Internal Medicine, University of Washington, Seattle, WA, USA.
Clin Transplant. 2013 Nov-Dec;27(6):809-22. doi: 10.1111/ctr.12217. Epub 2013 Sep 2.
Hospital length of stay (LOS) after liver transplantation has been determined to correlate with liver disease severity, post-transplant survival rates, and transplant-associated costs. A patient's model for end-stage liver disease (MELD) score and an organ's Donor risk index (DRI) have both been found to be significant predictors of LOS, but these two factors alone are insufficient to form an accurate prediction. Previous studies have identified other factors predictive of LOS, which can be incorporated with MELD and DRI to create more specific results. The objective of this study was to create an algorithm, or models, based on the most significant LOS predictors as identified from national data at different stages of the transplant process. Four models were developed predicting LOS using recipient factors, payment factors, donor factors, and postoperative factors. A medical care team member can enter a patient's data into the model and receive a reasonably accurate prediction of LOS for each phase of the liver transplant process, specifying the impact of each factor. These predictions would help predict the factors most likely to prolong LOS, inform resource allocation, and provide patients with more specific predictions of their LOS following transplantation.
肝移植后患者的住院时间（LOS）与肝病严重程度、移植后存活率和移植相关成本相关。患者的终末期肝病模型（MELD）评分和供体风险指数（DRI）都被发现是 LOS 的重要预测因素，但这两个因素本身不足以做出准确的预测。先前的研究已经确定了其他可预测 LOS 的因素，这些因素可以与 MELD 和 DRI 结合使用，以得出更具体的结果。本研究的目的是根据移植过程不同阶段的全国数据中确定的最显著的 LOS 预测因素，创建一个算法或模型。使用受者因素、支付因素、供者因素和术后因素，开发了四个预测 LOS 的模型。医疗团队成员可以将患者的数据输入模型，并获得肝移植过程每个阶段 LOS 的合理准确预测，同时指定每个因素的影响。这些预测将有助于预测最有可能延长 LOS 的因素，指导资源分配，并为患者提供移植后 LOS 的更具体预测。
