Jaume March

Escala de Zarit reducida en cuidados paliativos

BACKGROUND AND OBJECTIVE: We aimed to reduce the Zarit scale (ZS) for the assessment of caregiver burden in palliative care. PATIENTS AND METHOD: ZS was performed in 61 caregivers of palliative care patients admitted to hospital. The field explored by each item was decided by an expert committee and a logistic regression and ROC curve were performed. RESULTS: ZS reduction to 7 items was achieved with a sensitivity and specificity of 100%. CONCLUSIONS: A reduced Zarit scale is useful to identify caregiver burden in palliative care. It is also easy to use.

Estimating age-related trends in cross-sectional studies using S-distributions

Growth trends in children are often based on cross-sectional studies, in which a sample of the population is investigated at one given point in time. Estimating age-related percentiles in such studies involves fitting data distributions, each of which is specific for one age group, and a subsequent smoothing of the percentile curves. The first requirement for this process is the selection of a distributional form that is expected to be consistent with the observed data. If a goodness-of-fit test reveals significant discrepancies between the data and the best-fitting member of this distributional form, an alternative distribution must be found. In practice, there is seldom an objective argument for selecting any particular distribution. Also, different distributions can yield very similar fits, so that any selection is somewhat arbitrary. Finally, the shapes of the observed distributions may change throughout the age range so drastically that no single traditional distribution can fit them all in a satisfactory manner. To overcome these difficulties in population studies, non-parametric smoothing techniques and normalizing transformations have been used to derive percentile curves. In this paper we present an alternative strategy in the form of a flexible parametric family of statistical distributions: the S-distribution. We suggest a method that guides the search for well-fitting S-distributions for groups of observed distributions. The method is first tested with simulated data sets and subsequently applied to actual weight distributions of girls of different ages. As far as the results can be tested, they are consistent with observations and with results from other methods.

Stratification of the severity of critically ill patients with classification trees

BackgroundDevelopment of three classification trees (CT) based on the CART (Classification and Regression Trees), CHAID (Chi-Square Automatic Interaction Detection) and C4.5 methodologies for the calculation of probability of hospital mortality; the comparison of the results with the APACHE II, SAPS II and MPM II-24 scores, and with a model based on multiple logistic regression (LR).MethodsRetrospective study of 2864 patients. Random partition (70:30) into a Development Set (DS) n = 1808 and Validation Set (VS) n = 808. Their properties of discrimination are compared with the ROC curve (AUC CI 95%), Percent of correct classification (PCC CI 95%); and the calibration with the Calibration Curve and the Standardized Mortality Ratio (SMR CI 95%).ResultsCTs are produced with a different selection of variables and decision rules: CART (5 variables and 8 decision rules), CHAID (7 variables and 15 rules) and C4.5 (6 variables and 10 rules). The common variables were: inotropic therapy, Glasgow, age, (A-a)O2 gradient and antecedent of chronic illness. In VS: all the models achieved acceptable discrimination with AUC above 0.7. CT: CART (0.75(0.71-0.81)), CHAID (0.76(0.72-0.79)) and C4.5 (0.76(0.73-0.80)). PCC: CART (72(69-75)), CHAID (72(69-75)) and C4.5 (76(73-79)). Calibration (SMR) better in the CT: CART (1.04(0.95-1.31)), CHAID (1.06(0.97-1.15) and C4.5 (1.08(0.98-1.16)).ConclusionWith different methodologies of CTs, trees are generated with different selection of variables and decision rules. The CTs are easy to interpret, and they stratify the risk of hospital mortality. The CTs should be taken into account for the classification of the prognosis of critically ill patients.

Aproximación a la metodología basada en árboles de decisión (CART). Mortalidad hospitalaria del infarto agudo de miocardio

Objective: To provide an overview of decision trees based on CART (Classification and Regression Trees) methodology. As an example, we developed a CART model intended to estimate the probability of intrahospital death from acute myocardial infarction (AMI). Method: We employed the minimum data set (MDS) of Andalusia, Catalonia, Madrid and the Basque Country (20012002), which included 33,203 patients with a diagnosis of AMI. The 33,203 patients were randomly divided (70% and 30%) into the development (DS; n = 23,277) and the validation (VS; n = 9,926) sets. The CART inductive model was based on Breiman’s algorithm, with a sensitivity analysis based on the Gini index and cross-validation. We compared the results with those obtained by using both logistic regression (LR) and artificial neural network (ANN) (multilayer perceptron) models. The developed models were contrasted with the VS and their properties were evaluated with the area under the ROC curve (AUC) (95% confidence interval [CI]). Results: In the DS, the CART showed an AUC = 0.85 (0.860.88), LR 0.87 (0.86-0.88) and ANN 0.85 (0.85-0.86). In the VS, the CART showed an AUC = 0.85 (0.85-0.88), LR 0.86 (0.85-0.88) and ANN 0.84 (0.83-0.86). Conclusions: None of the methods tested outperformed the others in terms of discriminative ability. We found that the CART model was much easier to use and interpret, because the decision rules generated could be applied without the need for mathematical calculations.

Aproximación metodológica al uso de redes neuronales artificiales para la predicción de resultados en medicina

: In clinical practice, there is an increasing interest in obtaining adequate models of prediction. Within the possible available alternatives, the artificial neural networks (ANN) are progressively more used. In this review we first introduce the ANN methodology, describing the most common type of ANN, the Multilayer Perceptron trained with backpropagation algorithm (MLP). Then we compare the MLP with the Logistic Regression (LR). Finally, we show a practical scheme to make an application based on ANN by means of an example with actual data. The main advantage of the RN is its capacity to incorporate nonlinear effects and interactions between the variables of the model without need to include them a priori. As greater disadvantages, they show a difficult interpretation of their parameters and large empiricism in their process of construction and training. ANN are useful for the computation of probabilities of a given outcome based on a set of predicting variables. Furthermore, in some cases, they obtain better results than LR. Both methodologies, ANN and LR, are complementary and they help us to obtain more valid models.

Utilidad de la escala de Zarit reducida en demencias

BACKGROUND AND OBJECTIVES We aimed to reduce Zarit scale (ZS) for the assessment of caregiver burden in dementias. PATIENTS AND METHODS Prospective study in which the ZS was performed to 85 caregivers of patients with dementia admitted to hospital. The field explored by each item was decided by an expert committee and a logistic regression and ROC curve were performed. RESULTS A ZS reduction to 4 items was achieved with a sensitivity 98,5% and specificity of 94,7%. CONCLUSIONS A reduced Zarit scale is useful to identifiy caregiver burden in dementias. It is also easy to use.

Original breveUtilidad de la escala de Zarit reducida en demenciasShort Zarit scale in dementias

BACKGROUND AND OBJECTIVES We aimed to reduce Zarit scale (ZS) for the assessment of caregiver burden in dementias. PATIENTS AND METHODS Prospective study in which the ZS was performed to 85 caregivers of patients with dementia admitted to hospital. The field explored by each item was decided by an expert committee and a logistic regression and ROC curve were performed. RESULTS A ZS reduction to 4 items was achieved with a sensitivity 98,5% and specificity of 94,7%. CONCLUSIONS A reduced Zarit scale is useful to identifiy caregiver burden in dementias. It is also easy to use.

Relationship Between Traditional Games and the Intensity of Emotions Experienced by Participants

Purpose: The aims of this study were to analyze the intensity of emotions (positive, negative, or ambiguous) produced when players took part in traditional games with a different social structure and to examine the explanations given by those participants for these emotional experiences. Method: Participants (N = 556) were recruited from 4 Spanish universities. After taking part in each of the games, they were asked to complete the Games and Emotions Questionnaire to indicate the intensity of their emotional experiences and to explain what, in their view, had led to the strongest emotion felt. Results: The application of a mixed-methods approach identified statistically significant differences in relation to 3 variables. These were (a) the type of emotion, (b) motor domain, and (c) type of result (win, loss, and noncompetitive). The intensity of positive emotions was higher in cooperative games and lower in individual games. Comments referring to negative emotions were more frequent as the social structure of games became more complex (minimal presence of individual games and predominance of cooperation-opposition games). Winning was associated with the highest intensity ratings of positive and ambiguous emotions, whereas being defeated produced the highest values for negative emotions. The intensity ratings for negative emotions were lower in noncompetitive games than in games where players lost. Conclusion: The results confirm that traditional games can play a key role in relation to the emotional facets of physical education.

Approach to the methodology of classification and regression trees

OBJECTIVE To provide an overview of decision trees based on CART (Classification and Regression Trees) methodology. As an example, we developed a CART model intended to estimate the probability of intrahospital death from acute myocardial infarction (AMI). METHOD We employed the minimum data set (MDS) of Andalusia, Catalonia, Madrid and the Basque Country (2001-2002), which included 33,203 patients with a diagnosis of AMI. The 33,203 patients were randomly divided (70% and 30%) into the development (DS; n = 23,277) and the validation (VS; n = 9,926) sets. The CART inductive model was based on Breimans algorithm, with a sensitivity analysis based on the Gini index and cross-validation. We compared the results with those obtained by using both logistic regression (LR) and artificial neural network (ANN) (multilayer perceptron) models. The developed models were contrasted with the VS and their properties were evaluated with the area under the ROC curve (AUC) (95% confidence interval [CI]). RESULTS In the DS, the CART showed an AUC = 0.85 (0.86-0.88), LR 0.87 (0.86-0.88) and ANN 0.85 (0.85-0.86). In the VS, the CART showed an AUC = 0.85 (0.85-0.88), LR 0.86 (0.85-0.88) and ANN 0.84 (0.83-0.86). CONCLUSIONS None of the methods tested outperformed the others in terms of discriminative ability. We found that the CART model was much easier to use and interpret, because the decision rules generated could be applied without the need for mathematical calculations.

Skin lesions after intensive care procedures: Results of a prospective study ☆

PURPOSE To define the skin lesions produced by procedures used in the intensive care unit (ICU) and to examine patients 12 months after discharge from the ICU. MATERIAL AND METHODS This was a prospective clinical study in the 14-bed multidisciplinary ICU of a university hospital. Iatrogenic skin lesions (ISL) were examined in 316 patients after ICU discharge. RESULTS A total of 189 patients were interviewed 12 months after ICU discharge. More than 85% of the patients had ISL after being discharged from the ICU. The patients with the highest Acute Physiology and Chronic Health Evaluation II score and longest average stay presented the highest number of ISLs. A total of 93 patients (49%) reported some skin lesions after 12 months. All patients who had undergone surgical tracheostomy reported the presence of a scar, but 4 of 24 patients who had undergone percutaneous tracheostomy reported no tracheostomy scar. Only 22% of all patients reported scars caused by vascular catheter access. About half (54.5%) of the patients reported secondary lesions caused by chest draining, and these were predominantly caused by the large-bore tube drainage. All patients reported the presence of a laparatomy scar. CONCLUSIONS Most patients had identified skin lesions resulting from ICU procedures. Half of all patients were aware of their lesions and reported them at 12 months. Future research is needed to understand whether these lesions cause problems to survivors quality of life and whether the lesions lead to increased health care utilization.