Random-effects models for longitudinal data

Effect of chemotherapy on hippocampal volume and shape in older long-term breast cancer survivors

Purpose

The objective of this study was to assess changes in hippocampal volume and shape in older long-term breast cancer survivors who were exposed to chemotherapy 5-15 years prior.

Methods

This study recruited female long-term breast cancer survivors aged 65 years or older with a history of chemotherapy (C+), age-matched breast cancer survivors who did not receive chemotherapy (C-), and healthy controls (HC). The participants were recruited 5-15 years after chemotherapy at time point 1 (TP1) and were followed up for 2 years at time point 2 (TP2). Assessments included hippocampal volume and shape from brain MRI scans and neuropsychological (NP) tests.

Results

At TP1, each of the three groups was comprised of 20 participants. The C+ group exhibited a hippocampal volume loss estimated in proportion with total intracranial volume (ICV) in both the left and right hemispheres from TP1 to TP2. Regarding the hippocampal shape at TP1, the C+ group displayed inward changes compared to the control groups. Within the C+ group, changes in right hippocampal volume adjusted with ICV were positively correlated with crystalized composite scores (R = 0.450, p = 0.044). Additionally, in C+ groups, chronological age was negatively correlated with right hippocampal volume adjusted with ICV (R = -0.585, p = 0.007).

Conclusion

The observed hippocampal volume reduction and inward shape deformation within the C+ group may serve as neural basis for cognitive changes in older long-term breast cancer survivors with history of chemotherapy treatment.

Factors influencing inequality in government health expenditures within African regional economic communities

BACKGROUND

The unequal distribution of government health spending within African regional economic groupings is a significant barrier to achieving Universal Health Coverage and reaching health-related Sustainable Development targets. It also hampers the progress toward achieving the African Union's vision of an integrated and prosperous Africa, free of its heavy disease burden. Based on panel data from 36 countries nested into eight Regional Economic Communities (RECs), this study probes the effects of countries' macro-level factors on government health expenditure disparities within eight regional economic communities from 2000 to 2019.

METHOD

We use the multilevel linear mixed-effect method to show whether countries' trade gains, life expectancy at birth, poverty, urbanization, information and communication technology, and population aging worsen or reduce the differences for two government health expenditure indicators.

RESULTS

The insignificant effect of GDP per capita suggests that in most regional economic groupings, the health sector is still not considered a high-priority sector regarding overall government expenditures. Countries' poverty levels and urbanization increase the domestic general government health expenditure disparities as a percentage of general government expenditure within the regional groupings. However, trade gains and ICT diffusion reduce these disparities. Furthermore, the results reveal that external health expenditure per capita and life expectancy at birth positively impact within-regional inequalities in the domestic general government health expenditure per capita. In contrast, GDP per capita and trade gains tend to reduce them.

CONCLUSIONS

This study enriches the research on the determinants of government health expenditure inequality in Africa. Policies that can spur growth in trade and ICT access should be encouraged. Countries should also make more efforts to reduce poverty. Governments should also develop policies promoting economic growth and planned urbanization.

Motivational interviewing skills practice enhanced with artificial intelligence: ReadMI

BACKGROUND

Finding time in the medical curriculum to focus on motivational interviewing (MI) training is a challenge in many medical schools. We developed a software-based training tool, "Real-time Assessment of Dialogue in Motivational Interviewing" (ReadMI), that aims to advance the skill acquisition of medical students as they learn the MI approach. This human-artificial intelligence teaming may help reduce the cognitive load on a training facilitator.

METHODS

During their Family Medicine clerkship, 125 third-year medical students were scheduled in pairs to participate in a 90-minute MI training session, with each student doing two role-plays as the physician. Intervention group students received both facilitator feedback and ReadMI metrics after their first role-play, while control group students received only facilitator feedback.

RESULTS

While students in both conditions improved their MI approach from the first to the second role-play, those in the intervention condition used significantly more open-ended questions, fewer closed-ended questions, and had a higher ratio of open to closed questions.

CONCLUSION

MI skills practice can be gained with a relatively small investment of student time, and artificial intelligence can be utilized both for the measurement of MI skill acquisition and as an instructional aid.

Aorta pathology and pregnancy-related risks in adult congenital cardiac disease: does the aorta dilate during pregnancy?

Background

Aortic dilatation and pregnancy are major concerns in women with aortopathy (AOP). This single-centre retrospective analysis focuses on the evolution of aortic diameters during and after pregnancy in women with Marfan syndrome (MS), Turner syndrome (TS) and bicuspid aortic valve (BAV) aortopathy.

Methods and results

Thirty-eight women who had one or more single pregnancies were included. The ascending aorta was measured during pregnancy and postpartum. During pregnancy, a significant increase of diameters of the sinus aortae (median 1.4 mm; [-1.3; 3.8]) and ascending aorta (median 2.1 mm; [0.0; 4.0]) was noted. Systemic hypertension gives dilation of the aorta, but it did not influence the overall trajectory during pregnancy.

Conclusion

Significant aortic dilatation is noted during pregnancy in women with underlying AOP, even persisting in the long term. Pre-existing systemic hypertension is associated with larger aortic diameters prior to pregnancy. More research on a larger study population however is needed.

Pairwise fitting of piecewise mixed models for the joint modeling of multivariate longitudinal outcomes, in a randomized crossover trial

Many statistical models have been proposed in the literature for the analysis of longitudinal data. One may propose to model two or more correlated longitudinal processes simultaneously, with a goal of understanding their association over time. Joint modeling is then required to carefully study the association structure among the outcomes as well as drawing joint inferences about the different outcomes. In this study, we sought to model the associations among six nutrition outcomes while circumventing the computational challenge posed by their clustered and high-dimensional nature. We analyzed data from a 2 × $\times$ 2 randomized crossover trial conducted in Kenya, to compare the effect of high-dose and low-dose iodine in household salt on systolic blood pressure (SBP) and diastolic blood pressure (DBP) in women of reproductive age and their household matching pair of school-aged children. Two additional outcomes, namely, urinary iodine concentration (UIC) in women and children were measured repeatedly to monitor the amount of iodine excreted through urine. We extended the model proposed by Mwangi et al. (2021, Communications in Statistics: Case Studies, Data Analysis and Applications, 7(3), 413-431) allowing flexible piecewise joint models for six outcomes to depend on separate random effects, which are themselves correlated. This entailed fitting 15 bivariate general linear mixed models and deriving inference for the joint model using pseudo-likelihood theory. We analyzed the outcomes separately and jointly using piecewise linear mixed-effects (PLME) model and further validated the results using current state-of-the-art Jones and Kenward methodology (JKME model) used for analyzing randomized crossover trials. The results indicate that high-dose iodine in salt significantly reduced blood pressure (BP) compared to low-dose iodine in salt. Estimates for the random effects and residual error components showed that SBP and DBP had strong positive correlation, with effect of the random slope indicating that significantly related outcomes are strongly associated in their evolution. There was a moderately strong inverse relationship between evolutions of UIC and BP both in women and children. These findings confirmed the original hypothesis that high-dose iodine salt has significant lowering effect on BP. We further sought to evaluate the performance of our proposed PLME model against the widely used JKME model, within the multivariate joint modeling framework through a simulation study mimicking a2 × 2 $2\times 2$ crossover design. From our findings, the multivariate joint PLME model performed exceptionally well both in estimation of random-effects matrix (G) and Hessian matrix (H), allowing satisfactory model convergence during estimation. It allowed a more complex fit to the data with both random intercepts and slopes effects compared to the multivariate joint JKME model that allowed for random intercepts only. When a hierarchical viewpoint is adopted, in the sense that outcomes are specified conditionally upon random effects, the variance-covariance matrix of the random effects must be positive definite. In some cases, additional random effects could explain much variability in the data, thus improving precision in estimation of the estimands (effect size) parameters. The key highlight in this evaluation shows that multivariate joint JKME model is a powerful tool especially while fitting mixed models with random intercepts only, in crossover design settings. Addition of random slopes may lead to model complexities in most cases, resulting in unsatisfactory model convergence during estimation. To circumvent convergence pitfalls, extention of JKME model to PLME model allows a more flexible fit to the data (generated from crossover design settings), especially in the multivariate joint modeling framework.

Temporal Patterns of Change in Physical and Cognitive Performance

BACKGROUND

This study examined the relation between declines in physical and cognitive performance in older people.

METHODS

A population-based cohort of 7 483 adults (average age 72 years) were interviewed. Physical performance was assessed with 3 standardized tests and a combination of 4 cognitive tests was used to assess cognitive function. Rate of change in physical and cognitive performance was determined for each interval between interviews. In mixed effects linear regression models adjusted for age, sex, race, and study time, and change in each factor was used to predict change in the other factor. We examined time associations by using changes in the predictor measured at 1, 2, or 3 intervals before the outcome change.

RESULTS

Decline in cognitive function was most strongly predicted by physical decline in the same 3-year interval. The decline in cognitive function was weaker in the 1-time interval after the decline in physical function and was not significant in later intervals. When a decline in cognitive function was used to predict a decline in physical function, the results were similar. The strongest association occurred in the same time interval so that declines in cognitive and physical performance tend to occur together.

CONCLUSIONS

Decline in cognition and physical function seem to occur together in a short timeframe. It is important to investigate the reasons for these changes that are short-term to guide the development of interventions.

Estimating rate of change for nonlinear trajectories in the framework of individual measurement occasions: A new perspective on growth curves

Researchers are often interested in examining between-individual differences in within-individual processes. If the process under investigation is tracked for a long time, its trajectory may show a certain degree of nonlinearity, so that the rate of change is not constant. A fundamental goal of modeling such nonlinear processes is to estimate model parameters that reflect meaningful aspects of change, including the parameters related to change and other parameters that shed light on substantive hypotheses. However, if the measurement occasion is unstructured, existing models cannot simultaneously estimate these two types of parameters. This article has three goals. First, we view the change over time as the area under the curve (AUC) of the rate of change versus time ( r - t ) graph. Second, using the instantaneous rate of change midway through a time interval to approximate the average rate of change during that interval, we propose a new specification to describe longitudinal processes. In addition to obtaining the individual change-related parameters and other parameters related to specific research questions, the new specification allows for unequally spaced study waves and individual measurement occasions around each wave. Third, we derive the model-based interval-specific change and change from baseline, two common measures to evaluate change over time. We evaluate the proposed specification through a simulation study and a real-world data analysis. We also provide OpenMx and Mplus 8 code for each model with the novel specification.

Flexible Bayesian semiparametric mixed-effects model for skewed longitudinal data

BACKGROUND

In clinical trials and epidemiological research, mixed-effects models are commonly used to examine population-level and subject-specific trajectories of biomarkers over time. Despite their increasing popularity and application, the specification of these models necessitates a great deal of care when analysing longitudinal data with non-linear patterns and asymmetry. Parametric (linear) mixed-effect models may not capture these complexities flexibly and adequately. Additionally, assuming a Gaussian distribution for random effects and/or model errors may be overly restrictive, as it lacks robustness against deviations from symmetry.

METHODS

This paper presents a semiparametric mixed-effects model with flexible distributions for complex longitudinal data in the Bayesian paradigm. The non-linear time effect on the longitudinal response was modelled using a spline approach. The multivariate skew-t distribution, which is a more flexible distribution, is utilized to relax the normality assumptions associated with both random-effects and model errors.

RESULTS

To assess the effectiveness of the proposed methods in various model settings, simulation studies were conducted. We then applied these models on chronic kidney disease (CKD) data and assessed the relationship between covariates and estimated glomerular filtration rate (eGFR). First, we compared the proposed semiparametric partially linear mixed-effect (SPPLM) model with the fully parametric one (FPLM), and the results indicated that the SPPLM model outperformed the FPLM model. We then further compared four different SPPLM models, each assuming different distributions for the random effects and model errors. The model with a skew-t distribution exhibited a superior fit to the CKD data compared to the Gaussian model. The findings from the application revealed that hypertension, diabetes, and follow-up time had a substantial association with kidney function, specifically leading to a decrease in GFR estimates.

CONCLUSIONS

The application and simulation studies have demonstrated that our work has made a significant contribution towards a more robust and adaptable methodology for modeling intricate longitudinal data. We achieved this by proposing a semiparametric Bayesian modeling approach with a spline smoothing function and a skew-t distribution.

Longitudinal Associations of Social Support and Gait Speed Decline in Aging

BACKGROUND

Social support predicts functional and cognitive decline in aging. Yet, the associations between social support and gait speed decline-a functional vital sign-are not well understood. This study examined associations between social support and gait speed decline in aging.

METHODS

Social support and gait data from 542 older adults without dementia were examined (mean age 76.1 ± 6.5 years). Baseline emotional support, tangible support, affectionate support, positive social interactions, and overall support from the Medical Outcomes Study Social Support Survey were the predictors of interest. Annual change in simple (normal pace walking) and complex (walking while reciting alternate letters of the alphabet) gait speed (cm/s) were the outcomes of interest. Linear mixed effects models examined associations between social support and gait speed decline, after adjusting for gender, race, depressive symptoms, overall cognition, and comorbidities.

RESULTS

The mean annual change in gait speed was 1.8 cm/s during simple walking and 1.13 cm/s during complex walking. Tangible support was the only category of social support that predicted decline in simple and complex gait speed over a median follow-up of 3 years. The annual decline in gait speed was 0.51 cm/s (p = .008, 95% confidence intervals [CI] 0.13, 0.89) and 0.58 cm/s (p = .007, CI 0.16, 1.0) greater among those with low tangible support than in those with high tangible support during simple and complex walking, respectively.

CONCLUSIONS

Tangible support is a potentially modifiable risk factor for gait speed decline. Further study is needed to examine mechanisms behind the observed associations and the potential for intervention.

Alternative covariance structures in mixed-effects models: Addressing intra- and inter-individual heterogeneity

Mixed-effects models for repeated measures and longitudinal data include random coefficients that are unique to the individual, and thus permit subject-specific growth trajectories, as well as direct study of how the coefficients of a growth function vary as a function of covariates. Although applications of these models often assume homogeneity of the within-subject residual variance that characterizes within-person variation after accounting for systematic change and the variances of the random coefficients of a growth model that quantify individual differences in aspects of change, alternative covariance structures can be considered. These include allowing for serial correlations between the within-subject residuals to account for dependencies in data that remain after fitting a particular growth model or specifying the within-subject residual variance to be a function of covariates or a random subject effect to address between-subject heterogeneity due to unmeasured influences. Further, the variances of the random coefficients can be functions of covariates to relax the assumption that these variances are constant across subjects and to allow for the study of determinants of these sources of variation. In this paper, we consider combinations of these structures that permit flexibility in how mixed-effects models are specified to understand within- and between-subject variation in repeated measures and longitudinal data. Data from three learning studies are analyzed using these different specifications of mixed-effects models.

Trends in cognitive function before and after myocardial infarction: findings from the China Health and Retirement Longitudinal Study

Objectives

Incident stroke was associated with cognitive dysfunction after stroke and even before stroke. However, cognitive trends prior to myocardial infarction (MI) and the timeline of cognitive decline in a few years following incident MI remain unclear, especially among the Chinese population. We aimed to evaluate whether MI was associated with cognitive change both before and after MI in China.

Methods

This cohort study included 11,287 participants without baseline heart problems or stroke from the China Health and Retirement Longitudinal Study. The exposure was self-reported MI. The outcomes were scores of cognitive functions in five domains, which reflected abilities of episodic memory, visuospatial abilities, orientation, attention and calculation, and global cognition as a summary measure. A Linear mixed model was constructed to explore cognitive function before and after incident MI among the MI participants and the cognitive trends of participants free of MI.

Results

During the 7-year follow-up, 421 individuals [3.7% of 11,287, mean (SD) age, 60.0 (9.0) years; 59.1% female] experienced MI events. The cognitive scores of participants of both the MI group and the control group without MI declined gradually as time went by. The annual decline rate of the MI group before incident MI was similar to that of the control group during the whole follow-up period. Incident MI was not associated with acute cognitive decline in all five cognitive domains. Moreover, MI did not accelerate the cognitive decline rate after MI compared with the pre-MI cognitive trends. The decline rate of cognitive function after MI was similar to the rate before MI.

Conclusions

Different from stroke, participants who had an MI did not show steeper cognitive decline before MI. MI was not associated with acute cognitive decline and accelerated decline in several years after MI. Future studies are needed to learn the mechanisms behind the different patterns of cognitive decline between MI and stroke.

Reserve-building as a buffer for depression among individuals living with disability: a longitudinal study of current activities related to brain health

Aims

This study examined whether reserve-building activities are associated with attenuated reported depression among people who were disabled from work due to a medical condition as compared to employed, retired, and unemployed participants.

Methods

This secondary analysis included 771 individuals who provided data at three time points: baseline (late Spring 2020), follow-up 1 (Spring 2021), and follow-up 2 (Fall 2021). The DeltaQuest Reserve-Building Measure assessed current activities related to brain health. An analysis of variance and Pearson correlation coefficients assessed group differences in reserve-building activity scores. Classification and regression tree (CART) modeling investigated factors associated with higher and lower reported depression by employment group. The random effects (RE) models tested two buffering hypotheses: (1) comparing all groups to the employed group and (2) examining within-group effects.

Results

Engaging in outdoor activities, exercise, and religious/spiritual activities was associated with reduced depression over time in the overall sample. While disabled participants endorsed lower levels of being Active in the World, Outdoor activities, and Exercise and higher levels of Inner Life and Passive Media Consumption than the other employment groups, more reserve-building activities distinguished depression levels in the disabled group's CART models compared to the others. Among the disabled, unemployed, and retired participants, engaging in any reserve-building activities was also associated with lower depression scores, which was distinct from the employed participants. In the RE models that used the employed group as the reference category, only the disabled group's level of depression was buffered by engaging in creative activities. In the within-group RE models, the disabled group's engagement in Religious/Spiritual, Outdoors, and Games was associated with substantially reduced within-group depression, which was different from the other employment groups. In contrast, reserve-building activities were not implicated at all as buffers for employed participants.

Conclusion

This study revealed a beneficial effect of reserve-building activities on buffering depression over time during the COVID-19 pandemic, particularly for disabled people. It documented that even if such individuals engaged in lesser amounts of such activities as compared to other employment groups, the buffering effect was substantial. Given the low-cost and accessible nature of reserve-building activities, it would be worthwhile to encourage such activities for disabled individuals.

Using registry data to identify individual dairy cows with abnormal patterns in routinely recorded somatic cell counts

Data from the Danish milk recording system routinely enter the Danish Cattle Database, including somatic cell counts (SCC) for individual animals. Elevated SCC can signal intramammary inflammation, suggesting subclinical mastitis. Detecting mastitis is pivotal to limit severity, prevent pathogen spread, and target treatment or culling. This study aimed to differentiate normal and abnormal SCC patterns using recorded registry data. We used registry data from 2010 to 2020 for dairy cows in herds with 11 annual milk recordings. To create consistency across herds, we used data from 13,996 unique animals and eight different herds, selected based on the amount of data available, only selecting Holstein animals and conventional herds. We fitted log10-transformed SCC to days in milk (DIM) using the Wilmink and Wood's curve functions, originally developed for milk yield over the lactation. We used Nonlinear Least Square and Nonlinear Mixed Effect models to fit the log10-transformed SCC observations to DIM at animal level. Using mean squared residuals (MSR), we found a consistently better fit using a Wood's style function. Detection of MSR outliers in the model fitting process was used to identify animals with log10(SCC) curves deviating from the expected "normal" curve for that same animal. With this study, we propose a method to identify single animals with SCC patterns that indicate abnormalities, such as mastitis, based on registry data. This method could potentially lead to a registry data-based detection of mastitis cases in larger dairy herds.

The ICD-9 to ICD-10 transition has not improved identification of rapidly progressing stage 3 and stage 4 chronic kidney disease patients: a diagnostic test study

BACKGROUND

The International Classification of Diseases (ICD) coding system is the industry standard tool for billing, disease classification, and epidemiology purposes. Prior research has demonstrated ICD codes to have poor accuracy, particularly in relation to rapidly progressing chronic kidney disease (CKD) patients. In 2016, the ICD system moved to revision 10. This study examines subjects in a large insurer database to determine the accuracy of ICD-10 CKD-staging codes to diagnose patients rapidly progressing towards end-stage kidney disease (ESKD).

PATIENTS AND METHODS

Serial observations of outpatient serum creatinine measurements from 2016 to 2021 of 315,903 patients were transformed to estimated glomerular filtration rate (eGFR) to identify CKD stage-3 and advanced patients diagnosed clinically (eGFR-CKD). CKD-staging codes from the same time period of 59,386 patients and used to identify stage-3 and advanced patients diagnosed by ICD-code (ICD-CKD). eGFR-CKD and ICD-CKD diagnostic accuracy was compared between a total of 334,610 patients.

RESULTS

5,618 patients qualified for the progression analysis; 72 were identified as eGFR rapid progressors; 718 had multiple codes to qualify as ICD rapid progressors. Sensitivity was 5.56%, with positive predictive value (PPV) 5.6%. 34,858 patients were diagnosed as eGFR-CKD stage-3 patients; 17,549 were also diagnosed as ICD-CKD stage-3 patients, for a sensitivity of 50.34%, with PPV of 58.71%. 4,069 patients reached eGFR-CKD stage-4 with 2,750 ICD-CKD stage-4 patients, giving a sensitivity of 67.58%, PPV of 42.43%. 959 patients reached eGFR-CKD stage-5 with 566 ICD-CKD stage-5 patients, giving a sensitivity of 59.02%, PPV of 35.85%.

CONCLUSION

This research shows that recent ICD revisions have not improved identification of rapid progressors in diagnostic accuracy, although marked increases in sensitivity for stage-3 (50.34% vs. 24.68%), and PPV in stage-3 (58.71% vs. 40.08%), stage-4 (42.43% vs. 18.52%), and stage-5 (35.85% vs. 4.51%) were observed. However, sensitivity in stage-5 compares poorly (59.02% vs. 91.05%).

Identifying Shared Neuroanatomic Architecture between Cognitive Traits through Multiscale Morphometric Correlation Analysis

We introduce an informative metric, called morphometric correlation, as a measure of shared neuroanatomic similarity between two cognitive traits. Traditional estimates of trait correlations can be confounded by factors beyond brain morphology. To exclude these confounding factors, we adopt a Gaussian kernel to measure the morphological similarity between individuals and compare pure neuroanatomic correlations among cognitive traits. In our empirical study, we employ a multiscale strategy. Given a set of cognitive traits, we first perform morphometric correlation analysis for each pair of traits to reveal their shared neuroanatomic correlation at the whole brain (or global) level. After that, we extend our whole brain concept to regional morphometric correlation and estimate shared neuroanatomic similarity between two cognitive traits at the regional (or local) level. Our results demonstrate that morphometric correlation can provide insights into shared neuroanatomic architecture between cognitive traits. Furthermore, we also estimate the morphometricity of each cognitive trait at both global and local levels, which can be used to better understand how neuroanatomic changes influence individuals' cognitive status.

Association of firefighting exposures with lung function using a novel job exposure matrix (JEM)

OBJECTIVES

Characterisation of firefighters' exposures to dangerous chemicals in smoke from non-wildfire incidents, directly through personal monitoring and indirectly from work-related records, is scarce. The aim of this study was to evaluate the association between smoke particle exposures (P) and pulmonary function.

METHODS

The study period spanned from January 2010 through September 2021. Routine firefighting P were estimated using fire incident characteristics, response data and emission factors from a novel job exposure matrix. Linear mixed effects modelling was employed to estimate changes in pulmonary function as measured by forced expiratory volume in one second (FEV1). Models controlled for age, race/ethnicity, height, smoking and weight.

RESULTS

Every 1000 kg P was associated with 13 mL lower FEV1 (β=-13.34; 95% CI=-13.98 to -12.70) over the entire 12-year follow-up period. When analysing exposures within 3 months before PFT measurements, 1000 kg P was associated with 27 mL lower FEV1 (β=-26.87; 95% CI=-34.54 to -19.20). When evaluating P estimated within 3 months of a pulmonary function test (PFT), stronger associations were observed among those most highly exposed to the World Trade Center (WTC) disaster (β=-12.90; 95% CI=-22.70 to -2.89); the association of cumulative exposures was similar for both highly and less highly exposed individuals.

DISCUSSION

Smoke particle exposures were observed to have modest short-term and long-term associations with pulmonary function, particularly in those who, previously, had high levels of WTC exposure. Future work examining the association between P and pulmonary function among non-WTC exposed firefighters will be essential for disentangling the effects of ageing, routine firefighting and WTC exposures.

Longitudinal Associations of Diet, Food Insecurity, and Supplemental Nutrition Assistance Program Use with Global Cognitive Performance in Middle-Aged and Older Adults

BACKGROUND

Diet quality, food access, and food assistance policies may be key modifiable factors related to cognitive decline.

OBJECTIVE

We aimed to evaluate whether diet quality, food insecurity, and Supplemental Nutrition Assistance Program (SNAP) use are associated with longitudinal changes in cognition among older adults in the United States.

METHODS

Food intake data from the Health Care and Nutrition Study were linked with longitudinal health information from 5 waves of the Health and Retirement Study (2012-2020). The analytic sample (n = 6968) included community-dwelling United States adults aged ≥51 y without cognitive impairment. Global cognition was measured using a telephone-based cognitive status interview (range: 0-27). Diet quality was measured with the Healthy Eating Index, using participants' average intake of 13 dietary components. Questions regarding food access and affordability were used to determine food insecurity and use of SNAP benefits. Linear mixed-effects regression models were used to estimate longitudinal associations between diet-related factors and cognitive score changes.

RESULTS

Poorer diets [β: -0.24; 95% confidence interval (CI): -0.33, -0.15], food insecurity (β: -1.08; 95% CI: -1.31, -0.85), and SNAP use (β: -0.57; 95% CI: -0.82, -0.32) were associated with lower baseline cognitive scores. Poorer diets (β: -0.17; 95% CI: -0.29, -0.05) and food insecurity (β: -0.23; 95% CI: -0.47, -0.01) were associated with significantly steeper declines in cognitive scores over time, after 8 and 2 y of follow-up, respectively; however, SNAP use was not significantly associated with the rate of cognitive decline over time. Estimates were qualitatively similar when restricting the sample to participants aged ≥65 y.

CONCLUSIONS

Results suggest that food access and adherence to healthy diet recommendations may be important elements to maintain cognitive health in aging. SNAP benefits may be insufficient to prevent negative cognitive effects of poor diet and limited access to nutritious foods.

A modified Michaelis-Menten equation estimates growth from birth to 3 years in healthy babies in the USA

BACKGROUND

Standard pediatric growth curves cannot be used to impute missing height or weight measurements in individual children. The Michaelis-Menten equation, used for characterizing substrate-enzyme saturation curves, has been shown to model growth in many organisms including nonhuman vertebrates. We investigated whether this equation could be used to interpolate missing growth data in children in the first three years of life and compared this interpolation to several common interpolation methods and pediatric growth models.

METHODS

We developed a modified Michaelis-Menten equation and compared expected to actual growth, first in a local birth cohort (N = 97) then in a large, outpatient, pediatric sample (N = 14,695).

RESULTS

The modified Michaelis-Menten equation showed excellent fit for both infant weight (median RMSE: boys: 0.22 kg [IQR:0.19; 90% < 0.43]; girls: 0.20 kg [IQR:0.17; 90% < 0.39]) and height (median RMSE: boys: 0.93 cm [IQR:0.53; 90% < 1.0]; girls: 0.91 cm [IQR:0.50;90% < 1.0]). Growth data were modeled accurately with as few as four values from routine well-baby visits in year 1 and seven values in years 1-3; birth weight or length was essential for best fit. Interpolation with this equation had comparable (for weight) or lower (for height) mean RMSE compared to the best performing alternative models.

CONCLUSIONS

A modified Michaelis-Menten equation accurately describes growth in healthy babies aged 0-36 months, allowing interpolation of missing weight and height values in individual longitudinal measurement series. The growth pattern in healthy babies in resource-rich environments mirrors an enzymatic saturation curve.

Did the prevalence of depressive symptoms change during the COVID-19 pandemic? A multilevel analysis on longitudinal data from healthcare workers

BACKGROUND

Healthcare workers (HCW) are at high risk to develop mental health problems during the COVID-19 pandemic because of additional work load, perceived stress, and exposure to patients with COVID-19. Currently, there are few studies on change over time in the prevalence of depressive symptoms during pandemic start among HCW. Thus, the aims of the current study were to examine whether depressive symptoms increased during the pandemic and were associated with perceived stress and own COVID-19 infection and workplace exposure to virus-infected patients.

METHODS

The cohort study used longitudinal data from HCW collected monthly (July 2020 till December 2020) during the first year of the pandemic before vaccination became available. The sample of n = 166 was drawn from a German hospital and included medical (e.g. nurses, therapists, and physicians) and administrative staff. Using multilevel models, we analyzed the change in depressive symptoms [assessed with General Depression Scale (GDS), a validated German version of the Center for Epidemiological Studies Depression Scale (CES-D)] and its association with perceived stress across the study period. Laboratory-confirmed own infection was tested as a potential moderator in this context. Subscales of the GDS were used to examine change over time of depressive symptom modalities (e.g. emotional, somatic, and social interactions (β, 95% confidence interval).

RESULTS

Depression scores increased significantly during the study period (β = .03, 95% CI [0.02, 0.05]). Perceived stress was associated with depressive symptoms (β = .12, 95% CI [0.10, 0.14]) but did not change over time. Exposure to COVID-19 infection was associated with a higher increase of depressive symptoms (β = .12, 95% CI [0.10, .14]). Somatic symptoms of depression increased among medical HCW with workplace exposure to COVID-19 (β = .25, 95% CI [0.13, 0.38]), but not in administrators (β = .03, 95% CI [-0.04, 0.11]).

CONCLUSION

Research is needed to identify factors that promote the reduction of depressive symptoms in medical HCW with exposition to COVID-19 patients. Awareness of infection protection measures should be increased.

Motor training for young children with cerebral palsy: A single-blind randomized controlled trial

AIM

To compare the effect of iMOVE (Intensive Mobility training with Variability and Error) therapy with dose-matched conventional therapy on gross motor development and secondary outcomes in young children with cerebral palsy.

METHOD

This single-blind, randomized controlled trial included repeated assessments of gross motor function (using the Gross Motor Function Measure) and secondary outcomes during a 12- to 24-week intervention phase and at three follow-up points after treatment. Treatment was delivered three times per week in both groups. Forty-two children aged 12 to 36 months were stratified by age and motor function to ensure equivalence between groups at baseline.

RESULTS

Thirty-six children completed treatment and follow-up phases. Treatment fidelity was high and adherence was equivalent between groups (77.3% conventional therapy, 76.2% iMOVE). There were no group differences on the primary (gross motor function after 12 weeks p = 0.18; after 24 weeks p = 0.94) or any secondary (postural control p = 0.88, caregiver satisfaction p = 0.52, child engagement p = 0.98) measure after treatment or at the follow-up points. However, one-third of total participants exceeded predicted change after 12 weeks and 77% exceeded predicted change after 24 weeks of treatment.

INTERPRETATION

Our observations indicate a potential dose-response effect of rehabilitation therapy. We further demonstrated that individual therapeutic ingredients can be manipulated. When delivered consistently, both iMOVE and conventional therapy interventions might both be more effective than standard care.

WHAT THIS PAPER ADDS

Those receiving iMOVE therapy demonstrated more independent practice time, error, and child-initiation than those receiving the dose-matched control. iMOVE therapy was not superior to the control (conventional physical) therapy. Most participants exceeded predicted change after 24 weeks of treatment.

Effectiveness of the Wellness Together Canada Portal as a Digital Mental Health Intervention in Canada: Protocol for a Randomized Controlled Trial

BACKGROUND

The Wellness Together Canada (WTC) portal is a digital mental health intervention that was developed in response to an unprecedented rise in mental health and substance use concerns due to the COVID-19 pandemic, with funding from the Government of Canada. It is a mental health and substance use website to support people across Canada providing digital interventions and services at no cost. Two million people have visited the WTC portal over the course of 1 year since launching; however, rigorous evaluation of this potential solution to access to mental health care during and after the COVID-19 pandemic is urgently required.

OBJECTIVE

This study aims to better understand the effectiveness of the existing digital interventions in improving population mental health in Canada.

METHODS

The Let's Act on Mental Health study is designed as a longitudinal fully remote, equally randomized (1:1), double-blind, alternative intervention-controlled, parallel-group randomized controlled trial to be conducted between October 2023 and April 2024 with a prospective follow-up study period of 26 weeks. This trial will evaluate whether a digital intervention such as the WTC improves population mental health trajectories over time.

RESULTS

The study was approved by the research ethics board of CAMH (Centre for Addiction and Mental Health, Toronto, Ontario, Canada). It is ongoing and participant recruitment is underway. As of August 2023, a total of 453 participants in the age group of 18-72 years have participated, of whom 70% (n=359) are female.

CONCLUSIONS

This initiative provides a unique opportunity to match people's specific unmet mental health and substance use needs to evidence-based digital interventions.

Bayesian two-stage modeling of longitudinal and time-to-event data with an integrated fractional Brownian motion covariance structure

It is difficult to characterize complex variations of biological processes, often longitudinally measured using biomarkers that yield noisy data. While joint modeling with a longitudinal submodel for the biomarker measurements and a survival submodel for assessing the hazard of events can alleviate measurement error issues, the continuous longitudinal submodel often uses random intercepts and slopes to estimate both between- and within-patient heterogeneity in biomarker trajectories. To overcome longitudinal submodel challenges, we replace random slopes with scaled integrated fractional Brownian motion (IFBM). As a more generalized version of integrated Brownian motion, IFBM reasonably depicts noisily measured biological processes. From this longitudinal IFBM model, we derive novel target functions to monitor the risk of rapid disease progression as real-time predictive probabilities. Predicted biomarker values from the IFBM submodel are used as inputs in a Cox submodel to estimate event hazard. This two-stage approach to fit the submodels is performed via Bayesian posterior computation and inference. We use the proposed approach to predict dynamic lung disease progression and mortality in women with a rare disease called lymphangioleiomyomatosis who were followed in a national patient registry. We compare our approach to those using integrated Ornstein-Uhlenbeck or conventional random intercepts-and-slopes terms for the longitudinal submodel. In the comparative analysis, the IFBM model consistently demonstrated superior predictive performance.

Intraocular pressure increases the rate of macular vessel density loss in glaucoma

BACKGROUND/AIMS

To evaluate the relationship over time between intraocular pressure (IOP) and the rate of macula whole image vessel density (wiVD) loss and whole image ganglion cell complex (wiGCC) thinning in glaucoma METHODS: From 62 patients in the Diagnostic Innovations in Glaucoma Study, 59 Primary open-angle glaucoma and 27 glaucoma suspect eyes with mean follow-up of 3.2 years were followed. Optical coherence tomography angiography (OCT-A)-based vessel density and OCT-based structural thickness of the same 6×6 mm GCC scan slab were evaluated. Univariable and multivariable linear mixed models were performed for all eyes and also a subset of them in which peak IOP <18 mm Hg to investigate the effect of IOP parameters on the rate of wiVD and wiGCC change.

RESULTS

The mean baseline visual field mean deviation (95% CI) was -3.3 dB (-4.4 to -2.1). Higher mean IOP (-0.07%/year per 1 mm Hg (-0.14 to -0.01), p=0.033), peak IOP (-0.07%/year per 1 mm Hg (-0.13 to -0.02), p=0.004) and IOP fluctuation (IOP SD) (-0.17%/year per 1 mm Hg (-0.32 to 0.02), p=0.026) were associated with faster macular vessel density loss. Faster wiGCC thinning was associated with higher mean IOP (-0.05 µm/year per 1 mm Hg (-0.10 to -0.01), p=0.015), peak IOP (-0.05 µm/year per 1 mm Hg (-0.08 to -0.02), p=0.003) and IOP fluctuation (-0.12 µm/year per 1 mm Hg (-0.22 to -0.01), p=0.032). In eyes with peak <18 mm Hg, faster wiVD progression was associated with higher mean IOP (p=0.042). Faster wiGCC progression was associated with higher mean IOP in these eyes (p=0.025).

CONCLUSION

IOP metrics were associated with faster rates of overall macular microvascular loss and also in the eyes with peak IOP <18 mm Hg. Future studies are needed to examine whether additional IOP lowering reduces the rate of microvascular loss in patients with glaucoma.

TRIAL REGISTRATION NUMBER

NCT00221897.

Structural connectome architecture shapes the maturation of cortical morphology from childhood to adolescence

Cortical thinning is an important hallmark of the maturation of brain morphology during childhood and adolescence. However, the connectome-based wiring mechanism that underlies cortical maturation remains unclear. Here, we show cortical thinning patterns primarily located in the lateral frontal and parietal heteromodal nodes during childhood and adolescence, which are structurally constrained by white matter network architecture and are particularly represented using a network-based diffusion model. Furthermore, connectome-based constraints are regionally heterogeneous, with the largest constraints residing in frontoparietal nodes, and are associated with gene expression signatures of microstructural neurodevelopmental events. These results are highly reproducible in another independent dataset. These findings advance our understanding of network-level mechanisms and the associated genetic basis that underlies the maturational process of cortical morphology during childhood and adolescence.

Accounting for Twins and Other Multiple Births in Perinatal Studies Conducted Using Healthcare Administration Data

The analysis of perinatal studies is complicated by twins and other multiple births even when they are not the exposure, outcome, or a confounder of interest. Common approaches to handling multiples in studies of infant outcomes include restriction to singletons, counting outcomes at the pregnancy-level (i.e., by counting if at least one twin experienced a binary outcome), or infant-level analysis including all infants and, typically, accounting for clustering of outcomes by using generalised estimating equations or mixed effects models. Several healthcare administration databases only support restriction to singletons or pregnancy-level approaches. For example, in MarketScan insurance claims data, diagnoses in twins are often assigned to a single infant identifier, thereby preventing ascertainment of infant-level outcomes among multiples. Different approaches correspond to different causal questions, produce different estimands, and often rely on different assumptions. We demonstrate the differences that can arise from these different approaches using Monte Carlo simulations, algebraic formulas, and an applied example. Furthermore, we provide guidance on the handling of multiples in perinatal studies when using healthcare administration data.

Changes in Digital Speech Measures in Asymptomatic Carriers of Pathogenic Variants Associated With Frontotemporal Degeneration

BACKGROUND AND OBJECTIVES

Clinical trials developing therapeutics for frontotemporal degeneration (FTD) focus on pathogenic variant carriers at preclinical stages. Objective, quantitative clinical assessment tools are needed to track stability and delayed disease onset. Natural speech can serve as an accessible, cost-effective assessment tool. We aimed to identify early changes in the natural speech of FTD pathogenic variant carriers before they become symptomatic.

METHODS

In this cohort study, speech samples of picture descriptions were collected longitudinally from healthy participants in observational studies at the University of Pennsylvania and Columbia University between 2007 and 2020. Participants were asymptomatic but at risk for familial FTD. Status as "carrier" or "noncarrier" was based on screening for known pathogenic variants in the participant's family. Thirty previously validated digital speech measures derived from automatic speech processing pipelines were selected a priori based on previous studies in patients with FTD and compared between asymptomatic carriers and noncarriers cross-sectionally and longitudinally.

RESULTS

A total of 105 participants, all asymptomatic, included 41 carriers: 12 men [30%], mean age 43 ± 13 years; education, 16 ± 2 years; MMSE 29 ± 1; and 64 noncarriers: 27 men [42%]; mean age, 48 ± 14 years; education, 15 ± 3 years; MMSE 29 ± 1. We identified 4 speech measures that differed between carriers and noncarriers at baseline: mean speech segment duration (mean difference -0.28 seconds, 95% CI -0.55 to -0.02, p = 0.04); word frequency (mean difference 0.07, 95% CI 0.008-0.14, p = 0.03); word ambiguity (mean difference 0.02, 95% CI 0.0008-0.05, p = 0.04); and interjection count per 100 words (mean difference 0.33, 95% CI 0.07-0.59, p = 0.01). Three speech measures deteriorated over time in carriers only: particle count per 100 words per month (β = -0.02, 95% CI -0.03 to -0.004, p = 0.009); total narrative production time in seconds per month (β = -0.24, 95% CI -0.37 to -0.12, p < 0.001); and total number of words per month (β = -0.48, 95% CI -0.78 to -0.19, p = 0.002) including in 3 carriers who later converted to symptomatic disease.

DISCUSSION

Using automatic processing pipelines, we identified early changes in the natural speech of FTD pathogenic variant carriers in the presymptomatic stage. These findings highlight the potential utility of natural speech as a digital clinical outcome assessment tool in FTD, where objective and quantifiable measures for abnormal behavior and language are lacking.

A hierarchical approach for evaluating athlete performance with an application in elite basketball

In this paper, we present the ON score for evaluating the performance of athletes and teams that includes a season-long evaluation system, a single-game evaluation, and an evaluation of an athlete's overall contribution to their team. The approach used to calculate the ON score is based on mixed-effects regression models that take into account the hierarchical structure of the data and a principal component analysis to calculate athlete rating. We apply our methodology to a large dataset of National Basketball Association (NBA) games spanning four seasons from 2015-2016 to 2018-2019. Our model is validated using two systematic approaches, and our results demonstrate the reliability of our approach to calculate an athlete's performance. This provides coaches, General Managers and player agents with a powerful tool to gain deeper insights into their players' performance, make more informed decisions and ultimately improve team performance. Our methodology has several key advantages. First, by incorporating the hierarchical structure of the data, we can obtain valuable information about an athlete's contribution within their team. Second, the use of principal component analysis allows us to calculate a single score, the ON score, that captures the overall performance of an athlete. Third, our approach is based on classical restricted likelihood methods, which makes the calculation faster than Bayesian methods typically requiring 1000 posterior samples. With our approach, coaches and managers can evaluate athletes' performance throughout the season, compare athletes and teams over a year, and assess an athlete's performance during a single game. Our methodology can also complement other ratings and box score metrics to provide a more comprehensive assessment of an athlete's performance as our method uses the hierarchical nature of performance data (i.e. player nested within team over season) which is typically ignored in player rating systems. In summary, our methodology represents a significant contribution to the field of sports analytics and provides the foundation for future developments.

Optimizing CO2 field flooding during sternotomy: In vitro confirmation of the Karolinska studies

Although CO2 field-flooding was first used during cardiac surgery more than 60 years ago, its efficacy is still disputed. The invisible nature of the gas and the difficulty in determining the "safe" quantity to protect the patient are two of the main obstacles to overcome for its validation. Moreover, CO2 concentration in the chest cavity is highly sensitive to procedural aspects, such suction and hand movements. Based on our review of the existing literature, we identified four major factors that influence the intra-cavity CO2 concentration during open-heart surgery: type of delivery device (diffuser), delivery CO2 flow rate, diffuser position around the wound cavity, and its orientation inside the cavity. In this initial study, only steady state conditions were considered to establish a basic understanding on the effect of the four above-mentioned factors. Transient factors, such as suction or hand movements, will be reported separately.

Baseline levels and longitudinal rates of change in plasma Aβ42/40 among self-identified Black/African American and White individuals

Objective

The use of blood-based biomarkers of Alzheimer disease (AD) may facilitate access to biomarker testing of groups that have been historically under-represented in research. We evaluated whether plasma Aβ42/40 has similar or different baseline levels and longitudinal rates of change in participants racialized as Black or White.

Methods

The Study of Race to Understand Alzheimer Biomarkers (SORTOUT-AB) is a multi-center longitudinal study to evaluate for potential differences in AD biomarkers between individuals racialized as Black or White. Plasma samples collected at three AD Research Centers (Washington University, University of Pennsylvania, and University of Alabama-Birmingham) underwent analysis with C2N Diagnostics' PrecivityAD™ blood test for Aβ42 and Aβ40. General linear mixed effects models were used to estimate the baseline levels and rates of longitudinal change for plasma Aβ measures in both racial groups. Analyses also examined whether dementia status, age, sex, education, APOE ε4 carrier status, medical comorbidities, or fasting status modified potential racial differences.

Results

Of the 324 Black and 1,547 White participants, there were 158 Black and 759 White participants with plasma Aβ measures from at least two longitudinal samples over a mean interval of 6.62 years. At baseline, the group of Black participants had lower levels of plasma Aβ40 but similar levels of plasma Aβ42 as compared to the group of White participants. As a result, baseline plasma Aβ42/40 levels were higher in the Black group than the White group, consistent with the Black group having lower levels of amyloid pathology. Racial differences in plasma Aβ42/40 were not modified by age, sex, education, APOE ε4 carrier status, medical conditions (hypertension and diabetes), or fasting status. Despite differences in baseline levels, the Black and White groups had a similar longitudinal rate of change in plasma Aβ42/40.

Interpretation

Black individuals participating in AD research studies had a higher mean level of plasma Aβ42/40, consistent with a lower level of amyloid pathology, which, if confirmed, may imply a lower proportion of Black individuals being eligible for AD clinical trials in which the presence of amyloid is a prerequisite. However, there was no significant racial difference in the rate of change in plasma Aβ42/40, suggesting that amyloid pathology accumulates similarly across racialized groups.

Gender inequality and utilization of ART: an international cross-sectional and longitudinal analysis

STUDY QUESTION

What is the association between a country's level of gender equality and access to ART, as measured through ART utilization?

SUMMARY ANSWER

ART utilization is associated with a country's level of gender equality even after controlling for the level of development.

WHAT IS KNOWN ALREADY

Although gender equality is recognized as an important determinant of population health, its association with fertility care, a highly gendered condition, has not been explored.

STUDY DESIGN, SIZE, DURATION

A longitudinal cross-national analysis of ART utilization in 69 countries during 2002-2014 was carried out.

PARTICPANTS/MATERIALS, SETTING, METHODS

The Gender Inequality Index (GII), Human Development Index (HDI), and their component indicators were modelled against ART utilization using univariate regression models as well as mixed-effects regression methods (adjusted for country, time, and economic/human development) with multiple imputation to account for missing data.

MAIN RESULTS AND THE ROLE OF CHANCE

ART utilization is associated with the GII. In an HDI-adjusted analysis, a one standard deviation decrease in the GII (towards greater equality) is associated with a 59% increase in ART utilization. Gross national income per capita, the maternal mortality ratio, and female parliamentary representation were the index components most predictive of ART utilization.

LIMITATIONS, REASONS FOR CAUTION

Only ART was used rather than all infertility treatments (including less costly and non-invasive treatments such as ovulation induction). This was a country-level analysis and the results cannot be generalized to smaller groups. Not all modelled variables were available for each country across 2002-2014.

WIDER IMPLICATIONS OF THE FINDINGS

Access to fertility care is central to women's sexual and reproductive health, to women's rights, and to human rights. As gender equality improves, so does access to ART. This relation is likely to be reinforcing and bi-directional, with progress towards global, equitable access to fertility care also improving women's status and participation in societies.

STUDY FUNDING/COMPETING INTEREST(S)

External funding was not provided for this study. G.D.A. declares consulting fees from Labcorp and CooperSurgical. G.D.A. is the founder and CEO of Advanced Reproductive Care, Inc., as well as the Chair of the International Committee for Monitoring Assisted Reproductive Technologies (ICMART) and the World Endometriosis Research Foundation, both of which are unpaid roles. G.M.C. is an ICMART Board Representative, which is an unpaid role, and no payments are received from ICMART to UNSW, Sydney, or to G.M.C. to undertake this study. O.F., S.D., F.Z.-H., and E.K. report no conflicts of interest.

TRIAL REGISTRATION NUMBER

N/A.

Improving Regression Analysis with Imputation in a Longitudinal Study of Alzheimer's Disease

Background

Missing data is prevalent in the Alzheimer's Disease Neuroimaging Initiative (ADNI). It is common to deal with missingness by removing subjects with missing entries prior to statistical analysis; however, this can lead to significant efficiency loss and sometimes bias. It has yet to be demonstrated that the imputation approach to handling this issue can be valuable in some longitudinal regression settings.

Objective

The purpose of this study is to demonstrate the importance of imputation and how imputation is correctly done in ADNI by analyzing longitudinal Alzheimer's Disease Assessment Scale -Cognitive Subscale 13 (ADAS-Cog 13) scores and their association with baseline patient characteristics.

Methods

We studied 1,063 subjects in ADNI with mild cognitive impairment. Longitudinal ADAS-Cog 13 scores were modeled with a linear mixed-effects model with baseline clinical and demographic characteristics as predictors. The model estimates obtained without imputation were compared with those obtained after imputation with Multiple Imputation by Chained Equations (MICE). We justify application of MICE by investigating the missing data mechanism and model assumptions. We also assess robustness of the results to the choice of imputation method.

Results

The fixed-effects estimates of the linear mixed-effects model after imputation with MICE yield valid, tighter confidence intervals, thus improving the efficiency of the analysis when compared to the analysis done without imputation.

Conclusions

Our study demonstrates the importance of accounting for missing data in ADNI. When deciding to perform imputation, care should be taken in choosing the approach, as an invalid one can compromise the statistical analyses.

Profiles of lifestyle health behaviors and cognitive decline in older adults

INTRODUCTION

We aimed to identify profiles of modifiable, late-life lifestyle health behaviors related to subsequent maintenance of cognition and explore sociodemographics and health characteristics as effect modifiers.

METHODS

Analyses used data from 715 older adults without baseline dementia from the Rush Memory and Aging Project and with lifestyle health behaviors (physical activity, cognitive activity, healthy diet, social activity) at baseline and ≥ 2 annual assessments of cognition. We used latent profile analysis to group participants based on behavior patterns and assessed change in cognition by group.

RESULTS

Three latent profiles were identified: high (n = 183), moderate (n = 441), and low (n = 91) engagement in health behaviors. Compared to high engagement, the moderate (mean difference [MD] = -0.02, 95% CI = [-0.03;-0.0002], p = 0.048) and low (MD = -0.06, 95% CI = [-0.08;-0.03], p < 0.0001) groups had faster annual rates of decline in global cognition, with no significant effects modifiers (vascular risk factors, apolipoprotein E [APOE] ε4, motor function).

DISCUSSION

Avoiding low levels of lifestyle health behaviors may help maintain cognition.

HIGHLIGHTS

Latent profile analysis (LPA) captures lifestyle health behaviors associated with cognitive function. Such behavior include physical activity, cognitive activity, healthy diet, social activity. We used LPA to examine associations of behaviors and cognitive function over time. Older adults with low lifestyle health behaviors showed more rapid decline. To a lesser degree, so did those with moderate lifestyle health behaviors. Vascular conditions and risks, APOEε4, or motor function did not modify the effect.

Effect of gamma irradiation and supercritical carbon dioxide sterilization with Novakill™ or ethanol on the fracture toughness of cortical bone

Sterilization of structural bone allografts is a critical process prior to their clinical use in large cortical bone defects. Gamma irradiation protocols are known to affect tissue integrity in a dose dependent manner. Alternative sterilization treatments, such as supercritical carbon dioxide (SCCO2 ), are gaining popularity due to advantages such as minimal exposure to denaturants, the lack of toxic residues, superior tissue penetration, and minor impacts on mechanical properties including strength and stiffness. The impact of SCCO2 on the fracture toughness of bone tissue, however, remains unknown. Here, we evaluate crack initiation and growth toughness after 2, 6, and 24 h SCCO2 -treatment using Novakill™ and ethanol as additives on ~11 samples per group obtained from a pair of femur diaphyses of a canine. All mechanical testing was performed at ambient air after 24 h soaking in Hanks' balanced salt solution (HBSS). Results show no statistically significant difference in the failure characteristics of the Novakill™-treated groups whereas crack growth toughness after 6 and 24 h of treatment with ethanol significantly increases by 37% (p = .010) and 34% (p = .038), respectively, compared to an untreated control group. In contrast, standard 25 kGy gamma irradiation causes significantly reduced crack growth resistance by 40% (p = .007) compared to untreated bone. FTIR vibrational spectroscopy, conducted after testing, reveals a consistent trend of statistically significant differences (p < .001) with fracture toughness. These trends align with variations in the ratios of enzymatic mature to immature crosslinks in the collagen structure, suggesting a potential association with fracture toughness. Additional Raman spectroscopy after testing shows a similar trend with statistically significant differences (p < .005), which further supports that collagen structural changes occur in the SCF-treated groups with ethanol after 6 and 24 h. Our work reveals the benefits of SCCO2 sterilization compared to gamma irradiation.

Deep Learning Estimation of 10-2 Visual Field Map Based on Macular Optical Coherence Tomography Angiography Measurements

PURPOSE

To develop deep learning (DL) models estimating the central visual field (VF) from optical coherence tomography angiography (OCTA) vessel density (VD) measurements.

DESIGN

Development and validation of a deep learning model.

METHODS

A total of 1051 10-2 VF OCTA pairs from healthy, glaucoma suspects, and glaucoma eyes were included. DL models were trained on en face macula VD images from OCTA to estimate 10-2 mean deviation (MD), pattern standard deviation (PSD), 68 total deviation (TD) and pattern deviation (PD) values and compared with a linear regression (LR) model with the same input. Accuracy of the models was evaluated by calculating the average mean absolute error (MAE) and the R2 (squared Pearson correlation coefficients) of the estimated and actual VF values.

RESULTS

DL models predicting 10-2 MD achieved R2 of 0.85 (95% confidence interval [CI], 74-0.92) for 10-2 MD and MAEs of 1.76 dB (95% CI, 1.39-2.17 dB) for MD. This was significantly better than mean linear estimates for 10-2 MD. The DL model outperformed the LR model for the estimation of pointwise TD values with an average MAE of 2.48 dB (95% CI, 1.99-3.02) and R2 of 0.69 (95% CI, 0.57-0.76) over all test points. The DL model outperformed the LR model for the estimation of all sectors.

CONCLUSIONS

DL models enable the estimation of VF loss from OCTA images with high accuracy. Applying DL to the OCTA images may enhance clinical decision making. It also may improve individualized patient care and risk stratification of patients who are at risk for central VF damage.

Associations between recall of proper names in story recall and CSF amyloid and tau in adults without cognitive impairment

Neuropsychological measures sensitive to decline in the preclinical phase of Alzheimer's disease are needed. We previously demonstrated that higher amyloid-beta (Aβ) assessed by positron emission tomography in adults without cognitive impairment was associated with recall of fewer proper names in Logical Memory story recall. The current study investigated the association between proper names and cerebrospinal fluid biomarkers (Aβ42/40, phosphorylated tau181 [pTau181], neurofilament light) in 223 participants from the Wisconsin Registry for Alzheimer's Prevention. We assessed associations between biomarkers and delayed Logical Memory total score and proper names using binary logistic regressions. Sensitivity analyses used multinomial logistic regression and stratified biomarker groups. Lower Logical Memory total score and proper names scores from the most recent visit were associated with biomarker positivity. Relatedly, there was a 27% decreased risk of being classified Aβ42/40+/pTau181+ for each additional proper name recalled. A linear mixed effects model found that longitudinal change in proper names recall was predicted by biomarker status. These results demonstrate a novel relationship between proper names and Alzheimer's disease-cerebrospinal fluid pathology.

Addressing unmeasured confounders in cohort studies: Instrumental variable method for a time-fixed exposure on an outcome trajectory

Instrumental variable methods, which handle unmeasured confounding by targeting the part of the exposure explained by an exogenous variable not subject to confounding, have gained much interest in observational studies. We consider the very frequent setting of estimating the unconfounded effect of an exposure measured at baseline on the subsequent trajectory of an outcome repeatedly measured over time. We didactically explain how to apply the instrumental variable method in such setting by adapting the two-stage classical methodology with (1) the prediction of the exposure according to the instrumental variable, (2) its inclusion into a mixed model to quantify the exposure association with the subsequent outcome trajectory, and (3) the computation of the estimated total variance. A simulation study illustrates the consequences of unmeasured confounding in classical analyses and the usefulness of the instrumental variable approach. The methodology is then applied to 6224 participants of the 3C cohort to estimate the association of type-2 diabetes with subsequent cognitive trajectory, using 42 genetic polymorphisms as instrumental variables. This contribution shows how to handle endogeneity when interested in repeated outcomes, along with a R implementation. However, it should still be used with caution as it relies on instrumental variable assumptions hardly testable in practice.

The design and baseline characteristics for the HOPE Consortium Trial to reduce pain and opioid use in hemodialysis

The HOPE Consortium Trial to Reduce Pain and Opioid Use in Hemodialysis (HOPE Trial) is a multicenter randomized trial addressing chronic pain among patients receiving maintenance hemodialysis for end-stage kidney disease. The trial uses a sequential, multiple assignment design with a randomized component for all participants (Phase 1) and a non-randomized component for a subset of participants (Phase 2). During Phase 1, participants are randomized to Pain Coping Skills Training (PCST), an intervention designed to increase self-efficacy for managing pain, or Usual Care. PCST consists of weekly, live, coach-led cognitive behavioral therapy sessions delivered by video- or tele-conferencing for 12 weeks followed by daily interactive voice response sessions delivered by telephone for an additional 12 weeks. At 24 weeks (Phase 2), participants in both the PCST and Usual Care groups taking prescription opioid medications at an average dose of ≥20 morphine milligram equivalents per day are offered buprenorphine, a partial opioid agonist with a more favorable safety profile than full-agonist opioids. All participants are followed for 36 weeks. The primary outcome is pain interference ascertained, for the primary analysis, at 12 weeks. Secondary outcomes include additional patient-reported measures and clinical outcomes including falls, hospitalizations, and death. Exploratory outcomes include acceptability, tolerability, and efficacy of buprenorphine. The enrollment target of 640 participants was met 27 months after trial initiation. The findings of the trial will inform the management of chronic pain, a common and challenging issue for patients treated with maintenance hemodialysis. NCT04571619.

Longitudinal characteristics of physical frailty and its components in men and women post hip fracture

BACKGROUND

Frailty is an important geriatric syndrome predicting adverse health outcomes in older adults. However, the longitudinal characteristics of frailty components in post-hip fracture patients are less understood. Adopting the Fried frailty definition, we examined the longitudinal trends and sex trajectory differences in frailty and its components over 1 year post-fracture.

METHODS

Three hundred and twenty-seven hip fracture patients (162 men and 165 women with mean age 80.1 and 81.5) from Baltimore Hip Studies 7th cohort with measurements at 22 days after admission, and months 2, 6, and 12 post-fracture were analyzed. Frailty components included: grip strength, gait speed, weight, total energy expenditure, and exhaustion. Longitudinal analysis used mixed effect models.

RESULTS

At baseline, men were sicker with worse cognitive status, and had higher weight and grip strength, but lower total energy expenditure than women (p < 0.001). The prevalence of frailty was 31.5%, 30.2%, and 28.2% at months 2, 6, and 12 respectively, showing no longitudinal trends or sex differences. However, its components showed substantial recovery trends over the post-fracture year after confounding adjustments, including increasing gait speed, reducing risk of exhaustion, and stabilized weight loss and energy expenditure over time. Particularly, while men's grip strength tended to remain stable over first year post surgery within patients, women's grip strength reduced significantly over time within patients. On average over time within patients, women were more active with higher energy expenditures but lower grip strength and weight than men.

CONCLUSION

Significant recovery trends and sex differences were observed in frailty components during first year post-fracture. Overall frailty status did not show those trends over months 2-12 since a summary measure might obscure changes in components. Therefore, frailty components provided important multi-dimensional information on the complex recovery process of patients, indicating targets for intervention beyond the global binary measure of frailty.

Maternal Pre-Pregnancy BMI, Breastfeeding, and Child BMI

OBJECTIVES

Breastfeeding practices may protect against offspring obesity, but this relationship is understudied among women with obesity. We describe the associations between breastfeeding practices and child BMI for age z-score (BMIz), stratified by maternal BMI.

METHODS

We analyzed 8134 dyads from 21 cohorts in the Environmental Influences on Child Health Outcomes Program. Dyads with data for maternal pre-pregnancy BMI, infant feeding practices, and ≥1 child BMI assessment between the ages of 2 and 6 years were included. The associations between breastfeeding practices and continuous child BMIz were assessed by using multivariable linear mixed models.

RESULTS

Maternal pre-pregnancy BMI category prevalence was underweight: 2.5%, healthy weight: 45.8%, overweight: 26.0%, and obese: 25.6%. Median child ages at the cessation of any breastfeeding and exclusive breastfeeding across the 4 BMI categories were 19, 26, 24, and 17 weeks and 12, 20, 17, and 12 weeks, respectively. Results were in the hypothesized directions for BMI categories. Three months of any breastfeeding was associated with a lower BMIz among children whose mothers were a healthy weight (-0.02 [-0.04 to 0.001], P = .06), overweight (-0.04 [-0.07 to -0.004], P = .03), or obese (-0.04 [-0.07 to -0.006], P = .02). Three months of exclusive breastfeeding was associated with a lower BMIz among children whose mothers were a healthy weight (-0.06 [-0.10 to -0.02], P = .002), overweight (-0.05 [-0.10 to 0.005], P = .07), or obese (-0.08 [-0.12 to -0.03], P = .001).

CONCLUSIONS

Human milk exposure, regardless of maternal BMI category, was associated with a lower child BMIz in the Environmental Influences on Child Health Outcomes cohorts, supporting breastfeeding recommendations as a potential strategy for decreasing the risk of offspring obesity.

Progressive Macular Vessel Density Loss and Visual Field Progression in Open-angle Glaucoma Eyes with Central Visual Field Damage

PURPOSE

To investigate the association between the longitudinal changes in both macular vessel density (mVD) and macular ganglion cell-inner plexiform layer thickness (mGCIPLT) and visual field (VF) progression (including central VF progression) in open-angle glaucoma (OAG) patients with central visual field (CVF) damage at different glaucoma stages.

DESIGN

Retrospective longitudinal study.

PARTICIPANTS

This study enrolled 223 OAG eyes with CVF loss at baseline classified as early-to-moderate (133 eyes) or advanced (90 eyes) stage based on the VF mean deviation (MD) (-10 dB).

METHODS

Serial mVDs at parafoveal and perifoveal sectors and mGCIPLT measurements were obtained using OCT angiography and OCT during a mean follow-up of 3.5 years. Visual field progression was determined using both the event- and trend-based analyses during follow-up.

MAIN OUTCOME MEASURES

Linear mixed-effects models were used to compare the rates of change in each parameter between VF progressors and nonprogressors. Logistic regression analyses were performed to determine the risk factors for VF progression.

RESULTS

In early-to-moderate stage, progressors showed significantly faster rates of change in the mGCIPLT (-1.02 vs. -0.47 μm/year), parafoveal (-1.12 vs. -0.40%/year), and perifoveal mVDs (-0.83 vs. -0.44%/year) than nonprogressors (all P < 0.05). In advanced stage cases, only the rates of change in mVDs (parafoveal: -1.47 vs. -0.44%/year; perifoveal: -1.04 vs. -0.27%/year; all P < 0.05) showed significant differences between the groups. By multivariable logistic regression analyses, the faster rate of mVD loss was a predictor of VF progression regardless of glaucoma stage, while the rate of mGCIPLT loss was significantly associated with VF progression only in early-to-moderate stage cases.

CONCLUSIONS

Progressive mVD loss is significantly associated with VF progression (including central VF progression) in the OAG eyes with CVF loss regardless of the glaucoma stage.

FINANCIAL DISCLOSURE(S)

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Predictors associated with CD4 cell count changes over time among HIV-infected children on anti-retroviral therapy follow-up in Mekelle General Hospital, Northern Ethiopia, 2019: a retrospective longitudinal study

INTRODUCTION

AIDS continues to be a serious global public health issue. It targets CD4 cells and immunological cells, which are in charge of the body's resistance against pathogenic pathogens. In situations with limited resources, CD4 cell measurement is essential for assessing treatment responses and clinical judgments in HIV-infected children receiving Anti-Retroviral Therapy (ART). The volatility of CD4 cells during ART follow-up is still largely uncharacterized, and there are few new datasets on CD4 cell changes over time. Therefore, the purpose of this analysis was to identify the factors that were predictive of CD4 cell count changes over time in children who started ART at Mekelle General Hospital in northern Ethiopia.

METHODS

A retrospective follow-up study was done. 437 patients in Mekelle general hospital, northern Ethiopia, from 2014-2016 were involved. All patients who have started anti-retrieval treatment (ART) and measured their CD4 cell count at least twice, including the baseline and those who initiated ART treatment, were included in the study population. An exploratory data analysis and linear mixed model analysis were used to explore the predictors of CD4 cell count change in patients and consider variability within and between patients.

RESULTS

This study found the correlation variation explained in cells accounted for between patients was 61.3%, and the remaining 38.7% variation existed within. This indicates that there is a substantial change in random slope and intercept between and within patients. WHO clinical stage IV (β = -1.30, 95% CI: -2.37, -0.23), co-infection HIV/TB (β = -1.78, 95% CI: -2.58, -0.98), children aged 2-5 (β = -0.43; 95% CI: -0.82, -0.04), and 6-14 years (β = -1.02; 95% CI: -1.47, -0.56), non-opportunistic infection (β = 1.33, 95% CI: 0.51, 2.14), and bedridden functional status (β = -1.74, 95% CI: -2.81, -0.68) were predictors of cell changes over time.

CONCLUSIONS

This study found that patients receiving ART experienced a significant change in CD4 cells over time. Because 61.3% of the variation in CD4 cells explained between patients and the remaining 38.7% within patients, such nested data structures are often strong correlation evidence. Co-infection of HIV/TB, functional status, age category of children, WHO clinical stage, and opportunistic infections are potential predictors of CD4 cells count change. Hence, special guidance and attention is also required, especially for those patients who have an opportunistic infections, higher WHO clinical stages, co-infections with HIV and TB, and bedridden functional status.

Longitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture

From childhood to adolescence, the spatiotemporal development pattern of the human brain white matter connectome and its underlying transcriptomic and cellular mechanisms remain largely unknown. With a longitudinal diffusion MRI cohort of 604 participants, we map the developmental trajectory of the white matter connectome from global to regional levels and identify that most brain network properties followed a linear developmental trajectory. Importantly, connectome-transcriptomic analysis reveals that the spatial development pattern of white matter connectome is potentially regulated by the transcriptomic architecture, with positively correlated genes involve in ion transport- and development-related pathways expressed in excitatory and inhibitory neurons, and negatively correlated genes enriches in synapse- and development-related pathways expressed in astrocytes, inhibitory neurons and microglia. Additionally, the macroscale developmental pattern is also associated with myelin content and thicknesses of specific laminas. These findings offer insights into the underlying genetics and neural mechanisms of macroscale white matter connectome development from childhood to adolescence.

Updating test-day milk yield factors for use in genetic evaluations and dairy production systems: a comprehensive review

Various methods have been proposed to estimate daily yield from partial yields, primarily to deal with unequal milking intervals. This paper offers an exhaustive review of daily milk yields, the foundation of lactation records. Seminal advancements in the late 20th century concentrated on two main adjustment metrics: additive additive correction factors (ACF) and multiplicative correction factors (MCF). An ACF model provides additive adjustments to two times AM or PM milk yield, which then becomes the estimated daily yields, whereas an MCF is a ratio of daily yield to the yield from a single milking. Recent studies highlight the potential of alternative approaches, such as exponential regression and other nonlinear models. Biologically, milk secretion rates are not linear throughout the entire milking interval, influenced by the internal mammary gland pressure. Consequently, nonlinear models are appealing for estimating daily milk yields as well. MCFs and ACFs are typically determined for discrete milking interval classes. Nonetheless, large discrete intervals can introduce systematic biases. A universal solution for deriving continuous correction factors has been proposed, ensuring reduced bias and enhanced daily milk yield estimation accuracy. When leveraging test-day milk yields for genetic evaluations in dairy cattle, two predominant statistical models are employed: lactation and test-day yield models. A lactation model capitalizes on the high heritability of total lactation yields, aligning closely with dairy producers' needs because the total amount of milk production in a lactation directly determines farm revenue. However, a lactation yield model without harnessing all test-day records may ignore vital data about the shapes of lactation curves needed for informed breeding decisions. In contrast, a test-day model emphasizes individual test-day data, accommodating various intervals and recording plans and allowing the estimation of environmental effects on specific test days. In the United States, the patenting of test-day models in 1993 used to restrict the use of test-day models to regional and unofficial evaluations by the patent holders. Estimated test-day milk yields have been used as if they were accurate depictions of actual milk yields, neglecting possible estimation errors. Its potential consequences on subsequent genetic evaluations have not been sufficiently addressed. Moving forward, there are still numerous questions and challenges in this domain.

Growth and development of syphilis-exposed and -unexposed uninfected children during their first 18 months of life in Suzhou, China: a nested case-control study with propensity score matching

Background

With the successful implementation of Prevention of Mother-to-Child Transmission (PMTCT) policies, the proportion of infants with exposure to both syphilis and antibiotic medication in utero has increased in China, but there is limited evidence about the early growth and development of such infants.

Methods

We conducted a retrospective nested case-control study based on data from the China PMTCT program conducted in Suzhou from 2016 to 2021. Propensity score matching (PSM) was employed to extract 826 syphilis-exposed but uninfected (SEU) infants and 1,652 syphilis-unexposed uninfected (SUU) infants from a total of 712,653 infants. Maternal characteristics were collected through questionnaires, such as parity, age, education level, smoking and drinking habits during pregnancy. Infantile characteristics were retrieved from medical records or via questionnaires, such as gestational age, gender, mode of delivery, Apgar scores, birth weight and length, outdoor time, vitamin D intake, and feed pattern. Mixed effects models, adjusting for potential influencing factors, were used to investigate the early infantile growth pattern of SEU and SUU infants. All statistical analysis were conducted using R (version 4.2.0).

Results

Length and weight were slightly higher in SEU infants than in the SUU infants at some time points (months 0 and 18 for length, p-values <0.05; months 0, 6, and 18 for weight, p < 0.05). In the mixed effects model, SEU group was found to be associated with higher weight [exponentiated beta exp.(β) = 1.15, 95% Confidence Interval (CI) = 1.06, 1.25], length [exp(β) = 1.42, 95% CI = 1.14, 1.77], and BMI z-score [exp(β) = 1.09, 95% CI = 1.00, 1.19].

Conclusion

With the effective prevention of congenital syphilis under the PMTCT program, SEU infants have non-inferior growth patterns during their first 18 months of life compared with SUU controls in Suzhou, China.

Illustration of association between change in prostate-specific antigen (PSA) values and time to tumor status after treatment for prostate cancer patients: a joint modelling approach

BACKGROUND

Prostate cancer (PCa) is the most prevalent tumor in men, and Prostate-Specific Antigen (PSA) serves as the primary marker for diagnosis, recurrence, and disease-free status. PSA levels post-treatment guide physicians in gauging disease progression and tumor status (low or high). Clinical follow-up relies on monitoring PSA over time, forming the basis for dynamic prediction. Our study proposes a joint model of longitudinal PSA and time to tumor shrinkage, incorporating baseline variables. The research aims to assess tumor status post-treatment for dynamic prediction, utilizing joint assessment of PSA measurements and time to tumor status.

METHODS

We propose a joint model for longitudinal PSA and time to tumor shrinkage, taking into account baseline BMI and post-treatment factors, including external beam radiation therapy (EBRT), androgen deprivation therapy (ADT), prostatectomy, and various combinations of these interventions. The model employs a mixed-effect sub-model for longitudinal PSA and an event time sub-model for tumor shrinkage.

RESULTS

Results emphasize the significance of baseline factors in understanding the relationship between PSA trajectories and tumor status. Patients with low tumor status consistently exhibit low PSA values, decreasing exponentially within one month post-treatment. The correlation between PSA levels and tumor shrinkage is evident, with the considered factors proving to be significant in both sub-models.

CONCLUSIONS

Compared to other treatment options, ADT is the most effective in achieving a low tumor status, as evidenced by a decrease in PSA levels after months of treatment. Patients with an increased BMI were more likely to attain a low tumor status. The research enhances dynamic prediction for PCa patients, utilizing joint analysis of PSA and time to tumor shrinkage post-treatment. The developed model facilitates more effective and personalized decision-making in PCa care.

Nurse Practitioner Care Compared with Primary Care or Nephrologist Care in Early CKD

BACKGROUND

Early interventions in CKD have been shown to improve health outcomes; however, gaps in access to nephrology care remain common. Nurse practitioners can improve access to care; however, the quality and outcomes of nurse practitioner care for CKD are uncertain.

METHODS

In this propensity score-matched cohort study, patients with CKD meeting criteria for nurse practitioner care were matched 1:1 on their propensity scores for ( 1 ) nurse practitioner care versus primary care alone and ( 2 ) nurse practitioner versus nephrologist care. Processes of care were measured within 1 year after cohort entry, and clinical outcomes were measured over 5 years of follow-up and compared between propensity score-matched groups.

RESULTS

A total of 961 (99%) patients from the nurse practitioner clinic were matched on their propensity score to 961 (1%) patients receiving primary care only while 969 (100%) patients from the nurse practitioner clinic were matched to 969 (7%) patients receiving nephrologist care. After matching to patients receiving primary care alone, those receiving nurse practitioner care had greater use of angiotensin-converting enzyme inhibitors/angiotensin receptor blocker (82% versus 79%; absolute differences [ADs] 3.4% [95% confidence interval, 0.0% to 6.9%]) and statins (75% versus 66%; AD 9.7% [5.8% to 13.6%]), fewer prescriptions of nonsteroidal anti-inflammatory drugs (10% versus 17%; AD -7.2% [-10.4% to -4.2%]), greater eGFR and albuminuria monitoring, and lower rates of all-cause hospitalization (34.1 versus 43.3; rate difference -9.2 [-14.7 to -3.8] per 100 person-years) and all-cause mortality (3.3 versus 6.0; rate difference -2.7 [-3.6 to -1.7] per 100 person-years). When matched to patients receiving nephrologist care, those receiving nurse practitioner care were also more likely to be prescribed angiotensin-converting enzyme inhibitors/angiotensin receptor blockers and statins, with no difference in the risks of experiencing adverse clinical outcomes.

CONCLUSIONS

Nurse practitioner care for patients with CKD was associated with better guideline-concordant care than primary care alone or nephrologist care, with clinical outcomes that were better than or equivalent to primary care alone and similar to those with care by nephrologists.

PODCAST

This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_12_08_CJN0000000000000305.mp3.

Quantitative flood disaster loss-resilience with the multilevel hybrid evaluation model

The severity of global flood disasters is growing, causing loss of human life and property. Building a high-resilience social system, an important means of sustainable flood control, can address these flood-related issues. Numerous studies have carried out disaster resilience evaluations and explored the correlation between flood disaster loss and intensity, but neglected to analyze the role of resilience construction in disaster loss reduction. This study proposed a research route for linking flood loss and disaster loss to quantify the relationship between the two. Take Guangdong Province, China as a study case, the mixed-effects (ME) model and multilevel hybrid evaluation model (MHEM) were established to assess disaster loss and resilience of cities, respectively. Subsequently, disaster resilience curves were built to quantitatively evaluate disaster resilience and corresponding disaster loss. The results show that (1) the ME model can concurrently build the disaster intensity-loss curves of multiple cities with high fitting accuracy. The MHEM combines multiple methods to determine the evaluation result with the highest consistency, showing high reliability. (2) The central and southern regions of Guangdong Province have low disaster loss and high resilience, while the northern regions have high disaster loss and low resilience. (3) With the improvement of disaster resistance, the reduction in disaster loss gradually decreases. Disaster loss in low-resilience cities exhibits greater randomness than that in high-resilience cities, and increasing their resilience can more significantly reduce their level of loss. This study provides a quantitative basis and available methods for comprehensive responses to natural disasters and adaptation to global climate change.

Random survival forests with multivariate longitudinal endogenous covariates

Predicting the individual risk of clinical events using the complete patient history is a major challenge in personalized medicine. Analytical methods have to account for a possibly large number of time-dependent predictors, which are often characterized by irregular and error-prone measurements, and are truncated early by the event. In this work, we extended the competing-risk random survival forests to handle such endogenous longitudinal predictors when predicting event probabilities. The method, implemented in the R package DynForest, internally transforms the time-dependent predictors at each node of each tree into time-fixed features (using mixed models) that can then be used as splitting candidates. The final individual event probability is computed as the average of leaf-specific Aalen-Johansen estimators over the trees. Using simulations, we compared the performances of DynForest to accurately predict an event with (i) a joint modeling alternative when considering two longitudinal predictors only, and with (ii) a regression calibration method that ignores the informative truncation by the event when dealing with a large number of longitudinal predictors. Through an application in dementia research, we also illustrated how DynForest can be used to develop a dynamic prediction tool for dementia from multimodal repeated markers, and quantify the importance of each marker.

Classification of longitudinal profiles using semi-parametric nonlinear mixed models with P-Splines and the SAEM algorithm

In this work, we propose an extension of a semiparametric nonlinear mixed-effects model for longitudinal data that incorporates more flexibility with penalized splines (P-splines) as smooth terms. The novelty of the proposed approach consists of the formulation of the model within the stochastic approximation version of the EM algorithm for maximum likelihood, the so-called SAEM algorithm. The proposed approach takes advantage of the formulation of a P-spline as a mixed-effects model and the use of the computational advantages of the existing software for the SAEM algorithm for the estimation of the random effects and the variance components. Additionally, we developed a supervised classification method for these non-linear mixed models using an adaptive importance sampling scheme. To illustrate our proposal, we consider two studies on pregnant women where two biomarkers are used as indicators of changes during pregnancy. In both studies, information about the women's pregnancy outcomes is known. Our proposal provides a unified framework for the classification of longitudinal profiles that may have important implications for the early detection and monitoring of pregnancy-related changes and contribute to improved maternal and fetal health outcomes. We show that the proposed models improve the analysis of this type of data compared to previous studies. These improvements are reflected both in the fit of the models and in the classification of the groups.

Fast multiple-trait genome-wide association analysis for correlated longitudinal measurements

Large-scale longitudinal biobank data can be leveraged to identify genetic variation contributing to human diseases progression and traits trajectories. While methods for genome-wide association studies (GWAS) of multiple correlated traits have been proposed, an efficient multiple-trait approach to model longitudinal phenotypes is not currently available. We developed GAMUT, a genome-wide association approach for multiple longitudinal traits. GAMUT employs a mixed-effects model to fit longitudinal outcomes where a fast algorithm for inversion by recursive partitioning of the random effects submatrix is introduced. To evaluate performance of the algorithms introduced and assess their statistical power and type I error, stochastic simulation was conducted. Consistent with our expectation, power was greater for cross-sectional (CS) than longitudinal (LT) effects, particularly with a diminishing LT/CS ratio. With a minimum minor allele count of 3 within genotype by time categories, observed type I error was roughly equal to theoretical genome-wide significance. Additionally, 28 blood-based biomarkers measured at 2 time points on participants of the UK Biobank were used to compare GAMUT against single-trait standard and longitudinal GWAS (including rate of change). Across all biomarkers, we observed 539 (CS) and 248 (LT) significant independent variants for the GAMUT method, and 513 (CS) and 30 (LT) for single-trait longitudinal GWAS, respectively. Only 37 variants were identified by modeling rates of change using standard GWAS.