Intravenous lidocaine infusion for the treatment of acute pain in the pediatric intensive care unit: case series

Introducción:El uso de la infusión de lidocaína endovenosa ha aumentado en la última década como parte de un enfoque analgésico multimodal en los adultos; sin embargo, se dispone de información limitada sobre su seguridad y tolerabilidad en la población pediátrica. Métodos: Se presentan once casos de manejo de dolor agudo con lidocaína en infusión tratados en unidad de cuidado intensivo pediátrico. Resultados: Cinco casos fueron postoperatorio abdominal y seis casos tenían dolor abdominal no postoperatorio. Dos pacientes tenían cáncer y cursaban con colitis neutropénica. El control analgésico alcanzado fue bueno. Conclusión: Las infusiones de lidocaína parecen ser una opción segura para el manejo del dolor agudo ya sea posquirúrgico o no en la población pediátrica.Introduction: The use of intravenous lidocaine infusion has increased over the past decade as part of a multimodal approach to analgesia in adults; however, information about its safety and tolerability in the pediatric population is limited. Methods: Acute pain management using lidocaine infusion in eleven patients treated in the pediatric intensive care unit. Results: Five cases of postoperative abdominal pain and six cases of non-operative abdominal pain. Two cases were cancer patients affected by neutropenic colitis. Analgesic control achieved was good. Conclusion: Lidocaine infusions are apparently a safe option for the management of acute pain, either post-operative or not, in the pediatric population

Modeling the vital sign space to detect the deterioration of patients in a pediatric intensive care unit

ilustracionesResumen En el campo de la monitorización continua de los signos vitales en entornos de cuidados intensivos se ha observado que los signos de alerta temprana "de un deterioro fisiológico inminente” pueden no ser detectados a tiempo, hecho que se agrava no solo por la limitación de los recursos médicos, sino también por el "diluvio de datos" causado por la adquisición de información en pacientes cada vez más complejos durante la atención de rutina. El objetivo de este estudio es desarrollar un modelo probabilístico para predecir los episodios clínicos futuros de un paciente utilizando valores de signos vitales observados antes de un evento clínico. Los signos vitales (por ejemplo, frecuencia cardíaca, presión arterial) se utilizan para controlar las funciones fisiológicas de un paciente y sus cambios simultáneos indican las transiciones entre los estados de salud del paciente. Si tales cambios son anormales, puede conducir a un deterioro fisiológico grave. Se utilizó la metodología CRISP-DM (CRoss-Industry Standard Process for Data Mining) como proceso de minería de datos y luego utilizamos cadenas de Márkov para identificar los estados clínicos por los que pasa el paciente. Después, se aplicó un enfoque basado en un modelo oculto de Márkov (Hidden Márkov Model, HMM) para la clasificación y predicción del deterioro de un paciente calculando la probabilidad de estados clínicos futuros. Ambos modelos de aprendizaje fueron entrenados y evaluados utilizando seis bioseñales de 90 pacientes para un total de 94.678 instancias, recolectadas de una base de datos de pacientes reales que se encontraban en la Unidad de Cuidados Intensivos Pediátricos del Hospital Militar Central de la ciudad de Bogotá, Colombia. La técnica propuesta basada en el seguimiento de múltiples variables fisiológicas mostró resultados prometedores en la identificación precoz del deterioro de los pacientes críticos. (Texto tomado de la fuente)In the field of continuous vital-sign monitoring in critical care settings, it has been observed that the “early warning signs" of impending physiological deterioration can fail to be detected timely and sometimes by resource constrained clinical staff. This effect may be escalated by the “data deluge" caused by acquisition of more complex patient data during routine care. The objective of this study is to develop a probabilistic model for predicting the future clinical episodes of a patient using observed vital sign values prior to a clinical event. Vital signs (e.g., heart rate, blood pressure) are used to monitor a patient's physiological functions and their simultaneous changes indicate transitions between patient's health states. If such changes are abnormal then it may lead to serious physiological deterioration. The CRISP-DM (CRoss-Industry Standard Process for Data Mining) methodology was used as a data mining process and then we used Márkov chains to identify the clinical states through which the patient passes. Then, a Hidden Márkov model (HMM) based approach was applied for classification and prediction of patient's deterioration by computing the probability of future clinical states. Both learning models were trained and evaluated using six vital signs data from 94,678 records from 90 patients, collected from the database of real patients who were in the Pediatric Intensive Care Unit of the Central Military Hospital in the city of Bogota, Colombia. The proposed technique based on monitoring multiple physiological variables showed promising results in early identifying the deterioration of critically ill patients. (Texto tomado de la fuente)Abstract In the field of continuous vital-sign monitoring in critical care settings, it has been observed that the “early warning signs" of impending physiological deterioration can fail to be detected timely and sometimes by resource constrained clinical staff. This effect may be escalated by the “data deluge" caused by acquisition of more complex patient data during routine care. The objective of this study is to develop a probabilistic model for predicting the future clinical episodes of a patient using observed vital sign values prior to a clinical event. Vital signs (e.g., heart rate, blood pressure) are used to monitor a patient's physiological functions and their simultaneous changes indicate transitions between patient's health states. If such changes are abnormal then it may lead to serious physiological deterioration. The CRISP-DM (CRoss-Industry Standard Process for Data Mining) methodology was used as a data mining process and then we used Márkov chains to identify the clinical states through which the patient passes. Then, a Hidden Márkov model (HMM) based approach was applied for classification and prediction of patient's deterioration by computing the probability of future clinical states. Both learning models were trained and evaluated using six vital signs data from 94,678 records from 90 patients, collected from the database of real patients who were in the Pediatric Intensive Care Unit of the Central Military Hospital in the city of Bogota, Colombia. The proposed technique based on monitoring multiple physiological variables showed promising results in early identifying the deterioration of critically ill patients.MaestríaMagíster en Ingeniería BiomédicaEstudio analítico de corte transversal. Se tomaron muestras de monitoria de signos vitales de pacientes atendidos en la UCIP del Hospital Militar Central desde enero de 2018 a enero de 2020, desde 1 mes hasta los 18 meses de edad. Se realizo una descripción de las variables demográficas y clínicas utilizando las medidas más adecuadas de tendencia central y localización según la naturaleza de la variable y su distribución. Se realizo un análisis analítico mediante técnicas basadas en inteligencia computacional, identificando un modelo de aprendizaje automático de análisis, para la descripción de eventos clínicos normales/anormales, que tenga la capacidad de usar las tendencias temporales en datos continuos para la clasificación de eventos clínicos, tomando los datos temporales como una secuencia de cambios de estado clínico, y que se pudiera saber cuál es la probabilidad de que un evento clínico no solo dependa de los valores de signos vitales actuales en el paciente, sino también de una secuencia de mediciones del pasado. Se valido la herramienta computacional empleada a partir del modelo propuesto, adaptando diferentes métricas, para medir sensibilidad, especificidad y precisión, estableciendo las diferencias significativas y estableciendo un nivel de riesgo.Aprendizaje de MáquinasSistemas inteligente

Focused critical care echocardiography: training for pediatricians and pediatric intensivists in an intensive care unit

Introducción: La ecocardiografía, enfocada al cuidado crítico, se ha convertido hoy en día en una competencia necesaria del médico que labora en las unidades de cuidado intensivo pediátrico. Objetivo: Evaluar un programa de entrenamiento teórico y práctico, diseñado para explorar la adquisición de habilidades en la obtención de imágenes y algunas medidas ecocardiográficas sugeridas para la ecocardiografía enfocada al cuidado crítico. Materiales y métodos: Se realizó un entrenamiento teóricopráctico, de 26 horas de duración, por médicos pediatras y pediatras intensivistas, bajo la tutoría de un cardiólogo pediatra. El programa incluyó análisis cualitativos de las variables pertenecientes a las ventanas básicas y cuantitativos de la ecocardiografía enfocada al cuidado crítico. Resultados: Las diferencias significativas se presentaron entre tener conocimientos previos en ecocardiografía, que se asoció a un mejor puntaje en la ventana apical cuatro cámaras (media: 9,0 DE: 1,02 P=0,021), y mejor correlación con el cardiólogo pediatra en la medición de la funcionalidad del ventrículo izquierdo (media:92,2 DE:6,3 P=0,036). Conclusiones: Este programa de entrenamiento fue útil para el entrenamiento en nivel básico de la ecocardiografía enfocada al cuidado crítico, con un nivel óptimo en la adquisición de las principales ventanas ecocardiográficas, y para la toma de algunas medidas ecocardiográficas.Introduction: Focused critical care echocardiography (FCCE) has become a necessary competency for physicians working in pediatric intensive care units. Objective: To asses a theoretical and practical training program designed to explore skills acquisition for obtaining views and some echocardiographic measurements suggested for FCCE. Materials and methods: A 26-hour long theoretical and practical training for pediatricians and pediatric intensivists under the guidance of a pediatric cardiologist. The program included qualitative analysis of the variables pertaining to basic echocardiographic windows, and quantitative analysis of FCCE. Results: There were significant differences between having prior echocardiography knowledge, associated with a higher score in the 4-chamber apical window (mean: 9.0; standard deviation [SD]: 1.02; P=0.021), and better correlation with the pediatric cardiologist regarding left ventricular function measurements (mean: 92.02; SD: 6.3; P=0.036). Conclusion: The program was useful for basic level training in FCCE with an optimal level of acquisition of the main echocardiographic windows and some echocardiographic measurements

Adherence to Lung-Protective Ventilation Principles in Pediatric Acute Respiratory Distress Syndrome:A Pediatric Acute Respiratory Distress Syndrome Incidence and Epidemiology Study

OBJECTIVES: To describe mechanical ventilation management and factors associated with nonadherence to lung-protective ventilation principles in pediatric acute respiratory distress syndrome. DESIGN: A planned ancillary study to a prospective international observational study. Mechanical ventilation management (every 6 hr measurements) during pediatric acute respiratory distress syndrome days 0-3 was described and compared with Pediatric Acute Lung Injury Consensus Conference tidal volume recommendations (< 7 mL/kg in children with impaired respiratory system compliance, < 9 mL/kg in all other children) and the Acute Respiratory Distress Syndrome Network lower positive end-expiratory pressure/higher FIO2 grid recommendations. SETTING: Seventy-one international PICUs. PATIENTS: Children with pediatric acute respiratory distress syndrome. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Analyses included 422 children. On pediatric acute respiratory distress syndrome day 0, median tidal volume was 7.6 mL/kg (interquartile range, 6.3-8.9 mL/kg) and did not differ by pediatric acute respiratory distress syndrome severity. Plateau pressure was not recorded in 97% of measurements. Using delta pressure (peak inspiratory pressure - positive end-expiratory pressure), median tidal volume increased over quartiles of median delta pressure (p = 0.007). Median delta pressure was greater than or equal to 18 cm H2O for all pediatric acute respiratory distress syndrome severity levels. In severe pediatric acute respiratory distress syndrome, tidal volume was greater than or equal to 7 mL/kg 62% of the time, and positive end-expiratory pressure was lower than recommended by the positive end-expiratory pressure/FIO2 grid 70% of the time. In multivariable analysis, tidal volume nonadherence was more common with severe pediatric acute respiratory distress syndrome, fewer PICU admissions/yr, non-European PICUs, higher delta pressure, corticosteroid use, and pressure control mode. Adherence was associated with underweight stature and cuffed endotracheal tubes. In multivariable analysis, positive end-expiratory pressure/FIO2 grid nonadherence was more common with higher pediatric acute respiratory distress syndrome severity, ventilator decisions made primarily by the attending physician, pre-ICU cardiopulmonary resuscitation, underweight stature, and age less than 2 years. Adherence was associated with respiratory therapist involvement in ventilator management and longer time from pediatric acute respiratory distress syndrome diagnosis. Higher nonadherence to tidal volume and positive end-expiratory pressure recommendations were independently associated with higher mortality and longer duration of ventilation after adjustment for confounding variables. In stratified analyses, these associations were primarily influenced by children with severe pediatric acute respiratory distress syndrome. CONCLUSIONS: Nonadherence to lung-protective ventilation principles is common in pediatric acute respiratory distress syndrome and may impact outcome. Modifiable factors exist that may improve adherence

Predicting Mortality in Children With Pediatric Acute Respiratory Distress Syndrome:A Pediatric Acute Respiratory Distress Syndrome Incidence and Epidemiology Study

OBJECTIVES: Pediatric acute respiratory distress syndrome is heterogeneous, with a paucity of risk stratification tools to assist with trial design. We aimed to develop and validate mortality prediction models for patients with pediatric acute respiratory distress syndrome. DESIGN: Leveraging additional data collection from a preplanned ancillary study (Version 1) of the multinational Pediatric Acute Respiratory Distress syndrome Incidence and Epidemiology study, we identified predictors of mortality. Separate models were built for the entire Version 1 cohort, for the cohort excluding neurologic deaths, for intubated subjects, and for intubated subjects excluding neurologic deaths. Models were externally validated in a cohort of intubated pediatric acute respiratory distress syndrome patients from the Children's Hospital of Philadelphia. SETTING: The derivation cohort represented 100 centers worldwide; the validation cohort was from Children's Hospital of Philadelphia. PATIENTS: There were 624 and 640 subjects in the derivation and validation cohorts, respectively. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The model for the full cohort included immunocompromised status, Pediatric Logistic Organ Dysfunction 2 score, day 0 vasopressor-inotrope score and fluid balance, and PaO2/FIO2 6 hours after pediatric acute respiratory distress syndrome onset. This model had good discrimination (area under the receiver operating characteristic curve 0.82), calibration, and internal validation. Models excluding neurologic deaths, for intubated subjects, and for intubated subjects excluding neurologic deaths also demonstrated good discrimination (all area under the receiver operating characteristic curve ≥ 0.84) and calibration. In the validation cohort, models for intubated pediatric acute respiratory distress syndrome (including and excluding neurologic deaths) had excellent discrimination (both area under the receiver operating characteristic curve ≥ 0.85), but poor calibration. After revision, the model for all intubated subjects remained miscalibrated, whereas the model excluding neurologic deaths showed perfect calibration. Mortality models also stratified ventilator-free days at 28 days in both derivation and validation cohorts. CONCLUSIONS: We describe predictive models for mortality in pediatric acute respiratory distress syndrome using readily available variables from day 0 of pediatric acute respiratory distress syndrome which outperform severity of illness scores and which demonstrate utility for composite outcomes such as ventilator-free days. Models can assist with risk stratification for clinical trials

Global respiratory syncytial virus–related infant community deaths

Background Respiratory syncytial virus (RSV) is a leading cause of pediatric death, with >99% of mortality occurring in low- and lower middle-income countries. At least half of RSV-related deaths are estimated to occur in the community, but clinical characteristics of this group of children remain poorly characterized. Methods The RSV Global Online Mortality Database (RSV GOLD), a global registry of under-5 children who have died with RSV-related illness, describes clinical characteristics of children dying of RSV through global data sharing. RSV GOLD acts as a collaborative platform for global deaths, including community mortality studies described in this supplement. We aimed to compare the age distribution of infant deaths <6 months occurring in the community with in-hospital. Results We studied 829 RSV-related deaths <1 year of age from 38 developing countries, including 166 community deaths from 12 countries. There were 629 deaths that occurred <6 months, of which 156 (25%) occurred in the community. Among infants who died before 6 months of age, median age at death in the community (1.5 months; IQR: 0.8−3.3) was lower than in-hospital (2.4 months; IQR: 1.5−4.0; P < .0001). The proportion of neonatal deaths was higher in the community (29%, 46/156) than in-hospital (12%, 57/473, P < 0.0001). Conclusions We observed that children in the community die at a younger age. We expect that maternal vaccination or immunoprophylaxis against RSV will have a larger impact on RSV-related mortality in the community than in-hospital. This case series of RSV-related community deaths, made possible through global data sharing, allowed us to assess the potential impact of future RSV vaccines

Accidental oral paraphenylenediamine poisoning in a 5 year-old child

La parafenilendiamina (PPD) es una amina aromática con usos cosméticos e industriales. La dosis letal de PPD en adultos se estima en 7 g o más. Los efectos tóxicos de la PPD están relacionados con la dosis. A pesar de ser altamente tóxica, su uso no está restringido, sobre todo en países subdesarrollados. El tratamiento consta fundamentalmente de soporte vital avanzado. La intoxicación por PPD ocasiona una alta mortalidad y morbilidad con disfunción de órganos. Reportamos el caso de un escolar con intoxicación accidental por PPD, quien desarrolló una rápida insuficiencia respiratoria con lesión hepática, cardíaca y el desarrollo de falla renal. Durante su hospitalización en Unidad de Cuidado Intensivo Pediátrico, requirió soporte cardiovascular, asistencia ventilatoria y terapia de reemplazo renal. El paciente fue dado de alta de cuidado crítico una semana después con recuperación completa.Para-phenylenediamine (PPD) is an aromatic amine used in cosmetics and industrial applications. The PPD lethal dose in adults is estimated at 7 grams or more. The toxic effects of PPD are dose-related. Despite being highly toxic, its use is not restricted, especially in underdeveloped countries. Treatment consists primarily of advanced life support. PPD poisoning has a high mortality and morbidity, with multiorgan dysfunction. The case is presented of a school child with accidental poisoning PPD, who established a rapid respiratory failure with the development of liver damage, and cardiac and renal failure. During his hospitalization in the Paediatric Intensive Care Unit he required cardiovascular support, ventilatory support, and renal replacement therapy. The patient was discharged from critical care a week after full recovery