Community-Acquired Pneumonia: A Focused Review

Bacterial antigens and asthma: a comparative study of common respiratory pathogenic bacteria

Objective: In a previous study we have shown that, in the presence of interleukin (IL)-33, repeated, per-nasal challenge of murine airways with Streptococcus pneumoniae (S. pneumoniae) organisms induces human asthma-like airways inflammation. It is not clear, however, whether this effect is unique or manifest in response to other common respiratory pathogens.Methods: To explore this, airways of BALB/c mice were repeatedly challenged per-nasally with formaldehyde-inactivated bacterial bodies in the presence or absence of murine recombinant IL-33. Serum concentrations of S.pneumoniae, Moraxella catarrhalis (M.catarrhalis) and Haemophilus influenzae (H.influenzae) lysates-specific IgE were measured in patients with asthma and control subjects.Results: We showed that in the presence of IL-33, repeated, per-nasal airways exposure to the bodies of these bacteria induced airways hyperresponsiveness (AHR) in the experimental mice. This was accompanied by cellular infiltration into bronchoalveolar lavage fluid (BALF), eosinophilic infiltration and mucous hypertrophy of the lung tissue, with elevated local expression of some type 2 cytokines and elevated, specific IgG and IgE in the serum. The precise characteristics of the inflammation evoked by exposure to each bacterial species were distinguishable.Conclusions: These results suggest that in the certain circumstances, inhaled or commensal bacterial body antigens of both Gram-positive (S. pneumoniae) and Gram-negative (M. catarrhalis and H. influenzae) respiratory tract bacteria may initiate type 2 inflammation typical of asthma in the airways. In addition, we demonstrated that human asthmatic patients manifest elevated serum concentrations of M.catarrhalis- and H.influenzae-specific IgE.

Pulmonary Infection

Pneumonia is a significant cause of morbidity and mortality in the community and hospital settings. Bacterial, viral, mycobacterial, and fungal pathogens are all potential causative agents of pulmonary infection. Chest radiographs and computed tomography are frequently utilized in the assessment of pneumonia. Learning the imaging patterns of different potential organisms allows the radiologist to formulate an appropriate differential diagnosis. An organism-based approach is used to discuss the imaging findings of different etiologies of pulmonary infection.

Infectious Pulmonary Diseases

Pneumonia is a lower respiratory tract infection caused by the inability to clear pathogens from the lower airway and alveoli. Cytokines and local inflammatory markers are released, causing further damage to the lungs through the accumulation of white blood cells and fluid congestion, leading to pus in the parenchyma. The Infectious Diseases Society of America defines pneumonia as the presence of new lung infiltrate with other clinical evidence supporting infection, including new fever, purulent sputum, leukocytosis, and decline in oxygenation. Importantly, lower respiratory infections remain the most deadly communicable disease. Pneumonia is subdivided into three categories: (1) community acquired, (2) hospital acquired, and (3) ventilator associated. Therapy for each differs based on the severity of the disease and the presence of risk factors for methicillin-resistant Staphylococcus aureus or Pseudomonas aeruginosa.

Outcomes of patients admitted to the intensive care unit with community-acquired pneumonia in a tertiary care center in Riyadh, Saudi Arabia

BACKGROUND

Community-acquired pneumonia (CAP) is a leading cause of intensive care unit (ICU) morbidity and mortality. Despite extensive international epidemiological and clinical studies to improve those patients' outcomes, local statistics in Saudi Arabia are limited. The objective of this study is to describe the clinical characteristics and outcomes of patients admitted to the ICU with the diagnosis of CAP reflecting the experience of a tertiary center over an 18-year period.

METHODS

A retrospective cohort study included all consecutive adult ICU patients diagnosed with CAP between 1999 and 2017. Baseline demographics, patients' risk factors, and initial admission laboratory investigations were compared between survivors and nonsurvivors. A multivariate regression model was used to predict mortality.

RESULTS

During the study period, there were 3438 patients admitted to the ICU with CAP (median age 67 [Quartile 1, 3 (Q1, Q3) 51, 76] years) and 54.4% were males, of whom 1007 (29.2%) died. The survivors compared with nonsurvivors were younger (65 vs. 70 years), less likely to have chronic liver disease (2.4% vs. 10.5%), chronic renal failure (8.1% vs. 14.4%), and be immunocompromised (10.2% vs. 18.2%), and less frequently required mechanical ventilation or vasopressors (46.2% vs. 80.5% and 29.6% vs. 55.9%, respectively). Acute Physiology and Chronic Health Evaluation (APACHE) II score was significantly higher among nonsurvivors (median score 26 vs. 20) with a longer duration of mechanical ventilation and ICU stay. Using a multivariate regression model, age, APACHE II score, bilirubin level, vasopressors, and mechanical ventilation were significantly associated with increased mortality, while diabetes was associated with lower mortality.

CONCLUSION

Around one-third of patients admitted to the ICU with CAP died. Mortality was significantly associated with age, APACHE II score, vasopressor use, and mechanical ventilation. A comprehensive national registry is needed to enhance epidemiological data and to guide initiatives for improving CAP patients' outcomes.

Can we recognize severe community-acquired pneumonia without pneumonia severity index? Use of modified qSOFA with procalcitonin

Objective

The aim of this study is to analyze the diagnostic value of Quick Sequential Organ Failure Assessment (qSOFA), modified qSOFA, National Early Warning Score (NEWS) and NEWS + Lactate (NEWS + L) scores in the detection of severe community-acquired pneumonia (CAP).

Methods

This research is a retrospective cohort study. Patients admitted to the Emergency Department (ED) with the diagnosis of CAP were divided into severe and mild pneumonia regarding their Pneumonia Severity Index (PSI) scores. The accuracies of lactate, procalcitonin (PCT) values, NEWS and qSOFA scores, as well as score combinations (NEWS + L and qSOFA + PCT) in predicting patients with severe CAP were analyzed.

Results

Median qSOFA value in the severe CAP group (0 [IQR 0-1]) was found to be higher than median qSOFA value (0 [IQR 0-0]) in the mild CAP group (p < 0.001). The rate of qSOFA positivity (qSOFA ≥ 2) was significantly higher in the severe CAP group (17.1%, n = 21) compared to the mild CAP group (1.3%, n = 1) (p < 0.001). Median qSOFA + PCT for the severe CAP group (2 [IQR 1-2]) was higher than the mild CAP group (1 [IQR 0-1]) (p < 0.001). Mean NEWS score for severe CAP (4.95 ± 3.09) was found to be 1.69 (95% CI 0.92-2.46) higher than mean NEWS score of the mild CAP group (3.26 ± 2.39) (p < 0.001). The severe CAP NEWS + L score (6.97 ± 3.71) was higher than the mild CAP NEWS + L score (4.94 ± 2.48) (p < 0.001). Blood lactate level was not significant in the evaluation of severe CAP (p = 0.221). PCT (AUROC = 0.685 [95% CI 0.610-0.760]; p = 0.038), NEWS score (AUROC = 0.658 95% CI [0.582-0.733]; p < 0.001), qSOFA (AUROC = 0.686) were calculated to have adequate accuracy for the detection of severe CAP. [95% CI 0.613-0.759]; p = 0.037), NEWS + L score (AUROC = 0.665 [95% CI 0.589-0.740]; p = 0.038). The qSOFA + PCT score (AUROC = 0.758 [95% 0.691-0.825]; p = 0.034) was also found to be a highly accurate predictor of severe CAP.

Conclusion

In this study, we found a combination of qSOFA and PCT to be the most reliable method of detecting severe CAP.

Organizational Benefits of Ultra-Low-Dose Chest CT Compared to Chest Radiography in the Emergency Department for the Diagnostic Workup of Community-Acquired Pneumonia: A Real-Life Retrospective Analysis

Background and Objectives: Chest radiography remains the most frequently used examination in emergency departments (ED) for the diagnosis of community-acquired pneumonia (CAP), despite its poor diagnostic accuracy compared with ultra-low-dose (ULD) chest computed tomography (CT). However, although ULD CT appears to be an attractive alternative to radiography, its organizational impact in ED remains unknown. Our objective was to compare the relevant timepoints in ED management of CT and chest radiography. Materials and Methods: We conducted a retrospective study in two ED of a University Hospital including consecutive patients consulting for a CAP between 1 March 2019 and 29 February 2020 to assess the organizational benefits of ULD chest CT and chest radiography (length of stay (LOS) in the ED, time of clinical decision after imaging). Overlap weights (OW) were used to reduce covariate imbalance between groups. Results: Chest radiography was performed for 1476 patients (mean age: 76 years [63; 86]; 55% men) and ULD chest CT for 133 patients (mean age: 71 [57; 83]; 53% men). In the weighted population with OW, ULD chest CT did not significantly alter the ED LOS compared with chest radiography (11.7 to 12.2; MR 0.96 [0.85; 1.09]), although it did significantly reduce clinical decision time (6.9 and 9.5 h; MR 0.73 [0.59; 0.89]). Conclusion: There is real-life evidence that a strategy with ULD chest CT can be considered to be a relevant approach to replace chest radiography as part of the diagnostic workup for CAP in the ED without increasing ED LOS.

AI-based disease risk score for community-acquired pneumonia hospitalization

Community-acquired pneumonia (CAP) is an acute infection involving the parenchyma of the lungs, which is acquired outside of the hospital. Population-wide real-world data and artificial intelligence (AI) were used to develop a disease risk score for CAP hospitalization among older individuals. The source population included residents in Denmark aged 65 years or older in the period January 1, 1996, to July 30, 2018. 137344 individuals were hospitalized for pneumonia during the study period for which, 5 controls were matched leading to a study population of 620908 individuals. The disease risk had an average accuracy of 0.79 based on 5-fold cross-validation in predicting CAP hospitalization. The disease risk score can be useful in clinical practice to identify individuals at higher risk of CAP hospitalization and intervene to minimize their risk of being hospitalized for CAP.

Severity prediction of pulmonary diseases using chest CT scans via cost-sensitive label multi-kernel distribution learning

BACKGROUND AND OBJECTIVES

The progression of pulmonary diseases is a complex progress. Timely predicting whether the patients will progress to the severe stage or not in its early stage is critical to take appropriate hospital treatment. However, this task suffers from the "insufficient and incomplete" data issue since it is clinically impossible to have adequate training samples for one patient at each day. Besides, the training samples are extremely imbalanced since the patients who will progress to the severe stage is far less than those who will not progress to the non-severe stage.

METHOD

We consider the severity prediction of pulmonary diseases as a time estimation problem based on CT scans. To handle the issue of "insufficient and incomplete" training samples, we introduced label distribution learning (LDL). Specifically, we generate a label distribution for each patient, making a CT image contribute to not only the learning of its chronological day, but also the learning of its neighboring days. In addition, a cost-sensitive mechanism is introduced to explore the imbalance data issue. To identify the importance of pulmonary segments in pulmonary disease severity prediction, multi-kernel learning in composite kernel space is further incorporated and particle swarm optimization (PSO) is used to find the optimal kernel weights.

RESULTS

We compare the performance of the proposed CS-LD-MKSVR algorithm with several classical machine learning algorithms and deep learning (DL) algorithms. The proposed method has obtained the best classification results on the in-house data, fully indicating its effectiveness in pulmonary disease severity prediction.

CONTRIBUTIONS

The severity prediction of pulmonary diseases is considered as a time estimation problem, and label distribution is introduced to describe the conversion time from non-severe stage to severe stage. The cost-sensitive mechanism is also introduced to handle the data imbalance issue to further improve the classification performance.

Diagnostic and Prognostic Value of Deregulated Circulating Long Non-coding RNA TUG1 in Elderly Patients with Severe Pneumonia

To investigate the expression pattern of long non-coding RNA TUG1 in elderly patients with severe community-acquired pneumonia (sCAP) and evaluate its diagnostic and prognostic value for sCAP. Serum TUG1 levels were detected in 130 sCAP patients and 122 healthy volunteers via qRT-PCR method. The receiver operating characteristic (ROC) curve and k-M plot were drawn for the diagnostic and prognostic value evaluation. A diminished trend of TUG1 was detected in the serum of sCAP cases, and negatively correlated with the concentration of TNF-α, CRP, suPAR and sTREM-1. Among the 130 cases, 30 cases died from sCAP within 30 days after admission. Serum TUG1 had the diagnostic value for 30-day mortality prediction with the AUC of 0.823. In the non-survival group, more cases had old age, high CURB score and PSI score. K-M plot demonstrated that cases with low TUG1 levels showed poor survival than those carrying high TUG1 levels. Serum TUG1 was an independent risk factor for death in elderly patients with sCAP within 30 days after admission. Serum TUG1 was at low expression in sCAP patients, and it had the predictive value for the clinical prognosis of elderly sCAP patients.

A Systematic Review of Applications of Machine Learning and Other Soft Computing Techniques for the Diagnosis of Tropical Diseases

This systematic literature aims to identify soft computing techniques currently utilized in diagnosing tropical febrile diseases and explore the data characteristics and features used for diagnoses, algorithm accuracy, and the limitations of current studies. The goal of this study is therefore centralized around determining the extent to which soft computing techniques have positively impacted the quality of physician care and their effectiveness in tropical disease diagnosis. The study has used PRISMA guidelines to identify paper selection and inclusion/exclusion criteria. It was determined that the highest frequency of articles utilized ensemble techniques for classification, prediction, analysis, diagnosis, etc., over single machine learning techniques, followed by neural networks. The results identified dengue fever as the most studied disease, followed by malaria and tuberculosis. It was also revealed that accuracy was the most common metric utilized to evaluate the predictive capability of a classification mode. The information presented within these studies benefits frontline healthcare workers who could depend on soft computing techniques for accurate diagnoses of tropical diseases. Although our research shows an increasing interest in using machine learning techniques for diagnosing tropical diseases, there still needs to be more studies. Hence, recommendations and directions for future research are proposed.

[Pulmonary Opacities - What Lies Beneath?]

Pulmonary Opacities - What Lies Beneath? Abstract. Abstract: Pulmonary opacities are among the most common findings that general practitioners and internists have to interpret in everyday life. Conventional chest x-rays are still important, but computed tomograms often provide additional information. Patient history, clinical examination but also additionally collected laboratory findings are important prerequisites for the interpretation of imaging studies. Likewise, radiological patterns should be recognized and correctly described. The density, distribution to one or both sides, basal or apical, unifocal or multifocal, also the involvement of the interstitial tissue, bronchioles, the alveolar space and pleura can provide decisive differential diagnostic information. Space-occupying or shrinking processes may be suspected on behalf of the course of pleural lines. Tumours may be differentiated from shrinking lung volume as seen in atelectasis by shift of the mediastinum or the shape of pleural lines. Occasionally control images can support the interpretation of the radiological results.

Rapidly-Developing Pleural Effusion: Explosive Pleuritis Caused by Group A Streptococcal Infection

Community-acquired pneumonia is a leading cause of death from infectious diseases globally. Parapneumonic effusion is one of the most common complications of community-acquired pneumonia. As the infection progresses within the pleural space, loculation and empyema may develop. In rare cases, the parapneumonic effusions can progress significantly within 24 hours, which has been described as explosive pleuritis and may confer additional morbidity. Group A Streptococcus is the leading causative microorganism, which in itself has higher rates of parapneumonic effusions. We describe the case of a 30-year-old-female with a past medical history of asthma who presented to the emergency department with a sore throat, cough, and runny nose and was discharged on the same day after treatment of asthma exacerbation with upper respiratory tract infection. She re-presented within 24 hours with shortness of breath and right-sided pleuritic chest pain. Chest x-ray showed a new, large right-sided pleural effusion for which pleural fluid culture grew group A Streptococcus. She ultimately had prolonged hospitalization, requiring chest tube placement, and video-assisted thoracoscopic surgery (VATS). VATS was unsuccessful and she was treated with long-term antibiotics. This case demonstrates the dramatic evolution of explosive pleuritis and highlights the typical challenges encountered in these cases. 

Editorial Commentary: Community-acquired pneumonia, comparison of three mortality prediction scores in the emergency department

Successive measurements of global disease burden have documented that lower respiratory tract infections, including pneumonia, are among the top 10 causes of disability-adjusted life-years, and in 2019, pneumonia was the fourth cause of mortality for all ages. In Colombia, acute respiratory infections are the leading cause of mortality within the group of infectious diseases, 52.3% of the total reported between 2005 and 2019. Notably, the COVID-19 epidemic increased the impact of respiratory tract infections on the global disease burden, with estimates of 18 million excess deaths from January 2020 to December 2021 worldwide. The assessment of an adult with pneumonia or suspected pneumonia demands the identification of the likelihood of death and hospitalization. Several scales have been constructed to estimate this probability to improve the predictive capacity of clinical evaluation. Among these scales, the CRB-65 and the CURB-65 standout, being the first recommended for use with clinical criteria and the second when laboratory data such as urea nitrogen are available. Additionally,  for an individual with sepsis, there have been developed to predict mortality, such as  SOFA (Sequential Organ Failure Assessment) and, more recently, the qSOFA(quick SOFA), which has an accurate prediction of mortality in this population. Hincapié C et al. assessed the CURB-65, CRB-65 and SOFA scales to predict mortality and admission to the intensive care unit in adults with pneumonia in three cohorts of patients admitted in three medium- and high-complexity hospitals in the city of Medellin-Colombia. The study included 1110 patients with suspected pneumonia identified in the emergency department and followed up until discharge and death. The authors found that the highest discrimination capacity, measured by the ROC curve, for the outcome hospitalization in an intensive care unit was 0.61, 0.58, and 0.59 for the CURB-65, CRB-65, and SOFA, respectively. About mortality, the ROC found was 0.66, 0.63, and 0.63 for CURB-65, CRB65, and SOFA, respectively. The calibration was appropriate, that is, the ability to predict mortality and admission to the intensive care unit e for the three scales. Some readers have expressed their disagreement with the possible limited use of the scales, particularly the CURB-65 and the CRB-65, in the evaluation of an adult patient with pneumonia expressed by the authors.

Hematological- and Immunological-Related Biomarkers to Characterize Patients with COVID-19 from Other Viral Respiratory Diseases

COVID-19 has overloaded health system worldwide; thus, it demanded a triage method for an efficient and early discrimination of patients with COVID-19. The objective of this research was to perform a model based on commonly requested hematological variables for an early featuring of patients with COVID-19 form other viral pneumonia. This investigation enrolled 951 patients (mean of age 68 and 56% of male) who underwent a PCR test for respiratory viruses between January 2019 and January 2020, and those who underwent a PCR test for detection of SARS-CoV-2 between February 2020 and October 2020. A comparative analysis of the population according to PCR tests and logistic regression model was performed. A total of 10 variables were found for the characterization of COVID-19: age, sex, anemia, immunosuppression, C-reactive protein, chronic obstructive pulmonary disease, cardiorespiratory disease, metastasis, leukocytes and monocytes. The ROC curve revealed a sensitivity and specificity of 75%. A deep analysis showed low levels of leukocytes in COVID-19-positive patients, which could be used as a primary outcome of COVID-19 detection. In conclusion, this investigation found that commonly requested laboratory variables are able to help physicians to distinguish COVID-19 and perform a quick stratification of patients into different prognostic categories.

Introductory Chapter: Pneumonia

Collapse

Multi-drug resistant gram-negative bacterial pneumonia: etiology, risk factors, and drug resistance patterns

Bacterial pneumonia is one of the most serious public health issues owing to its medical and economic costs, which result in increased morbidity and mortality in people of all ages around the world. Furthermore, antimicrobial resistance has risen over time, and the advent of multi-drug resistance in GNB complicates therapy and has a detrimental impact on patient outcomes. The current review aimed to summarize bacterial pneumonia with an emphasis on gram-negative etiology, pathogenesis, risk factors, resistance mechanisms, treatment updates, and vaccine concerns to tackle the problem before it causes a serious consequence. In conclusion, the global prevalence of GNB in CAP was reported 49.7% to 83.1%, whereas in VAP patients ranged between 76.13% to 95.3%. The most commonly reported MDR-GNB causes of pneumonia were A. baumannii, K. pneumoniae, and P. aeruginosa, with A. baumannii isolated particularly in VAP patients and the elderly. In most studies, ampicillin, tetracyclines, amoxicillin-clavulanic acid, cephalosporins, and carbapenems were shown to be highly resistant. Prior MDR-GNB infection, older age, previous use of broad-spectrum antibiotics, high frequency of local antibiotic resistance, prolonged hospital stays, ICU admission, mechanical ventilation, and immunosuppression are associated with the MDR-GNB colonization. S. maltophilia was reported as a severe cause of HAP/VAP in patients with mechanically ventilated and having hematologic malignancy due to its ability of biofilm formation, site adhesion in respiratory devices, and its intrinsic and acquired drug resistance mechanisms. Effective combination therapies targeting PDR strains and drug-resistant genes, antibiofilm agents, gene-based vaccinations, and pathogen-specific lymphocytes should be developed in the future.

A-DROP Scoring System in Predicting Mortality within 30 Days of Hospitalization in Community-acquired Pneumonia Patients at H. Adam Malik General Hospital Medan

BACKGROUND: Community-acquired pneumonia (CAP) is the leading cause of death among infectious diseases, contributing significantly to patient morbidity and mortality. Therefore, an accurate initial assessment of CAP severity should be performed immediately to determine the prognosis before starting the management. A-DROP scoring system is one of the initial assessments. AIM: This study aimed to determine the accuracy of the A-DROP scoring system in predicting mortality within 30 days of hospitalization at H. Adam Malik General Hospital Medan. METHODS: This is an observational study with a retrospective and cohort study. Data were obtained from the medical records of 76 CAP patients hospitalized from January 2018 to December 2018. Each patient was assessed with an A-DROP scoring system and the presence or absence of mortality within 30 days of hospitalization. The data were processed using statistical analysis to calculate the area under curve (AUC) on the receiver operating characteristic (ROC) curve. The optimal cutoff point is also analyzed using the Youden index. RESULT: The ROC curve analysis showed that the AUC was 0.772 (95% CI: 0.666–0.978, p < 0.001). The accuracy of A-DROP scoring system is good (AUC:0.7−0.8). The optimal of the Youden index is 0.428 at the cutoff point A-DROP score >1.5, so that the optimal cutoff point is A-DROP score ≥2. CONCLUSION: The A-DROP scoring system has good accuracy in predicting mortality within 30 days of hospitalized CAP patients. The A-DROP scoring system has an accuracy similar to the PSI (Pneumonia Severity Index) and the CURB-65 scoring system.

Activation and Functional Alteration of Mucosal-Associated Invariant T Cells in Adult Patients With Community-Acquired Pneumonia

Community-acquired pneumonia (CAP) remains the significant infectious cause of morbidity and mortality worldwide. Although mucosal-associated invariant T cells (MAIT) play roles in the pathogenesis of children CAP and ICU-associated pneumonia, their roles in adult CAP are largely unexplored. In this study, we investigated the frequency, phenotype, and function of MAIT cells in peripheral blood and bronchoalveolar lavage fluid (BALF) of adult CAP patients. Our data indicate that MAIT-cell frequency is profoundly lower in the peripheral blood of CAP patients compared to that in healthy individuals. Furthermore, the circulatory MAIT cells express higher levels of CD69 and PD-1 compared to those in healthy individuals. In BALF of CAP patients, MAIT-cell frequency is higher and MAIT cells express higher levels of CD69 and PD-1 compared to their matched blood counterparts. Levels of IL-17A and IFN-γ are increased in BALF of CAP patients compared to those in BALF of patients with pulmonary small nodules. The IL-17A/IFN-γ ratio is significantly positively correlated with MAIT frequency in BALF of CAP patients, suggesting a pathogenic role of MAIT-17 cells in CAP. Of note, blood MAIT-cell frequency in CAP patients is strongly negatively correlated with high-sensitivity C-reactive protein (hsCRP) and neutrophil count percentage in blood. The ability of circulating MAIT cells in CAP patients to produce IFN-γ is significantly impaired compared to those in healthy individuals. In summary, our findings suggest the possible involvement of MAIT cells in the immunopathogenesis of adult CAP.

The Associations of Serum IL-37 With the Severity and Prognosis in Patients With Community-Acquired Pneumonia: A Retrospective Cohort Study

Background

Recent evidences suggested that IL-37 may participate in the pathophysiology of community-acquired pneumonia (CAP). Nevertheless, its exact biological role was unknown. The objective of this study was to determine the associations of serum IL-37 with the severity and prognosis in CAP patients based on a retrospective cohort study.

Methods

The whole of 120 healthy subjects and 240 CAP patients were summoned. Peripheral blood was collected and IL-37 was detected using ELISA.

Results

Serum IL-37 was obviously decreased in CAP patients on admission. In addition, serum IL-37 was gradually decreased in parallel with CAP severity scores. Correlative analysis revealed that serum IL-37 was negatively associated with CAP severity scores and inflammatory cytokines. Further logistical regression found that reduction of serum IL-37 augmented the severity of CAP patients. Moreover, the follow-up research was performed in CAP patients. Serum lower IL-37 on admission prolonged the hospital stay in CAP patients. Serum IL-37 combination with PSI and CURB-65 had a stronger predictive capacity for death than IL-37 and CAP severity score alone in CAP patients.

Conclusion

There are remarkably negative correlations between serum IL-37 with the severity and prognosis in CAP patients. Serum IL-37 on admission prolongs the hospital stay, demonstrating that IL-37 may involve in the process of CAP. Serum IL-37 may be regarded as a biomarker for diagnosis and prognosis for CAP patients.

Defining Post-COVID Symptoms (Post-Acute COVID, Long COVID, Persistent Post-COVID): An Integrative Classification

The pandemic of the coronavirus disease 2019 (COVID-19) has provoked a second pandemic, the "long-haulers", i.e., individuals presenting with post-COVID symptoms. We propose that to determine the presence of post-COVID symptoms, symptoms should appear after the diagnosis of SARS-CoV-2 infection; however, this situation has some problems due to the fact that not all people infected by SARS-CoV-2 receive such diagnosis. Based on relapsing/remitting nature of post-COVID symptoms, the following integrative classification is proposed: potentially infection related-symptoms (up to 4-5 weeks), acute post-COVID symptoms (from week 5 to week 12), long post-COVID symptoms (from week 12 to week 24), and persistent post-COVID symptoms (lasting more than 24 weeks). The most important topic is to establish the time reference points. The classification also integrates predisposing intrinsic and extrinsic factors and hospitalization data which could promote post-COVID symptoms. The plethora of symptoms affecting multiple systems exhibited by "long-haulers" suggests the presence of different underlying mechanisms.