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Completed

NCT Number: NCT04689711

Prognostic Models for COVID-19 Care

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.

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Key information

Conditions

Covid19 COVID-19 Coronaviridae Infections Coronavirus Infections Infections Lung Diseases Nidovirales Infections Pneumonia Pneumonia, Viral RNA Virus Infections Respiratory Tract Diseases Respiratory Tract Infections Virus Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Tufts Medical Center, Boston, Massachusetts, United States

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About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

  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 outcomes

  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.

Sponsors and collaborators

Lead sponsor

Tufts Medical Center

Other

Collaborators

  • Erasmus Medical Center
  • Northwell Health

Registry information

Official study title

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

Important dates

Study start
2020
Primary completion
2021
Study completion
2021
First posted
Dec 30, 2020
Registry last updated
Jan 20, 2022

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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