Clinical trial NCT04689711: Prognostic Models for COVID-19 Care
Official title: Generalizable Prognostic Models for Patient-Centered Decisions in COVID-19
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Covid19
Lead sponsor: Tufts Medical Center
Sponsor class: OTHER
Collaborators: name: Northwell Health
name: Erasmus Medical Center
First posted: 2020-12-30
Enrollment: 21
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The population will include COVID-19 survivors; family members of COVID-19 patients; caregivers for COVID-19 patients; critical care physicians; palliative care physicians; hospitalists; nurses; respiratory therapists; leaders of our clinical ethics committees and pastoral care representatives.

Brief summary: 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.

Detailed description: 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.

Eligibility criteria: 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

Primary outcomes:
measure: Changes in model discrimination (Model 1: need for MV in patients hospitalized with COVID-19)
timeFrame: 30 days from hospitalization
measure: Changes in model discrimination (Model 2: mortality in patients receiving MV)
timeFrame: 30 days from hospitalization
measure: Changes in model discrimination (Model 3: length of stay in the ICU)
timeFrame: 30 days from hospitalization
measure: Changes in model calibration (Model 1: need for MV in patients hospitalized with COVID-19)
timeFrame: 30 days from hospitalization
measure: Changes in model calibration (Model 2: mortality in patients receiving MV)
timeFrame: 30 days from hospitalization
measure: Changes in model calibration (Model 3: length of stay in the ICU)
timeFrame: 30 days from hospitalization
measure: Changes in net benefit (Model 1: need for MV in patients hospitalized with COVID-19)
timeFrame: 30 days from hospitalization
measure: Changes in net benefit (Model 2: mortality in patients receiving MV)
timeFrame: 30 days from hospitalization
measure: Changes in net benefit (Model 3: length of stay in the ICU)
timeFrame: 30 days from hospitalization
measure: Changes in model discrimination in external database after updating (Model 1: need for MV in patients hospitalized with COVID-19)
timeFrame: 30 days from hospitalization
measure: Changes in model discrimination in external database after updating (Model 2: mortality in patients receiving MV)
timeFrame: 30 days from hospitalization
measure: Changes in model discrimination in external database after updating (Model 3: length of stay in the ICU)
timeFrame: 30 days from hospitalization
measure: Changes in model calibration in external database after updating (Model 1: need for MV in patients hospitalized with COVID-19)
timeFrame: 30 days from hospitalization
measure: Changes in model calibration in external database after updating (Model 2: mortality in patients receiving MV)
timeFrame: 30 days from hospitalization
measure: Changes in model calibration in external database after updating (Model 3: length of stay in the ICU)
timeFrame: 30 days from hospitalization
measure: Changes in net benefit in external database after updating (Model 1: need for MV in patients hospitalized with COVID-19)
timeFrame: 30 days from hospitalization
measure: Changes in net benefit in external database after updating (Model 2: mortality in patients receiving MV)
timeFrame: 30 days from hospitalization
measure: Changes in net benefit in external database after updating (Model 3: length of stay in the ICU)
timeFrame: 30 days from hospitalization
Secondary outcomes:
measure: Stakeholder perceptions, beliefs and opinions on COVID prediction models
timeFrame: 6 months
Other outcomes:
Locations:
city: Boston
country: United States
facility: Tufts Medical Center
state: Massachusetts
city: Manhasset
country: United States
facility: Northwell Health (The Feinstein Institutes for Medical Research)
state: New York
Study officials:
affiliation: Tufts Medical Center
name: David M Kent, MD, MS
role: PRINCIPAL_INVESTIGATOR
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