Li Qian, He Zhe, Guo Yi, Zhang Hansi, George Thomas J, Hogan William, Charness Neil, Bian Jiang
University of Florida, Gainesville, FL, USA.
Florida State University, Tallahassee, FL, USA.
AMIA Annu Symp Proc. 2020 Mar 4;2019:1101-1110. eCollection 2019.
Existing trials had not taken enough consideration of their population representativeness, which can lower the effectiveness when the treatment is applied in real-world clinical practice. We analyzed the eligibility criteria of Bevacizumab colorectal cancer treatment trials, assessed their a priori generalizability, and examined how it affects patient outcomes when applied in real-world clinical settings. To do so, we extracted patient-level data from a large collection of electronic health records (EHRs) from the OneFlorida consortium. We built a zero-inflated negative binomial model using a composite patient-trial generalizability (cPTG) score to predict patients' clinical outcomes (i.e., number of serious adverse events, [SAEs]). Our study results provide a body of evidence that 1) the cPTG scores can predict patient outcomes; and 2) patients who are more similar to the study population in the trials that were used to develop the treatment will have a significantly lower possibility to experience serious adverse events.
现有试验对其人群代表性的考虑不足，这可能导致该治疗方法在实际临床实践中应用时效果降低。我们分析了贝伐单抗治疗结直肠癌试验的纳入标准，评估了其先验可推广性，并研究了在实际临床环境中应用时它如何影响患者预后。为此，我们从OneFlorida联盟的大量电子健康记录（EHR）中提取了患者层面的数据。我们使用综合患者试验可推广性（cPTG）评分构建了零膨胀负二项式模型，以预测患者的临床结局（即严重不良事件的数量，[SAEs]）。我们的研究结果提供了一系列证据，即1）cPTG评分可以预测患者结局；2）在用于开发该治疗方法的试验中，与研究人群更相似的患者发生严重不良事件的可能性将显著降低。
