Hospital Nacional de Niños

BackgroundVentilator-Associated Pneumonia (VAP) is a significant cause of morbidity and mortality in critically ill pediatric patients. Accurate diagnosis remains a challenge due to non-specific clinical presentations, inconsistent diagnostic criteria, and limitations in current microbiological and imaging methods. Optimizing the diagnostic approach is essential, so we aimed to measure prescription of bronchoalveolar lavages (BAL) in patients admitted to the pediatric intensive care unit (PICU) as well as consumption of third generation cephalosporins in the only tertiary pediatric hospital in Costa Rica.Number of BAL’s taken per month pre-intervention (from June 2021 to May 2022).Number of BAL’s taken per month post-intervention (from June 2022 to May 2023).MethodsA retrospective and prospective audit was conducted analyzing BAL indications and cefotaxime prescriptions during two periods: pre-intervention (June 2021–May 2022) and post-intervention (June 2022–May 2023). Data was extracted from bacteriology laboratory records, hospital admissions, and pharmacy databases. The intervention consisted of staff education on VAP diagnosis, dissemination of updated literature, implementation of a standardized VAP algorithm, and active participation in daily PICU rounds.ResultsIn the pre-intervention period, 812 BALs were performed, with a 38% positivity rate. Post-intervention, 854 BALs were performed, with a 35% positivity rate (Fig 1, 2). Cefotaxime use increased from 343 days of treatment (DOT) pre-intervention to 420 DOT post-intervention (Table 1,2). Factors potentially influencing these findings are outlined in Table 3.ConclusionDespite targeted educational efforts and the introduction of a diagnostic algorithm, there was no reduction in the number of BALs or cefotaxime prescriptions, and the proportion of positive cultures slightly declined. These findings highlight the complexity of changing clinical practice in high-stakes environments like the PICU. Continued efforts, including real-time decision support, strengthened stewardship programs, and ongoing engagement with multidisciplinary teams, are necessary to optimize diagnostic practices and antibiotic use in pediatric VAP.DisclosuresAll Authors: No reported disclosures

Se presenta el caso de un paciente con hallazgo incidental de miocardiopatía hipertrofia apical, en un servicio de emergencias. Se trata de un masculino de 33 años, que posterior a trauma cerrado a nivel torácico presenta dolor en hemitórax derecho y esternón por lo que se realiza un ECG, donde se documenta inversión de la onda T en precordiales de V2 a V6. Entre otros estudios se decide realizar un ETT que documenta a nivel de VI la presencia de hipertrofia confinada a la región apical, siendo el resto de los segmentos, de grosor normal. La miocardiopatía hipertrófica no obstructiva que se produce en el ápex del ventrículo izquierdo sin compromiso de la irrigación cardiaca por parte de las arterias coronarias se caracteriza por un buen pronóstico, con una mortalidad reportada de hasta el 1,9% de los casos (1). Es la variante menos frecuente de miocardiopatía hipertrófica, representa alrededor del 15-25% de la cohorte asiática y alrededor del 3% de la cohorte estadounidense (2, 3). En otros casos descritos a nivel mundial los pacientes consultaron por dolor torácico por lo que se abordaron inicialmente como síndromes coronarios agudos, terminando con angiografías coronarias que no revelaban evidencia de enfermedad de las arterias coronarias (4, 5, 6).

Process mining (PM) in the healthcare sector is a key discipline in terms of improving processes based on the data stored in clinical information systems. This discipline has been complemented by other techniques, such as process simulation (PS), in order to facilitate decision-making across the sector. In turn, this has enhanced the management of organizational indicators and strengthened the analysis, optimization, and improvement of processes. However, the combination of PM and PS in healthcare still presents a number of challenges, including the incorporation of additional medical specialties, the involvement of healthcare experts, the optimization of data quality, and the promotion of further research that seeks to strengthen the combination of these two areas. The present paper contributes to overcoming these challenges by introducing PROcess Mining & Simulation for Healthcare Analysis: PROMSHA-Methodology, a methodology that provides the necessary steps for process analysis by combining PM and PS in the field of healthcare. The methodology herein addresses certain challenges related to the quality of medical data and includes the participation of experts. To evaluate the usefulness of PROMSHA, a case study was conducted in the pediatric ophthalmology department of a Costa Rican hospital, in which PM techniques were applied to detect spaghetti models and repetitive activities, as well as to identify the causes of waiting lists and bottlenecks by means of several simulated scenarios.

ABSTRACTEvidence for the use of prophylactic anti‐seizure medication (ASM) in traumatic brain injury (TBI) in reducing the occurrence of early post‐traumatic seizure (EPTS) remains equivocal. This study aimed to analyze the prevalence of EPTS in children with TBI, compare clinical characteristics of those with and without EPTS, and explore the association between prophylactic ASM and EPTS. We performed an observational study among 28 pediatric intensive care units in 15 countries from January 2014 to October 2022. The rate of EPTS was compared between individuals prescribed prophylactic ASM and those who were not. Logistic regression was used to examine the association between ASM and EPTS. Among 697 children with TBI, 161 (23.1%) developed EPTS and 280 (40.2%) received prophylactic ASM treatment. Use of prophylactic ASM was associated with a lower likelihood of developing EPTS (27/280 (9.6%) vs. 134/417 (32.1%), p < 0.001). The most frequently used prophylactic ASMs were phenytoin, levetiracetam, and phenobarbital. Age ≤ 4 years and GCS ≤ 8 were associated with increased odds of developing EPTS (aOR 2.29, 95% CI 1.54–3.40, p < 0.001 and aOR 1.80, 95% CI 1.18–2.74, p = 0.01). Our data provide evidence supporting the potential protective role of prophylactic ASM against EPTS.

Liquid organic fertilizers (LOF) or bioles are liquid bioinput products derived from the fermentation of vegetable and fruit wastes, manure, waste from the dairy industry, and others; their metabolic and microbiological composition varies according to the source of its raw material. Its biostimulant effect has been demonstrated on various crops of agronomic interest in some Latin American countries, such as Costa Rica, where it is produced in an artisanal way. To our knowledge, this is the first literature review of LOF from a technical perspective, showing the stages of their fermentation process, as well as factors that may affect it, and technological tools that will allow us to monitor their quality and safe application in crops. We found that the application of this traditional and local practice has great potential to be projected worldwide; however, more research is needed to standardize its composition and effects according to the type of crop. The use of biotechnological tools for making LOF would allow the determination of their metabolic and microbiological composition and, at the same time, the monitoring of the quality of the product, allowing its safe use in agriculture. Finally, the production and application of LOF within a sustainable agricultural model would favor a circular economy by using organic waste as a raw material, thereby reducing the cost of chemical product consumption.

ABSTRACT More than half of the emerging or re‐emerging pathogens that affect human health have their origin in wildlife. Among these rodents are key zoonotic hosts due to their unique characteristics of life history and species diversity. The latter, added to logistical complexities and financial constraints, represents a significant challenge for rodent‐borne pathogens surveillance, particularly in regions such as Mexico and several countries in the Neotropics. This study examines host ecological traits associated with arenavirus and hepacivirus infections in American rodents and uses ecological trait‐based models to identify potential hosts that have not yet been sampled. To achieve this, we compile data on positive rodent species for mammarenaviruses and hepaciviruses infections, along with records of rodent species that inhabit the American continent, ecological traits and phylogenetic information related to these species. Subsequently, boosted regression trees (BRT) were used to explore how well different ecological traits could forecast viral positivity in rodent hosts. The models exhibited high predictive accuracy, with AUC values ranging from 0.92 to 0.96 for hepacivirus and mammarenavirus. Viral positivity was associated with family affiliations such as Muridae and Cricetidae, and ecological traits such as body mass, litter size, and longevity. Ultimately, we identified 46 potential hosts that warrant further surveillance to detect these viral agents. The application of ecological trait‐based models has the potential to optimize wildlife sampling and enhance the efficacy of monitoring initiatives, thereby facilitating the prediction and management of emerging infectious diseases.

ABSTRACTAimWe aimed to develop and test machine learning algorithms for the prediction of severe outcomes associated with MIS‐C.MethodAn observational ambispective cohort study was conducted including children aged from 1 month to 18 years old in 84 hospitals from the REKAMLATINA (Red de la Enfermedad de Kawasaki en America Latina) network diagnosed with MIS‐C from 1st January 2020 to 31st June 2022. Multiple models were developed to predict four main outcomes: paediatric intensive care unit (PICU) admission, need for inotropes, need for mechanical ventilation, and death. Performance measures were accuracy for PICU admission, inotropes use and mechanical ventilation, and the area under the receiver operating characteristic curve (AUROC) for death. Variable contribution was analysed using Shapley Additive Explanations (SHAP) values.ResultsWe included 1303 children with a diagnosis of MIS‐C. The model for the prediction of PICU admission (random forest [RF]) reached an accuracy of 0.80 (95% CI: 0.76–0.84), the model for inotrope use (RF) an accuracy of 0.86 (95% CI: 0.82–0.90), the model for mechanical ventilation (histogram‐based gradient boosting [HBGB]) an accuracy of 0.84 (95% CI 0.80–0.88), and the model for death (RF) reached an AUROC of 0.85 (95% CI 0.77–0.93).ConclusionsWe developed and validated machine learning models for the prediction of MIS‐C related outcomes that can help clinicians risk stratify patients to identify those most likely to have a severe outcome from MIS‐C.

In August 2020, the World Health Assembly approved the global strategy to eliminate cervical cancer, envisioning a world where it seizes to be a public health problem. The cervical cancer elimination initiative reinforces the commitment to fulfilling the rights of adolescent girls and women by reducing both health and economic inequities in the poorest populations that have more limited access to timely and effective services. This initiative improves the quality of life of women and their families by protecting them from a disease that causes disability and preventing avoidable mortality through public health measures. This article discusses the epidemiological situation and vaccination coverage and identifies weaknesses and opportunities in Latin America and the Caribbean to propose actions to reinforce progress toward the cervical cancer elimination goal.