Embedding a standardized whole-person health measure in electronic health records (EHR) could be instrumental to preventative care. The allostatic load index (ALI), calculated from ten component stressors across three body systems, offers a promising snapshot of holistic health. The ALI can be calculated from EHR data, but many components are missing, since not all patients undergo all tests. Using statistical modeling and machine learning, EHR data for 1000 patients from a large academic health system were used to predict in-patient hospitalization (as a count or binary) from ALI, controlling for age and sex. Various methods were evaluated to fill in information gaps for patients' missing ALI components, including summary measures combining components or using them separately. Performance was measured using receiver operating characteristic (ROC) curves and corresponding areas under the ROC curve (AUC). Count modeling of hospitalization did not improve upon binary, and logistic regression beat random forest. Overall, summary measures performed similarly, with the complete-case proportion (i.e., the proportion of non-missing components that were "unhealthy") performing best (AUC = 0.64) but by ≤ 0.01. When using components separately, the pattern submodel approach most accurately predicted hospitalization (AUC = 0.73) in sample, but did not cross-validate as well (AUC = 0.63). All summary measures performed similarly. However, when including the ALI components separately, tailoring models to subsets of patients with the same missing data pattern performed best. Next steps include EHR implementation to enable prediction and support clinician decision-making at scale.

The log-rank test and Kaplan–Meier plot are standard tools for analyzing time-to-event data in randomized clinical trials, yet neither provides a summary of the magnitude of the treatment effect. Practitioners typically fill this gap by reporting a hazard ratio from a Cox proportional-hazards model or an acceleration factor from an accelerated failure time (AFT) model, but both require assumptions beyond those needed for the log-rank test or Kaplan–Meier estimator. We propose two nonparametric confidence intervals for scalar effect-size summaries, an additive shift c and a multiplicative factor ρ, obtained by inverting the log-rank test under sharp null hypotheses of constant treatment effects. Building on the randomization-inference framework of Li and Small (2023), both intervals are valid under the randomization distribution alone, requiring no assumptions for the event-time distribution. We evaluate the proposed multiplicative interval via simulation, finding that it maintains nominal coverage across a range of censoring rates and sample sizes, including under data-generating processes that misspecify a parametric AFT model, while incurring only a modest efficiency loss compared to parametric AFT inference under correct specification. We illustrate the approach using data from a randomized trial of rhDNase for cystic fibrosis and provide R code and a Shiny application for ease of implementation.

Objectives: Electronic health record (EHR) data are prone to missingness and errors. Previously, we devised an enriched chart review protocol where a "roadmap" of auxiliary diagnoses was used to recover missing values. Still, chart reviews are expensive and time-intensive, limiting the number of patients whose data can be reviewed. Now, we investigate the accuracy and scalability of a roadmap-driven algorithm, based on International Classification of Diseases, 10th revision (ICD-10) codes, to mimic expert chart reviews and recover missing values. Materials and Methods: In addition to the clinicians' original roadmap from our previous work, we consider new versions that were iteratively refined using large language models (LLMs) in conjunction with clinical expertise to expand the list of auxiliary diagnoses. Using chart reviews for 100 patients from an extensive EHR, we examine algorithm performance. Results: Across 100 chart reviewed patients, there were 413 missing values in the EHR data. The expert chart reviews recovered 49 (12%), while the algorithms using LLMs-enhanced roadmaps recommended almost twice as many (83-89, 20%-22%). The final algorithm using clinician-approved LLMs' additions offered a balance (73, 18%), expanding the original roadmap with LLMs' suggestions but only when deemed clinically relevant. When applied to a larger study of 1000 patients from the same EHR, the per-patient median number of non-missing values increased from 6 to 7. Discussion: Clinically-driven algorithms (enhanced by LLMs) can recover missing EHR data with similar accuracy to chart reviews and feasibly be applied to large samples. Extending them to monitor other data quality dimensions is a promising future direction.

BACKGROUND:Food insecurity is a barrier to patients adhering to prescribed hypertension treatments and is strongly associated with worse blood pressure. Health systems are implementing interventions to assist patients with food insecurity, but rather than providing a single intervention, a stepped-care approach to providing interventions could be a more effective strategy. Our objective is to determine the effectiveness of a stepped-care food insecurity intervention on blood pressure and adherence among patients with uncontrolled hypertension. METHODS/DESIGN:We will conduct a sequential multiple assignment randomized trial. Adults (≥18 years) with uncontrolled hypertension (>130/80) who report food insecurity will be randomized to one of two first-stage interventions: 1) information about community resources or 2) community health worker (CHW) support. Participants who do not have ≥10 mmHg improvement in systolic blood pressure after 3 months will be re-randomized to one of two second-stage interventions for an additional 3 months: 1) CHW support or 2) delivery of medically tailored meals. In Aim 1, we will determine which first-stage intervention is more effective in improving blood pressure and adherence. In Aim 2, we will evaluate which intervention is the best next step for those who do not respond to the initial intervention. In Aim 3, we will advance our understanding of how and why participants achieved improvements through qualitative and quantitative data analysis. CONCLUSIONS:This will be the first study to test the effectiveness of a stepped-care food insecurity intervention. Given the growing interest among health systems, an efficacious, stepped-care food insecurity intervention could be broadly disseminated. TRIAL REGISTRATION:The study was registered with ClinicalTrials.gov (NCT07031739) on June 22, 2025.

Benchmarks of clinical management are essential for improving the quality of care. However, the lack of established quality metrics for pulmonary arterial hypertension (PAH) contributes to practice heterogeneity. We assessed our center's diagnostic practices, therapeutic practices, and risk-adjusted survival patterns over time for the purpose of establishing quality benchmarks. We analyzed the demographics, clinical characteristics, and diagnostic evaluation of 702 PAH patients enrolled between 1999 and 2019. We examined outcomes in this cohort, including an analysis of risk stratification, therapeutic practice patterns, hospitalizations, organ transplant, and survival. Initial diagnostic workup of incident PAH cases demonstrated excellent completion of echocardiographic (99%) and pulmonary function testing (91%), with improved completion of VQ scanning over the study time period (90% between 2015 and 2019). Right heart catheterization (RHC) was performed in all patients; RHC performed at our center was more likely to include complete hemodynamic data than those performed at referring institutions (55.4% and 30.4% respectively). The average number of PAH-specific medications prescribed increased over time; however, there was no significant increase in the use of parenteral therapy over time, even when stratified by the REVEAL risk score. Survival rates in the cohort were 94% at 1 year, 75% at 5 years, and 60% at 10 years, comparable to those of other PAH cohorts. Analysis of our well-characterized cohort of PAH patients reveals the extent to which guideline-directed diagnostic and therapeutic care is delivered at our specialty center, and the associated outcomes; these data may serve as a benchmark for continued improvements in quality of PAH care.

Neurodegenerative changes predominate in early stages of diabetic retinopathy but effective therapies are lacking. Insulin treatment decreases neurodegeneration and intranasal insulin has been shown to reach the central nervous system in neurodegenerative diseases like dementia. We tested the hypothesis that intranasal insulin can decrease retinal neurodegeneration using the C57BL/KsJ-db/db transgenic diabetic (db/db) mouse model. Compared to the non-diabetic wildtype mice given intranasal saline, we observed decreased electroretinogram b-wave and oscillatory potential amplitudes in db/db mice treated with intranasal saline but not in the db/db mice treated with 2 units of intranasal insulin daily over 10 weeks. When compared to the non-diabetic intranasal saline control, we also observed decreased outer retinal thickness in the db/db mice given intranasal saline but this effect was attenuated in the db/db mice treated with intranasal insulin. GFAP immunoreactivity and caspase cell count were similarly elevated in the db/db mice treated with intranasal saline but not intranasal insulin. Mean blood glucose measurements increased 30 minutes after both intranasal saline and insulin treatment. Transcriptomic analysis revealed downregulation of inflammatory and apoptotic genes in the retina of db/db mice treated with intranasal insulin when compared to saline. In summary, treatment with intranasal insulin prevents the depression of b-waves and oscillatory potentials, decreases the attenuation of outer retinal thickness, reduces caspase cell count and GFAP immunostaining, and downregulates the transcription of inflammatory and apoptotic genes in the retina of db/db mice without exerting peripheral glucose lowering effects. Taken together, our results suggest that intranasal insulin can reduce neurodegeneration in diabetic retinopathy by improving retinal neuronal function, decreasing reactive gliosis and cell death, and modulating the expression of inflammatory and apoptotic genes.

BACKGROUND:Food insecurity affects up to 30 % of pregnancies and is associated with worse maternal and infant health. Healthcare systems are implementing interventions to assist patients with food insecurity, but rather than providing a single intervention, adaptively providing interventions could be a more effective strategy. The objective of this study is to determine the feasibility of adaptively providing interventions to assist pregnant patients who report being food-insecure. METHODS/DESIGN:We will conduct a pilot sequential multiple assignment randomized trial at obstetrics clinics from one health system. Adults (N = 60) who are pregnant and food-insecure will be randomized at their initial prenatal visit to one of two first-stage interventions for 3 months: 1) electronic health record (EHR) referral to WIC or 2) EHR-referral to WIC + care navigation. Participants who do not have ≥2-point improvement in food insecurity after 3 months will be re-randomized to one of two second-stage interventions for an additional 3 months: weekly delivery of 1) produce or 2) medically-tailored meals. In Aim 1, we will determine the feasibility of recruitment, and in Aim 2, we will evaluate the feasibility of re-randomization, retention, and data collection. In Aim 3, we will advance our understanding of how, why, and under what circumstances participants achieved improvements through semi-structured interviews. CONCLUSIONS:This will be the first study to test an adaptive intervention to assist pregnant patients with food insecurity and will inform a future fully-powered trial. Given the growing interest among health systems, an efficacious, adaptive food insecurity intervention could be broadly disseminated. TRIAL REGISTRATION:The study was registered with ClinicalTrials.gov (NCT06942598) on April 23, 2025.

Introduction: More than 5 million children in the United States experience food insecurity (FI), yet little guidance exists regarding screening for FI. A prediction model of FI could be useful for healthcare systems and practices working to identify and address children with FI. Our objective was to predict FI using demographic, geographic, medical, and historic unmet health-related social needs data available within most electronic health records. Methods: This was a retrospective longitudinal cohort study of children evaluated in an academic pediatric primary care clinic and screened at least once for FI between January 2017 and August 2021. American Community Survey Data provided additional insight into neighborhood-level information such as home ownership and poverty level. Household FI was screened using two validated questions. Various combinations of predictor variables and modeling approaches, including logistic regression, random forest, and gradient-boosted machine, were used to build and validate prediction models. Results: A total of 25,214 encounters from 8521 unique patients were included, with FI present in 3820 (15%) encounters. Logistic regression with a 12-month look-back using census block group neighborhood variables showed the best performance in the test set (C-statistic 0.70, positive predictive value 0.92), had superior C-statistics to both random forest (0.65, p < 0.01) and gradient boosted machine (0.68, p = 0.01), and showed the best calibration. Results were nearly unchanged when coding missing data as a category. Conclusions: Although our models could predict FI, further work is needed to develop a more robust prediction model for pediatric FI.

Context Uric acid's role in cardiovascular health in youth with type 1 diabetes is unknown. Objective Investigate whether higher uric acid is associated with increased blood pressure (BP) and arterial stiffness over time in adolescents and young adults with type 1 diabetes and if overweight/obesity modifies this relationship. Methods Longitudinal analysis of data from adolescents and young adults with type 1 diabetes from 2 visits (mean follow up 4.6 years) in the SEARCH for Diabetes in Youth multicenter prospective cohort study from 2007 to 2018. Our exposure was uric acid at the first visit and our outcome measures were the change in BP, pulse wave velocity (PWV), and augmentation index between visits. We used multivariable linear mixed-effects models and assessed for effect modification by overweight/obesity. Results Of 1744 participants, mean age was 17.6 years, 49.4% were female, 75.9% non-Hispanic White, and 45.4% had a follow-up visit. Mean uric acid was 3.7 mg/dL (SD 1.0). Uric acid was not associated with increased BP, PWV-trunk, or augmentation index over time. Uric acid was marginally associated with PWV-upper extremity (β = .02 m/s/year, 95% CI 0.002 to 0.04). The magnitude of this association did not differ by overweight/obesity status. Conclusion Among adolescents and young adults with type 1 diabetes, uric acid was not consistently associated with increased BP or arterial stiffness over time. These results support findings from clinical trials in older adults with diabetes showing that lowering uric acid levels does not improve cardiovascular outcomes.

Abstract Background A prediction model that estimates the risk of elevated glycated hemoglobin (HbA1c) was developed from electronic health record (EHR) data to identify adult patients at risk for prediabetes who may otherwise go undetected. We aimed to assess the internal performance of a new penalized regression model using the same EHR data and compare it to the previously developed stepdown approximation for predicting HbA1c ≥ 5.7%, the cut-off for prediabetes. Additionally, we sought to externally validate and recalibrate the approximation model using 2017–2020 pre-pandemic National Health and Nutrition Examination Survey (NHANES) data. Methods We developed logistic regression models using EHR data through two approaches: the Least Absolute Shrinkage and Selection Operator (LASSO) and stepdown approximation. Internal validation was performed using the bootstrap method, with internal performance evaluated by the Brier score, C-statistic, calibration intercept and slope, and the integrated calibration index. We externally validated the approximation model by applying original model coefficients to NHANES, and we examined the approximation model’s performance after recalibration in NHANES. Results The EHR cohort included 22,635 patients, with 26% identified as having prediabetes. Both the LASSO and approximation models demonstrated similar discrimination in the EHR cohort, with optimism-corrected C-statistics of 0.760 and 0.763, respectively. The LASSO model included 23 predictor variables, while the approximation model contained 8. Among the 2,348 NHANES participants who met the inclusion criteria, 30.1% had prediabetes. External validation of the LASSO model was not possible due to the unavailability of some predictor variables. The approximation model discriminated well in the NHANES dataset, achieving a C-statistic of 0.787. Conclusion The approximation method demonstrated comparable performance to LASSO in the EHR development cohort, making it a viable option for healthcare organizations with limited resources to collect a comprehensive set of candidate predictor variables. NHANES data may be suitable for externally validating a clinical prediction model developed with EHR data to assess generalizability to a nationally representative sample, depending on the model’s intended use and the alignment of predictor variable definitions with those used in the model’s original development.

Objective: Evaluate the effectiveness of text messages to systematically engage parents/guardians ("caregivers") to reschedule a well-child visit (WCV) that was missed ("no-show") and attend that rescheduled WCV visits. Methods: Patients <18 years in one of five pediatrics or family medicine clinics, in one health system in the Southeast US, were eligible. Patients without a rescheduled WCV after a no-show were randomized into intervention (text messages) or care-as-usual comparison, stratified by language (English/Spanish). Enrollment occurred May-July 2022. Up to three text messages were sent to caregivers one week apart via REDCap and Twilio, advising how to reschedule the missed appointment by phone or health portal. Primary outcomes were 1) rescheduling a WCV within 6 weeks of no-show and 2) completing a rescheduled WCV within 6 weeks. Risk differences (RD) and odds ratios (OR) were used to evaluate the effect of text messages. Results: Seven hundred and twenty patients were randomized and analyzed (texts: 361, comparison: 359). The proportion rescheduling WCV after text versus usual care was English: 18.85% versus 15.02%, respectively, and Spanish: 5.94% versus 8.14%, with overall RD+ 1.98% (95% CI: -1.85, 5.81) and OR 1.21 (95% CI: 0.79, 1.84; P-value .38). Completed WCV rates in text or usual care were English: 13.08% versus 6.59%, and Spanish: 5.81% versus 5.94% with texts associated with RD+ 2.83% (95% CI: 1.66, 4.00) and OR 1.86 (95% CI: 1.09, 3.19). Conclusion: Text message follow-up after a no-show WCV may positively impact attendance at WCVs rescheduled in the subsequent 6 weeks. Trial registration: ClinicalTrials.gov NCT05086237.

Rationale and Objectives: Tools are needed for frailty screening of older adults. Opportunistic analysis of body composition could play a role. We aim to determine whether computed tomography (CT) -derived measurements of muscle and adipose tissue are associated with frailty. Materials and Methods: Outpatients aged >= 55 years consecutively imaged with contrast -enhanced abdominopelvic CT over a 3month interval were included. Frailty was determined from the electronic health record using a previously validated electronic frailty index (eFI). CT images at the level of the L3 vertebra were automatically segmented to derive muscle metrics (skeletal muscle area [SMA], skeletal muscle density [SMD], intermuscular adipose tissue [IMAT]) and adipose tissue metrics (visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT]). Distributions of demographic and CT -derived variables were compared between sexes. Sexspecific associations of muscle and adipose tissue metrics with eFI were characterized by linear regressions adjusted for age, race, ethnicity, duration between imaging and eFI measurements, and imaging parameters. Results: The cohort comprised 886 patients (449 women, 437 men, mean age 67.9 years), of whom 382 (43%) met the criteria for prefrailty (ie, 0.10 < eFI <= 0.21) and 138 (16%) for frailty (eFI > 0.21). In men, 1 standard deviation changes in SMD (beta = -0.01, 95% confidence interval [CI], -0.02 to -0.001, P = .02) and VAT area (beta = 0.008, 95% CI, 0.0005-0.02, P = .04), but not SMA, IMAT, or SAT, were associated with higher frailty. In women, none of the CT -derived muscle or adipose tissue metrics were associated with frailty. Conclusion: We observed a positive association between frailty and CT -derived biomarkers of myosteatosis and visceral adiposity in a sex -dependent manner.

Purpose: We reevaluated the Action for Health in Diabetes (Look AHEAD) intensive lifestyle intervention (ILI) to assess whether the effect of ILI on cardiovascular disease (CVD) prevention differed by baseline glycated hemoglobin (HbA1c). Methods: Look AHEAD randomized 5145 adults, aged 45 to 76 years with type 2 diabetes and overweight/obesity to ILI or a diabetes support and education (DSE) control group for a median of 9.6 years. ILI focused on achieving weight loss through decreased caloric intake and increased physical activity. We assessed the parent trial's primary composite CVD outcome. We evaluated additive and multiplicative heterogeneity of the intervention on CVD risk by baseline HbA1c. Results: Mean baseline HbA1c was 7.3% (SD 1.2) and ranged from 4.4% (quintile 1) to 14.5% (quintile 5). We observed additive and multiplicative heterogeneity of the association between ILI and CVD (all P < .001) by baseline HbA1c. Randomization to ILI was associated with lower CVD risk for HbA1c quintiles 1 [hazard ratio (HR): 0.68, 95% confidence interval (CI): 0.53, 0.88] and 2 (HR: 0.80, 95% CI: 0.66, 0.96) and associated with higher CVD risk for HbA1c quintile 5 (HR: 1.27, 95% CI: 1.02, 1.58), compared to DSE. Conclusion: Among adults with type 2 diabetes and overweight/obesity, randomization to a lifestyle intervention was differentially associated with CVD risk by baseline HbA1c such that it was associated with lower risk at lower HbA1c levels and higher risk at higher HbA1c levels. There is a critical need to develop and tailor lifestyle interventions to be successful for individuals with type 2 diabetes and high HbA1c.

Background: The Healthy Eating Index 2010 (HEI-2010) and Alternative Healthy Eating Index 2010 (AHEI-2010) are commonly used to measure dietary quality in research settings. Neither index is designed specifically to compare diet quality between low-carbohydrate (LC) and low-fat (LF) diets. It is unknown whether biases exist in making these comparisons. Objective: The aim was to determine whether HEI-2010 and AHEI-2010 contain biases when scoring LC and LF diets. Design: Secondary analyses of the Diet Intervention Examining the Factors Interacting With Treatment Success (DIETFITS) weight loss trial were conducted. The trial was conducted in the San Francisco Bay Area of California between January 2013 and May 2016. Three approaches were used to investigate whether biases existed for HEI-2010 and AHEI-2010 when scoring LC and LF diets. Participants/setting: DIETFITS participants were assigned to follow healthy LC or healthy LF diets for 12 months (n = 609). Main outcomes measures: Mean diet quality index scores for each diet were measured. Statistical analysis: Approach 1 examined both diet quality indices' scoring criteria. Approach 2 compared scores garnered by exemplary quality LC and LF menus created by registered dietitian nutritionists. Approach 3 used 2-sided t tests to compare the HEI-2010 and AHEI-2010 scores calculated from 24-hour dietary recalls of DIETFITS trial participants (n = 608). Results: Scoring criteria for both HEI-2010 (100 possible points) and AHEI-2010 (110 possible points) were estimated to favor an LF diet by 10 points. Mean scores for exemplary quality LF menus were higher than for LC menus using both HEI-2010 (91.8 vs 76.8) and AHEI-2010 (71.7 vs 64.4, adjusted to 100 possible points). DIETFITS participants assigned to a healthy LF diet scored significantly higher on HEI and AHEI than those assigned to a healthy LC diet at 3, 6, and 12 months (all, P < .001). Mean baseline scores were lower than mean scores at all follow-up time points regardless of diet assignment or diet quality index used. Conclusions: Commonly used diet quality indices, HEI-2010 and AHEI-2010, showed biases toward LF vs LC diets. However, both indices detected expected changes in diet quality within each diet, with HEI-2010 yielding greater variation in scores. Findings support the use of these indices in measuring diet quality differences within, but not between, LC and LF diets.

Objectives:Cardiovascular disease (CVD) is the leading cause of death and disability among persons with diabetes. Early intervention on cardiovascular risk factors (CRFs) is important in reducing CVD burden. The SEARCH for Diabetes in Youth study assessed CRFs in incident cohorts of youth aged <20 years established from 2002 to 2016. Research Design and Methods. Regression models assessed trends over each incident year for lipids (total cholesterol (TC), HDL-c, LDL-c, triglycerides (TG), VLDL-c, and non-HDL-c), kidney function (albumin/creatinine ratio (ACR) ≥30 and ≥300, cystatin C, serum creatinine and estimated glomerular filtration rate (eGFR)), systolic and diastolic blood pressure (BP) z-scores, BMI z-score, waist circumference (WC), and an inflammatory marker (C-reactive protein (CRP)). Models were stratified by diabetes type (type 1 diabetes (T1D), N = 4,600; type 2 diabetes (T2D), N = 932) and adjusted for age at diagnosis, sex, race/ethnicity, and diabetes duration. An interaction analysis assessed differential time trends by type. Results:For youth with T1D, all CRFs significantly improved over time, with the exception of ACR > 300, cystatin C, serum creatinine, eGFR, and CRP. For youth with T2D, TC, LDL-c, and non-HDL-c significantly improved, while eGFR, BMI z-score, and CRP significantly worsened. Significant differences in trends over time by type were seen for TC, HDL-c, BMI z-score, BP z-scores, WC, and CRP. Conclusions:Overall, improvements in CRFs were more often observed in youth with T1D. Youth with T2D had worsening trends over time in BMI z-score, CRP, and kidney function. Further research is needed to better understand these trends and their implications for long-term CVD risk.

Population health initiatives often rely on cold outreach to close gaps in preventive care, such as overdue screenings or immunizations. Tailoring messages to diverse patient populations remains challenging, as traditional A/B testing requires large sample sizes to test only two alternative messages. With increasing availability of large language models (LLMs), programs can utilize tiered testing among both LLM and manual human agents, presenting the dilemma of identifying which patients need different levels of human support to cost-effectively engage large populations. Using microsimulations, we compared both the statistical power and false positive rates of A/B testing and Sequential Multiple Assignment Randomized Trials (SMART) for developing personalized communications across multiple effect sizes and sample sizes. SMART showed better cost-effectiveness and net benefit across all scenarios, but superior power for detecting heterogeneous treatment effects (HTEs) only in later randomization stages, when populations were more homogeneous and subtle differences drove engagement differences.

BACKGROUND: Intensive blood pressure (BP) control in youth with chronic kidney disease (CKD) slows progression, delaying the need for kidney replacement therapy (KRT). Most youth with CKD have hypertension and BP control is difficult to achieve outside of controlled experimental settings. Implementing effective BP control strategies in this population may be cost-saving despite requiring additional resources. Our objective was to determine the economic and clinical impact of intensive versus usual care for BP management in youth with CKD in a microeconomic model. METHODS: We developed a decision tree from the US payer perspective to estimate the total costs and clinical effect of an intensified BP intervention over 5 years, modeled after the ESCAPE trial (Effect of Strict Blood Pressure Control and Angiotensin-Converting Enzyme [ACE] Inhibition on Progression of Chronic Renal Failure in Pediatric Patients) protocol. We compared this intervention to usual care in a hypothetical population of youth with mild-to-moderate CKD. Probabilities were informed by published literature; cost estimates were informed by publicly available data. Our outcomes were the net discounted cost of an intensive BP intervention, number needed to treat with the intervention to prevent 1 KRT episode, and incremental cost per KRT episode avoided. RESULTS: An intensive BP intervention, with a goal of an average 24-hour mean arterial pressure <50th percentile, improved outcomes with net cost savings of $9440 per participant over 5 years compared with usual care. To prevent 1 episode of KRT over 5 years, 13 participants need to receive intensive BP intervention. CONCLUSIONS: Routine use of the ESCAPE protocol for intensive BP control in youth with CKD could save overall costs for the payer and improve clinical outcomes.

Background: Eating a high-quality diet or adhering to a given dietary strategy may influence weight loss. However, these 2 factors have not been examined concurrently for those following macronutrient-limiting diets. Objective: To determine whether improvement in dietary quality, change in dietary macronutrient composition, or the combination of these factors is associated with differential weight loss when following a healthy low-carbohydrate (HLC) or healthy low-fat (HLF) diet. Design: Generally healthy adults were randomly assigned to HLC or HLF diets for 12 mo (n = 609) as part of a randomized controlled weight loss study. Participants with complete 24-h dietary recall data at baseline and 12-mo were included in this secondary analysis (total N = 448; N = 224 HLC, N = 224 HLF). Participants were divided into 4 subgroups according to 12-mo change in HEI-2010 score [above median = high quality (HQ) and below median = low quality (LQ)] and 12-mo change in macronutrient intake [below median = high adherence (HA) and above median = low adherence (LA) for net carbohydrate (g) or fat (g) for HLC and HLF, respectively]. Baseline to 12-mo changes in mean BMI were compared for those in HQ/HA, HQ/LA, LQ/ HA subgroups with the LQ/LA subgroup within HLC and HLF. Results: For HLC, changes (95 % confidence level [CI]) in mean BMI were-1.15 kg/m2 (-2.04,-0.26) for HQ/HA,-0.30 (-1.22, 0.61) for HQ/LA, and-0.80 (-1.74, 0.14) for LQ/HA compared with the LQ/LA subgroup. For HLF, changes (95% CI) in mean BMI were-1.11kg/m2 (-2.10,-0.11) for HQ/HA,-0.26 (-1.26, 0.75) for HQ/LA, and-0.66 (-1.74, 0.41) for LQ/HA compared with the LQ/LA subgroup. Conclusion: Within both HLC and HLF diet arms, 12-mo decrease in BMI was significantly greater in HQ/HA subgroups relative to LQ/LA subgroups. Neither HQ nor HA alone were significantly different than LQ/LA subgroups. Results of this analysis support the combination of dietary adherence and high-quality diets for weight loss.