Background Up to 30% of patients admitted to hospitals with invasive pneumococcal disease (IPD), experience major adverse cardiovascular event (MACE) including new/worsening heart failure, new/worsening arrhythmia, and/or myocardial infarction. Streptococcus pneumoniae (Spn) is the most frequently isolated bacterial pathogen among CAP patients and the only etiological agent linked independently to MACE. Nevertheless, no clinical data exists identifying which serotypes of Spn are principally responsible for MACE. Methods This was an observational multicenter retrospective study conducted through the Public Health Secretary of Bogotá, Colombia. We included patients with a confirmed clinical diagnosis of IPD with record of pneumococcal serotyping and clinical information between 2012 and 2019. Spn were serotyped using the quellung method by the National Center of Microbiology. MACE were determined by a retrospective chart review. Results The prevalence of MACE was 23% (71/310) in IPD patients; 28% (53/181) in patients admitted for CAP. The most prevalent S. pneumoniae serotype identified in our study was the 19A, responsible for the 13% (42/310) of IPD in our cohort, of which 21% (9/42) presented MACE. Serotypes independently associated with MACE in IPD patients were serotype 3 (OR 1, 48; 95% CI [1.21-2.27]; p=0.013) and serotype 9n (OR 1.29; 95% CI [1.08-2.24]; p=0.020). Bacteremia occurred in 87% of patients with MACE. Moreover, serum concentrations of C-reactive protein were elevated in patients with MACE versus in non-MACE patients (mean [SD], 138 [145] versus 73 [106], p=0.01). Conclusions MACE are common during IPD with serotype 3 and 9n independently of frequency.

Purpose The COVID-19 pandemic has spread worldwide, and almost 396 million people have been infected around the globe. Latin American countries have been deeply affected, and there is a lack of data in this regard. This study aims to identify the clinical characteristics, in-hospital outcomes, and factors associated with ICU admission due to COVID-19. Furthermore, to describe the functional status of patients at hospital discharge after the acute episode of COVID-19. Material and methods This was a prospective, multicenter, multinational observational cohort study of subjects admitted to 22 hospitals within Latin America. Data were collected prospectively. Descriptive statistics were used to characterize patients, and multivariate regression was carried out to identify factors associated with severe COVID-19. Results A total of 3008 patients were included in the study. A total of 64.3% of patients had severe COVID-19 and were admitted to the ICU. Patients admitted to the ICU had a higher mean (SD) 4C score (10 [3] vs. 7 [3)], p<0.001). The risk factors independently associated with progression to ICU admission were age, shortness of breath, and obesity. In-hospital mortality was 24.1%, whereas the ICU mortality rate was 35.1%. Most patients had equal self-care ability at discharge 43.8%; however, ICU patients had worse self-care ability at hospital discharge (25.7% [497/1934] vs. 3.7% [40/1074], p<0.001). Conclusions This study confirms that patients with SARS CoV-2 in the Latin American population had a lower mortality rate than previously reported. Systemic complications are frequent in patients admitted to the ICU due to COVID-19, as previously described in high-income countries.

Background The incidence of invasive pneumococcal disease (IPD) varies depending on a number of factors, including vaccine uptake, in both children and adults, the geographic location, and local serotype prevalence. There are limited data about the burden of Streptococcus pneumoniae (Spn), serotype distribution, and clinical characteristics of adults hospitalized due to IPD in Colombia. The objectives of this study included assessment of Spn serotype distribution, clinical characteristics, mortality, ICU admission, and the need for mechanical ventilation. Methods This was an observational, retrospective, a citywide study conducted between 2012 and 2019 in Bogotá, Colombia. We analyzed reported positive cases of IPD from 55 hospitals in a governmental pneumococcal surveillance program. Pneumococcal strains were isolated in each hospital and typified in a centralized laboratory. This is a descriptive study stratified by age and subtypes of IPD obtained through the analysis of medical records. Results A total of 310 patients with IPD were included, of whom 45.5% were female. The leading cause of IPD was pneumonia (60%, 186/310), followed by meningitis. The most frequent serotypes isolated were 19A (13.87%, 43/310) and 3 (11.94%, 37/310). The overall hospital mortality rate was 30.3% (94/310). Moreover, 52.6% (163/310 patients) were admitted to the ICU, 45.5% (141/310) required invasive mechanical ventilation and 5.1% (16/310) non-invasive mechanical ventilation. Conclusion Pneumococcal pneumonia is the most prevalent cause of IPD, with serotypes 19A and 3 being the leading cause of IPD in Colombian adults. Mortality due to IPD in adults continues to be very high.

Introduction: Patients with community-acquired pneumonia (CAP) admitted to the intensive care unit (ICU) have high mortality rates during the acute infection and up to ten years thereafter. Recommendations from international CAP guidelines include macrolide-based treatment. However, there is no data on the long-term outcomes of this recommendation. Therefore, we aimed to determine the impact of macrolide-based therapy on long-term mortality in this population. Methods Registered patients in the MIMIC-IV database 16 years or older and admitted to the ICU due to CAP were included. Multivariate analysis, targeted maximum likelihood estimation (TMLE) to simulate a randomised controlled trial, and survival analyses were conducted to test the effect of macrolide-based treatment on mortality six-month [6m] and twelve-month [12m] after hospital admission. A sensitivity analysis was performed excluding patients with Pseudomonas aeruginosa or MRSA pneumonia to control for Healthcare-Associated Pneumonia (HCAP). Results 3775 patients were included, and 1154 were treated with a macrolide-based treatment. The non-macrolide-based group had worse long-term clinical outcomes, represented by 6m (31.5 [363/1154] vs 39.5 [1035/2621], p < 0.001) and 12m mortality (39.0 [450/1154] vs 45.7 [1198/2621], p < 0.001). The main risk factors associated with long-term mortality were Charlson comorbidity index, SAPS II, septic shock, and respiratory failure. Macrolide-based treatment reduced the risk of dying at 6m (HR [95% CI] 0.69 [0.60, 0.78], p < 0.001) and 12m (0.72 [0.64, 0.81], p < 0.001]). After TMLE, the protective effect continued with an additive effect estimate of -0.069. Conclusion Macrolide-based treatment reduced the hazard risk of long-term mortality by almost one-third. This effect remains after simulating an RCT with TMLE and the sensitivity analysis for the HCAP classification.

BACKGROUNDPatients with COVID-19 could develop severe disease requiring admission to the Intensive Care Unit (ICU). This manuscript presents a novel method that predicts whether a patient will need admission to the ICU and assess the risk of in-hospital mortality by training a deep learning model that combines a set of clinical variables and features in the Chest-X-Rays.METHODSThis was a prospective diagnostic test study. Patients with confirmed SARS-CoV-2 infection between March 2020 and January 2021 were included. This study was designed to build predictive models obtained by training convolutional neural networks for Chest-X-ray images using an artificial intelligence (AI) tool and a Random Forest analysis to identify critical clinical variables. Then, both architectures were connected and fine-tuned to provide combined models.RESULTSA total of 2552 patients were included in the clinical cohort. The variables independently associated with ICU admission were age, the fraction of inspired oxygen - FiO2 on admission, dyspnoea on admission, and obesity. Moreover, the variables associated with hospital mortality were age, the fraction of inspired oxygen - FiO2 on admission, and dyspnoea. When implementing the AI model to interpret the Chest-X-rays and the clinical variable identified by random forest, we developed a model that accurately predicts ICU admission (AUC:0.92±0.04) and hospital mortality (AUC:0.81±0.06) in patients with confirmed COVID-19.CONCLUSIONSThis automated Chest-X-ray interpretation algorithm, along with clinical variables, is a reliable alternative to identify patients at risk of developing severe COVID-19 that might require admission to the ICU.