Microbial Etiology of Pneumonia: Epidemiology, Diagnosis and Resistance Patterns

Globally, pneumonia is a serious public health concern and a major cause of mortality and morbidity. Despite advances in antimicrobial therapies, microbiological diagnostic tests and prevention measures, pneumonia remains the main cause of death from infectious disease in the world. An important reason for the increased global mortality is the impact of pneumonia on chronic diseases, along with the increasing age of the population and the virulence factors of the causative microorganism. The increasing number of multidrug-resistant bacteria, difficult-to-treat microorganisms, and the emergence of new pathogens are a major problem for clinicians when deciding antimicrobial therapy. A key factor for managing and effectively guiding appropriate antimicrobial therapy is an understanding of the role of the different causative microorganisms in the etiology of pneumonia, since it has been shown that the adequacy of initial antimicrobial therapy is a key factor for prognosis in pneumonia. Furthermore, broad-spectrum antibiotic therapies are sometimes given until microbiological results are available and de-escalation cannot be performed quickly. This review provides an overview of microbial etiology, resistance patterns, epidemiology and microbial diagnosis of pneumonia

Predictors of treatment failure and clinical stability in patients with community acquired pneumonia

Community acquired pneumonia (CAP) is the leading infectious cause of mortality worldwide with approximately 10% of patients hospitalized requiring intensive care unit (ICU) admission. The ability to predict clinical stability (CS) and treatment failure (TF) enables the clinician to alter antibiotics appropriately, facilitate a timely ICU admission, or arrange a suitable discharge. The detection of CS and TF can be difficult and changes in clinical signs may be subtle or delayed. Thus clinical scores and biomarkers are routinely used to identify severity and monitor clinical progression. The evidence, however, is vast and the definitive role of these systems is at times difficult to elucidate. The aim of this review is to analyse the current literature and to provide a rational and clinically focused view of the predictive utility of various systems used to identify CS and TF in CAP

Ventilator-Associated Pneumonia and PaO(2)/F(I)O(2) Diagnostic Accuracy: Changing the Paradigm?

Background: Ventilator-associated pneumonia (VAP) is associated to longer stay and poor outcomes. Lacking definitive diagnostic criteria, worsening gas exchange assessed by PaO2/FIO2 ≤ 240 in mmHg has been proposed as one of the diagnostic criteria for VAP. We aim to assess the adequacy of PaO2/FIO2 ≤ 240 to diagnose VAP. Methods: Prospective observational study in 255 consecutive patients with suspected VAP, clustered according to PaO2/FIO2 ≤ 240 vs. > 240 at pneumonia onset. The primary analysis was the association between PaO2/FIO2 ≤ 240 and quantitative microbiologic confirmation of pneumonia, the most reliable diagnostic gold-standard. Results: Mean PaO2/FIO2 at VAP onset was 195 ± 82; 171 (67%) cases had PaO2/FIO2 ≤ 240. Patients with PaO2/FIO2 ≤ 240 had a lower APACHE-II score at ICU admission; however, at pneumonia onset they had higher CPIS, SOFA score, acute respiratory distress syndrome criteria and incidence of shock, and less microbiological confirmation of pneumonia (117, 69% vs. 71, 85%, p = 0.008), compared to patients with PaO2/FIO2 > 240. In multivariate logistic regression, PaO2/FIO2 ≤ 240 was independently associated with less microbiological confirmation (adjusted odds-ratio 0.37, 95% confidence interval 0.15-0.89, p = 0.027). The association between PaO2/FIO2 and microbiological confirmation of VAP was poor, with an area under the ROC curve 0.645. Initial non-response to treatment and length of stay were similar between both groups, while hospital mortality was higher in patients with PaO2/FIO2 ≤ 240. Conclusion: Adding PaO2/FIO2 ratio ≤ 240 to the clinical and radiographic criteria does not help in the diagnosis of VAP. PaO2/FIO2 ratio > 240 does not exclude this infection. Using this threshold may underestimate the incidence of VAP

Predicting treatment failure in patients with community acquired pneumonia: a case-control study

INTRODUCTION: Treatment failure in community-acquired-pneumonia (CAP) patients is associated with a high mortality rate, and therefore are a matter of great concern in clinical management. Those patients have increased mortality and are a target population for randomized clinical trials. METHODS: A case-control study was performed in patients with CAP (non-failure cases vs. failure cases, discriminating by late and early failure). CRP, PCT, interleukin 1, 6, 8 and 10 and TNF were determined at days 1 and 3 of hospitalization. RESULTS: A total of 253 patients were included in this study where 83 patients presented treatment failure. Of these, 40 (48.2%) had early failure. A discriminative effect was found for a higher CURB-65 score among late failure patients (p = 0.004). A significant increase on day 1 of hospitalization in CRP (p < 0.001), PCT (p = 0.004), IL-6 (p < 0.001) and IL-8 (p = 0.02), and a decrease in IL-1 (p = 0.06) in patients with failure was observed compared with patients without failure. On day 3, only the increase in CRP (p < 0.001), PCT (p = 0.007) and IL-6 (p < 0.001) remained significant. Independent predictors for early failure were higher IL-6 levels on day 1 (OR = 1.78, IC = 1.2-2.6) and pleural effusion (OR = 2.25, IC = 1.0-5.3), and for late failure, higher PCT levels on day 3 (OR = 1.60, IC = 1.0-2.5), CURB-65 score ≥ 3 (OR = 1.43, IC = 1.0-2.0), and multilobar involvement (OR = 4.50, IC = 2.1-9.9). CONCLUSIONS: There was a good correlation of IL-6 levels and CAP failure and IL-6 & PCT with late CAP failure. Pleural effusion and multilobar involvement were simple clinical predictors of early and late failure, respectively

Respiratory research networks in Europe and beyond: aims, achievements and aspirations for the 21st century

Healthcare-associated infection, such as intensive care unit (ICU)-related respiratory infections, remain the most frequently encountered morbidity of ICU admission, prolonging hospital stay and increasing mortality rates. The epidemiology of ICU-related respiratory infections, particularly nonventilated ICU-associated pneumonia and ventilator-associated tracheobronchitis, appears to be quite different among different countries. European countries have different prevalence, patterns and mechanism of resistance, as well as different treatments chosen by different attending physicians. The classical clinical research process in respiratory infections consists of the following loop: 1) identification of knowledge gaps; 2) systematic review and search for adequate answers; 3) generation of study hypotheses; 4) design of study protocols; 5) collection clinical data; 6) analysis and interpretation of the results; and 7) implementation of the results in clinical practic

Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain

Coronavirus SARS-CoV-2; COVID-19; 2019-nCoV; Fenotips; Factors de risc; Infecció greu per SARS-CoV-2Coronavirus SARS-CoV-2; COVID-19; 2019-nCoV; Fenotipos; Factores de riesgo; Infección grave por SARS-CoV-2Coronavirus SARS-CoV-2; COVID-19; 2019-nCoV; Phenotypes; Risk factors; Severe SARS-CoV-2 infectionBackground The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. Methods Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 ICUs in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient’s factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. Results The database included a total of 2022 patients (mean age 64 [IQR 5–71] years, 1423 (70.4%) male, median APACHE II score (13 [IQR 10–17]) and SOFA score (5 [IQR 3–7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A (mild) phenotype (537; 26.7%) included older age ( 65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications. Conclusion The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a “one-size-fits-all” model in practice.This study was supported by the Spanish Intensive Care Society (SEMICYUC) and Ricardo Barri Casanovas Foundation. The study sponsors have no role in the study design, data collection, data analysis, data interpretation, or writing of the report

Pandemic and post-pandemic Influenza A (H1N1) infection in critically ill patients

Background: There is a vast amount of information published regarding the impact of 2009 pandemic Influenza A (pH1N1) virus infection. However, a comparison of risk factors and outcome during the 2010-2011 post-pandemic period has not been described. Methods: A prospective, observational, multi-center study was carried out to evaluate the clinical characteristics and demographics of patients with positive RT-PCR for H1N1 admitted to 148 Spanish intensive care units (ICUs). Data were obtained from the 2009 pandemic and compared to the 2010-2011 post-pandemic period. Results: Nine hundred and ninety-seven patients with confirmed An/H1N1 infection were included. Six hundred and forty-eight patients affected by 2009 (pH1N1) virus infection and 349 patients affected by the post-pandemic Influenza (H1N1)v infection period were analyzed. Patients during the post-pandemic period were older, had more chronic comorbid conditions and presented with higher severity scores (Acute Physiology And Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA)) on ICU admission. Patients from the post-pandemic Influenza (H1N1)v infection period received empiric antiviral treatment less frequently and with delayed administration. Mortality was significantly higher in the post-pandemic period. Multivariate analysis confirmed that haematological disease, invasive mechanical ventilation and continuous renal replacement therapy were factors independently associated with worse outcome in the two periods. HIV was the only new variable independently associated with higher ICU mortality during the post-pandemic Influenza (H1N1)v infection period. Conclusion: Patients from the post-pandemic Influenza (H1N1)v infection period had an unexpectedly higher mortality rate and showed a trend towards affecting a more vulnerable population, in keeping with more typical seasonal viral infection

Impact of non-invasive mechanical ventilation (niv) in critical patients with influenza (H1N1) PDM09

The use of non-invasive mechanical ventilation (NIV) in patients with influenza A (H1N1)pdm09 admitted to intensive care units (ICU) has been controversial

Effect of combined β-Lactam/Macrolide therapy on mortality according to the microbial etiology and inflammatory status of patients with community-acquired pneumonia

Antibiotic combinations that include macrolides have shown lower mortality rates than β-lactams in monotherapy or combined with fluoroquinolones in patients with community-acquired pneumonia (CAP). However, this effect has not been studied according to the levels of C-reactive protein in CAP with identified microbial cause. In patients with CAP and known microbial cause we aimed to evaluate 30-day mortality of a β-lactam plus macrolide (BL + M) compared with a fluoroquinolone alone or with a β-lactam (FQ ± BL). METHODS: We analyzed a prospective observational cohort of patients with CAP admitted to the Hospital Clinic of Barcelona between 1996 and 2016. We included only patients with known microbial cause. RESULTS: Of 1,715 patients (29%) with known etiology, a total of 932 patients (54%) received BL + M. Despite lower crude mortality in the BL + M group in the overall population (BL + M, 5% vs FQ ± BL, 8%; P = .015), after adjustment by a propensity score and baseline characteristics, the combination of BL + M had a protective effect on mortality only in patients with high inflammatory response (C-reactive protein, > 15 mg/dL) and pneumococcal CAP (adjusted OR, 0.28; 95% CI, 0.09-0.93). No benefits on mortality were observed for the population without high inflammatory response and pneumococcal CAP or with other etiologies. CONCLUSIONS: The combination of a β-lactam with a macrolide was associated with decreased mortality in patients with pneumococcal CAP and in patients with high systemic inflammatory response. When both factors occurred together, BL + M was protective for mortality in the multivariate analysis

Pandemic and post-pandemic Influenza A (H1N1) infection in critically ill patients

Background: There is a vast amount of information published regarding the impact of 2009 pandemic Influenza A (pH1N1) virus infection. However, a comparison of risk factors and outcome during the 2010-2011 post-pandemic period has not been described. Methods: A prospective, observational, multi-center study was carried out to evaluate the clinical characteristics and demographics of patients with positive RT-PCR for H1N1 admitted to 148 Spanish intensive care units (ICUs). Data were obtained from the 2009 pandemic and compared to the 2010-2011 post-pandemic period. Results: Nine hundred and ninety-seven patients with confirmed An/H1N1 infection were included. Six hundred and forty-eight patients affected by 2009 (pH1N1) virus infection and 349 patients affected by the post-pandemic Influenza (H1N1)v infection period were analyzed. Patients during the post-pandemic period were older, had more chronic comorbid conditions and presented with higher severity scores (Acute Physiology And Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA)) on ICU admission. Patients from the post-pandemic Influenza (H1N1)v infection period received empiric antiviral treatment less frequently and with delayed administration. Mortality was significantly higher in the post-pandemic period. Multivariate analysis confirmed that haematological disease, invasive mechanical ventilation and continuous renal replacement therapy were factors independently associated with worse outcome in the two periods. HIV was the only new variable independently associated with higher ICU mortality during the post-pandemic Influenza (H1N1)v infection period. Conclusion: Patients from the post-pandemic Influenza (H1N1)v infection period had an unexpectedly higher mortality rate and showed a trend towards affecting a more vulnerable population, in keeping with more typical seasonal viral infection