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: 1. 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. 1. 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. 2. To evaluate temporal transportability, the investigators will examine both the main effect of calendar time and also examine calendar time as an effect modifier. 3. Engage stakeholders to facilitate best use of these CPMs in the care of patients with COVID-19.
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.
Inclusion Criteria:
- 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
Exclusion Criteria:
- Not proficient in reading or speaking English
Tufts Medical Center
Boston, Massachusetts, United States
Northwell Health (The Feinstein Institutes for Medical Research)
Manhasset, New York, United States
David M Kent, MD, MS, Principal Investigator
Tufts Medical Center