| Title:
|
High sensitivity troponin and COVID-19 outcomes |
| Abstract:
|
BACKGROUND: Recent reports have demonstrated high troponin levels in patients affected with COVID-19. In the present study we aimed to determine the association between admission and peak troponin levels and COVID-19 outcomes. METHODS: This was an observational multi-ethnic multi-centre study in a UK cohort of 434 patients admitted and diagnosed COVID-19 positive across six hospitals in London UK during the second half of March 2020. RESULTS: Myocardial injury defined as positive troponin during admission was observed in 288 (66.4%) patients. Age (OR: 1.68 [1.491.88] p < .001) hypertension (OR: 1.81 [1.102.99] p = .020) and moderate chronic kidney disease (OR: 9.12 [95% CI: 4.2419.64] p < .001) independently predicted myocardial injury. After adjustment patients with positive peak troponin were more likely to need non-invasive and mechanical ventilation (OR: 2.40 [95% CI: 1.274.56] p = .007 and OR: 6.81 [95% CI: 3.4013.62] p < .001 respectively) and urgent renal replacement therapy (OR: 4.14 [95% CI: 1.3412.78] p = .013). With regards to events and after adjustment positive peak troponin levels were independently associated with acute kidney injury (OR: 6.76 [95% CI: 3.4013.47] p < .001) venous thromboembolism (OR: 11.99 [95% CI: 3.2044.88] p < .001) development of atrial fibrillation (OR: 10.66 [95% CI: 1.3385.32] p = .026) and death during admission (OR: 2.40 [95% CI: 1.344.29] p = .003). Similar associations were observed for admission troponin. In addition median length of stay in days was shorter for patients with negative troponin levels: 8 (513) negative 14 (723) low-positive levels and 16 (1023) high-positive (p < .001). CONCLUSIONS: Admission and peak troponin appear to be predictors for cardiovascular and non-cardiovascular events and outcomes in COVID-19 patients and their utilisation may have an impact on patient management. |
| Published:
|
2021-03-08 |
| Journal:
|
Acta cardiologica |
| DOI:
|
10.1080/00015385.2021.1887586 |
| DOI_URL:
|
http://doi.org/10.1080/00015385.2021.1887586 |
| Author Name:
|
Papageorgiou Nikolaos |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/papageorgiou_nikolaos |
| Author Name:
|
Sohrabi Catrin |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/sohrabi_catrin |
| Author Name:
|
Prieto Merino David |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/prieto_merino_david |
| Author Name:
|
Tyrlis Angelos |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/tyrlis_angelos |
| Author Name:
|
Atieh Abed Elfattah |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/atieh_abed_elfattah |
| Author Name:
|
Saberwal Bunny |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/saberwal_bunny |
| Author Name:
|
Lim Wei Yao |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/lim_wei_yao |
| Author Name:
|
Creta Antonio |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/creta_antonio |
| Author Name:
|
Khanji Mohammed |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/khanji_mohammed |
| Author Name:
|
Rusinova Reni |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/rusinova_reni |
| Author Name:
|
Chooneea Bashistraj |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/chooneea_bashistraj |
| Author Name:
|
Khiani Raj |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/khiani_raj |
| Author Name:
|
Wijesuriya Nadeev |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/wijesuriya_nadeev |
| Author Name:
|
Chow Anna |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/chow_anna |
| Author Name:
|
Butt Haroun |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/butt_haroun |
| Author Name:
|
Browne Stefan |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/browne_stefan |
| Author Name:
|
Joshi Nikhil |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/joshi_nikhil |
| Author Name:
|
Kay Jamie |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/kay_jamie |
| Author Name:
|
Ahsan Syed |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/ahsan_syed |
| Author Name:
|
Providencia Rui |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/providencia_rui |
| sha:
|
ff6558dcf6cd2658d93d56968dbf007216649c05 |
| license:
|
no-cc |
| license_url:
|
[no creative commons license associated] |
| source_x:
|
Medline; PMC |
| source_x_url:
|
https://www.medline.com/https://www.ncbi.nlm.nih.gov/pubmed/ |
| pubmed_id:
|
33685354 |
| pubmed_id_url:
|
https://www.ncbi.nlm.nih.gov/pubmed/33685354 |
| pmcid:
|
PMC7970632 |
| pmcid_url:
|
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7970632 |
| url:
|
https://doi.org/10.1080/00015385.2021.1887586
https://www.ncbi.nlm.nih.gov/pubmed/33685354/ |
| has_full_text:
|
TRUE |
| Keywords Extracted from Text Content:
|
renal
macrovascular
Renal
left ventricular
kidney
ECMO
fibrinogen
hsTrop
Barts Health NHS Trust
CRP
Figure 2
cardiac
CK
cardiovascular
D-Dimers
Shi
COVID-19
heart
SARS-CoV-2
Wuhan cohorts
thrombin
Coronavirus disease 2019
patient
creatinine
angiotensin-converting enzyme inhibitors (
pulmonary
chronic/baseline
D-dimers
atrial
men
bloods
Guo
Diet
pulmonary embolism
D-Dimer
CKD
O2
troponin T
thromboplastin
vein
People
22/04/2020
C-reactive
[4]
coronary artery
myocardium
Wuhan
APTT
ACE-I
blood
myocardial infarction/
myocardial
LDH
creatine kinase
COVID-19 [9
statin
[6] [7] [8] .
patients
haemoglobin
people
Patients
INR
lactate dehydrogenase
RRT
Patient
blood cell
ventricular
NT-proBNP
glomerular
COVID-19 patients
UK
Troponin
troponin |
| Extracted Text Content in Record:
|
First 5000 Characters:Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 is a global pandemic [1] . So far, it has affected more than 100 million people worldwide, and is associated with multi-organ dysfunction and high mortality rates [1] .
Studies suggest that some patients present to hospital and have a relatively benign course, being discharged within a few days. However, for other patients the disease course is more aggressive, requiring multiple interventions, while they experience higher mortality and longer in-hospital stay [2] . It would be therefore of importance to have a biomarker which could help in distinguishing between these two groups of patients, not only for prognosis, but also, potentially, for treatment decisions.
Troponin is a marker of myocardial injury, but it is also found to be raised in several conditions. Recent reports demonstrated high troponin levels in patients affected by COVID-19. These were found to have higher mortality rates during the initial outbreak in Wuhan cohorts [3, 4] . In addition, higher mortality rates have been observed in the UK, as an older and multiethnic population with more comorbidities was affected by the disease [5] .
It is still unknown which patients are more likely to develop myocardial injury in the setting of COVID-19, and whether or not, after adjustment for confounders associated with rise in troponin, myocardial injury can be used as an independent predictor of the disease in Western multi-ethnic populations. Yet, it remains to be determined if admission troponin can be used as a predictor, and if the magnitude of troponin rise translates into different outcome rates (i.e. whether patients with higher rise in troponin levels experience a more severe disease progression than those with negative or mildly increase in troponin levels).
We aimed to assess: (1) the risk factors for myocardial injury; (2) the impact of myocardial injury on different COVID-19 associated outcomes and (3) whether admission troponin, peak troponin and magnitude of troponin rise have a similar prognostic capacity.
In this multi-centre study, we assessed the association between high-sensitivity troponin (hsTrop) and COVID-19 intra-hospital clinical trajectory (comprising mortality, utilisation of procedures, and cardiovascular and non-cardiovascular outcomes) in a UK cohort of 434 patients admitted and diagnosed positive, across six hospitals in London, UK. All patients admitted to the participant hospitals from the 16th to the 30th of March 2020 with a diagnosis of COVID-19 and having at least one troponin measurement were considered eligible for analysis. This observational study was approved by the Clinical Effectiveness Unit at Barts Health NHS Trust (Project ID: 11103; Title: COVID-19 and cardiovascular disease (CVD) outcomes) and by the Quality Governance Department at Royal Free London NHS Trust (Cardiovascular Implications of Outcomes of Patients With COVID-19; 22/04/2020).
Patient demographic characteristics, laboratory results, procedures, comorbidities, procedures and outcomes were extracted from the electronic records and paper notes. In order for patients to be included in the study, these should be diagnosed COVID-19 positive, as confirmed by polymerase chain reaction (PCR) swab. Patients with two positive swabs and older than 16 years were included in the study. Troponin T levels were measured with a high-sensitivity assay on and during admission as per Trusts' protocols.
Routine bloods were obtained from patients on and during admission. Routine hospital laboratory methods were used for the analysis. These were available on the electronic systems and included: full blood count parameters, high-sensitivity troponin T, C-reactive protein (CRP), lactate dehydrogenase (LDH), N-terminal pro B-type natriuretic peptide (NT-proBNP), international normalised ratio (INR), creatine kinase (CK), D-Dimers, activated partial thromboplastin time (APTT), fibrinogen, thrombin time and creatinine. Glomerular filtration rate (eGFR) was estimated using the Modification of Diet in Renal Disease (MDRD) Study equation. Patients were classified as having moderate CKD when eGFR was less than 60 ml/min.
Troponin measurements were performed based on clinical indication or based on local protocols for risk stratification of COVID-19 patients.
The term "myocardial injury" was used for patients with positive troponin levels (defined as 15 ng/mLthe 99th percentile in the normal population according to our lab). Peak troponin for each patient was measured and based on these values, three troponin levels were defined, utilising tertiles to define the cutoffs, as: "negative" if <15 ng/mL (no myocardial injury according to our definition above), "low-positive" if levels 15 ng/mL and <47 ng/mL and "high-positive" level if 47 ng/mL.
The study primary endpoint was defined as all-cause mortality. Secondary outcomes were: (i) pneumonia, (ii) acute kidney injury (defined as a 50% increase in creatinine compared |
| Keywords Extracted from PMC Text:
|
creatinine
myocardium
thromboplastin
LDH
thrombin
heart
pulmonary embolism
Coronavirus disease 2019
Shi
APTT
D-dimers
Diet
troponin T
bloods
Guo
troponin
's T
People
CK
pulmonary
NT-proBNP
ventricular
blood cell
D-Dimers
fibrinogen
INR
statin
Troponin
SARS-CoV-2
Patients
chronic/baseline
O2
people
Renal
ACE-I
blood
myocardial
kidney
macro-vascular
CRP
patient
Figure 2
glomerular
CKD
ECMO
lactate dehydrogenase
Wuhan
Wuhan cohorts
renal
COVID-19 patients
creatine kinase
RRT
COVID-19 [9
cardiac
COVID-19
angiotensin-converting enzyme inhibitors (
patients
coronary artery
haemoglobin
C-reactive
"
UK
≥15
myocardial infarction/ischaemic
vein
cardiovascular
atrial
[4] |
| Extracted PMC Text Content in Record:
|
First 5000 Characters:Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 is a global pandemic [1]. So far, it has affected more than 100 million people worldwide, and is associated with multi-organ dysfunction and high mortality rates [1].
Studies suggest that some patients present to hospital and have a relatively benign course, being discharged within a few days. However, for other patients the disease course is more aggressive, requiring multiple interventions, while they experience higher mortality and longer in-hospital stay [2]. It would be therefore of importance to have a biomarker which could help in distinguishing between these two groups of patients, not only for prognosis, but also, potentially, for treatment decisions.
Troponin is a marker of myocardial injury, but it is also found to be raised in several conditions. Recent reports demonstrated high troponin levels in patients affected by COVID-19. These were found to have higher mortality rates during the initial outbreak in Wuhan cohorts [3,4]. In addition, higher mortality rates have been observed in the UK, as an older and multi-ethnic population with more comorbidities was affected by the disease [5].
It is still unknown which patients are more likely to develop myocardial injury in the setting of COVID-19, and whether or not, after adjustment for confounders associated with rise in troponin, myocardial injury can be used as an independent predictor of the disease in Western multi-ethnic populations. Yet, it remains to be determined if admission troponin can be used as a predictor, and if the magnitude of troponin rise translates into different outcome rates (i.e. whether patients with higher rise in troponin levels experience a more severe disease progression than those with negative or mildly increase in troponin levels).
We aimed to assess: (1) the risk factors for myocardial injury; (2) the impact of myocardial injury on different COVID-19 associated outcomes and (3) whether admission troponin, peak troponin and magnitude of troponin rise have a similar prognostic capacity.
Routine bloods were obtained from patients on and during admission. Routine hospital laboratory methods were used for the analysis. These were available on the electronic systems and included: full blood count parameters, high-sensitivity troponin T, C-reactive protein (CRP), lactate dehydrogenase (LDH), N-terminal pro B-type natriuretic peptide (NT-proBNP), international normalised ratio (INR), creatine kinase (CK), D-Dimers, activated partial thromboplastin time (APTT), fibrinogen, thrombin time and creatinine. Glomerular filtration rate (eGFR) was estimated using the Modification of Diet in Renal Disease (MDRD) Study equation. Patients were classified as having moderate CKD when eGFR was less than 60 ml/min.
Troponin measurements were performed based on clinical indication or based on local protocols for risk stratification of COVID-19 patients.
The term "myocardial injury" was used for patients with positive troponin levels (defined as ≥15 ng/mL – the 99th percentile in the normal population according to our lab). Peak troponin for each patient was measured and based on these values, three troponin levels were defined, utilising tertiles to define the cut-offs, as: "negative" if <15 ng/mL (no myocardial injury according to our definition above), "low-positive" if levels ≥15 ng/mL and <47 ng/mL and "high-positive" level if ≥47 ng/mL.
The study primary endpoint was defined as all-cause mortality. Secondary outcomes were: (i) pneumonia, (ii) acute kidney injury (defined as a 50% increase in creatinine compared to chronic/baseline levels), (iii) myocardial injury (defined as high-sensitivity troponin above the 99th percentile of normal), (iv) acute heart failure, (v) acute atrial fibrillation episode, (vi) stroke, (vii) venous thromboembolic disease (including pulmonary embolism and/or deep vein thrombosis) and (viii) utilisation of procedures (non-invasive ventilation, mechanical ventilation, ECMO and renal replacement therapy).
Descriptive statistics are presented as proportions for binary variables and median and inter-quartile for continuous variables. Parametric (Student's T) or equivalent non-parametric tests (Mann–Whitney) were used where appropriate for comparisons of continuous variables among groups. Proportions were compared with a Chi2 test.
Binary logistic regression was used to assess for predictors of myocardial injury using the forward likelihood ratio (LR) method, with probability for stepwise .05. Significant predictors identified through univariate analysis are added in the multivariable model through a stepwise process with predictors entering at each step as long as they significantly improve the predictive capacity of the model.
The association of troponin levels with each pre-defined procedure and clinical outcome was assessed with a separate logistic regression. Each model was adjusted for the clinical variables that showed significant association with myoca |
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