Covid19NCT04689711
Prognostic Models for COVID-19 Care
An observational study of Covid19, run by Tufts Medical Center at 2 sites in United States.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Covid19
- How many people
- 21 participants
- Where
- 2 sitesUnited States
- Study dates
- 7 Dec 2020to 31 Aug 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- Not proficient in reading or speaking English
Trial locations and contacts
2 sites in 1 country.
United States2 sites
Tufts Medical Center
Boston, Massachusetts, 02111
Northwell Health (The Feinstein Institutes for Medical Research)
Manhasset, New York, 11030
About this study
Official title: Generalizable Prognostic Models for Patient-Centered Decisions in COVID-19
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:
- 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.
Read the 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.
- Conditions
- Covid19
- Medical subject headings
- COVID-19
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 30 days from hospitalization
Secondary outcomes (1)
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.
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Dec 2020
- Study start7 Dec 2020
- Primary completion31 Aug 2021
- Study completion31 Aug 2021
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (12)Papers the sponsor has linked to this study.
- Other trials for Covid19Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 21 participants (actual)
Sponsor and investigators
- Lead sponsor
- Tufts Medical Center
- Sponsor type
- University, hospital or other
- Collaborators
- Northwell Health, Erasmus Medical Center
- Responsible party
- Sponsor
- Investigators
- David M Kent, MD, MS, Principal investigator, Tufts Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04689711
- Sponsor's protocol ID
- PCORI-ME-1606-35555
- Status last verified
- Jan 2022
- First submitted
- 20 Nov 2020
- Last updated
- 20 Jan 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW; the Northwell COVID-19 Research Consortium; Barnaby DP, Becker LB, Chelico JD, Cohen SL, Cookingham J, Coppa K, Diefenbach MA, Dominello AJ, Duer-Hefele J, Falzon L, Gitlin J, Hajizadeh N, Harvin TG, Hirschwerk DA, Kim EJ, Kozel ZM, Marrast LM, Mogavero JN, Osorio GA, Qiu M, Zanos TP. Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA. 2020 May 26;323(20):2052-2059. doi: 10.1001/jama.2020.6775.
PubMed 32320003Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, Guan L, Wei Y, Li H, Wu X, Xu J, Tu S, Zhang Y, Chen H, Cao B. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020 Mar 28;395(10229):1054-1062. doi: 10.1016/S0140-6736(20)30566-3. Epub 2020 Mar 11.
PubMed 32171076Clarification of Mortality Rate and Data in Abstract, Results, and Table 2. JAMA. 2020 May 26;323(20):2098. doi: 10.1001/jama.2020.7681. No abstract available.
PubMed 32330939Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, Bonten MMJ, Dahly DL, Damen JAA, Debray TPA, de Jong VMT, De Vos M, Dhiman P, Haller MC, Harhay MO, Henckaerts L, Heus P, Kammer M, Kreuzberger N, Lohmann A, Luijken K, Ma J, Martin GP, McLernon DJ, Andaur Navarro CL, Reitsma JB, Sergeant JC, Shi C, Skoetz N, Smits LJM, Snell KIE, Sperrin M, Spijker R, Steyerberg EW, Takada T, Tzoulaki I, van Kuijk SMJ, van Bussel B, van der Horst ICC, van Royen FS, Verbakel JY, Wallisch C, Wilkinson J, Wolff R, Hooft L, Moons KGM, van Smeden M. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. 2020 Apr 7;369:m1328. doi: 10.1136/bmj.m1328.
PubMed 32265220Levy 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.
Putter H, Fiocco M, Geskus RB. Tutorial in biostatistics: competing risks and multi-state models. Stat Med. 2007 May 20;26(11):2389-430. doi: 10.1002/sim.2712.
PubMed 17031868de Wreede LC, Fiocco M, Putter H. The mstate package for estimation and prediction in non- and semi-parametric multi-state and competing risks models. Comput Methods Programs Biomed. 2010 Sep;99(3):261-74. doi: 10.1016/j.cmpb.2010.01.001. Epub 2010 Mar 15.
PubMed 20227129van Klaveren D, Steyerberg EW, Gonen M, Vergouwe Y. The calibrated model-based concordance improved assessment of discriminative ability in patient clusters of limited sample size. Diagn Progn Res. 2019 Jun 6;3:11. doi: 10.1186/s41512-019-0055-8. eCollection 2019.
PubMed 31183411Jones AE, Trzeciak S, Kline JA. The Sequential Organ Failure Assessment score for predicting outcome in patients with severe sepsis and evidence of hypoperfusion at the time of emergency department presentation. Crit Care Med. 2009 May;37(5):1649-54. doi: 10.1097/CCM.0b013e31819def97.
PubMed 19325482Lim WS, van der Eerden MM, Laing R, Boersma WG, Karalus N, Town GI, Lewis SA, Macfarlane JT. Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax. 2003 May;58(5):377-82. doi: 10.1136/thorax.58.5.377.
PubMed 12728155Griffith GJ, Morris TT, Tudball MJ, Herbert A, Mancano G, Pike L, Sharp GC, Sterne J, Palmer TM, Davey Smith G, Tilling K, Zuccolo L, Davies NM, Hemani G. Collider bias undermines our understanding of COVID-19 disease risk and severity. Nat Commun. 2020 Nov 12;11(1):5749. doi: 10.1038/s41467-020-19478-2.
PubMed 33184277
Study at a glance
From the registry record, last updated by the sponsor on 20 Jan 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Generalizable Prognostic Models for Patient-Centered Decisions in COVID-19
- ClinicalTrials.gov ID
- NCT04689711
- Acronym / sponsor ID
- PCORI-ME-1606-35555
- Conditions
- Covid19
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 21 participants (actual)
- Lead sponsor
- Tufts Medical CenterUniversity, hospital or other
- Collaborators
- Northwell Health, Erasmus Medical Center
- Study start
- 7 Dec 2020 (actual)
- Primary completion
- 31 Aug 2021 (actual)
- Study completion
- 31 Aug 2021 (actual)
- First posted
- 30 Dec 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- 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
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (1 listed)
- Not proficient in reading or speaking English
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
1 secondary outcome
Stakeholder perceptions, beliefs and opinions on COVID prediction models
Time frame: 6 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Jan 2022.
- Background: PubMed 32320003Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW; the Northwell COVID-19 Research Consortium; Barnaby DP, Becker LB, Chelico JD, Cohen SL, Cookingham J, Coppa K, Diefenbach MA, Dominello AJ, Duer-Hefele J, Falzon L, Gitlin J, Hajizadeh N, Harvin TG, Hirschwerk DA, Kim EJ, Kozel ZM, Marrast LM, Mogavero JN, Osorio GA, Qiu M, Zanos TP. Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA. 2020 May 26;323(20):2052-2059. doi: 10.1001/jama.2020.6775.
- Background: PubMed 32171076Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, Guan L, Wei Y, Li H, Wu X, Xu J, Tu S, Zhang Y, Chen H, Cao B. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020 Mar 28;395(10229):1054-1062. doi: 10.1016/S0140-6736(20)30566-3. Epub 2020 Mar 11.
- Background: PubMed 32330939Clarification of Mortality Rate and Data in Abstract, Results, and Table 2. JAMA. 2020 May 26;323(20):2098. doi: 10.1001/jama.2020.7681. No abstract available.
- Background: PubMed 32265220Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, Bonten MMJ, Dahly DL, Damen JAA, Debray TPA, de Jong VMT, De Vos M, Dhiman P, Haller MC, Harhay MO, Henckaerts L, Heus P, Kammer M, Kreuzberger N, Lohmann A, Luijken K, Ma J, Martin GP, McLernon DJ, Andaur Navarro CL, Reitsma JB, Sergeant JC, Shi C, Skoetz N, Smits LJM, Snell KIE, Sperrin M, Spijker R, Steyerberg EW, Takada T, Tzoulaki I, van Kuijk SMJ, van Bussel B, van der Horst ICC, van Royen FS, Verbakel JY, Wallisch C, Wilkinson J, Wolff R, Hooft L, Moons KGM, van Smeden M. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. 2020 Apr 7;369:m1328. doi: 10.1136/bmj.m1328.
- BackgroundLevy 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.
- BackgroundTibshirani R. Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society Series B (Methodological). 1996;58(1):267-288.
- Background: PubMed 17031868Putter H, Fiocco M, Geskus RB. Tutorial in biostatistics: competing risks and multi-state models. Stat Med. 2007 May 20;26(11):2389-430. doi: 10.1002/sim.2712.
- Background: PubMed 20227129de Wreede LC, Fiocco M, Putter H. The mstate package for estimation and prediction in non- and semi-parametric multi-state and competing risks models. Comput Methods Programs Biomed. 2010 Sep;99(3):261-74. doi: 10.1016/j.cmpb.2010.01.001. Epub 2010 Mar 15.
- Background: PubMed 31183411van Klaveren D, Steyerberg EW, Gonen M, Vergouwe Y. The calibrated model-based concordance improved assessment of discriminative ability in patient clusters of limited sample size. Diagn Progn Res. 2019 Jun 6;3:11. doi: 10.1186/s41512-019-0055-8. eCollection 2019.
- Background: PubMed 19325482Jones AE, Trzeciak S, Kline JA. The Sequential Organ Failure Assessment score for predicting outcome in patients with severe sepsis and evidence of hypoperfusion at the time of emergency department presentation. Crit Care Med. 2009 May;37(5):1649-54. doi: 10.1097/CCM.0b013e31819def97.
- Background: PubMed 12728155Lim WS, van der Eerden MM, Laing R, Boersma WG, Karalus N, Town GI, Lewis SA, Macfarlane JT. Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax. 2003 May;58(5):377-82. doi: 10.1136/thorax.58.5.377.
- Background: PubMed 33184277Griffith GJ, Morris TT, Tudball MJ, Herbert A, Mancano G, Pike L, Sharp GC, Sterne J, Palmer TM, Davey Smith G, Tilling K, Zuccolo L, Davies NM, Hemani G. Collider bias undermines our understanding of COVID-19 disease risk and severity. Nat Commun. 2020 Nov 12;11(1):5749. doi: 10.1038/s41467-020-19478-2.
- Parent study funded through the Patient Centered Outcome Research Institute (PCORI) to develop evaluation and updating framework for clinical prediction models used in this study.Link provided by the sponsor.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
David M Kent, MD, MS principal investigator
Tufts Medical Center
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Covid19, run by Tufts Medical Center at 2 sites in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 20 Jan 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Changes in model discrimination (Model 1: need for MV in patients hospitalized with COVID-19) (time frame: 30 days from hospitalization). The study lists 18 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04689711, which also lists 12 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Jan 2022.