Cognitive and clinical predictors of a long-term course in obsessive compulsive disorder: A machine learning approach in a prospective cohort study.

BACKGROUND: Obsessive compulsive disorder (OCD) is a disabling illness with a chronic course, yet data on long-term outcomes are scarce. This study aimed to examine the long-term course of OCD in patients treated with different approaches (drugs, psychotherapy, and psychosurgery) and to identify predictors of clinical outcome by machine learning. METHOD: We included outpatients with OCD treated at our referral unit. Demographic and neuropsychological data were collected at baseline using standardized instruments. Clinical data were collected at baseline, 12 weeks after starting pharmacological treatment prescribed at study inclusion, and after follow-up. RESULTS: Of the 60 outpatients included, with follow-up data available for 5-17 years (mean = 10.6 years), 40 (67.7 %) were considered non-responders to adequate treatment at the end of the study. The best machine learning model achieved a correlation of 0.63 for predicting the long-term Yale-Brown Obsessive Compulsive Scale (Y-BOCS) score by adding clinical response (to the first pharmacological treatment) to the baseline clinical and neuropsychological characteristics. LIMITATIONS: Our main limitations were the sample size, modest in the context of traditional ML studies, and the sample composition, more representative of rather severe OCD cases than of patients from the general community. CONCLUSIONS: Many patients with OCD showed persistent and disabling symptoms at the end of follow-up despite comprehensive treatment that could include medication, psychotherapy, and psychosurgery. Machine learning algorithms can predict the long-term course of OCD using clinical and cognitive information to optimize treatment options.

Late-onset postoperative angioedema triggered by angiotensin-converting enzyme inhibitor: An emergent airway forethought.

Angioedema is a potentially life-threatening condition due to the risk of airway compromise leading to deterioration of respiratory function, hypoxia, and ultimately, cardiopulmonary arrest. It can be either unprovoked or triggered by pharmaceutical agents, emotional or physiologic factors, upper airway trauma, or surgical stress. A 46-year-old man previously prescribed perindopril developed angioedema of the tongue 4 h after being discharged from the Post Anesthesia Care Unit (PACU). A multidisciplinary team was called and they outlined an airway management strategy to use in the event of worsening. The strategy consisted of either fiberoptic intubation by an anesthesiologist or surgical tracheostomy performed by the surgical team, both performed with the patient awake and in spontaneous ventilation. The aim of this case report is to raise awareness that angioedema is a potentially life-threatening condition. For optimal management, it is important to prepare in advance a detailed airway management strategy to be implemented by a multidisciplinary team.

An extensive experimental survey of regression methods.

Regression is a very relevant problem in machine learning, with many different available approaches. The current work presents a comparison of a large collection composed by 77 popular regression models which belong to 19 families: linear and generalized linear models, generalized additive models, least squares, projection methods, LASSO and ridge regression, Bayesian models, Gaussian processes, quantile regression, nearest neighbors, regression trees and rules, random forests, bagging and boosting, neural networks, deep learning and support vector regression. These methods are evaluated using all the regression datasets of the UCI machine learning repository (83 datasets), with some exceptions due to technical reasons. The experimental work identifies several outstanding regression models: the M5 rule-based model with corrections based on nearest neighbors (cubist), the gradient boosted machine (gbm), the boosting ensemble of regression trees (bstTree) and the M5 regression tree. Cubist achieves the best squared correlation ( R2) in 15.7% of datasets being very near to it, with difference below 0.2 for 89.1% of datasets, and the median of these differences over the dataset collection is very low (0.0192), compared e.g. to the classical linear regression (0.150). However, cubist is slow and fails in several large datasets, while other similar regression models as M5 never fail and its difference to the best R2 is below 0.2 for 92.8% of datasets. Other well-performing regression models are the committee of neural networks (avNNet), extremely randomized regression trees (extraTrees, which achieves the best R2 in 33.7% of datasets), random forest (rf) and ε-support vector regression (svr), but they are slower and fail in several datasets. The fastest regression model is least angle regression lars, which is 70 and 2,115 times faster than M5 and cubist, respectively. The model which requires least memory is non-negative least squares (nnls), about 2 GB, similarly to cubist, while M5 requires about 8 GB. For 97.6% of datasets there is a regression model among the 10 bests which is very near (difference below 0.1) to the best R2, which increases to 100% allowing differences of 0.2. Therefore, provided that our dataset and model collection are representative enough, the main conclusion of this study is that, for a new regression problem, some model in our top-10 should achieve R2 near to the best attainable for that problem.

Angioedema postoperatorio de inicio tardío, desencadenado por un inhibidor de la enzima convertidora de angiotensina: previsión de vía aérea de emergencia / Late-onset postoperative angioedema triggered by angiotensin-converting enzyme inhibitor: an emergent airway forethought

El angioedema es una situación potencialmente mortal, debido al riesgo de compromiso de la vía aérea que da lugar a un deterioro de la función respiratoria, hipoxia y, por último, paro cardiopulmonar. Puede ser provocado o desencadenado por agentes farmacéuticos, factores emocionales o fisiológicos, traumatismo de la vía aérea superior, o estrés quirúrgico.Un varón de 46 años de edad, a quien se había prescrito previamente perindopril, desarrolló angioedema de lengua 4h después de recibir el alta de la UCPA (unidad de cuidados postanestésicos). Se convocó a un equipo multidisciplinar, que destacó una estrategia de manejo de la vía aérea en caso de empeoramiento. Dicha estrategia consistió en intubación con fibroscopio por parte del anestesiólogo, o bien traqueostomía quirúrgica practicada por el equipo de cirugía, realizándose ambas técnicas con el paciente despierto y ventilación espontánea.El objetivo de este informe de caso es alertar de que el angioedema es una situación potencialmente mortal. Para un manejo óptimo, es importante preparar de antemano una estrategia detallada de manejo de la vía aérea, a implementar por parte de un equipo multidisciplinar. (AU)

Angioedema is a potentially life-threatening condition due to the risk of airway compromise leading to deterioration of respiratory function, hypoxia, and ultimately, cardiopulmonary arrest. It can be either unprovoked or triggered by pharmaceutical agents, emotional or physiologic factors, upper airway trauma, or surgical stress.A 46-year-old man previously prescribed perindopril developed angioedema of the tongue 4h after being discharged from the Post Anesthesia Care Unit (PACU). A multidisciplinary team was called and they outlined an airway management strategy to use in the event of worsening. The strategy consisted of either fiberoptic intubation by an anesthesiologist or surgical tracheostomy performed by the surgical team, both performed with the patient awake and in spontaneous ventilation.The aim of this case report is to raise awareness that angioedema is a potentially life-threatening condition. For optimal management, it is important to prepare in advance a detailed airway management strategy to be implemented by a multidisciplinary team. (AU)