Generalized Estimating Equations Models Research Articles

Association of sarcopenia and its prognostic value in symptomatic knee osteoarthritis among older people in China: the first longitudinal evidence from CHARLS

BackgroundSarcopenia and knee osteoarthritis (KOA) are two common musculoskeletal disorders, often coexisting in aged population and potentially influencing each other. However, the underlying relationship between two conditions remains unclear and controversial. To fill this knowledge gap, we aimed to investigate the longitudinal association among the older Chinese population.MethodsData were attracted from 2 waves of the China Health and Retirement Longitudinal Study (CHARLS), and 6212 individuals aged ≥ 60 years were included. Sarcopenia status was defined by the Asian Working Group for Sarcopenia 2019 criteria. Multivariate logistic regression and generalized estimation equation models were applied to estimate the impact of sarcopenia on KOA. A prognostic nomogram was developed through train-test cross-validation.ResultsAt baseline in CHARLS 2015, the prevalence of symptomatic KOA was 12.7% (792/6212) in total population, 9% (270/2996) in no-sarcopenia group, 17.5% (286/1638) in possible sarcopenia group, and 15.0% (236/1578) in sarcopenia group. Over a 3-year follow-up, 4980 respondents were included, with incident KOA cases identified as 56 (2.2%), 38 (3.0%), and 43 (3.6%) in no-sarcopenia, possible sarcopenia, and sarcopenia groups, respectively. Sarcopenia was significantly associated with increased new-onset KOA compared to no-sarcopenia peers in the fully adjusted model (OR: 1.91, 95% CI: 1.15–3.18), whereas this association was non-significant for possible sarcopenia. In females, low muscle mass alone significantly increased the incident risk of KOA (OR: 2.58, 95% CI: 1.05–6.49). The final prognostic model and nomogram, including sarcopenia status, age, gender, body mass index, self-reported health status, comorbidities, history of falls and physical activity, had a considerable discrimination (AUC = 0.744, C-index = 0.660). The calibration curve indicated significant agreement between predicted and actual observations. Decision curve analysis revealed net benefits for clinical intervention at a probability threshold of 1–17%.ConclusionsSarcopenia was associated with a higher incident risk of symptomatic KOA, wherein low muscle mass may play an important role. The inferior prognosis of sarcopenia in KOA needs more attention in clinical practice.

Prematurity and Genetic Liability for Autism Spectrum Disorder.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by diverse presentations and a strong genetic component. Environmental factors, such as prematurity, have also been linked to increased liability for ASD, though the interaction between genetic predisposition and prematurity remains unclear. This study aims to investigate the impact of genetic liability and preterm birth on ASD conditions. We analyzed phenotype and genetic data from two large ASD cohorts, the Simons Foundation Powering Autism Research for Knowledge (SPARK) and Simons Simplex Collection (SSC), encompassing 78,559 individuals for phenotype analysis, 12,519 individuals with genome sequencing data, and 8,104 individuals with exome sequencing data. Statistical significance of differences in clinical measures were evaluated between individuals with different ASD and preterm status. We assessed the rare variants burden using generalized estimating equations (GEE) models and polygenic load using ASD-associated polygenic risk score (PRS). Furthermore, we developed a machine learning model to predict ASD in preterm children using phenotype and genetic features available at birth. Individuals with both preterm birth and ASD exhibit more severe phenotypic outcomes despite similar levels of genetic liability for ASD across the term and preterm groups. Notable, preterm ASD individuals showed an elevated rate of de novo variants identified in exome sequencing (GEE model with Poisson family, p-value = 0.005) in comparison to the non-ASD preterm group. Additionally, a GEE model showed that a higher ASD PRS, preterm birth, and male sex were positively associated with a higher predicted probability for ASD, reaching a probability close to 90%. Lastly, we developed a machine learning model using phenotype and genetic features available at birth with limited predictive power (AUROC = 0.65). Preterm birth may exacerbate the multimorbidity present in ASD, which was not due to the ASD genetic factors. However, increased genetic factors may elevate the likelihood of a preterm child being diagnosed with ASD. Additionally, a polygenic load of ASD-associated variants had an additive role with preterm birth in the predicted probability for ASD, especially for boys. We propose that incorporating genetic assessment into neonatal care could benefit early ASD identification and intervention for preterm infants.

Effect of Male Partner-Targeted Breastfeeding Education and Support Interventions on Optimal Breastfeeding Practices in Central Ethiopia: A Cluster Randomized Controlled Trial.

A male partner or husband is the most influential person in the family, especially in Ethiopia. The role of a male partner or husband in promoting and supporting breastfeeding has hardly been investigated in low- and middle-income countries, including Ethiopia. Therefore, the objective of this study was to evaluate the effect of breastfeeding education and support interventions that focus on male partners, with the goal of promoting optimal breastfeeding practices in central Ethiopia. A two-arm parallel design cluster randomized controlled trial was carried out among couples in a community setting in the Hadiya Zone, Central Ethiopia. Fathers and mothers in the intervention group received breastfeeding education and support via social events, home visits and using printed materials, while those in the control group received existing routine care. A total of 408 couples from 16 clusters were randomly assigned to either the intervention group (n = 204) or the control group (n = 204). A Generalized Estimating Equation (GEE) model was used to evaluate the effectiveness of the interventions. At the end of the interventions, the proportion of optimal breastfeeding practice increased by 19.5% in the intervention group and by 2% in the control group. The differences-in-difference in optimal breastfeeding practice between the intervention and control groups was 17.5% [95% CI: 13.8%-21.2%; p = 0.001]. In the GEE model, babies born to mothers in the mother-father pair group had a 38% higher likelihood [RR = 1.38, 95% CI (1.106, 1.723)] of being optimally breastfed at the sixth month compared with babies born to mothers who received standard care. Breastfeeding education and support interventions targeting male partners in low-resource settings improve optimal breastfeeding practices, highlighting the need to give due emphasis to integrating breastfeeding promotion and counselling for male partners into existing maternal and child health services. Trial Registration: ClinicalTrials.gov identifier (NCT number): NCT05173454, First registered on 30/12/2021.

Using Multilevel Models and Generalized Estimating Equation Models to Account for Clustering in Neurology Clinical Research.

In clinical and health services research, clustered data (also known as data with a multilevel or hierarchical structure) are frequently encountered. For example, patients may be clustered or nested within hospitals. Understanding when data have a multilevel structure is important because clustering of individuals can induce a homogeneity in outcomes within clusters, so that, even after adjusting for measured covariates, outcomes for 2 individuals in the same cluster are more likely to be similar than outcomes for 2 individuals from different clusters. Using conventional statistical regression models to analyze clustered data can result in incorrect conclusions being drawn. In particular, estimated CIs may be artificially narrow, and significance levels may be artificially low. As a result, one may conclude that there is a statistically significant association when there is none. To avoid this problem, investigators should ensure that their analyses use techniques that account for clustering of data. Generalized linear models estimated using generalized estimating equation (GEE) methods and multilevel regression models (also known as hierarchical regression models, mixed-effects models, or random-effects models) are two such techniques. We provide an introduction to clustered or multilevel data and describe how GEE models or multilevel models can be used for the analysis of multilevel data.

Norwegian and Swedish value sets for the EORTC QLU-C10D utility instrument.

This study aimed to develop utility weights for the European Organization for Research and Treatment of Cancer (EORTC) QLU-C10D, a cancer-specific utility instrument, tailored to the Norwegian and Swedish populations. The utility weights are intended for use in the specific welfare contexts of Norway and Sweden to support more precise healthcare decision-making in cancer treatment and care. This cross-sectional study included 1019 Norwegian and 1048 Swedish participants representative in age and gender of the two general populations. Participants completed a discrete choice experiment involving 960 choice sets, each consisting of two EORTC QLU-C10D health states described by the instrument's domains and the duration of each state. Utility weights were calculated using generalized estimation equation models, and non-monotonic levels were merged to ensure consistent valuation. In the Norwegian participants, the largest utility decrements were seen for the domain of physical functioning (decrement of -0.263 for highest level "very much"), followed by pain (decrement -0.205 for level "very much") and role functioning (-0.139). Among the cancer-specific domains, nausea had the largest utility decrement (-0.124). In the Swedish participants, the largest utility decrements were also observed for physical functioning (-0.207 for the response "very much"), followed by pain (-0.139), role functioning (-0.133), and nausea (-0.119). Emotional functioning also exhibited a sizable utility decrement (-0.115). This study provides the first set of utility weights for the EORTC QLU-C10D specific to Norway and Sweden, reflecting the unique health preferences of these populations. The generated utility decrements can inform cost-utility analyses and optimize resource allocation in cancer care within the Norwegian and Swedish healthcare systems.

Individual factors that affect laypeople's understanding of definitions of medical jargon

ObjectivePatients have difficulty understanding medical jargon in electronic health record (EHR) notes. Lay definitions can improve patient comprehension, which is the goal of the NoteAid project. We assess whether the NoteAid definitions are understandable to laypeople and whether understandability differs with respect to layperson characteristics. MethodsDefinitions for jargon terms were written for laypersons with a 4th-to-7th-grade reading level. 300 definitions were randomly sampled from a corpus of approximately 30,000 definitions. 280 laypeople (crowdsource workers) were recruited; each layperson rated the understandability of 20 definitions. Understandability was rated on a 5-point scale. Using a generalized estimating equation model (GEE) we analyzed the relationship between understandability and age, sex, race/ethnicity, education level, native language, health literacy, and definition writer. ResultsOverall, 81.1 % (95 % CI: 76.5–85.7 %) of the laypeople reported that the definitions were understandable. Males were less likely to report understanding the definitions than females (OR: 0.73, 95 % CI: 0.63–0.84). Asians, Hispanics, and those who marked their race/ethnicity as “other” were more likely to report understanding the definitions than whites (Asians: OR: 1.43, 95 % CI: 1.17–1.73; Hispanics: OR: 1.86, 95 % CI: 1.33–2.59; Other: OR: 2.48, 95 % CI: 1.65–3.74). Laypeople whose native language was not English were less likely to report understanding the definitions (OR: 0.51, 95 % CI: 0.36–0.74). Laypeople with lower health literacy were less likely to report understanding definitions (health literacy score 3: OR: 0.51, 95 % CI: 0.43–0.62; health literacy score 4: OR: 0.40, 95 % CI: 0.29–0.55). ConclusionUnderstandability of definitions among laypeople was high. There were statistically significant race/ethnic differences in self-reported understandability, even after controlling for multiple demographics. Public interest summaryWe conducted a study to ensure that definitions written for the NoteAid EHR jargon identification tool are understandable. We recruited a diverse group of crowdsource workers and found that overall, the definitions were understandable, but understanding levels varied based on several demographic characteristics.

Behavioural economics to improve and motivate vaccination in primary care using nudges through the electronic health record: rationale and design of the BE IMMUNE randomised clinical trial

IntroductionAnnual influenza vaccination reduces disease burden but vaccination rates are suboptimal, with persistent disparities among subpopulations. The purpose of this trial is to evaluate multicomponent behavioural economic nudge interventions to clinicians and patients to increase influenza vaccination. This trial also includes an intensification nudge to reduce disparities in vaccination among older adult, primary care patients.MethodsThis is a two-part, multisite cluster randomised, pragmatic clinical trial. In the first part, a multicomponent nudge intervention will be tested over approximately 6 months (September 2023–February 2024). The second part consists of a replication trial conducted at an additional site during the following influenza season (September 2024–February 2025). Primary care clinics will be randomised to the nudge intervention or usual care. Eligible clinicians and patients at intervention clinics will receive the intervention, and patients deemed high risk for not receiving a vaccine will be further randomised to receive an intensification nudge. The primary outcome is vaccine completion during the eligible visit and the secondary outcome is vaccine completion within 3 months of the eligible visit.AnalysisThe effect of the clinic-level nudge intervention on the primary and secondary outcomes will be evaluated using generalised estimating equations (GEEs) with a clinic-level exchangeable working correlation to account for clustering of observations within the clinic. GEE models with an independent working correlation will be used to evaluate the impact of the additional intensification nudge on the primary and secondary outcomes.Ethics and disseminationThe University of Pennsylvania Institutional Review Board (IRB) approved this trial and serves as the single IRB of record (IRB #851838). Results will be disseminated via peer-reviewed publication and conference presentations.Trial registration numberNCT06057727.

Functional Outcomes Following Injury in Pediatric Patients without Traumatic Brain Injury

BackgroundPediatric trauma patients often have disability after successful resuscitation for hemorrhage. Clinical characteristics related to dependency on living following injury were elucidated among pediatric patients without traumatic brain injury. MethodsThis retrospective cohort study used the 2019–2021 Japan Trauma Data Bank and included pediatric patients aged ≤16 years without head injury (Abbreviated Injury Scale [AIS] ≥ 2). The unfavorable function was defined as dependency in daily life at discharge (Glasgow Outcome Scale ≤ 3), and predictors for unfavorable functional outcomes were examined using a generalized estimating equations (GEE) model, including age, sex, physical disability before injury, injury mechanism, transportation time, prehospital procedures, vital signs on arrival, surgery in each body region, transfusion, AIS in each region, and institutions. Furthermore, these factors were analyzed separately in toddler/preschool (≤5 years), school age (6–11 years), and adolescence (12–16 years). ResultsAmong 1,412 patients eligible for the study, 137 had an unfavorable physical function at discharge. The GEE model revealed that female sex, physical disability before injury, prehospital transfusion, lower Glasgow Coma Score (GCS) score on arrival, neck surgery, and higher AIS in the neck and extremity/pelvis were independently associated with unfavorable function at discharge. In age-specific analyses, physical disability before injury strongly predicted worse functional outcomes in toddlers/preschoolers, whereas higher AIS in the chest and lower systolic blood pressure on arrival were additional predictors of dependent living among adolescents. ConclusionsSevere neck/extremity/pelvis injury and lower GCS on arrival are associated with unfavorable functional outcomes in patients with pediatric trauma.

Serum anti-NMDA receptor antibodies are linked to memory impairment 12 months after stroke.

Patients suffering from strokes are at increased risk of developing post-stroke dementia. Serum anti-NMDA receptor autoantibodies (NMDAR1-abs) have been associated with unfavorable post-stroke outcomes. However, their effect on specific cognitive domains remains unclear. We used data from the prospective multicenter DZNE-mechanisms after stroke (DEMDAS) cohort, and measured NMDAR1-abs in serum at baseline. Cognitive function was assessed with a comprehensive neuropsychological test battery at 6- and 12-months follow-up. We employed crude and stepwise confounder adjusted linear and logistic regression models as well as generalized estimating equation models (GEE) to determine the relevance of NMDAR1-abs seropositivity on cognitive function after stroke. 10.2% (58/569) DEMDAS patients were NMDAR1-abs seropositive (IgM:n = 44/IgA:n = 21/IgG:n = 2). Seropositivity was not associated with global cognitive impairment after stroke. However, NMDAR1-abs seropositive patients performed lower in the memory domain (βadjusted = -0.11; 95%CI = -0.57 to -0.03) and were at increased risk for memory impairment (ORadjusted = 3.8; 95%CI = 1.33-10.82) compared to seronegative patients, 12 months after stroke. Further, NMDAR1-abs were linked to memory impairment over time in GEE from 6- to 12-months follow-up (ORadjusted = 2.41; 95%CI = 1.05-5.49). Our data suggests that NMDAR1-abs contribute to memory dysfunction 1 year after stroke while not affecting other cognitive subdomains. Hence, antineuronal autoimmunity may be involved in distinct mechanisms of post-stroke memory impairment. Clinical trial name and registration number: The Determinants of Dementia After Stroke (DEMDAS; study identifier on clinical trials.gov: NCT01334749).

The evolution of carotid atherosclerotic plaque composition: a prospective study

Abstract Background The rupture of carotid atherosclerotic plaque is one of the major causes of ischemic stroke, and largely determined by the composition of the plaque. To date, most studies on carotid plaque composition focused on individual plaque components, whilst the interplay between different components within the same plaque may be a critical driving force for plaque evolution and rupture. Objectives To determine the interplay between carotid plaque components over time and describe the dynamics of plaque composition with a specific focus on age- and sex-specific changes in a population-based setting. Methods From a population-based cohort in Netherlands, 802 participants (mean age ± SD 68 ± 8 years, 43% women) with subclinical carotid atherosclerosis underwent repeat magnetic resonance imaging (MRI) of carotid plaque (first scan between 2007 and 2012, second scan between 2014 to 2017). We evaluated carotid plaque composition, including calcification, lipid-rich necrotic core (LRNC) and intraplaque hemorrhage (IPH). To assess the relationship between the incidence of one specific component and baseline plaque composition, plaques with this specific component present at the baseline were excluded. Generalized estimation equation models were used with adjustments for baseline plaque size and cardiovascular risk factors. We demonstrated the evolution of plaque composition along age and by sex. Results Baseline presence of calcification in carotid plaque is independently associated with a higher incidence of IPH (adjusted odds ratio: 2.25; 95% confidence interval: 1.40 to 3.63). No associations were found between baseline plaque composition and the new onset of calcification or LRNC. The proportion of complex plaques with multiple components significantly increased at follow-up (Figure 1a). The plaque composition dramatically evolved over time and the proportion of plaques with two or more components started with 10% around 55 years and increased to over 50% after 70 years of age (Figure 1b). Although the age distribution was comparable between men and women, men were more likely to have and develop plaques with multiple vulnerable plaque components such as IPH (Figure 2). Conclusions A higher incidence of IPH was found in plaques with baseline calcification. The pre-existing plaque composition strongly determines how the plaque will evolve over time. Given the interplay between individual components, the overall plaque composition exhibits significant age- and sex- specific characteristics.

Discrete Choice Experiment on Financial Incentives for Engaging Young Adults in Vaping Cessation Programs

Introduction Contingency management involves rewarding individuals based on objective evidence of behavioral changes. This study explores preferences for financial incentives in vaping cessation programs. Methods A discrete choice experiment (DCE) was conducted among young adult current e-cigarette users aged 19-29. DCE attributes (and levels) included reward amount ($100, $300), reward schedule (consistent value, escalating value), reward procedure (gain-framed, loss-framed), vaping education modules (yes, no), text messaging support (yes, no), and cost ($30, $100). Participants were randomized into one of the four blocks, each containing four choice sets. Each choice set presented two hypothetical vaping cessation programs. Choice data (n × choice sets × alternative programs = 154 × 4 × 2 = 1,232 observations) were analyzed using generalized estimation equation models. Results Participants showed a preference for vaping cessation programs offering a higher reward amount (odds ratio [OR] = 11.5; 95% CI,7.0-19.2), gain-framed rewards for sustained abstinence (OR = 1.4; 95% CI,1.0-1.8), text messaging support (OR = 1.9; 95% CI,1.4-2.5), and a lower program cost (OR = 2.6; 95% CI,1.8-3.8). Interaction tests showed significant effects of the rewards procedure among daily e-cigarette users (adjusted OR [AOR] = 2.1; 95% CI,1.3-3.5), but not some-day users; and among those with no quit attempts in the past six months (AOR = 2.7; 95% CI,1.5-4.7), but not those with previous quit attempts. Female participants preferred text messaging support (AOR = 3.6; 95% CI,2.3-5.4), whereas male participants did not show this preference. Conclusion A multifaceted vaping cessation intervention augmented with financial incentives might improve participation and engagement among young adults. Future studies should investigate how these attributes can enhance program reach and vaping abstinence outcomes in real-world settings.

Impact of surgical technique on postoperative ventricular arrhythmias in arrhythmic mitral valve prolapse

Abstract Background Mitral valve prolapse (MVP) is associated with progressive mitral regurgitation (MR) requiring surgical correction. A subset of patients with MVP may also constitute an "arrhythmic" phenotype suffering from malignant ventricular arrhythmias (VAs). Purpose To assess the impact of surgical technique for correction of degenerative MR including repair using artificial chords, repair with resection and mitral valve replacement (MVR) on longitudinal risk of VAs in patients with arrhythmic MVP. Methods Patients who had undergone mitral valve surgery for MVP with moderate to severe degenerative MR between 2010-2022 at a university hospital in Sweden were retrospectively included. Patients with ischemic heart disease, primary cardiomyopathy, channelopathy, or endocarditis were excluded. To examine changes in VAs (defined as sustained- or non-sustained ventricular tachycardias) as assessed by continuous Holter monitoring before and after surgery, generalized estimating equation (GEE) models were performed to account for the correlations between repeated monitoring in each patient and for differences in the time of measurement. Results Of 599 patients undergoing mitral valve surgery, 423 (71%) underwent repair using artificial chords, 62 (10%) repair with resection and 114 (19%) MVR. For those presenting with symptoms such as palpitations, presyncope, and/or syncope, Holter monitoring was conducted before (n=171; 29%) and/or after mitral valve surgery (n=184; 31%) (Figure 1). The distribution of VAs before and after surgery, stratified by surgical technique, is illustrated in Figure 2. In GEE models, repair using artificial chords was associated with increased postoperative VAs compared with the other surgical techniques (OR 2.14; 95% CI 1.19-3.83; p=0.01), repair with resection with reduced postoperative VAs compared with the other surgical techniques (OR 0.15; 95% CI 0.04-0.61; p=0.01), and no change in postoperative VAs was observed for MVR compared with the other surgical techniques (OR 0.724; 95% CI 0.39-1.35; p=0.31). The results remained similar after adjustment for age, sex, myxomatous disease/fibroelastic deficiency or bileaflet prolapse. Conclusion In patients with symptoms of palpitations, presyncope and/or syncope before or after mitral valve surgery, repair using artificial chords was associated with an increased risk of postoperative VAs. Prospective studies including continuous monitoring before and after surgery are needed to confirm these findings.

Chronic condition change and its longitudinal association with health care utilization among rural older adults: intergenerational financial support as a possible moderator?

BackgroundThe prevalence of chronic conditions increases rapidly among older population. However, it is unclear how different chronic conditions progression contributes to the health care utilization, and whether intergenerational support modified this relationship. This study aimed to explore the longitudinal link between chronic condition progression and health care utilization, and examine whether intergenerational financial support is a moderator in this relationship among Chinese rural older people.MethodsData was derived from the Shandong Rural Elderly Health Cohort (SREHC), which was conducted from 2019 to 2020. A total of 2,785 participants were included in this study. Kruskal-Wallis rank tests and generalized estimating equation (GEE) models were employed to analyze the association between chronic condition progression and health care utilization. Moderating effect analysis was performed using GEE model and margins plot.ResultsOlder people with progressive chronic conditions used more outpatient (no chronic condition to emerging multimorbidity: OR = 1.83; p = 0.028; one chronic condition to emerging multimorbidity: OR = 2.17; p < 0.001; remained multimorbidity while chronic conditions increased: OR = 3.26; p < 0.001) and inpatient services (no chronic condition to emerging multimorbidity: OR = 2.76; p < 0.001; one chronic condition to emerging multimorbidity: OR = 3.40; p < 0.001; remained multimorbidity while chronic conditions increased: OR = 5.32; p < 0.001) than those remained no chronic conditions. Intergenerational financial support may alleviate outpatient utilization of older people with multimorbidity (remained multimorbidity × intergenerational financial support: OR = 0.40; p = 0.013; remained multimorbidity while chronic conditions increased × intergenerational financial support: OR = 0.35; p = 0.019).ConclusionThis study showed that the changes of chronic condition were associated with health care utilization, and intergenerational financial support moderated this relationship. It is vital to continuously monitor and timely intervene the chronic condition progression among rural older people.

Longitudinal lipidomic profiles of left ventricular mass and left ventricular hypertrophy in American Indians.

BACKGROUNDLeft ventricular hypertrophy (LVH) and dyslipidemia are strong, independent predictors for cardiovascular disease, but their relationship is less well studied. A longitudinal lipidomic profiling of left ventricular mass (LVM) and LVH is still lacking.METHODSUsing liquid chromatography-mass spectrometry (LC-MS), we repeatedly measured 1,542 lipids from 1,755 unique American Indians attending 2 exams (mean, 5 years apart). Cross-sectional associations of individual lipid species with LVM index (LVMI) were examined by generalized estimating equation (GEE), followed by replication in an independent biracial cohort (65% White, 35% Black). Baseline plasma lipids associated with LVH risk beyond traditional risk factors were identified by logistic GEE model in American Indians. Longitudinal associations between changes in lipids and changes in LVMI were examined by GEE, adjusting for baseline lipids, baseline LVMI, and covariates.RESULTSMultiple lipid species were significantly associated with LVMI or the risk of LVH in American Indians. Some lipids were confirmed in Black and White individuals. Moreover, some LVH-related lipids were inversely associated with risk of coronary heart disease (CHD). Longitudinal changes in several lipid species were significantly associated with changes in LVMI.CONCLUSIONAltered fasting plasma lipidome and its longitudinal change over time were significantly associated with LVMI and risk for LVH in American Indians. Our results offer insight into the role of individual lipid species in LV remodeling and risk of LVH, independent of known risk factors.FUNDINGThis study was supported by the NIH grant (R01DK107532). The Strong Heart Study has been funded in whole or in part with federal funds from the National Heart, Lung, and Blood Institute, NIH, Department of Health and Human Services, under contract nos. 75N92019D00027, 75N92019D00028, 75N92019D00029, and 75N92019D00030.

Enhancement of a social risk score in the electronic health record to identify social needs among medically underserved patients: using structured data and free-text provider notes.

To improve the performance of a social risk score (a predictive risk model) using electronic health record (EHR) structured and unstructured data. We used EPIC-based EHR data from July 2016 to June 2021 and linked it to community-level data from the US Census American Community Survey. We identified predictors of interest within the EHR structured data and applied natural language processing (NLP) techniques to identify patients' social needs in the EHR unstructured data. We performed logistic regression models with and without information from the unstructured data (Models I and II) and compared their performance with generalized estimating equation (GEE) models with and without the unstructured data (Models III and IV). The logistic model (Model I) performed well (Area Under the Curve [AUC] 0.703, 95% confidence interval [CI] 0.701:0.705) and the addition of EHR unstructured data (Model II) resulted in a slight change in the AUC (0.701, 95% CI 0.699:0.703). In the logistic models, the addition of EHR unstructured data resulted in an increase in the area under the precision-recall curve (PRC 0.255, 95% CI 0.254:0.256 in Model I versus 0.378, 95% CI 0.375:0.38 in Model II). The GEE models performed similarly to the logistic models and the addition of EHR unstructured data resulted in a slight change in the AUC (0.702, 95% CI 0.699:0.705 in Model III versus 0.699, 95% CI 0.698:0.702 in Model IV). Our work presents the enhancement of a novel social risk score that integrates community-level data with patient-level data to systematically identify patients at increased risk of having future social needs for in-depth assessment of their social needs and potential referral to community-based organizations to address these needs. The addition of information on social needs extracted from unstructured EHR resulted in an improved prediction of positive cases presented by the improvement in the PRC.

Regression Approaches to Assess Effect of Treatments That Arrest Progression of Symptoms.

Motivated by a small sample example in neonatal onset multisystem inflammatory disease (NOMID), we propose a method that can be used when the interest is testing for an association between a changes in disease progression with start of treatment compared to historical disease progression prior to treatment. Our method estimates the longitudinal trajectory of the outcome variable and adds an interaction term between an intervention indicator variable and the time since initiation of the intervention. This method is appropriate for a situation in which the intervention slows or arrests the effect of the disease on the outcome, as is the case in our motivating example. By simulation in small samples and restricted sets of treatment initiation times, we show that the generalized estimating equations (GEE) formulation with small sample adjustments can bound the Type I error rate better than GEE and linear mixed models without small sample adjustments. Permutation tests (permuting the time of treatment initiation) is another valid approach that can also be useful. We illustrate the methodology through an application to a prospective cohort of NOMID patients enrolled at the NIH clinical center.

Machine learning-based phenogroups and prediction model in patients with functional gastrointestinal disorders to reveal distinct disease subsets associated with gas production.

Symptom-based subtyping for functional gastrointestinal disorders (FGIDs) has limited value in identifying underlying mechanisms and guiding therapeutic strategies. Small intestinal dysbiosis is implicated in the development of FGIDs. We tested if machine learning (ML) algorithms utilizing both gastrointestinal (GI) symptom characteristics and lactulose breath tests could provide distinct clusters. This was a prospective cohort study. We performed lactulose hydrogen methane breath tests and hydrogen sulfide breath tests in 508 patients with GI symptoms. An unsupervised ML algorithm was used to categorize subjects by integrating GI symptoms and breath gas characteristics. Generalized Estimating Equation (GEE) models were used to examine the longitudinal associations between cluster patterns and breath gas time profiles. An ML-based prediction model for identifying excessive gas production in FGIDs patients was developed and internal validation was performed. FGIDs were confirmed in 300 patients. K-means clustering identified 4 distinct clusters. Cluster 2, 3, and 4 showed enrichments for abdominal distention and diarrhea with a high proportion of excessive gas production, whereas Cluster 1 was characterized by moderate lower abdominal discomforts with the most psychological complaints and the lowest proportion of excessive gas production. GEE models showed that breath gas concentrations varied among different clusters over time. We further sought to develop an ML-based prediction model to determine excessive gas production. The model exhibited good predictive capabilities. ML-based phenogroups and prediction model approaches could provide distinct FGIDs subsets and efficiently determine FGIDs subsets with greater gas production, thereby facilitating clinical decision-making and guiding treatment.

Gender disparity regarding the impact of retirement on marital satisfaction: Evidence from a longitudinal study of older Korean adults.

To investigate the impact of retirement on marital satisfaction by gender on Koreans aged >45 years in a large, nationally representative cohort. Retirement status and marital satisfaction data of 5867 individuals were analysed. Marital dissatisfaction was defined as a satisfaction score reduction of more than 10 points versus the previous wave. Lagged generalised estimating equation (GEE) models adjusted for demographics (age, sex, education level and household income), health-related habits (smoking and drinking status) and comorbidities (obesity, hypertension, type 2 diabetes, and depression and frailty) were used to confirm the relationship between retirement and marital dissatisfaction. The final GEE model adjusted for covariates revealed sex-specific differences in marital satisfaction after retirement. Whereas male retirees reported higher satisfaction than non-retired men, female retiree responses revealed lower satisfaction than non-retired women. Furthermore, these trends were consistent regardless of the time elapsed after retirement. Marital satisfaction should be screened in women during the transition to retirement.

Clinical characteristics and risk factors of osteoporosis among older Asian men with type-2 diabetes mellitus, hypertension, or hyperlipidaemia

SummaryThis study investigated osteoporosis risk factors among older Asian men with type-2 diabetes mellitus, hypertension, or hyperlipidaemia in primary care. Advanced age, dementia, depression, and polypharmacy were associated with higher risks for osteoporosis. Screening strategies targeting these factors are crucial for improving bone health as part of comprehensive preventive care.PurposeAsian patients with type-2 diabetes mellitus (T2DM), hypertension, or hyperlipidaemia (DHL) are predominantly managed in primary care. They are also at risk of osteoporosis, but men are often under-screened and under-treated for this preventable bone disorder. This study aimed to identify the clinical characteristics and risk factors of osteoporosis among older men with DHL in primary care for early intervention.MethodsThis retrospective study included men aged 65 years and older managed in public primary care clinics for their DHL between 1st July 2017 and 30th June 2018. Demographic, clinical, laboratory, and imaging data were extracted from their electronic medical records based on their International Classification of Diseases-10 (ICD-10) diagnosis codes. Descriptive statistical analyses, with statistical significance set at p < 0.05, were conducted, followed by generalized estimating equation (GEE) modelling.ResultsMedical records of 17,644 men (83.1% Chinese, 16.9% minority ethnic groups, median age 71 years) were analysed. 2.3% of them had diagnosis of osteoporosis, 0.15% had fragility fracture, and 26.0% of those diagnosed with osteoporosis were treated with bisphosphonates. Their mean HbA1c was 6.9%; mean systolic and diastolic blood pressure were 133 and 69 mmHg. The GEE model showed that age (OR = 1.07, 95%CI = 1.05–1.09, p < 0.001), dementia (OR = 2.24, 95%CI = 1.33–3.77, p = 0.002), depression (OR = 2.38, 95%CI = 1.03–5.50, p = 0.043), and polypharmacy (OR = 6.85, 95%CI = 3.07–15.26, p < 0.001) were significantly associated with higher risks for osteoporosis.ConclusionAge, dementia, depression, and polypharmacy are associated with osteoporosis risks in men with DHL. Strategies to incorporate osteoporosis screening among older men with these risk factors are needed to improve their bone health.

Prospective associations between an empirically derived dietary pattern and life satisfaction in Malaysian adolescents

BackgroundLittle is known on the prospective associations between an empirically derived dietary pattern (DP) and life satisfaction among adolescents. This PUTRA-Adol follow-up study aimed to assess the prospective associations between the empirically derived DP and life satisfaction during adolescence.MethodsA total of 585 and 262 adolescents participated in the baseline PUTRA-Adol study in 2016 and PUTRA-Adol follow-up study in 2019–2020, respectively. These adolescents were recruited from three southern states in peninsular Malaysia, namely Negeri Sembilan, Melaka and Johor. Dietary assessments were estimated using a validated food frequency questionnaire (FFQ) while a Multidimensional Students’ Life Satisfaction Scale (MSLSS) was used to measure life satisfaction at baseline in 2016 as well as at follow-up in 2019–2020. A DP characterised by foods high in free sugar and energy dense was identified using reduced rank regression (RRR), cross-sectionally at baseline and was reported elsewhere. Similar RRR analysis was used to find a DP that best explained the variation in response variables linked to poorer life satisfaction, including dietary energy density (DED), fiber density, and percentage of energy from total fat and sugar at the PUTRA-Adol follow-up study. Prospective relationship between adherence to the identified DPs and overall life satisfaction scores as well as its domains between 2016 and 2019–2020 were evaluated using generalized estimating equation models (GEE).ResultsA DP characterized by high intakes of percentage energy from sugar, fibre and low in DED and percentage energy from total fat was identified at the 2019–2020 follow-up. The identified DP explained 11% of total variations in the response variables and was characterized by high intakes of sweets, sugar sweetened beverages (SSB) and fruits. Female adolescents [67.6(8.9)] had a mean (SD) of life satisfaction that was higher than male adolescents [67.5(10.8)] p < 0.05 in 2019–2020. Prospective analysis found a significant positive association between the identified DP and school domain, in male adolescents (β = 0.117; 95% CI 0.001, 0.234) and an inverse relationship between the DP z-score and self-domain in all adolescents (β = − 0.060; 95% CI − 0.115, − 0.005) from 2016 to 2019–2020.ConclusionsAn increasing score for the predominant ‘High sugar and High fibre’ DP was prospectively associated with increasing life satisfaction score for the school domain in male adolescents and decreasing score for self-domain in both male and females during adolescence. A lifestyle intervention targeting reduced dietary intakes, particularly sugar, may improve life satisfaction in adolescents and promote healthier future without compromising dietary intakes for chronic disease prevention later in life.