Factors Shaping the Implementation of Strategies to Prevent Acute Kidney Injury: A Qualitative Study

Reducing the prevalence of acute kidney injury (AKI) is an important patient safety objective set forth by the National Quality Forum. Despite international guidelines to prevent AKI, there continues to be an inconsistent uptake of these interventions by cardiac teams across practice settings. The IMPROVE-AKI study was designed to test the effectiveness and implementation of AKI preventive strategies delivered through team-based coaching activities. Qualitative methods were used to identify factors that shaped sites' implementation of AKI prevention strategies. Semi-structured interviews were conducted with staff in a range of roles within the cardiac catheterization laboratories, including nurses, laboratory managers, and interventional cardiologists (N = 50) at multiple time points over the course of the study. Interview transcripts were qualitatively coded, and aggregated code reports were reviewed to construct main themes through memoing. In this paper, we report insights from semi-structured interviews regarding workflow, organizational culture, and leadership factors that impacted implementation of AKI prevention strategies.

Latent Class Analysis of Prenatal Substance Exposure and Child Behavioral Outcomes

OBJECTIVES

To predict behavioral disruptions in middle childhood, we identified latent classes of prenatal substance use.

STUDY DESIGN

As part of the Environmental influences on Child Health Outcomes Program, we harmonized prenatal substance use data and child behavior outcomes from 2195 women and their 6- to 11-year-old children across 10 cohorts in the US and used latent class-adjusted regression models to predict parent-rated child behavior.

RESULTS

Three latent classes fit the data: low use (90.5%; n = 1986), primarily using no substances; licit use (6.6%; n = 145), mainly using nicotine with a moderate likelihood of using alcohol and marijuana; and illicit use (2.9%; n = 64), predominantly using illicit substances along with a moderate likelihood of using licit substances. Children exposed to primarily licit substances in utero had greater levels of externalizing behavior than children exposed to low or no substances (P = .001, d = .64). Children exposed to illicit substances in utero showed small but significant elevations in internalizing behavior than children exposed to low or no substances (P < .001, d = .16).

CONCLUSIONS

The differences in prenatal polysubstance use may increase risk for specific childhood problem behaviors; however, child outcomes appeared comparably adverse for both licit and illicit polysubstance exposure. We highlight the need for similar multicohort, large-scale studies to examine childhood outcomes based on prenatal substance use profiles.

Team-Based Coaching Intervention to Improve Contrast-Associated Acute Kidney Injury: A Cluster-Randomized Trial

BACKGROUND

Up to 14% of patients in the United States undergoing cardiac catheterization each year experience AKI. Consistent use of risk minimization preventive strategies may improve outcomes. We hypothesized that team-based coaching in a Virtual Learning Collaborative (Collaborative) would reduce postprocedural AKI compared with Technical Assistance (Assistance), both with and without Automated Surveillance Reporting (Surveillance).

METHODS

The IMPROVE AKI trial was a 2×2 factorial cluster-randomized trial across 20 Veterans Affairs medical centers (VAMCs). Participating VAMCs received Assistance, Assistance with Surveillance, Collaborative, or Collaborative with Surveillance for 18 months to implement AKI prevention strategies. The Assistance and Collaborative approaches promoted hydration and limited NPO and contrast dye dosing. We fit logistic regression models for AKI with site-level random effects accounting for the clustering of patients within medical centers with a prespecified interest in exploring differences across the four intervention arms.

RESULTS

Among VAMCs' 4517 patients, 510 experienced AKI (235 AKI events among 1314 patients with preexisting CKD). AKI events in each intervention cluster were 110 (13%) in Assistance, 122 (11%) in Assistance with Surveillance, 190 (13%) in Collaborative, and 88 (8%) in Collaborative with Surveillance. Compared with sites receiving Assistance alone, case-mix-adjusted differences in AKI event proportions were -3% (95% confidence interval [CI], -4 to -3) for Assistance with Surveillance, -3% (95% CI, -3 to -2) for Collaborative, and -5% (95% CI, -6 to -5) for Collaborative with Surveillance. The Collaborative with Surveillance intervention cluster had a substantial 46% reduction in AKI compared with Assistance alone (adjusted odds ratio=0.54; 0.40-0.74).

CONCLUSIONS

This implementation trial estimates that the combination of Collaborative with Surveillance reduced the odds of AKI by 46% at VAMCs and is suggestive of a reduction among patients with CKD.

CLINICAL TRIAL REGISTRY NAME AND REGISTRATION NUMBER

IMPROVE AKI Cluster-Randomized Trial (IMPROVE-AKI), NCT03556293.

Characteristics of Individuals in the United States Who Used Opioids During Pregnancy

Background: Opioid use has disproportionally impacted pregnant people and their fetuses. Previous studies describing opioid use among pregnant people are limited by geographic location, type of medical coverage, and small sample size. We described characteristics of a large, diverse group of pregnant people who were enrolled in the Environmental Influences on Child Health Outcomes (ECHO) Program, and determined which characteristics were associated with opioid use during pregnancy. Materials and Methods: Cross-sectional data obtained from 21,905 pregnancies of individuals across the United States enrolled in the ECHO between 1990 and 2021 were analyzed. Medical records, laboratory testing, and self-report were used to determine opioid-exposed pregnancies. Multiple imputation methods using fully conditional specification with a discriminant function accounted for missing characteristics data. Results: Opioid use was present in 2.8% (n = 591) of pregnancies. The majority of people who used opioids in pregnancy were non-Hispanic White (67%) and had at least some college education (69%). Those who used opioids reported high rates of alcohol use (32%) and tobacco use (39%) during the pregnancy; although data were incomplete, only 5% reported heroin use and 86% of opioid use originated from a prescription. After adjustment, non-Hispanic White race, pregnancy during the years 2010-2012, higher parity, tobacco use, and use of illegal drugs during pregnancy were each significantly associated with opioid use during pregnancy. In addition, maternal depression was associated with increased odds of opioid use during pregnancy by more than two-fold (adjusted odds ratio 2.42, 95% confidence interval: 1.95-3.01). Conclusions: In this large study of pregnancies from across the United States, we found several factors that were associated with opioid use among pregnant people. Further studies examining screening for depression and polysubstance use may be useful for targeted interventions to prevent detrimental opioid use during pregnancy, while further elucidation of the reasons for use of prescription opioids during pregnancy should be further explored.

Abstract 245: Increased Prevalence Of Children With Congenital Heart Disease In Colorado From 2012 - 2019

Introduction: Congenital heart defects (CHD) are the most common birth defects and are estimated to affect almost 1% of births per year in the US. Most CHD prevalence estimates are based on data from population-based birth defects surveillance systems and these estimates are inconsistent due to varied definitions. It is therefore important to derive high-quality, population-based estimates of the prevalence of CHD to help care for this vulnerable population.

Methods: We analyzed all payer claims data (APCD) from Colorado from 2012-2019. Children with CHD were identified by applying CHD ICD-9 and ICD-10 diagnoses codes from the Society of Thoracic Surgeons (STS) International Society for Nomenclature of Paediatric and Congenital Heart Disease (ISNPCHD) harmonized cardiac codes. We included children with CHD < 18 years of age who resided in Colorado, had a documented zip code, and had at least one ambulatory healthcare claim. We analyzed the test for linear trends in the proportion of CHD diagnoses from 2012-2019 with the Cochran-Armitage (Z) test. Differences among patient characteristics and CHD diagnosis were tested using the Pearson Chi-square test and Wilcoxon rank sum tests as appropriate.

Results: Overall the current study analyzed 1,565,438 children with 36,567 CHD diagnoses (i.e. 23.4 per 1,000 live births), comprising 2.3% of the pediatric population. Between 2012 and 2019 the statewide rate of children diagnosed with CHD significantly increased from 21.9 to 32.3 per 1,000 children per year (Z: 5.38; p<0.001). There were statistically significant differences in the magnitude of the trend in CHD prevalence rate by region (Z: -31.82), urban-rural residence (Z:-24.02), degree of chronic complex conditions (Z: -38.78), disease severity (Z: -44.11), age (Z: -72.89), insurance type (Z: 46.51) and median household income (Z: 12.87; all p<0.001).

Conclusion: The current study is the first population-level analysis of pediatric CHD in the US and these findings suggest that the statewide CHD prevalence rate has increased significantly since 2012. Children with CHD are a priority population for quality improvement in pediatrics given their growing prevalence and corresponding risk of adverse outcomes.

Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?

Background

Social risk factors influence rehospitalization rates yet are challenging to incorporate into prediction models. Integration of social risk factors using natural language processing (NLP) and machine learning could improve risk prediction of 30‐day readmission following an acute myocardial infarction.

Methods and Results

Patients were enrolled into derivation and validation cohorts. The derivation cohort included inpatient discharges from Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary diagnosis of acute myocardial infarction, who were discharged alive, and not transferred from another facility. The validation cohort included patients from Dartmouth‐Hitchcock Health Center between April 2, 2011, and December 31, 2016, meeting the same eligibility criteria described above. Data from both sites were linked to Centers for Medicare & Medicaid Services administrative data to supplement 30‐day hospital readmissions. Clinical notes from each cohort were extracted, and an NLP model was deployed, counting mentions of 7 social risk factors. Five machine learning models were run using clinical and NLP‐derived variables. Model discrimination and calibration were assessed, and receiver operating characteristic comparison analyses were performed. The 30‐day rehospitalization rates among the derivation (n=6165) and validation (n=4024) cohorts were 15.1% (n=934) and 10.2% (n=412), respectively. The derivation models demonstrated no statistical improvement in model performance with the addition of the selected NLP‐derived social risk factors.

Conclusions

Social risk factors extracted using NLP did not significantly improve 30‐day readmission prediction among hospitalized patients with acute myocardial infarction. Alternative methods are needed to capture social risk factors.

Improving the prediction of long-term readmission and mortality using a novel biomarker panel

OBJECTIVE

Several short-term readmission and mortality prediction models have been developed using clinical risk factors or biomarkers among patients undergoing coronary artery bypass graft (CABG) surgery. The use of biomarkers for long-term prediction of readmission and mortality is less well understood. Given the established association of cardiac biomarkers with short-term adverse outcomes, we hypothesized that 5-year prediction of readmission or mortality may be significantly improved using cardiac biomarkers.

MATERIALS AND METHODS

Plasma biomarkers from 1149 patients discharged alive after isolated CABG surgery from eight medical centers were measured in a cohort from the Northern New England Cardiovascular Disease Study Group between 2004 and 2007. We assessed the added predictive value of a biomarker panel with a clinical model against the clinical model alone and compared the model discrimination using the area under the receiver operating characteristic (AUROC) curves.

RESULTS

In our cohort, 461 (40%) patients were readmitted or died within 5 years. Long-term outcomes were predicted by applying the STS ASCERT clinical model with an AUROC of 0.69. The biomarker panel with the clinical model resulted in a significantly improved AUROC of 0.74 (p value <.0001). Across 5 years, the hazard ratio for patients in the second to fifth quintile predicted probabilities from the biomarker augmented STS ASCERT model ranged from 2.2 to 7.9 (p values <.001).

CONCLUSIONS

We report that a panel of biomarkers significantly improved prediction of long-term readmission or mortality risk following CABG surgery. Our findings suggest biomarkers help clinical care teams better assess the long-term risk of readmission or mortality.

Development of Electronic Health Record-Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction

IMPORTANCE

In the US, more than 600 000 adults will experience an acute myocardial infarction (AMI) each year, and up to 20% of the patients will be rehospitalized within 30 days. This study highlights the need for consideration of calibration in these risk models.

OBJECTIVE

To compare multiple machine learning risk prediction models using an electronic health record (EHR)-derived data set standardized to a common data model.

DESIGN, SETTING, AND PARTICIPANTS

This was a retrospective cohort study that developed risk prediction models for 30-day readmission among all inpatients discharged from Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary diagnosis of AMI who were not transferred from another facility. The model was externally validated at Dartmouth-Hitchcock Medical Center from April 2, 2011, to December 31, 2016. Data analysis occurred between January 4, 2019, and November 15, 2020.

EXPOSURES

Acute myocardial infarction that required hospital admission.

MAIN OUTCOMES AND MEASURES

The main outcome was thirty-day hospital readmission. A total of 141 candidate variables were considered from administrative codes, medication orders, and laboratory tests. Multiple risk prediction models were developed using parametric models (elastic net, least absolute shrinkage and selection operator, and ridge regression) and nonparametric models (random forest and gradient boosting). The models were assessed using holdout data with area under the receiver operating characteristic curve (AUROC), percentage of calibration, and calibration curve belts.

RESULTS

The final Vanderbilt University Medical Center cohort included 6163 unique patients, among whom the mean (SD) age was 67 (13) years, 4137 were male (67.1%), 1019 (16.5%) were Black or other race, and 933 (15.1%) were rehospitalized within 30 days. The final Dartmouth-Hitchcock Medical Center cohort included 4024 unique patients, with mean (SD) age of 68 (12) years; 2584 (64.2%) were male, 412 (10.2%) were rehospitalized within 30 days, and most of the cohort were non-Hispanic and White. The final test set AUROC performance was between 0.686 to 0.695 for the parametric models and 0.686 to 0.704 for the nonparametric models. In the validation cohort, AUROC performance was between 0.558 to 0.655 for parametric models and 0.606 to 0.608 for nonparametric models.

CONCLUSIONS AND RELEVANCE

In this study, 5 machine learning models were developed and externally validated to predict 30-day readmission AMI hospitalization. These models can be deployed within an EHR using routinely collected data.

Heart failure biomarker levels correlate with invasive haemodynamics in pulmonary valve replacement

BACKGROUND

Although widely used in cardiology, relation of heart failure biomarkers to cardiac haemodynamics in patients with CHD (and in particular with pulmonary insufficiency undergoing pulmonary valve replacement) remains unclear. We hypothesised that the cardiac function biomarkers N-terminal pro-brain natriuretic peptide (NT-proBNP), soluble suppressor of tumorigenicity 2, and galectin-3 would have significant associations to right ventricular haemodynamic derangements.

METHODS

Consecutive patients ( n = 16) undergoing cardiac catheterisation for transcatheter pulmonary valve replacement were studied. NT-proBNP, soluble suppressor of tumorigenicity 2, and galectin-3 levels were measured using a multiplex enzyme-linked immunosorbent assay from a pre-intervention blood sample obtained after sheath placement. Spearman correlation was used to identify significant correlations (p ≤ 0.05) of biomarkers with baseline cardiac haemodynamics. Cardiac MRI data (indexed right ventricular and left ventricular end-diastolic volumes and ejection fraction) prior to device placement were also compared to biomarker levels.

RESULTS

NT-proBNP and soluble suppressor of tumorigenicity 2 were significantly correlated (p < 0.01) with baseline mean right atrial pressure and right ventricular end-diastolic pressure. Only NT-proBNP was significantly correlated with age. Galectin-3 did not have significant associations in this cohort. Cardiac MRI measures of right ventricular function and volume were not correlated to biomarker levels or right heart haemodynamics.

CONCLUSIONS

NT-proBNP and soluble suppressor of tumorigenicity 2, biomarkers of myocardial strain, significantly correlated to invasive pressure haemodynamics in transcatheter pulmonary valve replacement patients. Serial determination of soluble suppressor of tumorigenicity 2, as it was not associated with age, may be superior to serial measurement of NT-proBNP as an indicator for timing of pulmonary valve replacement.

The Association Between Cardiac Biomarker NT-proBNP and 30-Day Readmission or Mortality After Pediatric Congenital Heart Surgery

BACKGROUND

Very little is known about clinical and biomarker predictors of readmissions following pediatric congenital heart surgery. The cardiac biomarker N-terminal pro-brain natriuretic peptide (NT-proBNP) can help predict readmission in adult populations, but the estimated utility in predicting risk of readmission or mortality after pediatric congenital heart surgery has not previously been studied. Our objective was to evaluate the association between pre- and postoperative serum biomarker levels and 30-day readmission or mortality for pediatric patients undergoing congenital heart surgery.

METHODS

We measured pre- and postoperative NT-proBNP levels in two prospective cohorts of 522 pediatric patients <18 years of age who underwent at least one congenital heart operation from 2010 to 2014. Blood samples were collected before and after surgery. We evaluated the association between pre- and postoperative NT-proBNP with readmission or mortality within 30 days of discharge, using multivariate logistic regression, adjusting for covariates based on the Society of Thoracic Surgeons (STS) Congenital Heart Surgery Mortality Risk Model.

RESULTS

The Johns Hopkins Children's Center cohort and the Translational Research Investigating Biomarker Endpoints in Acute Kidney Injury (TRIBE-AKI) cohort demonstrate event rates of 12.9% and 9.4%, respectively, for the composite end point. After adjustment for covariates in the STS congenital risk model, we did not find an association between elevated levels of NT-proBNP and increased risk of readmission or mortality following congenital heart surgery for either cohort.

CONCLUSIONS

In our two cohorts, preoperative and postoperative values of NT-proBNP were not significantly associated with readmission or mortality following pediatric congenital heart surgery. These findings will inform future studies evaluating multimarker risk assessment models in the pediatric population.

Biomarkers associated with 30-day readmission and mortality after pediatric congenital heart surgery

OBJECTIVES

Novel cardiac biomarkers serum (suppression of tumorigenicity [ST2]) and Galectin-3 may be associated with an increased likelihood of important events after cardiac surgery. Our objective was to explore the association between pre- and postoperative serum biomarker levels and 30-day readmission or mortality for pediatric patients.

METHODS

We prospectively enrolled pediatric patients <18 years of age who underwent at least one cardiac surgical operation at Johns Hopkins Children's Center from 2010 to 2014 (N = 162). Blood samples were collected immediately before surgery and at the end of bypass. We evaluated the association between pre- and postoperative Galectin-3 and ST2 with 30-day readmission or mortality, using backward stepwise logistic regression, adjusting for covariates based on the Society of Thoracic Surgeons (STS) Congenital Heart Surgery Mortality Risk Model.

RESULTS

In our cohort, 21 (12.9%) patients experienced readmission or mortality 30-days from discharge. Before adjustment, preoperative ST2 terciles demonstrated a strong association with readmission and/or mortality after surgery (OR: 2.58; 95% CI: 1.17-3.66 and OR: 4.37; 95% CI: 1.31-14.57). After adjustment for covariates based on the STS congenital risk model, Galectin-3 postoperative mid-tercile was significantly associated with 30-day readmission or mortality (OR: 6.17; 95% CI: 1.50-0.43) as was the highest tercile of postoperative ST2 (OR: 4.98; 95% CI: 1.06-23.32).

CONCLUSIONS

Elevated pre-and postoperative levels of ST2 and Galectin-3 are associated with increased risk of readmission or mortality after pediatric heart surgery. These clinically available biomarkers can be used for improved risk stratification and may guide improved patient care management.

sST2 as a novel biomarker for the prediction of in-hospital mortality after coronary artery bypass grafting

Objectives: Soluble suppression of tumorigenicity 2 (sST2) biomarker is an emerging predictor of adverse clinical outcomes, but its prognostic value for in-hospital mortality after coronary artery bypass grafting (CABG) is not well understood. This study measured the association between operative sST2 levels and in-hospital mortality after CABG. Methods: A prospective cohort of 1560 CABG patients were analyzed from the Northern New England Cardiovascular Disease Study Group Biomarker Study. The primary outcome was in-hospital mortality after CABG surgery (n = 32). Results: After risk adjustment, patients in the third tercile of pre-, post- and pre-to-postoperative sST2 values experienced significantly greater odds of in-hospital death compared to patients in the first tercile of sST2 values. The addition of both postoperative and pre-to-postoperative sST2 biomarker significantly improved ability to predict in-hospital mortality status following CABG surgery, compared to using the EuroSCORE II mortality model alone, (c-statistic: 0.83 [95% CI: 0.75, 0.92], p value 0.0213) and (c-statistic: 0.83 [95% CI: 0.75, 0.92], p value 0.0215), respectively. Conclusion: sST2 values are associated with in-hospital mortality after CABG surgery and postoperative and pre-to-post operative sST2 values improve prediction. Our findings suggest that sST2 can be used as a biomarker to identify adult patients at greatest risk of in-hospital death after CABG surgery.

Neonatal Abstinence Syndrome in West Virginia Substate Regions, 2007-2013

PURPOSE

The opioid epidemic is a public health threat with consequences affecting newborns. Neonatal Abstinence Syndrome (NAS) is a constellation of withdrawal symptoms resulting primarily from in utero opioid exposure. The purpose of this study was to examine NAS and drug-specific trends in West Virginia (WV), where rurality-related issues are largely present.

METHODS

The 2007-2013 WV Health Care Authority, Uniform Billing Data were analyzed for 119,605 newborn admissions with 1,974 NAS diagnoses. NAS (ICD9-CM 779.5) and exposure diagnostic codes for opioids, hallucinogens, and cocaine were utilized as incidence rate (IR) per 1,000 live births.

FINDINGS

Between 2007 and 2013, NAS IR significantly increased from 7.74 to 31.56 per 1,000 live births per year (Z: -19.10, P < .0001). During this time period, opioid exposure increased (Z: -9.56, P < .0001), while cocaine exposure decreased (Z: 3.62, P = .0003). In 2013, the southeastern region of the state had the highest NAS IR of 48.76 per 1,000 live births. NAS infants were more likely to experience other clinical conditions, longer hospital stay, and be insured by Medicaid.

CONCLUSIONS

Statewide NAS IR increased 4-fold over the study period, with rates over 3 times the national annual averages. This alarming trend is deleterious for the health of WV mother-child dyads and it strains the state's health care system. Therefore, WV has a unique need for prenatal public health drug treatment and prevention resources, specifically targeting the southeastern region. Further examination of maternal drug-specific trends and general underutilization of neonatal exposure ICD-9-CM codes is indicated.

Understanding Genomic Knowledge in Rural Appalachia: The West Virginia Genome Community Project

PURPOSE

Rural communities have limited knowledge about genetics and genomics and are also underrepresented in genomic education initiatives. The purpose of this project was to assess genomic and epigenetic knowledge and beliefs in rural West Virginia.

SAMPLE

A total of 93 participants from three communities participated in focus groups and 68 participants completed a demographic survey. The age of the respondents ranged from 21 to 81 years. Most respondents had a household income of less than $40,000, were female and most were married, completed at least a HS/GED or some college education working either part-time or full-time.

METHOD

A Community Based Participatory Research process with focus groups and demographic questionnaires was used.

FINDINGS

Most participants had a basic understanding of genetics and epigenetics, but not genomics. Participants reported not knowing much of their family history and that their elders did not discuss such information. If the conversations occurred, it was only during times of crisis or an illness event. Mental health and substance abuse are topics that are not discussed with family in this rural population.

CONCLUSIONS

Most of the efforts surrounding genetic/genomic understanding have focused on urban populations. This project is the first of its kind in West Virginia and has begun to lay the much needed infrastructure for developing educational initiatives and extending genomic research projects into our rural Appalachian communities. By empowering the public with education, regarding the influential role genetics, genomics, and epigenetics have on their health, we can begin to tackle the complex task of initiating behavior changes that will promote the health and well-being of individuals, families and communities.

Guided Imagery for Arthritis and Other Rheumatic Diseases: A Systematic Review of Randomized Controlled Trials

Many individuals suffering from arthritis and other rheumatic diseases (AORD) supplement pharmacologic treatments with psychosocial interventions. One promising approach, guided imagery, has been reported to have positive results in randomized controlled trials (RCTs) and is a highly scalable treatment for those with AORD. The main purpose of this study was to conduct a systematic review of RCTs that have examined the effects of guided imagery on pain, function, and other outcomes such as anxiety, depression, and quality of life in adults with AORD. Ten electronic bibliographic databases were searched for reports of RCTs published between 1960 and 2013. Selection criteria included adults with AORD who participated in RCTs that used guided imagery as a partial or sole intervention strategy. Risk of bias was assessed using the Cochrane Risk of Bias Assessment Instrument. Results were synthesized qualitatively. Seven studies representing 306 enrolled and 287 participants who completed the interventions met inclusion criteria. The average age of the participants was 62.9 years (standard deviation = 12.2). All interventions used guided imagery scripts that were delivered via audio technology. The interventions ranged from a one-time exposure to 16 weeks in duration. Risk of bias was low or unclear in all but one study. All studies reported statistically significant improvements in the observed outcomes. Guided imagery appears to be beneficial for adults with AORD. Future theory-based studies with cost-benefit analyses are warranted.

Linking parent perceptions of children's weight to early coronary risk factors: results from the CARDIAC Project

INTRODUCTION

Obesity is a public health threat because of the increasing prevalence in childhood and its causal relationship to the leading cause of death in America, heart disease. Detecting early signs of cardiovascular disease (CVD) risk factors in children and appropriately intervening to reverse the unhealthy trajectory associated with childhood obesity is of great importance. The objective of this study is to observe parental perception of their child's body mass index (BMI) and find associations between inaccurately estimated children and CVD risk factors.

METHODS

This study analyzed the association between 147 rural fifth grade students' lipid profiles and parents' self-reported survey who participated in the 2008-2011 Coronary Artery Risk Detection in Appalachian Communities study.

RESULTS

After controlling for covariates, underestimated children were more likely to have higher log-transformed triglyceride and systolic blood pressure values and overestimated children were more likely to have lower systolic blood pressure.

CONCLUSIONS

Underestimating a child's BMI is associated with coronary risk-related factors, while overestimating a child's BMI is associate with a protective CVD marker. A follow-up study examining the development of CVD risk factors in children of parents who inaccurately estimate their BMI would help clarify this relationship. Knowledge of how parental perceptions directly influence higher lipid levels in children could have an impact on public health efforts in the fight against childhood obesity in rural environments.