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Community-acquired pneumonia (CAP) is an infectious disease caused by bacteria, viruses, or a combination of these infectious agents. The severity of the clinical manifestations of CAP varies significantly. Consequently, both the differentiation of viral from bacterial CAP cases and the accurate assessment and prediction of disease severity are critical for effectively managing individuals with CAP. To solve questionable cases, several biomarkers indicating the etiology and severity of CAP have been studied. Unfortunately, only a few studies have examined the roles of these biomarkers in pediatric practice. The main aim of this paper is to detail current knowledge regarding the use of biomarkers to diagnose and treat CAP in children, analyzing the most recently published relevant studies. Despite several attempts, the etiologic diagnosis of pediatric CAP and the estimation of the potential outcome remain unsolved problems in most cases. Among traditional biomarkers, procalcitonin (PCT) appears to be the most effective for both selecting bacterial cases and evaluating the severity. However, a precise cut-off separating bacterial from viral and mild from severe cases has not been defined. The three-host protein assay based on C-reactive protein (CRP), tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), plasma interferon-γ protein-10 (IP-10), and micro-array-based whole genome expression arrays might offer more advantages in comparison with former biomarkers. However, further studies are needed before the routine use of those presently in development can be recommended.

Background Distinguishing bacterial from viral community-acquired pneumonia (CAP) remains a major clinical challenge, often leading to inappropriate antimicrobial use. Alpha-1 antitrypsin (AAT) is an acute-phase protein that regulates neutrophil protease activity and is cleaved during inflammation, generating bioactive peptides. We investigated whether circulating AAT and its peptides could discriminate bacterial from viral CAP. Methods Serum samples were obtained from 81 prospectively enrolled adults with CAP (bacterial, n  = 36; viral, n  = 45) at hospital admission (day 0) and day 3. AAT concentrations were measured by ELISA, and nine AAT-derived C-terminal peptides were quantified by LC-MS/MS. Associations with CAP etiology were assessed using multivariable logistic regression and receiver operating characteristic (ROC) analyses. Results AAT concentrations were significantly higher in bacterial than viral CAP at both admission ( p  = 0.006) and day 3 ( p  < 0.001) and remained independently associated with bacterial etiology after adjustment for clinical covariates, whereas C-reactive protein (CRP) did not. A predictive model combining AAT, age, and leukocyte count demonstrated the highest discriminatory performance (AUC = 0.803). Four of nine analyzed peptides (C36, C37, C40, and C42) were consistently detectable. C37 levels were higher in bacterial CAP at admission ( p  = 0.010). C36 showed a similar trend but declined from day 0 to day 3 ( p  = 0.006). In contrast, C40 levels increased in viral CAP ( p  = 0.017), resulting in a higher C40/AAT ratio at admission compared with bacterial CAP ( p  = 0.014). Correlations between AAT, peptides, and inflammatory markers were observed in bacterial but not in viral CAP, indicating distinct patterns of AAT processing. Conclusions Circulating AAT independently discriminates bacterial from viral CAP and, when combined with age and leukocyte count, show improved discriminatory performance compared with CRP-based models. Distinct patterns of AAT-derived peptides suggest etiology-specific proteolytic processing and merit further evaluation as markers for differentiating bacterial and viral CAP.

Severe acute respiratory syndrome coronavirus2 has caused a global pandemic of coronavirus disease 2019 (COVID-19). High-density lipoproteins (HDLs), particles chiefly known for their reverse cholesterol transport function, also display pleiotropic properties, including anti-inflammatory or antioxidant functions. HDLs and low-density lipoproteins (LDLs) can neutralize lipopolysaccharides and increase bacterial clearance. HDL cholesterol (HDL-C) and LDL cholesterol (LDL-C) decrease during bacterial sepsis, and an association has been reported between low lipoprotein levels and poor patient outcomes. The goal of this study was to characterize the lipoprotein profiles of severe ICU patients hospitalized for COVID-19 pneumonia and to assess their changes during bacterial ventilator-associated pneumonia (VAP) superinfection. A prospective study was conducted in a university hospital ICU. All consecutive patients admitted for COVID-19 pneumonia were included. Lipoprotein levels were assessed at admission and daily thereafter. The assessed outcomes were survival at 28 days and the incidence of VAP. A total of 48 patients were included. Upon admission, lipoprotein concentrations were low, typically under the reference values ([HDL-C] = 0.7[0.5-0.9] mmol/L; [LDL-C] = 1.8[1.3-2.3] mmol/L). A statistically significant increase in HDL-C and LDL-C over time during the ICU stay was found. There was no relationship between HDL-C and LDL-C concentrations and mortality on day 28 (log-rank p = 0.554 and p = 0.083, respectively). A comparison of alive and dead patients on day 28 did not reveal any differences in HDL-C and LDL-C concentrations over time. Bacterial VAP was frequent (64%). An association was observed between HDL-C and LDL-C concentrations on the day of the first VAP diagnosis and mortality ([HDL-C] = 0.6[0.5-0.9] mmol/L in survivors vs. [HDL-C] = 0.5[0.3-0.6] mmol/L in nonsurvivors, p = 0.036; [LDL-C] = 2.2[1.9-3.0] mmol/L in survivors vs. [LDL-C] = 1.3[0.9-2.0] mmol/L in nonsurvivors, p = 0.006). HDL-C and LDL-C concentrations upon ICU admission are low in severe COVID-19 pneumonia patients but are not associated with poor outcomes. However, low lipoprotein concentrations in the case of bacterial superinfection during ICU hospitalization are associated with mortality, which reinforces the potential role of these particles during bacterial sepsis.

Purpose To determine the global metabolomic profile as measured in circulating plasma from surviving and non-surviving patients with community-acquired pneumonia (CAP) and sepsis. Methods Random, outcome-stratified case–control sample from a prospective study of 1,895 patients hospitalized with CAP and sepsis. Cases ( n  = 15) were adults who died before 90 days, and controls ( n  = 15) were adults who survived, matched on demographics, infection type, and procalcitonin. We determined the global metabolomic profile in the first emergency department blood sample using non-targeted mass-spectrometry. We derived metabolite-based prognostic models for 90-day mortality. We determined if metabolites stimulated cytokine production by differentiated Thp1 monocytes in vitro, and validated metabolite profiles in mouse liver and kidney homogenates at 8 h in cecal ligation and puncture (CLP) sepsis. Results We identified 423 small molecules, of which the relative levels of 70 (17 %) were different between survivors and non-survivors ( p  ≤ 0.05). Broad differences were present in pathways of oxidative stress, bile acid metabolism, and stress response. Metabolite-based prognostic models for 90-day survival performed modestly (AUC = 0.67, 95 % CI 0.48, 0.81). Five nucleic acid metabolites were greater in non-survivors ( p  ≤ 0.05). Of these, pseudouridine increased monocyte expression of TNFα and IL1β versus control ( p  < 0.05). Pseudouridine was also increased in liver and kidney homogenates from CLP mice versus sham ( p  < 0.05 for both). Conclusions Although replication is required, we show the global metabolomic profile in plasma broadly differs between survivors and non-survivors of CAP and sepsis. Metabolite-based prognostic models had modest performance, though metabolites of oxidative stress may act as putative damage-associated molecular patterns.

ObjectivesTo prospectively investigate in patients with severe COVID-19-associated cytokine storm syndrome (CSS) whether an intensive course of glucocorticoids with or without tocilizumab accelerates clinical improvement, reduces mortality and prevents invasive mechanical ventilation, in comparison with a historic control group of patients who received supportive care only.MethodsFrom 1 April 2020, patients with COVID-19-associated CSS, defined as rapid respiratory deterioration plus at least two out of three biomarkers with important elevations (C-reactive protein >100 mg/L; ferritin >900 µg/L; D-dimer >1500 µg/L), received high-dose intravenous methylprednisolone for 5 consecutive days (250 mg on day 1 followed by 80 mg on days 2–5). If the respiratory condition had not improved sufficiently (in 43%), the interleukin-6 receptor blocker tocilizumab (8 mg/kg body weight, single infusion) was added on or after day 2. Control patients with COVID-19-associated CSS (same definition) were retrospectively sampled from the pool of patients (n=350) admitted between 7 March and 31 March, and matched one to one to treated patients on sex and age. The primary outcome was ≥2 stages of improvement on a 7-item WHO-endorsed scale for trials in patients with severe influenza pneumonia, or discharge from the hospital. Secondary outcomes were hospital mortality and mechanical ventilation.ResultsAt baseline all patients with COVID-19 in the treatment group (n=86) and control group (n=86) had symptoms of CSS and faced acute respiratory failure. Treated patients had 79% higher likelihood on reaching the primary outcome (HR: 1.8; 95% CI 1.2 to 2.7) (7 days earlier), 65% less mortality (HR: 0.35; 95% CI 0.19 to 0.65) and 71% less invasive mechanical ventilation (HR: 0.29; 95% CI 0.14 to 0.65). Treatment effects remained constant in confounding and sensitivity analyses.ConclusionsA strategy involving a course of high-dose methylprednisolone, followed by tocilizumab if needed, may accelerate respiratory recovery, lower hospital mortality and reduce the likelihood of invasive mechanical ventilation in COVID-19-associated CSS.

Background Community–acquired pneumonia is a major cause of emergency department visits, hospitalization, and death. In the emergency department, decisions to diagnose pneumonia and initiate antibiotics are typically guided by clinical assessment (symptoms and physical examination), chest imaging, and laboratory tests including inflammatory markers. However, discrimination between infectious pneumonia and non–infectious conditions remains only moderately accurate, and imaging is not always immediately available. There is therefore a need for simple, rapid point–of–care testing (POCT) that can screen for infectious pneumonia early in the evaluation. Serum metabolomics using liquid chromatography–mass spectrometry (LC–MS) is a potential POCT approach, but most prior studies have focused on relatively small panels of identified metabolites and have made limited use of unidentified spectral information. We therefore applied machine learning to full–scan serum mass spectral fingerprints, including unidentified peaks, to distinguish infectious pneumonia from non–infectious cases in the emergency department setting. Methods We conducted a single–center proof–of–concept observational study using a serum biobank from adult patients in a secondary–care ED in Japan. To evaluate the diagnostic models, we selected from this biobank 20 clearly non–infectious cases without gray–zone presentations and 20 cases of clinically diagnosed infectious pneumonia based on prespecified stringent clinical criteria. We performed LC–MS–based profiling to quantify metabolites and to acquire mass spectral fingerprints. Two machine–learning models were evaluated with stratified 5–fold cross–validation, and diagnostic performance for distinguishing these predefined groups was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Results We analyzed 20 cases of infectious pneumonia and 20 non–infectious cases. The metabolite model achieved an AUC of 0.885 (95% confidence interval 0.754–0.985) for discriminating infectious pneumonia from non–infectious cases, whereas the mass spectral fingerprint model showed perfect discrimination in internal cross–validation (AUC = 1.000). Conclusions In this proof–of–concept study, machine–learning models trained on serum mass spectral fingerprints demonstrated promising performance in discriminating infectious pneumonia from non–infectious conditions in this selected cohort. This approach may serve as a foundation for future POCT applications, although larger multicenter external validation studies are warranted.

The albumin-to-globulin ratio (AGR) has been associated with infectious diseases; however, its association with pneumonia in patients with aneurysmal subarachnoid hemorrhage (aSAH) is unclear. This research aims to investigate the connection between AGR and hospital-acquired pneumonia (HAP). This retrospective analysis included 4,813 patients with aSAH from West China Hospital, Sichuan University. Serum samples were collected within seven days after admission. Logistic regression analysis was conducted to assess the association between AGR and HAP. Among the 4,813 patients, 1,249 (26.0%) developed HAP. The patients were classified into quartiles according to their AGR levels. Significant differences in odds ratios (ORs) were found in admission AGR: 0.76 (95% CI 0.63–0.93, p  = 0.007) for Q2, 0.78 (95% CI 0.64–0.94, p  = 0.016) for Q3, and 0.77 (95% CI 0.63–0.94, p  = 0.012) for Q4 versus Q1. ORs of minimum AGR were: 0.67 (95% CI 0.57–0.80, p  < 0.001) for Q2, 0.45(95% CI 0.37–0.54, p  < 0.001) for Q3, and 0.24 (95% CI 0.20–0.30, p  < 0.001) for Q4. ORs for AGR drift: 1.19 (95% CI 0.93–1.52, p  = 0.18) for Q2, 1.35 (95% CI 1.06–1.73, p  = 0.015) for Q3, and 2.00 (95% CI 1.57–2.55, p  < 0.001) for Q4. Furthermore, admission AGR (< 1.21) and minimum AGR (< 0.99) were linked to elevated CURB-65 scores 3 , 4 – 5 in HAP patients, yielding ORs of 0.46 (95% CI 0.27–0.77, p  = 0.003) and 0.44 (95% CI 0.27–0.69, p  < 0.001). AGR was associated with an increased risk of HAP. Furthermore, lower minimum AGR was associated with increased severity of HAP among aSAH patients. Further research is necessary to confirm this finding and investigate the underlying mechanisms.

Abstract Rationale Plasma-detectable biomarkers that rapidly and accurately diagnose bacterial infections in children with suspected pneumonia could reduce the morbidity of respiratory disease and decrease the unnecessary use of antibiotic therapy. Objectives Using 56 markers measured in a multiplexed immunoassay, we sought to identify proteins and protein combinations that could discriminate bacterial from viral or malarial diagnoses. Methods We selected 80 patients with clinically diagnosed pneumonia (as defined by the World Health Organization) who also met criteria for bacterial, viral, or malarial infection based on clinical, radiographic, and laboratory results. Ten healthy community control subjects were enrolled to assess marker reliability. Patients were subdivided into two sets: one for identifying potential markers and another for validating them. Measurements and Main Results Three proteins (haptoglobin, tumor necrosis factor receptor 2 or IL-10, and tissue inhibitor of metalloproteinases 1) were identified that, when combined through a classification tree signature, accurately classified patients into bacterial, malarial, and viral etiologies and misclassified only one patient with bacterial pneumonia from the validation set. The overall sensitivity and specificity of this signature for the bacterial diagnosis were 96 and 86%, respectively. Alternative combinations of markers with comparable accuracy were selected by support vector machine and regression models and included haptoglobin, IL-10, and creatine kinase–MB. Conclusions Combinations of plasma proteins accurately identified children with a respiratory syndrome who were likely to have bacterial infections and who would benefit from antibiotic therapy. When used in conjunction with malaria diagnostic tests, they may improve diagnostic specificity and simplify treatment decisions for clinicians.

Procalcitonin (PCT) is a biomarker for bacterial infections, with controversial utility in diagnosing hospital-acquired pneumonia (HAP) in neurosurgical intensive care unit (NICU) patients. Establishing an optimal PCT cutoff value could enhance diagnostic accuracy. This retrospective single-center study included NICU patients hospitalized between January 1, 2021, and December 31, 2022, who underwent routine serum PCT measurement. HAP was diagnosed based on clinical, biochemical, microbiological, and radiological data. The optimal PCT cutoff value was identified using the Youden Index. Associations between PCT levels, radiological findings, sputum cultures, and confirmed HAP were analyzed using chi-square tests. A multivariate logistic regression was performed to identify independent predictors of elevated PCT. Among 2363 patients, 193 met inclusion criteria, and 148 were diagnosed with HAP. The optimal PCT cutoff value was 0.095 ng/mL, yielding a sensitivity of 89.2% and specificity of 93.3% ( p  < 0.001). This cutoff resulted in a positive likelihood ratio of 13.3 and a negative likelihood ratio of 0.116. Radiological signs of pneumonia and positive sputum cultures were observed in 48.4% and 78.4% of HAP cases, respectively, but neither showed a significant association with HAP ( p  = 0.135 and p  = 0.056). Leukocytosis was significantly associated with HAP but had low specificity, while CRP showed a non-significant trend. In multivariate analysis, only confirmed HAP independently predicted PCT elevation. PCT, with a cutoff value of 0.095 ng/mL, shows high diagnostic accuracy for HAP in NICU patients and could enhance early identification and treatment. Our findings suggest that elevated PCT is primarily driven by HAP rather than non-infectious inflammatory triggers such as trauma or recent surgery. Further prospective studies are warranted to validate these findings.

Lower respiratory tract infections are often treated with antibiotics without evidence of clinically relevant bacterial disease. Serum calcitonin precursor concentrations, including procalcitonin, are raised in bacterial infections. We aimed to assess a procalcitonin-based therapeutic strategy to reduce antibiotic use in lower respiratory tract infections with a new rapid and sensitive assay. 243 patients admitted with suspected lower respiratory tract infections were randomly assigned standard care (standard group; n=119) or procalcitonin-guided treatment (procalcitonin group; n=124). On the basis of serum procalcitonin concentrations, use of antibiotics was more or less discouraged (<0·1 μg/L or <0·25 μg/L) or encouraged (≥;0·5 μg/L or ≥0·25 μg/L), respectively. Reevaluation was possible after 6–24 h in both groups. Primary endpoint was use of antibiotics and analysis was by intention to treat. Final diagnoses were pneumonia (n=87; 36%), acute exacerbation of chronic obstructive pulmonary disease (60; 25%), acute bronchitis (59; 24%), asthma (13; 5%), and other respiratory affections (24; 10%). Serological evidence of viral infection was recorded in 141 of 175 tested patients (81%). Bacterial cultures were positive from sputum in 51 (21%) and from blood in 16 (7%). In the procalcitonin group, the adjusted relative risk of antibiotic exposure was 0·49 (95% CI 0·44–0·55; p<0·0001) compared with the standard group. Antibiotic use was significantly reduced in all diagnostic subgroups. Clinical and laboratory outcome was similar in both groups and favourable in 235 (97%). Procalcitonin guidance substantially reduced antibiotic use in lower respiratory tract infections. Withholding antimicrobial treatment did not compromise outcome. In view of the current overuse of antimicrobial therapy in often self-limiting acute respiratory tract infections, treatment based on procalcitonin measurement could have important clinical and financial implications. Published online Feb 10, 2004. http://image.thelancet.com/extras/04art1162web.pdf