Design Lab, University of California San Diego, La Jolla, CA, United States.
Division of Infectious Diseases and Global Public Health, Department of Medicine, UC San Diego Health, La Jolla, CA, United States.
J Med Internet Res. 2021 Dec 3;23(12):e23571. doi: 10.2196/23571.
There is a pressing need for digital tools that can leverage big data to help clinicians select effective antibiotic treatments in the absence of timely susceptibility data. Clinical presentation and local epidemiology can inform therapy selection to balance the risk of antimicrobial resistance and patient risk. However, data and clinical expertise must be appropriately integrated into clinical workflows.
The aim of this study is to leverage available data in electronic health records, to develop a data-driven, user-centered, clinical decision support system to navigate patient safety and population health.
We analyzed 5 years of susceptibility testing (1,078,510 isolates) and patient data (30,761 patients) across a large academic medical center. After curating the data according to the Clinical and Laboratory Standards Institute guidelines, we analyzed and visualized the impact of risk factors on clinical outcomes. On the basis of this data-driven understanding, we developed a probabilistic algorithm that maps these data to individual cases and implemented iBiogram, a prototype digital empiric antimicrobial clinical decision support system, which we evaluated against actual prescribing outcomes.
We determined patient-specific factors across syndromes and contexts and identified relevant local patterns of antimicrobial resistance by clinical syndrome. Mortality and length of stay differed significantly depending on these factors and could be used to generate heuristic targets for an acceptable risk of underprescription. Combined with the developed remaining risk algorithm, these factors can be used to inform clinicians' reasoning. A retrospective comparison of the iBiogram-suggested therapies versus the actual prescription by physicians showed similar performance for low-risk diseases such as urinary tract infections, whereas iBiogram recognized risk and recommended more appropriate coverage in high mortality conditions such as sepsis.
The application of such data-driven, patient-centered tools may guide empirical prescription for clinicians to balance morbidity and mortality with antimicrobial stewardship.
迫切需要能够利用大数据的数字工具，在缺乏及时药敏数据的情况下帮助临床医生选择有效的抗生素治疗方案。临床特征和当地的流行病学情况可以为治疗方案的选择提供信息，以平衡抗菌药物耐药的风险和患者的风险。然而，数据和临床专业知识必须被恰当地整合到临床工作流程中。
本研究旨在利用电子健康记录中的现有数据，开发一种数据驱动、以用户为中心的临床决策支持系统，以保障患者安全和人群健康。
我们分析了一家大型学术医疗中心 5 年来的药敏试验（1078510 份分离株）和患者数据（30761 名患者）。根据临床和实验室标准协会的指南对数据进行整理后，我们分析并可视化了风险因素对临床结果的影响。在此数据驱动的理解基础上，我们开发了一种概率算法，将这些数据映射到个体病例，并实现了 iBiogram，这是一个原型数字化经验性抗菌药物临床决策支持系统，我们将其与实际处方结果进行了评估。
我们确定了特定于患者的综合征和背景下的因素，并通过临床综合征确定了相关的局部抗菌药物耐药模式。死亡率和住院时间因这些因素而有显著差异，可用于生成可接受的过度用药风险的启发式目标。将这些因素与开发的剩余风险算法相结合，可以为临床医生的决策提供信息。对 iBiogram 建议的治疗方案与医生实际开出的处方进行回顾性比较，结果表明，对于尿路感染等低风险疾病，两者的性能相似，而 iBiogram 则可以识别风险，并在脓毒症等高死亡率情况下推荐更合适的覆盖范围。
应用这种数据驱动、以患者为中心的工具可能有助于指导临床医生进行经验性处方，以平衡发病率和死亡率与抗菌药物管理。
