Cognitive function mediates the relationship between age and anaesthesia-induced oscillatory-specific alpha power.

Cognitive decline is common among older individuals, and although the underlying brain mechanisms are not entirely understood, researchers have suggested using EEG frontal alpha activity during general anaesthesia as a potential biomarker for cognitive decline. This is because frontal alpha activity associated with GABAergic general anaesthetics has been linked to cognitive function. However, oscillatory-specific alpha power has also been linked with chronological age. We hypothesize that cognitive function mediates the association between chronological age and (oscillatory-specific) alpha power. We analysed data from 380 participants (aged over 60) with baseline screening assessments and intraoperative EEG. We utilized the telephonic Montreal Cognitive Assessment to assess cognitive function. We computed total band power, oscillatory-specific alpha power, and aperiodics to measure anaesthesia-induced alpha activity. To test our mediation hypotheses, we employed structural equation modelling. Pairwise correlations between age, cognitive function and alpha activity were significant. Cognitive function mediated the association between age and classical alpha power [age → cognitive function → classical alpha; ß = -0.0168 (95% confidence interval: -0.0313 to -0.00521); P = 0.0016] as well as the association between age and oscillatory-specific alpha power [age → cognitive function → oscillatory-specific alpha power; ß = -0.00711 (95% confidence interval: -0.0154 to -0.000842); P = 0.028]. However, cognitive function did not mediate the association between age and aperiodic activity (1/f slope, P = 0.43; offset, P = 0.0996). This study is expected to provide valuable insights for anaesthesiologists, enabling them to make informed inferences about a patient's age and cognitive function from an analysis of anaesthetic-induced EEG signals in the operating room. To ensure generalizability, further studies across different populations are needed.

Validation of the CALL score as a mortality prediction tool in a cohort of hospitalized COVID-19 patients in Chile.

Introduction: The CALL score is a predictive tool for respiratory failure progression in COVID-19. Whether the CALL score is useful to predict short- and medium-term mortality in an unvaccinated population is unknown. Materials and methods: This is a prospective cohort study in unvaccinated inpatients with a COVID-19 pneumonia diagnosis upon hospital admission. Patients were followed up for mortality at 28 days, 3, 6, and 12 months. Associations between CALL score and mortality were analyzed using logistic regression. The prediction performance was evaluated using the area under a receiver operating characteristic curve (AUROC). Results: A total of 592 patients were included. On average, the CALL score was 9.25 (±2). Higher CALL scores were associated with increased mortality at 28 days [univariate: odds ratio (OR) 1.58 (95% CI, 1.34-1.88), p < 0.001; multivariate: OR 1.54 (95% CI, 1.26-1.87), p < 0.001] and 12 months [univariate OR 1.63 (95% CI, 1.38-1.93), p < 0.001; multivariate OR 1.63 (95% CI, 1.35-1.97), p < 0.001]. The prediction performance was good for both univariate [AUROC 0.739 (0.687-0.791) at 28 days and 0.869 (0.828-0.91) at 12 months] and multivariate models [AUROC 0.752 (0.704-0.8) at 28 days and 0.862 (0.82-0.905) at 12 months]. Conclusion: The CALL score exhibits a good predictive capacity for short- and medium-term mortality in an unvaccinated population.

Compound climate-pollution extremes in Santiago de Chile.

Cities in the global south face dire climate impacts. It is in socioeconomically marginalized urban communities of the global south that the effects of climate change are felt most deeply. Santiago de Chile, a major mid-latitude Andean city of 7.7 million inhabitants, is already undergoing the so-called "climate penalty" as rising temperatures worsen the effects of endemic ground-level ozone pollution. As many cities in the global south, Santiago is highly segregated along socioeconomic lines, which offers an opportunity for studying the effects of concurrent heatwaves and ozone episodes on distinct zones of affluence and deprivation. Here, we combine existing datasets of social indicators and climate-sensitive health risks with weather and air quality observations to study the response to compound heat-ozone extremes of different socioeconomic strata. Attributable to spatial variations in the ground-level ozone burden (heavier for wealthy communities), we found that the mortality response to extreme heat (and the associated further ozone pollution) is stronger in affluent dwellers, regardless of comorbidities and lack of access to health care affecting disadvantaged population. These unexpected findings underline the need of a site-specific hazard assessment and a community-based risk management.

The effect of age on electroencephalogram measures of anesthesia hypnosis: A comparison of BIS, Alpha Power, Lempel-Ziv complexity and permutation entropy during propofol induction.

Background: Improving anesthesia administration for elderly population is of particular importance because they undergo considerably more surgical procedures and are at the most risk of suffering from anesthesia-related complications. Intraoperative brain monitors electroencephalogram (EEG) have proved useful in the general population, however, in elderly subjects this is contentious. Probably because these monitors do not account for the natural differences in EEG signals between young and older patients. In this study we attempted to systematically characterize the age-dependence of different EEG measures of anesthesia hypnosis. Methods: We recorded EEG from 30 patients with a wide age range (19-99 years old) and analyzed four different proposed indexes of depth of hypnosis before, during and after loss of behavioral response due to slow propofol infusion during anesthetic induction. We analyzed Bispectral Index (BIS), Alpha Power and two entropy-related EEG measures, Lempel-Ziv complexity (LZc), and permutation entropy (PE) using mixed-effect analysis of variances (ANOVAs). We evaluated their possible age biases and their trajectories during propofol induction. Results: All measures were dependent on anesthesia stages. BIS, LZc, and PE presented lower values at increasing anesthetic dosage. Inversely, Alpha Power increased with increasing propofol at low doses, however this relation was reversed at greater effect-site propofol concentrations. Significant group differences between elderly patients (>65 years) and young patients were observed for BIS, Alpha Power, and LZc, but not for PE. Conclusion: BIS, Alpha Power, and LZc show important age-related biases during slow propofol induction. These should be considered when interpreting and designing EEG monitors for clinical settings. Interestingly, PE did not present significant age differences, which makes it a promising candidate as an age-independent measure of hypnotic depth to be used in future monitor development.

Inteligencia artificial en medicina: Selección de métodos, aplicaciones y consideraciones (Parte II) / Artificial Intelligence in medicine: Methods selection, applications and considerations (Part II)

The spreading of artificial intelligence and machine learning (ML) methods in different healthcare areas is common. The se- cond part of this review article describes the foresights when selecting different ML methods. It also presents an updated review of examples and the precautions or challenges that we will face in the future when using these technologies. We will describe how to know whether to use a descriptive or predictive approach, the characteristics of these methods and their potential applications. Later, we will discuss how the irruption of digital data, together with freely available algorithms and greater com- putational power, has made it possible to enhance the implementation of these models in medicine. We will review how ML has contributed to the development of diagnostic imaging, as well as the prediction of monitoring and clinical outcomes. Finally, we will analyze the challenges and ethical considerations associated with the implementation of ML in clinical practice.

La difusión de los métodos de inteligencia artificial y machine learning (ML) en diversas áreas de la salud es transversal. La segunda parte de este artículo de revisión describe las consideraciones que se deben tener al seleccionar distintos métodos de ML. Además, presenta una revisión actualizada de ejemplos de su uso y de las precauciones o desafíos a los que nos veremos enfrentados en el futuro al utilizar estas tecnologías. Describiremos cómo saber si utilizar un enfoque descriptivo o predictivo, las características de estas aproximaciones y sus potenciales aplicaciones. Posteriormente, discutiremos cómo la irrupción de datos digitales, en conjunto con algoritmos de libre disposición y mayor poder computacional, ha permitido potenciar la implementa- ción de estos modelos en medicina. Revisaremos como el ML ha contribuido en el desarrollo del diagnóstico por imágenes, como también en la predicción de monitorización y desenlaces clínicos. Finalmente, analizaremos los desafíos y consideraciones éticas asociadas a la implementación del ML en la práctica clínica.

Inteligencia artificial en medicina: Métodos de modelamiento (Parte I) / Artificial Intelligence in medicine: Modeling methods (Part I)

The emergence of artificial intelligence and machine learning in medicine determines that healthcare professionals should understand generalities of their methodologies. This narrative review consists of two parts. The first consists of an exploration of the main methods used to model in machine learning, described in a simple way by medical and mathematical authors, with the purpose to bring this methodology healthcare workers. Here we will describe the basic structure of a machine learning algorithm (input information, task to execute, output result, optimization, and adjustment), its main classifications (supervised, unsupervised and by reinforcement) and the main modeling methods used. We will review regression and then explore decision trees, support vector machines, principal component analysis, clustering, K-means, hierarchical clustering, deep learning, and convolutional neural networks. In this way, we hope to bring this methodology closer to healthcare personnel to increase the interpretability of the published work in medicine that use these methodologies.

La irrupción de la inteligencia artificial y el amplio desarrollo de aplicaciones de machine learning que se ha experimentado en el campo de la medicina en los últimos años, requiere que los profesionales de la salud conozcan generalidades de sus metodologías. Esta revisión narrativa se compone de dos partes. La primera consta de una exploración de los principales métodos utilizados para modelar algoritmos de machine learning, descrito de manera sencilla por autores médicos y matemáticos, con el fin de acercar esta metodología a un público que se desempeña en el área de la salud. Aquí describiremos la estructura básica y los objetivos de un algoritmo de machine learning, los diferentes modelos existentes (supervisado, no supervisado y por refuerzo) y los principales métodos y técnicas estadísticas que lo componen. Revisaremos generalidades de regresiones para luego profundizar en árboles de decisiones y sus derivados, máquinas de vector de soporte, análisis de componentes principales, clustering, K-medias, clustering jerarquizado, aprendizaje profundo y redes neuronales convolucionales. De esta forma, esperamos acercar esta metodología al personal de salud con el fin de aumentar la interpretabilidad de los trabajos publicados en medicina que utilizan estas metodologías.

Revisión de metadona intraoperatoria para el manejo de dolor posquirúrgico / Intraoperative methadone for the management of postsurgical pain a review

Pain management associated with surgery is a constant concern of the health team as well as the patient. Multiple proposals for analgesia have been made in the perioperative context. The use of opioids with rapid effect and easy titration in the intraoperative period are currently frequent; to then perform a postoperative analgesic control with drugs with a longer half-life, usually achieving adequate pain management. However, sometimes the standard analgesic scheme is not enough. The problems associated with this situation have led to the need for high doses of opioids in the postoperative period, with the requirement for monitoring, health personnel, and the adverse effects that these involve. Methadone is a long-acting, rapid-onset opioid, the latter secondary to its long elimination half-life. It is presumed that these characteristics have led patients to report adequate pain management, which has been related to a decrease in the need and dose of rescue opioids, in addition to delaying the requirement of these if necessary during the postoperative. These properties allow methadone to be a potential solution to perioperative pain management.

El manejo del dolor asociado a la cirugía es una preocupación constante del equipo de salud al igual que del paciente. Se han planteado múltiples propuestas de analgesia en el contexto perioperatorio, siendo actualmente frecuente el uso de opioides de rápido efecto y fácil titulación en el intraoperatorio; para luego realizar un control analgésico postoperatorio con fármacos de mayor vida media, logrando habitualmente un manejo adecuado del dolor. Sin embargo, a veces el esquema analgésico estándar no es suficiente. La problemática asociada a esta situación ha llevado a la necesidad de altas dosis de opioides en el posoperatorio, con el requerimiento de monitorización, personal de salud y efectos adversos que estos involucran. La metadona es un opioide de inicio de acción rápido y larga duración, este último secundario a su vida media de eliminación prolongada. Se presume que estas características han logrado que los pacientes reporten un adecuado manejo de su dolor, lo que se ha relacionado a una disminución en la necesidad y dosis de opioides de rescate, además de retrasar el requerimiento de éstos en el caso de ser necesarios durante el postoperatorio. Estas propiedades permiten que la metadona pueda ser una potencial solución al manejo del dolor perioperatorio.

[Analysis of incident reports in an anesthesiology unit of a university hospital]. / De la anestesia a la seguridad de la atención: experiencia de 6 años en el análisis de reportes de incidentes en un hospital universitario.

BACKGROUND: Incident reporting is an effective strategy used to enhance patient safety. An incident is an event that could eventually result in harm to a patient. AIM: To classify and analyze incidents reported by an Anesthesiology division at a University hospital in Chile. MATERIAL AND METHODS: A retrospective analysis of the reported incidents registered in our institutional database from January 2008 to January 2014. They were classified according to three variables proposed by the World Health Organization system to determine the type of incident and patients’ potential harm. RESULTS: There were 297 reports registered. Etiologic classification according to the WHO system showed that 29% (n = 85) were related with management, 20% (59) with drugs, 20% (59) with medical devices, 16% (48) with procedures and 15% (46) with human factors. Seventy two percent (58) of incidents caused low or moderate harm and 28% (22) resulted in a severe adverse event or death. CONCLUSIONS: Our analysis highlights that a high rate of incidents are associated with management, the leading cause of reports in our center. Due to the low incident report rate in our country, it is difficult to perform appropriate comparisons with other centers. In the future, local incident reporting systems should be improved.

De la anestesia a la seguridad de la atención: experiencia de 6 años en el análisis de reportes de incidentes en un hospital universitario / Analysis of incident reports in an anesthesiology unit of a university hospital

Background: Incident reporting is an effective strategy used to enhance patient safety. An incident is an event that could eventually result in harm to a patient. Aim: To classify and analyze incidents reported by an Anesthesiology division at a University hospital in Chile. Material and Methods: A retrospective analysis of the reported incidents registered in our institutional database from January 2008 to January 2014. They were classified according to three variables proposed by the World Health Organization system to determine the type of incident and patients’ potential harm. Results: There were 297 reports registered. Etiologic classification according to the WHO system showed that 29% (n = 85) were related with management, 20% (59) with drugs, 20% (59) with medical devices, 16% (48) with procedures and 15% (46) with human factors. Seventy two percent (58) of incidents caused low or moderate harm and 28% (22) resulted in a severe adverse event or death. Conclusions: Our analysis highlights that a high rate of incidents are associated with management, the leading cause of reports in our center. Due to the low incident report rate in our country, it is difficult to perform appropriate comparisons with other centers. In the future, local incident reporting systems should be improved.