Secondary bacterial infection in COVID-19 patients is associated with increased mortality and disproportionately affects critically ill patients. This single-centre retrospective observational study investigates the comparative efficacy of change in procalcitonin (PCT) and other commonly available biomarkers in revealing or predicting microbiologically proven secondary infection in critical COVID-19 patients. Adult patients admitted to an intensive care unit (ICU) with confirmed SARS-CoV-2 infection between 9 March 2020 and 5 June 2020 were recruited to the study. For daily biomarker and secondary infection, laboratory-confirmed bloodstream infection (LCBI) and ventilator-associated pneumonia/tracheobronchitis (VAP/VAT) data were collected. We observed a PCT rise in 53 (81.5%) of the patients, a C-reactive protein (CRP) rise in 55 (84.6%) and a white blood cell count (WBC) rise in 61 (93.8%). Secondary infection was confirmed in 33 (50.8%) of the patients. A PCT rise was present in 97.0% of patients with at least one confirmed VAP/VAT and/or LCBI event. CRP and WBC rises occurred in 93.9% and 97.0% of patients with confirmed VAP/VAT and/or LCBI, respectively. Logistic regression analysis found that, when including all biomarkers in the same model, there was a significant association between PCT rise and the occurrence of LCBI and/or VAP/VAT (OR = 14.86 95%CI: 2.20, 342.53; p = 0.021). Conversely, no statistically significant relationship was found between either a CRP rise (p = 0.167) or a WBC rise (p = 0.855) and the occurrence of VAP/VAT and/or LCBI. These findings provide a promising insight into the usefulness of PCT measurement in predicting the emergence of secondary bacterial infection in ICU.

Procalcitonin Increase Is Associated with the Development of Critical Care-Acquired Infections in COVID-19 ARDS.

Overuse of antibiotics during the Coronavirus Disease 2019 (COVID-19) pandemic could increase the selection of extensively resistant bacteria (XDR). However, it is unknown what impact they could have on the evolution of patients, particularly critically ill patients. This study aimed to evaluate the characteristics and impact of ICU-acquired infections in patients with COVID-19. A retrospective cohort study was conducted, evaluating all patients with critical COVID-19 admitted to the intensive care unit (ICU) of a hospital in Southern Peru from 28 March 2020 to 1 March 2021. Of the 124 patients evaluated, 50 (40.32%) developed a healthcare-associated infection (HAI), which occurred at a median of 8 days (IQR 6-17) after ICU admission. The proportion of patients with HAI that required ceftriaxone was significantly higher; the same was true for the use of dexamethasone. Forty bacteria isolations (80%) were classified as XDR to antibiotics, with the most common organisms being Acinetobacter baumannii (54%) and Pseudomonas aeruginosa (22%); 33% (41/124) died at the ICU during the follow-up. In the adjusted analysis, healthcare-associated infection was associated with an increased risk of mortality (aHR= 2.7; 95% CI: 1.33-5.60) and of developing acute renal failure (aRR = 3.1; 95% CI: 1.42-6.72). The incidence of healthcare infection mainly by XDR pathogens is high in critically ill patients with COVID-19 and is associated with an increased risk of complications or death.

https://www.mdpi.com/2079-6382/10/8/959/pdf

Antibiotic Use and Fatal Outcomes among Critically Ill Patients with COVID-19 in Tacna, Peru

Abstract Purpose Low dose radiation therapy (LDRT) using doses in the range of 30–150 cGy has been proposed as a means of mitigating the pneumonia associated with COVID-19. However, preliminary results from ongoing clinical trials have been mixed. The aim of this work is to develop a mathematical model of the viral infection and associated systemic inflammation in a patient based on the time evolution of the viral load. The model further proposes an immunomodulatory response to LDRT based on available data. Inflammation kinetics are then explored and compared to clinical results. Methods The time evolution of a viral infection, inflammatory signaling factors, and inflammatory response are modeled by a set of coupled differential equations. Adjustable parameters are taken from the literature where available and otherwise iteratively adjusted to fit relevant data. Simple functions modeling both the suppression of pro-inflammatory signal factors and the enhancement of anti-inflammatory factors in response to low doses of radiation are developed. The inflammation response is benchmarked against C-reactive protein (CRP) levels measured for cohorts of patients with severe COVID-19. Results The model fit the time-evolution of viral load data, cytokine data, and inflammation (CRP) data. When LDRT was applied early, the model predicted a reduction in peak inflammation consistent with the difference between the non-surviving and surviving cohorts. This reduction of peak inflammation diminished as the application of LDRT was delayed. Conclusion The model tracks the available data on viral load, cytokine levels, and inflammatory biomarkers well. An LDRT effect is large enough in principle to provide a life-saving immunomodulatory effect, though patients treated with LDRT already near the peak of their inflammation trajectory are unlikely to see drastic reductions in that peak. This result potentially explains some discrepancies in the preliminary clinical trial data.

/pdf/a-model-of-infection-and-immune-response-to-low-dose-24twdl03.pdf

A model of infection and immune response to low dose radiation

Background. The COVID-19 pandemic is associated with significant number of complications and mortality and a burden on the healthcare system. In 1015% of hospitalized patients, the invasive and non-invasive mechanical ventilation (IMV/NIMV) is required. At the same time, it is important to stratify the risk of mechanical ventilation upon admission to the hospital. Aims to identify clinical and laboratory risk factors for transfer to IMV and NIMV in hospitalized patients with COVID-19-associated pneumonia. Methods. A retrospective one-center nonrandomized study of 386 consecutive hospitalized patients with COVID-19-associated pneumonia was performed. The primary endpoints were IMV (n=22) and NIMV (n=28). Risk factors of artificial ventilation were considered for periods up to 14 and 28 days for both variants. To select a risk predictor, a univariate analysis based on Cox survival regression was performed, followed by multivariate analysis to determine risk factors at these time points. Results. After 28 days from admission the mortal exit was registered in 20 patients from 386 patients (5.2%). 22 patients (5.7%) were transferred to IMV, and 28 patients (7.3%) to NIV, and 9 of the latter were transferred later to IMV. As a result of univariate and multivariate analyzes, the risk factors for transfer to mechanical ventilation on 14th day were: age 65 years (OR=5.91), a history of stroke (OR=17.04), an increased serum level of urea (OR=6.36), LDH (OR=7.39), decreased sodium (OR=12.32), GFR 80 mL/min/1.73 m2 (OR=13.75) and platelets (OR=4.14); on the 28th day age 65 years (OR=4.58), J-wave on the ECG (OR=2.98), an increase of LDH (OR=9.99) and a decrease in albumin (OR=2.77) in serum. Predictors of the transfer of patients with COVID-19 to NIV within the period up to 14 days from the beginning of hospitalization were the age 65 years (OR=5.09), procalcitonin level in the blood 0.25 ng/ml (OR=0.19), leukocytes 11109 (OR=19.64) and increased LDH (OR=3.9). Conclusions. In patients with COVID-19, the risk factors for transfer to IMV/NIVL in the period of 14 and 28 days from the beginning of hospitalization were identified, which enable patients mechanical ventilation stratification and to plan respiratory support resources.

Risk Factors of Transfer to Mechanical Ventilation of COVID-19 Patients in a Retrospective Non-Randomized Study

OBJECTIVES:
To compare the values of the hematological and inflammatory markers in 1st and 4th waves to predict the outcome of COVID-19 in a hospital-based study.
METHODOLOGY:
This comparative study was conducted in the Department of Hematology, Hayatabad Medical Complex Peshawar, from April 2020 to 20 August 2021. Tests of significance (Independent t-test/Mann Whitney U test) and Chi-square test were used. Relevant information was recorded on a pre-designed proforma prepared following the study's objectives.
RESULTS:
A total of 178 patients, 71 from (the 1st wave) and 107 from (the 4th wave) with known outcomes, were studied. A statistically significant difference exists between the groups (1st vs 4th wave) regarding hematological markers; neutrophil to lymphocyte ratio (NLR) (p=0.02), Absolute Neutrophilic count (ANC) (p=0.01) and platelet count (p=0.001). Similarly, significantly higher inflammatory markers values were recorded in the 1st  wave compared with the 4th wave regarding inflammatory markers; CRP (p=0.002) and D-dimer (p=0.001). During the 1st wave, Total Leukocyte Count (TLC), ANC and d-dimer were the leading prognostic indicators to predict mortality/worst outcome in COVID-19 with an Area Under Curve (AUC) of 0.74, 0.70 and 0.7  on receiver operating characteristics (ROC) respectively. In 4th, the Area under the curve (AUC) of d-dimer was 0.84 to predict mortality. 
CONCLUSION:
TLC, ANC, NLR, and low platelet count were the worst hematological markers in COVID-19 in the first wave, while d-dimer and CRP were the primary prognostic inflammatory markers. Unlikely in the 4th wave, the prognostic values of hematological markers were merely significant. The d-dimer values in both the waves proved to be reliable for predicting the severity and mortality of COVID-19.

/pdf/comparison-of-hematological-and-inflammatory-markers-to-2c86pjt3.pdf

Comparison of Hematological and Inflammatory Markers to Predict Outcome in Covid-19 in 1st and 4th Wave

Background: We sought to determine if there was a difference in the longitudinal inflammatory response measured by white blood cell count (WBC), C-reactive protein (CRP), procalcitonin (PCT), and ferritin levels between the first and the second COVID-19 wave of ICU patients. Methods: In a single-center retrospective observational study, ICU patients were enrolled during the first and second waves of the COVID-19 pandemic. Data were collected on patient demographics, comorbidities, laboratory results, management strategies, and complications during the ICU stay. The inflammatory response was evaluated using WBC count, CRP, PCT, and Ferritin levels on the day of admission until Day 28, respectively. Organ dysfunction was measured by the SOFA score. Results: 65 patients were admitted during the first and 113 patients during the second wave. WBC and ferritin levels were higher in the second wave. CRP and PCT showed markedly different longitudinal kinetics up until day 28 of ICU stay between the first and second wave, with significantly lower levels in the second wave. Steroid and immunomodulatory therapy use was significantly greater in the second wave. Mortality was similar in both waves. Conclusions: We found that there was a significantly reduced inflammatory response in the second wave, which is likely to be attributable to the more widespread use of immunomodulatory therapies.

Differences in Inflammatory Marker Kinetics between the First and Second Wave of COVID-19 Patients Admitted to the ICU: A Retrospective, Single-Center Study.

Elsa Harte

Papers

Differences in Inflammatory Marker Kinetics between the First and Second Wave of COVID-19 Patients Admitted to the ICU: A Retrospective, Single-Center Study.