A comprehensive prognostic model for older adults discharged to skilled nursing facilities.

针对出院到熟练护理机构的老年人的综合预后模型。

• 批准号: 10723510
• 负责人: William James Deardorff
• 金额: $ 16.15万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10723510
• 关键词: Acute; Admission activity; Advance Care Planning; Adverse event; Age; Antihypertensive Agents; Award; Calibration; Caring; Cessation of life; Characteristics; Chronic; Chronic Disease; Classification; Clinical; Clinical Management; Communities; Complex; Data Set; Decision Making; Diabetes Mellitus; Diagnosis; Discrimination; Elderly; Event; Family; Fee-for-Service Plans; Foundations; Funding; Future; Goals; Healthcare Systems; Home; Hospitalization; Hospitals; Individual; Internet; K-Series Research Career Programs; Length of Stay; Life; Long-Term Care; Measures; Medical; Medicare; Modeling; Outcome; Palliative Care; Patient Care; Patient-Focused Outcomes; Patients; Performance; Pharmaceutical Preparations; Play; Rehabilitation therapy; Research Personnel; Research Project Grants; Risk; Risk Estimate; Role; Sampling; Skilled Nursing Facilities; Techniques; Testing; Time; Update; Visit; Vulnerable Populations; Work; adverse outcome; aged; behavior measurement; beneficiary; career; clinical care; clinical practice; clinical predictive model; clinical predictors; clinically relevant; cohort; comorbidity; expectation; experience; falls; high risk; hospice environment; hospital readmission; improved; innovation; interest; mortality; mortality risk; outcome prediction; payment; predictive modeling; preference; prognostic; prognostic model; prognostic tool; randomized, clinical trials; readmission risk; restraint; satisfaction; shared decision making

PROJECT SUMMARY/ABSTRACT An estimated 20% of hospitalized older adults were discharged to a skilled nursing facility (SNF) in 2019 with many subsequently experiencing potentially adverse outcomes, including hospital re-admission, entering long- term care (LTC) rather than returning to the community, and death within 6 months. During this critical transition period following a hospitalization, clinicians, patients, and families often have discordant expectations about the trajectory of the SNF stay which can lead to dissatisfaction with care and disagreements over care plans. For SNF clinicians managing these patients, decision making around management of acute and chronic conditions, treatment preferences, and advance care planning is hindered by a lack of accurate prognostic information. Providing individualized risk estimates for a variety of outcomes following SNF admission can help frame these important discussions and facilitate shared decision making. Given that there are no widely used prognostic tools for older adults specifically discharged to a SNF, the objective of this study is to develop an easy-to-use and parsimonious prediction model that jointly models multiple outcomes. A 20% sample of community-dwelling Medicare beneficiaries aged 65 years and older discharged from a hospital to a SNF will be used to investigate two specific aims: (1) develop and internally validate a Day 1 prognostic model to be used on day 1 of SNF admission that provides risk estimates of multiple outcomes including hospital re- admission, discharge home without readmission, prolonged SNF stay >100 days (suggesting transition to LTC), and 6-month mortality and (2) develop an Updated model which provides refined estimates using detailed information from the Minimum Data Set (MDS) assessment that may not be readily available or widely collected by clinicians on day 1 of SNF admission. The result of these aims will be 2 easy-to-use parsimonious models that can provide accurate and well calibrated estimates of outcomes following a SNF admission. Significance and Innovation: Results from the proposed research project will directly inform clinical practice by allowing SNF clinicians to input specific patient characteristics into a web calculator to obtain risk estimates for multiple outcomes that can frame conversations with patients and families around clinical management decisions and future planning. This project is innovative because it will be the first SNF outcome prediction model to predict multiple clinically relevant outcomes simultaneously using a minimal number of variables that can be easily implemented in clinical practice. Future directions of this work will involve investigating more advanced modeling techniques, such as dynamic predictions and multi-state modeling, and ultimately explore how providing this prognostic information can improve satisfaction with care and other patient-centered outcomes in a randomized clinical trial.

项目总结/摘要 据估计，2019年有20%的住院老年人出院到专业护理机构（SNF）， 许多人随后经历了潜在的不良后果，包括再次入院，进入长期- 长期护理（LTC）而不是返回社区，以及6个月内死亡。在这个关键 在住院后的过渡期，临床医生、患者和家属往往有不一致的期望 关于SNF停留的轨迹，这可能导致对护理的不满和对护理的分歧 布局对于管理这些患者的SNF临床医生，围绕急性和慢性 条件，治疗偏好和提前护理计划受到缺乏准确预后的阻碍， 信息.提供SNF入院后各种结局的个体化风险估计可能有所帮助 组织这些重要的讨论，促进共同决策。由于没有广泛使用的 预后的工具，老年人专门出院到SNF，这项研究的目的是开发一个 易于使用和简约的预测模型，共同模拟多个结果。20%的样本 65岁及以上的社区居住医疗保险受益人从医院出院到SNF将 用于研究两个具体目标：（1）开发和内部验证第1天的预后模型， 在SNF入院的第1天使用，提供多种结局的风险估计，包括医院再访， 入院，出院回家，无再入院，SNF停留时间延长>100天（建议过渡到 LTC）和6个月死亡率，以及（2）开发更新模型，使用 来自最小数据集（MDS）评估的详细信息可能无法随时获得或广泛使用 由临床医生在SNF入院的第1天收集。这些目标的结果将是2易于使用的吝啬 模型，可以提供准确和良好的校准估计的结果后，SNF入院。 意义和创新：拟议研究项目的结果将直接告知临床实践， 允许SNF临床医生将特定的患者特征输入网络计算器，以获得风险估计， 多种结果，可以围绕临床管理与患者和家属进行对话 决策和未来规划。这个项目是创新的，因为它将是第一个SNF结果预测 使用最少数量的变量同时预测多个临床相关结局的模型， 可以很容易地在临床实践中实现。这项工作的未来方向将涉及调查更多 先进的建模技术，如动态预测和多状态建模，并最终探索 提供这些预后信息如何提高对护理和其他以患者为中心的 随机临床试验的结果。