COVID-CLIMATE: COVID-19: The role of CLinical and IMAging TEsts


In de beginperiode van de pandemie werden patiënten behandeld met zuurstof of beademing. Ook werden medicijnen geprobeerd met wisselend resultaat. Enkele medicijnen bleken effectief, maar het is nog onduidelijk aan wie deze medicijnen het beste gegeven kunnen worden. In deze periode zijn veel CT-scans gemaakt om de diagnose te stellen, omdat er weinig PCR-testen waren. Deze scans geven echter ook informatie over de ernst van de ziekte en mogelijk over het verloop ervan. In dit project wordt onderzocht of met de scans beter te voorspellen is, wie de medicijnen nodig zullen hebben.


Bepalen of het met CT-scans mogelijk is om te voorspellen welke COVID-19 patiënten medicijnen nodig zullen hebben. 


Een deel van de patiënten houdt klachten na herstel. De onderzoekers proberen per persoon te voorspellen hoe dit zal verlopen. Daarvoor gebruiken ze CT-scans en andere informatie over de gezondheid van ex-patiënten. Bij enkele patienten zullen ze enkele maanden na herstel een nieuw soort scan maken. Hiermee kunnen ze zien of de blijvende afwijkingen worden veroorzaakt door ontsteking of beginnende verlittekening.


Samenvatting van de aanvraag

Research question While the vast majority of COVID-19 patients only experience mild symptoms, a minority of patients develop severe COVID-19. Initial treatment of these patients consisted of pure supportive care, but more recently, anti-inflammatory and immune modulating (IMM) therapies have been shown to reduce mortality in severe COVID-19. However, not all patients benefit and thus the optimal patient selection is unknown. Besides, persisting symptoms and disabilities after surviving COVID-19 cannot be predicted nor adequately explained, so far. In the current project, we aim to determine whether computer tomography (CT) scans may be helpful for better patient stratification over clinical parameters alone. Although it was hypothesized that more extensive parenchymal involvement is predictive for ICU admission, more parameters on intrapulmonary and extrapulmonary characteristics can be extracted and their predictive value is unclear. Low muscle mass and high ectopic fat mass derived from baseline chest CTs have been suggested to be predictive. Hypothesis We hypothesize that data on severity of COVID-19 pneumonia, in combination with data on body composition extracted from baseline chest CTs can increase predictive value of currently available models including clinical, demographic and laboratory parameters on disease course and outcome and this way be helpful in patient selection for therapy. Moreover, we aim predict outcome at least one year after hospital discharge. Besides, we will investigate the role of a new tracer (FAPI) in separating ongoing inflammation from fibrosis at three months follow-up. Study design This project is a multicenter cohort study separated in two work packages. Study population 1. To investigate the role of baseline chest CTs, we will collect data already available in the participating centers including the CT scans. 2. To investigate patient outcome at one year from baseline data and data collected at three months after hospital discharge will be gathered. Patient outcome at one year after discharge will be investigated using data collected from patients visiting the outpatient clinic at 1-year. In a subset of patients, a more extensive multidimensional health assessment will be performed. Twenty patients will undergo FAPI-PET three months after hospital discharge to investigate whether residual abnormalities are due to persistent inflammation, which can be treated, or fibrosis. Intervention Not applicable Outcome measures 1. We will start to validate a model including the most important published predictive parameters on our own dataset. Secondly, data obtained from baseline CT-scans on both disease extent, type of abnormalities and extrapulmonary body composition will be added to investigate whether these parameters will improve the model. 2. The obtained retrospective clinical outcome data and CT-derived changes in pulmonary and extrapulmonary tissues will be used to predict health status (EQ-5D) after one year of discharge. Additionally, prospectively obtained multidimensional health measures will be assessed to find a pathophysiological basis for long-term health impact. FAPI-PET will be performed to distinguish between persistent inflammation and fibrosis to predict whether residual changes can disappear or will persist. Sample size 1. We will included all COVID-19 patients who have a baseline chest CT available in one of the participating centers. We expect to include around 1000 patients. We expect 20% rate of clinical deterioration at day 10-14 after admission. 2. Including 1000 patients at baseline and 450 at three months follow-up, we can successfully estimate twelve chest CT-derived, demographic and clinical predictors in binary logistic regression. For the prospective prediction study including ~200 patients, we will be able to estimate 6 predictors. Data analysis WP1. We will use the best existing model according to the BMJ living review and add CT parameters using logistic regression. WP2. Changes in CT-derived pulmonary and extrapulmonary features at three and twelve months together with clinical data will be used to analyze their combined impact on multidimensional health outcome after 12 months using logistic regression. FAPI-PETs will be analyzed for lesion size and uptake.

Onderdeel van programma:
Projectleider en penvoerder:
Dr. H.A. Gietema
Verantwoordelijke organisatie:
Maastricht University