Extracting relevant predictive variables for COVID-19 severity prognosis: An exhaustive comparison of feature selection techniques.

With the COVID-19 pandemic having caused unprecedented numbers of infections and deaths, large research efforts have been undertaken to increase our understanding of the disease and the factors which determine diverse clinical evolutions. Here we focused on a fully data-driven exploration regarding which factors (clinical or otherwise) were most informative for SARS-CoV-2 pneumonia severity prediction via machine learning (ML). In particular, feature selection techniques (FS), designed to reduce the dimensionality of data, allowed us to characterize which of our variables were the most useful for ML prognosis. We conducted a multi-centre clinical study, enrolling n = 1548 patients hospitalized due to SARS-CoV-2 pneumonia: where 792, 238, and 598 patients experienced low, medium and high-severity evolutions, respectively. Up to 106 patient-specific clinical variables were collected at admission, although 14 of them had to be discarded for containing ⩾60% missing values. Alongside 7 socioeconomic attributes and 32 exposures to air pollution (chronic and acute), these became d = 148 features after variable encoding. We addressed this ordinal classification problem both as a ML classification and regression task. Two imputation techniques for missing data were explored, along with a total of 166 unique FS algorithm configurations: 46 filters, 100 wrappers and 20 embeddeds. Of these, 21 setups achieved satisfactory bootstrap stability (⩾0.70) with reasonable computation times: 16 filters, 2 wrappers, and 3 embeddeds. The subsets of features selected by each technique showed modest Jaccard similarities across them. However, they consistently pointed out the importance of certain explanatory variables. Namely: patient's C-reactive protein (CRP), pneumonia severity index (PSI), respiratory rate (RR) and oxygen levels -saturation Sp O2, quotients Sp O2/RR and arterial Sat O2/Fi O2-, the neutrophil-to-lymphocyte ratio (NLR) -to certain extent, also neutrophil and lymphocyte counts separately-, lactate dehydrogenase (LDH), and procalcitonin (PCT) levels in blood. A remarkable agreement has been found a posteriori between our strategy and independent clinical research works investigating risk factors for COVID-19 severity. Hence, these findings stress the suitability of this type of fully data-driven approaches for knowledge extraction, as a complementary to clinical perspectives.

Automated location of orofacial landmarks to characterize airway morphology in anaesthesia via deep convolutional neural networks.

BACKGROUND: A reliable anticipation of a difficult airway may notably enhance safety during anaesthesia. In current practice, clinicians use bedside screenings by manual measurements of patients' morphology. OBJECTIVE: To develop and evaluate algorithms for the automated extraction of orofacial landmarks, which characterize airway morphology. METHODS: We defined 27 frontal + 13 lateral landmarks. We collected n=317 pairs of pre-surgery photos from patients undergoing general anaesthesia (140 females, 177 males). As ground truth reference for supervised learning, landmarks were independently annotated by two anaesthesiologists. We trained two ad-hoc deep convolutional neural network architectures based on InceptionResNetV2 (IRNet) and MobileNetV2 (MNet), to predict simultaneously: (a) whether each landmark is visible or not (occluded, out of frame), (b) its 2D-coordinates (x,y). We implemented successive stages of transfer learning, combined with data augmentation. We added custom top layers on top of these networks, whose weights were fully tuned for our application. Performance in landmark extraction was evaluated by 10-fold cross-validation (CV) and compared against 5 state-of-the-art deformable models. RESULTS: With annotators' consensus as the 'gold standard', our IRNet-based network performed comparably to humans in the frontal view: median CV loss L=1.277·10-3, inter-quartile range (IQR) [1.001, 1.660]; versus median 1.360, IQR [1.172, 1.651], and median 1.352, IQR [1.172, 1.619], for each annotator against consensus, respectively. MNet yielded slightly worse results: median 1.471, IQR [1.139, 1.982]. In the lateral view, both networks attained performances statistically poorer than humans: median CV loss L=2.141·10-3, IQR [1.676, 2.915], and median 2.611, IQR [1.898, 3.535], respectively; versus median 1.507, IQR [1.188, 1.988], and median 1.442, IQR [1.147, 2.010] for both annotators. However, standardized effect sizes in CV loss were small: 0.0322 and 0.0235 (non-significant) for IRNet, 0.1431 and 0.1518 (p<0.05) for MNet; therefore quantitatively similar to humans. The best performing state-of-the-art model (a deformable regularized Supervised Descent Method, SDM) behaved comparably to our DCNNs in the frontal scenario, but notoriously worse in the lateral view. CONCLUSIONS: We successfully trained two DCNN models for the recognition of 27 + 13 orofacial landmarks pertaining to the airway. Using transfer learning and data augmentation, they were able to generalize without overfitting, reaching expert-like performances in CV. Our IRNet-based methodology achieved a satisfactory identification and location of landmarks: particularly in the frontal view, at the level of anaesthesiologists. In the lateral view, its performance decayed, although with a non-significant effect size. Independent authors had also reported lower lateral performances; as certain landmarks may not be clear salient points, even for a trained human eye.

Changes in health-related quality of life as a marker in the prognosis in COPD patients.

Chronic obstructive pulmonary disease (COPD) is understood as a complex, heterogeneous and multisystem airway obstructive disease. The association of deterioration in health-related quality of life (HRQoL) with mortality and hospitalisation for COPD exacerbation has been explored in general terms. The specific objectives of this study were to determine whether a change in HRQoL is related, over time, to mortality and hospitalisation. Overall, 543 patients were recruited through Galdakao Hospital's five outpatient respiratory clinics. Patients were assessed at baseline, and the end of the first and second year, and were followed up for 3 years. At each assessment, measurements were made of several variables, including HRQoL using the St George's Respiratory Questionnaire (SGRQ). The cohort had moderate obstruction (forced expiratory volume in 1 s 55% of the predicted value). SGRQ total, symptoms, activity and impact scores at baseline were 39.2, 44.5, 48.7 and 32.0, respectively. Every 4-point increase in the SGRQ was associated with an increase in the likelihood of death: "symptoms" domain odds ratio 1.04 (95% CI 1.00-1.08); "activity" domain OR 1.12 (95% CI 1.08-1.17) and "impacts" domain OR 1.11 (95% CI 1.06-1.15). The rate of hospitalisations per year was 5% (95% CI 3-8%) to 7% (95% CI 5-10%) higher for each 4-point increase in the separate domains of the SGRQ. Deterioration in HRQoL by 4 points in SGRQ domain scores over 1 year was associated with an increased likelihood of death and hospitalisation.

Facilitators and barriers to participation in population-based colorectal cancer screening programme from the perspective of healthcare professionals: Qualitative research study.

OBJECTIVE: High participation determines the success of colorectal cancer screening programmes in reducing incidence and mortality. The factors that determine participation must be studied from the perspective of professionals that implement the programme. The aim was to identify factors that facilitate or hinder the participation of the invited people in the bowel cancer screening programme of the Basque Country (Spain) from professional's perspective. METHODS: Qualitative design based on individual interviews and focus groups. Thirty-eight primary care professionals who implement the programme participated (administrative staff, nurses and general practitioners). Thematic analysis was performed. RESULTS: Professionals show high satisfaction with the programme, and they believe firmly in its benefits. Facilitators for participation include professionals' commitment to the programme, their previous positive experiences, their optimistic attitude towards the prognosis of cancer and their trust in the health system and accessibility. Barriers include invitees' lack of independence to make decisions, fear of a positive test result and patient vulnerability and labour mobility of the health professionals. CONCLUSIONS: Professionals show a high degree of involvement and identify primary care is an appropriate place to carry out disease prevention. They identify the closeness to patients, the personal attitude and the characteristics of the health system as key factors that influence participation.

[Involvement of the Primary Healthcare professionals in the Colorectal Cancer Screening Programme of the Basque Country.] / Implicación de los profesionales de atención primaria en el programa de detección precoz de cáncer colorrectal del País Vasco.

OBJECTIVE: The Basque Government (Spain) approved a population based Colorectal Cancer Screening Programme in 2008 with its base on Primary Healthcare. Since then, a coverage of 100% of the population and an average participation rate of 68.4% have been achieved. General Practitioners and nurses play a central role on its implementation. The aim of this work was to describe the characteristics, involvement and attitudes of the health professionals that implement the programme. METHODS: A cross-sectional descriptive study was conducted in Primary Healthcare to general practitioners and nurses between May and June of 2016. An ad-hoc online questionnaire was designed. The data included socio-demographic information and questions regarding their involvement on the programme. RESULTS: 1,216 health professionals answered the questionnaire, 50.7% were general practitioners and 49.3% nurses. 78% of the responders were women. The 75.8% considered the programme very important although differences were found between general practitioners and nurses. The 89% of the professionals attended training and 34% scientific workshops about screening at least once. There were differences between general practitioners and nurses on the attendance to the training and importance they give to the programme, and on their participation on workshops. CONCLUSIONS: There is a high level of involvement of Primary Healthcare professionals in the programme as they consider it very important; this could be one of the keys for its success. The differences between professionals on their opinion and experience should be taken into account on its design, as they are the ones with a closer contact with the population.

OBJETIVO: El Gobierno Vasco (España) aprobó en 2008 un programa de detección del cáncer colorrectal de base poblacional en Atención Primaria. Se ha logrado una cobertura del 100% con una tasa de participación media del 68,4%. Los profesionales de medicina y enfermería desempeñan un papel fundamental en su implementación. El objetivo fue describir las características, implicación y actitudes de los profesionales de la salud que implementan el programa. METODOS: Estudio descriptivo transversal a personal de medicina y enfermería entre mayo y junio de 2016. Se diseñó un cuestionario en línea ad-hoc. Los datos incluían información sociodemográfica y preguntas relativas a su implicación en el programa. RESULTADOS: 1.216 profesionales de la salud respondieron al cuestionario (50,7% medicina y 49,3% enfermería). El 78% eran mujeres. El 75,8% consideró que el programa era muy importante, aunque se encontraron diferencias entre profesionales de medicina y de enfermería. El 89% asistió a formación y el 34% a jornadas científicas sobre el cribado por lo menos una vez. Se observaron diferencias entre médicos y profesionales de la enfermería en cuanto a la asistencia a formación y jornadas y en la importancia que daban al programa. CONCLUSIONES: Existe un alto nivel de participación de los profesionales de la atención primaria de la salud en el programa y lo consideran muy importante. Las diferencias entre los profesionales en cuanto a su opinión y experiencia deben ser tenidas en cuenta en el diseño de los programas, ya que son ellos los que tienen un contacto más estrecho con la población.

Selecting the number of categories of the lymph node ratio in cancer research: A bootstrap-based hypothesis test.

The high impact of the lymph node ratio as a prognostic factor is widely established in colorectal cancer, and is being used as a categorized predictor variable in several studies. However, the cut-off points as well as the number of categories considered differ considerably in the literature. Motivated by the need to obtain the best categorization of the lymph node ratio as a predictor of mortality in colorectal cancer patients, we propose a method to select the best number of categories for a continuous variable in a logistic regression framework. Thus, to this end, we propose a bootstrap-based hypothesis test, together with a new estimation algorithm for the optimal location of the cut-off points called BackAddFor, which is an updated version of the previously proposed AddFor algorithm. The performance of the hypothesis test was evaluated by means of a simulation study, under different scenarios, yielding type I errors close to the nominal errors and good power values whenever a meaningful difference in terms of prediction ability existed. Finally, the methodology proposed was applied to the CCR-CARESS study where the lymph node ratio was included as a predictor of five-year mortality, resulting in the selection of three categories.

Predictive factors over time of health-related quality of life in COPD patients.

BACKGROUND: Health-related quality of life (HRQoL) should be seen as a tool that provides an overall view of the general clinical condition of a COPD patient. The aims of this study were to identify variables associated with HRQoL and whether they continue to have an influence in the medium term, during follow-up. METHODS: Overall, 543 patients with COPD were included in this prospective observational longitudinal study. At all four visits during a 5-year follow-up, the patients completed the Saint George's Respiratory Questionnaire (SGRQ), pulmonary function tests, the 6-min walk test (6MWT), and a physical activity (PA) questionnaire, among others measurements. Data on hospitalization for COPD exacerbations and comorbidities were retrieved from the personal electronic clinical record of each patient at every visit. RESULTS: The best fit to the data of the cohort was obtained with a beta-binomial distribution. The following variables were related over time to SGRQ components: age, inhaled medication, smoking habit, forced expiratory volume in one second, handgrip strength, 6MWT distance, body mass index, residual volume, diffusing capacity of the lung for carbon monoxide, PA (depending on level, 13 to 35% better HRQoL, in activity and impacts components), and hospitalizations (5 to 45% poorer HRQoL, depending on the component). CONCLUSIONS: Among COPD patients, HRQoL was associated with the same variables throughout the study period (5-year follow-up), and the variables with the strongest influence were PA and hospitalizations.

Anxiety and depressive symptoms are related to core symptoms, general health outcome, and medical comorbidities in eating disorders.

OBJECTIVE: The goal of this study is to identify potential factors that have a significant effect on anxiety and depression of patients with eating disorders (ED) using the beta-binomial regression (BBR) approach on a broad sample of patients. METHOD: This cross-sectional study involved 520 ED patients. The effect of sociodemographics, core symptoms, general health outcomes, and medical comorbidities in anxiety and depression were analysed jointly using the beta-binomial mixed-effects model. RESULTS: Two hundred fifty-five (49.4%) patients had anorexia nervosa, 173 (33.3%) patients had bulimia nervosa, and 92 (17.7%) had ED not otherwise specified. A high level of anxiety was associated, among other variables, with having a restrictive ED subtype as compared with purgative and binge ED subtypes (ß = -0.2, p < 0.001, OR = 0.82 and ß = -0.16, p < 0.03, OR = 0.85, respectively), with having a high level of ED symptomatology or with living not alone (ß = -0.23, p = 0.002, OR = 0.80). A high level of depression was associated, among other variables, with older age (ß = 0.74, p < 0.001, OR = 2.1). CONCLUSIONS: The results of our study suggest that depressive and anxiety symptoms are related to ED symptoms, health status, ED subtype, medical comorbitidy, and educational level. In addition, another interesting finding is the inverse association between symptomatology of anxiety and living alone. Finally, BBR may be a useful approach in interpreting patient-reported outcome as odds.

A Computer Application to Predict Adverse Events in the Short-Term Evolution of Patients With Exacerbation of Chronic Obstructive Pulmonary Disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a common chronic disease. Exacerbations of COPD (eCOPD) contribute to the worsening of the disease and the patient's evolution. There are some clinical prediction rules that may help to stratify patients with eCOPD by their risk of poor evolution or adverse events. The translation of these clinical prediction rules into computer applications would allow their implementation in clinical practice. OBJECTIVE: The goal of this study was to create a computer application to predict various outcomes related to adverse events of short-term evolution in eCOPD patients attending an emergency department (ED) based on valid and reliable clinical prediction rules. METHODS: A computer application, Prediction of Evolution of patients with eCOPD (PrEveCOPD), was created to predict 2 outcomes related to adverse events: (1) mortality during hospital admission or within a week after an ED visit and (2) admission to an intensive care unit (ICU) or an intermediate respiratory care unit (IRCU) during the eCOPD episode. The algorithms included in the computer tool were based on clinical prediction rules previously developed and validated within the Investigación en Resultados y Servicios de Salud COPD study. The app was developed for Windows and Android systems, using Visual Studio 2008 and Eclipse, respectively. RESULTS: The PrEveCOPD computer application implements the prediction models previously developed and validated for 2 relevant adverse events in the short-term evolution of patients with eCOPD. The application runs under Windows and Android systems and it can be used locally or remotely as a Web application. Full description of the clinical prediction rules as well as the original references is included on the screen. Input of the predictive variables is controlled for out-of-range and missing values. Language can be switched between English and Spanish. The application is available for downloading and installing on a computer, as a mobile app, or to be used remotely via internet. CONCLUSIONS: The PrEveCOPD app shows how clinical prediction rules can be summarized into simple and easy to use tools, which allow for the estimation of the risk of short-term mortality and ICU or IRCU admission for patients with eCOPD. The app can be used on any computer device, including mobile phones or tablets, and it can guide the clinicians to a valid stratification of patients attending the ED with eCOPD. TRIAL REGISTRATION: ClinicalTrials.gov NCT00102401; https://clinicaltrials.gov/ct2/show/results/NCT02434536 (Archived by WebCite at http://www.webcitation.org/76iwTxYuA). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/1472-6963-11-322.

COPD classification models and mortality prediction capacity.

OBJECTIVE: Our aim was to assess the impact of comorbidities on existing COPD prognosis scores. PATIENTS AND METHODS: A total of 543 patients with COPD (FEV1 <80% and FEV1/FVC <70%) were included between January 2003 and January 2004. Patients were stable for at least 6 weeks before inclusion and were followed for 5 years without any intervention by the research team. Comorbidities and causes of death were established from medical reports or information from primary care medical records. The GOLD system and the body mass index, obstruction, dyspnea and exercise (BODE) index were used for COPD classification. Patients were also classified into four clusters depending on the respiratory disease and comorbidities. Cluster analysis was performed by combining multiple correspondence analyses and automatic classification. Receiver operating characteristic curves and the area under the curve (AUC) were calculated for each model, and the DeLong test was used to evaluate differences between AUCs. Improvement in prediction ability was analyzed by the DeLong test, category-free net reclassification improvement and the integrated discrimination index. RESULTS: Among the 543 patients enrolled, 521 (96%) were male, with a mean age of 68 years, mean body mass index 28.3 and mean FEV1% 55%. A total of 167 patients died during the study follow-up. Comorbidities were prevalent in our cohort, with a mean Charlson index of 2.4. The most prevalent comorbidities were hypertension, diabetes mellitus and cardiovascular diseases. On comparing the BODE index, GOLDABCD, GOLD2017 and cluster analysis for predicting mortality, cluster system was found to be superior compared with GOLD2017 (0.654 vs 0.722, P=0.006), without significant differences between other classification models. When cardiovascular comorbidities and chronic renal failure were added to the existing scores, their prognostic capacity was statistically superior (P<0.001). CONCLUSION: Comorbidities should be taken into account in COPD management scores due to their prevalence and impact on mortality.

A beta-binomial mixed-effects model approach for analysing longitudinal discrete and bounded outcomes.

Patient-reported outcomes (PROs) are currently being increasingly used as primary outcome measures in observational and experimental studies since they inform clinicians and researchers about the health-status of patients and generate data to facilitate improved care. PROs usually appear as discrete and bounded with U, J, or inverse J shapes, and hence, exponential family members offer inadequate distributional fits. The beta-binomial distribution has been proposed in the literature to fit PROs. However, the fact that the beta-binomial distribution does not belong to the exponential family limits its applicability in the regression model context, and classical estimation approaches are not straightforward. Moreover, PROs are usually measured in a longitudinal framework in which individuals are followed up for a certain period. Hence, each individual obtains several scores of the PRO over time, which leads to the repeated measures and defines the correlation structure in the data. In this work, we have developed and proposed an estimation procedure for the analysis of correlated discrete and bounded outcomes, particularly PROs, by a beta-binomial mixed-effects model. Additionally, we have implemented the methodology in the PROreg package in R. Because there are similar approaches in the literature to address the same issue, this work also incorporates a comparison study between our proposal and alternative methodologies commonly implemented in R and shows the superior performance of our estimation procedure. This paper was motivated by the analysis of the health-status of patients with chronic obstructive pulmonary disease, where the main objective is the assessment of risk factors that may affect the evolution of the disease. The application of the proposed approach in the study leads to clinically relevant results.

Predictors of one and two years' mortality in patients with colon cancer: A prospective cohort study.

BACKGROUND: Tools to aid in the prognosis assessment of colon cancer patients in terms of risk of mortality are needed. Goals of this study are to develop and validate clinical prediction rules for 1- and 2-year mortality in these patients. METHODS: This is a prospective cohort study of patients diagnosed with colon cancer who underwent surgery at 22 hospitals. The main outcomes were mortality at 1 and 2 years after surgery. Background, clinical parameters, and diagnostic tests findings were evaluated as possible predictors. Multivariable multilevel logistic regression and survival models were used in the analyses to create the clinical prediction rules. Models developed in the derivation sample were validated in another sample of the study. RESULTS: American Society of Anesthesiologists Physical Status Classification System (ASA), Charlson comorbidity index (> = 4), age (>75 years), residual tumor (R2), TNM stage IV and log of lymph nodes ratio (> = -0.53) were predictors of 1-year mortality (C-index (95% CI): 0.865 (0.792-0.938)). Adjuvant chemotherapy was an additional predictor. Again ASA, Charlson Index (> = 4), age (>75 years), log of lymph nodes ratio (> = -0.53), TNM, and residual tumor were predictors of 2-year mortality (C-index:0.821 (0.766-0.876). Chemotherapy was also an additional predictor. CONCLUSIONS: These clinical prediction rules show very good predictive abilities of one and two years survival and provide clinicians and patients with an easy and quick-to-use decision tool for use in the clinical decision process while the patient is still in the index admission.

Validación externa de la escala EAHFE-3D para la evaluación del pronóstico en insuficiencia cardiaca aguda / External validity of a prognostic score for acute heart failure based on the Epidemiology of Acute Heart Failure in Emergency Departments registry: the EAHFE-3D scale

Objetivo. Validar la escala pronóstica EAHFE-3D en una cohorte externa de pacientes atendidos por insuficiencia cardiaca aguda (ICA). Método. Estudio de cohortes multipropósito que incluyó pacientes con ICA en 3 centros hospitalarios del País Vasco entre 2011 y 2013. Se recogieron los datos demográficos (edad), clase funcional basal (New York Heart Association), clínicos (presión arterial sistólica y saturación de oxígeno basal), analíticos (natremia) y terapéuticos (ventilación mecá- nica no invasiva e inotrópicos y vasopresores) en el servicio de urgencias (SU) necesarios para el cálculo de la escala EAHFE-3D. La variable de resultado principal fue la mortalidad a tres días de la llegada al SU. Resultados. Analizamos 717 pacientes con información completa. El intercepto β el modelo fue 0,5 (IC95%: -2,7- 3,7) y la pendiente α fue de 1,3 (IC95%: 0,4-2,2). El área bajo la curva AUC (ROC) fue 0,76 (IC95%: 0,58-0,94). Conclusiones. La escala EAHFE-3D presentó una buena capacidad predictiva en nuestra muestra, no diferente a la obtenida por los autores originales, aunque no ha mostrado buena calibración. Se recomienda continuar con el proceso de validación antes de ser implementada en la práctica clínica (AU)

Objective. To validate the EAHFE-3D scale, based on the Acute Heart Failure in Emergency Departments registry, in a cohort of patients attended for acute heart failure. Methods. Study of a multipurpose cohort of patients with acute heart failure in 3 hospitals in the Basque Country between 2011 and 2013. We extracted age, baseline New York Heart Association functional class, systolic blood pressure, baseline arterial oxygen saturation, sodium level in blood, and emergency department treatments (noninvasive mechanical ventilation, use of inotropic agents and vasopressors) in order to calculate each patient's EAHFE-3D score. The main outcome variable was mortality within 3 days of arrival at the emergency department. Results. The patient sample for score validation consisted of 717 patients with complete information. The model's intercept was 0.5 (95% CI, -2.7 to 3.7) and the slope was 1.3 (95% CI, 0.4 to 2.2). The area under the receiver operating characteristic curve was 0.76 (95% CI, 0.58 to 0.94). Conclusions. The EAHFE-3D scale's ability to discriminate was good in this patient sample and similar to that reported by the authors who developed the scale; however, calibration was poor. The scale should be studied further before it is applied in clinical practice (AU)

Combining statistical techniques to predict postsurgical risk of 1-year mortality for patients with colon cancer.

INTRODUCTION: Colorectal cancer is one of the most frequently diagnosed malignancies and a common cause of cancer-related mortality. The aim of this study was to develop and validate a clinical predictive model for 1-year mortality among patients with colon cancer who survive for at least 30 days after surgery. METHODS: Patients diagnosed with colon cancer who had surgery for the first time and who survived 30 days after the surgery were selected prospectively. The outcome was mortality within 1 year. Random forest, genetic algorithms and classification and regression trees were combined in order to identify the variables and partition points that optimally classify patients by risk of mortality. The resulting decision tree was categorized into four risk categories. Split-sample and bootstrap validation were performed. ClinicalTrials.gov Identifier: NCT02488161. RESULTS: A total of 1945 patients were enrolled in the study. The variables identified as the main predictors of 1-year mortality were presence of residual tumor, American Society of Anesthesiologists Physical Status Classification System risk score, pathologic tumor staging, Charlson Comorbidity Index, intraoperative complications, adjuvant chemotherapy and recurrence of tumor. The model was internally validated; area under the receiver operating characteristic curve (AUC) was 0.896 in the derivation sample and 0.835 in the validation sample. Risk categorization leads to AUC values of 0.875 and 0.832 in the derivation and validation samples, respectively. Optimal cut-off point of estimated risk had a sensitivity of 0.889 and a specificity of 0.758. CONCLUSION: The decision tree was a simple, interpretable, valid and accurate prediction rule of 1-year mortality among colon cancer patients who survived for at least 30 days after surgery.

External validity of a prognostic score for acute heart failure based on the Epidemiology of Acute Heart Failure in Emergency Departments registry: the EAHFE-3D scale. / Validación externa de la escala EAHFE-3D para la evaluación del pronóstico en insuficiencia cardiaca aguda.

OBJECTIVES: To validate the EAHFE-3D scale, based on the Acute Heart Failure in Emergency Departments registry, in a cohort of patients attended for acute heart failure. MATERIAL AND METHODS: Study of a multipurpose cohort of patients with acute heart failure in 3 hospitals in the Basque Country between 2011 and 2013. We extracted age, baseline New York Heart Association functional class, systolic blood pressure, baseline arterial oxygen saturation, sodium level in blood, and emergency department treatments (noninvasive mechanical ventilation, use of inotropic agents and vasopressors) in order to calculate each patient's EAHFE-3D score. The main outcome variable was mortality within 3 days of arrival at the emergency department. RESULTS: The patient sample for score validation consisted of 717 patients with complete information. The model's intercept was 0.5 (95% CI, -2.7 to 3.7) and the slope was 1.3 (95% CI, 0.4 to 2.2). The area under the receiver operating characteristic curve was 0.76 (95% CI, 0.58 to 0.94). CONCLUSION: The EAHFE-3D scale's ability to discriminate was good in this patient sample and similar to that reported by the authors who developed the scale; however, calibration was poor. The scale should be studied further before it is applied in clinical practice.

OBJETIVO: Validar la escala pronóstica EAHFE-3D en una cohorte externa de pacientes atendidos por insuficiencia cardiaca aguda (ICA). METODO: Estudio de cohortes multipropósito que incluyó pacientes con ICA en 3 centros hospitalarios del País Vasco entre 2011 y 2013. Se recogieron los datos demográficos (edad), clase funcional basal (New York Heart Association), clínicos (presión arterial sistólica y saturación de oxígeno basal), analíticos (natremia) y terapéuticos (ventilación mecánica no invasiva e inotrópicos y vasopresores) en el servicio de urgencias (SU) necesarios para el cálculo de la escala EAHFE-3D. La variable de resultado principal fue la mortalidad a tres días de la llegada al SU. RESULTADOS: Analizamos 717 pacientes con información completa. El intercepto ß el modelo fue 0,5 (IC95%: ­2,7- 3,7) y la pendiente α fue de 1,3 (IC95%: 0,4-2,2). El área bajo la curva AUC (ROC) fue 0,76 (IC95%: 0,58-0,94). CONCLUSIONES: La escala EAHFE-3D presentó una buena capacidad predictiva en nuestra muestra, no diferente a la obtenida por los autores originales, aunque no ha mostrado buena calibración. Se recomienda continuar con el proceso de validación antes de ser implementada en la práctica clínica.

Cytotoxicity and cellular mechanisms of toxicity of CuO NPs in mussel cells in vitro and comparative sensitivity with human cells.

There is a need to assess human and ecosystem health effects of copper oxide nanoparticles (CuO NPs), extensively used in many industrial products. Here, we aimed to determine the cytotoxicity and cellular mechanisms involved in the toxicity of CuO NPs in mussel cells (hemocytes and gill cells) in parallel with exposures to ionic Cu and bulk CuO, and to compare the sensitivity of mussel primary cells with a well-established human cell line (pulmonary TT1 cells). At similar doses, CuO NPs promoted dose-dependent cytotoxicity and increased reactive oxygen species (ROS) production in mussel and human cells. In mussel cells, ionic Cu was more toxic than CuO NPs and the latter more than bulk CuO. Ionic Cu and CuO NPs increased catalase and acid phosphatase activities in both mussel cells and decreased gill cells Na-K-ATPase activity. All Cu forms produced DNA damage in hemocytes, whereas in gill cells only ionic Cu and CuO NPs were genotoxic. Induction of the MXR transport activity was found in gill cells exposed to all forms of Cu and in hemocytes exposed to ionic Cu and CuO NPs. Phagocytosis increased only in hemocytes exposed to CuO NPs, indicating a nanoparticle-specific immunostimulatory effect. In conclusion, toxicity of CuO NPs is driven by ROS in human and mussel cells. Mussel cells respond to CuO NP exposure by triggering an array of defensive mechanisms.

Comparison of beta-binomial regression model approaches to analyze health-related quality of life data.

Health-related quality of life has become an increasingly important indicator of health status in clinical trials and epidemiological research. Moreover, the study of the relationship of health-related quality of life with patients and disease characteristics has become one of the primary aims of many health-related quality of life studies. Health-related quality of life scores are usually assumed to be distributed as binomial random variables and often highly skewed. The use of the beta-binomial distribution in the regression context has been proposed to model such data; however, the beta-binomial regression has been performed by means of two different approaches in the literature: (i) beta-binomial distribution with a logistic link; and (ii) hierarchical generalized linear models. None of the existing literature in the analysis of health-related quality of life survey data has performed a comparison of both approaches in terms of adequacy and regression parameter interpretation context. This paper is motivated by the analysis of a real data application of health-related quality of life outcomes in patients with Chronic Obstructive Pulmonary Disease, where the use of both approaches yields to contradictory results in terms of covariate effects significance and consequently the interpretation of the most relevant factors in health-related quality of life. We present an explanation of the results in both methodologies through a simulation study and address the need to apply the proper approach in the analysis of health-related quality of life survey data for practitioners, providing an R package.

Acute toxicity, bioaccumulation and effects of dietary transfer of silver from brine shrimp exposed to PVP/PEI-coated silver nanoparticles to zebrafish.

The extensive use and release to the aquatic environment of silver nanoparticles (NPs) could lead to their incorporation into the food web. Brine shrimp larvae of 24h showed low sensitivity to the exposure to PVP/PEI-coated Ag NPs (5nm), with EC50 values at 24h of 19.63mgAgL-1, but they significantly accumulated silver after 24h of exposure to 100µgL-1 of Ag NPs. Thus, to assess bioaccumulation and effects of silver transferred by the diet in zebrafish, brine shrimp larvae were exposed to 100ngL-1 of Ag NPs as an environmentally relevant concentration or to 100µgL-1 as a potentially effective concentration and used to feed zebrafish for 21days. Autometallography revealed a dose- and time-dependent metal accumulation in the intestine and in the liver of zebrafish. Three-day feeding with brine shrimps exposed to 100ngL-1 of Ag NPs was enough to impair fish health as reflected by the significant reduction of lysosomal membrane stability and the presence of vacuolization and necrosis in the liver. However, dietary exposure to 100µgL-1 of Ag NPs for 3days did not significantly alter gene transcription levels, neither in the liver nor in the intestine. After 21days, biological processes such as lipid transport and localization, cellular response to chemical stimulus and response to xenobiotic stimulus were significantly altered in the liver. Overall, these results indicate an effective dietary transfer of silver and point out to liver as the main target organ for Ag NP toxicity in zebrafish after dietary exposure.

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.

Chronic Obstructive Pulmonary Disease Subtypes. Transitions over Time.

BACKGROUND: Although subtypes of chronic obstructive pulmonary disease are recognized, it is unknown what happens to these subtypes over time. Our objectives were to assess the stability of cluster-based subtypes in patients with stable disease and explore changes in clusters over 1 year. METHODS: Multiple correspondence and cluster analysis were used to evaluate data collected from 543 stable patients included consecutively from 5 respiratory outpatient clinics. RESULTS: Four subtypes were identified. Three of them, A, B, and C, had marked respiratory profiles with a continuum in severity of several variables, while the fourth, subtype D, had a more systemic profile with intermediate respiratory disease severity. Subtype A was associated with less dyspnea, better health-related quality of life and lower Charlson comorbidity scores, and subtype C with the most severe dyspnea, and poorer pulmonary function and quality of life, while subtype B was between subtypes A and C. Subtype D had higher rates of hospitalization the previous year, and comorbidities. After 1 year, all clusters remained stable. Generally, patients continued in the same subtype but 28% migrated to another cluster. Together with movement across clusters, patients showed changes in certain characteristics (especially exercise capacity, some variables of pulmonary function and physical activity) and changes in outcomes (quality of life, hospitalization and mortality) depending on the new cluster they belonged to. CONCLUSIONS: Chronic obstructive pulmonary disease clusters remained stable over 1 year. Most patients stayed in their initial subtype cluster, but some moved to another subtype and accordingly had different outcomes.