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.

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.

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.

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 decision tree to assess short-term mortality after an emergency department visit for an exacerbation of COPD: a cohort study.

BACKGROUND: Creating an easy-to-use instrument to identify predictors of short-term (30/60-day) mortality after an exacerbation of chronic obstructive pulmonary disease (eCOPD) could help clinicians choose specific measures of medical care to decrease mortality in these patients. The objective of this study was to develop and validate a classification and regression tree (CART) to predict short term mortality among patients evaluated in an emergency department (ED) for an eCOPD. METHODS: We conducted a prospective cohort study including participants from 16 hospitals in Spain. COPD patients with an exacerbation attending the emergency department (ED) of any of the hospitals between June 2008 and September 2010 were recruited. Patients were randomly divided into derivation (50%) and validation samples (50%). A CART based on a recursive partitioning algorithm was created in the derivation sample and applied to the validation sample. RESULTS: Two thousand four hundred eighty-seven patients, 1252 patients in the derivation sample and 1235 in the validation sample, were enrolled in the study. Based on the results of the univariate analysis, five variables (baseline dyspnea, cardiac disease, the presence of paradoxical breathing or use of accessory inspiratory muscles, age, and Glasgow Coma Scale score) were used to build the CART. Mortality rates 30 days after discharge ranged from 0% to 55% in the five CART classes. The lowest mortality rate was for the branch composed of low baseline dyspnea and lack of cardiac disease. The highest mortality rate was in the branch with the highest baseline dyspnea level, use of accessory inspiratory muscles or paradoxical breathing upon ED arrival, and Glasgow score <15. The area under the receiver-operating curve (AUC) in the derivation sample was 0.835 (95% CI: 0.783, 0.888) and 0.794 (95% CI: 0.723, 0.865) in the validation sample. CART was improved to predict 60-days mortality risk by adding the Charlson Comorbidity Index, reaching an AUC in the derivation sample of 0.817 (95% CI: 0.776, 0.859) and 0.770 (95% CI: 0.716, 0.823) in the validation sample. CONCLUSIONS: We identified several easy-to-determine variables that allow clinicians to classify eCOPD patients by short term mortality risk, which can provide useful information for establishing appropriate clinical care. TRIAL REGISTRATION: NCT02434536.

The Use of Pulse Oximetry to Determine Hypoxemia in Acute Exacerbations of COPD.

BACKGROUND: There is little evidence that the guideline-recommended oxygen saturation of 92% is the best cut-off point for detecting hypoxemia in COPD exacerbations. OBJECTIVE: To detect and validate pulse oximetry oxygen saturation cut-off values likely to detect hypoxemia in patients with aeCOPD, to explore the correlation between oxygen saturation measured by pulse oximetry and hypoxemia or hypercapnic respiratory failure. METHODOLOGY: Cross-sectional study nested in the IRYSS-COPD study with 2,181 episodes of aeCOPD recruited between 2008 and 2010 in 16 hospitals belonging to the Spanish Public Health System. Data collected include determination of oxygen saturation by pulse oximetry upon arrival in the emergency department (ED), first arterial blood gasometry values, sociodemographic information, background medical history and clinical variables upon ED arrival. Logistic regression models were performed using as the dependent variables hypoxemia (PaO2 < 60 mmHg) and hypercapnic respiratory failure (PaO2 < 60 mmHg and PaCO2 > 45). Optimal cut-off points were calculated. RESULTS: The correlation coefficient between oxygen saturation and pO2 measured by arterial blood gasometry was 0.89. The area under the curve (AUC) for the hypoxemia model was 0.97 (0.96-0.98) and the optimal cut-off point for hypoxemia was an oxygen saturation of 90%. The AUC for hypercapnic respiratory failure was 0.90 (0.87-0.92) and the optimal cut-off point was an oxygen saturation of 88%. CONCLUSIONS: Our results support current recommendations for ordering blood gasometry based on pulse oximetry oxygen saturation cut-offs for hypoxemia. We also provide easy to use formulae to calculate pO2 from oxygen saturation measured by pulse oximetry.

Predictive score for mortality in patients with COPD exacerbations attending hospital emergency departments.

BACKGROUND: Limited information is available about predictors of short-term outcomes in patients with exacerbation of chronic obstructive pulmonary disease (eCOPD) attending an emergency department (ED). Such information could help stratify these patients and guide medical decision-making. The aim of this study was to develop a clinical prediction rule for short-term mortality during hospital admission or within a week after the index ED visit. METHODS: This was a prospective cohort study of patients with eCOPD attending the EDs of 16 participating hospitals. Recruitment started in June 2008 and ended in September 2010. Information on possible predictor variables 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. Main short-term outcomes were death during hospital admission or within 1 week of discharge to home from the ED, as well as at death within 1 month of the index ED visit. Multivariate logistic regression models were developed in a derivation sample and validated in a validation sample. The score was compared with other published prediction rules for patients with stable COPD. RESULTS: In total, 2,487 patients were included in the study. Predictors of death during hospital admission, or within 1 week of discharge to home from the ED were patient age, baseline dyspnea, previous need for long-term home oxygen therapy or non-invasive mechanical ventilation, altered mental status, and use of inspiratory accessory muscles or paradoxical breathing upon ED arrival (area under the curve (AUC) = 0.85). Addition of arterial blood gas parameters (oxygen and carbon dioxide partial pressures (PO2 and PCO2)) and pH) did not improve the model. The same variables were predictors of death at 1 month (AUC = 0.85). Compared with other commonly used tools for predicting the severity of COPD in stable patients, our rule was significantly better. CONCLUSIONS: Five clinical predictors easily available in the ED, and also in the primary care setting, can be used to create a simple and easily obtained score that allows clinicians to stratify patients with eCOPD upon ED arrival and guide the medical decision-making process.

Influence of changes in physical activity on frequency of hospitalization in chronic obstructive pulmonary disease.

BACKGROUND AND OBJECTIVE: To evaluate whether changes in regular physical activity (PA) in patients with chronic obstructive pulmonary disease (COPD) affect the rate of hospitalizations for COPD exacerbation (eCOPD). METHODS: Five hundred forty-three ambulatory clinic patients being treated for COPD were prospectively identified. PA was self-reported by patients, and the level was established by the distance they walked (km/day) at least 3 days per week. Hospitalizations were recorded from hospital databases. All patients with at least a 2-year follow-up after enrollment were included in the analysis. The response variable was the number of hospitalizations for eCOPD within the 3-year period from 2 to 5 years after study enrollment. RESULTS: Three hundred ninety-one survivors were studied. Mean forced expiratory volume in 1 s was 52% (±14%) of the predicted value. Patients who maintained a lower level of PA had an increased rate of hospitalization (odds ratio 1.901; 95% confidence interval 1.090-3.317). After having had the highest level of PA, those patients who decreased their PA in the follow-up showed an increasing rate of hospitalizations (odds ratio 2.134; 95% confidence interval 1.146-3.977). CONCLUSIONS: Patients with COPD with a low level of PA or who reduced their PA over time were more likely to experience a significant increase in the rate of hospitalization for eCOPD. Changes to a higher level of PA or maintaining a moderate or high level of PA over time, with a low intensity activity such as walking for at least 3-6 km/day, could reduce the rate of hospitalizations for eCOPD.

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

BACKGROUND: In 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. METHODS: We 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. RESULTS: The 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). CONCLUSIONS: Our 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.

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.

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.

Amino acid signatures in the Ovar-DRB1 peptide-binding pockets are associated with Ovine Pulmonary Adenocarcinoma susceptibility/resistance.

Potential relationships between amino acid motifs of various alleles of the ovine major histocompatibility complex DR (Ovar-DR) molecule and occurrence of clinical OPA caused by JSRV were investigated in a case-control study. Latxa sheep (n=132) screened for presence/absence of pulmonary OPA lesions were typed for their Ovar-DRB1 2nd exon alleles by PCR and sequence-based typing (PCR-SBT). The polymorphic amino acid residues derived from the obtained 34 DRB1 protein variants were subjected to a logistic regression-based association study. The amino acids at several positions showed significant associations with the presence/absence of pulmonary OPA lesions; some of the residues were located within the peptide binding cleft of the DRB molecule, including pockets P1, P4, P7 and P9.

What mechanism of niche segregation allows the coexistence of sympatric sibling rhinolophid bats?

INTRODUCTION: Our purpose was to assess how pairs of sibling horseshoe bats coexists when their morphology and echolocation are almost identical. We collected data on echolocation, wing morphology, diet, and habitat use of sympatric Rhinolophus mehelyi and R. euryale. We compared our results with literature data collected in allopatry with similar protocols and at the same time of the year (breeding season). RESULTS: Echolocation frequencies recorded in sympatry for R. mehelyi (mean = 106.8 kHz) and R. euryale (105.1 kHz) were similar to those reported in allopatry (R. mehelyi 105-111 kHz; R. euryale 101-109 kHz). Wing parameters were larger in R. mehelyi than R. euryale for both sympatric and allopatric conditions. Moths constitute the bulk of the diet of both species in sympatry and allopatry, with minor variation in the amounts of other prey. There were no inter-specific differences in the use of foraging habitats in allopatry in terms of structural complexity, however we found inter-specific differences between sympatric populations: R. mehelyi foraged in less complex habitats. The subtle inter-specific differences in echolocation frequency seems to be unlikely to facilitate dietary niche partitioning; overall divergences observed in diet may be explained as a consequence of differential prey availability among foraging habitats. Inter-specific differences in the use of foraging habitats in sympatry seems to be the main dimension for niche partitioning between R. mehelyi and R. euryale, probably due to letter differences in wing morphology. CONCLUSIONS: Coexistence between sympatric sibling horseshoe bats is likely allowed by a displacement in spatial niche dimension, presumably due to the wing morphology of each species, and shifts the niche domains that minimise competition. Effective measures for conservation of sibling/similar horseshoe bats should guarantee structural diversity of foraging habitats.

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.

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

BACKGROUND: Patients 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/DESIGN: The 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. DISCUSSION: The 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.

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.

[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.

MHC class II DRB1 gene polymorphism in the pathogenesis of Maedi-Visna and pulmonary adenocarcinoma viral diseases in sheep.

Ovine pulmonary adenocarcinoma (OPA) and Maedi-Visna (Maedi) are two chronic respiratory diseases of retroviral origin which occur worldwide. It is known that different host genetic factors influence the outcome of viral infections. To determine if variation in the Mhc-DRB1 gene was associated with progression to these ovine diseases, sheep lungs with and without OPA and Maedi lesions were collected. A sequence-based method was applied and 40 different alleles were detected in the sample analysed. In the allele-by-allele association analysis, allele DRB1*0325 had a significant association with susceptibility to Maedi (P = 0.045). For OPA, DRB1*0143 and DRB1*0323 were significantly associated with susceptibility (P = 0.024 and P = 0.029), and allele DRB1*0702 was significantly associated with resistance (P = 0.012). Based on these results, the Mhc-DRB1 alleles were classified by effect in three categories-susceptible (S), resistant (R) and neutral (N)-and animals were reassigned the genotypes as S/S, S/R, S/N, R/R, R/N and N/N. In a second analysis, penalised logistic regression models including a flock effect were run. In Maedi, significant association was detected for the N/S heterozygote (P = 0.0007), but not for the S/S homozygote, probably as a result of the low number of S/S animals. In OPA, association was detected for both the S/S and R/R homozygotes (P = 0.005 and P = 0.047). This allele grouping method may be applied in association studies with highly variable genes. This is the first study demonstrating significant associations between sheep Mhc-DRB1 alleles and susceptibility to OPA and Maedi. Therefore, both diseases are suitable candidates for more comprehensive genetic studies.

Decision trees for indication of cataract surgery based on changes in visual acuity.

OBJECTIVE: To develop decision trees based on prospectively collected data for determining the appropriateness of cataract extraction. DESIGN: Prospective observational cohort study. PARTICIPANTS: Consecutive patients with a diagnosis of cataract who are on waiting lists to undergo cataract extraction by phacoemulsification. METHODS: Patients were randomly assigned to 1 of 2 independent cohorts: The derivation cohort included 3691 patients, and the validation cohort included 2416 patients. Sociodemographic and clinical data, including visual acuity (VA) and the Visual Function Index 14 (VF-14), were collected before and after cataract extraction. Univariate and multivariate linear regression, and regression trees analysis were performed in the derivation cohort. Decision trees obtained in the derivation cohort were validated in the validation cohort. Final results were divided into appropriate or inappropriate indications and compared with a previously established benchmark of desirable VA and VF-14 gain in relation to preintervention VA classes. MAIN OUTCOME MEASURES: Preintervention VA and changes 6 weeks after the intervention. RESULTS: Among patients with simple cataract, predictors of significant improvement in VA after cataract extraction were preintervention VA and negative surgical complexity. Among patients with cataract and other ocular comorbidity, preintervention visual function and expected postintervention VA also predicted change in VA. When compared with a benchmark based on the minimal clinically important difference in VA after cataract extraction, sensitivity for the decision trees was 83% for both diagnostic groups and specificities ranged from 36.2 to 54.8. CONCLUSIONS: A simple decision tree based on changes in VA can help identify appropriate patients for cataract extraction and be used to evaluate clinical practice or for quality control.

Assessment of HRQoL in patients with eating disorders by the beta-binomial regression approach.

OBJECTIVE: To study the influence of clinical variables on health-related quality of life (HRQoL) among women with eating disorder (ED) using beta-binomial regression (BBR) to analyze scores on the Short-Form 36 (SF-36) as dependent variable. METHOD: Female patients diagnosed with ED completed the SF-36 at the beginning of the study and after 2 years of treatment. Sociodemographic and clinical information was recorded. For the multivariate analysis, we used BBR models to identify factors that influence SF-36. RESULTS: Questionnaires were completed by 193 women at baseline and 158 (82%) after 2 years of treatment. Anxiety, depression, and the severity of ED, explained scores in most domains of the SF-36 at baseline. The main predictor of HRQoL after 2 years of follow-up was the HRQoL in the same domain at baseline. However, depression, anxiety, and duration of symptoms at baseline also significantly influenced HRQoL after 2 years of treatment in some domains. Higher levels of anxiety or depression, longer duration of symptoms, and poorer SF-36 scores at baseline were associated with worse HRQoL after 2 years of treatment and follow-up. DISCUSSION: BBR models provide understandable results for clinicians and can be used in multivariate models with HRQoL dependent variables.