics in Singapore. Multiple clinical variables including patient demographics, comorbidities, laboratory test results, existing medications, and trajectory patterns of haemoglobin A (HbA) were used to identify similar patients. The DMRS was evaluated on four groups of patients with comorbidities such as hyperlipidaemia (HLD) and hypertension (HTN). Recommendations were assessed using hit ratio which represents the percentage of patients with at least one recommended sets of medication matches exactly the diabetes prescriptions in both the type and dosage. Recall, precision, and mean reciprocal ranking of the recommendation against the diabetes prescriptions in the EHR records were also computed. Evaluation against the EHR prescriptions revealed that the DMRS recommendations can achieve hit ratio of 81% for diabetes patients with no comorbidity, 84% for those with HLD, 78% for those with HTN, and 75% for those with both HLD and HTN. By considering patients' clinical profiles and their trajectory patterns of HbA, the DMRS can provide an individualized recommendation that resembles the actual prescribed medication and dosage. Such a system is useful as a shared decision-making tool to assist clinicians in selecting the appropriate medications for patients with T2DM.
2 型糖尿病（T2DM）是一种医疗状况，当饮食治疗失败后，患者可使用口服药物来控制高血糖。然而，由于个体的临床特征、合并症、生活方式和医疗依从性等因素的不同，他们对规定的药物治疗的反应可能会有所不同。一种方法是在同一社区内识别相似的患者，以预测他们对规定的糖尿病药物的可能反应。本研究旨在提出一种基于患者相似性分析的循证糖尿病药物推荐系统（DMRS）。DMRS 使用来自新加坡六个初级保健诊所的 54933 名成年 T2DM 患者的 10 年电子健康记录开发。使用了多种临床变量，包括患者人口统计学、合并症、实验室检查结果、现有药物以及血红蛋白 A（HbA）的轨迹模式，以识别相似的患者。DMRS 在四个患有合并症（如高脂血症[HLD]和高血压[HTN]）的患者组中进行了评估。推荐使用命中率进行评估，命中率表示至少有一组推荐药物与糖尿病处方在类型和剂量上完全匹配的患者的百分比。还计算了推荐与 EHR 记录中的糖尿病处方的召回率、精度和平均倒数排名。根据 EHR 处方进行评估表明，DMRS 推荐可以为无合并症的糖尿病患者达到 81%的命中率，为 HLD 患者达到 84%，为 HTN 患者达到 78%，为同时患有 HLD 和 HTN 的患者达到 75%。通过考虑患者的临床特征和 HbA 的轨迹模式，DMRS 可以提供类似于实际规定药物和剂量的个体化推荐。这种系统可用作辅助临床医生为 T2DM 患者选择适当药物的共同决策工具。
