Hospital length of stay after serious injury

严重受伤后的住院时间

• 批准号: 6781332
• 负责人: DAVID E CLARK
• 金额: $ 4.83万
• 依托单位: MAINEHEALTH
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-05-01 至 2006-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6781332
• 关键词: analytical method; health care model; health care service evaluation; health services research tag; hospital length of stay; hospital utilization; human data; human mortality; injury; intensive care; method development; outcomes research; prognosis

DESCRIPTION (PROVIDED BY APPLICANT): This project proposes to improve prediction of hospital length of stay (LOS) and mortality in seriously injured patients, using a recently developed piecewise exponential (PWE) model allowing for multiple outcomes and time-varying covariates. LOS can be difficult to predict in patient populations with significant mortality, because the cases with shortest LOS include both the least serious (discharged alive) and the most serious (fatalities). The multiple-outcome PWE model, designed to overcome this difficulty, will be applied to data from the National Trauma Data Bank(r) of the American College of Surgeons. Half of the database will be randomly selected for model development. First, standard regression models for the estimation of mortality and LOS will be derived. Using similar variables, a multiple-outcome PWE model will also be developed: For specified times after admission, selected cases will be compartmentalized as 1) still in hospital, 2) discharged alive, or 3) died. After distinguishing time periods during which the transition rates from the hospitalized state to the other states are relatively constant, the effects of different covariates on these rates will be determined using Poisson regression. Standard classifications of injury severity, injury mechanism, and preexisting health status will be used, along with standard modeling approaches and data transformations. Where possible, the model will be simplified by combining estimates of covariate effects for adjacent time periods. Mathematical formulas for LOS and mortality for any given set of covariates, assuming piecewise constant rates occurring simultaneously for each transition, can be derived analytically from the PWE model. Using the remaining half of the database, multiple-outcome PWE model predictions of LOS and mortality will be compared to the standard regression predictions and to the actual experience. Covariate effects on early and late mortality, as well as LOS, will be contrasted and analyzed. Predictions from the multiple-outcome PWE model may be useful for cost-effectiveness studies, as well as for determining appropriate reimbursement for the care of injured patients. Time-varying multistate models may also be useful in other areas of medical outcomes research.

描述(由申请人提供)：该项目建议使用最近开发的分段指数(PWE)模型来改进对严重受伤患者的住院时间(LOS)和死亡率的预测，该模型允许多种结果和时变的协变量。在有显著死亡率的患者群体中，LOS可能很难预测，因为LOS最短的病例既包括最轻的(活着出院)，也包括最严重的(死亡)。为克服这一困难而设计的多结果PWE模型将应用于美国外科医师学会国家创伤数据库(R)的数据。 数据库的一半将被随机选择用于模型开发。首先，将推导出用于估计死亡率和LOS的标准回归模型。使用类似的变量，也将开发一个多结局PWE模型：在入院后的特定时间内，选定的病例将被划分为1)仍在医院，2)活着出院，或3)死亡。在区分从住院状态到其他状态的转换率相对恒定的时间段后，将使用泊松回归来确定不同协变量对这些转换率的影响。将使用损伤严重程度、损伤机制和先前存在的健康状态的标准分类，以及标准建模方法和数据转换。在可能的情况下，模型将通过合并对相邻时间段的协变量影响的估计来简化。假设每个转变同时发生分段恒定的速率，可以从PWE模型解析地推导出任何给定协变量集合的LOS和死亡率的数学公式。 使用数据库的其余一半，多结果PWE模型对LOS和死亡率的预测将与标准回归预测和实际经验进行比较。将对比和分析协变量对早期和晚期死亡率以及LOS的影响。 多结果PWE模型的预测可能有助于成本效益研究，以及确定受伤患者护理的适当补偿。时变多态模型在医疗结果研究的其他领域也可能有用。