Characteristics and outcomes of COVID-19 patients admitted to hospital with and without respiratory symptoms.

Background: COVID-19 is primarily known as a respiratory illness; however, many patients present to hospital without respiratory symptoms. The association between non-respiratory presentations of COVID-19 and outcomes remains unclear. We investigated risk factors and clinical outcomes in patients with no respiratory symptoms (NRS) and respiratory symptoms (RS) at hospital admission. Methods: This study describes clinical features, physiological parameters, and outcomes of hospitalised COVID-19 patients, stratified by the presence or absence of respiratory symptoms at hospital admission. RS patients had one or more of: cough, shortness of breath, sore throat, runny nose or wheezing; while NRS patients did not. Results: Of 178,640 patients in the study, 86.4 % presented with RS, while 13.6 % had NRS. NRS patients were older (median age: NRS: 74 vs RS: 65) and less likely to be admitted to the ICU (NRS: 36.7 % vs RS: 37.5 %). NRS patients had a higher crude in-hospital case-fatality ratio (NRS 41.1 % vs. RS 32.0 %), but a lower risk of death after adjusting for confounders (HR 0.88 [0.83-0.93]). Conclusion: Approximately one in seven COVID-19 patients presented at hospital admission without respiratory symptoms. These patients were older, had lower ICU admission rates, and had a lower risk of in-hospital mortality after adjusting for confounders.

Hospital-acquired bacterial pneumonia in critically ill patients: from research to clinical practice.

INTRODUCTION: Hospital-acquired pneumonia (HAP) represents a significant cause of mortality among critically ill patients admitted to Intensive Care Units (ICUs). Timely and precise diagnosis is imperative to enhance therapeutic efficacy and patient outcomes. However, the diagnostic process is challenged by test limitations and a wide-ranging list of differential diagnoses, particularly in patients exhibiting escalating oxygen requirements, leukocytosis, and increased secretions. AREAS COVERED: This narrative review aims to update diagnostic modalities, facilitating the prompt identification of nosocomial pneumonia while guiding, developing, and assessing therapeutic interventions. A comprehensive literature review was conducted utilizing the MEDLINE/PubMed database from 2013 to April 2024. EXPERT OPINION: An integrated approach that integrates clinical, microbiological, and imaging tools is paramount. Progress in diagnostic techniques, including novel molecular methods, the expanding utilization and accuracy of bedside ultrasound, and the emergence of Artificial Intelligence, coupled with an improved comprehension of lung microbiota and host-pathogen interactions, continues to enhance our capability to accurately and swiftly identify HAP and its causative agents. This advancement enables the refinement of treatment strategies and facilitates the implementation of precision medicine approaches.

Epidemiology and Burden of Ventilator-Associated Pneumonia among Adult Intensive Care Unit Patients: A Portuguese, Multicenter, Retrospective Study (eVAP-PT Study).

Ventilator-associated pneumonia (VAP) is a prevailing nosocomial infection in critically ill patients requiring invasive mechanical ventilation (iMV). The impact of VAP is profound, adversely affecting patient outcomes and placing a significant burden on healthcare resources. This study assessed for the first time the contemporary VAP epidemiology in Portugal and its burden on the healthcare system and clinical outcomes. Additionally, resource consumption (duration of iMV, intensive care unit (ICU), hospital length of stay (LOS)) and empirical antimicrobial therapy were also evaluated. This multicenter, retrospective study included patients admitted to the hospital between July 2016 and December 2017 in a participating ICU, who underwent iMV for at least 48 h. Patients with a VAP diagnosis were segregated for further analysis (n = 197). Control patients, ventilated for >48 h but without a VAP diagnosis, were also included in a 1:1 ratio. Cumulative VAP incidence was computed. All-cause mortality was assessed at 28, 90, and 365 days after ICU admission. Cumulative VAP incidence was 9.2% (95% CI 8.0-10.5). The all-cause mortality rate in VAP patients was 24.9%, 34.0%, and 40.6%, respectively, and these values were similar to those observed in patients without VAP diagnosis. Further, patients with VAP had significantly longer ICU (27.5 vs. 11.0 days, p < 0.001) and hospital LOS (61 vs. 35.9 days, p < 0.001), more time under iMV (20.7 vs. 8.0 days, p < 0.001) and were more often subjected to tracheostomy (36.5 vs. 14.2%; p < 0.001). Patients with VAP who received inappropriate empirical antimicrobials had higher 28-day mortality, 34.3% vs. 19.5% (odds ratio 2.16, 95% CI 1.10-4.23), although the same was not independently associated with 1-year all-cause mortality (p = 0.107). This study described the VAP impact and burden on the Portuguese healthcare system, with approximately 9% of patients undergoing iMV for >48 h developing VAP, leading to increased resource consumption (longer ICU and hospital LOS). An unexpectedly high incidence of inappropriate, empirical antimicrobial therapy was also noted, being positively associated with a higher mortality risk of these patients. Knowledge of the Portuguese epidemiology characterization of VAP and its multidimensional impact is essential for efficient treatment and optimized long-term health outcomes of these patients.

Applicability of an unsupervised cluster model developed on first wave COVID-19 patients in second/third wave critically ill patients.

OBJECTIVE: To validate the unsupervised cluster model (USCM) developed during the first pandemic wave in a cohort of critically ill patients from the second and third pandemic waves. DESIGN: Observational, retrospective, multicentre study. SETTING: Intensive Care Unit (ICU). PATIENTS: Adult patients admitted with COVID-19 and respiratory failure during the second and third pandemic waves. INTERVENTIONS: None. MAIN VARIABLES OF INTEREST: Collected data included demographic and clinical characteristics, comorbidities, laboratory tests and ICU outcomes. To validate our original USCM, we assigned a phenotype to each patient of the validation cohort. The performance of the classification was determined by Silhouette coefficient (SC) and general linear modelling. In a post-hoc analysis we developed and validated a USCM specific to the validation set. The model's performance was measured using accuracy test and area under curve (AUC) ROC. RESULTS: A total of 2330 patients (mean age 63 [53-82] years, 1643 (70.5%) male, median APACHE II score (12 [9-16]) and SOFA score (4 [3-6]) were included. The ICU mortality was 27.2%. The USCM classified patients into 3 clinical phenotypes: A (n = 1206 patients, 51.8%); B (n = 618 patients, 26.5%), and C (n = 506 patients, 21.7%). The characteristics of patients within each phenotype were significantly different from the original population. The SC was -0.007 and the inclusion of phenotype classification in a regression model did not improve the model performance (0.79 and 0.78 ROC for original and validation model). The post-hoc model performed better than the validation model (SC -0.08). CONCLUSION: Models developed using machine learning techniques during the first pandemic wave cannot be applied with adequate performance to patients admitted in subsequent waves without prior validation.

Invasive Fungal Diseases in Adult Patients in Intensive Care Unit (FUNDICU): 2024 consensus definitions from ESGCIP, EFISG, ESICM, ECMM, MSGERC, ISAC, and ISHAM.

PURPOSE: The aim of this document was to develop standardized research definitions of invasive fungal diseases (IFD) in non-neutropenic, adult patients without classical host factors for IFD, admitted to intensive care units (ICUs). METHODS: After a systematic assessment of the diagnostic performance for IFD in the target population of already existing definitions and laboratory tests, consensus definitions were developed by a panel of experts using the RAND/UCLA appropriateness method. RESULTS: Standardized research definitions were developed for proven invasive candidiasis, probable deep-seated candidiasis, proven invasive aspergillosis, probable invasive pulmonary aspergillosis, and probable tracheobronchial aspergillosis. The limited evidence on the performance of existing definitions and laboratory tests for the diagnosis of IFD other than candidiasis and aspergillosis precluded the development of dedicated definitions, at least pending further data. The standardized definitions provided in the present document are aimed to speed-up the design, and increase the feasibility, of future comparative research studies.

Challenges for a broad international implementation of the current severe community-acquired pneumonia guidelines.

Severe community-acquired pneumonia (sCAP) remains one of the leading causes of admission to the intensive care unit, thus consuming a large share of resources and is associated with high mortality rates worldwide. The evidence generated by clinical studies in the last decade was translated into recommendations according to the first published guidelines focusing on severe community-acquired pneumonia. Despite the advances proposed by the present guidelines, several challenges preclude the prompt implementation of these diagnostic and therapeutic measures. The present article discusses the challenges for the broad implementation of the sCAP guidelines and proposes solutions when applicable.

New Guidelines for Severe Community-acquired Pneumonia.

In 2023, the new European guidelines on severe community-acquired pneumonia, providing clinical practice recommendations for the management of this life-threatening infection, characterized by a high burden of mortality, morbidity, and costs for the society. This review article aims to summarize the principal evidence related to eight different questions covered in the guidelines, by also highlighting the future perspectives for research activity.

Major alteration of Lung Microbiome and the Host Reaction in critically ill COVID-19 Patients with high viral load.

Background: Patients with COVID-19 under invasive mechanical ventilation are at higher risk of developing ventilator-associated pneumonia (VAP), associated with increased healthcare costs, and unfavorable prognosis. The underlying mechanisms of this phenomenon have not been thoroughly dissected. Therefore, this study attempted to bridge this gap by performing a lung microbiota analysis and evaluating the host immune responses that could drive the development of VAP. Materials and methods: In this prospective cohort study, mechanically ventilated patients with confirmed SARS-CoV-2 infection were enrolled. Nasal swabs (NS), endotracheal aspirates (ETA), and blood samples were collected initially within 12 hours of intubation and again at 72 hours post-intubation. Plasma samples underwent cytokine and metabolomic analyses, while NS and ETA samples were sequenced for lung microbiome examination. The cohort was categorized based on the development of VAP. Data analysis was conducted using RStudio version 4.3.1. Results: In a study of 36 COVID-19 patients on mechanical ventilation, significant differences were found in the nasal and pulmonary microbiome, notably in Staphylococcus and Enterobacteriaceae, linked to VAP. Patients with VAP showed a higher SARS-CoV-2 viral load, elevated neutralizing antibodies, and reduced inflammatory cytokines, including IFN-δ, IL-1ß, IL-12p70, IL-18, IL-6, TNF-α, and CCL4. Metabolomic analysis revealed changes in 22 metabolites in non-VAP patients and 27 in VAP patients, highlighting D-Maltose-Lactose, Histidinyl-Glycine, and various phosphatidylcholines, indicating a metabolic predisposition to VAP. Conclusions: This study reveals a critical link between respiratory microbiome alterations and ventilator-associated pneumonia in COVID-19 patients, with elevated SARS-CoV-2 levels and metabolic changes, providing novel insights into the underlying mechanisms of VAP with potential management and prevention implications.

ARDS Mortality Prediction Model Using Evolving Clinical Data and Chest Radiograph Analysis.

INTRODUCTION: Within primary ARDS, SARS-CoV-2-associated ARDS (C-ARDS) emerged in late 2019, reaching its peak during the subsequent two years. Recent efforts in ARDS research have concentrated on phenotyping this heterogeneous syndrome to enhance comprehension of its pathophysiology. METHODS AND RESULTS: A retrospective study was conducted on C-ARDS patients from April 2020 to February 2021, encompassing 110 participants with a mean age of 63.2 ± 11.92 (26-83 years). Of these, 61.2% (68) were male, and 25% (17) experienced severe ARDS, resulting in a mortality rate of 47.3% (52). Ventilation settings, arterial blood gases, and chest X-ray (CXR) were evaluated on the first day of invasive mechanical ventilation and between days two and three. CXR images were scrutinized using a convolutional neural network (CNN). A binary logistic regression model for predicting C-ARDS mortality was developed based on the most influential variables: age, PaO2/FiO2 ratio (P/F) on days one and three, CNN-extracted CXR features, and age. Initial performance assessment on test data (23 patients out of the 110) revealed an area under the receiver operating characteristic (ROC) curve of 0.862 with a 95% confidence interval (0.654-0.969). CONCLUSION: Integrating data available in all intensive care units enables the prediction of C-ARDS mortality by utilizing evolving P/F ratios and CXR. This approach can assist in tailoring treatment plans and initiating early discussions to escalate care and extracorporeal life support. Machine learning algorithms for imaging classification can uncover otherwise inaccessible patterns, potentially evolving into another form of ARDS phenotyping. The combined features of these algorithms and clinical variables demonstrate superior performance compared to either element alone.

Practical Lessons on Antimicrobial Therapy for Critically Ill Patients.

Sepsis stands as a formidable global health challenge, with persistently elevated mortality rates in recent decades. Each year, sepsis not only contributes to heightened morbidity but also imposes substantial healthcare costs on survivors. This narrative review aims to highlight the targeted measures that can be instituted to alleviate the incidence and impact of sepsis in intensive care. Here we discuss measures to reduce nosocomial infections and the prevention of equipment and patient colonisation by resilient pathogens. The overarching global crisis of bacterial resistance to newly developed antimicrobial agents intensifies the imperative for antimicrobial stewardship and de-escalation. This urgency has been accentuated in recent years, notably during the COVID-19 pandemic, as high-dose steroids and opportunistic infections presented escalating challenges. Ongoing research into airway colonisation's role in influencing disease outcomes among critically ill patients underscores the importance of tailoring treatments to disease endotypes within heterogeneous populations, which are important lessons for intensivists in training. Looking ahead, the significance of novel antimicrobial delivery systems and drug monitoring is poised to increase. This narrative review delves into the multifaceted barriers and facilitators inherent in effectively treating critically ill patients vulnerable to nosocomial infections. The future trajectory of intensive care medicine hinges on the meticulous implementation of vigilant stewardship programs, robust infection control measures, and the continued exploration of innovative and efficient technological solutions within this demanding healthcare landscape.

Blood-brain barrier disruption and sustained systemic inflammation in individuals with long COVID-associated cognitive impairment.

Vascular disruption has been implicated in coronavirus disease 2019 (COVID-19) pathogenesis and may predispose to the neurological sequelae associated with long COVID, yet it is unclear how blood-brain barrier (BBB) function is affected in these conditions. Here we show that BBB disruption is evident during acute infection and in patients with long COVID with cognitive impairment, commonly referred to as brain fog. Using dynamic contrast-enhanced magnetic resonance imaging, we show BBB disruption in patients with long COVID-associated brain fog. Transcriptomic analysis of peripheral blood mononuclear cells revealed dysregulation of the coagulation system and a dampened adaptive immune response in individuals with brain fog. Accordingly, peripheral blood mononuclear cells showed increased adhesion to human brain endothelial cells in vitro, while exposure of brain endothelial cells to serum from patients with long COVID induced expression of inflammatory markers. Together, our data suggest that sustained systemic inflammation and persistent localized BBB dysfunction is a key feature of long COVID-associated brain fog.

Perspectives on the use of ceftolozane/tazobactam: a review of clinical trial data and real-world evidence.

Hospital-acquired bacterial pneumonia (HABP) and ventilator-associated bacterial pneumonia (VABP) are common healthcare-associated infections linked to high morbidity and mortality. Gram-negative pathogens, such as Pseudomonas aeruginosa, exhibit multidrug resistance and are recognized as major public health concerns, particularly among critically ill patients with HABP/VABP. Ceftolozane/tazobactam is a novel combination antibacterial agent comprising ceftolozane (a potent antipseudomonal cephalosporin) and tazobactam (a ß-lactamase inhibitor). Phase III trials have demonstrated non-inferiority of ceftolozane/tazobactam to comparators, leading to the approval of ceftolozane/tazobactam for the treatment of complicated urinary tract infections, complicated intra-abdominal infections, and nosocomial pneumonia. In this article, we review the clinical trial evidence and key real-world effectiveness data of ceftolozane/tazobactam for the treatment of serious healthcare-associated Gram-negative infections, focusing on patients with HABP/VABP.

Highlights from a review of ceftolozane/tazobactam for the treatment of serious infections Serious infections that can affect people in hospitals can cause serious illness or loss of life. Antibiotics are a type of medicine designed to kill the bacteria that cause these infections. However, bacteria have evolved over time, which means that antibiotics are not as effective at killing the bacteria and treating the infection. This is known as antibiotic resistance. To treat serious infections in hospital, there is a need for new antibiotics that can overcome this resistance and successfully fight off bacteria. This paper looks at an antibiotic known as ceftolozane/tazobactam (C/T), which can be used to treat people with serious infections that are picked up in hospitals. Clinical and laboratory studies have been reviewed to evaluate how effective, safe, and suitable C/T is for patients. The studies discussed in this paper highlight how well C/T works in people with serious infections, including those who are already ill and have been put on a ventilator to help with their breathing. Some of these studies showed that C/T worked well against lots of different types of bacteria that are known to cause serious infections in hospital and are linked to a high risk of death. Antibiotic resistance is a major problem all over the world. There is a need for effective antibiotics that can treat a range of infections caused by resistant bacteria. The results of this paper show that there is a lot of evidence to support the use of C/T in hospitals for people with serious bacterial infections.

Optimizing artificial intelligence in sepsis management: Opportunities in the present and looking closely to the future.

Sepsis remains a major challenge internationally for healthcare systems. Its incidence is rising due to poor public awareness and delays in its recognition and subsequent management. In sepsis, mortality increases with every hour left untreated. Artificial intelligence (AI) is transforming worldwide healthcare delivery at present. This review has outlined how AI can augment strategies to address this global disease burden. AI and machine learning (ML) algorithms can analyze vast quantities of increasingly complex clinical datasets from electronic medical records to assist clinicians in diagnosing and treating sepsis earlier than traditional methods. Our review highlights how these models can predict the risk of sepsis and organ failure even before it occurs. This gives providers additional time to plan and execute treatment plans, thereby avoiding increasing complications associated with delayed diagnosis of sepsis. The potential for cost savings with AI implementation is also discussed, including improving workflow efficiencies, reducing administrative costs, and improving healthcare outcomes. Despite these advantages, clinicians have been slow to adopt AI into clinical practice. Some of the limitations posed by AI solutions include the lack of diverse data sets for model building so that they are widely applicable for routine clinical use. Furthermore, the subsequent algorithms are often based on complex mathematics leading to clinician hesitancy to embrace such technologies. Finally, we highlight the need for robust political and regulatory frameworks in this area to achieve the trust and approval of clinicians and patients to implement this transformational technology.

ICU-acquired infections in immunocompromised patients.

Immunocompromised patients account for an increasing proportion of the typical intensive care unit (ICU) case-mix. Because of the increased availability of new drugs for cancer and auto-immune diseases, and improvement in the care of the most severely immunocompromised ICU patients (including those with hematologic malignancies), critically ill immunocompromised patients form a highly heterogeneous patient population. Furthermore, a large number of ICU patients with no apparent immunosuppression also harbor underlying conditions altering their immune response, or develop ICU-acquired immune deficiencies as a result of sepsis, trauma or major surgery. While infections are associated with significant morbidity and mortality in immunocompromised critically ill patients, little specific data are available on the incidence, microbiology, management and outcomes of ICU-acquired infections in this population. As a result, immunocompromised patients are usually excluded from trials and guidelines on the management of ICU-acquired infections. The most common ICU-acquired infections in immunocompromised patients are ventilator-associated lower respiratory tract infections (which include ventilator-associated pneumonia and tracheobronchitis) and bloodstream infections. Recently, several large observational studies have shed light on some of the epidemiological specificities of these infections-as well as on the dynamics of colonization and infection with multidrug-resistant bacteria-in these patients, and these will be discussed in this review. Immunocompromised patients are also at higher risk than non-immunocompromised hosts of fungal and viral infections, and the diagnostic and therapeutic management of these infections will be covered. Finally, we will suggest some important areas of future investigation.

Molecular antimicrobial susceptibility testing in sepsis.

Rapidly detecting and identifying pathogens is crucial for appropriate antimicrobial therapy in patients with sepsis. Conventional diagnostic methods have been a great asset to medicine, though they are time consuming and labor intensive. This work will enable healthcare professionals to understand the bacterial community better and enhance their diagnostic capacity by using novel molecular methods that make obtaining quicker, more precise results possible. The authors discuss and critically assess the merits and drawbacks of molecular testing and the added value of these tests, including the shift turnaround time, the implication for clinicians' decisions, gaps in knowledge, future research directions and novel insights or innovations. The field of antimicrobial molecular testing has seen several novel insights and innovations to improve the diagnosis and management of infectious diseases.

Sepsis is a life-threatening reaction to an infection. This infection is normally caused by a bacteria. Identifying the bacteria that has caused the infection is very important to choosing the best treatment. This is usually done using molecular testing. This article discusses the advantages and disadvantages of molecular testing, which tests are available and the value of these tests in clinical practice, the implication of molecular tests for clinicians' decisions and the gaps in our knowledge. It also discusses future innovations in molecular testing.

Comprehensive study of mechanical power in controlled mechanical ventilation: Prevalence of elevated mechanical power and component analysis.

OBJECTIVE: To determine the prevalence of elevated mechanical power (MP) values (>17J/min) used in routine clinical practice. DESIGN: Observational, descriptive, cross-sectional, analytical, multicenter, international study conducted on November 21, 2019, from 8:00 AM to 3:00 PM. NCT03936231. SETTING: One hundred thirty-three Critical Care Units. PATIENTS: Patients receiving invasive mechanical ventilation for any cause. INTERVENTIONS: None. MAIN VARIABLES OF INTEREST: Mechanical power. RESULTS: A population of 372 patients was analyzed. PM was significantly higher in patients under pressure-controlled ventilation (PC) compared to volume-controlled ventilation (VC) (19.20±8.44J/min vs. 16.01±6.88J/min; p<0.001), but the percentage of patients with PM>17J/min was not different (41% vs. 35%, respectively; p=0.382). The best models according to AICcw expressing PM for patients in VC are described as follows: Surrogate Strain (Driving Pressure) + PEEP+Surrogate Strain Rate (PEEP/Flow Ratio) + Respiratory Rate. For patients in PC, it is defined as: Surrogate Strain (Expiratory Tidal Volume/PEEP) + PEEP+Surrogate Strain Rate (Surrogate Strain/Ti) + Respiratory Rate+Expiratory Tidal Volume+Ti. CONCLUSIONS: A substantial proportion of mechanically ventilated patients may be at risk of experiencing elevated levels of mechanical power. Despite observed differences in mechanical power values between VC and PC ventilation, they did not result in a significant disparity in the prevalence of high mechanical power values.

Association of obesity on the outcome of critically ill patients affected by COVID-19.

OBJECTIVE: To evaluate the impact of obesity on ICU mortality. DESIGN: Observational, retrospective, multicentre study. SETTING: Intensive Care Unit (ICU). PATIENTS: Adults patients admitted with COVID-19 and respiratory failure. INTERVENTIONS: None. PRIMARY VARIABLES OF INTEREST: Collected data included demographic and clinical characteristics, comorbidities, laboratory tests and ICU outcomes. Body mass index (BMI) impact on ICU mortality was studied as (1) a continuous variable, (2) a categorical variable obesity/non-obesity, and (3) as categories defined a priori: underweight, normal, overweight, obesity and Class III obesity. The impact of obesity on mortality was assessed by multiple logistic regression and Smooth Restricted cubic (SRC) splines for Cox hazard regression. RESULTS: 5,206 patients were included, 20 patients (0.4%) as underweight, 887(17.0%) as normal, 2390(46%) as overweight, 1672(32.1) as obese and 237(4.5%) as class III obesity. The obesity group patients (n = 1909) were younger (61 vs. 65 years, p < 0.001) and with lower severity scores APACHE II (13 [9-17] vs. 13[10-17, p < 0.01) than non-obese. Overall ICU mortality was 28.5% and not different for obese (28.9%) or non-obese (28.3%, p = 0.65). Only Class III obesity (OR = 2.19, 95%CI 1.44-3.34) was associated with ICU mortality in the multivariate and SRC analysis. CONCLUSIONS: COVID-19 patients with a BMI > 40 are at high risk of poor outcomes in the ICU. An effective vaccination schedule and prolonged social distancing should be recommended.

Profiling the dysregulated immune response in sepsis: overcoming challenges to achieve the goal of precision medicine.

Sepsis is characterised by a dysregulated host immune response to infection. Despite recognition of its significance, immune status monitoring is not implemented in clinical practice due in part to the current absence of direct therapeutic implications. Technological advances in immunological profiling could enhance our understanding of immune dysregulation and facilitate integration into clinical practice. In this Review, we provide an overview of the current state of immune profiling in sepsis, including its use, current challenges, and opportunities for progress. We highlight the important role of immunological biomarkers in facilitating predictive enrichment in current and future treatment scenarios. We propose that multiple immune and non-immune-related parameters, including clinical and microbiological data, be integrated into diagnostic and predictive combitypes, with the aid of machine learning and artificial intelligence techniques. These combitypes could form the basis of workable algorithms to guide clinical decisions that make precision medicine in sepsis a reality and improve patient outcomes.