Modeling Risk Factors for Intraindividual Variability: A Mixed-Effects Beta-Binomial Model Applied to Cognitive Function in Older People in the English Longitudinal Study of Ageing.

Cognitive functioning in older age profoundly impacts quality of life and health. While most research on cognition in older age has focused on mean levels, intraindividual variability (IIV) around this may have risk factors and outcomes independent of the mean value. Investigating risk factors associated with IIV has typically involved deriving a summary statistic for each person from residual error around a fitted mean. However, this ignores uncertainty in the estimates, prohibits exploring associations with time-varying factors, and is biased by floor/ceiling effects. To address this, we propose a mixed-effects location scale beta-binomial model for estimating average probability and IIV in a word recall test in the English Longitudinal Study of Ageing. After adjusting for mean performance, an analysis of 9,873 individuals across 7 (mean = 3.4) waves (2002-2015) found IIV to be greater at older ages, with lower education, in females, with more difficulties in activities of daily living, in later birth cohorts, and when interviewers recorded issues potentially affecting test performance. Our study introduces a novel method for identifying groups with greater IIV in bounded discrete outcomes. Our findings have implications for daily functioning and care, and further work is needed to identify the impact for future health outcomes.

Risk Factors Associated with PTLD Related Mortality in Adult Multivisceral Transplant Recipients - A Single Centre Cohort Study.

Background: PTLD is a heterogeneous group of lymphoproliferative diseases which can add significant mortality following multivisceral transplantation (MVTx). Our study aimed to identify potential risk factors of mortality in adult MVTx recipients who developed PTLD. Methods: All adult recipients of intestinal-containing grafts transplanted in our institution between 2013 and 2022, and who developed PTLD, were included in the study. Results: PTLD-associated mortality was 28.6% (6/21). Increased relative risk of mortality was associated with Stage 3 ECOG performance score (p=0.005; HR 34.77; 95%CI 2.94-410.91), if the recipients had a splenectomy (p=0.036; HR 14.36; 95%CI 1.19-172.89), or required retransplantation (p=0.039; HR 11.23; 95% CI 1.13-112.12). There was a significant trend for increased risk of PTLD mortality with higher peak EBV load (p=0.008), longer time from MVTx to PTLD diagnosis (p=0.008), and higher donor age (p 0.001). Peak LDH before treatment commencement was significantly higher in the mortality group vs the survival group (520.3 +- 422.8 IU/L vs 321.8 +- 154.4 IU/L; HR 1.00, 95%CI 1.00 to 1.01, p=0.019). Peak viral load prior to treatment initiation (Cycle Threshold (CT) cutoff = 32) correlated with the relative risk of death in MVTx patients who developed PTLD [29.4 (3.5) CTs in survivors compared to 23.0 (4.0) CTs in the mortality group]. Conclusions: This is the first study to identify risk factors for PTLD-associated mortality in an adult MVTx recipient cohort. Validation in larger multicentre studies and subsequent risk stratification according to these risk factors may contribute to better survival in this group of patients.

Sociodemographic characteristics and longitudinal progression of multimorbidity: A multistate modelling analysis of a large primary care records dataset in England.

BACKGROUND: Multimorbidity, characterised by the coexistence of multiple chronic conditions in an individual, is a rising public health concern. While much of the existing research has focused on cross-sectional patterns of multimorbidity, there remains a need to better understand the longitudinal accumulation of diseases. This includes examining the associations between important sociodemographic characteristics and the rate of progression of chronic conditions. METHODS AND FINDINGS: We utilised electronic primary care records from 13.48 million participants in England, drawn from the Clinical Practice Research Datalink (CPRD Aurum), spanning from 2005 to 2020 with a median follow-up of 4.71 years (IQR: 1.78, 11.28). The study focused on 5 important chronic conditions: cardiovascular disease (CVD), type 2 diabetes (T2D), chronic kidney disease (CKD), heart failure (HF), and mental health (MH) conditions. Key sociodemographic characteristics considered include ethnicity, social and material deprivation, gender, and age. We employed a flexible spline-based parametric multistate model to investigate the associations between these sociodemographic characteristics and the rate of different disease transitions throughout multimorbidity development. Our findings reveal distinct association patterns across different disease transition types. Deprivation, gender, and age generally demonstrated stronger associations with disease diagnosis compared to ethnic group differences. Notably, the impact of these factors tended to attenuate with an increase in the number of preexisting conditions, especially for deprivation, gender, and age. For example, the hazard ratio (HR) (95% CI; p-value) for the association of deprivation with T2D diagnosis (comparing the most deprived quintile to the least deprived) is 1.76 ([1.74, 1.78]; p < 0.001) for those with no preexisting conditions and decreases to 0.95 ([0.75, 1.21]; p = 0.69) with 4 preexisting conditions. Furthermore, the impact of deprivation, gender, and age was typically more pronounced when transitioning from an MH condition. For instance, the HR (95% CI; p-value) for the association of deprivation with T2D diagnosis when transitioning from MH is 2.03 ([1.95, 2.12], p < 0.001), compared to transitions from CVD 1.50 ([1.43, 1.58], p < 0.001), CKD 1.37 ([1.30, 1.44], p < 0.001), and HF 1.55 ([1.34, 1.79], p < 0.001). A primary limitation of our study is that potential diagnostic inaccuracies in primary care records, such as underdiagnosis, overdiagnosis, or ascertainment bias of chronic conditions, could influence our results. CONCLUSIONS: Our results indicate that early phases of multimorbidity development could warrant increased attention. The potential importance of earlier detection and intervention of chronic conditions is underscored, particularly for MH conditions and higher-risk populations. These insights may have important implications for the management of multimorbidity.

Post-mastectomy radiotherapy for women with early breast cancer and one to three positive lymph nodes.

BACKGROUND: Continual improvement in adjuvant therapies has resulted in a better prognosis for women diagnosed with breast cancer. A surrogate marker used to detect the spread of disease after treatment of breast cancer is local and regional recurrence. The risk of local and regional recurrence after mastectomy increases with the number of axillary lymph nodes affected by cancer. There is a consensus to use radiotherapy as an adjuvant treatment after mastectomy (postmastectomy radiotherapy (PMRT)) in women diagnosed with breast cancer and found to have disease in four or more positive axillary lymph nodes. Despite data showing almost double the risk of local and regional recurrence in women treated with mastectomy and found to have one to three positive lymph nodes, there is a lack of international consensus on the use of PMRT in this group. OBJECTIVES: To assess the effects of PMRT in women diagnosed with early breast cancer and found to have one to three positive axillary lymph nodes. SEARCH METHODS: We searched the Cochrane Breast Cancer Group's Specialised Register, CENTRAL, MEDLINE, Embase, the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP) and ClinicalTrials.gov up to 24 September 2021. SELECTION CRITERIA: We included randomised controlled trials (RCTs). The inclusion criteria included women diagnosed with breast cancer treated with simple or modified radical mastectomy and axillary surgery (sentinel lymph node biopsy (SLNB) alone or those undergoing axillary lymph node clearance with or without prior SLNB). We included only women receiving PMRT using X-rays (electron and photon radiation), and we defined the radiotherapy dose to reflect what is currently being recommended (i.e. 40 Gray (Gy) to 50 Gy in 15 to 25/28 fractions in 3 to 5 weeks. The included studies did not administer any boost to the tumour bed. In this review, we excluded studies using neoadjuvant chemotherapy as a supportive treatment before surgery. DATA COLLECTION AND ANALYSIS: We used Covidence to screen records. We collected data on tumour characteristics, adjuvant treatments and the outcomes of local and regional recurrence, overall survival, disease-free survival, time to progression, short- and long-term adverse events and quality of life. We reported on time-to-event outcome measures using the hazard ratio (HR) and subdistribution HR. We used Cochrane's risk of bias tool (RoB 1), and we presented overall certainty of the evidence using the GRADE approach. MAIN RESULTS: The RCTs included in this review were subgroup analyses of original RCTs conducted in the 1980s to assess the effectiveness of PMRT. Hence, the type and duration of adjuvant systemic treatments used in the studies included in this review were suboptimal compared to the current standard of care. The review involved three RCTs with a total of 829 women diagnosed with breast cancer and low-volume axillary disease. Amongst the included studies, only a single study pertained to the modern-day radiotherapy practice. The results from this one study showed a reduction of local and regional recurrence (HR 0.20, 95% confidence interval (CI) 0.13 to 0.33, 1 study, 522 women; low-certainty evidence) and improvement in overall survival with PMRT (HR 0.76, 95% CI 0.60 to 0.97, 1 study, 522 women; moderate-certainty evidence). One of the other studies using radiotherapy techniques that do not reflect modern-day practice reported on disease-free survival in women with low-volume axillary disease (subdistribution HR 0.63, 95% CI 0.41 to 0.96, 1 study, 173 women). None of the included studies reported on PMRT side effects or quality-of-life outcome measures. AUTHORS' CONCLUSIONS: Based on one study, the use of PMRT in women diagnosed with breast cancer and low-volume axillary disease indicated a reduction in locoregional recurrence and an improvement in survival. There is a need for more research to be conducted using modern-day radiotherapy equipment and methods to support and supplement the review findings.

Skin-sparing mastectomy for the treatment of breast cancer.

BACKGROUND: Skin-sparing mastectomy (SSM) is a surgical technique that aims to maximize skin preservation, facilitate breast reconstruction, and improve cosmetic outcomes. Despite its use in clinical practice, the benefits and harms related to SSM are not well established. OBJECTIVES: To assess the effectiveness and safety of skin-sparing mastectomy for the treatment of breast cancer. SEARCH METHODS: We searched Cochrane Breast Cancer's Specialized Register, CENTRAL, MEDLINE, Embase, LILACS, the World Health Organization's International Clinical Trials Registry Platform (WHO ICTRP), and ClinicalTrials.gov on 9 August 2019. SELECTION CRITERIA: Randomized controlled trials (RCTs), quasi-randomized or non-randomized studies (cohort and case-control) comparing SSM to conventional mastectomy for treating ductal carcinoma in situ (DCIS) or invasive breast cancer. DATA COLLECTION AND ANALYSIS: We used standard methodological procedures expected by Cochrane. The primary outcome was overall survival. Secondary outcomes were local recurrence free-survival, adverse events (including overall complications, breast reconstruction loss, skin necrosis, infection and hemorrhage), cosmetic results, and quality of life. We performed a descriptive analysis and meta-analysis of the data. MAIN RESULTS: We found no RCTs or quasi-RCTs. We included two prospective cohort studies and twelve retrospective cohort studies. These studies included 12,211 participants involving 12,283 surgeries (3183 SSM and 9100 conventional mastectomies). It was not possible to perform a meta-analysis for overall survival and local recurrence free-survival due to clinical heterogeneity across studies and a lack of data to calculate hazard ratios (HR).  Based on one study, the evidence suggests that SSM may not reduce overall survival for participants with DCIS tumors (HR 0.41, 95% CI 0.17 to 1.02; P = 0.06; 399 participants; very low-certainty evidence) or for participants with invasive carcinoma (HR 0.81, 95% CI 0.48 to 1.38; P = 0.44; 907 participants; very low-certainty evidence). For local recurrence-free survival, meta-analysis was not possible, due to high risk of bias in nine of the ten studies that measured this outcome. Informal visual examination of effect sizes from nine studies suggested the size of the HR may be similar between groups. Based on one study that adjusted for confounders, SSM may not reduce local recurrence-free survival (HR 0.82, 95% CI 0.47 to 1.42; P = 0.48; 5690 participants; very low-certainty evidence).  The effect of SSM on overall complications is unclear (RR 1.55, 95% CI 0.97 to 2.46; P = 0.07, I2 = 88%; 4 studies, 677 participants; very low-certainty evidence). Skin-sparing mastectomy may not reduce the risk of breast reconstruction loss (RR 1.79, 95% CI 0.31 to 10.35; P = 0.52; 3 studies, 475 participants; very low-certainty evidence), skin necrosis (RR 1.15, 95% CI 0.62 to 2.12; P = 0.22, I2 = 33%; 4 studies, 677 participants; very low-certainty evidence), local infection (RR 2.04, 95% CI 0.03 to 142.71; P = 0.74, I2 = 88%; 2 studies, 371 participants; very low-certainty evidence), nor hemorrhage (RR 1.23, 95% CI 0.47 to 3.27; P = 0.67, I2 = 0%; 4 studies, 677 participants; very low-certainty evidence). We downgraded the certainty of the evidence due to the risk of bias, imprecision, and inconsistency among the studies. There were no data available on the following outcomes: systemic surgical complications, local complications, explantation of implant/expander, hematoma, seroma, rehospitalization, skin necrosis with revisional surgery, and capsular contracture of the implant. It was not possible to perform a meta-analysis for cosmetic and quality of life outcomes due to a lack of data. One study performed an evaluation of aesthetic outcome after SSM: 77.7% of participants with immediate breast reconstruction had an overall aesthetic result of excellent or good versus 87% of participants with delayed breast reconstruction. AUTHORS' CONCLUSIONS: Based on very low-certainty evidence from observational studies, it was not possible to draw definitive conclusions on the effectiveness and safety of SSM for breast cancer treatment. The decision for this technique of breast surgery for treatment of DCIS or invasive breast cancer must be individualized and shared between the physician and the patient while considering the potential risks and benefits of available surgical options.

Use of Evidence-Based Interventions to Promote Healthy Weight, Nutrition, and Physical Activity in Community Health Improvement Plans from Large Local Health Departments.

We sought to identify evidence-based healthy weight, nutrition, and physical activity strategies related to obesity prevention in large local health department (LHD) Community Health Improvement Plans (CHIPs). We analyzed the content of the most recent, publicly available plans from 72 accredited LHDs serving a population of at least 500 000 people. We matched CHIP strategies to the County Health Rankings and Roadmaps' What Works for Health (WWFH) database of interventions. We identified 739 strategies across 55 plans, 62.5% of which matched a "WWFH intervention" rated for effectiveness on diet and exercise outcomes. Among the 20 most commonly identified WWFH interventions in CHIPs, 10 had the highest evidence for effectiveness while 4 were rated as likely to decrease health disparities according to WWFH. Future prioritization of strategies by health agencies could focus on strategies with the strongest evidence for promoting healthy weight, nutrition, and physical activity outcomes and reducing health disparities.

Projected U.S. State-Level Prevalence of Adult Obesity and Severe Obesity.

BACKGROUND: Although the national obesity epidemic has been well documented, less is known about obesity at the U.S. state level. Current estimates are based on body measures reported by persons themselves that underestimate the prevalence of obesity, especially severe obesity. METHODS: We developed methods to correct for self-reporting bias and to estimate state-specific and demographic subgroup-specific trends and projections of the prevalence of categories of body-mass index (BMI). BMI data reported by 6,264,226 adults (18 years of age or older) who participated in the Behavioral Risk Factor Surveillance System Survey (1993-1994 and 1999-2016) were obtained and corrected for quantile-specific self-reporting bias with the use of measured data from 57,131 adults who participated in the National Health and Nutrition Examination Survey. We fitted multinomial regressions for each state and subgroup to estimate the prevalence of four BMI categories from 1990 through 2030: underweight or normal weight (BMI [the weight in kilograms divided by the square of the height in meters], <25), overweight (25 to <30), moderate obesity (30 to <35), and severe obesity (≥35). We evaluated the accuracy of our approach using data from 1990 through 2010 to predict 2016 outcomes. RESULTS: The findings from our approach suggest with high predictive accuracy that by 2030 nearly 1 in 2 adults will have obesity (48.9%; 95% confidence interval [CI], 47.7 to 50.1), and the prevalence will be higher than 50% in 29 states and not below 35% in any state. Nearly 1 in 4 adults is projected to have severe obesity by 2030 (24.2%; 95% CI, 22.9 to 25.5), and the prevalence will be higher than 25% in 25 states. We predict that, nationally, severe obesity is likely to become the most common BMI category among women (27.6%; 95% CI, 26.1 to 29.2), non-Hispanic black adults (31.7%; 95% CI, 29.9 to 33.4), and low-income adults (31.7%; 95% CI, 30.2 to 33.2). CONCLUSIONS: Our analysis indicates that the prevalence of adult obesity and severe obesity will continue to increase nationwide, with large disparities across states and demographic subgroups. (Funded by the JPB Foundation.).

Lead Concentrations in US School Drinking Water: Testing Programs, Prevalence, and Policy Opportunities, 2016‒2018.

Objectives. To detail baseline drinking water sample lead concentrations and features of US state-level programs and policies to test school drinking water for lead in 7 states' operating programs between 2016 and 2018. Methods. We coded program and policy documents using structured content analysis protocols and analyzed state-provided data on lead concentration in drinking water samples collected in public schools during initial testing phases. Results. We analyzed data from 5688 public schools, representing 35% of eligible schools in 7 states. The number of samples per school varied. The proportion of schools identifying any sample lead concentration exceeding 5 parts per billion varied (13%-81%). Four states exceeded 20%. Other program features varied among states. Instances of lead above the state action level were identified in all states. Conclusions. In 2018, many US public school students attended schools in states without drinking water lead-testing programs. Testing all drinking water sources may be recommended. Public Health Implications. Initiating uniform school drinking water lead testing programs and surveillance over time could be used to reduce risk of lead exposure in drinking water. (Am J Public Health. 2022;112(S7):S679-S689. https://doi.org/10.2105/AJPH.2022.306961).

Sample size estimation using a latent variable model for mixed outcome co-primary, multiple primary and composite endpoints.

Mixed outcome endpoints that combine multiple continuous and discrete components are often employed as primary outcome measures in clinical trials. These may be in the form of co-primary endpoints, which conclude effectiveness overall if an effect occurs in all of the components, or multiple primary endpoints, which require an effect in at least one of the components. Alternatively, they may be combined to form composite endpoints, which reduce the outcomes to a one-dimensional endpoint. There are many advantages to joint modeling the individual outcomes, however in order to do this in practice we require techniques for sample size estimation. In this article we show how the latent variable model can be used to estimate the joint endpoints and propose hypotheses, power calculations and sample size estimation methods for each. We illustrate the techniques using a numerical example based on a four-dimensional endpoint and find that the sample size required for the co-primary endpoint is larger than that required for the individual endpoint with the smallest effect size. Conversely, the sample size required in the multiple primary case is similar to that needed for the outcome with the largest effect size. We show that the empirical power is achieved for each endpoint and that the FWER can be sufficiently controlled using a Bonferroni correction if the correlations between endpoints are less than 0.5. Otherwise, less conservative adjustments may be needed. We further illustrate empirically the efficiency gains that may be achieved in the composite endpoint setting.

Prediction of Cardiovascular Disease Risk Accounting for Future Initiation of Statin Treatment.

Cardiovascular disease (CVD) risk-prediction models are used to identify high-risk individuals and guide statin initiation. However, these models are usually derived from individuals who might initiate statins during follow-up. We present a simple approach to address statin initiation to predict "statin-naive" CVD risk. We analyzed primary care data (2004-2017) from the UK Clinical Practice Research Datalink for 1,678,727 individuals (aged 40-85 years) without CVD or statin treatment history at study entry. We derived age- and sex-specific prediction models including conventional risk factors and a time-dependent effect of statin initiation constrained to 25% risk reduction (from trial results). We compared predictive performance and measures of public-health impact (e.g., number needed to screen to prevent 1 event) against models ignoring statin initiation. During a median follow-up of 8.9 years, 103,163 individuals developed CVD. In models accounting for (versus ignoring) statin initiation, 10-year CVD risk predictions were slightly higher; predictive performance was moderately improved. However, few individuals were reclassified to a high-risk threshold, resulting in negligible improvements in number needed to screen to prevent 1 event. In conclusion, incorporating statin effects from trial results into risk-prediction models enables statin-naive CVD risk estimation and provides moderate gains in predictive ability but had a limited impact on treatment decision-making under current guidelines in this population.

Long-term maintenance rituximab for ANCA-associated vasculitis: relapse and infection prediction models.

OBJECTIVES: Following a maintenance course of rituximab (RTX) for ANCA-associated vasculitis (AAV), relapses occur on cessation of therapy, and further dosing is considered. This study aimed to develop relapse and infection risk prediction models to help guide decision making regarding extended RTX maintenance therapy. METHODS: Patients with a diagnosis of AAV who received 4-8 grams of RTX as maintenance treatment between 2002 and 2018 were included. Both induction and maintenance doses were included; most patients received standard departmental protocol consisting of 2× 1000 mg 2 weeks apart, followed by 1000 mg every 6 months for 2 years. Patients who continued on repeat RTX dosing long-term were excluded. Separate risk prediction models were derived for the outcomes of relapse and infection. RESULTS: A total of 147 patients were included in this study with a median follow-up of 63 months [interquartile range (IQR): 34-93]. Relapse: At time of last RTX, the model comprised seven predictors, with a corresponding C-index of 0.54. Discrimination between individuals using this model was not possible; however, discrimination could be achieved by grouping patients into low- and high-risk groups. When the model was applied 12 months post last RTX, the ability to discriminate relapse risk between individuals improved (C-index 0.65), and once again, clear discrimination was observed between patients from low- and high-risk groups. Infection: At time of last RTX, five predictors were retained in the model. The C-index was 0.64 allowing discrimination between low and high risk of infection groups. At 12 months post RTX, the C-index for the model was 0.63. Again, clear separation of patients from two risk groups was observed. CONCLUSION: While our models had insufficient power to discriminate risk between individual patients they were able to assign patients into risk groups for both relapse and infection. The ability to identify risk groups may help in decisions regarding the potential benefit of ongoing RTX treatment. However, we caution the use of these prediction models until prospective multi-centre validation studies have been performed.

Opioid stewardship training during the transition to residency to prepare medical students to recognize and treat opioid use disorder.

Background: With a drastic shortage of addiction medicine specialists-and an ever-growing number of patients with opioid use disorder (OUD)-there is a dire need for more clinicians to feel confident in prevention and management of OUD and obtain a DEA-X waiver to prescribe medications to treat OUD. Here we determine if it is feasible to certify 4th year medical students with DEA-X waiver training as a component of the PROUD (Prevent and Reduce Opioid Use Disorder) curriculum, and if PROUD enhanced preparedness for medical students to manage OUD as interns. Methods: We implemented a sequential mixed-methods IRB approved study to assess feasibility (completing all required components of DEA-X waiver training) and impact of PROUD (measured by knowledge growth, enhancement for residency, and utilization of training during internship). Students completed 11 hours of required OUD training. Quantitative data included pre-/post- knowledge and curriculum satisfaction assessments as well as long-term impact with follow up survey as interns. Qualitative data was collected by survey and semi-structured focus groups. Results: All 120 graduating medical students completed the required components of the curriculum. Knowledge improved on the Provider Clinical Support Services (12.9-17.3, p < 0.0001) and Brief Opioid Overdose Knowledge assessments (10.15-10.81, p < 0.0001). Course satisfaction was high: 90% recommended online modules; 85% recommended training overall. Six qualitative themes emerged: (1) curriculum content was practical, (2) online modules allowed flexibility, (3) in-person seminars ensured authenticity, (4) timing at the transition to residency was optimal, (5) curriculum enhanced awareness and confidence, and (6) training was applicable to future careers. At 3 months, 60% reported using their training during internship; 64% felt more prepared to treat OUD than peers. Conclusions: PROUD trained 4th year medical students in opioid stewardship. As interns, students felt ready to serve as change agents to prevent, diagnose, and treat OUD.

Explaining the Sex Effect on Survival in Cystic Fibrosis: a Joint Modeling Study of UK Registry Data.

BACKGROUND: Male sex is associated with better lung function and survival in people with cystic fibrosis but it is unclear whether the survival benefit is solely due to the sex-effect on lung function. METHODS: This study analyzes data between 1996 and 2015 from the longitudinal registry study of the UK Cystic Fibrosis Registry. We jointly analyze repeated measurements and time-to-event outcomes to assess how much of the sex effect on lung function also explains survival. These novel methods allow examination of association between percent of forced expiratory volume in 1 second (%FEV1) and covariates such as sex and genotype, and survival, in the same modeling framework. We estimate the probability of surviving one more year with a probit model. RESULTS: The dataset includes 81,129 lung function measurements of %FEV1 on 9,741 patients seen between 1996 and 2015 and captures 1,543 deaths. Males compared with females experienced a more gradual decline in %FEV1 (difference 0.11 per year 95% confidence interval [CI] = 0.08, 0.14). After adjusting for confounders, both overall level of %FEV1 and %FEV1 rate of change are associated with the concurrent hazard for death. There was evidence of a male survival advantage (probit coefficient 0.15; 95% CI = 0.10, 0.19) which changed little after adjustment for %FEV1 using conventional approaches but was attenuated by 37% on adjustment for %FEV1 level and slope in the joint model (0.09; 95% CI = 0.06, 0.12). CONCLUSIONS: We estimate that about 37% of the association of sex on survival in cystic fibrosis is mediated through lung function.

Greater variability in lipid measurements associated with cardiovascular disease and mortality: A 10-year diabetes cohort study.

AIM: To examine the associations between variability in lipids and the risk of cardiovascular disease (CVD) and mortality in patients with type 2 diabetes based on low-density lipoprotein-cholesterol (LDL-C), the total cholesterol (TC) to high-density lipoprotein-cholesterol (HDL-C) ratio and triglycerides (TG). MATERIALS AND METHODS: A retrospective cohort study included 125 047 primary care patients with type 2 diabetes aged 45-84 years without CVD during 2008-2012. The variability of LDL-C, TC to HDL-C and TG was determined using the standard deviation of variables in a mixed effects model to minimize regression dilution bias. The associations between variability in lipids and CVD and mortality risk were assessed by Cox regression. Subgroup analyses based on patients' baseline characteristics were also conducted. RESULTS: A total of 19 913 CVD events and 15 329 mortalities were recorded after a median follow-up period of 77.5 months (0.8 million person-years), suggesting a positive linear relationship between variability in lipids and the risk of CVD and mortality. Each unit increase in the variability of LDL-C (mmol/L), the TC to HDL-C ratio and TG (mmol/L) was associated with a 27% (HR: 1.27 [95% CI: 1.20-1.34]), 31% (HR:1.31 [95% CI: 1.25-1.38]) and 9% (HR: 1.09 [95% CI: 1.04-1.15]) increase in the risk of composite endpoint of CVD and mortality, respectively. Age-specific effects were also found when comparing LDL-C variability, with patients aged 45-54 years (HR: 1.70 [95% CI: 1.42-2.02]) exhibiting a 53% increased risk for the composite endpoints than those aged 75-84 years (HR: 1.11 [95% CI: 1.01-1.23]). Similar age effects were observed for both the TC to HDL-C ratio and TG variability. Significant associations remained consistent among most of the subgroups. CONCLUSIONS: Variability in respective lipids are significant factors in predicting CVD and mortality in primary care patients with type 2 diabetes, with the strongest effects related to LDL-C and the TC to HDL-C ratio and most significant in the younger age group of patients aged 45-54 years. Further study is warranted to confirm these findings.

Factors predicting poor glycemic control in the first two years of childhood onset type 1 diabetes in a cohort from East London, UK: Analyses using mixed effects fractional polynomial models.

BACKGROUND/OBJECTIVE: Poor early glycemic control in childhood onset type 1 diabetes (T1D) is associated with future risk of acute and chronic complications. Our aim was to identify the predictors of higher glycated hemoglobin (HbA1c) within 24 months of T1D diagnosis in children and adolescents. METHODS: Mixed effects models with fractional polynomials were used to analyze longitudinal data of patients <19 years of age, followed from T1D diagnosis for up to 2 years, at three diabetes clinics in East London, United Kingdom. RESULTS: A total of 2209 HbA1c observations were available for 356 patients (52.5% female; 64.4% non-white), followed from within 3 months of diagnosis during years 2005 to 2015, with a mean ± SD of 6.2 ± 2.5 HbA1c observations/participant. The mean age and HbA1c at diagnosis were 8.9 ± 4.3 years and 10.7% ±4.3% (or expressed as mmol/mol HbA1c mean ± SD 92.9 ± 23.10 mmol/mol) respectively. Over the 2 years following T1D diagnosis, HbA1c levels were mostly above the National Institute for Health, Care and Excellence (NICE), UK recommendations of 7.5% (<58 mmol/mol). Significant (P < .05) predictors of poorer glycemic control were: Age at diagnosis (12-18 years), higher HbA1c at baseline (>9.5%, ie, >80 mmol/mol), clinic site, non-white ethnicity, and period (pre-year 2011) of diagnosis. Additionally in univariable analyses, frequency of clinic visits, HbA1c at diagnosis, and type of insulin treatment regimen showed association with poor glycemic control (P < .05). CONCLUSIONS: Major risk factors of poorer glycemic control during 3-24 months following childhood onset T1D are: diagnosis prior to 2011, higher HbA1c levels at baseline, age at diagnosis, non-white ethnicity, and clinic site.

Improvements in psychologists' metacommunication self-efficacy, willingness, and skill following online training and a supervision exercise.

OBJECTIVE(S): Psychologists' experiences of an online training tool in metacommunication as well as an in-supervisory metacommunication exercise were examined. METHOD: A total of 101 participants completed a training tool in metacommunication and changes in self-efficacy (SE) to use metacommunication with clients, the proportion of metacommunication used in vignette-responses, and their willingness to use metacommunication in supervision were assessed pre- and posttraining and at 6-week follow-up. A total of 48 participants elected to undertake the in-supervision exercise. RESULTS: Participants reported significantly higher willingness and self-efficacy after completing the online training. They also showed a higher proportion of metacommunicative statements in their posttraining vignette responses compared with pretraining. The increase in willingness was retained at 6-week follow-up. There was an increase in self-efficacy from pre- to postonline-training, and this increased at follow-up. CONCLUSIONS: This opens the door to better developing metacommunication skills in supervisees through both online training and the metacommunication supervisory exercise. Areas for continued research are outlined.

Mixed-effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study.

Electronic health records are being increasingly used in medical research to answer more relevant and detailed clinical questions; however, they pose new and significant methodological challenges. For instance, observation times are likely correlated with the underlying disease severity: Patients with worse conditions utilise health care more and may have worse biomarker values recorded. Traditional methods for analysing longitudinal data assume independence between observation times and disease severity; yet, with health care data, such assumptions unlikely hold. Through Monte Carlo simulation, we compare different analytical approaches proposed to account for an informative visiting process to assess whether they lead to unbiased results. Furthermore, we formalise a joint model for the observation process and the longitudinal outcome within an extended joint modelling framework. We illustrate our results using data from a pragmatic trial on enhanced care for individuals with chronic kidney disease, and we introduce user-friendly software that can be used to fit the joint model for the observation process and a longitudinal outcome.

Dynamic Prediction of Survival in Cystic Fibrosis: A Landmarking Analysis Using UK Patient Registry Data.

BACKGROUND: Cystic fibrosis (CF) is an inherited, chronic, progressive condition affecting around 10,000 individuals in the United Kingdom and over 70,000 worldwide. Survival in CF has improved considerably over recent decades, and it is important to provide up-to-date information on patient prognosis. METHODS: The UK Cystic Fibrosis Registry is a secure centralized database, which collects annual data on almost all CF patients in the United Kingdom. Data from 43,592 annual records from 2005 to 2015 on 6181 individuals were used to develop a dynamic survival prediction model that provides personalized estimates of survival probabilities given a patient's current health status using 16 predictors. We developed the model using the landmarking approach, giving predicted survival curves up to 10 years from 18 to 50 years of age. We compared several models using cross-validation. RESULTS: The final model has good discrimination (C-indexes: 0.873, 0.843, and 0.804 for 2-, 5-, and 10-year survival prediction) and low prediction error (Brier scores: 0.036, 0.076, and 0.133). It identifies individuals at low and high risk of short- and long-term mortality based on their current status. For patients 20 years of age during 2013-2015, for example, over 80% had a greater than 95% probability of 2-year survival and 40% were predicted to survive 10 years or more. CONCLUSIONS: Dynamic personalized prediction models can guide treatment decisions and provide personalized information for patients. Our application illustrates the utility of the landmarking approach for making the best use of longitudinal and survival data and shows how models can be defined and compared in terms of predictive performance.

A sensitivity analysis approach for informative dropout using shared parameter models.

Shared parameter models (SPMs) are a useful approach to addressing bias from informative dropout in longitudinal studies. In SPMs it is typically assumed that the longitudinal outcome process and the dropout time are independent, given random effects and observed covariates. However, this conditional independence assumption is unverifiable. Currently, sensitivity analysis strategies for this unverifiable assumption of SPMs are underdeveloped. In principle, parameters that can and cannot be identified by the observed data should be clearly separated in sensitivity analyses, and sensitivity parameters should not influence the model fit to the observed data. For SPMs this is difficult because it is not clear how to separate the observed data likelihood from the distribution of the missing data given the observed data (i.e., 'extrapolation distribution'). In this article, we propose a new approach for transparent sensitivity analyses for informative dropout that separates the observed data likelihood and the extrapolation distribution, using a typical SPM as a working model for the complete data generating mechanism. For this model, the default extrapolation distribution is a skew-normal distribution (i.e., it is available in a closed form). We propose anchoring the sensitivity analysis on the default extrapolation distribution under the specified SPM and calibrate the sensitivity parameters using the observed data for subjects who drop out. The proposed approach is used to address informative dropout in the HIV Epidemiology Research Study.

Estimating the association between blood pressure variability and cardiovascular disease: An application using the ARIC Study.

The association between visit-to-visit systolic blood pressure variability and cardiovascular events has recently received a lot of attention in the cardiovascular literature. But, blood pressure variability is usually estimated on a person-by-person basis and is therefore subject to considerable measurement error. We demonstrate that hazard ratios estimated using this approach are subject to bias due to regression dilution, and we propose alternative methods to reduce this bias: a two-stage method and a joint model. For the two-stage method, in stage one, repeated measurements are modelled using a mixed effects model with a random component on the residual standard deviation (SD). The mixed effects model is used to estimate the blood pressure SD for each individual, which, in stage two, is used as a covariate in a time-to-event model. For the joint model, the mixed effects submodel and time-to-event submodel are fitted simultaneously using shared random effects. We illustrate the methods using data from the Atherosclerosis Risk in Communities study.