Role of methemoglobin and carboxyhemoglobin levels in predicting COVID-19 prognosis: an observational study

Predictive markers related to local and systemic inflammation in severe COVID-19-associated ARDS: a prospective single-center analysis

BACKGROUND

As the COVID-19 pandemic strains healthcare systems worldwide, finding predictive markers of severe courses remains urgent. Most research so far was limited to selective questions hindering general assumptions for short- and long-term outcome.

METHODS

In this prospective single-center biomarker study, 47 blood- and 21 bronchoalveolar lavage (BAL) samples were collected from 47 COVID-19 intensive care unit (ICU) patients upon admission. Expression of inflammatory markers toll-like receptor 3 (TLR3), heme oxygenase-1 (HO-1), interleukin (IL)-6, IL-8, leukocyte counts, procalcitonin (PCT) and carboxyhemoglobin (CO-Hb) was compared to clinical course. Clinical assessment comprised acute local organ damage, acute systemic damage, mortality and outcome after 6 months.

RESULTS

PCT correlated with acute systemic damage and was the best predictor for quality of life (QoL) after 6 months (r = - 0.4647, p = 0.0338). Systemic TLR3 negatively correlated with impaired lung function (ECMO/ECLS: r = - 0.3810, p = 0.0107) and neurological short- (RASS mean: r = 0.4474, p = 0.0023) and long-term outcome (mRS after 6 m: r = - 0.3184, p = 0.0352). Systemic IL-8 correlated with impaired lung function (ECMO/ECLS: r = 0.3784, p = 0.0161) and neurological involvement (RASS mean: r = - 0.5132, p = 0.0007). IL-6 in BAL correlated better to the clinical course than systemic IL-6. Using three multivariate regression models, we describe prediction models for local and systemic damage as well as QoL. CO-Hb mean and max were associated with higher mortality.

CONCLUSIONS

Our predictive models using the combination of Charlson Comorbidity Index, sex, procalcitonin, systemic TLR3 expression and IL-6 and IL-8 in BAL were able to describe a broad range of clinically relevant outcomes in patients with severe COVID-19-associated ARDS. Using these models might proof useful in risk stratification and predicting disease course in the future. Trial registration The trial was registered with the German Clinical Trials Register (Trial-ID DRKS00021522, registered 22/04/2020).

The Trend of Arterial Carboxyhemoglobin in Non-smokers as a Prognostic Tool in Severe COVID-19 Patients: A Single-Centre Prospective Study

Introduction  Carboxyhemoglobinemia is characterised by decreased oxygen delivery to tissues. In severe and critical coronavirus disease 2019 (COVID-19) illness with hypoxia, this can herald a grave and protracted course of illness. Patients with COVID-19 experience respiratory impairment, lowering the pace at which carbon monoxide (CO) is eliminated and raising the likelihood of carboxyhemoglobinemia. We set out to explore early arterial carboxyhemoglobin (COHb) and COVID-19 patient outcomes in non-smokers and its potential as a predictive tool for mortality. Methods  Forty-five patients, non-smokers with severe/critical severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection requiring admission in a North Indian 1200-bedded tertiary care hospital, were recruited prospectively from October 2020 to March 2021. Arterial COHb% was evaluated with arterial blood gases using an analyser, which were taken at the time of admission and then every alternate day for the first 10 days. Carboxyhemoglobinemia was defined as COHb% more than 1%. The primary outcome was defined as the patient's hospital outcome (survivor/non-survivor). Results Of the total 45 subjects, 51.1% (n=23) survived. Patients developed carboxyhemoglobinemia with an incidence of 51% during the course of their hospital stay. The mean ± SD of COHb% on admission was 1.0 ± 0.58 and 1.03 ± 0.8 in non-survivors and survivors, respectively (p=0.870). Maximal individual values of 5.3% and 6.1% were seen in survivors and non-survivors, respectively. On serial COHb measurement, non-survivors had significantly higher COHb% on days 6 and 10. No co-relation of COHb% with inflammatory markers was noted. Conclusion  Arterial COHb levels in non-survivors were significantly higher than in survivors on days 6 and 10. Our study did not show a prognostic value of serial COHb measurement in patients with severe COVID-19. To establish COHb as a predictive marker in severely ill COVID-19 patients, additional research is required.

Assessment of Methemoglobin, Hemoglobin, Ferritin and Lactate dehydrogenase in COVID-19 Patients

Today world is trying to cope with the biggest pandemic caused by Coronavirus disease 2019 (COVID-19). The disease is graded as mild, moderate, serious and critical illness. Very few studies are done with methemoglobin along with other parameters for the assessment of the severity of COVID-19 disease. The objectives of the study were to estimate methemoglobin (Met-Hb), hemoglobin (Hb), ferritin and lactate dehydrogenase (LDH) levels in patients with COVID-19 disease and to investigate the interaction between these parameters and the severity of the disease. This observational study was conducted in three groups of COVID-19 patients- moderate, severe and critical, each group containing 30 patients, between June 2021 and September 2021 in the biochemistry department of a tertiary care hospital. For all patients, Met-Hb, Hb, ferritin, and LDH levels were estimated on the 2nd-3rd day of hospital admission. Patients in the critical group were older and had significantly high values of Met-Hb, ferritin and LDH and significantly low values of Hb (P<0.05). In multivariate ordinal regression analysis, older age (OR-3.08; 95%CI:1.19-7.19;P-0.019), higher values of LDH (OR-8.66; 95%CI:2.53-29.5; P-0.001) and ferritin (OR-3.08; 95%CI:1.09-8.7;P-0.033) were independently associated with severity of the disease. A cut-off value of 410.50 U/L for LDH predicted the severity of the disease with 90% sensitivity and 88.3% specificity. In conclusion, higher levels of LDH and ferritin were related to the severity of the disease in COVID-19 cases. Although Met-Hb showed a minimal increase without any association with severity, it may be an underlying cause of hypoxia that may go unnoticed. So, monitoring of all these parameters should be done at intervals.

Coexistence of carbon monoxide intoxication and COVID-19

Introduction This case report aimed to report two patients admitted to the emergency department with a preliminary diagnosis of carbon monoxide intoxication and was diagnosed with COVID-19 during their follow-up. Case 1 A 73-year-old female patient presented with weakness and shortness of breath complaints to the emergency department. Carboxyhemoglobin (COhgb) reached 36.2 %. Atypical pneumonic infiltration with peripheral and central patchy consolidations in the zones in hemothorax images from computerized tomography. The patient was tested positive for COVID-19 after a PCR test. The COhgb values of the patient reached 16 % after 3 h. Then, it dropped to 3.0 % after 8 h. Case 2 A 77-year-old male patient presented with shortness of breath and nausea complaints to the emergency department. COhgb (carboxyhemoglobin) reached 30%. Emphysematous changes in the lung parenchyma and increased peribronchial densities in the lobes were shown in computed tomography. The patient was tested positive for COVID-19 after a PCR test. The COhgb values of the patient reached 13 after 3 h. It dropped to 2.4 after 8 h. Conclusion Carbon monoxide intoxication is a significant public health problem with a high probability of death. Detailed studies and meta-analyses are needed to affect the prognosis of COVID-19 disease.

Prediction of diagnosis and prognosis of COVID-19 disease by blood gas parameters using decision trees machine learning model: a retrospective observational study

The coronavirus disease 2019 (COVID-19) epidemic went down in history as a pandemic caused by corona-viruses that emerged in 2019 and spread rapidly around the world. The different symptoms of COVID-19 made it difficult to understand which variables were more influential on the diagnosis, course and mortality of the disease. Machine learning models can accurately assess hidden patterns among risk factors by analyzing large-datasets to quickly predict diagnosis, prognosis and mortality of diseases. Because of this advantage, the use of machine learning models as decision support systems in health services is increasing. The aim of this study is to determine the diagnosis and prognosis of COVID-19 disease with blood-gas data using the Chi-squared Automatic Interaction Detector (CHAID) decision-tree-model, one of the machine learning methods, which is a subfield of artificial intelligence. This study was carried out on a total of 686 patients with COVID-19 (n = 343) and non-COVID-19 (n = 343) treated at Erzincan-Mengücek-Gazi-Training and Research-Hospital between April 1, 2020 and March 1, 2021. Arterial blood gas values of all patients were obtained from the hospital registry system. While the total-accuracyratio of the decision-tree-model was 65.0% in predicting the prognosis of the disease, it was 68.2% in the diagnosis of the disease. According to the results obtained, the low ionized-calcium value (< 1.10 mM) significantly predicted the need for intensive care of COVID-19 patients. At admission, low-carboxyhemoglobin (< 1.00%), high-pH (> 7.43), low-sodium (< 135.0 mM), hematocrit (< 40.0%), and methemoglobin (< 1.30%) values are important biomarkers in the diagnosis of COVID-19 and the results were promising. The findings in the study may aid in the early-diagnosis of the disease and the intensive-care treatment of patients who are severe. The study was approved by the Ministry of Health and Erzincan University Faculty of Medicine Clinical Research Ethics Committee.