The peak levels of highly sensitive troponin I predicts in-hospital mortality in COVID-19 patients with cardiac injury: a retrospective study.
05 Mar 2021-European heart journal. Acute cardiovascular care (Eur Heart J Acute Cardiovasc Care)-Vol. 10, Iss: 1, pp 6-15
TL;DR: In this paper, the authors investigated the association between levels of highly sensitive troponin I (hs-troponinI) and mortality in novel coronavirus disease 2019 (COVID-19) patients with cardiac injury.
Abstract: AIMS: To investigate the association between levels of highly sensitive troponin I (hs-troponin I) and mortality in novel coronavirus disease 2019 (COVID-19) patients with cardiac injury. METHODS AND RESULTS: We retrospectively reviewed the medical records of all COVID-19 patients with increased levels of hs-troponin I from two hospitals in Wuhan, China. Demographic information, laboratory test results, cardiac ultrasonographic findings, and electrocardiograms were collected, and their predictive value on in-hospital mortality was explored using multivariable logistic regression. Of 1500 patients screened, 242 COVID-19 patients were enrolled in our study. Their median age was 68 years, and (48.8%) had underlying cardiovascular diseases. One hundred and seventy-six (72.7%) patients died during hospitalization. Multivariable logistic regression showed that C-reactive protein (>75.5 mg/L), D-dimer (>1.5 µg/mL), and acute respiratory distress syndrome were risk factors of mortality, and the peak hs-troponin I levels (>259.4 pg/mL) instead of the hs-troponin I levels at admission was predictor of death. The area under the receiver operating characteristic curve of the peak levels of hs-troponin I for predicting in-hospital mortality was 0.79 (95% confidence interval, 0.73-0.86; sensitivity, 0.80; specificity, 0.72; P < 0.0001). CONCLUSION: Our results demonstrated that the risk of in-hospital death among COVID-19 patients with cardiac injury can be predicted by the peak levels of hs-troponin I during hospitalization and was significantly associated with oxygen supply-demand mismatch, inflammation, and coagulation.
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TL;DR: In this article , a systematic search of indexed articles in PubMed, Embase, CINAHL, Cochrane, Web of Science, and Scopus was conducted to quantify the association between biomarkers of myocardial injury, coagulation, and severe COVID-19 and death in hospitalized patients.
12 citations
TL;DR: In the era of the coronavirus disease 2019 (COVID-19) pandemic, acute cardiac injury (ACI), as reflected by elevated cardiac troponin above the 99th percentile, has been observed in 8%-62% of patients with COVID-2019 infection with highest incidence and mortality recorded in patients with severe infection.
Abstract: In the era of the coronavirus disease 2019 (COVID-19) pandemic, acute cardiac injury (ACI), as reflected by elevated cardiac troponin above the 99th percentile, has been observed in 8%-62% of patients with COVID-19 infection with highest incidence and mortality recorded in patients with severe infection. Apart from the clinically and electrocardiographically discernible causes of ACI, such as acute myocardial infarction (MI), other cardiac causes need to be considered such as myocarditis, Takotsubo syndrome, and direct injury from COVID-19, together with noncardiac conditions, such as pulmonary embolism, critical illness, and sepsis. Acute coronary syndromes (ACS) with normal or near-normal coronary arteries (ACS-NNOCA) appear to have a higher prevalence in both COVID-19 positive and negative patients in the pandemic compared to the pre-pandemic era. Echocardiography, coronary angiography, chest computed tomography and/or cardiac magnetic resonance imaging may render a correct diagnosis, obviating the need for endomyocardial biopsy. Importantly, a significant delay has been recorded in patients with ACS seeking advice for their symptoms, while their routine care has been sharply disrupted with fewer urgent coronary angiographies and/or primary percutaneous coronary interventions performed in the case of ST-elevation MI (STEMI) with an inappropriate shift toward thrombolysis, all contributing to a higher complication rate in these patients. Thus, new challenges have emerged in rendering a diagnosis and delivering treatment in patients with ACI/ACS in the pandemic era. These issues, the various mechanisms involved in the development of ACI/ACS, and relevant current guidelines are herein reviewed.
11 citations
TL;DR: In this paper , a systematic review and meta-analysis synthesized the evidence on the impacts of demographics and comorbidities on the clinical outcomes of COVID-19, as well as the sources of the heterogeneity and publication bias of the relevant studies.
Abstract: This systematic review and meta-analysis synthesized the evidence on the impacts of demographics and comorbidities on the clinical outcomes of COVID-19, as well as the sources of the heterogeneity and publication bias of the relevant studies. Two authors independently searched the literature from PubMed, Embase, Cochrane library, and CINAHL on 18 May 2021; removed duplicates; screened the titles, abstracts, and full texts by using criteria; and extracted data from the eligible articles. The variations among the studies were examined by using Cochrane, Q.; I2, and meta-regression. Out of 11,975 articles that were obtained from the databases and screened, 559 studies were abstracted, and then, where appropriate, were analyzed by meta-analysis (n = 542). COVID-19-related severe illness, admission to the ICU, and death were significantly correlated with comorbidities, male sex, and an age older than 60 or 65 years, although high heterogeneity was present in the pooled estimates. The study design, the study country, the sample size, and the year of publication contributed to this. There was publication bias among the studies that compared the odds of COVID-19-related deaths, severe illness, and admission to the ICU on the basis of the comorbidity status. While an older age and chronic diseases were shown to increase the risk of developing severe illness, admission to the ICU, and death among the COVID-19 patients in our analysis, a marked heterogeneity was present when linking the specific risks with the outcomes.
8 citations
TL;DR: Patients with SARS-CoV-2 infection should be carefully assessed in terms of cardiac medical history and possible cardiological complications, as cardiac injury is an independent risk factor, which multiplies the death rate several times in comparison to infected patients without myocardial injury.
Abstract: SARS-CoV-2 virus can not only damage the respiratory system but may also pose a threat to other organs, such as the heart or vessels. This review focuses on cardiovascular complications of COVID-19, including acute cardiac injury, arrhythmias, biomarkers, accompanying comorbidities and outcomes in patients diagnosed with SARS-CoV-2 infection. The research was conducted on the databases: PubMed, Springer, ScienceDirect, UpToDate, Oxford Academic, Wiley Online Library, ClinicalKey. Fifty-six publications from 1 November 2020 till 15 August 2021 were included in this study. The results show that cardiac injury is present in about 1 in 4 patients with COVID-19 disease, and it is an independent risk factor, which multiplies the death rate several times in comparison to infected patients without myocardial injury. New-onset cardiac injury occurs in nearly every 10th patient of the COVID-19-suffering population. Comorbidities (such as hypertension, cardiovascular disease and diabetes) severely deteriorate the outcome. Therefore, patients with SARS-CoV-2 infection should be carefully assessed in terms of cardiac medical history and possible cardiological complications.
5 citations
17 Aug 2021
TL;DR: In this paper, a rule extraction technique was proposed to determine whether an axis-parallel hyperplane is discriminative or not, with a greedy algorithm that progressively removes rule antecedents.
Abstract: A natural method aiming at explaining the answers of a black-box model is by means of propositional rules. Nevertheless, rule extraction from ensembles of Machine Learning models was rarely achieved. Moreover, experiments in this context have rarely been evaluated by cross-validation trials. Based on stratified tenfold cross-validation, we performed experiments with several ensemble models on Covid-19 prognostic data. Specifically, we compared the characteristics of the propositional rules generated from: Random Forests; Shallow Trees trained by Gradient Boosting; Decision Stumps trained by several variants of Boosting; and ensembles of transparent neural networks trained by Bagging. The Discretized Interpretable Multi Layer Perceptron (DIMLP) allowed us to generate rules from all the used ensembles by transforming Decision Trees into DIMLPs. Our rule extraction technique simply determines whether an axis-parallel hyperplane is discriminative or not, with a greedy algorithm that progressively removes rule antecedents. Rules extracted from Decision Stumps trained by modest Adaboost were the simplest with the highest fidelity. Our best average predictive accuracy result was equal to 96.5%. Finally, we described a particular ruleset extracted from an ensemble of Decision Stumps and it turned out that the rule antecedents seem to be plausible with respect to several recent works related to the Covid-19 virus.
1 citations
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