Population-Based Estimates of the Prevalence of Children With Congenital Heart Disease and Associated Comorbidities in the United States.

BACKGROUND: Congenital heart defects (CHD) are the most common birth defects and previous estimates report the disease affects 1% of births annually in the United States. To date, CHD prevalence estimates are inconsistent due to varied definitions, data reliant on birth registries, and are geographically limited. These data sources may not be representative of the total prevalence of the CHD population. 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 performed a descriptive, retrospective 8-year analysis using all-payer claims data from Colorado from 2012 to 2019. Children with CHD were identified by applying International Classification of Diseases-Ninth Revision (ICD-9) and International Classification of Diseases-Tenth Revision (ICD-10) diagnosis codes from the American Heart Association-American College of Cardiology 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 1 health care claim. CHD type was categorized as simple, moderate, and severe disease. Association with comorbid conditions and genetic diagnoses were analyzed using χ2 test. We used direct standardization to calculate adjusted prevalence rates, controlling for age, sex, primary insurance provider, and urban-rural residence. RESULTS: We identified 1 566 328 children receiving care in Colorado from 2012 to 2019. Of those, 30 512 children had at least 1 CHD diagnosis, comprising 1.95% (95% CI, 1.93-1.97) of the pediatric population. Over half of the children with CHD also had at least 1 complex chronic condition. After direct standardization, the adjusted prevalence rates show a small increase in simple severity diagnoses across the study period (adjusted rate of 11.5 [2012]-14.4 [2019]; P<0.001). CONCLUSIONS: The current study is the first population-level analysis of pediatric CHD in the United States. Using administrative claims data, our study found a higher CHD prevalence and comorbidity burden compared with previous estimates.

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.

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.

ST2 Predicts Risk of Unplanned Readmission Within 1 Year After Pediatric Congenital Heart Surgery.

BACKGROUND: Approximately 10% to 20% of children are readmitted after congenital heart surgery. Very little is known about biomarkers as predictors of risk of unplanned readmission after pediatric congenital heart surgery. Novel cardiac biomarker ST2 may be associated with risk of unplanned readmission. ST2 concentrations are believed to reflect cardiovascular stress and fibrosis. Our objective was to explore the relationship between pre- and postoperative ST2 biomarker levels and risk of readmission within 1 year after congenital heart surgery. METHODS: We prospectively enrolled pediatric patients aged < 18 years who underwent at least 1 congenital heart operation at Johns Hopkins Hospital from 2010 to 2014. Plasma samples were collected immediately before surgery and at the end of bypass. We used Kaplan-Meier survival analysis and multivariable Cox regression models to adjust for variables used in The Society of Thoracic Surgeons Congenital Heart Surgery Database mortality risk model. RESULTS: Of our cohort of 145 patients, we found 39 children with readmissions within 365 days. The median time to unplanned readmission was 54 days (interquartile range, 10-153). Kaplan-Meier analysis demonstrated a significant difference across terciles of pre- and postoperative ST2 biomarker levels. After adjustment, elevated serum levels of ST2 measured preoperatively and postoperatively were associated with increased risk of readmission (hazard ratio, 2.5-3.7; all P < .05). CONCLUSIONS: Elevated levels of ST2 are significantly associated with increased risk of unplanned readmission within 1 year after pediatric congenital heart surgery. Novel serum biomarker ST2 can be used for risk stratification or estimating postsurgical prognosis.

Novel Biomarkers Improve Prediction of 365-Day Readmission After Pediatric Congenital Heart Surgery.

BACKGROUND: The purpose of this study was to evaluate the association between preoperative biomarker levels and 365-day readmission or mortality after pediatric congenital heart surgery. METHODS: Children aged 18 years or younger undergoing congenital heart surgery (n = 145) at Johns Hopkins Hospital from 2010 to 2014 were enrolled in the prospective cohort. Novel biomarkers suppression of tumorgenicity 2, galectin-3, N-terminal prohormone brain natriuretic peptide, and glial fibrillary acidic protein were measured. The composite study endpoint was unplanned readmission within 365 days after discharge or mortality either in hospital during the surgical admission or within 365 days after discharge. A clinical model based on covariates used in The Society of Thoracic Surgeons Congenital Heart Surgery Database mortality risk model and an augmented model using the clinical model in conjunction with a novel biomarker panel were evaluated. RESULTS: Readmission or mortality within 365 days of surgery occurred among 39 pediatric patients (27%). The clinical model alone resulted in a c-statistic of 0.719 (95% confidence interval, 0.63 to 0.81). The clinical model in conjunction with the log-transformed biomarkers improved the c-statistic to 0.805 (95% confidence interval, 0.73 to 0.88). The addition of biomarkers resulted in a significant improvement to the clinical model alone (P value = 0.035). CONCLUSIONS: Novel biomarkers may add predictive value when assessing the likelihood of 365-day readmission or mortality after pediatric congenital heart surgery. After adjusting for clinical and novel biomarkers, preoperative and postoperative suppression of tumorgenicity 2 remained associated with 365-day readmission or mortality. Currently, The Society of Thoracic Surgeons clinical congenital mortality risk model can be applied to identify children with increased risk of repeat hospitalizations and postdischarge mortality and may inform preventative care interventions that aim to reduce these adverse events.

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.

Biomarkers improve prediction of 30-day unplanned readmission or mortality after paediatric congenital heart surgery.

OBJECTIVE: To evaluate the association between novel pre- and post-operative biomarker levels and 30-day unplanned readmission or mortality after paediatric congenital heart surgery. METHODS: Children aged 18 years or younger undergoing congenital heart surgery (n = 162) at Johns Hopkins Hospital from 2010 to 2014 were enrolled in the prospective cohort. Collected novel pre- and post-operative biomarkers include soluble suppression of tumorgenicity 2, galectin-3, N-terminal prohormone of brain natriuretic peptide, and glial fibrillary acidic protein. A model based on clinical variables from the Society of Thoracic Surgery database was developed and evaluated against two augmented models. RESULTS: Unplanned readmission or mortality within 30 days of cardiac surgery occurred among 21 (13%) children. The clinical model augmented with pre-operative biomarkers demonstrated a statistically significant improvement over the clinical model alone with a receiver-operating characteristics curve of 0.754 (95% confidence interval: 0.65-0.86) compared to 0.617 (95% confidence interval: 0.47-0.76; p-value: 0.012). The clinical model augmented with pre- and post-operative biomarkers demonstrated a significant improvement over the clinical model alone, with a receiver-operating characteristics curve of 0.802 (95% confidence interval: 0.72-0.89; p-value: 0.003). CONCLUSIONS: Novel biomarkers add significant predictive value when assessing the likelihood of unplanned readmission or mortality after paediatric congenital heart surgery. Further exploration of the utility of these novel biomarkers during the pre- or post-operative period to identify early risk of mortality or readmission will aid in determining the clinical utility and application of these biomarkers into routine risk assessment.

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.

Capturing the statewide incidence of neonatal abstinence syndrome in real time: the West Virginia experience.

BACKGROUND: Neonatal abstinence syndrome (NAS) is one of the consequences at birth affecting the newborn after discontinuation of prenatal drug exposure to mainly opioids. The objective of this study was to determine the extent of the problem in the state of West Virginia (WV) using a real-time statewide surveillance system. METHODS: Project WATCH is a surveillance tool that since 1998 collects data on all infants born in the state of WV. NAS surveillance item was added to the tool in October 2016. This study examined all births (N = 23,667) in WV from October to December 2017. The data from six WV birthing facilities were audited for 1 month to evaluate how well this tool was capturing NAS data using κ-statistics. RESULTS: The 2017 annual incidence rate of NAS was 51.3 per 1000 live births per year for all births and 50.6 per 1000 live births per year for WV residents only. The κ-coefficient between the hospital medical records and Project WATCH data was 0.74 (95% confidence interval: 0.66-0.82) for NAS. CONCLUSION: The study provides justification to develop effective systems of care for the mother-infant dyad affected by substance use, especially targeting pregnant women in rural communities.

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.

Association Between Childhood Residential Mobility and Non-medical Use of Prescription Drugs Among American Youth.

INTRODUCTION: Prescription drug abuse is a public health epidemic, resulting in 15,000 deaths annually. Disruption of childhood residence has been shown to increase drug-seeking behavior among adolescents; however, little research has explored its association specifically with non-medical use of prescription drugs (NMUPD). The objective of the study was to measure the association between residential mobility and NMUPD. METHODS: The 2010 National Survey on Drug Use and Health data were analyzed for 15,745 participants aged 12-17 years. NMUPD was defined as self-report of any non-medical use (i.e., taking a prescription drug that was not prescribed to them or consumption for recreational purposes) of tranquilizers, pain relievers, sedatives, or stimulants. Logistic regression for survey data was used to estimate the association between residential mobility and NMUPD, adjusting for potential confounders. RESULTS: After controlling for demographic, intrapersonal, interpersonal, and community factors, adolescents with low mobility (1-2 moves in the past 5 years) and residential instability (≥3 moves) were 16% (OR 1.16, 95% CI 1.01, 1.33) and 25% (OR 1.25, 95% CI 1.00, 1.56) more likely to report NMUPD compared to non-mobile adolescents (0 moves). Low-mobile adolescents were 18% (OR 1.18, 95% CI 1.01, 1.38) more likely to abuse pain relievers, specifically. No relationship was found between moving and tranquilizer, stimulant, or sedative use. DISCUSSION: Increasing childhood residential mobility is associated with NMUPD; therefore, efforts to prevent NMUPD should target mobile adolescents. Further examination of the psychological effects of moving and its association with pain reliever abuse is indicated.