Lipitz-Snyderman Allison, Classen David, Pfister David, Killen Aileen, Atoria Coral L, Fortier Elizabeth, Epstein Andrew S, Anderson Christopher, Weingart Saul N
Memorial Sloan Kettering Cancer Center; Columbia University; AIG, New York, NY; Pascal Metrics, Washington, DC; University of Utah School of Medicine, Salt Lake City, UT; and Tufts Medical Center and Tufts University School of Medicine, Boston, MA.
J Oncol Pract. 2017 Mar;13(3):e223-e230. doi: 10.1200/JOP.2016.016634. Epub 2017 Jan 17.
Although patient safety is a priority in oncology, few tools measure adverse events (AEs) beyond treatment-related toxicities. The study objective was to assemble a set of clinical triggers in the medical record and assess the extent to which triggered events identified AEs.
We performed a retrospective cohort study to assess the performance of an oncology medical record screening tool at a comprehensive cancer center. The study cohort included 400 patients age 18 years or older diagnosed with breast (n = 128), colorectal (n = 136), or lung cancer (n = 136), observed as in- and outpatients for up to 1 year.
We identified 790 triggers, or 1.98 triggers per patient (range, zero to 18 triggers). Three hundred four unique AEs were identified from medical record reviews and existing AE databases. The overall positive predictive value (PPV) of the original tool was 0.40 for total AEs and 0.15 for preventable or mitigable AEs. Examples of high-performing triggers included return to the operating room or interventional radiology within 30 days of surgery (PPV, 0.88 and 0.38 for total and preventable or mitigable AEs, respectively) and elevated blood glucose (> 250 mg/dL; PPV, 0.47 and 0.40 for total and preventable or mitigable AEs, respectively). The final modified tool included 49 triggers, with an overall PPV of 0.48 for total AEs and 0.18 for preventable or mitigable AEs.
A valid medical record screening tool for AEs in oncology could offer a powerful new method for measuring and improving cancer care quality. Future improvements could optimize the tool's efficiency and create automated electronic triggers for use in real-time AE detection and mitigation algorithms.
尽管患者安全是肿瘤学的首要任务,但除治疗相关毒性外,很少有工具能衡量不良事件(AE)。本研究的目的是在病历中收集一组临床触发因素,并评估触发事件识别AE的程度。
我们进行了一项回顾性队列研究,以评估某综合癌症中心肿瘤病历筛查工具的性能。研究队列包括400名18岁及以上的患者,他们被诊断为乳腺癌(n = 128)、结直肠癌(n = 136)或肺癌(n = 136),作为门诊和住院患者观察长达1年。
我们识别出790个触发因素,即每位患者1.98个触发因素(范围为零至18个触发因素)。通过病历审查和现有的AE数据库识别出304个独特的AE。原始工具对总AE的总体阳性预测值(PPV)为0.40,对可预防或可减轻的AE为0.15。高性能触发因素的例子包括术后30天内返回手术室或介入放射科(总AE和可预防或可减轻AE的PPV分别为0.88和0.38)以及血糖升高(> 250 mg/dL;总AE和可预防或可减轻AE的PPV分别为0.47和0.40)。最终修改后的工具包括49个触发因素,总AE的总体PPV为0.48,可预防或可减轻AE的总体PPV为0.18。
一种有效的肿瘤学AE病历筛查工具可为衡量和改善癌症护理质量提供一种强大的新方法。未来的改进可以优化该工具的效率,并创建用于实时AE检测和缓解算法的自动化电子触发因素。