Piecewise Linear Mixed-effects Models Research Articles

Sex-Specific Association of Ambient Temperature With Urine Biomarkers in Southwest Coastal Bangladesh

IntroductionMen are vulnerable to ambient heat-related kidney disease burden, but limited evidence exists on how vulnerable women are when exposed to high ambient heat. We evaluated the sex-specific association between ambient temperature and urine electrolytes (concentration and 24-hour excretion), and 24-hour urine total protein, and volume. MethodsWe pooled a longitudinal data of 1,175 participants' concentration and 24-hour excretion of urine electrolytes and other biomarkers (24-hour urine total protein and volume) (5624 person-visits) collected in southwest coastal Bangladesh (Khulna, Satkhira, and Mongla districts) during November 2016 - April 2017. We then spatiotemporally linked ambient temperature data from local weather stations to participants' health outcomes. For evaluating the relationships between average ambient temperature and urine electrolytes and other biomarkers, we plotted confounder-adjusted restricted cubic spline (RCS) plots using participant-, household-, and community-level random intercepts. We then used piece-wise linear mixed-effects models for different ambient temperature segments determined by inflection points in RCS plots and reported the maximum likelihood estimates and cluster robust standard errors. By applying interaction terms for sex and ambient temperature, we determined the overall significance using the Wald test. Bonferroni correction was used for multiple comparisons. ResultsThe RCS plots demonstrated non-linear associations between ambient heat and urine biomarkers for males and females. Piecewise linear mixed-effects models suggested sex did not modify the relationship of ambient temperature with any of the urine parameters after Bonferroni correction (p-value <0.004). ConclusionOur findings suggest that women are as susceptible to the effects of high ambient temperature exposure as men.

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 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 etal. (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 a 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 modelingframework.

Assessment of Mobility Trajectories Using Wearable Inertial Sensors During Autologous Hematopoietic Cell Transplant

ObjectiveThis study aimed to characterize mobility patterns using wearable inertial sensors and serial assessment across autologous hematopoietic cell transplant (autoHCT) and investigate the relation between mobility and perceived function in patients with hematologic cancer. DesignProspective longitudinal study. SettingHospital adult transplant clinic followed by discharge. Participants78 patients with hematological cancer receiving autoHCT. Main Outcome MeasuresMobility was measured across 3 clinical phases (pretransplant, pre-engraftment, and post-engraftment) in using inertial sensors worn during prescribed performance tests in the hospital. Perceived function was assessed using validated provider-reported (Eastern Cooperative Oncology Group [ECOG] Performance Status Scale) and patient-reported [European Organization for Research and Treatment of Cancer Quality of Life Questionnaire [EORTC QLQ-C30]) measures. Trajectories of 5 selected mobility characteristics (turn duration, gait speed, stride time variability, double support time, and heel strike angle) across the clinical phases were also evaluated using piecewise linear mixed-effects models. ResultsUsing Principal Components Analysis, 4 mobility patterns were identified pretransplant: Gait Limitation, Sagittal Sway, Coronal Sway, and Balance Control. Gait Limitation measured pretransplant was significantly inversely associated with perceived function reported by the provider- (β = -0.11; 95% CI: -0.19, -0.02) and patient- (β = -4.85; 95% CI: -7.72, -1.99) post-engraftment in age-adjusted linear regression models. Mobility characteristics demonstrated immediate declines early pre-engraftment with stabilization by late pre-engraftment. ConclusionPatients with hematological cancer experiencing gait limitations pretransplant are likely to have worse perceived function post-engraftment. Mobility declines in early phases post-transplant and may not fully recover, indicating an opportunity for timely rehabilitation referrals. Wearable inertial sensors can be used to identify early mobility problems and patients who may be at risk for future functional decline who may be candidates for early physical rehabilitation.

Neurological intervention transition model for dynamic prediction of good outcome in spontaneous subarachnoid haemorrhage.

Deterioration of neurovascular conditions can be rapid in patients with spontaneous subarachnoid haemorrhage (SAH) and often lead to poor clinical outcomes. Therefore, it is crucial to promptly assess and continually track the progression of the disease. This study incorporated baseline clinical conditions, repeatedly measured neurological grades and haematological biomarkers for dynamic outcome prediction in patients with spontaneous SAH. Neurological intervention, mainly aneurysm clipping and endovascular embolisation, was also incorporated as an intermediate event in developing a neurological intervention transition (NIT) joint model. A retrospective cohort study was performed on 701 patients in spontaneous SAH with a study period of 14days from the MIMIC-IV dataset. A dynamic prognostic model predicting outcome of patients was developed based on combination of Cox model and piecewise linear mixed-effect models to incorporate different types of prognostic information. Clinical baseline covariates, including cerebral oedema, cerebral infarction, respiratory failure, hydrocephalus and vasospasm, as well as repeated measured Glasgow Coma Scale (GCS), glucose and white blood cell (WBC) levels were covariates contributing to the optimal model. Incorporation of neurological intervention as an intermediate event increases the prediction performance compared with baseline joint modelling approach. The average AUC of the optimal model proposed in this study is 0.7783 across different starting points of prediction and prediction intervals. The model proposed in this study can provide dynamic prognosis for spontaneous SAH patients and significant potential benefits in critical care management.

Evaluation of a flexible piecewise linear mixed-effects model in the analysis of randomized cross-over trials.

Cross-over designs are commonly used in randomized clinical trials to estimate efficacy of a new treatment. They have received a lot of attention, particularly in connection with regulatory requirements for new drugs. The main advantage of using cross-over designs over conventional parallel designs is increased precision, thanks to within-subject comparisons. In the statistical literature, more recent developments are discussed in the analysis of cross-over trials, in particular regarding repeated measures. A piecewise linear model within the framework of mixed effects has been proposed in the analysis of cross-over trials. In this article, we report on a simulation study comparing performance of a piecewise linear mixed-effects (PLME) model against two commonly cited models-Grizzle's mixed-effects (GME) and Jones & Kenward's mixed-effects (JKME) models-used in the analysis of cross-over trials. Our simulation study tried to mirror real-life situation by deriving true underlying parameters from empirical data. The findings from real-life data confirmed the original hypothesis that high-dose iodine salt have significantly lowering effect on diastolic blood pressure (DBP). We further sought to evaluate the performance of PLME model against GME and JKME models, within univariate modeling framework through a simulation study mimicking a 2 × 2 cross-over design. The fixed-effects, random-effects and residual error parameters used in the simulation process were estimated from DBP data, using a PLME model. The initial results with full specification of random intercept and slope(s), showed that the univariate PLME model performed better than the GME and JKME models in estimation of variance-covariance matrix (G) governing the random effects, allowing satisfactory model convergence during estimation. When a hierarchical view-point 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. The PLME model is preferred especially in modeling an increased number of random effects, compared to the GME and JKME models that work equally well with random intercepts only. In some cases, additional random effects could explain much variability in the data, thus improving precision in estimation of the estimands (effect size) parameters.

TRAJECTORIES OF C-REACTIVE PROTEIN OVER THE MENOPAUSE TRANSITION: THE STUDY OF WOMEN’S HEALTH ACROSS THE NATION

Abstract Inflammation is a mechanistic pillar of aging contributing to the pathogenesis of several common medical conditions. Numerous in-vitro and animal experiments support a link between estrogen decline over the menopause transition (MT) and inflammation. Whether the MT per-se is associated with increased inflammatory load is not obvious. Using longitudinal data from 1,459 (11,373 observations) women (at baseline: 42-52 years old; underweight/normal 39.6%, overweight 28%, obese 32.4%; White 44.5%, Black 27.8%, Chinese 10.7%, Japanese 12.1%, Hispanic 4.9%) with a known final-menstrual-period (FMP) date and at least 3-measures of high sensitivity C-reactive protein (hs-CRP) spanning between 10 years before to 14 years after FMP, we characterized trajectories of hs-CRP and their associations with premenopausal obesity and race/ethnicity. Observations indicating acute inflammation were deleted and data were censored at first use of hormone therapy. Group-based trajectory modeling adjusted for time-varying smoking-status and steroids/statin use was utilized to identify trajectories. LOESS plots and piecewise linear mixed-effects models were used to determine if trajectories were linear or had inflection points around FMP. Three distinct hs-CRP trajectories were identified: low-rise (low-level significantly increased 2.5Yrs after FMP; 26.6%), medium-stable (remained stable across MT; 41.7%), and high-decline (high-level significantly declined at FMP; 31.7%). White women were more likely to show the medium-stable trajectory, Black and Hispanic the high-decline, and Chinese and Japanese the low-rise trajectory. Premenopausal obesity predicted the high-decline trajectory, underweight/normal predicted the low-rise, while overweight predicted the medium-stable trajectory. Heterogeneous patterns of hs-CRP over the MT exist and are related to women’s race/ethnicity and premenopausal obesity.

Changes in Body Mass Index and Incident Mild Cognitive Impairment Among African American Older Adults.

Previous research suggests a decline in body mass index (BMI) among older adults is associated with negative health outcomes, including mild cognitive impairment (MCI) and incident dementia. However, no studies have examined the effects of education or developing MCI on BMI trajectories over time. The purpose of this investigation was to characterize trajectories of change in BMI among older adults who develop MCI. Participants were from the Minority Aging Research Study (MARS), a longitudinal cohort study of cognitive decline and Alzheimer's disease in older African Americans living in the greater Chicago, Illinois, area. The study included annual clinical evaluations of cognitive status, as well as measurements of height and weight for BMI calculation. Older African American participants without cognitive impairment at baseline were included in the present analysis (N = 436, 78% women, mean baseline age = 72 [SD = 5.7], mean education = 15 [SD = 3.5]). In piecewise linear mixed-effects models that included a random intercept and 2 random slopes, BMI declined over time (B = -0.20, SE = 0.02, p < .001), with a faster decline after MCI diagnosis (additional decline, B = -0.15, SE = 0.06, p = .019). Older age was associated with lower baseline BMI (B = -0.19, SE = 0.05, p < .001), as was higher education (B = -0.34, SE = 0.09, p < .001). Further, higher education was associated with a slower decline in BMI before MCI (B = 0.02, SE = 0.006, p = .001), but a faster decline after MCI (B = -0.06, SE = 0.022, p = .003). These results suggest an accelerated decline in BMI following an MCI diagnosis, with higher education related to an even faster BMI decline.

Changes in Life-Space Mobility With Loss of Relatives and Friends Among Older Adults: Results From the UAB Study of Aging.

Increasing age brings a greater risk of death of friends and family (hereafter referred to as loss) potentially impacting individuals' life-space mobility (LSM) trajectory. Using the UAB study of aging, we examined differences in LSM trajectories of 1,000 community-dwelling older Alabamians (65 + years) with and without loss over 8.5 years. We measured LSM using UAB's Life-Space Assessment (LSA), a validated instrument assessing movement through zones ranging from their bedroom to out of town. We assessed loss every 6 months using a standard bereavement questionnaire capturing spousal, other relative, or friend loss. We used piecewise linear mixed-effects models to compare LSA trajectories. At baseline, those who later experienced loss, compared with those who did not were younger, more likely to be female, and overall in better health. Those without loss had a baseline mean LSA score of 49.5 and a decline of 0.08 points per year (p < .001). Those with loss had a baseline LSA score of 60 and declined by 1.0 point per year before loss (p < .001), accelerating to 1.8 points per year after loss (p < .001). Those with loss do not experience acute decline postloss but do have an acceleration of the preexisting decline. Although additional research may explain the impact of loss on LSM; this finding suggests that more interventions such as social, mental, or health care services, may be needed for those who experience loss. Specifically, bereaved individuals may benefit from it.

Trajectories of lipids around the menopause transition in Chinese women: results of the Kailuan cohort study

To investigate changes in lipid parameters around the final menstrual period (FMP) in Chinese women. A prospective community-based cohort study. Three thousand seven hundred fifty six Chinese women from the Kailuan cohort study who participated in the first examination and reached their FMP by the end of the seventh examination. Health examinations were performed every 2 years. Multivariable piece-wise linear mixed-effect models were used for repeated measures of lipids as a function of time around FMP. Number of years before or after FMP at each examination. Lipids at each examination, including total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TGs). Total cholesterol, LDL-C, and TGs began to increase in early transition, regardless of baseline age. Moreover, TC and LDL-C had a maximum annual increase from 1 year before to 2 years after FMP; TGs had a maximum annual increase from early transition to the fourth-year after menopause. The trajectories in other postmenopause segments differed across subgroups of different baseline ages. Furthermore, HDL-C remained stable around FMP if baseline age was <45 years, whereas if baseline age was ≥45 years, HDL-C would first decline and then rise during postmenopause. Women with a higher body mass index (BMI) underwent less adverse changes in TC and TGs during postmenopause and had decline in HDL-C before menopause. A later FMP age was associated with less adverse changes in TC, LDL-C, and TGs and greater increase in HDL-C during postmenopause; it was associated with a greater increase in LDL-C during early transition. This repeated measurement cohort study of indigenous Chinese women demonstrated that, regardless of baseline age, the adverse effect of menopause on lipids was since early transition, and the most adverse change time was from 1 year before to 2 years after FMP; HDL-C decreased first and then increased during postmenopause in older women; BMI and FMP age affected lipid trajectory mainly during postmenopause. We highlighted positive lipid management during menopause to reduce the burden of postmenopausal dyslipidemia. For lipid stratification management in postmenopausal women, BMI and FMP age are important factors.

Substantial Lipid Increases During Menopausal Transition in Korean Middle-Aged Women.

Adverse lipid profiles are observed in postmenopausal women. However, there is insufficient evidence of the association between lipids and reproductive aging in Korean women. We aimed to characterize lipid changes with respect to timing relative to menopause in Korean middle-aged women. This study included 1,436 premenopausal women who had a natural menopause during the follow-up period (median = 15.76 years) from the Korean Genome and Epidemiology Study (KoGES) Ansan and Anseong cohort. Lipid levels were measured every 2 years, and the magnitudes of annual lipid changes and differences in the changes by premenopausal body mass index were estimated using piecewise linear mixed-effects models. All lipid levels increased greatly from 3 or 5 years before menopause to 1 year after menopause in all women, regardless of their premenopausal body mass index. During the period, high-density lipoprotein cholesterol (HDL-C) levels increased at 0.42 mg/dL per year (95% confidence interval [CI], 0.29 to 0.55 mg/dL). Nevertheless, non-HDL-C levels simultaneously increased at 3.42 mg/dL per year (95% CI, 3.11 to 3.72 mg/dL), and an annual change in the non-HDL-C to HDL-C ratio was 0.05 (95% CI, 0.04 to 0.06). One year after menopause, changes in all lipid parameters significantly slowed down, except for the non-HDL-C to HDL-C ratio (P < 0.001 for all). The ratio continued to increase until 3 years after menopause, but thereafter, the change leveled off. Women experienced remarkable increases in lipid levels during menopausal transition, highlighting the need for early intervention strategies for cardiovascular disease prevention in women.

CHANGES IN LIFE-SPACE WITH LOSS OF RELATIVES AND FRIENDS AMONG OLDER ADULTS: RESULTS FROM THE UAB STUDY OF AGING

Abstract Increasing age brings greater risk of the death of friends and family (hereafter referred to as loss). Loss may impact individuals’ life-space mobility (LSM); however, no research has explored relationships between loss and LSM trajectories. Using the UAB Study of Aging, we examined differences in LSM trajectories of 1000 community-dwelling older Alabamians (65+years) with and without loss during the study period. We assessed LSM using the UAB Life-Space Assessment (LSA), a validated instrument measuring individuals’ ability to move through zones ranging from their bedroom to out of town. We assessed loss every 6 months using a standard bereavement questionnaire capturing spousal, other relative, or friend loss. We used piecewise linear mixed effects models to compare LSA trajectories. Those who do not experience loss had a baseline mean LSA score of 49.5 and show a decline of 0.08 points per year (p &amp;lt; 0.001). Those that do experience loss had a baseline LSA score of 60 and decline by 1.0 point per year before the loss (p &amp;lt; 0.001), accelerating to 1.8 points per year after the loss (p &amp;lt; 0.001). Those with loss do not experience acute decline post loss but do have an acceleration of the pre-existing decline. More research is needed on this topic to better understand the impact of loss on LSM trajectories; but this finding suggests that more public health and clinical interventions may be needed for those who experience loss. Specifically, bereaved individuals may benefit from social, mental, or healthcare services for loss-related challenges.

Tracking vocabulary and reading growth in children from lower and higher socioeconomic backgrounds during the transition from primary to secondary education.

We examined the relation between socioeconomic status (SES), vocabulary, and reading in middle childhood, during the transition from primary (elementary) to secondary (high) school. Children (N=279, 163 girls) completed assessments of everyday and curriculum-related vocabulary, (non)word reading, and reading comprehension at five timepoints from age 10 to 13. Piecewise linear mixed-effects models showed significant growth in everyday vocabulary and word reading between every time point. Curriculum vocabulary and reading comprehension showed significant growth during the school year, but not during the summer holidays. There were significant effects of SES on all measures except word reading; yet, SES differences did not widen over time. Our findings motivate targeted reading and vocabulary support for secondary school students from lower SES backgrounds.

Predictions of machine learning with mixed-effects in analyzing longitudinal data under model misspecification

We consider predictions in longitudinal studies, and investigate the well known statistical mixed-effects model, piecewise linear mixed-effects model and six different popular machine learning approaches: decision trees, bagging, random forest, boosting, support-vector machine and neural network. In order to consider the correlated data in machine learning, the random effects is combined into the traditional tree methods and random forest. Our focus is the performance of statistical modelling and machine learning especially in the cases of the misspecification of the fixed effects and the random effects. Extensive simulation studies have been carried out to evaluate the performance using a number of criteria. Two real datasets from longitudinal studies are analysed to demonstrate our findings. The R code and dataset are freely available at https://github.com/shuwen92/MEML.

Clinical outcomes of adults and children with cystic fibrosis during the COVID-19 pandemic

BackgroundThe onset of the COVID-19 pandemic was associated with restricted community movement and limited access to healthcare facilities, resulting in changed clinical service delivery to people with cystic fibrosis (CF). This study aimed to determine clinical outcomes of Australian adults and children with CF in the 12-months following the onset of the COVID-19 pandemic. MethodsThis longitudinal cohort study used national registry data. Primary outcomes were 12-month change in percent predicted forced expiratory volume in one second (FEV1 %pred), body mass index (BMI) in adults and BMI z-scores in children. A piecewise linear mixed-effects model was used to determine trends in outcomes before and after pandemic onset. ResultsData were available for 3662 individuals (median age 19.6 years, range 0-82). When trends in outcomes before and after pandemic onset were compared; FEV1 %pred went from a mean annual decline of -0.13% (95%CI -0.36 to 0.11) to a mean improvement of 1.76% (95%CI 1.46-2.05). Annual trend in BMI improved from 0.03 kg/m2 (95%CI -0.02-0.08) to 0.30 kg/m2 (95%CI 0.25-0.45) and BMI z-scores improved from 0.05 (95%CI 0.03-0.07) to 0.12 (95%CI 0.09-0.14). Number of hospitalisations decreased from a total of 2656 to 1957 (p < 0.01). Virtual consultations increased from 8% to 47% and average number of consultations per patient increased from median (IQR) of 4(2-5) to 5(3-6) (p < 0.01). ConclusionIn the 12-months following the onset of the COVID-19 pandemic, there was an improvement in the clinical outcomes of people with CF when compared to the pre-pandemic period.

Douching cessation and molecular bacterial vaginosis: a reanalysis of archived specimens

ObjectivesObservational studies demonstrate an association between vaginal douching and bacterial vaginosis (BV) characterised by Gram stain. We sought to describe the effect of a douching cessation intervention on the composition...

Increased Joint Space Narrowing After Arthroscopic Partial Meniscectomy: Data From the Osteoarthritis Initiative

Background: Arthroscopic partial meniscectomy (APM) is widely performed and remains an important therapeutic option for patients with a meniscal tear. However, it is debated whether or not APM accelerates the progression of osteoarthritis (OA) in the long term. Purpose/Hypothesis: The purpose was to compare the progression of OA measured by the change in tibiofemoral joint space width (JSW)—a quantitative measure of OA radiographic severity—across 3 groups with a midterm follow-up: (1) patients undergoing APM; (2) those with a meniscal tear treated nonoperatively; and (3) those without a tear. We hypothesized that the reduction in JSW would be greatest in patients undergoing APM and least in those patients without a tear. Study Design: Cohort study; Level of evidence, 3. Methods: Using the Osteoarthritis Initiative cohort, a total of 144 patients were identified that underwent APM with at least 12 months of follow up and without previous knee surgery. Those with a meniscal tear who did not have APM (n = 144) and those without a tear (n = 144) were matched to patients who had APM by sex, age, Kellgren-Lawrence (KL) grade, and follow up time. Participants underwent magnetic resonance imaging at baseline. Knee radiographs to assess JSW were collected annually or biannually. The change in minimum medial compartment JSW was calculated using a validated automated method. A piecewise linear mixed effects model was constructed to examine the relationship between JSW decline over time and treatment group—adjusting for age, body mass index, smoking status, KL grade, and baseline JSW. Results: All groups had comparable baseline JSW—ranging from 4.33 mm to 4.38 mm. The APM group had a rate of JSW decline of −0.083 mm/mo in the first 12 months and −0.014 mm/mo between 12 and 72 months. The rate of JSW decline in the APM group was approximately 27 times greater in the first 12 months than that in the nonsurgical group (−0.003 mm/mo) and 5 times greater than that in the no tear group (−0.015 mm/mo); however, there was no significant difference between groups for 12 to 72 months (nonsurgical group: −0.009 mm/mo; no tear group: −0.010 mm/mo). The adjusted JSW in the APM group was 4.38 mm at baseline and decreased to 2.57 mm at 72 months; the JSW in the nonsurgical group declined from 4.31 mm to 3.73 mm, and in the no tear group it declined from 4.33 mm to 3.54 mm. There was a statistically significant difference in JSW change between baseline and 72 months for the APM group compared with the other groups (P < .001), but not between the nonsurgical and no tear groups (P = .12). Conclusion: In the first postoperative year, APM results in a faster rate of joint space narrowing compared with knees undergoing nonsurgical management of meniscal tears. Thereafter, there are comparable rates of OA progression regardless of the chosen management. APM results in a persistent decrease in JSW over at least 72 months. An untreated meniscal tear does not contribute to radiographic progression—assessed by JSW—as compared with an intact meniscus.

Potential Effects of Elimination of the Black Race Coefficient in eGFR Calculations in the CREDENCE Trial.

The effect of including race in the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation on screening, recruitment, and outcomes of clinical trials is unclear. The inclusion and outcomes of participants in the Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation (CREDENCE) trial, which randomized individuals with type 2 diabetes and CKD to canagliflozin or placebo, were evaluated after calculating eGFR using the 2009 CKD-EPI creatinine equation with and without a race-specific coefficient or the 2021 CKD-EPI creatinine equation. Treatment effects were estimated using proportional hazards models and piecewise linear mixed effects models for eGFR slope. Of 4401 randomized participants, 2931 (67%) were White participants, 224 (5%) were Black participants, 877 (20%) were Asian participants, and 369 (8%) participants were other race. Among randomized participants, recalculation of screening eGFR using the 2009 equation without a race-specific coefficient had no effect on the likelihood of non-Black participants meeting inclusion criteria but would have excluded 22 (10%) randomized Black participants for eGFR<30 ml/min per 1.73 m2. Recalculation with the 2021 equation would have excluded eight (4%) Black participants for low eGFR and one (0.4%) Black participant for eGFR≥90 ml/min per 1.73 m2, whereas 30 (0.7%) and 300 (7%) non-Black participants would have been excluded for low and high eGFR, respectively. A high proportion (eight of 22; 36%) of end points in Black participants occurred in individuals who would have been excluded following recalculation using the race-free 2009 equation but not when recalculated with the 2021 equation (one of eight; 13%). Cardiovascular and kidney treatment effects remained consistent across eGFR categories following recalculation with either equation. Changes in estimated treatment effects on eGFR slope were modest but were qualitatively larger following recalculation using the 2021 equation. However, the effect of canagliflozin on chronic change in eGFR was attenuated by 7% among Black participants and increased 6% in non-Black participants. In the CREDENCE trial, eGFR recalculation without the race-specific coefficient had small but potentially important effects on event rates and the relative proportion of Black participants without substantially changing efficacy estimates. Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation (CREDENCE), NCT02065791.

Bayesian modeling of associations in bivariate piecewise linear mixed-effects models.

Longitudinal processes rarely occur in isolation; often the growth curves of 2 or more variables are interdependent. Moreover, growth curves rarely exhibit a constant pattern of change. Many educational and psychological phenomena are comprised of different developmental phases (segments). Bivariate piecewise linear mixed-effects models (BPLMEM) are a useful and flexible statistical framework that allow simultaneous modeling of 2 processes that portray segmented change and investigates their associations over time. The purpose of the present study was to develop a BPLMEM using a Bayesian inference approach allowing the estimation of the association between the error variances and providing a more robust modeling choice for the joint random-effects of the 2 processes. This study aims to improve upon the limitations of the prior literature on bivariate piecewise mixed-effects models, such as only allowing the modeling of uncorrelated residual errors across the 2 longitudinal processes and restricting modeling choices for the random effects. The performance of the BPLMEM was investigated via a Monte Carlo simulation study. Furthermore, the utility of BPLMEM was illustrated by using a national educational dataset, Early Childhood Longitudinal Study-Kindergarten Cohort (ECLS-K), where we examined the joint development of mathematics and reading achievement scores and the association between their trajectories over 7 measurement occasions. The findings obtained shed new light on the relationship between these 2 prominent educational domains over time. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Trajectory of Viral RNA Load Among Persons With Incident SARS-CoV-2 G614 Infection (Wuhan Strain) in Association With COVID-19 Symptom Onset and Severity

The SARS-CoV-2 viral trajectory has not been well characterized in incident infections. These data are needed to inform natural history, prevention practices, and therapeutic development. To characterize early SARS-CoV-2 viral RNA load (hereafter referred to as viral load) in individuals with incident infections in association with COVID-19 symptom onset and severity. This prospective cohort study was a secondary data analysis of a remotely conducted study that enrolled 829 asymptomatic community-based participants recently exposed (<96 hours) to persons with SARS-CoV-2 from 41 US states from March 31 to August 21, 2020. Two cohorts were studied: (1) participants who were SARS-CoV-2 negative at baseline and tested positive during study follow-up, and (2) participants who had 2 or more positive swabs during follow-up, regardless of the initial (baseline) swab result. Participants collected daily midturbinate swab samples for SARS-CoV-2 RNA detection and maintained symptom diaries for 14 days. Laboratory-confirmed SARS-CoV-2 infection. The observed SARS-CoV-2 viral load among incident infections was summarized, and piecewise linear mixed-effects models were used to estimate the characteristics of viral trajectories in association with COVID-19 symptom onset and severity. A total of 97 participants (55 women [57%]; median age, 37 years [IQR, 27-52 years]) developed incident infections during follow-up. Forty-two participants (43%) had viral shedding for 1 day (median peak viral load cycle threshold [Ct] value, 38.5 [95% CI, 38.3-39.0]), 18 (19%) for 2 to 6 days (median Ct value, 36.7 [95% CI, 30.2-38.1]), and 31 (32%) for 7 days or more (median Ct value, 18.3 [95% CI, 17.4-22.0]). The cycle threshold value has an inverse association with viral load. Six participants (6%) had 1 to 6 days of viral shedding with censored duration. The peak mean (SD) viral load was observed on day 3 of shedding (Ct value, 33.8 [95% CI, 31.9-35.6]). Based on the statistical models fitted to 129 participants (60 men [47%]; median age, 38 years [IQR, 25-54 years]) with 2 or more SARS-CoV-2-positive swab samples, persons reporting moderate or severe symptoms tended to have a higher peak mean viral load than those who were asymptomatic (Ct value, 23.3 [95% CI, 22.6-24.0] vs 30.7 [95% CI, 29.8-31.4]). Mild symptoms generally started within 1 day of peak viral load, and moderate or severe symptoms 2 days after peak viral load. All 535 sequenced samples detected the G614 variant (Wuhan strain). This cohort study suggests that having incident SARS-CoV-2 G614 infection was associated with a rapid viral load peak followed by slower decay. COVID-19 symptom onset generally coincided with peak viral load, which correlated positively with symptom severity. This longitudinal evaluation of the SARS-CoV-2 G614 with frequent molecular testing serves as a reference for comparing emergent viral lineages to inform clinical trial designs and public health strategies to contain the spread of the virus.

Body Mass Index Trajectory and Incident Mild Cognitive Impairment Among African American Older Adults

Previous research suggests a decline in body mass index (BMI) among older adults is associated with negative health outcomes, including mild cognitive impairment (MCI) and incident dementia (Gao et al., 2011). However, few studies have examined BMI longitudinal trajectories and how they change after MCI diagnosis among older African Americans. To characterize trajectories of change in BMI among older African American participants with no cognitive impairment at baseline we used data from the Minority Aging Research Study, MARS (N=408, 76.5% women, mean age = 73.5, mean education = 15.0). We constructed piecewise linear mixed-effects models that included a random intercept and two random slopes. The first slope began at baseline. The second slope began at MCI diagnosis allowing for acceleration in the rate of decline after the diagnosis. The results showed BMI declined over time (B=-0.19, SE=0.04, p<.001), and there was a faster decline after MCI (additional decline, B=-0.18, SE=0.068, p=.007). In a second model controlling for age, higher education was associated with a lower BMI at baseline (B=-0.36, SE=0.092, p<.001) but slower decline before MCI (B=0.02, SE=0.006, p=.001). However, after MCI the decline of participants with higher education was faster (B=-0.06, SE=0.022, p=.003). These results suggest an accelerated decline in BMI following MCI diagnosis, with higher education related to an even faster BMI decline, possibly a consequence of cognitive reserve.