Predictores del estado post-ictus en el alta hospitalaria. Importancia en enfermería / Predictors of the post-stroke status in the discharge from the hospital. Importance in nursing

A menudo, por parte del paciente y de la familia, se solicita a los profesionales de enfermería que predigan los factores que influyen en el estado post-ictus. Se han realizado numerosos estudios para determinar los factores que influyen en el estado neurológico post-ictus en el momento del alta hospitalaria. Sin embargo, las técnicas de aprendizaje automático no se han utilizado para este propósito. Con el objetivo de obtener reglas de asociación del pronóstico neurológico, se ha llevado a cabo un doble análisis, tanto clínico como con técnicas de aprendizaje automático, de las posibles asociaciones de factores que influyen en el estado neurológico de los pacientes post-ictus. El algoritmo Apriori detectó varias reglas de asociación con alta confianza (≥ 95%), con el siguiente patrón: En pacientes en el rango de edad de 50-80 años, la asociación de un NIHSS entre 11 y 15 puntos (NIHSS intermedio/bajo), junto con la trombectomía, conduce a la recuperación ad integrum al alta. Con la técnica de remuestreo SMOTE, se alcanzó el 100% de confianza para la asociación de NIHSS elevado (>20) y afectación de las arterias carótida y basilar, con pronóstico nefasto (exitus). Estas reglas confirman, por primera vez con aprendizaje automático, la importancia de la asociación de algunos predictores, en el pronóstico post-ictus. El conocimiento por parte de las enfermeras de estas reglas puede mejorar los resultados del ictus. Adicionalmente, el papel de la enfermería en los programas de educación sobre los factores de riesgo, y pronóstico de un ictus se torna imprescindible. (AU)

Nurses are often asked to predict factors that influence post-stroke outcome by the patient and family. Many studies have been carried out in order to determine the factors that influence the neurological status of the post-stroke patient at the moment of the discharge from the hospital. However, machine learning techniques have not been used for this purpose. Therefore, with the objective of obtaining association rules of neurological prognosis, a double analysis, both clinical and with machine learning techniques of the possible associations of factors that influence the neurological status of the post-stroke patients has been carried out. The Apriori algorithm detected several association rules with high confidence (≥ 95%), from which the following pattern: In patients in the age range of 50-80 years, the association of a NIHSS between 11 and 15 points (intermediate/low NIHSS), along with thrombectomy, leads to recovery ad integrum at discharge. With the SMOTE resampling technique, the 100% confidence was reached for the association of high NIHSS (>20) and involvement of the carotid and basilar arteries, with a dire prognosis (exitus). These rules confirm, for the first time with machine learning, the importance of the association of some predictors, in the post-stroke prognosis. The knowledge by the nurses of these association rules can successfully improve stroke outcome. In addition, the role of nurses in education programs that teach knowledge of risk factors and stroke prognosis becomes essential. (AU)

Comparing fixed-array and functionally-defined channel of interest approaches to infant functional near-infrared spectroscopy data.

Functional near-infrared spectroscopy (fNIRS) is increasingly used to study brain function in infants, but the development and standardization of analysis techniques for use with infant fNIRS data have not paced other technical advances. Here we quantify and compare the effects of different methods of analysis of infant fNIRS data on two independent fNIRS datasets involving 6-9-month-old infants and a third simulated infant fNIRS dataset. With each, we contrast results from a traditional, fixed-array analysis with several functional channel of interest (fCOI) analysis approaches. In addition, we tested the effects of varying the number and anatomical location of potential data channels to be included in the fCOI definition. Over three studies we find that fCOI approaches are more sensitive than fixed-array analyses, especially when channels of interests were defined within-subjects. Applying anatomical restriction and/or including multiple channels in the fCOI definition does not decrease and in some cases increases sensitivity of fCOI methods. Based on these results, we recommend that researchers consider employing fCOI approaches to the analysis of infant fNIRS data and provide some guidelines for choosing between particular fCOI approaches and settings for the study of infant brain function and development.

Triaje por enfermería en el ictus agudo / Nursing triage in acute stroke

En las ultimas décadas, el papel de la enfermería en el triaje y cribado de pacientes en los servicios de urgencias y emergencias, tanto en el entorno prehospitalario como sobre todo en los hospitales, es esencial e indiscutible. Con el objetivo de analizar el triaje realizado por enfermería para detectar los pacientes con ictus agudo, y llegado el caso mejorarlo, se ha realizado un estudio retrospectivo de las presentaciones cardinales del ictus, la escala del Instituto Nacional de la Salud de Estados Unidos, y la escala modificada de Rankin, aplicadas en el set del triaje por enfermería en el Hospital Universitario de Salamanca, durante el período comprendido entre los años 2016 y 2019, ambos inclusive. El total de historias clínicas analizadas ha sido de 1572. El análisis está centrado en la fiabilidad, evaluado por enfermería, de la presentación cardinal del ictus agudo, y de las dos escalas, frente a los algoritmos tradicionales rápidos de detección del ictus, en particular el método FAST, y la escala de Cincinnati. Nuestro estudio demuestra que son varias las presentaciones clínicas que escapan a las escalas rápidas, por lo que es esencial ampliar los métodos de triaje del ictus agudo realizados por enfermería, con el fin de evitar retardos en la detección y el tratamiento definitivo (enfermedad tiempo-dependiente). Así pues, la identificación ampliada de las presentaciónes cardinales, junto con el uso de escalas más detalladas aplicadas por enfermería entrenada, se muestran como herramientas muy útiles de detección del ictus agudo. (AU)

Multiclassifier Systems for Predicting Neurological Outcome of Patients with Severe Trauma and Polytrauma in Intensive Care Units.

This paper presents an ensemble based classification proposal for predicting neurological outcome of severely traumatized patients. The study comprises both the whole group of patients and a subgroup containing those patients suffering traumatic brain injury (TBI). Data was gathered from patients hospitalized in the Intensive Care Unit (ICU) of the University Hospital in Salamanca. Predictive models were induced from both epidemiologic and clinical variables taken at the emergency room and along the stay in the ICU. The large number of variables leads to a low accuracy in the classifiers even when feature selection methods are used. In addition, the presence of a much larger number of instances of one of the classes in the subgroup of TBI patients produces a significantly lesser precision for the minority class. Usual ways of dealing with the last problem is to use undersampling and oversampling strategies, which can lead to the loss of valuable data and overfitting problems respectively. Our proposal for dealing with these problems is based in the use of ensemble multiclassifiers as well as in the use of an ensemble playing the role of base classifier in multiclassifiers. The proposed strategy gave the best values of the selected quality measures (accuracy, precision, sensitivity, specificity, F-measure and area under the Receiver Operator Characteristic curve) as well as the closest values of precision for the two classes under study in the case of the classification from imbalanced data.

Success/Failure Prediction of Noninvasive Mechanical Ventilation in Intensive Care Units. Using Multiclassifiers and Feature Selection Methods.

OBJECTIVES: This paper addresses the problem of decision-making in relation to the administration of noninvasive mechanical ventilation (NIMV) in intensive care units. METHODS: Data mining methods were employed to find out the factors influencing the success/failure of NIMV and to predict its results in future patients. These artificial intelligence-based methods have not been applied in this field in spite of the good results obtained in other medical areas. RESULTS: Feature selection methods provided the most influential variables in the success/failure of NIMV, such as NIMV hours, PaCO2 at the start, PaO2 / FiO2 ratio at the start, hematocrit at the start or PaO2 / FiO2 ratio after two hours. These methods were also used in the preprocessing step with the aim of improving the results of the classifiers. The algorithms provided the best results when the dataset used as input was the one containing the attributes selected with the CFS method. CONCLUSIONS: Data mining methods can be successfully applied to determine the most influential factors in the success/failure of NIMV and also to predict NIMV results in future patients. The results provided by classifiers can be improved by preprocessing the data with feature selection techniques.