Functional proportional hazards mixture cure model with applications in cancer mortality in NHANES and post ICU recovery.

We develop a functional proportional hazards mixture cure model with scalar and functional covariates measured at the baseline. The mixture cure model, useful in studying populations with a cure fraction of a particular event of interest is extended to functional data. We employ the expectation-maximization algorithm and develop a semiparametric penalized spline-based approach to estimate the dynamic functional coefficients of the incidence and the latency part. The proposed method is computationally efficient and simultaneously incorporates smoothness in the estimated functional coefficients via roughness penalty. Simulation studies illustrate a satisfactory performance of the proposed method in accurately estimating the model parameters and the baseline survival function. Finally, the clinical potential of the model is demonstrated in two real data examples that incorporate rich high-dimensional biomedical signals as functional covariates measured at the baseline and constitute novel domains to apply cure survival models in contemporary medical situations. In particular, we analyze (i) minute-by-minute physical activity data from the National Health And Nutrition Examination Survey 2003-2006 to study the association between diurnal patterns of physical activity at baseline and all cancer mortality through 2019 while adjusting for other biological factors; (ii) the impact of daily functional measures of disease severity collected in the intensive care unit on post intensive care unit recovery and mortality event. Our findings provide novel epidemiological insights into the association between daily patterns of physical activity and cancer mortality. Software implementation and illustration of the proposed estimation method are provided in R.

Reproducibility of continuous glucose monitoring results under real-life conditions in an adult population: a functional data analysis.

Continuous glucose monitoring systems (CGM) are a very useful tool to understand the behaviour of glucose in different situations and populations. Despite the widespread use of CGM systems in both clinical practice and research, our understanding of the reproducibility of CGM data remains limited. The present work examines the reproducibility of the results provided by a CGM system in a random sample of a free-living adult population, from a functional data analysis approach. Functional intraclass correlation coefficients (ICCs) and their 95% confidence intervals (CI) were calculated to assess the reproducibility of CGM results in 581 individuals. 62% were females 581 participants (62% women) mean age 48 years (range 18-87) were included, 12% had previously been diagnosed with diabetes. The inter-day reproducibility of the CGM results was greater for subjects with diabetes (ICC 0.46 [CI 0.39-0.55]) than for normoglycaemic subjects (ICC 0.30 [CI 0.27-0.33]); the value for prediabetic subjects was intermediate (ICC 0.37 [CI 0.31-0.42]). For normoglycaemic subjects, inter-day reproducibility was poorer among the younger (ICC 0.26 [CI 0.21-0.30]) than the older subjects (ICC 0.39 [CI 0.32-0.45]). Inter-day reproducibility was poorest among normoglycaemic subjects, especially younger normoglycaemic subjects, suggesting the need to monitor some patient groups more often than others.

Handgrip strength conditional tolerance regions suggest the need for personalized sarcopenia definition: an analysis of the American NHANES database.

BACKGROUND: Handgrip strength (HGS) is a well-established clinical biomarker that assesses functional capacity in older populations. In addition, HGS is a diagnostic tool that forecasts aging health conditions, such as sarcopenia. AIMS: This paper provides HGS statistical tolerance regions and presents the need to establish HGS reference values according to patients' characteristics. METHODS: For this purpose, we used a conditional tolerance algorithm for HGS, and we observed the tolerances regions in different age strata and sex of non-sarcopenic individuals from the National Health and Nutrition Examination Survey (NHANES, wave 2011-2012). RESULTS AND DISCUSSION: Our results have critical implications for sarcopenia, since conventional and available HGS cut-offs do not consider age range. CONCLUSIONS: This paper offers new perspectives on the evolution of traditional definitions of sarcopenia according to the principles of precision medicine.

Identification of Asthma Phenotypes in the Spanish MEGA Cohort Study Using Cluster Analysis

Introduction: The definition of asthma phenotypes has not been fully established, neither there are cluster studies showing homogeneous results to solidly establish clear phenotypes. The purpose of this study was to develop a classification algorithm based on unsupervised cluster analysis, identifying clusters that represent clinically relevant asthma phenotypes that may share asthma-related outcomes. Methods: We performed a multicentre prospective cohort study, including adult patients with asthma (N=512) from the MEGA study (Mechanisms underlying the Genesis and evolution of Asthma). A standardised clinical history was completed for each patient. Cluster analysis was performed using the kernel k-groups algorithm. Results: Four clusters were identified. Cluster 1 (31.5% of subjects) includes adult-onset atopic patients with better lung function, lower BMI, good asthma control, low ICS dose, and few exacerbations. Cluster 2 (23.6%) is made of adolescent-onset atopic asthma patients with normal lung function, but low adherence to treatment (59% well-controlled) and smokers (48%). Cluster 3 (17.1%) includes adult-onset patients, mostly severe non-atopic, with overweight, the worse lung function and asthma control, and receiving combination of treatments. Cluster 4 (26.7%) consists of the elderly-onset patients, mostly female, atopic (64%), with high BMI and normal lung function, prevalence of smokers and comorbidities. Conclusion: We defined four phenotypes of asthma using unsupervised cluster analysis. These clusters are clinically relevant and differ from each other as regards FEV1, age of onset, age, BMI, atopy, asthma severity, exacerbations, control, social class, smoking and nasal polyps. (AU)

Identification of Asthma Phenotypes in the Spanish MEGA Cohort Study Using Cluster Analysis.

INTRODUCTION: The definition of asthma phenotypes has not been fully established, neither there are cluster studies showing homogeneous results to solidly establish clear phenotypes. The purpose of this study was to develop a classification algorithm based on unsupervised cluster analysis, identifying clusters that represent clinically relevant asthma phenotypes that may share asthma-related outcomes. METHODS: We performed a multicentre prospective cohort study, including adult patients with asthma (N=512) from the MEGA study (Mechanisms underlying the Genesis and evolution of Asthma). A standardised clinical history was completed for each patient. Cluster analysis was performed using the kernel k-groups algorithm. RESULTS: Four clusters were identified. Cluster 1 (31.5% of subjects) includes adult-onset atopic patients with better lung function, lower BMI, good asthma control, low ICS dose, and few exacerbations. Cluster 2 (23.6%) is made of adolescent-onset atopic asthma patients with normal lung function, but low adherence to treatment (59% well-controlled) and smokers (48%). Cluster 3 (17.1%) includes adult-onset patients, mostly severe non-atopic, with overweight, the worse lung function and asthma control, and receiving combination of treatments. Cluster 4 (26.7%) consists of the elderly-onset patients, mostly female, atopic (64%), with high BMI and normal lung function, prevalence of smokers and comorbidities. CONCLUSION: We defined four phenotypes of asthma using unsupervised cluster analysis. These clusters are clinically relevant and differ from each other as regards FEV1, age of onset, age, BMI, atopy, asthma severity, exacerbations, control, social class, smoking and nasal polyps.

Physical activity phenotypes and mortality in older adults: a novel distributional data analysis of accelerometry in the NHANES.

Physical activity is deemed critical to successful ageing. Despite evidence and progress, there is still a need to determine more precisely the direction, magnitude, intensity, and volume of physical activity that should be performed on a daily basis to effectively promote the health of individuals. This study aimed to assess the clinical validity of new physical activity phenotypes derived from a novel distributional functional analysis of accelerometer data in older adults. A random sample of participants aged between 65 and 80 years with valid accelerometer data from the National Health and Nutrition Examination Survey (NHANES) 2011-2014 was used. Five major clinical phenotypes were identified, which provided a greater sensitivity for predicting 5-year mortality and survival outcomes than age alone, and our results confirm the importance of moderate-to-vigorous physical activity. The new clinical physical activity phenotypes are a promising tool for improving patient prognosis and for directing to more targeted intervention planning, according to the principles of precision medicine. The use of distributional representations shows clear advantages over more traditional metrics to explore the effects of the full spectrum of the physical activity continuum on human health.

Gait and Neuromuscular Changes Are Evident in Some Masters Club Level Runners 24-h After Interval Training Run.

Purpose: To examine the time course of recovery for gait and neuromuscular function immediately after and 24-h post interval training. In addition, this study compared the impact of different statistical approaches on detecting changes. Methods: Twenty (10F, 10M) healthy, recreational club runners performed a high-intensity interval training (HIIT) session consisting of six repetitions of 800 m. A 6-min medium intensity run was performed pre, post, and 24-h post HIIT to assess hip and knee kinematics and coordination variability. Voluntary activation and twitch force of the quadriceps, along with maximum isometric force were examined pre, post, and 24-h post significance HIIT. The time course of changes were examined using two different statistical approaches: traditional null hypothesis significance tests and "real" changes using minimum detectable change. Results: Immediately following the run, there were significant (P < 0.05) increases in the hip frontal kinematics and coordination variability. The runners also experienced a loss of muscular strength and neuromuscular function immediately post HIIT (P < 0.05). Individual assessment, however, showed that not all runners experienced fatigue effects immediately post HIIT. Null hypothesis significance testing revealed a lack of recovery in hip frontal kinematics, coordination variability, muscle strength, and neuromuscular function at 24-h post, however, the use of minimum detectable change suggested that most runners had recovered. Conclusion: High intensity interval training resulted in altered running kinematics along with central and peripheral decrements in neuromuscular function. Most runners had recovered within 24-h, although a minority still exhibited signs of fatigue. The runners that were not able to recover prior to their run at 24-h were identified to be at an increased risk of running-related injury.

Kernel machine learning methods to handle missing responses with complex predictors. Application in modelling five-year glucose changes using distributional representations.

BACKGROUND AND OBJECTIVES: Missing data is a ubiquitous problem in longitudinal studies due to the number of patients lost to follow-up. Kernel methods have enriched the machine learning field by successfully managing non-vectorial predictors, such as graphs, strings, and probability distributions, and have emerged as a promising tool for the analysis of complex data stemming from modern healthcare. This paper proposes a new set of kernel methods to handle missing data in the response variables. These methods will be applied to predict long-term changes in glycated haemoglobin (A1c), the primary biomarker used to diagnose and monitor the progression of diabetes mellitus, making emphasis on exploring the predictive potential of continuous glucose monitoring (CGM). METHODS: We propose a new framework of non-linear kernel methods for testing statistical independence, selecting relevant predictors, and quantifying the uncertainty of the resultant predictive models. As a novelty in the clinical analysis, we used a distributional representation of CGM as a predictor and compared its performance with that of traditional diabetes biomarkers. RESULTS: The results show that, after the incorporation of CGM information, predictive ability increases from R2=0.61 to R2=0.71. In addition, uncertainty analysis is useful for characterising some subpopulations where predictivity is worsened, and a more personalised clinical follow-up is advisable according to expected patient uncertainty in glucose values. CONCLUSIONS: The proposed methods have proven to deal effectively with missing data. They also have the potential to improve the results of predictive tasks by including new complex objects as explanatory variables and modelling arbitrary dependence relations. The application of these methods to a longitudinal study of diabetes showed that the inclusion of a distributional representation of CGM data provides greater sensitivity in predicting five-year A1c changes than classical diabetes biomarkers and traditional CGM metrics.

COVID-19: Estimation of the transmission dynamics in Spain using a stochastic simulator and black-box optimization techniques.

BACKGROUND AND OBJECTIVES: Epidemiological models of epidemic spread are an essential tool for optimizing decision-making. The current literature is very extensive and covers a wide variety of deterministic and stochastic models. However, with the increase in computing resources, new, more general, and flexible procedures based on simulation models can assess the effectiveness of measures and quantify the current state of the epidemic. This paper illustrates the potential of this approach to build a new dynamic probabilistic model to estimate the prevalence of SARS-CoV-2 infections in different compartments. METHODS: We propose a new probabilistic model in which, for the first time in the epidemic literature, parameter learning is carried out using gradient-free stochastic black-box optimization techniques simulating multiple trajectories of the infection dynamics in a general way, solving an inverse problem that is defined employing the daily information from mortality records. RESULTS: After the application of the new proposal in Spain in the first and successive waves, the result of the model confirms the accuracy to estimate the seroprevalence and allows us to know the real dynamics of the pandemic a posteriori to assess the impact of epidemiological measures by the Spanish government and to plan more efficiently the subsequent decisions with the prior knowledge obtained. CONCLUSIONS: The model results allow us to estimate the daily patterns of COVID-19 infections in Spain retrospectively and examine the population's exposure to the virus dynamically in contrast to seroprevalence surveys. Furthermore, given the flexibility of our simulation framework, we can model situations -even using non-parametric distributions between the different compartments in the model- that other models in the existing literature cannot. Our general optimization strategy remains valid in these cases, and we can easily create other non-standard simulation epidemic models that incorporate more complex and dynamic structures.

LIPG endothelial lipase and breast cancer risk by subtypes.

Experimental data showed that endothelial lipase (LIPG) is a crucial player in breast cancer. However, very limited data exists on the role of LIPG on the risk of breast cancer in humans. We examined the LIPG-breast cancer association within our population-based case-control study from Galicia, Spain, BREOGAN (BREast Oncology GAlicia Network). Plasma LIPG and/or OxLDL were measured on 114 breast cancer cases and 82 controls from our case-control study, and were included in the present study. The risk of breast cancer increased with increasing levels of LIPG (multivariable OR for the highest category (95% CI) 2.52 (1.11-5.81), P-trend = 0.037). The LIPG-breast cancer association was restricted to Pre-menopausal breast cancer (Multivariable OR for the highest LIPG category (95% CI) 4.76 (0.94-28.77), P-trend = 0.06, and 1.79 (0.61-5.29), P-trend = 0.372, for Pre-menopausal and Post-menopausal breast cancer, respectively). The LIPG-breast cancer association was restricted to Luminal A breast cancers (Multivariable OR for the highest LIPG category (95% CI) 3.70 (1.42-10.16), P-trend = 0.015, and 2.05 (0.63-7.22), P-trend = 0.311, for Luminal A and non-Luminal A breast cancers, respectively). Subset analysis only based on HER2 receptor indicated that the LIPG-breast cancer relationship was restricted to HER2-negative breast cancers (Multivariable OR for the highest LIPG category (95% CI) 4.39 (1.70-12.03), P-trend = 0.012, and 1.10 (0.28-4.32), P-trend = 0.745, for HER2-negative and HER2-positive tumors, respectively). The LIPG-breast cancer association was restricted to women with high total cholesterol levels (Multivariable OR for the highest LIPG category (95% CI) 6.30 (2.13-20.05), P-trend = 0.018, and 0.65 (0.11-3.28), P-trend = 0.786, among women with high and low cholesterol levels, respectively). The LIPG-breast cancer association was also restricted to non-postpartum breast cancer (Multivariable OR for the highest LIPG category (95% CI) 3.83 (1.37-11.39), P-trend = 0.003, and 2.35 (0.16-63.65), P-trend = 0.396, for non-postpartum and postpartum breast cancer, respectively), although we lacked precision. The LIPG-breast cancer association was more pronounced among grades II and III than grade I breast cancers (Multivariable ORs for the highest category of LIPG (95% CI) 2.73 (1.02-7.69), P-trend = 0.057, and 1.90 (0.61-6.21), P-trend = 0.170, for grades II and III, and grade I breast cancers, respectively). No association was detected for OxLDL levels and breast cancer (Multivariable OR for the highest versus the lowest category (95% CI) 1.56 (0.56-4.32), P-trend = 0.457).

Glucodensities: A new representation of glucose profiles using distributional data analysis.

Biosensor data have the potential to improve disease control and detection. However, the analysis of these data under free-living conditions is not feasible with current statistical techniques. To address this challenge, we introduce a new functional representation of biosensor data, termed the glucodensity, together with a data analysis framework based on distances between them. The new data analysis procedure is illustrated through an application in diabetes with continuous-time glucose monitoring (CGM) data. In this domain, we show marked improvement with respect to state-of-the-art analysis methods. In particular, our findings demonstrate that (i) the glucodensity possesses an extraordinary clinical sensitivity to capture the typical biomarkers used in the standard clinical practice in diabetes; (ii) previous biomarkers cannot accurately predict glucodensity, so that the latter is a richer source of information and; (iii) the glucodensity is a natural generalization of the time in range metric, this being the gold standard in the handling of CGM data. Furthermore, the new method overcomes many of the drawbacks of time in range metrics and provides more in-depth insight into assessing glucose metabolism.

Author Correction: Neutrophil to lymphocyte ratio and breast cancer risk: analysis by subtype and potential interactions.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Modelado estadístico y matemático en la epidemia del coronavirus: algunas consideraciones para minimizar los sesgos en los resultados / Statistical and Mathematical Modeling in the Coronavirus Epidemic: Some Considerations to Minimize Biases in the Result

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Modelado estadístico y matemático en la epidemia del coronavirus: algunas consideraciones para minimizar los sesgos en los resultados / Statistical and Mathematical Modeling in the Coronavirus Epidemic: Some Considerations to Minimize Biases in the Results

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Neutrophil to lymphocyte ratio and breast cancer risk: analysis by subtype and potential interactions.

Multiple studies have found the neutrophil to lymphocyte ratio (NLR) to be associated with adverse breast cancer (BC) prognosis and survival. Very limited data exist on the role of NLR and risk of BC. The BREOGAN study is a population-based case-control study conducted in Galicia, Spain. We examined the WBC- and NLR-BC relationships. The risk of BC increased with increasing levels of neutrophils percentage (NE%) (multivariable OR for the highest category (95% CI) = 2.14 (1.39-3.32), P-trend < 0.001) and of the NLR (multivariable OR for the highest category (95% CI) = 1.93 (1.26-2.97), P-trend < 0.001). Lymphocytes absolute (L#) and percentage (L%) were associated with a decreased risk of BC (multivariable OR for the highest category (95% CI) = 0.54 (0.35-0.83), and 0.51 (0.33-0.79), P-trend = 0.001 and < 0.001, respectively). The NLR-BC association was more pronounced among Luminal A BC (multivariable OR for the highest category (95% CI) = 2.00 (1.17-3.45), P-trend < 0.001), HER2-negative BC (multivariable OR for the highest category (95% CI) = 1.87 (1.16-3.02), P-trend < 0.001), and those with high total cholesterol and low H2O2 levels.

Fatigue Induced Changes in Muscle Strength and Gait Following Two Different Intensity, Energy Expenditure Matched Runs.

PURPOSE: To investigate changes in hip and knee strength, kinematics, and running variability following two energy expenditure matched training runs; a medium intensity continuous run (MICR) and a high intensity interval training session (HIIT). METHODS: Twenty (10 Females, 10 Males) healthy master class runners were recruited. Each participant completed the HIIT consisting of six repetitions of 800 m with a 1:1 work: rest ratio. The MICR duration was set to match energy expenditure of the HIIT session. Hip and knee muscular strength were examined pre and post both HIIT and MICR. Kinematics and running variability for hip and knee, along with spatiotemporal parameters were assessed at start and end of each run-type. Changes in variables were examined using both 2 × 2 ANOVAs with repeated measures and on an individual level when the change in a variable exceeded the minimum detectable change (MDC). RESULTS: All strength measures exhibited significant reductions at the hip and knee (P < 0.05) with time for both run-types; 12% following HIIT, 10.6% post MICR. Hip frontal plane kinematics increased post run for both maximum angle (P < 0.001) and range of motion (P = 0.003). Runners exhibited increased running variability for nearly all variables, with the HIIT having a greater effect. Individual assessment revealed that not all runners were effected post run and that following HIIT more runners had reduced muscular strength, altered kinematics and increased running variability. CONCLUSION: Runners exhibited fatigue induced changes following typical training runs, which could potentially present risk of injury development. Group and individual assessment revealed different findings where the use of MDC is recommended over that of P-values.

Prediction of Maximal Oxygen Uptake From Submaximal Exercise Testing in Chronic Respiratory Patients. New Perspectives / Predicción del consumo máximo de oxígeno mediante pruebas de ejercicio submáximo en pacientes con patología respiratoria crónica

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