Division of Pulmonary and Critical Care Medicine, Rutgers Robert Wood Johnson Medical School, 125 Paterson Street, Suite 5200B, New Brunswick, NJ, 08901, USA.
Department of Computer Science, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA.
J Gen Intern Med. 2021 Apr;36(4):901-907. doi: 10.1007/s11606-020-06572-w. Epub 2021 Jan 22.
Although many predictive models have been developed to risk assess medical intensive care unit (MICU) readmissions, they tend to be cumbersome with complex calculations that are not efficient for a clinician planning a MICU discharge.
To develop a simple scoring tool that comprehensively takes into account not only patient factors but also system and process factors in a single model to predict MICU readmissions.
Retrospective chart review.
We included all patients admitted to the MICU of Robert Wood Johnson University Hospital, a tertiary care center, between June 2016 and May 2017 except those who were < 18 years of age, pregnant, or planned for hospice care at discharge.
Logistic regression models and a scoring tool for MICU readmissions were developed on a training set of 409 patients, and validated in an independent set of 474 patients.
Readmission rate in the training and validation sets were 8.8% and 9.1% respectively. The scoring tool derived from the training dataset included the following variables: MICU admission diagnosis of sepsis, intubation during MICU stay, duration of mechanical ventilation, tracheostomy during MICU stay, non-emergency department admission source to MICU, weekend MICU discharge, and length of stay in the MICU. The area under the curve of the scoring tool on the validation dataset was 0.76 (95% CI, 0.68-0.84), and the model fit the data well (Hosmer-Lemeshow p = 0.644). Readmission rate was 3.95% among cases in the lowest scoring range and 50% in the highest scoring range.
We developed a simple seven-variable scoring tool that can be used by clinicians at MICU discharge to efficiently assess a patient's risk of MICU readmission. Additionally, this is one of the first studies to show an association between MICU admission diagnosis of sepsis and MICU readmissions.
尽管已经开发出许多预测模型来评估医学重症监护病房(MICU)的再入院风险,但它们往往计算复杂,对于计划出院的临床医生来说效率不高。
开发一种简单的评分工具,该工具不仅全面考虑患者因素,还全面考虑单一模型中的系统和流程因素,以预测 MICU 再入院。
回顾性图表审查。
我们纳入了 2016 年 6 月至 2017 年 5 月期间入住罗格斯大学罗伯特伍德约翰逊医院 MICU 的所有患者(除 18 岁以下、孕妇或计划出院时接受临终关怀的患者)。
在一个包含 409 名患者的训练集中开发了 MICU 再入院的逻辑回归模型和评分工具,并在一个包含 474 名患者的独立集中进行了验证。
训练集和验证集的再入院率分别为 8.8%和 9.1%。从训练数据集中得出的评分工具包括以下变量:MICU 入院诊断为败血症、MICU 住院期间插管、机械通气持续时间、MICU 住院期间行气管切开术、非急诊部门进入 MICU、周末 MICU 出院和 MICU 住院时间。验证数据集上评分工具的曲线下面积为 0.76(95%CI,0.68-0.84),并且模型拟合数据良好(Hosmer-Lemeshow p=0.644)。在评分最低的范围内,再入院率为 3.95%,而在评分最高的范围内,再入院率为 50%。
我们开发了一种简单的七变量评分工具,临床医生可以在 MICU 出院时使用该工具来有效地评估患者 MICU 再入院的风险。此外,这是第一个表明 MICU 入院诊断为败血症与 MICU 再入院之间存在关联的研究之一。