COVID-19 association with multidrug-resistant bacteria superinfections: Lessons for future challenges

Susan, Monica; Susan, Razvan; Viorica, Lazăr; Bagiu, Iulia Cristina; Mihu, Alin; Bagiu, Radu Vasile; Ionescu, A; Iana, Andreea; Dehelean, Cristina; Lighezan, Daniel; Marti, Daniela

doi:10.3892/etm.2023.11953

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…According to Susan et al, MDR bacteria were involved in 73.17% of COVID-19 patients with bacterial superinfections. Klebsiella pneumoniae, Enterococcus spp., and MRSA were the most common MDR bacteria identified in late infections after hospitalization in 20.43%, 4.30%, and 4.30% of all infections, respectively [ 116 ].…”

Section: Resultsmentioning

confidence: 99%

COVID-19 and Laboratory Markers from Romanian Patients—A Narrative Review

Musat,

Sorop,

Sorop

et al. 2023

Life

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COVID-19 has significantly impacted the whole world, and Romania was no exception. Biomarkers play a crucial role in understanding and managing the disease. However, research regarding laboratory analyses for patients with COVID-19 is fairly limited. For detection, PCR testing is still considered the golden standard, while antibodies are still useful for monitoring both patients and their vaccination status. In our country, biomarkers such as CRP, LDH, transaminases, cardiac, and iron markers have been used to assess the status of patients and even predict illness outcome. CRP, IL-6, LDH, FER, fibrinogen, creatinine, and vitamin D levels have been associated with increased severity, risk of ICU admission, and death. Cardiac markers and D-dimers are also good predictors, but their role seems more important in patients with complications. HDL cholesterol and BUN levels were also suggested as potential biomarkers. Hematological issues in SARS-CoV-2 infections include neutrophilia, lymphopenia and their ratio, while PCT, which is a marker of bacterial infections, is better to be used in patients with co- or supra-infections. The current research is a narrative review that focuses on the laboratory results of Romanian COVID-19 patients. The goal of this article is to provide an update on the research on biomarkers and other laboratory tests conducted inside the borders of Romania and identify gaps in this regard. Secondly, options for further research are discussed and encouraged.

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Section: Resultsmentioning

confidence: 99%

COVID-19 and Laboratory Markers from Romanian Patients—A Narrative Review

Musat,

Sorop,

Sorop

et al. 2023

Life

Self Cite

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“…Of note, in the present study, following an initial decrease, a second increase in CRP values was observed on days 10 and 15. A plausible explanation for this increase may be the onset of nosocomial infections, usually complicating the clinical course ( 38 , 39 ). Indeed, in patients critically ill with COVID-19, ICU-acquired bacteremia developed after a median time of 11 days after ICU admission, as shown in a previous study from the ICU ( 40 ).…”

Section: Discussionmentioning

confidence: 99%

Temporal trends in laboratory parameters in survivors and non‑survivors of critical COVID‑19 illness and the effect of dexamethasone treatment

Kokkoris,

Kanavou,

Katsaros

et al. 2023

Biomed Rep

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Although coronavirus disease 2019 (COVID-19)-induced changes in laboratory parameters in patients upon admission have been well-documented, information on their temporal changes is limited. The present study describes the laboratory trends and the effect of dexamethasone treatment on these parameters, in patients with COVID-19 in the intensive care unit (ICU). Routine laboratory parameters, namely white blood cell (WBC), neutrophil, lymphocyte and platelet (PLT) counts, fibrinogen, C-reactive protein (CRP), lactate dehydrogenase (LDH) and albumin concentrations, were recorded upon admission to the ICU and, thereafter, on days 3, 5, 10, 15 and 21; these values were compared between survivors and non-survivors, as well as between those who were treated with dexamethasone and those who were not. Among the 733 patients in the ICU, (mean age, 65±13 years; 68% males; ICU mortality rate 45%; 76% of patients treated with dexamethasone), the WBC and neutrophil counts were persistently high in all patients, without significant differences over the first 15 days. Initially, low lymphocyte counts exhibited increasing trends, but remained higher in survivors compared to non-survivors (P=0.01). The neutrophil-to-lymphocyte ratio (NLR) was persistently elevated in all patients, although it was significantly higher in non-survivors compared to survivors (P < 0.001). The PLT count was initially increased in all patients, although it was significantly decreased in non-survivors over time. The fibrinogen and LDH values remained similarly elevated in all patients. However, the increased levels of CRP, which did not differ between patients upon admission, further increased in non-survivors compared to survivors after day 10 (P=0.001). Declining trends in albumin levels over time, overall, with a significant decrease in non-survivors compared to survivors, were observed. Dexamethasone treatment significantly affected the temporal progression of fibrinogen and CRP in survivors and that of NLR in non-survivors. On the whole, the present study demonstrates that patients in the ICU with COVID-19 present persistently abnormal laboratory findings and significant differences in laboratory trends of NLR, CRP, PLT and albumin, but not in WBC and neutrophil count, and fibrinogen and LDH levels, between survivors and non-survivors. The temporal progression of fibrinogen, CRP and NLR is affected by dexamethasone treatment.

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PROCAL: A SHap-based approach to explain Random Forest predictions of COVID-19- Procalcitonin levels

Naldan,

Kırboğa,

Işık

et al. 2024

Preprint

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In COVID-19, the inflammatory cytokine storm is a critical factor that increases the severity of the disease. Procalcitonin (PCT) is a costly, time-consuming and important biomarker involved in the cytokine storm that exacerbates the severity of COVID-19. This study aims to develop an algorithm that can predict the PCT value in an explainable and interpretable way using explainable artificial intelligence (XAI) methods. The dataset consists of 1068 COVID-19 patients registered at Erzurum Regional Research Center in Turkey between March 2020 and March 2021 (ethical decision number: 2023/3–17). The Permutation Feature Significance (PFI) method was used to identify essential features and build the model. Among the seven-machine learning (ML) models, RandomForestClassifier performed best. RandomForestClassifier's performance metrics training accuracy: 0.89, test accuracy: 0.88, precision: 0.91, recall: 0.88, F-1 score: 0.88, Brier score: 0.11, AUC (area under the curve): 0.935, confidence intervals: 0.877, 0.883. The importance of the features in the model's predictions was analysed with the Shapley additive annotation (SHap) method integrated into the model. The results showed that LDH U/L, CRP mg/L and lymphocytes are important in predicting PCT. This study showed that PCT plays a vital role in assessing the condition of COVID-19 patients, and XAI methods can be helpful in this assessment.

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COVID-19 association with multidrug-resistant bacteria superinfections: Lessons for future challenges

Cited by 3 publications

References 26 publications

COVID-19 and Laboratory Markers from Romanian Patients—A Narrative Review

COVID-19 and Laboratory Markers from Romanian Patients—A Narrative Review

Temporal trends in laboratory parameters in survivors and non‑survivors of critical COVID‑19 illness and the effect of dexamethasone treatment

PROCAL: A SHap-based approach to explain Random Forest predictions of COVID-19- Procalcitonin levels

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