Early-Life Slow Enteral Feeding Progression Pattern Is Associated with Longitudinal Head-Size Growth Faltering and Neurodevelopmental Impairment Outcomes in Extremely Preterm Infants.

OBJECTIVE: To determine whether feeding progression patterns in the first eight postnatal weeks, depicted by clustering analysis of daily enteral feeding volume, are associated with longitudinal head-circumference (HC) growth and neurodevelopmental outcomes in extremely preterm (EP) infants. METHODS: 200 infants who were admitted at gestational ages 23-27 weeks between 2011 and 2018; survived to discharge; and underwent longitudinal HC growth measurements at birth, term-equivalent age (TEA), corrected age (CA) 6-month, 12-month, and 24-month; and neurodevelopmental assessment using the Bayley Scales of Infant Development at CA 24 months were included for analysis. RESULTS: kmlShape analysis identified two distinct enteral feeding progression patterns: fast progression in 131 (66%) infants and slow progression in 69 (34%) infants. Compared to the fast progression group, the slow progression group showed significantly lower daily enteral volumes after day 13, was older in postnatal age reaching full feeding, had a higher rate of Delta z scores of HC (zHC) < -1 (p < 0.001) between birth and TEA, and displayed lower longitudinal zHC from TEA to CA 24 months. The slow progression group also showed higher rates of microcephaly [42% vs. 16%, p < 0.001; adjusted odd ratio (aOR): 3.269, p = 0.001] and neurodevelopmental impairment (NDI) (38% vs. 19%, p = 0.007; aOR: 2.095, p = 0.035) at CA 24 months. For NDI, the model including feeding progression patterns showed a lower Akaike information criterion score and a better goodness of fit than the model that did not include feeding patterns. CONCLUSION: Characterizing feeding progression pattern may help identify EP infants at high-risk of head-size growth faltering and NDI at early childhood.

Risk patterns associated with transient hearing impairment and permanent hearing loss in infants born very preterm: A retrospective study.

AIM: To determine the risk patterns associated with transient hearing impairment (THI) and permanent hearing loss (PHL) of infants born very preterm who failed hearing screenings. METHOD: We enrolled 646 infants (347 males, 299 females) born at no more than 30 weeks' gestation between 2006 and 2020 who received auditory brainstem response screening at term-equivalent age. Audiological examinations of infants who failed the screening revealed THI, when hearing normalized, or PHL, defined as a persistent unilateral or bilateral hearing threshold above 20 dB. Principal component analysis (PCA) was used to characterize risk patterns. RESULTS: Among the 646 infants, 584 (90.4%) had normal hearing, 42 (6.5%) had THI, and 20 (3.1%) had PHL. Compared with the group with normal hearing, the THI and PHL groups had significantly higher rates of neurodevelopmental impairment at 24 months corrected age. PCA of risk patterns showed the THI group and especially the PHL group had more severe haemodynamic and respiratory instability. Moreover, severe intraventricular haemorrhage (IVH) was also a risk for PHL. Propensity score matching revealed an association of haemodynamic and respiratory instability with PHL. INTERPRETATION: In infants born preterm, the severity and duration of haemodynamic and respiratory instability are risk patterns for both THI and PHL; severe IVH is an additional risk for PHL. WHAT THIS PAPER ADDS: Neurodevelopmental delay was more common in infants born preterm who failed hearing screening. Principal component analysis revealed the risk patterns associated with hearing impairment. Haemodynamic-respiratory instability was associated with transient and permanent hearing impairment outcomes. Severe haemodynamic-respiratory instability and intraventricular haemorrhage was associated with permanent hearing loss.

Early-life respiratory trajectories and neurodevelopmental outcomes in infants born very and extremely preterm: A retrospective study.

AIM: To determine whether early-life respiratory trajectories are associated with neurodevelopmental impairment (NDI) in infants born very and extremely preterm. METHOD: The daily type of respiratory supports in the first 8 weeks after birth were analysed in 546 infants (285 males, 261 females; median gestational age = 28.0 weeks, interquartile range = 3 weeks), comprising 301 infants born very preterm (gestation = 28-30 weeks) and 245 infants born extremely preterm (gestation <28 weeks), who survived to discharge from 2004 to 2018 and received follow-up assessment by Bayley Scales of Infant and Toddler Development at a corrected age of 24 months. NDI included cognition or motor impairment, moderate and severe cerebral palsy, or visual and hearing impairment. RESULTS: Clustering analysis identified three respiratory patterns with increasing severity: improving; slowly improving; and delayed improvement. These were significantly associated with increasing rates of NDI in infants born very and extremely preterm and smaller head circumference in infants born extremely preterm (both p < 0.001). By day 28, the proportion of infants who were under different categories of ventilation support significantly differed according to the three trajectory groups in infants born very and extremely preterm (both p < 0.05). Models that included adverse respiratory trajectories demonstrated more negative impacts on neurodevelopment than those without. INTERPRETATION: An adverse early-life respiratory trajectory was associated with NDI at follow-up, especially in infants born extremely preterm, suggesting a lung-brain axis of preterm birth. WHAT THIS PAPER ADDS: Clustering analysis identified three respiratory trajectories with increasing severity in infants born preterm. Increasing severity of respiratory trajectories was associated with increasing rates of neurodevelopmental impairment. Adverse respiratory trajectories had a significantly negative impact on neurodevelopmental outcomes.

OBJETIVO: Determinar se as trajetórias respiratórias no início da vida estão associadas ao comprometimento do neurodesenvolvimento (CND) em bebês nascidos muito e extremamente prematuros. MÉTODOS: O tipo diário de suporte respiratório nas primeiras 8 semanas após o nascimento foi analisado em 546 bebês (285 meninos, 261 meninas; idade gestacional mediana = 28,0 semanas, intervalo interquartil = 3 semanas), compreendendo 301 bebês nascidos muito prematuros (gestação = 28-30 semanas) e 245 bebês nascidos extremamente prematuros (gestação < 28 semanas), que sobreviveram à alta entre 2004 e 2018 e receberam avaliação de seguimento por meio da Bayley Scales of Infant and Toddler Development na idade corrigida de 24 meses. O CND incluiu deficiência cognitiva ou motora, paralisia cerebral moderada e grave ou deficiência visual e auditiva. RESULTADOS: A análise de agrupamento identificou três padrões respiratórios com gravidade crescente: melhorando; melhorando lentamente; e melhora tardia. Estes foram significativamente associados com taxas crescentes de CND em bebês nascidos muito e extremamente prematuros e menor perímetro cefálico em bebês nascidos extremamente prematuros (ambos p < 0,001). No dia 28, a proporção de bebês que estavam sob diferentes categorias de suporte ventilatório diferiu significativamente de acordo com os três grupos de trajetória em bebês nascidos muito prematuros e extremamente prematuros (ambos p < 0,05). Os modelos que incluíram trajetórias respiratórias adversas demonstraram mais impactos negativos no neurodesenvolvimento do que aqueles sem. INTERPRETAÇÃO: Uma trajetória respiratória adversa no início da vida foi associada ao CND no seguimento, especialmente em bebês nascidos extremamente prematuros, sugerindo um eixo pulmão-cérebro de nascimento prematuro.

Gestational Age-Related Associations between Early-Life Feeding Trajectories and Growth Outcomes at Term Equivalent Age in Very Preterm Infants.

Establishing the different feeding trajectories based on daily enteral feeding data in preterm infants at different gestational ages (GAs), may help to identify the risks and extrauterine growth restriction (EUGR) outcomes associated with the adverse feeding pattern. In a single center, we retrospectively included 625 infants born at 23-30 weeks of gestation who survived to term-equivalent age (TEA) from 2009 to 2020. The infants were designated into three GA groups: 23-26, 27-28, and 29-30 weeks. The daily enteral feeding amounts in the first 56 postnatal days were analyzed to determine the feeding trajectories. The primary outcomes were EUGR in body weight and head circumference calculated, respectively, by the changes between birth and TEA. Clustering analysis identified two feeding trajectories, namely the improving and adverse patterns in each GA group. The adverse feeding pattern that occurred in 49%, 20%, and 17% of GA 23-26, 27-28, and 29-30 weeks, respectively, was differentiated from the improving feeding pattern as early as day 7 in infants at GA 23-26 and 27-28 weeks, in contrast to day 21 in infants at GA 29-30 weeks. The adverse feeding patterns were associated with sepsis, respiratory, and gastrointestinal morbidities at GA 23-26 weeks; sepsis, hemodynamic and gastrointestinal morbidities at GA 27-28 weeks; and preeclampsia, respiratory, and gastrointestinal morbidities at GA 29-30 weeks. Using the improving feeding group as a reference, the adverse feeding group showed significantly higher adjusted odds ratios of EUGR in body weight and head circumference in infants at GA 23-26 and 27-28 weeks. Identifying the early-life adverse feeding trajectories may help recognize the related EUGR outcomes of preterm infants in a GA-related manner.

Determination of correlations in multivariate longitudinal data with modified Cholesky and hypersphere decomposition using Bayesian variable selection approach.

In this article, we present a Bayesian framework for multivariate longitudinal data analysis with a focus on selection of important elements in the generalized autoregressive matrix. An efficient Gibbs sampling algorithm was developed for the proposed model and its implementation in a comprehensive R package called MLModelSelection is available on the comprehensive R archive network. The performance of the proposed approach was studied via a comprehensive simulation study. The effectiveness of the methodology was illustrated using a nonalcoholic fatty liver disease dataset to study correlations in multiple responses over time to explain the joint variability of lung functions and body mass index. Supplementary materials for this article, including a standardized description of the materials needed to reproduce the work, are available as an online supplement.

Hybrid algorithms for generating optimal designs for discriminating multiple nonlinear models under various error distributional assumptions.

Finding a model-based optimal design that can optimally discriminate among a class of plausible models is a difficult task because the design criterion is non-differentiable and requires 2 or more layers of nested optimization. We propose hybrid algorithms based on particle swarm optimization (PSO) to solve such optimization problems, including cases when the optimal design is singular, the mean response of some models are not fully specified and problems that involve 4 layers of nested optimization. Using several classical examples, we show that the proposed PSO-based algorithms are not models or criteria specific, and with a few repeated runs, can produce either an optimal design or a highly efficient design. They are also generally faster than the current algorithms, which are generally slow and work for only specific models or discriminating criteria. As an application, we apply our techniques to find optimal discriminating designs for a dose-response study in toxicology with 5 possible models and compare their performances with traditional and a recently proposed algorithm. In the supplementary material, we provide a R package to generate different types of discriminating designs and evaluate efficiencies of competing designs so that the user can implement an informed design.

Recognizing spatial and temporal clustering patterns of dengue outbreaks in Taiwan.

BACKGROUND: Dengue fever is the most common arboviral infection in humans, with viral transmissions occurring in more than 100 countries in tropical regions. A global strategy for dengue prevention and control was established more than 10 years ago. However, the factors that drive the transmission of the dengue virus and subsequent viral infection continue unabated. The largest dengue outbreaks in Taiwan since World War II occurred in two recent successive years: 2014 and 2015. METHODS: We performed a systematic analysis to detect and recognize spatial and temporal clustering patterns of dengue incidence in geographical areas of Taiwan, using the map-based pattern recognition procedure and scan test. Our aim was to recognize geographical heterogeneity patterns of varying dengue incidence intensity and detect hierarchical incidence intensity clusters. RESULTS: Using the map-based pattern recognition procedure, we identified and delineated two separate hierarchical dengue incidence intensity clusters that comprise multiple mutually adjacent geographical units with high dengue incidence rates. We also found that that dengue incidence tends to peak simultaneously and homogeneously among the neighboring geographic units with high rates in the same cluster. CONCLUSION: Beyond significance testing, this study is particularly desired by and useful for health authorities who require optimal characteristics of disease incidence patterns on maps and over time. Among the integrated components for effective prevention and control of dengue and dengue hemorrhagic fever are active surveillance and community-based integrated mosquito control, for which this study provides valuable inferences. Effective dengue prevention and control programs in Taiwan are critical, and have the added benefit of controlling the potential emergence of Zika.

Standardized maximim D-optimal designs for enzyme kinetic inhibition models.

Locally optimal designs for nonlinear models require a single set of nominal values for the unknown parameters. An alternative is the maximin approach that allows the user to specify a range of values for each parameter of interest. However, the maximin approach is difficult because we first have to determine the locally optimal design for each set of nominal values before maximin types of optimal designs can be found via a nested optimization process. We show that particle swarm optimization (PSO) techniques can solve such complex optimization problems effectively. We demonstrate numerical results from PSO can help find, for the first time, formulae for standardized maximin D-optimal designs for nonlinear model with 3 or 4 parameters on the compact and nonnegative design space. Additionally, we show locally and standardized maximin D-optimal designs for inhibition models are not necessarily supported at a minimum number of points. To facilitate use of such designs, we create a web-based tool for practitioners to find tailor-made locally and standardized maximin optimal designs.

Optimizing Two-level Supersaturated Designs using Swarm Intelligence Techniques.

Supersaturated designs (SSDs) are often used to reduce the number of experimental runs in screening experiments with a large number of factors. As more factors are used in the study, the search for an optimal SSD becomes increasingly challenging because of the large number of feasible selection of factor level settings. This paper tackles this discrete optimization problem via an algorithm based on swarm intelligence. Using the commonly used E(s2) criterion as an illustrative example, we propose an algorithm to find E(s2)-optimal SSDs by showing that they attain the theoretical lower bounds in Bulutoglu and Cheng (2004) and Bulutoglu (2007). We show that our algorithm consistently produces SSDs that are at least as efficient as those from the traditional CP exchange method in terms of computational effort, frequency of finding the E(s2)-optimal SSD and also has good potential for finding D3-, D4- and D5-optimal SSDs.

A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models.

Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also compare the modified PSO performance with Fedorov's algorithm, a popular algorithm used to generate optimal designs, Cocktail algorithm, and the recent algorithm proposed by [1].

Using Animal Instincts to Design Efficient Biomedical Studies via Particle Swarm Optimization.

Particle swarm optimization (PSO) is an increasingly popular metaheuristic algorithm for solving complex optimization problems. Its popularity is due to its repeated successes in finding an optimum or a near optimal solution for problems in many applied disciplines. The algorithm makes no assumption of the function to be optimized and for biomedical experiments like those presented here, PSO typically finds the optimal solutions in a few seconds of CPU time on a garden-variety laptop. We apply PSO to find various types of optimal designs for several problems in the biological sciences and compare PSO performance relative to the differential evolution algorithm, another popular metaheuristic algorithm in the engineering literature.

Optimal minimax designs over a prespecified interval in a heteroscedastic polynomial model.

Minimax optimal designs can be useful for estimating response surface but they are notoriously difficult to study analytically. We provide formulae for three types of minimax optimal designs over a user-specified region. We focus on polynomial models with various types of heteroscedastic errors but the design strategy is applicable to other types of linear models and optimality criteria. Relationships among the three types of minimax optimal designs are discussed.