Predictive Medicine Group, Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.
Clalit Research Institute, Ramat Gan, Israel.
J Am Med Inform Assoc. 2023 Nov 17;30(12):1915-1924. doi: 10.1093/jamia/ocad154.
To determine whether data-driven family histories (DDFH) derived from linked EHRs of patients and their parents can improve prediction of patients' 10-year risk of diabetes and atherosclerotic cardiovascular disease (ASCVD).
A retrospective cohort study using data from Israel's largest healthcare organization. A random sample of 200 000 subjects aged 40-60 years on the index date (January 1, 2010) was included. Subjects with insufficient history (<1 year) or insufficient follow-up (<10 years) were excluded. Two separate XGBoost models were developed-1 for diabetes and 1 for ASCVD-to predict the 10-year risk for each outcome based on data available prior to the index date of January 1, 2010.
Overall, the study included 110 734 subject-father-mother triplets. There were 22 153 cases of diabetes (20%) and 11 715 cases of ASCVD (10.6%). The addition of parental information significantly improved prediction of diabetes risk (P < .001), but not ASCVD risk. For both outcomes, maternal medical history was more predictive than paternal medical history. A binary variable summarizing parental disease state delivered similar predictive results to the full parental EHR.
The increasing availability of EHRs for multiple family generations makes DDFH possible and can assist in delivering more personalized and precise medicine to patients. Consent frameworks must be established to enable sharing of information across generations, and the results suggest that sharing the full records may not be necessary.
DDFH can address limitations of patient self-reported family history, and it improves clinical predictions for some conditions, but not for all, and particularly among younger adults.
确定从患者及其父母的电子健康记录（EHR）中提取的数据驱动的家族史（DDFH）是否可以提高患者糖尿病和动脉粥样硬化性心血管疾病（ASCVD） 10 年风险的预测能力。
这是一项回顾性队列研究，使用了以色列最大的医疗保健组织的数据。纳入了索引日期（2010 年 1 月 1 日）年龄在 40-60 岁之间的 20 万例随机样本。排除了病史不足（<1 年）或随访不足（<10 年）的患者。根据 2010 年 1 月 1 日前的可用数据，为每个结局分别建立了两个独立的 XGBoost 模型-1 个用于糖尿病，1 个用于 ASCVD-预测 10 年风险。
总体而言，该研究共纳入了 110734 例患者-父亲-母亲三胞胎。共有 22153 例糖尿病（20%）和 11715 例 ASCVD（10.6%）。添加父母信息显著提高了糖尿病风险的预测能力（P < 0.001），但对 ASCVD 风险没有影响。对于两种结局，母亲的病史比父亲的病史更具预测性。概括父母疾病状态的二进制变量提供了与完整父母 EHR 相似的预测结果。
随着越来越多的多代家庭的 EHR 可用，DDFH 成为可能，并有助于为患者提供更个性化和更精准的医疗服务。必须建立同意框架以实现跨代信息共享，并且结果表明共享完整记录可能并非必要。
DDFH 可以解决患者自我报告的家族史的局限性，并且可以改善某些疾病的临床预测，但并非所有疾病，特别是在年轻成年人中。
