MagicalRsq-X: A cross-cohort transferable genotype imputation quality metric.

Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

Risk factors for 1-year allograft loss in pediatric heart transplant patients using machine learning: An analysis of the pediatric heart transplant society database.

BACKGROUND: Pediatric heart transplant patients are at greatest risk of allograft loss in the first year. We assessed whether machine learning could improve 1-year risk assessment using the Pediatric Heart Transplant Society database. METHODS: Patients transplanted from 2010 to 2019 were included. The primary outcome was 1-year graft loss free survival. We developed a prediction model using cross-validation, by comparing Cox regression, gradient boosting, and random forests. The modeling strategy with the best discrimination and calibration was applied to fit a final prediction model. We used Shapley additive explanation (SHAP) values to perform variable selection and to estimate effect sizes and importance of individual variables when interpreting the final prediction model. RESULTS: Cumulative incidence of graft loss or mortality was 7.6%. Random forests had favorable discrimination and calibration compared to Cox proportional hazards with a C-statistic (95% confidence interval [CI]) of 0.74 (0.72, 0.76) versus 0.71 (0.69, 0.73), and closer alignment between predicted and observed risk. SHAP values computed using the final prediction model indicated that the diagnosis of congenital heart disease (CHD) increased 1 year predicted risk of graft loss by 1.7 (i.e., from 7.6% to 9.3%), need for mechanical circulatory support increased predicted risk by 2, and single ventricle CHD increased predicted risk by 1.9. These three predictors, respectively, were also estimated to be the most important among the 15 predictors in the final model. CONCLUSIONS: Risk prediction models used to facilitate patient selection for pediatric heart transplant can be improved without loss of interpretability using machine learning.

Risk Prediction Models for Atherosclerotic Cardiovascular Disease in Patients with Chronic Kidney Disease: The CRIC Study.

BACKGROUND: Individuals with CKD may be at high risk for atherosclerotic cardiovascular disease (ASCVD). However, there are no ASCVD risk prediction models developed in CKD populations to inform clinical care and prevention. METHODS: We developed and validated 10-year ASCVD risk prediction models in patients with CKD that included participants without self-reported cardiovascular disease from the Chronic Renal Insufficiency Cohort (CRIC) study. ASCVD was defined as the first occurrence of adjudicated fatal and nonfatal stroke or myocardial infarction. Our models used clinically available variables and novel biomarkers. Model performance was evaluated based on discrimination, calibration, and net reclassification improvement. RESULTS: Of 2604 participants (mean age 55.8 years; 52.0% male) included in the analyses, 252 had incident ASCVD within 10 years of baseline. Compared with the American College of Cardiology/American Heart Association pooled cohort equations (area under the receiver operating characteristic curve [AUC]=0.730), a model with coefficients estimated within the CRIC sample had higher discrimination (P=0.03), achieving an AUC of 0.736 (95% confidence interval [CI], 0.649 to 0.826). The CRIC model developed using clinically available variables had an AUC of 0.760 (95% CI, 0.678 to 0.851). The CRIC biomarker-enriched model had an AUC of 0.771 (95% CI, 0.674 to 0.853), which was significantly higher than the clinical model (P=0.001). Both the clinical and biomarker-enriched models were well-calibrated and improved reclassification of nonevents compared with the pooled cohort equations (6.6%; 95% CI, 3.7% to 9.6% and 10.0%; 95% CI, 6.8% to 13.3%, respectively). CONCLUSIONS: The 10-year ASCVD risk prediction models developed in patients with CKD, including novel kidney and cardiac biomarkers, performed better than equations developed for the general population using only traditional risk factors.

Development and Validation of Machine Learning-Based Race-Specific Models to Predict 10-Year Risk of Heart Failure: A Multicohort Analysis.

BACKGROUND: Heart failure (HF) risk and the underlying risk factors vary by race. Traditional models for HF risk prediction treat race as a covariate in risk prediction and do not account for significant parameters such as cardiac biomarkers. Machine learning (ML) may offer advantages over traditional modeling techniques to develop race-specific HF risk prediction models and to elucidate important contributors of HF development across races. METHODS: We performed a retrospective analysis of 4 large, community cohort studies (ARIC [Atherosclerosis Risk in Communities], DHS [Dallas Heart Study], JHS [Jackson Heart Study], and MESA [Multi-Ethnic Study of Atherosclerosis]) with adjudicated HF events. The study included participants who were >40 years of age and free of HF at baseline. Race-specific ML models for HF risk prediction were developed in the JHS cohort (for Black race-specific model) and White adults from ARIC (for White race-specific model). The models included 39 candidate variables across demographic, anthropometric, medical history, laboratory, and electrocardiographic domains. The ML models were externally validated and compared with prior established traditional and non-race-specific ML models in race-specific subgroups of the pooled MESA/DHS cohort and Black participants of ARIC. The Harrell C-index and Greenwood-Nam-D'Agostino χ2 tests were used to assess discrimination and calibration, respectively. RESULTS: The ML models had excellent discrimination in the derivation cohorts for Black (n=4141 in JHS, C-index=0.88) and White (n=7858 in ARIC, C-index=0.89) participants. In the external validation cohorts, the race-specific ML model demonstrated adequate calibration and superior discrimination (Black individuals, C-index=0.80-0.83; White individuals, C-index=0.82) compared with established HF risk models or with non-race-specific ML models derived with race included as a covariate. Among the risk factors, natriuretic peptide levels were the most important predictor of HF risk across both races, followed by troponin levels in Black and ECG-based Cornell voltage in White individuals. Other key predictors of HF risk among Black individuals were glycemic parameters and socioeconomic factors. In contrast, prevalent cardiovascular disease and traditional cardiovascular risk factors were stronger predictors of HF risk in White adults. CONCLUSIONS: Race-specific and ML-based HF risk models that integrate clinical, laboratory, and biomarker data demonstrated superior performance compared with traditional HF risk and non-race-specific ML models. This approach identifies distinct race-specific contributors of HF.

Inflammation biomarkers and incident coronary heart disease: the Reasons for Geographic And Racial Differences in Stroke Study.

BACKGROUND: Individual inflammation biomarkers are associated with incident coronary heart disease (CHD) events. However, there is limited research on whether the risk for incident CHD is progressively higher with a higher number of inflammation biomarkers in abnormal levels. METHODS: We used data from 15,758 Reasons for Geographic and Racial Differences in Stroke (REGARDS) study participants aged ≥45 years without a history of CHD at baseline in 2003-2007. Abnormal levels of baseline high-sensitivity C-reactive protein, leukocyte count and serum albumin were defined as ≥3.8 mg/L (3rd tertile), ≥6.3 x 109 cells/L (3rd tertile), and <4.0 g/dL (1st tertile), respectively. The outcome was a composite of incident myocardial infarction or CHD death. RESULTS: Overall, 38.9% (n = 6,123) had 0, 36.6% (n = 5,774) had 1, 19.8% (n = 3,113) had 2 and 4.7% (n = 748) had 3 biomarkers of inflammation in abnormal levels. Over a median follow-up of 11.4 years, 954 (6.1%) participants had incident CHD. The rate of incident CHD per 1000 person-years for individuals with 0, 1, 2, and 3 biomarkers of inflammation in abnormal levels was 4.4 (95% confidence interval [CI]: 3.9-5.0), 6.3 (95% CI: 5.6-6.9), 8.8 (95% CI: 7.8-9.9), and 10.6 (95% CI: 8.1-13.1), respectively. Multi-variable adjusted hazard ratios for incident CHD associated with 1, 2 and 3 versus no inflammation biomarker in abnormal levels were 1.26 (95% CI: 1.07-1.49), 1.72 (95% CI: 1.43-2.07), and 1.84 (95% CI: 1.37-2.47), respectively (P-trend < .001). CONCLUSIONS: The number of inflammation markers in abnormal levels was associated with increased risk of incident CHD after multi-variable adjustment.

Spike-and-slab least absolute shrinkage and selection operator generalized additive models and scalable algorithms for high-dimensional data analysis.

There are proposals that extend the classical generalized additive models (GAMs) to accommodate high-dimensional data ( p ≫ n $$ p\gg n $$ ) using group sparse regularization. However, the sparse regularization may induce excess shrinkage when estimating smooth functions, damaging predictive performance. Moreover, most of these GAMs consider an "all-in-all-out" approach for functional selection, rendering them difficult to answer if nonlinear effects are necessary. While some Bayesian models can address these shortcomings, using Markov chain Monte Carlo algorithms for model fitting creates a new challenge, scalability. Hence, we propose Bayesian hierarchical generalized additive models as a solution: we consider the smoothing penalty for proper shrinkage of curve interpolation via reparameterization. A novel two-part spike-and-slab LASSO prior for smooth functions is developed to address the sparsity of signals while providing extra flexibility to select the linear or nonlinear components of smooth functions. A scalable and deterministic algorithm, EM-Coordinate Descent, is implemented in an open-source R package BHAM. Simulation studies and metabolomics data analyses demonstrate improved predictive and computational performance against state-of-the-art models. Functional selection performance suggests trade-offs exist regarding the effect hierarchy assumption.

Development and validation of optimal phenomapping methods to estimate long-term atherosclerotic cardiovascular disease risk in patients with type 2 diabetes.

AIMS/HYPOTHESIS: Type 2 diabetes is a heterogeneous disease process with variable trajectories of CVD risk. We aimed to evaluate four phenomapping strategies and their ability to stratify CVD risk in individuals with type 2 diabetes and to identify subgroups who may benefit from specific therapies. METHODS: Participants with type 2 diabetes and free of baseline CVD in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial were included in this study (N = 6466). Clustering using Gaussian mixture models, latent class analysis, finite mixture models (FMMs) and principal component analysis was compared. Clustering variables included demographics, medical and social history, laboratory values and diabetes complications. The interaction between the phenogroup and intensive glycaemic, combination lipid and intensive BP therapy for the risk of the primary outcome (composite of fatal myocardial infarction, non-fatal myocardial infarction or unstable angina) was evaluated using adjusted Cox models. The phenomapping strategies were independently assessed in an external validation cohort (Look Action for Health in Diabetes [Look AHEAD] trial: n = 4211; and Bypass Angioplasty Revascularisation Investigation 2 Diabetes [BARI 2D] trial: n = 1495). RESULTS: Over 9.1 years of follow-up, 789 (12.2%) participants had a primary outcome event. FMM phenomapping with three phenogroups was the best-performing clustering strategy in both the derivation and validation cohorts as determined by Bayesian information criterion, Dunn index and improvement in model discrimination. Phenogroup 1 (n = 663, 10.3%) had the highest burden of comorbidities and diabetes complications, phenogroup 2 (n = 2388, 36.9%) had an intermediate comorbidity burden and lowest diabetes complications, and phenogroup 3 (n = 3415, 52.8%) had the fewest comorbidities and intermediate burden of diabetes complications. Significant interactions were observed between phenogroups and treatment interventions including intensive glycaemic control (p-interaction = 0.042) and combination lipid therapy (p-interaction < 0.001) in the ACCORD, intensive lifestyle intervention (p-interaction = 0.002) in the Look AHEAD and early coronary revascularisation (p-interaction = 0.003) in the BARI 2D trial cohorts for the risk of the primary composite outcome. Favourable reduction in the risk of the primary composite outcome with these interventions was noted in low-risk participants of phenogroup 3 but not in other phenogroups. Compared with phenogroup 3, phenogroup 1 participants were more likely to have severe/symptomatic hypoglycaemic events and medication non-adherence on follow-up in the ACCORD and Look AHEAD trial cohorts. CONCLUSIONS/INTERPRETATION: Clustering using FMMs was the optimal phenomapping strategy to identify replicable subgroups of patients with type 2 diabetes with distinct clinical characteristics, CVD risk and response to therapies.

Racial Differences in Blood Pressure Control Following Stroke: The REGARDS Study.

BACKGROUND AND PURPOSE: In the general population, Black adults are less likely than White adults to have controlled blood pressure (BP), and when not controlled, they are at greater risk for stroke compared with White adults. High BP is a major modifiable risk factor for recurrent stroke, but few studies have examined racial differences in BP control among stroke survivors. METHODS: We used data from the REGARDS study (Reasons for Geographic and Racial Differences in Stroke) to examine disparities in BP control between Black and White adults, with and without a history of stroke. We studied participants taking antihypertensive medication who did and did not experience an adjudicated stroke (n=306 and 7693 participants, respectively) between baseline (2003-2007) and a second study visit (2013-2016). BP control at the second study visit was defined as systolic BP <130 mm Hg and diastolic BP <80 mm Hg except for low-risk adults ≥65 years of age (ie, those without diabetes, chronic kidney disease, history of cardiovascular disease, and with a 10-year predicted atherosclerotic cardiovascular disease risk <10%) for whom BP control was defined as systolic BP <130 mm Hg. RESULTS: Among participants with a history of stroke, 50.3% of White compared with 39.3% of Black participants had controlled BP. Among participants without a history of stroke, 56.0% of White compared with 50.2% of Black participants had controlled BP. After multivariable adjustment, there was a tendency for Black participants to be less likely than White participants to have controlled BP (prevalence ratio, 0.77 [95% CI, 0.59-1.02] for those with a history of stroke and 0.92 [95% CI, 0.88-0.97] for those without a history of stroke). CONCLUSIONS: There was a lower proportion of controlled BP among Black compared with White adults with or without stroke, with no statistically significant differences after multivariable adjustment.

Factors associated with breast cancer screening behaviors in a sample of Jamaican women in 2013.

In this study authors aimed to investigate the barriers to participation of a sample of Jamaican women in mammography screening. The data were obtained from a previous survey that was conducted in Jamaica from June to August 2013 in the four parishes (Hanover, St. James, Trelawny, and Westmoreland) served by the Western Regional Health Authority (WRHA). WRHA served four hospitals, five non-randomized health centers, and five sites of community events. The sample consisted of women ranging in age between 35 and 83 years, with the mean age of 50.2 (SD = 10.6). The authors used a logistic regression to determine the extent to which variables were associated with breast cancer screening. The level of significace was 0.1 for the bivariate and multivariable analysis. The main factors associated with the lack of breast cancer screening among the women were: being less than 50 years old, single, never thought about breast cancer screening, in need of childcare, and having more than three children. We identified significant barriers to participation in mammography screening experienced by a sample of Jamaican women. Our results suggest that it is necessary to increase the awareness of the importance of breast screening importance among the women who may never have thought about mammography.

Trends in Blood Pressure Control Among US Adults With Hypertension, 1999-2000 to 2017-2018.

Importance: Controlling blood pressure (BP) reduces the risk for cardiovascular disease. Objective: To determine whether BP control among US adults with hypertension changed from 1999-2000 through 2017-2018. Design, Setting, and Participants: Serial cross-sectional analysis of National Health and Nutrition Examination Survey data, weighted to be representative of US adults, between 1999-2000 and 2017-2018 (10 cycles), including 18 262 US adults aged 18 years or older with hypertension defined as systolic BP level of 140 mm Hg or higher, diastolic BP level of 90 mm Hg or higher, or use of antihypertensive medication. The date of final data collection was 2018. Exposures: Calendar year. Main Outcomes and Measures: Mean BP was computed using 3 measurements. The primary outcome of BP control was defined as systolic BP level lower than 140 mm Hg and diastolic BP level lower than 90 mm Hg. Results: Among the 51 761 participants included in this analysis, the mean (SD) age was 48 (19) years and 25 939 (50.1%) were women; 43.2% were non-Hispanic White adults; 21.6%, non-Hispanic Black adults; 5.3%, non-Hispanic Asian adults; and 26.1%, Hispanic adults. Among the 18 262 adults with hypertension, the age-adjusted estimated proportion with controlled BP increased from 31.8% (95% CI, 26.9%-36.7%) in 1999-2000 to 48.5% (95% CI, 45.5%-51.5%) in 2007-2008 (P < .001 for trend), remained stable and was 53.8% (95% CI, 48.7%-59.0%) in 2013-2014 (P = .14 for trend), and then declined to 43.7% (95% CI, 40.2%-47.2%) in 2017-2018 (P = .003 for trend). Compared with adults who were aged 18 years to 44 years, it was estimated that controlled BP was more likely among those aged 45 years to 64 years (49.7% vs 36.7%; multivariable-adjusted prevalence ratio, 1.18 [95% CI, 1.02-1.37]) and less likely among those aged 75 years or older (37.3% vs 36.7%; multivariable-adjusted prevalence ratio, 0.81 [95% CI, 0.65-0.97]). It was estimated that controlled BP was less likely among non-Hispanic Black adults vs non-Hispanic White adults (41.5% vs 48.2%, respectively; multivariable-adjusted prevalence ratio, 0.88; 95% CI, 0.81-0.96). Controlled BP was more likely among those with private insurance (48.2%), Medicare (53.4%), or government health insurance other than Medicare or Medicaid (43.2%) vs among those without health insurance (24.2%) (multivariable-adjusted prevalence ratio, 1.40 [95% CI, 1.08-1.80], 1.47 [95% CI, 1.15-1.89], and 1.36 [95% CI, 1.04-1.76], respectively). Controlled BP was more likely among those with vs those without a usual health care facility (48.4% vs 26.5%, respectively; multivariable-adjusted prevalence ratio, 1.48 [95% CI, 1.13-1.94]) and among those who had vs those who had not had a health care visit in the past year (49.1% vs 8.0%; multivariable-adjusted prevalence ratio, 5.23 [95% CI, 2.88-9.49]). Conclusions and Relevance: In a series of cross-sectional surveys weighted to be representative of the adult US population, the prevalence of controlled BP increased between 1999-2000 and 2007-2008, did not significantly change from 2007-2008 through 2013-2014, and then decreased after 2013-2014.

Prevalence of Activity Limitations and Association with Multimorbidity Among US Adults 50 to 64 Years Old.

BACKGROUND: Functional limitations may be more common in middle-aged adults than previously recognized. However, there are few published data on the prevalence of activity limitations, and their association with multimorbidity, among adults 50 to 64 years old. OBJECTIVE: To describe the prevalence of activity limitations and the association with multimorbidity in middle-aged adults. DESIGN: Cross-sectional analysis of US population-based National Health and Nutrition Examination Survey (NHANES) 2011-2016. PARTICIPANTS: The total number of community-dwelling NHANES participants aged 50-64 years old is 4217. MAIN MEASURES: Chronic conditions included hypertension, high cholesterol, diabetes mellitus, obesity, chronic kidney disease, cancer, stroke, coronary heart disease, heart failure, chronic obstructive pulmonary disease/asthma, arthritis, and depression. Activity limitations were defined as any difficulty within each of four International Classification of Functioning (ICF) domains: functional limitations (kneeling, carrying, standing, sitting, reaching, grasping, pulling), mobility (walking » mile, climbing 10 steps), basic activities of daily living (BADLs; walking, transferring, eating, dressing), and instrumental activities of daily living (IADLs; finances, chores, cooking). We calculated prevalence ratios for activity limitations using generalized estimating equations. KEY RESULTS: The prevalence of functional limitations, mobility limitations, BADL difficulty, and IADL difficulty was 34%, 11%, 15%, and 17%, respectively. Seventy-two percent of participants had two or more chronic conditions; 23% had two, 18% had three, 15% had four, and 16% had five or more. Multivariable adjusted prevalence ratios (95% CI) for functional limitations among those with 2, 3, 4, and 5 or more chronic conditions, compared with 0-1 conditions, were 1.94 (1.43-2.63), 2.50 (1.93-3.23), 3.26 (2.48-4.27), and 4.54 (3.48-5.93), respectively (p trend < 0.001). Larger prevalence ratios at a higher number of chronic conditions were present for mobility limitations, BADL difficulty, and IADL difficulty. CONCLUSIONS: Problems with function are not limited to older adults and multimorbidity may be helpful for identifying middle-aged adults with a high prevalence of activity limitations.

Impact of bladder cancer on health-related quality of life.

OBJECTIVES: To identify changes in health-related quality of life (HRQoL) after diagnosis of bladder cancer in older adults in comparison with a group of adults without bladder cancer (controls). PATIENTS AND METHODS: Data from the Surveillance, Epidemiology and End Results registries were linked with Medicare Health Outcomes Survey (MHOS) data. Medicare beneficiaries aged ≥65 years in the period 1998-2013, who were diagnosed with bladder cancer between baseline and follow-up through the MHOS, were matched with control subjects without cancer using propensity scores. Linear mixed models were used to estimate predictors of HRQoL changes. RESULTS: After matching, 535 patients with bladder cancer (458 non-muscle-invasive bladder cancer [NMIBC] and 77 with muscle-invasive bladder cancer [MIBC]) and 2 770 control subjects without cancer were identified. Both patients with NMIBC and those with MIBC reported significant declines in HRQoL scores over time vs controls: physical component summary -2 and -5.3 vs -0.4, respectively; bodily pain -1.9 and -3.6 vs -0.7; role physical -2.7 and -4.7 vs -0.7; general health -2.4 and -6.1 vs 0; vitality -1.2 and -3.5 vs -0.1; and social functioning -2.1 and -5.7 vs -0.8. All scores ranged from 0 to 100. When stratified by time since diagnosis, HRQoL improved over 1 year for some domains (role physical), but remained lower across most domains. CONCLUSIONS: After diagnosis, patients with bladder cancer experienced significant declines in physical, mental and social HRQoL relative to controls. Decrements were most pronounced among individuals with MIBC. Methods to better understand and address HRQoL decrements among patients with bladder cancer are needed.

Assessing responsiveness over time of the PROMIS® pediatric symptom and function measures in cancer, nephrotic syndrome, and sickle cell disease.

PURPOSE: Previous studies provided evidence for the validity of the PROMIS Pediatric measures in cross-sectional studies. This study evaluated the ability of the PROMIS Pediatric measures to detect change over time in children and adolescents with cancer, nephrotic syndrome (NS), or sickle cell disease (SCD). METHODS: Participants (8-17 years) completed measures of fatigue, pain interference, anger, anxiety, depressive symptoms, mobility, upper extremity, and peer relationships at three or four time points (T1-T4). Between T1 and T2, children with cancer received chemotherapy and children with SCD experienced a pain exacerbation. Children with NS were first assessed during active disease (T2), with T3 and T4 conducted at disease remission. For the primary analysis of responsiveness, we expected better scores at T3 (recovery) compared to T2 (event) for all diseases. T1 and T4 are also expected to have better scores than T2. Linear mixed models were used and adjusted for time, gender, age, race/ethnicity, education, comorbid conditions, and disease. RESULTS: Enrolled were 96 children with cancer, 121 children with SCD, and 127 children with NS. Fatigue, pain interference, mobility, and upper extremity scores worsened from T1 (baseline) to T2 (event) (p < 0.01), and significantly improved from T2 to T3 and T4 (p < 0.01). Similarly, anxiety and depressive symptoms significantly improved from T2 to T3 and T4 (p < 0.01). CONCLUSIONS: This study provides evidence for the responsiveness of seven PROMIS Pediatric measures to clinical disease state in three chronic illnesses. The findings support use of PROMIS Pediatric measures in clinical research.

Low agreement between modified-Schwartz and CKD-EPI eGFR in young adults: a retrospective longitudinal cohort study.

BACKGROUND: While there is a great deal of research updating methods for estimating renal function, many of these methods are being developed in either adults with CKD or younger children. Currently, there is limited understanding of the agreement between the modified new bedside Schwartz estimated glomerular filtration rate (eGFR) formula and the adult CKD-EPI formula in adolescents and young adults (AYAs) with chronic kidney disease (CKD) measured longitudinally. METHODS: Longitudinal cohort study of 242 patients (10-30 years) with CKD, followed retrospectively in a single tertiary centre as they transitioned from the paediatric- to adult-focused settings. The study population came from a longitudinal cohort of AYAs undergoing healthcare transition at the STARx Program at the University of North Carolina, in the South-Eastern USA, from 2006 to 2015. We calculated and compared the eGFR using the new bedside Schwartz formula and the CKD-EPI eGFR. Measurements were repeated for each age in years. Agreement was tested using Bland & Altman analysis. Subgroup analysis was performed using the following age groups 10-15, 15-20, 20-25 and 25-30 years, glomerular and non-glomerular causes of CKD and height z-score. RESULTS: Using repeated measures, concordance between the new Schwartz and CKD-EPI eGFR was low at 0.74 (95% C.I. 0.67, 0.79) at the lowest age range of 10-15, 0.78 (95% C.I. 0.71, 0.84) at age 15-20, 0.80 (0.70, 0.87) at ages 20-25, and 0.82 (95% C.I. 0.70, 0.90) at age 25-30. Discordance was worse in males and largest in the 10-15 year-old age group, and in patients with stunted growth. CONCLUSIONS: The Schwartz and CKD-EPI equations exhibit poor agreement in patients before and during the transition period with CKD-EPI consistently yielding higher eGFRs, especially in males. Further studies are required to determine the appropriate age for switching to the CKD-EPI equation after age 18.

Responsiveness of PROMIS® Pediatric Measures to Hospitalizations for Sickle Pain and Subsequent Recovery.

BACKGROUND: The Patient-Reported Outcomes Measurement Information System(®) (PROMIS(®) ) created pediatric self-report scales measuring a variety of health attributes (domains), but their responsiveness to changes in health status has not yet been determined in children with sickle cell disease (SCD). PROCEDURE: A convenience cohort of symptomatic SCD children, aged 8-17 years, was asked to complete PROMIS pediatric scales at an initial clinic visit, at the end of a subsequent hospitalization for sickle pain, at a subsequent clinic visit or at home 2-3 weeks after hospitalization, and at a clinic visit 1-2 years after their initial assessment. RESULTS: A total of 121 participants (mean age 12.5 ± 3.1 years, 56.2% female) participated in the study. Pain interference and fatigue domain scores were elevated at baseline, increased substantially during hospitalization, and largely returned to baseline by the recovery period, whereas the depressive symptoms, anger, and anxiety domain scores displayed a less pronounced elevation during hospitalizations and a slower return to baseline levels. The two physical functioning scales showed a substantial decline in response to hospitalization, but only modest improvements at the recovery assessment, likely representing incomplete recovery. CONCLUSIONS: Several PROMIS pediatric measures were responsive to changes in health status associated with occurrence and resolution of acute vaso-occlusive pain requiring hospitalization. The substantial differences in these domains during SCD-related pain exacerbations support their potential usefulness in clinical research or in clinical practice. Further studies to characterize variations in symptom patterns over time may provide insights into strategies for more effective management of sickle pain.

Five-Year Cumulative Cardiovascular Health and Clinical Events in Patients With Chronic Kidney Disease: The CRIC Study.

BACKGROUND: Higher cardiovascular health (CVH) score is associated with lower risks of cardiovascular disease (CVD) and mortality in the general population. However, it is unclear whether cumulative CVH is associated with CVD, end-stage kidney disease (ESKD), and death in patients with chronic kidney disease. METHODS AND RESULTS: Among individuals from the prospective CRIC (Chronic Renal Insufficiency Cohort) Study, we used the percentage of the maximum possible CVH score attained from baseline to the year 5 visit to calculate cumulative CVH score. Multivariable-adjusted Cox proportional hazards regression was used to investigate the associations of cumulative CVH with risks of adjudicated CVD (myocardial infarction, stroke, and heart failure), ESKD, and all-cause mortality. A total of 3939 participants (mean age, 57.7 years; 54.9% men) were included. The mean (SD) cumulative CVH score attained during 5 years was 55.5% (12.3%). Over a subsequent median 10.2-year follow-up, 597 participants developed CVD, 656 had ESKD, and 1324 died. A higher cumulative CVH score was significantly associated with lower risks of CVD, ESKD, and mortality, independent of the CVH score at year 5. Multivariable-adjusted hazard ratios and 95% CIs per 10% higher cumulative CVH score during 5 years were 0.81 (0.69-0.95) for CVD, 0.82 (0.70-0.97) for ESKD, and 0.80 (0.72-0.89) for mortality. CONCLUSIONS: Among patients with chronic kidney disease stages 2 to 4, a better CVH status maintained throughout 5 years is associated with lower risks of CVD, ESKD, and all-cause mortality. The findings support the need for interventions to maintain ideal CVH status for prevention of adverse outcomes in the population with chronic kidney disease.

Social Determinants of Health and Incident Apparent Treatment-Resistant Hypertension Among White and Black US Adults: The REGARDS Study.

BACKGROUND: We examined the association of multilevel social determinants of health with incident apparent treatment-resistant hypertension (aTRH). METHODS AND RESULTS: We analyzed data from 2774 White and 2257 Black US adults from the REGARDS (Reasons for Geographic and Racial Differences in Stroke) study taking antihypertensive medication without aTRH at baseline to estimate the association of social determinants of health with incident aTRH. Selection of social determinants of health was guided by the Healthy People 2030 domains of education, economic stability, social context, neighborhood environment, and health care access. Blood pressure (BP) was measured during study visits, and antihypertensive medication classes were identified through a pill bottle review. Incident aTRH was defined as (1) systolic BP ≥140 mm Hg or diastolic BP ≥90 mm Hg, or systolic BP ≥130 mm Hg or diastolic BP ≥80 mm Hg for those with diabetes or chronic kidney disease while taking ≥3 classes of antihypertensive medication or (2) taking ≥4 classes of antihypertensive medication regardless of BP level, at the follow-up visit. Over a median 9.5 years of follow-up, 15.9% of White and 24.0% of Black adults developed aTRH. A percent of the excess aTRH risk among Black versus White adults was mediated by low education (14.2%), low income (16.0%), not seeing a friend or relative in the past month (8.1%), not having someone to care for them if ill or disabled (7.6%), lack of health insurance (10.6%), living in a disadvantaged neighborhood (18.0%), and living in states with poor public health infrastructure (6.0%). CONCLUSIONS: Part of the association between race and incident aTRH risk was mediated by social determinants of health.

Race differences in ambulatory blood pressure monitoring parameters.

BACKGROUND: Mean systolic and diastolic blood pressure (SBP and DBP) on ambulatory blood pressure (BP) monitoring (ABPM) are higher among Black compared with White adults. With 48 to 72 BP measurements obtained over 24 h, ABPM can generate parameters other than mean BP that are associated with increased risk for cardiovascular events. There are few data on race differences in ABPM parameters other than mean BP. METHODS: To estimate differences between White and Black participants in ABPM parameters, we used pooled data from five US-based studies in which participants completed ABPM (n = 2580). We calculated measures of SBP and DBP level, including mean, load, peak, and measures of SBP and DBP variability, including average real variability (ARV) and peak increase. RESULTS: There were 1513 (58.6%) Black and 1067 (41.4%) White participants with mean ages of 56.1 and 49.0 years, respectively. After multivariable adjustment, asleep SBP and DBP load were 5.7% (95% CI: 3.5-7.9%) and 2.7% (95% CI: 1.1-4.3%) higher, respectively, among Black compared with White participants. Black compared with White participants also had higher awake DBP ARV (0.3 [95%CI: 0.0-0.6] mmHg) and peak increase in DBP (0.4 [95% CI: 0.0-0.8] mmHg). There was no evidence of Black:White differences in awake measures of SBP level, asleep peak SBP or DBP, awake and asleep measures of SBP variability or asleep measures of DBP variability after multivariable adjustment. CONCLUSION: Asleep SBP load, awake DBP ARV and peak increase in awake DBP were higher in Black compared to White participants, independent of mean BP on ABPM.

Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.