Assessment of the Performance of Imputation Techniques in Observational Studies with Two Measurements

: In observational studies with two measurements when the measured outcome pertains to a health related quality of life (HRQoL) variable, one motivation of the research may be to determine the potential predictors of the mean change of the outcome of interest. It is very common in such studies for data to be missing, which can bias the results. Different imputation techniques have been proposed to cope with missing data in outcome variables. We compared five analysis approaches (Complete Case, Available Case, K- Nearest Neighbour, Propensity Score, and a Markov Chain Monte Carlo algorithm) to assess their performance when handling missing data at different missingness rates and mechanisms (MCAR, MAR and MNAR). These strategies were applied to a pre-post study of patients with Chronic Obstructive Pulmonary Disease. We analyzed the relationship of the changes in subjects HRQoL over one year with clinical and socio-demographic characteristics. A simulation study was also performed to illustrate the performance of the imputation methods. Relative and standardized bias was assessed on each scenario. For all missingness mechanisms, not imputing and using MCMC method, both combined with mixed-model analysis, showed lowest standardized bias. Conversely, Propensity Score showed worst bias values. When missingness pattern is MCAR or MAR and rate small, we recommend using mixed models. Nevertheless, when missingness percentage is high, in order to gain sample size and statistical power, MCMC is preferred, although there are no bias differences compared with the mixed models without imputation. For a MNAR scenario, a further sensitivity analysis should be made

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.This research is supported by the Spanish State Research Agency AEI under the project S3M1P4R PID2020-115882RB-I00, as well as by the Basque Government EJ-GV under the grant ‘Artificial Intelligence in BCAM’ 2019/00432, under the strategy ‘Mathematical Modelling Applied to Health’, and under the BERC 2018–2021 and 2022–2025 programmes, and also by the Spanish Ministry of Science and Innovation: BCAM Severo Ochoa accreditation CEX2021-001142-S / MICIN / AEI / 10.13039/501100011033. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Modelling physical activity profiles in COPD patients: a fully functional approach to variable domain functional regression models

Physical activity plays a significant role in the well-being of individuals with Chronic obstructive Pulmonary Disease (COPD). Specifically, it has been directly associated with changes in hospitalization rates for these patients. However, previous investigations have primarily been conducted in a cross-sectional or longitudinal manner and have not considered a continuous perspective. Using the telEPOC program we use telemonitoring data to analyze the impact of physical activity adopting a functional data approach. However, Traditional functional data methods, including functional regression models, typically assume a consistent data domain. However, the data in the telEPOC program exhibits variable domains, presenting a challenge since the majority of functional data methods, are based on the fact that data are observed in the same domain. To address this challenge, we introduce a novel fully functional methodology tailored to variable domain functional data, eliminating the need for data alignment, which can be computationally taxing. Although models designed for variable domain data are relatively scarce and may have inherent limitations in their estimation methods, our approach circumvents these issues. We substantiate the effectiveness of our methodology through a simulation study, comparing our results with those obtained using established methodologies. Finally, we apply our methodology to analyze the impact of physical activity in COPD patients using the telEPOC program's data. Software for our method is available in the form of R code on request at \url{https://github.com/Pavel-Hernadez-Amaro/V.D.F.R.M-new-estimation-approach.git}

Inputazio tekniken errendimenduaren ebaluazioa bi neurketako luzeranzko datuetan

Neurketa errepikatuetan oinarrituriko behaketa-ikerketak menpeko aldagaien aldaketak denboran zehar aztertzeko erabiltzen dira. Bi neurketa baizik bakarrik egiten ez direnean, ikerketa helburu nagusietariko bat izan daiteke menpeko aldagaiaren batez besteko aldaketa aurresaten dituzten faktoreak zehaztea. Menpeko aldagaian faltako balioak ohikoak dira ikerketa mota hauetan, behaturiko datuen analisiaren emaitzak alboratuak gerta daitezkeelarik. Lan honetan inputazio teknika desberdinak proposatuko ditugu datu-analisiak egiterakoan faltako balioei aurre egiteko aukera gisa. Hiru inputazio metodoren errendimendua aztertu dugu (K-Nearest Neighbor, Propensity Score eta Markov Chain Monte Carlo algoritmoak), faltako balioek datu multzo osoaren % 10a eta % 30a osatzen dutenean

Eredu aurresaleetako aldagai jarraituen kategorizaziorik hoberena lortzeko metodologia proposamena: aplikazio zehatza medikuntzan

Medikuntzan parametro kliniko asko kategorizatzen dira erabaki prozesuak errazteko. Areago arau aurresale klinikoen garapenean ohiko teknika bat da aldagaien kategorizazio hau. Aldagai aurresale bat kategorizatzerakoan kategoria kopurua aldagai aurresale eta erantzulearen arteko erlazioaren menpe dagoenez bi kategoria baino gehiagoren beharra aztertu behar da. Guk metodo bat proposatzen dugu aldagai aurresaleak kategorizatzeko eredu aurresaleetan: eredutik lortutako funtzio leunaren arabera gutxienez batez besteko arriskuko kategoria bat eta arrisku altu eta arrisku baxuko behar beste kategoriak sortzea. Metodologia hau bihotz-gutxiegitasun desorekatu larria duten pazienteen kohorte prospektibo batean aplikatu dugu aldagai aurresalea arteria-tentsioa eta aldagai erantzulea epe laburreko heriotza izan direlarik. Erregresio logistiko gehigarria erabili da aldagai aurresale eta erantzulearen arteko erlazioa erakusteko. Proposatutako metodoa erabiliz lortutako kategoria-aldagaia jatorrizko aldagai jarraituarekin konparatu dugu AIC eta AUC parametroak erabiliz. Lau kategorietako arteria-tentsio sistolikoko proposamena honako hau da = 120 (120,136] (136,158] eta 158 baino handiagoa. Lau kategoria horietarako AIC=344,59 eta AUC=0,72 balioak lortu dira. Aldagai jarraiturako AIC=345,7 eta AUC=0,718 balioak lortu dira bi AUC balioen artean diferentzia adierazgarririk egon gabe (p = 0,974). Guk proposaturiko metodoaren bitartez aldagai jarraitua kategorizatzeko beharrezkoak diren mozketa-puntu kopurua eta puntuen kokapenik hoberena lortzen da. Horrela lortutako kategoria-aldagaiak jatorrizko aldagai jarraituak bezainbesteko errendimendu ona ematen du

Eredu aurresaleetako aldagai jarraituen kategorizaziorik hoberena lortzeko metodologia proposamena: aplikazio zehatza medikuntzan

Medikuntzan parametro kliniko asko kategorizatzen dira erabaki prozesuak errazteko. Areago arau aurresale klinikoen garapenean ohiko teknika bat da aldagaien kategorizazio hau. Aldagai aurresale bat kategorizatzerakoan kategoria kopurua aldagai aurresale eta erantzulearen arteko erlazioaren menpe dagoenez bi kategoria baino gehiagoren beharra aztertu behar da. Guk metodo bat proposatzen dugu aldagai aurresaleak kategorizatzeko eredu aurresaleetan: eredutik lortutako funtzio leunaren arabera gutxienez batez besteko arriskuko kategoria bat eta arrisku altu eta arrisku baxuko behar beste kategoriak sortzea. Metodologia hau bihotz-gutxiegitasun desorekatu larria duten pazienteen kohorte prospektibo batean aplikatu dugu aldagai aurresalea arteria-tentsioa eta aldagai erantzulea epe laburreko heriotza izan direlarik. Erregresio logistiko gehigarria erabili da aldagai aurresale eta erantzulearen arteko erlazioa erakusteko. Proposatutako metodoa erabiliz lortutako kategoria-aldagaia jatorrizko aldagai jarraituarekin konparatu dugu AIC eta AUC parametroak erabiliz. Lau kategorietako arteria-tentsio sistolikoko proposamena honako hau da = 120 (120,136] (136,158] eta 158 baino handiagoa. Lau kategoria horietarako AIC=344,59 eta AUC=0,72 balioak lortu dira. Aldagai jarraiturako AIC=345,7 eta AUC=0,718 balioak lortu dira bi AUC balioen artean diferentzia adierazgarririk egon gabe (p = 0,974). Guk proposaturiko metodoaren bitartez aldagai jarraitua kategorizatzeko beharrezkoak diren mozketa-puntu kopurua eta puntuen kokapenik hoberena lortzen da. Horrela lortutako kategoria-aldagaiak jatorrizko aldagai jarraituak bezainbesteko errendimendu ona ematen du

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

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.Euskal Herriko Unibertsitatea; Spanish Ministry of Science, Innovation and Universities, Grant/Award Number: SEV-2017-0718; Spanish Ministry of Economy and Competitiveness MINECO and FEDER, Grant/Award Number: MTM2016-74931-P; Department of Education, Language Policy and Culture from the Basque Government, Grant/Award Numbers: BERC 2018-2021, IT620-1

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

[EN] 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.This work was supported by the Spanish Health Research Fund (FIS grant number PI020510), and by funding from the Dept of Health of the Basque Government (grant number 200111002), Spanish Ministry of Economy and Competitiveness (MTM2016-74931-P and BCAM Severo Ochoa excellence accreditation SEV-2017-0718), Dept of Education, Linguistic Policy and Culture of the Basque Government (IT1294-19 and BERC 2018-2021), and the University of the Basque Country (COLAB20/01)

Validation of a screening questionnaire for hip and knee osteoarthritis in old people

Es reproducción del documentoa publicado en http://dx.doi.org/10.1186/1471-2474-8-84Background: To develop a sensitive and specific screening tool for knee and hip osteoarthritis in the general population of elderly people. Methods: The Knee and Hip OsteoArthritis Screening Questionnaire (KHOA- SQ) was developed based on previous studies and observed data and sent to 11,002 people aged 60 to 90 years, stratified by age and gender, who were selected by random sampling. Algorithms of the KHOA- SQ were created. Respondents positive for knee or hip OA on the KHOA- SQ were invited to be evaluated by an orthopedic surgeon. A sample of 300 individuals negative for knee or hip OA on the KHOA- SQ were also invited for evaluation. Sensitivity and specificity were determined for the KHOA- SQ, as well as for KHOA- SQ questions. Classification and Regression Tree analysis was used to find alternative screening algorithms from the questionnaire. Results: Of 11,002 individuals contacted, 7,577 completed the KHOA- SQ. Of 1,115 positive for knee OA, on the KHOA- SQ, 710 ( 63.6%) were diagnosed with it. For hip OA, 339 of the 772 who screened positive ( 43.9%) were diagnosed it. Sensitivity for the hip algorithm was 87.4% and specificity 59.8%; for the knee, sensitivity was 94.5% and specificity 43.8%. Two alternative algorithms provided lower specificity. Conclusion: The KHOA- SQ offers high sensitivity and moderate specificity. Although this tool correctly identifies individuals with knee or hip OA, the high false positive rate could pose problems. Based on our questions, no better algorithm was found