Role of a lower cutoff of high sensitivity troponin I in identification of early cardiac damage in non-severe patients with COVID-19

Detectable troponin below the 99th percentile predicts survival in patients undergoing coronary angiography

Background

Cardiac troponin I (cTnI) above the 99th percentile is associated with an increased risk of major adverse events. Patients with detectable cTnI below the 99th percentile are a heterogeneous group with a less well-defined risk profile. The purpose of this study is to investigate the prognostic relevance of detectable cTnI below the 99th percentile in patients undergoing coronary angiography.

Methods

The study included 14,776 consecutive patients (mean age of 65.4 ± 12.7 years, 71.3 % male) from the Essen Coronary Artery Disease (ECAD) registry. Patients with cTnI levels above the 99th percentile and patients with ST-segment elevation acute myocardial infarction were excluded. All-cause mortality was defined as the primary endpoint.

Results

Detectable cTnI below the 99th percentile was present in 2811 (19.0 %) patients, while 11,965 (81.0 %) patients were below detection limit of the employed assay. The mean follow-up was 4.25 ± 3.76 years. All-cause mortality was 20.8 % for patients with detectable cTnI below the 99th percentile and 15.0 % for those without detectable cTnI. In a multivariable Cox regression analysis, detectable cTnI was independently associated with all-cause mortality with a hazard ratio of 1.60 (95 % CI 1.45-1.76; p < 0.001). There was a stepwise relationship with increasing all-cause mortality and tertiles of detectable cTnI levels with hazard ratios of 1.63 (95 % CI 1.39-1.90) for the first tertile to 2.02 (95 % CI 1.74-2.35) for the third tertile.

Conclusions

Detectable cTnI below the 99th percentile is an independent predictor of mortality in patients undergoing coronary angiography with the risk of death growing progressively with increasing troponin levels.

Utilizing machine learning for survival analysis to identify risk factors for COVID-19 intensive care unit admission: A retrospective cohort study from the United Arab Emirates

BACKGROUND

The current situation of the unprecedented COVID-19 pandemic leverages Artificial Intelligence (AI) as an innovative tool for addressing the evolving clinical challenges. An example is utilizing Machine Learning (ML) models-a subfield of AI that take advantage of observational data/Electronic Health Records (EHRs) to support clinical decision-making for COVID-19 cases. This study aimed to evaluate the clinical characteristics and risk factors for COVID-19 patients in the United Arab Emirates utilizing EHRs and ML for survival analysis models.

METHODS

We tested various ML models for survival analysis in this work we trained those models using a different subset of features extracted by several feature selection methods. Finally, the best model was evaluated and interpreted using goodness-of-fit based on calibration curves,Partial Dependence Plots and concordance index.

RESULTS

The risk of severe disease increases with elevated levels of C-reactive protein, ferritin, lactate dehydrogenase, Modified Early Warning Score, respiratory rate and troponin. The risk also increases with hypokalemia, oxygen desaturation and lower estimated glomerular filtration rate and hypocalcemia and lymphopenia.

CONCLUSION

Analyzing clinical data using AI models can provide vital information for clinician to measure the risk of morbidity and mortality of COVID-19 patients. Further validation is crucial to implement the model in real clinical settings.

A review of cardiac troponin I detection by surface enhanced Raman spectroscopy: Under the spotlight of point-of-care testing

Cardiac troponin I (cTnI) is a biomarker widely related to acute myocardial infarction (AMI), one of the leading causes of death around the world. Point-of-care testing (POCT) of cTnI not only demands a short turnaround time for its detection but the highest accuracy levels to set expeditious and adequate clinical decisions. The analytical technique Surface-enhanced Raman spectroscopy (SERS) possesses several properties that tailor to the POCT format, such as its flexibility to couple with rapid assay platforms like microfluidics and paper-based immunoassays. Here, we analyze the strategies used for the detection of cTnI by SERS considering POCT requirements. From the detection ranges reported in the reviewed literature, we suggest the diseases other than AMI that could be diagnosed with this technique. For this, a section with information about cardiac and non-cardiac diseases with cTnI release, including their release kinetics or cut-off values are presented. Likewise, POCT features, the use of SERS as a POCT technique, and the biochemistry of cTnI are discussed. The information provided in this review allowed the identification of strengths and lacks of the available SERS-based point-of-care tests for cTnI and the disclosing of requirements for future assays design.

COVID-19 in Older Individuals Requiring Hospitalization

Older individuals have an increased risk for severe coronavirus disease 2019 (COVID-19) and a higher risk for complications and death. The aim of this study was to investigate the clinical characteristics of older patients admitted with COVID-19 and describe their outcomes. This was a retrospective cohort study of patients older than 65 years admitted to the COVID-19 Department of the University Hospital of Heraklion. Data recorded and evaluated included age, gender, Infectious Diseases Society of America (IDSA) severity score, Charlson comorbidity index (CCI), high-flow nasal oxygen (HFNO) use, admission to the Intensive Care Unit (ICU), laboratory exams, treatment administered, and outcome. In total, 224 patients were evaluated in the present study. The median age was 75 years and 105 (46.9%) were female. In 50 patients (22.7%), HFNO was used and 23 (10.3%) were admitted to the ICU. Mortality was 13.4% (30 patients). Patients that died had higher age, were more likely to be male, had an IDSA severity score of 3, had prior HFNO use, had been admitted to the ICU, and were also more likely to have a higher white blood cell (WBC) count, CRP, ferritin, procalcitonin, d-dimers, and troponin. A multivariate logistic regression analysis identified age and the need for HFNO use to be independently positively associated with mortality. To conclude, COVID-19 carries significant mortality in hospitalized older patients, which increases with age, while the need for HFNO also increased the likelihood of worse outcomes. Clinicians caring for patients with COVID-19 should bear in mind these two factors. Future studies could elaborate on the effect of new variants on the dynamics of mortality in older patients.