Rothman Michael J, Tepas Joseph J, Nowalk Andrew J, Levin James E, Rimar Joan M, Marchetti Albert, Hsiao Allen L
PeraHealth, Inc., 6302 Fairview Rd., Suite 310, Charlotte, NC 28203, United States.
University of Florida, Jacksonville, FL, United States.
J Biomed Inform. 2017 Feb;66:180-193. doi: 10.1016/j.jbi.2016.12.013. Epub 2017 Jan 3.
Awareness of a patient's clinical status during hospitalization is a primary responsibility for hospital providers. One tool to assess status is the Rothman Index (RI), a validated measure of patient condition for adults, based on empirically derived relationships between 1-year post-discharge mortality and each of 26 clinical measurements available in the electronic medical record. However, such an approach cannot be used for pediatrics, where the relationships between risk and clinical variables are distinct functions of patient age, and sufficient 1-year mortality data for each age group simply do not exist. We report the development and validation of a new methodology to use adult mortality data to generate continuously age-adjusted acuity scores for pediatrics. Clinical data were extracted from EMRs at three pediatric hospitals covering 105,470 inpatient visits over a 3-year period. The RI input variable set was used as a starting point for the development of the pediatric Rothman Index (pRI). Age-dependence of continuous variables was determined by plotting mean values versus age. For variables determined to be age-dependent, polynomial functions of mean value and mean standard deviation versus age were constructed. Mean values and standard deviations for adult RI excess risk curves were separately estimated. Based on the "find the center of the channel" hypothesis, univariate pediatric risk was then computed by applying a z-score transform to adult mean and standard deviation values based on polynomial pediatric mean and standard deviation functions. Multivariate pediatric risk is estimated as the sum of univariate risk. Other age adjustments for categorical variables were also employed. Age-specific pediatric excess risk functions were compared to age-specific expert-derived functions and to in-hospital mortality. AUC for 24-h mortality and pRI scores prior to unplanned ICU transfers were computed. Age-adjusted risk functions correlated well with similar functions in Bedside PEWS and PAWS. Pediatric nursing data correlated well with risk as measured by mortality odds ratios. AUC for pRI for 24-h mortality was 0.93 (0.92, 0.94), 0.93 (0.93, 0.93) and 0.95 (0.95, 0.95) at the three pediatric hospitals. Unplanned ICU transfers correlated with lower pRI scores. Moreover, pRI scores declined prior to such events. A new methodology to continuously age-adjust patient acuity provides a tool to facilitate timely identification of physiologic deterioration in hospitalized children.
了解住院患者的临床状况是医院医护人员的首要职责。一种评估病情的工具是罗斯曼指数(RI),它是一种经过验证的衡量成人患者病情的指标,基于出院后1年死亡率与电子病历中26项临床测量指标之间的经验性推导关系。然而,这种方法不能用于儿科,因为风险与临床变量之间的关系是患者年龄的不同函数,而且每个年龄组的1年死亡率数据并不充分。我们报告了一种新方法的开发和验证,该方法利用成人死亡率数据为儿科生成持续年龄调整后的急性病评分。临床数据从三家儿科医院的电子病历中提取,涵盖了三年期间的105470次住院就诊。RI输入变量集被用作开发儿科罗斯曼指数(pRI)的起点。通过绘制均值与年龄的关系图来确定连续变量的年龄依赖性。对于确定为年龄依赖性的变量,构建均值和均值标准差与年龄的多项式函数。分别估计成人RI超额风险曲线的均值和标准差。基于“找到通道中心”假说,然后通过基于多项式儿科均值和标准差函数对成人均值和标准差进行z分数变换来计算单变量儿科风险。多变量儿科风险估计为单变量风险之和。还对分类变量进行了其他年龄调整。将特定年龄的儿科超额风险函数与特定年龄的专家推导函数以及住院死亡率进行比较。计算无计划ICU转科前24小时死亡率和pRI评分的AUC。年龄调整后的风险函数与床边PEWS和PAWS中的类似函数相关性良好。儿科护理数据与死亡率比值比衡量的风险相关性良好。三家儿科医院24小时死亡率的pRI的AUC分别为0.93(0.92,0.94)、0.93(0.93,0.93)和0.95(0.95,0.95)。无计划ICU转科与较低的pRI评分相关。此外,在此类事件发生前pRI评分会下降。一种持续年龄调整患者急性病的新方法提供了一种工具,有助于及时识别住院儿童的生理恶化情况。