A predictive score at admission for respiratory failure among hospitalized patients with confirmed 2019 Coronavirus Disease: a simple tool for a complex problem

A Comparison between SARS-CoV-2 and Gram-Negative Bacteria-Induced Hyperinflammation and Sepsis

Sepsis is a life-threatening condition caused by the body's overwhelming response to an infection, such as pneumonia or urinary tract infection. It occurs when the immune system releases cytokines into the bloodstream, triggering widespread inflammation. If not treated, it can lead to organ failure and death. Unfortunately, sepsis has a high mortality rate, with studies reporting rates ranging from 20% to over 50%, depending on the severity and promptness of treatment. According to the World Health Organization (WHO), the annual death toll in the world is about 11 million. One of the main toxins responsible for inflammation induction are lipopolysaccharides (LPS, endotoxin) from Gram-negative bacteria, which rank among the most potent immunostimulants found in nature. Antibiotics are consistently prescribed as a part of anti-sepsis-therapy. However, antibiotic therapy (i) is increasingly ineffective due to resistance development and (ii) most antibiotics are unable to bind and neutralize LPS, a prerequisite to inhibit the interaction of endotoxin with its cellular receptor complex, namely Toll-like receptor 4 (TLR4)/MD-2, responsible for the intracellular cascade leading to pro-inflammatory cytokine secretion. The pandemic virus SARS-CoV-2 has infected hundreds of millions of humans worldwide since its emergence in 2019. The COVID-19 (Coronavirus disease-19) caused by this virus is associated with high lethality, particularly for elderly and immunocompromised people. As of August 2023, nearly 7 million deaths were reported worldwide due to this disease. According to some reported studies, upregulation of TLR4 and the subsequent inflammatory signaling detected in COVID-19 patients "mimics bacterial sepsis". Furthermore, the immune response to SARS-CoV-2 was described by others as "mirror image of sepsis". Similarly, the cytokine profile in sera from severe COVID-19 patients was very similar to those suffering from the acute respiratory distress syndrome (ARDS) and sepsis. Finally, the severe COVID-19 infection is frequently accompanied by bacterial co-infections, as well as by the presence of significant LPS concentrations. In the present review, we will analyze similarities and differences between COVID-19 and sepsis at the pathophysiological, epidemiological, and molecular levels.

Improving prediction of COVID-19 mortality using machine learning in the Spanish SEMI-COVID-19 registry

COVID-19 is responsible for high mortality, but robust machine learning-based predictors of mortality are lacking. To generate a model for predicting mortality in patients hospitalized with COVID-19 using Gradient Boosting Decision Trees (GBDT). The Spanish SEMI-COVID-19 registry includes 24,514 pseudo-anonymized cases of patients hospitalized with COVID-19 from 1 February 2020 to 5 December 2021. This registry was used as a GBDT machine learning model, employing the CatBoost and BorutaShap classifier to select the most relevant indicators and generate a mortality prediction model by risk level, ranging from 0 to 1. The model was validated by separating patients according to admission date, using the period 1 February to 31 December 2020 (first and second waves, pre-vaccination period) for training, and 1 January to 30 November 2021 (vaccination period) for the test group. An ensemble of ten models with different random seeds was constructed, separating 80% of the patients for training and 20% from the end of the training period for cross-validation. The area under the receiver operating characteristics curve (AUC) was used as a performance metric. Clinical and laboratory data from 23,983 patients were analyzed. CatBoost mortality prediction models achieved an AUC performance of 84.76 (standard deviation 0.45) for patients in the test group (potentially vaccinated patients not included in model training) using 16 features. The performance of the 16-parameter GBDT model for predicting COVID-19 hospital mortality, although requiring a relatively large number of predictors, shows a high predictive capacity.

Validation of the COVID-19-12O score for predicting readmissions/revisits in patients with SARS-CoV-2 pneumonia discharged from the emergency department

OBJECTIVE

The COVID-19-12O-score has been validated to determine the risk of respiratory failure in patients hospitalized for COVID-19. Our study aims to assess whether the score is effective in patients with SARS-CoV-2 pneumonia discharged from a hospital emergency department (HED) to predict readmission and revisit.

METHOD

Retrospective cohort of patients with SARS-CoV-2 pneumonia discharged consecutively from an HUS of a tertiary hospital, from January 7 to February 17, 2021, where we applied the COVID-19-12O -score, with a cut-off point of 9 points to define the risk of admission or revisit. The primary outcome variable was revisit with or without hospital readmission after 30 days of discharge from HUS.

RESULTS

We included 77 patients, with a median age of 59 years, 63.6% men and Charlson index of 2. 9.1% had an emergency room revisit and 15.3% had a deferred hospital admission. The relative risk (RR) for emergency journal was 0.46 (0.04-4.62, 95% CI, p=0.452), and the RR for hospital readmission was 6.88 (1.20-39.49, 95% CI, p<0.005).

CONCLUSIONS

The COVID-19-12O -score is effective in determining the risk of hospital readmission in patients discharged from HED with SARS-CoV-2 pneumonia, but is not useful for assessing the risk of revisit.

[Validation of the COVID-19-12O scale for predicting readmissions/revisits in patients with SARS-CoV-2 pneumonia discharged from the emergency department]

OBJECTIVE

The COVID-19-12O scale has been validated for determining the risk of respiratory failure in patients hospitalized due to COVID-19. This study aims to assess whether the scale is effective for predicting readmissions and revisits in patients with SARS-CoV-2 pneumonia discharged from a hospital emergency department (HED).

METHOD

This work is a retrospective cohort of consecutive patients with SARS-CoV-2 pneumonia discharged from the HED of a tertiary hospital from January 7 to February 17, 2021. The COVID-19-12O scale with a cut-off point of nine points was used to define the risk of admissions or revisits. The primary outcome variable was a revisit with or without hospital readmission after 30 days of discharge from the HED.

RESULTS

Seventy-seven patients were included. The median age was 59 years, 63.6% were men, and the Charlson Comorbidity Index was 2. A total of 9.1% had an emergency room revisit and 15.3% had a deferred hospital admission. The relative risk (RR) for an HED revisit was 0.46 (0.04-4.62, 95% CI p=0.452) and the RR for hospital readmission was 6.88 (1.20-39.49, 95% CI, p<0.005).

CONCLUSIONS

The COVID-19-12O scale is effective in determining the risk of hospital readmission in patients discharged from an HED with SARS-CoV-2 pneumonia, but is not useful for assessing the risk of revisit.

Derivation and external validation of a simple prediction rule for the development of respiratory failure in hospitalized patients with influenza

BACKGROUND

Influenza viruses cause seasonal epidemics worldwide with a significant morbimortality burden. Clinical spectrum of Influenza is wide, being respiratory failure (RF) one of its most severe complications. This study aims to elaborate a clinical prediction rule of RF in hospitalized Influenza patients.

METHODS

A prospective cohort study was conducted during two consecutive Influenza seasons (December 2016-March 2017 and December 2017-April 2018) including hospitalized adults with confirmed A or B Influenza infection. A prediction rule was derived using logistic regression and recursive partitioning, followed by internal cross-validation. External validation was performed on a retrospective cohort in a different hospital between December 2018 and May 2019.

RESULTS

Overall, 707 patients were included in the derivation cohort and 285 in the validation cohort. RF rate was 6.8% and 11.6%, respectively. Chronic obstructive pulmonary disease, immunosuppression, radiological abnormalities, respiratory rate, lymphopenia, lactate dehydrogenase and C-reactive protein at admission were associated with RF. A four category-grouped seven point-score was derived including radiological abnormalities, lymphopenia, respiratory rate and lactate dehydrogenase. Final model area under the curve was 0.796 (0.714-0.877) in the derivation cohort and 0.773 (0.687-0.859) in the validation cohort (p < 0.001 in both cases). The predicted model showed an adequate fit with the observed results (Fisher's test p > 0.43).

CONCLUSION

we present a simple, discriminating, well-calibrated rule for an early prediction of the development of RF in hospitalized Influenza patients, with proper performance in an external validation cohort. This tool can be helpful in patient's stratification during seasonal Influenza epidemics.