Generalizable Prognostic Models for Patient-Centered Decisions in COVID-19

Tufts Medical Center2 个研究点分布在 1 个国家目标入组 21 人开始时间: 2020年12月7日最近更新: 2022年1月20日

适应症

试验速览

阶段: 不适用
状态: 已完成
发起方: Tufts Medical Center
入组人数: 21
试验地点: 2
主要终点: Changes in model calibration (Model 1: need for MV in patients hospitalized with COVID-19)

研究概览研究设计入排标准结局指标研究者研究点记录与标识符相似试验

研究概览

简要总结

Approximately 20% of patients hospitalized with COVID-19 require intensive care and possibly invasive mechanical ventilation (MV). Patient preferences with COVID-19 for MV may be different, because intubation for these patients is often prolonged (for several weeks), is administered in settings characterized by social isolation and is associated with very high average mortality rates. Supporting patients facing this decision requires providing an accurate forecast of their likely outcomes based on their individual characteristics.

The investigators therefore aim to:

Develop 3 CPMs in each of 2 hospital systems (i.e., 6 distinct models) to predict:

i) the need for MV in patients hospitalized with COVID-19; ii) mortality in patients receiving MV; iii) length of stay in the ICU. 2. Evaluate the geographic and temporal transportability of these models and examine updating approaches.

To evaluate geographic transportability, the investigators will apply the evaluation and updating framework developed (in the parent PCORI grant) to assess CPM validity and generalizability across the different datasets.
To evaluate temporal transportability, the investigators will examine both the main effect of calendar time and also examine calendar time as an effect modifier.
Engage stakeholders to facilitate best use of these CPMs in the care of patients with COVID-19.

详细描述

There has been a proliferation of COVID-19 clinical prediction models (CPMs) reported in the literature across health systems, but the validity and potential generalizability of these models to other settings is unknown. Generally, most hospitals (and systems) do not have a sufficient number of cases (and outcomes) to develop models fit to their local population, and predictor variables are not uniformly and reliably obtained across systems. Therefore, pooling and harmonizing data resources and assessing generalizability across different sites is urgently needed to create tools that may help support decision making across settings. In addition, since best practices are rapidly evolving over time (e.g., proning, minimizing paralytics, lung-protective volumes, remdesivir, dexamethasone or other treatments), updating and recalibrating these CPMs is crucially important.

In the current PCORI Methods project, the investigators developed a CPM evaluation and updating framework including both conventional and novel performance measures. The investigators will use this framework to evaluate COVID-19 prognostic models in the largest cohort of COVID-19 patients examined to date, spanning 2 datasets from very different settings. As the COVID-19 pandemic affects different regions, with subsequent waves expected, identifying the most accurate, robust and generalizable prognostic tools is needed to guide patient-centered decision making across diverse populations and settings.

研究设计

研究类型: Observational
观察模型: Cohort
时间视角: Prospective

入排标准

年龄范围: 18 Years 至 —（Adult, Older Adult）
性别: All
接受健康志愿者: 是

入选标准

•COVID-19 patient survivor
•Family member/caregiver of patient hospitalized for COVID-19
•Physician with experience caring for COVID-19 patients
•Other provider (pastoral care, nursing, respiratory therapy) with experience caring for COVID-19 patients

排除标准

•Not proficient in reading or speaking English