A latent class linear mixed model for monotonic continuous processes measured with error.

Motivated by measurement errors in radiographic diagnosis of osteoarthritis, we propose a Bayesian approach to identify latent classes in a model with continuous response subject to a monotonic, that is, non-decreasing or non-increasing, process with measurement error. A latent class linear mixed model has been introduced to consider measurement error while the monotonic process is accounted for via truncated normal distributions. The main purpose is to classify the response trajectories through the latent classes to better describe the disease progression within homogeneous subpopulations.

Analysis of Adverse Drug Reactions in Pediatric Patients with Epilepsy: An Intensive Pharmacovigilance Study.

Epilepsy is a chronic neurological disease characterized by the presence of spontaneous seizures, with a higher incidence in the pediatric population. Anti-seizure medication (ASM) may produce adverse drug reactions (ADRs) with an elevated frequency and a high severity. Thus, the objective of the present study was to analyze, through intensive pharmacovigilance over 112 months, the ADRs produced by valproic acid (VPA), oxcarbazepine (OXC), phenytoin (PHT), and levetiracetam (LEV), among others, administered to monotherapy or polytherapy for Mexican hospitalized pediatric epilepsy patients. A total of 1034 patients were interviewed; 315 met the inclusion criteria, 211 patients presented ADRs, and 104 did not. A total of 548 ASM-ADRs were identified, and VPA, LEV, and PHT were the main culprit drugs. The most frequent ADRs were drowsiness, irritability, and thrombocytopenia, and the main systems affected were hematologic, nervous, and dermatologic. LEV and OXC caused more nonsevere ADRs, and PHT caused more severe ADRs. The risk analysis showed an association between belonging to the younger groups and polytherapy with ADR presence and between polytherapy and malnutrition with severe ADRs. In addition, most of the severe ADRs were preventable, and most of the nonsevere ADRs were nonpreventable.

Incomplete Antiretroviral Therapy Adherence Is Associated with Lower CD4-CD8 Ratio in Virally Suppressed Patients with HIV Infection in Mexico.

Suboptimal adherence to antiretroviral therapy (ART) in people with HIV, even during sustained viral suppression, is associated with persistent inflammation, immune activation, and coagulopathy. Persistently low CD4-CD8 Ratio has been also associated with residual inflammation, is a good predictor of increased risk of death and more widely available than inflammatory biomarkers. We tested the hypothesis that the CD4-CD8 Ratio is associated with ART adherence during periods of complete viral suppression. We used the Medication Possession Ratio based in pharmacy registries as measure of adherence and time-varying, routine care CD4 and CD8 measurements as outcome. We used a linear mixed model for longitudinal data, including fixed effects for sex, age, education, date of ART initiation, AIDS-related conditions, and baseline CD4 to model the outcome. In 988 adults with a median follow-up of 4.13 years, higher ART adherence was independently associated with a modest increase in CD4-CD8. For each increasing percentage point in adherence, the CD4-CD8 Ratio increased 0.000857 (95% confidence interval [CI] -0.000494 to 0.002209, p = .213731) in the first year after achieving viral suppression; 0.001057 (95% CI 0.000262-0.001853, p = .009160) in years 1 to 3; 0.000323 (95% CI -0.000448 to 0.001095, p = .411441) in years 3 to 5; and 0.000850 (95% CI 0.000272-0.001429, p = .003946) 5-10 years after achieving viral suppression. The magnitude of the effect of adherence over CD4-CD8 Ratios varied over time and by baseline CD4 count, with increasing adherence having a larger effect early after ART initiation in people with higher baseline CD4 (>500 cells/µL) and in later years in people with lower baseline CD4 count (≥200 cells/µL). Our findings expand on previous evidence suggesting that the benefits of optimal adherence to modern ART regimens goes beyond maintaining viral suppression. These results highlight the importance of including objective measurements of adherence as part of routine care, even in patients with complete HIV suppression over long-term follow-up.

Habilitation of Executive Functions in Pediatric Congenital Heart Disease Patients through LEGO®-Based Therapy: A Quasi-Experimental Study.

Congenital heart disease is defined as an abnormality in the cardiocirculatory structure or function. Various studies have shown that patients with this condition may present cognitive deficits. To compensate for this, several therapeutic strategies have been developed, among them, the LEGO® Education sets, which use the pedagogic enginery to modify cognitive function by didactic material based on mechanics and robotics principles. Accordingly, the goal of this study was to evaluate the effect of cognitive habilitation by using LEGO®-based therapy in pediatric congenital heart disease patients. This was a quasi-experimental study; eligible patients were identified, and their general data were obtained. In the treatment group, an initial evaluation with the neuropsychological BANFE-2 test was applied; then, once a week, the interventions were performed, with a final test at the end of the interventions. In the control group, after the initial evaluation, a second appointment was scheduled for the final evaluation. Our results show that >50% of children presented cognitive impairment; nevertheless, there was an overall improvement in treatment patients, showing a significant increase in BANFE scores in areas related to executive functions. LEGO®-based therapy may be useful to improve cognitive abilities; however, future research should be performed to strengthen the data.

Mixed kernel SVR addressing Parkinson's progression from voice features.

Parkinson's Disease (PD) is a progressive neurodegenerative disease with multiple motor and non-motor characteristics. PD patients commonly face vocal impairments during the early stages of the disease. In this article, the aim is to explain the Unified Parkinson's Disease Rating Scale (UPDRS) as a measure of the progression of Parkinson's disease using a set of covariates obtained from voice signals. In particular, a Support Vector Regression (SVR) model based on a combination of kernel functions is introduced. Theoretically, this proposal, that relies on a mixed kernel (global and local) produces an admissible kernel function. The optimal fitting was obtained for the combination given by the product of radial and polynomial basis. Important results are the non-linear relationships inferred from the features to the response, as well as a considerable improvement in prediction performance metrics, when compared to other learning approaches. Furthermore, with knowledge on factors such as age and gender, it is possible to describe the dynamics of patients' UPDRS from the data collected during their monitoring. In summary, these advances could expand learning processes and intelligent systems to assist in monitoring the evolution of Parkinson's disease.

A spatio-temporal study of state-wide case-fatality risks during the first wave of the COVID-19 pandemic in Mexico.

spatio-temporal analysis of the first wave of the coronavirus (COVID-19) pandemic in Mexico (April to September 2020) was performed by state. Descriptive analyses through diagrams, mapping, animations and time series representations were carried out. Greater risks were observed at certain times in specific regions. Various trends and clusters were observed and analysed by fitting linear mixed models and time series clustering. The association of co-morbidities and other variables were studied by fitting a spatial panel data linear model (SPLM). On average, the greatest risks were observed in Baja California Norte, Chiapas and Sonora, while some other densely populated states, e.g., Mexico City, had lower values. The trends varied by state and a four-order polynomial, including fixed and random effects, was necessary to model them. The most common risk development was observed in states belonging to two clusters and consisted of an initial increase followed by a decrease. Some states presented cluster configurations with a retarded risk increase before the decrease, while the risk increased throughout the time of study in others. A cyclic behaviour with a second increasing trend was also observed in some states. The SPLM approach revealed a positive significant association with respect to case fatality risk between certain groups, such as males and individuals aged 50 years and more, and the prevalence of chronic kidney disease, cardiovascular disease, asthma and hypertension. The analysis may provide valuable insight into COVID-19 dynamics applicable in future outbreaks, as well as identify determinants signifying certain trends at the state level. The combination of spatial and temporal information may provide a better understanding of the fatalities due to COVID-19.

Replication-based regularization approaches to diagnose Reinke's edema by using voice recordings.

Reinke's edema is one of the most prevalent laryngeal pathologies. Its detection can be addressed by using computer-aided diagnosis systems based on features extracted from speech recordings. When extracting acoustic features from different voice recordings of a particular subject at a concrete moment, imperfections in technology and the very biological variability result in values that are close, but they are not identical. This suggests that the within-subject variability must be properly addressed in the statistical methodology. Regularization-based regression approaches can be used to reduce the classification errors by favoring the best predictors and penalizing the worst ones. Three replication-based regularization approaches for variable selection and classification have been specifically designed and implemented to take into account the underlying within-subject variability. In order to illustrate the applicability of these approaches, an experiment has been specifically conducted to discriminate Reinke's edema patients (30 subjects) from healthy people (30 subjects) in a hospital environment. The features have been extracted from four phonations of the sustained vowel /a/ recorded for each subject, leading to a database that has fed the proposed machine learning approaches. The proposed replication-based approaches have been proved to be reliable in terms of selected features and predictive ability, leading to a stable accuracy rate of 0.89 under a cross-validation framework. Also, a comparison with traditional independence-based regularization methods reports a great variability of the latter in terms of selected features and accuracy metrics. Therefore, the proposed approaches contribute to fill a gap in the scientific literature on statistical approaches considering within-subject variability and can be used to build a robust expert system.

Monitoring Parkinson's disease progression based on recorded speech with missing ordinal responses and replicated covariates.

Monitoring Parkinson's Disease (PD) progression is an important task to improve the life quality of the affected people. This task can be performed by extracting features from voice recordings and applying specifically designed statistical models, leading to systems that improve the ability of monitoring the progression of PD in an objective, remote, non-invasive, fast, and economically sustainable way. An experiment has been conducted with 36 subjects to study the progression of the PD over 4 years by using the Hoehn and Yahr (HY) scale and features extracted from the phonation of the vowel/a/. The collected dataset had many missing data, which should be addressed jointly with the non-decreasing nature of the disease and the within-subject variability due to the use of replicated features. In order to handle these issues, a Hidden Markov model for longitudinal data was designed and implemented by using a data augmentation scheme based on different latent variables. Markov chain Monte Carlo methods were used to generate from the posterior distribution. The proposed approach has been tested on simulated data, providing good accuracy rates in the context of a multiclass problem. It also has been applied to the real data obtained from the conducted experiment, providing imputed and predicted HY stages compatible with the progression of PD. The conducted experiment and the proposed approach contribute to fill a gap in the scientific literature on experiments and methodologies for tracking PD progression based on acoustic features and the HY scale. This would help to derive an expert system that can be integrated into the protocols of neurology units in hospital centers.

Random intercept and linear mixed models including heteroscedasticity in a logarithmic scale: Correction terms and prediction in the original scale.

Random intercept models are linear mixed models (LMM) including error and intercept random effects. Sometimes heteroscedasticity is included and the response variable is transformed into a logarithmic scale, while inference is required in the original scale; thus, the response variable has a log-normal distribution. Hence, correction terms should be included to predict the response in the original scale. These terms multiply the exponentiated predicted response variable, which subestimates the real values. We derive the correction terms, simulations and real data about the income of elderly are presented to show the importance of using them to obtain more accurate predictions. Generalizations for any LMM are also presented.

A hidden Markov model addressing measurement errors in the response and replicated covariates for continuous nondecreasing processes.

Motivated by a study tracking the progression of Parkinson's disease (PD) based on features extracted from voice recordings, an inhomogeneous hidden Markov model with continuous state-space is proposed. The approach addresses the measurement error in the response, the within-subject variability of the replicated covariates and presumed nondecreasing response. A Bayesian framework is described and an efficient Markov chain Monte Carlo method is developed. The model performance is evaluated through a simulation-based example and the analysis of a PD tracking progression dataset is presented. Although the approach was motivated by a PD tracking progression problem, it can be applied to any monotonic nondecreasing process whose continuous response variable is subject to measurement errors and where replicated covariates play a key role.

A two-stage variable selection and classification approach for Parkinson's disease detection by using voice recording replications.

BACKGROUND AND OBJECTIVE: In the scientific literature, there is a lack of variable selection and classification methods considering replicated data. The problem motivating this work consists in the discrimination of people suffering Parkinson's disease from healthy subjects based on acoustic features automatically extracted from replicated voice recordings. METHODS: A two-stage variable selection and classification approach has been developed to properly match the replication-based experimental design. The way the statistical approach has been specified allows that the computational problems are solved by using an easy-to-implement Gibbs sampling algorithm. RESULTS: The proposed approach produces an acceptable predictive capacity for PD discrimination with the considered database, despite the fact that the sample size is relatively small. Specifically, the accuracy rate, sensitivity and specificity are 86.2%, 82.5%, and 90.0%, respectively. However, the most important fact is that there is an improvement in the interpretability of the results at the same time that it is shown a better chain mixing and a lower computation time with respect to the only-classification approaches presented in the scientific literature. CONCLUSIONS: To the best of the authors' knowledge, this is the first approach developed to properly consider intra-subject variability for variable selection and classification. Although the proposed approach has been applied for PD discrimination, it can be applied in other contexts with similar replication-based experimental designs.

Addressing voice recording replications for tracking Parkinson's disease progression.

Tracking Parkinson's disease symptom severity by using characteristics automatically extracted from voice recordings is a very interesting and challenging problem. In this context, voice features are automatically extracted from multiple voice recordings from the same subjects. In principle, for each subject, the features should be identical at a concrete time, but the imperfections in technology and the own biological variability result in nonidentical replicated features. The involved within-subject variability must be addressed since replicated measurements from voice recordings can not be directly used in independence-based pattern recognition methods as they have been routinely used through the scientific literature. Besides, the time plays a key role in the experimental design. In this paper, for the first time, a Bayesian linear regression approach suitable to handle replicated measurements and time is proposed. Moreover, a version favoring the best predictors and penalizing the worst ones is also presented. Computational difficulties have been avoided by developing Gibbs sampling-based approaches.