Hospital length of stay after serious injury

描述（由申请人提供）：该项目旨在改善严重受伤患者住院时间（LOS）和死亡率的预测，使用最近开发的分段指数（PWE）模型，允许多个结局和随时间变化的协变量。 在具有显著死亡率的患者人群中，LOS可能难以预测，因为具有最短LOS的病例包括最不严重（活着出院）和最严重（死亡）。 多结局PWE模型旨在克服这一困难，将应用于美国外科医生学会国家创伤数据库（r）的数据。 将随机选择一半数据库用于模型开发。 首先，标准回归模型的估计死亡率和LOS将被导出。 使用类似的变量，还将开发多结局PWE模型：对于入院后的指定时间，将选定病例划分为1）仍在住院，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，将使用泊松回归确定不同协变量对这些比率的影响。 将使用损伤严重程度、损伤机制和预先存在的健康状态的标准分类，沿着标准建模方法和数据转换。 在可能的情况下，将通过合并相邻时间段的协变量效应估计值来简化模型。 假设每次转换同时发生分段恒定速率，任何给定协变量集的LOS和死亡率的数学公式可以从PWE模型中解析得出。 使用数据库的剩余一半，将LOS和死亡率的多结局PWE模型预测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。 从多结局PWE模型的预测可能是有用的成本效益研究，以及确定适当的补偿受伤的病人的护理。 时变多状态模型也可能在医疗结果研究的其他领域有用。

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.