Community-acquired pneumonia represents the third-highest cause of mortality in industrialized countries and the first due to infection. Although guidelines for the approach to this infection model are widely implemented in international health schemes, information continually emerges that generates controversy or requires updating its management. This paper reviews the most important issues in the approach to this process, such as an aetiologic update using new molecular platforms or imaging techniques, including the diagnostic stewardship in different clinical settings. It also reviews both the Intensive Care Unit admission criteria and those of clinical stability to discharge. An update in antibiotic, in oxygen, or steroidal therapy is presented. It also analyzes the management out-of-hospital in CAP requiring hospitalization, the main factors for readmission, and an approach to therapeutic failure or rescue. Finally, the main strategies for prevention and vaccination in both immunocompetent and immunocompromised hosts are reviewed.

Infections due to Klebsiella pneumoniae have been increasing in intensive care units (ICUs) in the last decade. Such infections pose a serious problem, especially when antimicrobial resistance is present. We created a task force of experts, including specialists in intensive care medicine, anaesthesia, microbiology and infectious diseases, selected on the basis of their varied experience in the field of nosocomial infections, who conducted a comprehensive review of the recently published literature on the management of carbapenemase-producing Enterobacterales (CPE) infections in the intensive care setting from 2012 to 2022 to summarize the best available treatment. The group established priorities regarding management, based on both the risk of developing infections caused by K. pneumoniae and the risk of poor outcome. Moreover, we reviewed and updated the most important clinical entities and the new antibiotic treatments recently developed. After analysis of the priorities outlined, this group of experts established a series of recommendations and designed a management algorithm.

OBJECTIVE: To describe the clinical characteristics and epidemiological features of severe (non-ICU) and critically patients (ICU) with COVID-19 at triage, prior hospitalization, in one of the main hospitals in The Balearic Islands health care system. DESIGN: Retrospective observational study SETTING: Son Llatzer University Hospital in Palma de Mallorca (Spain) PARTICIPANTS: Among a cohort of 52 hospitalized patients as of 31 March 2020, 48 with complete demographic information and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive test, were analyzed. Data were collected between March 15th, 2020, and March 31th 2020, inclusive of these dates. MAIN OUTCOMES: Clinical, vital signs and routine laboratory outcomes at the time of hospitalization, including symptoms reported prior to hospitalization. Demographics and baseline comorbidities were also collected. Mortality was reported at the end of the study. RESULTS: 48 patients (27 non-ICU and 21 ICU) resident in Mallorca, Spain (mean age, 66 years, [range, 33-88 years]; 67% males) with positive SARS-CoV-2 infection were analyzed. There were no differences in age or sex among groups (p >.05). Initial symptoms included fever (100%), coughing (85%), dyspnea (76%), diarrhea (42%) and asthenia (21%). The majority of patients in this case series were hospitalized because of low SpO2 (SpO2 below 90%) and presentation of bilateral pneumonia (94%) at triage. ICU patients had a higher prevalence of dyspnea compared to non-ICU patients (95% vs 61%, p = .022). Acute respiratory syndrome (ARDS) was presented in 100% of the ICU-patients. All the patients included in the study required oxygen therapy. ICU-patients had lymphopenia as well as hypoalbuminemia. Inflammatory markers such as lactate dehydrogenase (LDH), C-reactive protein (CRP), and procalcitonin were significantly higher in ICU patients compared to non-ICU (p < .001).Lower albumin levels were associated with poor prognosis measured as longer hospital length (r= −0.472, p <.001) and mortality (r= −0.424, p=.003). Interestingly we also found, that MCV was lower among of those patients who died (p=.0002). As of April 28, 2020, 10 patients (8 ICU and 2 non-ICU) had died (21% mortality) and while 100% of the non-ICU patients had been discharged, 33% of ICU patients still remained hospitalized (5 in ICU and 2 had been transferred to ward). CONCLUSION: Critically ill patients with COVID-19 present lymphopenia, hypoalbuminemia as well high levels of inflammation. Lower levels of albumin were associated with poorer outcomes in COVID-19 patients. Albumin might be of importance because of its association with disease severity in patients infected with SARS-CoV-2. WHAT IS ALREADY KNOWN IN THIS TOPIC: Spain has been hit particularly hard by the pandemic. By the time that this manuscript was written more than 25.000 deaths related to COVID-19 have been confirmed. There is limited information available describing the clinical and epidemiological features of Spanish patients requiring hospitalization for COVID-19. Also, it is important to know the characteristics of the hospitalized patients who become critically ill WHAT THIS STUDY ADDS: This small case series provides the first steps towards a comprehensive clinical characterization of severe and critical COVID-19 adult patients in Spain. The overall mortality in our patients was 21%. To our knowledge this is the first report with reporting these features in Spain. At triage the majority of patients had lower SpO2 (<90%) and bilateral pneumonia. The most common comorbidities were hypertension (70%), dyslipidemia (62%) and cardiovascular disease (30%). Critically ill patients present hypoalbuminemia and lymphopenia, as well as higher levels of inflammation. Albumin might be of importance because of its association with disease severity and mortality in patients infected with SARS-CoV-2.

Background: Sepsis remains a leading cause of mortality in hospitalized patients. We evaluated characteristics and outcomes of patients identified through a comprehensive hospital-wide sepsis protocol over a 16-year period. Methods: This retrospective cohort study analyzed hospital-wide sepsis protocol activations at a tertiary care hospital in Spain from 2006 to 2022. The protocol required at least two SIRS criteria plus evidence of organ dysfunction in patients over 14 years old. We analyzed demographics, activation criteria, hospital location, mortality predictors using univariate and multivariate analyses, including propensity score modeling, and resource utilization trends. Results: A total of 10,919 patients with 14,546 protocol activations were identified. The median age was 69 years (IQR: 56–78), with 60.9% male patients. Protocol activations occurred in the emergency department (54%), ICU (34.2%), and inpatient wards (11.8%). The most common SIRS criteria were tachycardia (75.6%), tachypnea (50.4%), and fever (48.5%). Prevalent organ dysfunctions included hypotension (53%), hypoxemia (50.1%), oliguria (28.9%), and altered mental status (22%). Overall in-hospital mortality showed a significant linear downward trend from 26.5% in the first year to 13.6% in later years (p < 0.01). Propensity score analysis confirmed independent mortality predictors included hyperlactatemia (aOR 2.21), altered consciousness (aOR 2.09), hypotension (aOR 1.87), and leukopenia (aOR 1.79). ICU admission rate decreased from 58% to 24% over the study period. Conclusions: This 16-year analysis shows that comprehensive hospital-wide sepsis protocols achieve sustained mortality reduction with improved resource utilization efficiency. These findings support implementing comprehensive sepsis protocols as an effective strategy for improving sepsis outcomes.

Background/Objectives: Sepsis detection remains challenging due to clinical heterogeneity and limitations of traditional scoring systems. This study developed and validated a hospital-wide machine learning model for sepsis detection using retrospectively developed data from prospectively expert-validated cases, aiming to improve diagnostic accuracy beyond conventional approaches. Methods: This retrospective cohort study analysed 218,715 hospital episodes (2014–2018) at a tertiary care centre. Sepsis cases (n = 11,864, 5.42%) were prospectively validated in real-time by a Multidisciplinary Sepsis Unit using modified Sepsis-2 criteria with organ dysfunction. The model integrated structured data (26.95%) and unstructured clinical notes (73.04%) extracted via natural language processing from 2829 variables, selecting 230 relevant predictors. Thirty models including random forests, support vector machines, neural networks, and gradient boosting were developed and evaluated. The dataset was randomly split (5/7 training, 2/7 testing) with preserved patient-level independence. Results: The BiAlert Sepsis model (random forest + Sepsis-2 ensemble) achieved an AUC-ROC of 0.95, sensitivity of 0.93, and specificity of 0.84, significantly outperforming traditional approaches. Compared to the best rule-based method (Sepsis-2 + qSOFA, AUC-ROC 0.90), BiAlert reduced false positives by 39.6% (13.10% vs. 21.70%, p < 0.01). Novel predictors included eosinopenia and hypoalbuminemia, while traditional variables (MAP, GCS, platelets) showed minimal univariate association. The model received European Medicines Agency approval as a medical device in June 2024. Conclusions: This hospital-wide machine learning model, trained on prospectively expert-validated cases and integrating extensive NLP-derived features, demonstrates superior sepsis detection performance compared to conventional scoring systems. External validation and prospective clinical impact studies are needed before widespread implementation.