Length of Stay Predictions Improvements Through the Use of Automated Laboratory and Comorbidity Variables
Length of Stay Predictions Improvements Through the Use of Automated Laboratory and Comorbidity Variables
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DOI：
10.1097/mlr.0b013e3181e359f3
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发表时间：
2010-08-01
期刊：
影响因子：
3
通讯作者：
Escobar, Gabriel J.
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文献类型：
--
作者：
Liu, Vincent;Kipnis, Patricia;Escobar, Gabriel J.
背景：住院时间（LOS）是衡量医院资源利用率的常用指标。大多数风险调整服务水平的方法都受到仅使用行政数据的限制。最近的研究表明，将自动化临床数据添加到这些models.Objectives中可以提高性能：为了评估将“入院点”自动化实验室和合并症测量-实验室急性生理学评分（LAPS）和合并症点评分（COPS）-添加到基于管理数据的风险调整模型中的实用性。我们对2002年至2005年期间在北方加州17家Kaiser Permanente医院住院的155，474例患者进行了回顾性分析。我们评估的好处，增加LAPS和COPS在线性回归模型中使用完整的，修剪，截断，对数转换LOS，以及在logistic和广义线性models.Results：平均年龄为61 +/- 19岁，女性占55.2%的科目。平均LOS为4.5 ± 7.7天;中位LOS为2.8天（四分位距，1.3-5.1天）。将LAPS和COPS添加到线性回归模型中，将R-2从0.113提高到0.146，提高了29%。在其他回归模型中也观察到了纳入LAPS和COPS后的类似改进。总之，这些变量负责>50%的预测能力的逻辑回归模型，确定离群值与较长的LOS.Conclusions：列入自动化的实验室和合并症的数据提高了LOS预测在所有模型中，强调需要更广泛地采用全面的电子病历。
Background: Length of stay (LOS) is a common measure of hospital resource utilization. Most methods for risk-adjusting LOS are limited by the use of only administrative data. Recent studies suggest that adding automated clinical data to these models improves performance.Objectives: To evaluate the utility of adding "point of admission" automated laboratory and comorbidity measures-the Laboratory Acute Physiology Score (LAPS) and Comorbidity Point Score (COPS)-to risk adjustment models that are based on administrative data.Methods: We performed a retrospective analysis of 155,474 hospitalizations between 2002 and 2005 at 17 Northern California Kaiser Permanente hospitals. We evaluated the benefit of adding LAPS and COPS in linear regression models using full, trimmed, truncated, and log-transformed LOS, as well as in logistic and generalized linear models.Results: Mean age was 61 +/- 19 years; females represented 55.2% of subjects. The mean LOS was 4.5 +/- 7.7 days; median LOS was 2.8 days (interquartile range, 1.3-5.1 days). Adding LAPS and COPS to the linear regression model improved R-2 by 29% from 0.113 to 0.146. Similar improvements with the inclusion of LAPS and COPS were observed in other regression models. Together, these variables were responsible for >50% of the predictive ability of logistic regression models that identified outliers with longer LOS.Conclusions: The inclusion of automated laboratory and comorbidity data improved LOS predictions in all models, underscoring the need for more widespread adoption of comprehensive electronic medical records.