Purpose of reviewCommunity-acquired pneumonia (CAP) is known as a major worldwide health concern considering it has been shown to account for 78% of infection-related deaths in the USA. It is a common cause for hospitalization with a continued incidence rise in the elderly, high mortality rate and long-term sequelae in critically ill patients. Severe CAP (sCAP) is an accepted terminology used to describe ICU admitted patients with CAP. The aim of this review is to further report on the major advances in treatment for patients with sCAP including new antibiotic treatments despite macrolide resistance as seen in the ICU, and multifaceted antibiotic stewardship interventions that may lead to the reduction broad-spectrum antibiotic use in CAP. Recent findingsWe aim to examine the most recent findings in order to determine appropriate empirical antibiotic choices, timing regimens and evidence for clinical effectiveness. This will be addressed by focusing on the use combination therapies, the usefulness of severity scores and the difficulty to treat multidrug-resistant pathogens, including gram negatives such as Pseudomonas aeruginosa and methicillin-resistant Staphylococcus aureus. Relevant reports referenced within included randomized controlled trials, metaanalyses, observational studies, systematic reviews and international guidelines where applicable. SummaryNew antibiotics have been recently launched with direct agent-specific properties that have been shown to avoid the overuse of previous broad-spectrum antibiotics when treating patients sCAP. Although narrowspectrum antibiotics are now recommended and imperative in improving a patients' prognosis, there are also some considerations when prescribing antibiotics that are beyond the spectrum. There is a need to implement effective policies of de-escalation to avoid antibiotic resistance and the risk for developing subsequent infections by combining informed clinical judgement and the application of biomarkers. Reaching clinical stability and avoidance of treatment failure are the most important pillars in treatment success.
IntroductionMillions of deaths worldwide are a result of sepsis (viral and bacterial) and septic shock syndromes which originate from microbial infections and cause a dysregulated host immune response. These diseases share both clinical and immunological patterns that involve a plethora of biomarkers that can be quantified and used to explain the severity level of the disease. Therefore, we hypothesize that the severity of sepsis and septic shock in patients is a function of the concentration of biomarkers of patients.MethodsIn our work, we quantified data from 30 biomarkers with direct immune function. We used distinct Feature Selection algorithms to isolate biomarkers to be fed into machine learning algorithms, whose mapping of the decision process would allow us to propose an early diagnostic tool.ResultsWe isolated two biomarkers, i.e., Programmed Death Ligand-1 and Myeloperoxidase, that were flagged by the interpretation of an Artificial Neural Network. The upregulation of both biomarkers was indicated as contributing to increase the severity level in sepsis (viral and bacterial induced) and septic shock patients.DiscussionIn conclusion, we built a function considering biomarker concentrations to explain severity among sepsis, sepsis COVID, and septic shock patients. The rules of this function include biomarkers with known medical, biological, and immunological activity, favoring the development of an early diagnosis system based in knowledge extracted from artificial intelligence.
Background The severe form of COVID-19 can cause a dysregulated host immune syndrome that might lead patients to death. To understand the underlying immune mechanisms that contribute to COVID-19 disease we have examined 28 different biomarkers in two cohorts of COVID-19 patients, aiming to systematically capture, quantify, and algorithmize how immune signals might be associated to the clinical outcome of COVID-19 patients. Methods The longitudinal concentration of 28 biomarkers of 95 COVID-19 patients was measured. We performed a dimensionality reduction analysis to determine meaningful biomarkers for explaining the data variability. The biomarkers were used as input of artificial neural network, random forest, classification and regression trees, k-nearest neighbors and support vector machines. Two different clinical cohorts were used to grant validity to the findings. Results We benchmarked the classification capacity of two COVID-19 clinicals studies with different models and found that artificial neural networks was the best classifier. From it, we could employ different sets of biomarkers to predict the clinical outcome of COVID-19 patients. First, all the biomarkers available yielded a satisfactory classification. Next, we assessed the prediction capacity of each protein separated. With a reduced set of biomarkers, our model presented 94% accuracy, 96.6% precision, 91.6% recall, and 95% of specificity upon the testing data. We used the same model to predict 83% and 87% (recovered and deceased) of unseen data, granting validity to the results obtained. Conclusions In this work, using state-of-the-art computational techniques, we systematically identified an optimal set of biomarkers that are related to a prediction capacity of COVID-19 patients. The screening of such biomarkers might assist in understanding the underlying immune response towards inflammatory diseases.
Critically ill COVID-19 patients start developing single respiratory organ failure that often evolves into multiorgan failure. Understanding the immune mechanisms in severe forms of an infectious disease (either critical COVID-19 or bacterial septic shock) would help to achieve a better understanding of the patient’s clinical trajectories and the success of potential therapies. We hypothesized that a dysregulated immune response manifested by the abnormal activation of innate and adaptive immunity might be present depending on the severity of the clinical presentation in both COVID-19 and bacterial sepsis. We found that critically ill COVID-19 patients demonstrated a different clinical endotype that resulted in an inflammatory dysregulation in mild forms of the disease. Mild cases (COVID-19 and bacterial non severe sepsis) showed significant differences in the expression levels of CD8 naïve T cells, CD4 naïve T cells, and CD4 memory T cells. On the other hand, in the severe forms of infection (critical COVID-19 and bacterial septic shock), patients shared immune patterns with upregulated single-cell transcriptome sequencing at the following levels: B cells, monocyte classical, CD4 and CD8 naïve T cells, and natural killers. In conclusion, we identified significant gene expression differences according to the etiology of the infection (COVID-19 or bacterial sepsis) in the mild forms; however, in the severe forms (critical COVID-19 and bacterial septic shock), patients tended to share some of the same immune profiles related to adaptive and innate immune response. Severe forms of the infections were similar independent of the etiology. Our findings might promote the implementation of co-adjuvant therapies and interventions to avoid the development of severe forms of disease that are associated with high mortality rates worldwide.
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