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Prognostic Models for COVID-19 Care

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ClinicalTrials.gov Identifier: NCT04689711
Recruitment Status : Enrolling by invitation
First Posted : December 30, 2020
Last Update Posted : December 30, 2020
Sponsor:
Collaborators:
Northwell Health
Erasmus Medical Center
Information provided by (Responsible Party):
Tufts Medical Center

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.

Condition or disease
Covid19

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.

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Study Type : Observational
Estimated Enrollment : 16 participants
Observational Model: Cohort
Time Perspective: Prospective
Official Title: Generalizable Prognostic Models for Patient-Centered Decisions in COVID-19
Actual Study Start Date : December 7, 2020
Estimated Primary Completion Date : August 31, 2021
Estimated Study Completion Date : August 31, 2021



Primary Outcome Measures :
  1. Changes in model discrimination (Model 1: need for MV in patients hospitalized with COVID-19) [ Time Frame: 30 days from hospitalization ]
    Aim 1 Outcome: Changes in Area under receiver operating characteristic curve (AUC) [delta AUC] for models predicting the probability of: the need for MV in patients hospitalized with COVID-19.

  2. Changes in model discrimination (Model 2: mortality in patients receiving MV) [ Time Frame: 30 days from hospitalization ]
    Aim 1 Outcome: Changes in Area under receiver operating characteristic curve (AUC) [delta AUC] for models predicting the probability of: mortality in patients receiving MV.

  3. Changes in model discrimination (Model 3: length of stay in the ICU) [ Time Frame: 30 days from hospitalization ]
    Aim 1 Outcome: Changes in Area under receiver operating characteristic curve (AUC) [delta AUC] for models predicting the probability of: length of stay in the ICU.

  4. Changes in model calibration (Model 1: need for MV in patients hospitalized with COVID-19) [ Time Frame: 30 days from hospitalization ]
    Aim 1 Outcome-Changes in Harrell's E for models predicting the probability of: the need for MV in patients hospitalized with COVID-19.

  5. Changes in model calibration (Model 2: mortality in patients receiving MV) [ Time Frame: 30 days from hospitalization ]
    Aim 1 Outcome-Changes in Harrell's E for models predicting the probability of: mortality in patients receiving MV.

  6. Changes in model calibration (Model 3: length of stay in the ICU) [ Time Frame: 30 days from hospitalization ]
    Aim 1 Outcome-Changes in Harrell's E for models predicting the probability of: length of stay in the ICU.

  7. Changes in net benefit (Model 1: need for MV in patients hospitalized with COVID-19) [ Time Frame: 30 days from hospitalization ]
    Aim 1 Outcome-Changes in Net Benefit for models predicting the probability of: the need for MV in patients hospitalized with COVID-19.

  8. Changes in net benefit (Model 2: mortality in patients receiving MV) [ Time Frame: 30 days from hospitalization ]
    Aim 1 Outcome-Changes in Net Benefit for models predicting the probability of: mortality in patients receiving MV.

  9. Changes in net benefit (Model 3: length of stay in the ICU) [ Time Frame: 30 days from hospitalization ]
    Aim 1 Outcome-Changes in Net Benefit for models predicting the probability of: length of stay in the ICU.

  10. Changes in model discrimination in external database after updating (Model 1: need for MV in patients hospitalized with COVID-19) [ Time Frame: 30 days from hospitalization ]
    Aim 2 Outcome-Changes in Area under receiver operating characteristic curve (AUC) [delta AUC] for models predicting the probability of: the need for MV in patients hospitalized with COVID-19.

  11. Changes in model discrimination in external database after updating (Model 2: mortality in patients receiving MV) [ Time Frame: 30 days from hospitalization ]
    Aim 2 Outcome-Changes in Area under receiver operating characteristic curve (AUC) [delta AUC] for models predicting the probability of: mortality in patients receiving MV.

  12. Changes in model discrimination in external database after updating (Model 3: length of stay in the ICU) [ Time Frame: 30 days from hospitalization ]
    Aim 2 Outcome-Changes in Area under receiver operating characteristic curve (AUC) [delta AUC] for models predicting the probability of: length of stay in the ICU.

  13. Changes in model calibration in external database after updating (Model 1: need for MV in patients hospitalized with COVID-19) [ Time Frame: 30 days from hospitalization ]
    Aim 2 Outcome-Changes in Harrell's E for models predicting the probability of: the need for MV in patients hospitalized with COVID-19.

  14. Changes in model calibration in external database after updating (Model 2: mortality in patients receiving MV) [ Time Frame: 30 days from hospitalization ]
    Aim 2 Outcome-Changes in Harrell's E for models predicting the probability of: mortality in patients receiving MV.

  15. Changes in model calibration in external database after updating (Model 3: length of stay in the ICU) [ Time Frame: 30 days from hospitalization ]
    Aim 2 Outcome-Changes in Harrell's E for models predicting the probability of: length of stay in the ICU.

  16. Changes in net benefit in external database after updating (Model 1: need for MV in patients hospitalized with COVID-19) [ Time Frame: 30 days from hospitalization ]
    Aim 2 Outcome-Changes in Net Benefit for models predicting the probability of: the need for MV in patients hospitalized with COVID-19.

  17. Changes in net benefit in external database after updating (Model 2: mortality in patients receiving MV) [ Time Frame: 30 days from hospitalization ]
    Aim 2 Outcome-Changes in Net Benefit for models predicting the probability of: mortality in patients receiving MV.

  18. Changes in net benefit in external database after updating (Model 3: length of stay in the ICU) [ Time Frame: 30 days from hospitalization ]
    Aim 2 Outcome-Changes in Net Benefit for models predicting the probability of: length of stay in the ICU.


Secondary Outcome Measures :
  1. Stakeholder perceptions, beliefs and opinions on COVID prediction models [ Time Frame: 6 months ]
    Aim 3 Outcome-The outcome will be assessed with a codebook derived deductively from our structured interview guide to identify themes that emerge in the semi-structured sessions. Through focus groups held via synchronous video conferences, we will engage with patients and clinical providers to identify patient- and provider-reported themes that emerge in how clinical prediction models can support decision making in the care of patients with COVID-19. Themes will be identified through qualitative analysis of patient and provider feedback. We expect to elicit patient and provider beliefs, opinions and values around the scientific, ethical and pragmatic aspects of use of these models to support decision making.



Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
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.
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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04689711


Locations
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United States, Massachusetts
Tufts Medical Center
Boston, Massachusetts, United States, 02111
United States, New York
Northwell Health (The Feinstein Institutes for Medical Research)
Manhasset, New York, United States, 11030
Sponsors and Collaborators
Tufts Medical Center
Northwell Health
Erasmus Medical Center
Investigators
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Principal Investigator: David M Kent, MD, MS Tufts Medical Center
  Study Documents (Full-Text)

Documents provided by Tufts Medical Center:
Additional Information:
Publications:
Levy TJ, Richardson S, Coppa K, et al. Development and Validation of a Survival Calculator for Hospitalized Patients with COVID-19. medRxiv. 2020:2020.2004.2022.20075416.
Tibshirani R. Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society Series B (Methodological). 1996;58(1):267-288.

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Responsible Party: Tufts Medical Center
ClinicalTrials.gov Identifier: NCT04689711    
Other Study ID Numbers: PCORI-ME-1606-35555
First Posted: December 30, 2020    Key Record Dates
Last Update Posted: December 30, 2020
Last Verified: December 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No