Irantzu Barrio

The IRYSS-COPD appropriateness study: objectives, methodology, and description of the prospective cohort

BackgroundPatients with chronic obstructive pulmonary disease (COPD) often experience exacerbations of the disease that require hospitalization. Current guidelines offer little guidance for identifying patients whose clinical situation is appropriate for admission to the hospital, and properly developed and validated severity scores for COPD exacerbations are lacking. To address these important gaps in clinical care, we created the IRYSS-COPD Appropriateness Study.Methods/DesignThe RAND/UCLA Appropriateness Methodology was used to identify appropriate and inappropriate scenarios for hospital admission for patients experiencing COPD exacerbations. These scenarios were then applied to a prospective cohort of patients attending the emergency departments (ED) of 16 participating hospitals. Information was recorded during the time the patient was evaluated in the ED, at the time a decision was made to admit the patient to the hospital or discharge home, and during follow-up after admission or discharge home. While complete data were generally available at the time of ED admission, data were often missing at the time of decision making. Predefined assumptions were used to impute much of the missing data.DiscussionThe IRYSS-COPD Appropriateness Study will validate the appropriateness criteria developed by the RAND/UCLA Appropriateness Methodology and thus better delineate the requirements for admission or discharge of patients experiencing exacerbations of COPD. The study will also provide a better understanding of the determinants of outcomes of COPD exacerbations, and evaluate the equity and variability in access and outcomes in these patients.

A randomized controlled trial of a 10 week group psychotherapeutic treatment added to standard medical treatment in patients with functional dyspepsia.

OBJECTIVE Epidemiological evidence suggests an association between psychological factors and functional dyspepsia (FD). Yet few randomized controlled trials (RCTs) of psychological interventions have been conducted for FD. We conducted an RCT to evaluate the efficacy of psychotherapy among chronic FD. METHODS One hundred fifty-eight consecutive patients with FD were randomized to medical therapy plus psychotherapy consisted in 8 group and 2 individual sessions focused on teaching techniques for coping with FD (intensive treatment (IT); n=76) or medical therapy alone (conventional treatment (CT); n=82). Patients completed validated self-reported questionnaires before and after the 10-week treatment and 6 months later. Linear mixed-effects models were used, in intention-to-treat analysis. RESULTS At the end of treatment period, statistically significant improvements were observed for IT compared with CT for dyspepsia-related quality of life (DRQoL). DRQoL mean changes of 6.09 and 3.54 were obtained in IT and CT patients, respectively (p=<0.0001); and SS mean changes of 11.55 and 4.57 were obtained in IT and CT patients, respectively (p=0.0013). Those improvements, measured by minimum clinically important difference (MCID), were clinically significant (DRQoL: 77% of the IT patients exceeded the MCID vs. the 45% of the CT; SS: 75% vs. 48%). Six months after treatment, those statistically significant improvements persisted for DRQoL (p=0.0067) and for SS (p=0.0405). Clinical improvements persisted for SS (63% vs. 41%). CONCLUSIONS These findings suggest that adding psychotherapy to standard medical therapy improves short-term outcomes in patients with FD and may have long-term effects as well. The cost-effectiveness of intensive therapy needs to be evaluated. Registration number and name of trial registry: NCT01802710.

Impact of clinical and patient‐reported outcomes on patient satisfaction with cataract extraction

To date, factors that influence satisfaction with cataract surgery have not been broadly explored.

A new approach to categorising continuous variables in prediction models: Proposal and validation.

When developing prediction models for application in clinical practice, health practitioners usually categorise clinical variables that are continuous in nature. Although categorisation is not regarded as advisable from a statistical point of view, due to loss of information and power, it is a common practice in medical research. Consequently, providing researchers with a useful and valid categorisation method could be a relevant issue when developing prediction models. Without recommending categorisation of continuous predictors, our aim is to propose a valid way to do it whenever it is considered necessary by clinical researchers. This paper focuses on categorising a continuous predictor within a logistic regression model, in such a way that the best discriminative ability is obtained in terms of the highest area under the receiver operating characteristic curve (AUC). The proposed methodology is validated when the optimal cut points’ location is known in theory or in practice. In addition, the proposed method is applied to a real data-set of patients with an exacerbation of chronic obstructive pulmonary disease, in the context of the IRYSS-COPD study where a clinical prediction rule for severe evolution was being developed. The clinical variable PCO2 was categorised in a univariable and a multivariable setting.

Use of generalised additive models to categorise continuous variables in clinical prediction

BackgroundIn medical practice many, essentially continuous, clinical parameters tend to be categorised by physicians for ease of decision-making. Indeed, categorisation is a common practice both in medical research and in the development of clinical prediction rules, particularly where the ensuing models are to be applied in daily clinical practice to support clinicians in the decision-making process. Since the number of categories into which a continuous predictor must be categorised depends partly on the relationship between the predictor and the outcome, the need for more than two categories must be borne in mind.MethodsWe propose a categorisation methodology for clinical-prediction models, using Generalised Additive Models (GAMs) with P-spline smoothers to determine the relationship between the continuous predictor and the outcome. The proposed method consists of creating at least one average-risk category along with high- and low-risk categories based on the GAM smooth function. We applied this methodology to a prospective cohort of patients with exacerbated chronic obstructive pulmonary disease. The predictors selected were respiratory rate and partial pressure of carbon dioxide in the blood (PCO2), and the response variable was poor evolution. An additive logistic regression model was used to show the relationship between the covariates and the dichotomous response variable. The proposed categorisation was compared to the continuous predictor as the best option, using the AIC and AUC evaluation parameters. The sample was divided into a derivation (60%) and validation (40%) samples. The first was used to obtain the cut points while the second was used to validate the proposed methodology.ResultsThe three-category proposal for the respiratory rate was ≤ 20;(20,24];> 24, for which the following values were obtained: AIC=314.5 and AUC=0.638. The respective values for the continuous predictor were AIC=317.1 and AUC=0.634, with no statistically significant differences being found between the two AUCs (p =0.079). The four-category proposal for PCO2 was ≤ 43;(43,52];(52,65];> 65, for which the following values were obtained: AIC=258.1 and AUC=0.81. No statistically significant differences were found between the AUC of the four-category option and that of the continuous predictor, which yielded an AIC of 250.3 and an AUC of 0.825 (p =0.115).ConclusionsOur proposed method provides clinicians with the number and location of cut points for categorising variables, and performs as successfully as the original continuous predictor when it comes to developing clinical prediction rules.

Developmental and reproductive toxicity of PVP/PEI-coated silver nanoparticles to zebrafish

Cellular and molecular mechanisms of toxicity of silver nanoparticles (NPs) and their toxicity to fish embryos after waterborne exposure have been widely investigated, but much less information is available regarding the effect of Ag NPs on physiological functions such as growth or reproduction. In this work, the effects of waterborne exposure of adult zebrafish (Danio rerio) to PVP/PEI coated Ag NPs (~5nm) on reproduction (fecundity) were investigated. Moreover, the development of the embryos after parental exposure was compared with the development of embryos after direct waterborne exposure to the NPs. For this, two experiments were run: 1) embryos from unexposed parents were treated for 5days with Ag NPs (10μgAgL-1-10mgAgL-1) and development was monitored, and 2) selected breeding zebrafish were exposed for 3weeks to 100ngAgL-1 (environmentally relevant concentration) or to 10μgAgL-1 of Ag NPs, fecundity was scored and development of resulting embryos was monitored up to 5days. Waterborne exposure of embryos to Ag NPs resulted in being highly toxic (LC50 at 120h=50μgAgL-1), causing 100% mortality during the first 24h of exposure at 0.1mgAgL-1. Exposure of adults, even at the environmentally relevant silver concentration, caused a significant reduction of fecundity by the second week of treatment and resulting embryos showed a higher prevalence of malformations than control embryos. Exposed adult females presented higher prevalence of vacuolization in the liver. These results show that Ag NPs at an environmentally relevant concentration are able to affect population level parameters in zebrafish.

Predictors of hospital admission in exacerbations of chronic obstructive pulmonary disease.

OBJECTIVE To identify factors predictive of hospital admission among patients attending an emergency department (ED) with exacerbation of chronic obstructive pulmonary disease (COPD) and to determine if these were consistent with Global Initiative for Chronic Obstructive Lung Disease (GOLD) recommendations. DESIGN In a prospective cohort of 2487 COPD patients with exacerbations conducted in 16 EDs, clinical data were obtained and physical examination and blood gas analyses were performed on arrival at the ED and at decision time about hospitalisation. Multivariate analyses were performed using hospital admission as the dependent measure. RESULTS In multivariate analysis, factors predictive of hospital admission on ED arrival were previous hospitalisation for COPD exacerbation (OR 2.03, 95%CI 1.32-3.11), resting dyspnoea (OR 3.05, 95%CI 2.39-3.88) and altered blood gas (PaO2 = 45-60 mmHg, OR 2.7, 95%CI 2.12-3.44; PaO2 < 45 mmHg, OR 3.24, 95%CI 2.14-4.92; PaCO2 = 56-65 mmHg, OR 2.35, 95%CI 1.58-3.51; and PaCO2 > 65 mmHg, OR 6.98, 95%CI 4.03-12.09). The predictive capacity of the model using variables available at decision time was better than for those at ED arrival (area under the receiver operating characteristic curve 0.89 and 0.83). These factors are included in the GOLD recommendations. CONCLUSION Among COPD patients presenting to the ED with exacerbation, factors immediately associated with episode severity were independent predictors of hospitalisation. Our criteria for hospitalisation are in line with GOLD recommendations.

Comparison of two discrimination indexes in the categorisation of continuous predictors in time-to-event studies

The Cox proportional hazards model is the most widely used survival prediction model for analysing time-to-event data. To measure the discrimination ability of a survival model the concordance probability index is widely used. In this work we studied and compared the performance of two different estimators of the concordance probability when a continuous predictor variable is categorised in a Cox proportional hazards regression model. In particular, we compared the c-index and the concordance probability estimator. We evaluated the empirical performance of both estimators through simulations. To categorise the predictor variable we propose a methodology which considers the maximal discrimination attained for the categorical variable. We applied this methodology to a cohort of patients with chronic obstructive pulmonary disease, in particular, we categorised the predictor variable forced expiratory volume in one second in percentage.

Sample Size Impact on the Categorisation of Continuous Variables in Clinical Prediction

Recent advances in information technologies are generating a growth in the amount of available biomedical data. In this paper, we studied the impact sample size may have on the categorisation of a continuous predictor variable in a logistic regression setting. Two different approaches to categorise predictor variables were compared.

Applying a coping with stress questionnaire for cancer patients to patients with non-cancer chronic illnesses:

One of the few instruments to evaluate coping skills among patients with chronic illnesses is the Cuestionario de Afrontamiento al Estrés para Pacientes Oncológicos (CAEPO), created initially for cancer patients. We evaluate how well CAEPO applies to patients with non-cancer chronic illnesses. A total of 344 patients (115 with chronic hepatitis C, 120 with inflammatory bowel disease and 109 with recurrent vertigo) completed the CAEPO. Exploratory factor analysis and Cronbach’s alpha provide only partial support for the seven factors suggested by the original CAEPO. A streamlined version with fewer dimensions and items may be a better solution for identifying coping strategies among these patients.