比较预测住院患者死亡率和住院时间的指标。
Comparison of Measures to Predict Mortality and Length of Stay in Hospitalized Patients.
机构信息
Jianfang Liu, PhD, is Assistant Professor, School of Nursing, Columbia University, New York, New York. Elaine Larson, RN, PhD, FAAN, CIC, is Associate Dean for Research and Anna C. Maxwell Professor of Nursing Research, School of Nursing, and Professor of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York. Amanda Hessels, PhD, MPH, RN, CIC, CPHQ, FAPIC, is Assistant Professor, School of Nursing, Columbia University, New York, New York, and Nurse Scientist, Hackensack Meridian Health, Neptune, New Jersey. Bevin Cohen, PhD, MPH, RN, is Associate Research Scientist, School of Nursing, Columbia University, New York, New York. Philip Zachariah, MD, MS, is Assistant Professor, Columbia University Medical Center & New York-Presbyterian Morgan Stanley Children's Hospital. David Caplan, BS, is Senior Technical Specialist, Division of Quality Analytics, New York-Presbyterian Hospital. Jingjing Shang, PhD, RN, is Associate Professor, School of Nursing, Columbia University, New York, New York.
出版信息
Nurs Res. 2019 May/Jun;68(3):200-209. doi: 10.1097/NNR.0000000000000350.
BACKGROUND
Patient risk adjustment is critical for hospital benchmarking and allocation of healthcare resources. However, considerable heterogeneity exists among measures.
OBJECTIVES
The performance of five measures was compared to predict mortality and length of stay (LOS) in hospitalized adults using claims data; these include three comorbidity composite scores (Charlson/Deyo age-comorbidity score, V W Elixhauser comorbidity score, and V W Elixhauser age-comorbidity score), 3 M risk of mortality (3 M ROM), and 3 M severity of illness (3 M SOI) subclasses.
METHODS
Binary logistic and zero-truncated negative binomial regression models were applied to a 2-year retrospective dataset (2013-2014) with 123,641 adult inpatient admissions from a large hospital system in New York City.
RESULTS
All five measures demonstrated good to strong model fit for predicting in-hospital mortality, with C-statistics of 0.74 (95% confidence interval [CI] [0.74, 0.75]), 0.80 (95% CI [0.80, 0.81]), 0.81(95% CI [0.81, 0.82]), 0.94 (95% CI [0.93, 0.94]), and 0.90 (95% CI [0.90, 0.91]) for Charlson/Deyo age-comorbidity score, V W Elixhauser comorbidity score, V W Elixhauser age-comorbidity score, 3 M ROM, and 3 M SOI, respectively. The model fit statistics to predict hospital LOS measured by the likelihood ratio index were 0.3%, 1.2%, 1.1%, 6.2%, and 4.3%, respectively.
DISCUSSION
The measures tested in this study can guide nurse managers in the assignment of nursing care and coordination of needed patient services and administrators to effectively and efficiently support optimal nursing care.
背景
患者风险调整对于医院基准测试和医疗资源分配至关重要。然而，各种衡量标准之间存在很大的异质性。
目的
使用索赔数据比较五种衡量标准在预测住院成年人死亡率和住院时间（LOS）方面的表现；这些衡量标准包括三种合并症综合评分（Charlson/Deyo 年龄合并症评分、VW Elixhauser 合并症评分和 VW Elixhauser 年龄合并症评分）、3M 死亡率风险（3M ROM）和 3M 疾病严重程度（3M SOI）亚类。
方法
应用二元逻辑回归和零截断负二项回归模型对来自纽约市一家大型医院系统的 123641 名成年住院患者的 2 年回顾性数据集（2013-2014 年）进行分析。
结果
所有五种衡量标准在预测院内死亡率方面均表现出良好到较强的模型拟合度，C 统计量分别为 0.74（95%置信区间 [0.74, 0.75]）、0.80（95%置信区间 [0.80, 0.81]）、0.81（95%置信区间 [0.81, 0.82]）、0.94（95%置信区间 [0.93, 0.94]）和 0.90（95%置信区间 [0.90, 0.91]），用于 Charlson/Deyo 年龄合并症评分、VW Elixhauser 合并症评分、VW Elixhauser 年龄合并症评分、3M ROM 和 3M SOI。预测 LOS 的似然比指数模型拟合统计量分别为 0.3%、1.2%、1.1%、6.2%和 4.3%。
讨论
本研究测试的衡量标准可以指导护士长分配护理工作，并协调所需的患者服务，帮助管理人员有效地支持最佳护理。
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