Prognostic Factors Keeping Track for COVID-19 Pneumonia
NCT ID: NCT04324684
Last Updated: 2020-05-14
Study Results
Results pending
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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Recruitment Status
COMPLETED
Total Enrollment
198 participants
Study Classification
OBSERVATIONAL
Study Start Date
2020-03-31
Study Completion Date
2020-05-07
Brief Summary
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It has been reported that nearly half of the patients who are hospitalized for Covid-19 pneumonia have on admission old age or comorbidities.
In particular, hypertension was present in 30% of the cases, diabetes in 19%, coronary heart disease in 8% and chronic obstructive lung disease in 3% of the patients.
Amazingly, in the two major studies published in the Lancet (Zhou F et al Lancet 2020) and in the New England Journal of Medicine (Guan W et al 2020), the weight of the subjects as well their body mass index (BMI) were omitted. However, obesity, alone or in association with diabetes, can be a major predisposition factor for Covid-19 infection.
The primary end-point of our prospective, observational study is to assess the recovery rate in patients with diagnosis of Covid-19 pneumonia. Among the other secondary end-points, we intend to find the predictors of the time to clinical improvement or hospital discharge in patients affected by Covid-19 pneumonia.
In particular, hypertension was present in 30% of the cases, diabetes in 19%, coronary heart disease in 8% and chronic obstructive lung disease in 3% of the patients.
Amazingly, in the two major studies published in the Lancet (Zhou F et al Lancet 2020) and in the New England Journal of Medicine (Guan W et al 2020), the weight of the subjects as well their body mass index (BMI) were omitted. However, obesity, alone or in association with diabetes, can be a major predisposition factor for Covid-19 infection.
The primary end-point of our prospective, observational study is to assess the recovery rate in patients with diagnosis of Covid-19 pneumonia. Among the other secondary end-points, we intend to find the predictors of the time to clinical improvement or hospital discharge in patients affected by Covid-19 pneumonia.
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Detailed Description
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It has been reported that nearly half of the patients who are hospitalized for Covid-19 pneumonia have on admission old age or comorbidities.
In particular, hypertension was present in 30% of the cases, diabetes in 19%, coronary heart disease in 8% and chronic obstructive lung disease in 3% of the patients.
Amazingly, in the two major studies published in the Lancet and in the New England Journal of Medicine, the weight of the subjects as well their body mass index (BMI) were omitted. However, obesity, alone or in association with diabetes, can be a major predisposition factor for Covid-19 infection.
Obesity is associated with a systemic low-grade inflammation state with increase circulating levels on many pro-inflammatory cytokines, such as IL-1β and IL-6 .
Belonging to the innate immune system but sharing characteristics with the adaptive immunity, natural killer (NK) cells are activated in the white adipose tissue of subjects with obesity where they proliferate and trigger M1 macrophage accumulation.
NK cells are the first line of defense against viral infections. They mediate cytolysis or apoptosis of virus-infected cells. Moreover, NK cells release pro-inflammatory cytokines with antiviral activity.
Not only NK cells frequency is reduced in subjects with obesity but also their cytotoxic capabilities are reduced.
A lower NK cell activity is also present in subjects with type 2 diabetes .Therefore, subjects with obesity and/or type 2 diabetes should have an enhanced susceptibility to viral infections.
It has been shown that hypertension is associated with Covid19 infection in 24-30% of the cases while diabetes was present in 12% to 22% of the patients.
It is now recognized that lipids perform numerous indispensable cellular functions and some of them are involved in the activation of the immune active cells. In addition, lipids are involved in multiple steps in the virus replication cycle, and a recent article showed how metabolic remodelling of host lipids is significantly associated with the propagation of the human-pathogenic coronavirus.
Lipids show both pro-inflammatory and anti-inflammatory activities and interact with the immune response through the activation of lipid-reactive T cells. Ceramides (Cer), phospholipid or sphingolipid, but also amino acids and free fatty acids (FFA), activate the pro-inflammatory pathways resulting in the activation of toll like receptor-4 (TLR-4) and Lysophosphatidylcholines (LPC) that play a role in cell proliferation and activation of T-cells.
The platelet-activating factor, (also known as PAF, PAF-acether or AGEPC, i.e. acetyl-glyceryl-ether-phosphorylcholine), can also be involved. PAF is a potent phospholipid activator and mediator of many leukocyte functions, platelet aggregation and degranulation, inflammation, and anaphylaxis. Moreover, it is an important mediator of bronchoconstriction.
We hypothesize that several lipids may serve as biomarkers of patients who will develop a more severe reaction to the virus. Measurement of plasma lipidomic profile will help in finding subjects more at risk to severe pulmonary disease and in helping to target treatment strategy.
The primary end-point of our prospective, observational study is to assess the recovery rate in patients with diagnosis of Covid-19 pneumonia. Among the other secondary end-points, we intend to find the predictors of the time to clinical improvement or hospital discharge in patients affected by Covid-19 pneumonia.
Clinical improvement is defined as the reduction in severity of Covid-19 pneumonia expressed as the transition from a higher severity to a less severity condition. The possible outcomes are 1. Death; 2. hospitalization, requiring extracorporeal membrane oxygenation and/or invasive mechanical ventilation; 3. hospitalization, requiring nasal high-flow oxygen therapy and/or noninvasive mechanical ventilation; 4. hospitalization, requiring supplemental oxygen; 5. hospital discharge.
Secondary endpoints will include liver, kidney or multiorgan failure, cardiac failure, the efficacy of different pharmaceutical treatment against Covid-19 and the development of predictors and biomarkers of the severity of Covid-19 infection.
Methods Before starting the study, the protocol will be submitted to and approved by the local Ethical Committees at the Fondazione Policlinico Universitario A. Gemelli IRCCS, Catholic University, Rome, Italy. Before enrollment each subject will sign the informed consent.
Inclusion criteria: hospitalized subjects of both sexes aged 18 years or older with diagnosis of pneumonia, confirmed by chest imaging and oxygen saturation (SaO2) ≤ 94% in ambient air, Covid-19 test positive, given informed consent to data collection from the patient or from the patient's legal representative if the patient is too unwell to provide consent.
Exclusion criteria: age lower than 18 years, pregnancy or breast-feeding. Nasopharyngeal swab samples will be taken for quantitative real-time polymerase chain reaction to make diagnosis of Covid19 (2 repeated tests).
Data collected include time of symptoms (cough, fever, dyspnea, conjunctivitis, diarrhea, asthenia, arthralgia) age, sex, height, weight, education, alcohol and smoking habits, morbidities, plasma glucose, creatinine, transaminases, γ-GT, total cholesterol, HDL-cholesterol, triglycerides, complete blood count, D-dimer, lactic acid dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP), creatinkinase (CK), ferritin, albumin, HbA1c, chest X rays, chest CT scan, therapy for pneumonia, other treatments including anti-hypertensive and anti-hyperglycemic agents, body temperature, blood pressure, and oxygen flow rate or other types of oxygen treatment.
Five ml of plasma divided in aliquots of 1 ml each will be also obtained and stored at -80°C in anonymized way for future analysis, including third parties.
Primary end-point The primary end-point of the study is to compare the mean recovery rate in patients with diagnosis of Covid-19 pneumonia, who present with complications at the time of hospital admission (such as diabetes, obesity, cardiovascular disease, hypertension or respiratory failure), with the mean recovery rate in patients without any of the above-mentioned complications.
Secondary end-points
A secondary end-point of the study is the comparison of the survival curves (times to improvement) in the two groups (patients with and without complications) and among patients presenting with different types of complications:
1. Hypertension
2. Obesity and/or type 2 diabetes
3. Cardiovascular disease
4. Chronic obstructive lung disease
5. None of the above diseases Other endpoints are liver, kidney or multiorgan failure, cardiac failure, the efficacy of different pharmaceutical treatment against Covid-19 and the development of predictors and biomarkers of the severity of Covid-19 infection.
Sample size The sample size computation (20) is performed under the following hypotheses: the rate of recovery for patients without complications is supposed to be 98%; the average rate of recovery for patients with one of the following complications: diabetes, obesity, cardiovascular disease, hypertension or chronic respiratory failure, is supposed to be 88%. Moreover, it is supposed that the ratio between the sizes of the two groups is k=Nc/Nwc = 1, under the assumption that 50% of patients with Covid-19 pneumonia have one of the above-mentioned complications. We are, in fact, including overweight and obesity. To reach a power of 0.80, with a ratio k of 1, the probabilities of improvement equal to pc = 0.88 and pwc =0.98 and with an expected difference rates of 0.10, the sample size required is 198 patients if α is equal to 0.05.
Statistics The association between recovery and patient groups will be tested by means of a Fisher exact test. A Cox Proportional-Hazard regression will be used to compare survival curves (times to improvement) among the studied groups by correcting for the administered therapy and for all the quantitative collected variables. Quantitative variables, measured at hospital admission, will be compared among groups using ANOVA. In univariable analyses, categorical variables, as gender, education, alcohol consumption and smoke habits will be analysed by means of a Chi-Squared test to study their association with the recovery, while a logistic regression model will be used to test possible quantitative predictors of recovery. A multivariable logistic model, with a stepwise selection procedure, will be then used to test all the variables that are significant in a univariable analysis.
In particular, hypertension was present in 30% of the cases, diabetes in 19%, coronary heart disease in 8% and chronic obstructive lung disease in 3% of the patients.
Amazingly, in the two major studies published in the Lancet and in the New England Journal of Medicine, the weight of the subjects as well their body mass index (BMI) were omitted. However, obesity, alone or in association with diabetes, can be a major predisposition factor for Covid-19 infection.
Obesity is associated with a systemic low-grade inflammation state with increase circulating levels on many pro-inflammatory cytokines, such as IL-1β and IL-6 .
Belonging to the innate immune system but sharing characteristics with the adaptive immunity, natural killer (NK) cells are activated in the white adipose tissue of subjects with obesity where they proliferate and trigger M1 macrophage accumulation.
NK cells are the first line of defense against viral infections. They mediate cytolysis or apoptosis of virus-infected cells. Moreover, NK cells release pro-inflammatory cytokines with antiviral activity.
Not only NK cells frequency is reduced in subjects with obesity but also their cytotoxic capabilities are reduced.
A lower NK cell activity is also present in subjects with type 2 diabetes .Therefore, subjects with obesity and/or type 2 diabetes should have an enhanced susceptibility to viral infections.
It has been shown that hypertension is associated with Covid19 infection in 24-30% of the cases while diabetes was present in 12% to 22% of the patients.
It is now recognized that lipids perform numerous indispensable cellular functions and some of them are involved in the activation of the immune active cells. In addition, lipids are involved in multiple steps in the virus replication cycle, and a recent article showed how metabolic remodelling of host lipids is significantly associated with the propagation of the human-pathogenic coronavirus.
Lipids show both pro-inflammatory and anti-inflammatory activities and interact with the immune response through the activation of lipid-reactive T cells. Ceramides (Cer), phospholipid or sphingolipid, but also amino acids and free fatty acids (FFA), activate the pro-inflammatory pathways resulting in the activation of toll like receptor-4 (TLR-4) and Lysophosphatidylcholines (LPC) that play a role in cell proliferation and activation of T-cells.
The platelet-activating factor, (also known as PAF, PAF-acether or AGEPC, i.e. acetyl-glyceryl-ether-phosphorylcholine), can also be involved. PAF is a potent phospholipid activator and mediator of many leukocyte functions, platelet aggregation and degranulation, inflammation, and anaphylaxis. Moreover, it is an important mediator of bronchoconstriction.
We hypothesize that several lipids may serve as biomarkers of patients who will develop a more severe reaction to the virus. Measurement of plasma lipidomic profile will help in finding subjects more at risk to severe pulmonary disease and in helping to target treatment strategy.
The primary end-point of our prospective, observational study is to assess the recovery rate in patients with diagnosis of Covid-19 pneumonia. Among the other secondary end-points, we intend to find the predictors of the time to clinical improvement or hospital discharge in patients affected by Covid-19 pneumonia.
Clinical improvement is defined as the reduction in severity of Covid-19 pneumonia expressed as the transition from a higher severity to a less severity condition. The possible outcomes are 1. Death; 2. hospitalization, requiring extracorporeal membrane oxygenation and/or invasive mechanical ventilation; 3. hospitalization, requiring nasal high-flow oxygen therapy and/or noninvasive mechanical ventilation; 4. hospitalization, requiring supplemental oxygen; 5. hospital discharge.
Secondary endpoints will include liver, kidney or multiorgan failure, cardiac failure, the efficacy of different pharmaceutical treatment against Covid-19 and the development of predictors and biomarkers of the severity of Covid-19 infection.
Methods Before starting the study, the protocol will be submitted to and approved by the local Ethical Committees at the Fondazione Policlinico Universitario A. Gemelli IRCCS, Catholic University, Rome, Italy. Before enrollment each subject will sign the informed consent.
Inclusion criteria: hospitalized subjects of both sexes aged 18 years or older with diagnosis of pneumonia, confirmed by chest imaging and oxygen saturation (SaO2) ≤ 94% in ambient air, Covid-19 test positive, given informed consent to data collection from the patient or from the patient's legal representative if the patient is too unwell to provide consent.
Exclusion criteria: age lower than 18 years, pregnancy or breast-feeding. Nasopharyngeal swab samples will be taken for quantitative real-time polymerase chain reaction to make diagnosis of Covid19 (2 repeated tests).
Data collected include time of symptoms (cough, fever, dyspnea, conjunctivitis, diarrhea, asthenia, arthralgia) age, sex, height, weight, education, alcohol and smoking habits, morbidities, plasma glucose, creatinine, transaminases, γ-GT, total cholesterol, HDL-cholesterol, triglycerides, complete blood count, D-dimer, lactic acid dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP), creatinkinase (CK), ferritin, albumin, HbA1c, chest X rays, chest CT scan, therapy for pneumonia, other treatments including anti-hypertensive and anti-hyperglycemic agents, body temperature, blood pressure, and oxygen flow rate or other types of oxygen treatment.
Five ml of plasma divided in aliquots of 1 ml each will be also obtained and stored at -80°C in anonymized way for future analysis, including third parties.
Primary end-point The primary end-point of the study is to compare the mean recovery rate in patients with diagnosis of Covid-19 pneumonia, who present with complications at the time of hospital admission (such as diabetes, obesity, cardiovascular disease, hypertension or respiratory failure), with the mean recovery rate in patients without any of the above-mentioned complications.
Secondary end-points
A secondary end-point of the study is the comparison of the survival curves (times to improvement) in the two groups (patients with and without complications) and among patients presenting with different types of complications:
1. Hypertension
2. Obesity and/or type 2 diabetes
3. Cardiovascular disease
4. Chronic obstructive lung disease
5. None of the above diseases Other endpoints are liver, kidney or multiorgan failure, cardiac failure, the efficacy of different pharmaceutical treatment against Covid-19 and the development of predictors and biomarkers of the severity of Covid-19 infection.
Sample size The sample size computation (20) is performed under the following hypotheses: the rate of recovery for patients without complications is supposed to be 98%; the average rate of recovery for patients with one of the following complications: diabetes, obesity, cardiovascular disease, hypertension or chronic respiratory failure, is supposed to be 88%. Moreover, it is supposed that the ratio between the sizes of the two groups is k=Nc/Nwc = 1, under the assumption that 50% of patients with Covid-19 pneumonia have one of the above-mentioned complications. We are, in fact, including overweight and obesity. To reach a power of 0.80, with a ratio k of 1, the probabilities of improvement equal to pc = 0.88 and pwc =0.98 and with an expected difference rates of 0.10, the sample size required is 198 patients if α is equal to 0.05.
Statistics The association between recovery and patient groups will be tested by means of a Fisher exact test. A Cox Proportional-Hazard regression will be used to compare survival curves (times to improvement) among the studied groups by correcting for the administered therapy and for all the quantitative collected variables. Quantitative variables, measured at hospital admission, will be compared among groups using ANOVA. In univariable analyses, categorical variables, as gender, education, alcohol consumption and smoke habits will be analysed by means of a Chi-Squared test to study their association with the recovery, while a logistic regression model will be used to test possible quantitative predictors of recovery. A multivariable logistic model, with a stepwise selection procedure, will be then used to test all the variables that are significant in a univariable analysis.
Conditions
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Pneumonia, Viral
Hypertension
Diabetes Mellitus
Obesity
Cardiovascular Diseases
Obstructive Lung Disease
Study Design
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Observational Model Type
CASE_CONTROL
Study Time Perspective
PROSPECTIVE
Study Groups
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Covid19 pneumonia with comorbidities
Patients with pneumonia from Covid 19 with at least one of the following comorbidities:
1. Hypertension
2. Obesity and/or type 2 diabetes
3. Cardiovascular disease
4. Chronic obstructive lung disease
No interventions assigned to this group
Covid2 pneumonia without comorbidities
Without any of the following comorbidities
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
diagnosis of pneumonia; Covid-19 test positive; hospitalized subjects; both sexes aged; given informed consent.
Exclusion Criteria
age lower than 18 years; pregnancy; breast-feeding.
Minimum Eligible Age
18 Years
Maximum Eligible Age
100 Years
Eligible Sex
ALL
Accepts Healthy Volunteers
No
Sponsors
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Catholic University of the Sacred Heart
OTHER
Sponsor Role
lead
Responsible Party
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Geltrude Mingrone
Associate Professor of Internal Medicine
Responsibility Role
PRINCIPAL_INVESTIGATOR
Principal Investigators
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Geltrude Mingrone, MD PhD
Role: PRINCIPAL_INVESTIGATOR
Fondazione Policlinico Universitario A. Gemelli, IRCCS
Locations
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Fondazione Policlinico Universitario A. Gemelli IRCCS
Roma, , Italy
Site Status
Countries
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Italy
References
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Zhou 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.
Reference Type
BACKGROUND
Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, Liu L, Shan H, Lei CL, Hui DSC, Du B, Li LJ, Zeng G, Yuen KY, Chen RC, Tang CL, Wang T, Chen PY, Xiang J, Li SY, Wang JL, Liang ZJ, Peng YX, Wei L, Liu Y, Hu YH, Peng P, Wang JM, Liu JY, Chen Z, Li G, Zheng ZJ, Qiu SQ, Luo J, Ye CJ, Zhu SY, Zhong NS; China Medical Treatment Expert Group for Covid-19. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med. 2020 Apr 30;382(18):1708-1720. doi: 10.1056/NEJMoa2002032. Epub 2020 Feb 28.
Reference Type
BACKGROUND
Bornstein SR, Dalan R, Hopkins D, Mingrone G, Boehm BO. Endocrine and metabolic link to coronavirus infection. Nat Rev Endocrinol. 2020 Jun;16(6):297-298. doi: 10.1038/s41574-020-0353-9.
Reference Type
BACKGROUND
Masters SL, Dunne A, Subramanian SL, Hull RL, Tannahill GM, Sharp FA, Becker C, Franchi L, Yoshihara E, Chen Z, Mullooly N, Mielke LA, Harris J, Coll RC, Mills KH, Mok KH, Newsholme P, Nunez G, Yodoi J, Kahn SE, Lavelle EC, O'Neill LA. Activation of the NLRP3 inflammasome by islet amyloid polypeptide provides a mechanism for enhanced IL-1beta in type 2 diabetes. Nat Immunol. 2010 Oct;11(10):897-904. doi: 10.1038/ni.1935. Epub 2010 Sep 12.
Reference Type
BACKGROUND
Qu D, Liu J, Lau CW, Huang Y. IL-6 in diabetes and cardiovascular complications. Br J Pharmacol. 2014 Aug;171(15):3595-603. doi: 10.1111/bph.12713.
Reference Type
BACKGROUND
Other Identifiers
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20202503
Identifier Type: -
Identifier Source: org_study_id
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