Amarasingham Ruben, Velasco Ferdinand, Xie Bin, Clark Christopher, Ma Ying, Zhang Song, Bhat Deepa, Lucena Brian, Huesch Marco, Halm Ethan A
Parkland Center for Clinical Innovation, 8435 Stemmons Freeway, Suite 1150, Dallas, TX, 75247, USA.
Division of General Internal Medicine, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, USA.
BMC Med Inform Decis Mak. 2015 May 20;15:39. doi: 10.1186/s12911-015-0162-6.
There is increasing interest in using prediction models to identify patients at risk of readmission or death after hospital discharge, but existing models have significant limitations. Electronic medical record (EMR) based models that can be used to predict risk on multiple disease conditions among a wide range of patient demographics early in the hospitalization are needed. The objective of this study was to evaluate the degree to which EMR-based risk models for 30-day readmission or mortality accurately identify high risk patients and to compare these models with published claims-based models.
Data were analyzed from all consecutive adult patients admitted to internal medicine services at 7 large hospitals belonging to 3 health systems in Dallas/Fort Worth between November 2009 and October 2010 and split randomly into derivation and validation cohorts. Performance of the model was evaluated against the Canadian LACE mortality or readmission model and the Centers for Medicare and Medicaid Services (CMS) Hospital Wide Readmission model.
Among the 39,604 adults hospitalized for a broad range of medical reasons, 2.8% of patients died, 12.7% were readmitted, and 14.7% were readmitted or died within 30 days after discharge. The electronic multicondition models for the composite outcome of 30-day mortality or readmission had good discrimination using data available within 24 h of admission (C statistic 0.69; 95% CI, 0.68-0.70), or at discharge (0.71; 95% CI, 0.70-0.72), and were significantly better than the LACE model (0.65; 95% CI, 0.64-0.66; P =0.02) with significant NRI (0.16) and IDI (0.039, 95% CI, 0.035-0.044). The electronic multicondition model for 30-day readmission alone had good discrimination using data available within 24 h of admission (C statistic 0.66; 95% CI, 0.65-0.67) or at discharge (0.68; 95% CI, 0.67-0.69), and performed significantly better than the CMS model (0.61; 95% CI, 0.59-0.62; P < 0.01) with significant NRI (0.20) and IDI (0.037, 95% CI, 0.033-0.041).
A new electronic multicondition model based on information derived from the EMR predicted mortality and readmission at 30 days, and was superior to previously published claims-based models.
利用预测模型来识别出院后再入院或死亡风险患者的兴趣日益浓厚,但现有模型存在显著局限性。需要基于电子病历(EMR)的模型,以便在住院早期针对广泛的患者人口统计学特征中的多种疾病状况预测风险。本研究的目的是评估基于EMR的30天再入院或死亡风险模型准确识别高危患者的程度,并将这些模型与已发表的基于理赔数据的模型进行比较。
对2009年11月至2010年10月期间达拉斯/沃思堡地区3个医疗系统所属7家大型医院内科收治的所有连续成年患者的数据进行分析,并随机分为推导队列和验证队列。根据加拿大LACE死亡率或再入院模型以及医疗保险和医疗补助服务中心(CMS)的全院再入院模型评估该模型的性能。
在因各种医疗原因住院的39604名成年人中,2.8%的患者死亡,12.7%的患者再入院,14.7%的患者在出院后30天内再入院或死亡。用于30天死亡率或再入院综合结局的电子多病情模型,利用入院后24小时内(C统计量0.69;95%CI,0.68 - 0.70)或出院时(0.71;95%CI,0.70 - 0.72)可用的数据,具有良好的区分度,并且显著优于LACE模型(0.65;95%CI,0.64 - 0.66;P = 0.02),具有显著的净重新分类指数(NRI,0.16)和综合鉴别改善指数(IDI,0.039,95%CI,0.035 - 0.044)。仅用于30天再入院的电子多病情模型,利用入院后24小时内(C统计量0.66;95%CI,0.65 - 0.67)或出院时(0.68;95%CI,0.67 - 0.69)可用的数据,具有良好的区分度,并且显著优于CMS模型(0.61;95%CI,0.59 - 0.62;P < 0.01),具有显著的NRI(0.20)和IDI(0.037,95%CI,0.033 - 0.04)。
一种基于EMR衍生信息的新型电子多病情模型可预测30天死亡率和再入院情况,且优于先前发表的基于理赔数据的模型。