PeraHealth, Inc, Sarasota, Florida, USA.
BMJ Open. 2013 May 14;3(5):e002367. doi: 10.1136/bmjopen-2012-002367.
To explore the hypothesis that placing clinical variables of differing metrics on a common linear scale of all-cause postdischarge mortality provides risk functions that are directly correlated with in-hospital mortality risk.
Modelling study.
An 805-bed community hospital in the southeastern USA.
42302 inpatients admitted for any reason, excluding obstetrics, paediatric and psychiatric patients.
All-cause in-hospital and postdischarge mortalities, and associated correlations.
Pearson correlation coefficients comparing in-hospital risks with postdischarge risks for creatinine, heart rate and a set of 12 nursing assessments are 0.920, 0.922 and 0.892, respectively. Correlation between postdischarge risk heart rate and the Modified Early Warning System (MEWS) component for heart rate is 0.855. The minimal excess risk values for creatinine and heart rate roughly correspond to the normal reference ranges. We also provide the risks for values outside that range, independent of expert opinion or a regression model. By summing risk functions, a first-approximation patient risk score is created, which correctly ranks 6 discharge categories by average mortality with p<0.001 for differences in category means, and Tukey's Honestly Significant Difference Test confirmed that the means were all different at the 95% confidence level.
Quantitative or categorical clinical variables can be transformed into risk functions that correlate well with in-hospital risk. This methodology provides an empirical way to assess inpatient risk from data available in the Electronic Health Record. With just the variables in this paper, we achieve a risk score that correlates with discharge disposition. This is the first step towards creation of a universal measure of patient condition that reflects a generally applicable set of health-related risks. More importantly, we believe that our approach opens the door to a way of exploring and resolving many issues in patient assessment.
探索将具有不同度量标准的临床变量置于全因出院后死亡率的通用线性尺度上的假设，提供与住院死亡率风险直接相关的风险函数。
建模研究。
美国东南部的一家 805 床位社区医院。
因任何原因住院的 42302 名患者，不包括产科、儿科和精神科患者。
全因住院和出院后死亡率，以及相关相关性。
比较肌酐、心率和一组 12 项护理评估的住院风险与出院后风险的皮尔逊相关系数分别为 0.920、0.922 和 0.892。出院后风险心率与心率改良早期预警系统（MEWS）组成部分之间的相关性为 0.855。肌酐和心率的最小超额风险值大致对应于正常参考范围。我们还提供了超出该范围值的风险，独立于专家意见或回归模型。通过对风险函数求和，创建了一个初步近似的患者风险评分，该评分通过平均死亡率正确对 6 个出院类别进行排名，p<0.001 表示类别均值的差异，Tukey 的诚实显著差异检验证实了在 95%置信水平下所有均值均不同。
定量或分类临床变量可以转化为与住院风险密切相关的风险函数。这种方法提供了一种从电子健康记录中可用数据评估住院患者风险的经验方法。仅使用本文中的变量，我们就可以创建一个与出院处置相关的风险评分。这是创建反映一般适用健康相关风险的通用患者状况衡量标准的第一步。更重要的是，我们相信我们的方法为探索和解决患者评估中的许多问题开辟了道路。
