Clinical Presentation and Outcomes of Kawasaki Disease in Children from Latin America: A Multicenter Observational Study from the REKAMLATINA Network

Objetivos: Describir la presentación clínica, el manejo y los resultados de la enfermedad de Kawasaki (EK) en Latinoamérica y evaluar los indicadores pronósticos tempranos de aneurisma de la arteria coronaria (AAC). Diseño del estudio: Se realizó un estudio observacional basado en el registro de la EK en 64 centros pediátricos participantes de 19 países latinoamericanos de forma retrospectiva entre el 1 de enero de 2009 y el 31 de diciembre de 2013, y de forma prospectiva desde el 1 de junio de 2014 hasta el 31 de mayo de 2017. Se recopilaron datos demográficos, clínicos y de laboratorio iniciales. Se utilizó una regresión logística que incorporaba factores clínicos y la puntuación z máxima de la arteria coronaria en la presentación inicial (entre 10 días antes y 5 días después de la inmunoglobulina intravenosa [IGIV]) para desarrollar un modelo pronóstico de AAC durante el seguimiento (>5 días después de la IGIV). Resultados: De 1853 pacientes con EK, el ingreso tardío (>10 días tras el inicio de la fiebre) se produjo en el 16%, el 25% tuvo EK incompleta y el 11% fue resistente a la IGIV. Entre los 671 sujetos con puntuación z de la arteria coronaria notificada durante el seguimiento (mediana: 79 días; IQR: 36, 186), el 21% presentaba AAC, incluido un 4% con aneurismas gigantes. Un modelo pronóstico simple que utilizaba sólo una puntuación z de la arteria coronaria máxima ≥2,5 en la presentación inicial fue óptimo para predecir la AAC durante el seguimiento (área bajo la curva: 0,84; IC del 95%: 0,80, 0,88). Conclusiones: De nuestra población latinoamericana, la puntuación z de la arteria coronaria ≥2,5 en la presentación inicial fue el factor pronóstico más importante que precedió a la AAC durante el seguimiento. Estos resultados resaltan la importancia de la ecocardiografía temprana durante la presentación inicial de la EK. © 2023 Los autoresObjectives: To describe the clinical presentation, management, and outcomes of Kawasaki disease (KD) in Latin America and to evaluate early prognostic indicators of coronary artery aneurysm (CAA). Study design: An observational KD registry-based study was conducted in 64 participating pediatric centers across 19 Latin American countries retrospectively between January 1, 2009, and December 31, 2013, and prospectively from June 1, 2014, to May 31, 2017. Demographic and initial clinical and laboratory data were collected. Logistic regression incorporating clinical factors and maximum coronary artery z-score at initial presentation (between 10 days before and 5 days after intravenous immunoglobulin [IVIG]) was used to develop a prognostic model for CAA during follow-up (>5 days after IVIG). Results: Of 1853 patients with KD, delayed admission (>10 days after fever onset) occurred in 16%, 25% had incomplete KD, and 11% were resistant to IVIG. Among 671 subjects with reported coronary artery z-score during follow-up (median: 79 days; IQR: 36, 186), 21% had CAA, including 4% with giant aneurysms. A simple prognostic model utilizing only a maximum coronary artery z-score ≥2.5 at initial presentation was optimal to predict CAA during follow-up (area under the curve: 0.84; 95% CI: 0.80, 0.88). Conclusion: From our Latin American population, coronary artery z-score ≥2.5 at initial presentation was the most important prognostic factor preceding CAA during follow-up. These results highlight the importance of early echocardiography during the initial presentation of KD. © 2023 The Author(s

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

Pattern recognition applied to seismic signals of Llaima volcano (Chile): An evaluation of station-dependent classifiers

Automatic pattern recognition applied to seismic signals from volcanoes may assist seismic monitoring by reducing the workload of analysts, allowing them to focus on more challenging activities, such as producing reports, implementing models, and understanding volcanic behaviour. In a previous work, we proposed a structure for automatic classification of seismic events in Llaima volcano, one of the most active volcanoes in the Southern Andes, located in the Araucania Region of Chile. A database of events taken from three monitoring stations on the volcano was used to create a classification structure, independent of which station provided the signal. The database included three types of volcanic events: tremor, long period, and volcano-tectonic and a contrast group which contains other types of seismic signals. In the present work, we maintain the same classification scheme, but we consider separately the stations information in order to assess whether the complementary information provided by different stations improves the performance of the classifier in recognising seismic patterns. This paper proposes two strategies for combining the information from the stations: i) combining the features extracted from the signals from each station and ii) combining the classifiers of each station. In the first case, the features extracted from the signals from each station are combined forming the input for a single classification structure. In the second, a decision stage combines the results of the classifiers for each station to give a unique output. The results confirm that the station-dependent strategies that combine the features and the classifiers from several stations improves the classification performance, and that the combination of the features provides the best performance. The results show an average improvement of 9% in the classification accuracy when compared with the station-independent method.Direccion de Investigacion at the Universidad de La Frontera DIUFRO10-0020 project CONICYT-PIA ANILLO ACT 1120 CONICYT-FONDEF IDeA CA13I10273 Project STIC-AmSud 15STIC-0