Prognostic Models for COVID-19 Care

Study Overview
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Brief Summary
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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. 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.

Detailed Description
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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.

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

Conditions
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Covid19

Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Changes in model discrimination (Model 1: need for MV in patients hospitalized with COVID-19) | 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. | 30 days from hospitalization | 
| Changes in model discrimination (Model 2: mortality in patients receiving MV) | 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. | 30 days from hospitalization | 
| Changes in model discrimination (Model 3: length of stay in the ICU) | 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. | 30 days from hospitalization | 
| Changes in model calibration (Model 1: need for MV in patients hospitalized with COVID-19) | Aim 1 Outcome-Changes in Harrell's E for models predicting the probability of: the need for MV in patients hospitalized with COVID-19. | 30 days from hospitalization | 
| Changes in model calibration (Model 2: mortality in patients receiving MV) | Aim 1 Outcome-Changes in Harrell's E for models predicting the probability of: mortality in patients receiving MV. | 30 days from hospitalization | 
| Changes in model calibration (Model 3: length of stay in the ICU) | Aim 1 Outcome-Changes in Harrell's E for models predicting the probability of: length of stay in the ICU. | 30 days from hospitalization | 
| Changes in net benefit (Model 1: need for MV in patients hospitalized with COVID-19) | Aim 1 Outcome-Changes in Net Benefit for models predicting the probability of: the need for MV in patients hospitalized with COVID-19. | 30 days from hospitalization | 
| Changes in net benefit (Model 2: mortality in patients receiving MV) | Aim 1 Outcome-Changes in Net Benefit for models predicting the probability of: mortality in patients receiving MV. | 30 days from hospitalization | 
| Changes in net benefit (Model 3: length of stay in the ICU) | Aim 1 Outcome-Changes in Net Benefit for models predicting the probability of: length of stay in the ICU. | 30 days from hospitalization | 
| Changes in model discrimination in external database after updating (Model 1: need for MV in patients hospitalized with COVID-19) | 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. | 30 days from hospitalization | 
| Changes in model discrimination in external database after updating (Model 2: mortality in patients receiving MV) | 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. | 30 days from hospitalization | 
| Changes in model discrimination in external database after updating (Model 3: length of stay in the ICU) | 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. | 30 days from hospitalization | 
| Changes in model calibration in external database after updating (Model 1: need for MV in patients hospitalized with COVID-19) | Aim 2 Outcome-Changes in Harrell's E for models predicting the probability of: the need for MV in patients hospitalized with COVID-19. | 30 days from hospitalization | 
| Changes in model calibration in external database after updating (Model 2: mortality in patients receiving MV) | Aim 2 Outcome-Changes in Harrell's E for models predicting the probability of: mortality in patients receiving MV. | 30 days from hospitalization | 
| Changes in model calibration in external database after updating (Model 3: length of stay in the ICU) | Aim 2 Outcome-Changes in Harrell's E for models predicting the probability of: length of stay in the ICU. | 30 days from hospitalization | 
| Changes in net benefit in external database after updating (Model 1: need for MV in patients hospitalized with COVID-19) | Aim 2 Outcome-Changes in Net Benefit for models predicting the probability of: the need for MV in patients hospitalized with COVID-19. | 30 days from hospitalization | 
| Changes in net benefit in external database after updating (Model 2: mortality in patients receiving MV) | Aim 2 Outcome-Changes in Net Benefit for models predicting the probability of: mortality in patients receiving MV. | 30 days from hospitalization | 
| Changes in net benefit in external database after updating (Model 3: length of stay in the ICU) | Aim 2 Outcome-Changes in Net Benefit for models predicting the probability of: length of stay in the ICU. | 30 days from hospitalization | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Stakeholder perceptions, beliefs and opinions on COVID prediction models | 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. | 6 months |