Calvert Jacob, Mao Qingqing, Hoffman Jana L, Jay Melissa, Desautels Thomas, Mohamadlou Hamid, Chettipally Uli, Das Ritankar
Dascena Inc., Hayward, CA, USA.
Kaiser Permanente South San Francisco Medical Center, South San Francisco, CA, USA; Department of Emergency Medicine, University of California San Francisco, San Francisco, CA, USA.
Ann Med Surg (Lond). 2016 Sep 6;11:52-57. doi: 10.1016/j.amsu.2016.09.002. eCollection 2016 Nov.
Clinical decision support systems are used to help predict patient stability and mortality in the Intensive Care Unit (ICU). Accurate patient information can assist clinicians with patient management and in allocating finite resources. However, systems currently in common use have limited predictive value in the clinical setting. The increasing availability of Electronic Health Records (EHR) provides an opportunity to use medical information for more accurate patient stability and mortality prediction in the ICU.
Develop and evaluate an algorithm which more accurately predicts patient mortality in the ICU, using the correlations between widely available clinical variables from the EHR.
We have developed an algorithm, , which uses eight common clinical variables from the EHR to assign patient mortality risk scores. Each clinical variable produces a subscore, and combinations of two or three discretized clinical variables also produce subscores. A combination of weighted subscores produces the overall score. We validated the performance of this algorithm in a retrospective study on the MIMIC III medical ICU dataset.
12 h mortality prediction yields an Area Under Receiver Operating Characteristic value of 0.88 (95% confidence interval 0.86 to 0.88). At a sensitivity of 80%, maintains a specificity of 81% with a diagnostic odds ratio of 16.26.
Through the multidimensional analysis of the correlations between eight common clinical variables, provides an improvement in the specificity and sensitivity of patient mortality prediction over existing prediction methods.
临床决策支持系统用于帮助预测重症监护病房（ICU）患者的稳定性和死亡率。准确的患者信息可协助临床医生进行患者管理并分配有限的资源。然而，目前常用的系统在临床环境中的预测价值有限。电子健康记录（EHR）可用性的不断提高为利用医疗信息在ICU中更准确地预测患者稳定性和死亡率提供了机会。
开发并评估一种算法，该算法利用电子健康记录中广泛可用的临床变量之间的相关性，更准确地预测ICU患者的死亡率。
我们开发了一种算法，该算法使用电子健康记录中的八个常见临床变量来分配患者死亡风险评分。每个临床变量产生一个子评分，两个或三个离散化临床变量的组合也产生子评分。加权子评分的组合产生总体评分。我们在对MIMIC III医疗ICU数据集的回顾性研究中验证了该算法的性能。
12小时死亡率预测的受试者工作特征曲线下面积值为0.88（95%置信区间0.86至0.88）。在灵敏度为80%时，特异性保持在81%，诊断比值比为16.26。
通过对八个常见临床变量之间相关性的多维度分析，该算法在患者死亡率预测的特异性和灵敏度方面比现有预测方法有所提高。
