Noyd David H, Chen Sixia, Bailey Anna M, Janitz Amanda E, Baker Ashley A, Beasley William H, Etzold Nancy C, Kendrick David C, Kibbe Warren A, Oeffinger Kevin C
Department of Pediatrics, The University of Oklahoma Health Sciences Center, College of Medicine, Oklahoma City, Oklahoma, USA.
Department of Biostatistics and Epidemiology, The University of Oklahoma Health Sciences Center, Hudson College of Public Health, Oklahoma City, Oklahoma, USA.
Pediatr Blood Cancer. 2023 Jun 7:e30474. doi: 10.1002/pbc.30474.
Clinical informatics tools to integrate data from multiple sources have the potential to catalyze population health management of childhood cancer survivors at high risk for late heart failure through the implementation of previously validated risk calculators.
The Oklahoma cohort (n = 365) harnessed data elements from Passport for Care (PFC), and the Duke cohort (n = 274) employed informatics methods to automatically extract chemotherapy exposures from electronic health record (EHR) data for survivors 18 years old and younger at diagnosis. The Childhood Cancer Survivor Study (CCSS) late cardiovascular risk calculator was implemented, and risk groups for heart failure were compared to the Children's Oncology Group (COG) and the International Guidelines Harmonization Group (IGHG) recommendations. Analysis within the Oklahoma cohort assessed disparities in guideline-adherent care.
The Oklahoma and Duke cohorts both observed good overall concordance between the CCSS and COG risk groups for late heart failure, with weighted kappa statistics of .70 and .75, respectively. Low-risk groups showed excellent concordance (kappa > .9). Moderate and high-risk groups showed moderate concordance (kappa .44-.60). In the Oklahoma cohort, adolescents at diagnosis were significantly less likely to receive guideline-adherent echocardiogram surveillance compared with survivors younger than 13 years old at diagnosis (odds ratio [OD] 0.22; 95% confidence interval [CI]: 0.10-0.49).
Clinical informatics tools represent a feasible approach to leverage discrete treatment-related data elements from PFC or the EHR to successfully implement previously validated late cardiovascular risk prediction models on a population health level. Concordance of CCSS, COG, and IGHG risk groups using real-world data informs current guidelines and identifies inequities in guideline-adherent care.
整合来自多个来源数据的临床信息学工具，有可能通过实施先前验证的风险计算器，促进对晚期心力衰竭高危儿童癌症幸存者的人群健康管理。
俄克拉荷马队列（n = 365）利用了护理护照（PFC）中的数据元素，杜克队列（n = 274）采用信息学方法从电子健康记录（EHR）数据中自动提取诊断时18岁及以下幸存者的化疗暴露情况。实施了儿童癌症幸存者研究（CCSS）晚期心血管风险计算器，并将心力衰竭风险组与儿童肿瘤学组（COG）和国际指南协调组（IGHG）的建议进行了比较。俄克拉荷马队列中的分析评估了遵循指南护理方面的差异。
俄克拉荷马队列和杜克队列均观察到CCSS与COG晚期心力衰竭风险组之间总体一致性良好，加权卡方统计量分别为0.70和0.75。低风险组显示出极好的一致性（卡方>0.9）。中风险组和高风险组显示出中等一致性（卡方0.44 - 0.60）。在俄克拉荷马队列中，诊断时的青少年与诊断时年龄小于13岁的幸存者相比，接受遵循指南的超声心动图监测的可能性显著降低（优势比[OR] 0.22；95%置信区间[CI]：0.10 - 0.49）。
临床信息学工具是一种可行的方法，可利用来自PFC或EHR的离散治疗相关数据元素，在人群健康层面成功实施先前验证的晚期心血管风险预测模型。使用真实世界数据的CCSS、COG和IGHG风险组的一致性为当前指南提供了信息，并确定了遵循指南护理中的不公平现象。
