Objective: To describe the implementation, scale, and stakeholder experience of a statewide clinic-to-clinic pediatric subspecialty telemedicine program improving access to care for children in rural and underserved communities. Methods: A hub-and-spoke telemedicine network was developed linking pediatric subspecialists at an academic children’s hospital with advanced practice providers (APPs) at five regional spoke clinics. In this hybrid clinic-to-clinic model, APPs performed in-person assessments and facilitated synchronous telemedicine consultations for all new patients. Program utilization, travel burden reduction, and parent survey responses were analyzed descriptively; free-text parent comments and a provider panel discussion underwent qualitative content analysis. Results: From 2018 through 2024, the program completed 21,838 visits across 21 pediatric subspecialties, involving 98 clinicians. Families avoided an estimated 5.9 million travel miles, 99,000 travel hours, and approximately 2,400 metric tons of CO 2 emissions. Among 251 surveys returned, 90.0% of the 239 parents answering the rating item rated care as excellent and 8.8% as good. Providers attributed the model’s success to deliberate program infrastructure and standardization, strong trust and communication between subspecialists and site APPs, and improved access to timely subspecialty care closer to home. Full-time spoke sites achieved operational sustainability, whereas part-time sites were limited by staffing and workflow barriers. Conclusions: A clinic-to-clinic telemedicine model incorporating trained APPs at the point of care can sustainably expand pediatric subspecialty access while reducing travel burden and supporting provider satisfaction and retention. This scalable hybrid framework may help health systems address geographic barriers and pediatric subspecialty shortages.

Objectives: To build a proof of concept clinical mathematical model estimating postoperative urine output (UOP) utilizing pre-operative, intra-operative and immediate postoperative variables in children underwent cardiopulmonary bypass (CPB) for congenital heart surgery. Methods: Single-center, retrospective cohort study in a university-affiliated children's hospital. Patients younger than 21 years old who underwent CPB for congenital heart surgery and were postoperatively admitted to West Virginia University Children's Hospital`s pediatric intensive care unit (PICU) between September 1, 2007, and June 31, 2013 were included in the study. Body surface area, CPB duration, first measured hematocrit, serum pH, central venous pressure and vasoactive-inotropic score in the PICU were used to build the mathematical model. A randomly selected 50% of the dataset was used to calculate model parameters. A cross validation was used to assess model performance. Results: A total of 256 patients met inclusion criteria. The model was able to achieve mean absolute error of 1.065 ml/kg/hr (95% Confidence Interval (CI): 1.062-1.067 ml/kg/hr), root mean squared error of 1.80 ml/kg/hr (95% CI: 1.799-1.804 ml/kg/hr) and R2 of 0.648 (95% CI: 0.646-0.650) in estimating UOP in the first 32 hours of postoperative period. Conclusions: The mathematical model utilizing pre-operative, intra-operative and immediate postoperative variables may be a potentially useful clinical tool in estimating UOP in the first 32 hours postoperative period.

Prediction of cardiac surgery-associated acute kidney injury (CS-AKI) in pediatric patients is crucial to improve outcomes and guide clinical decision-making. This study aimed to develop a supervised machine learning (ML) model for predicting moderate to severe CS-AKI at postoperative day 2 (POD2). This retrospective cohort study analyzed data from 402 pediatric patients who underwent cardiac surgery at a university-affiliated children’s hospital, who were separated into an 80

Background Acute kidney injury (AKI) is a common complication after cardiac surgery and is associated with worse outcomes. Its management relies on early diagnosis, and therefore, electronic alerts have been used to alert clinicians for development of AKI. Electronic alerts are, however, associated with high rates of alert fatigue.Objectives We designed this study to assess the acceptance of user-centered electronic AKI alert by clinicians.Methods We developed a user-centered electronic AKI alert that alerted clinicians of development of AKI in a persistent yet noninterruptive fashion. As the goal of the alert was to alert toward new or worsening AKI, it disappeared 48 hours after being activated. We assessed the acceptance of the alert using surveys at 6 and 12 months after the alert went live.Results At 6 months after their implementation, 38.9% providers reported that they would not have recognized AKI as early as they did without this alert. This number increased to 66.7% by 12 months of survey. Most providers also shared that they re-dosed or discontinued medications earlier, provided earlier management of volume status, avoided intravenous contrast use, and evaluated patients by using point-of-care ultrasounds more due to the alert. Overall, 83.3% respondents reported satisfaction with the electronic AKI alerts at 6 months and 94.4% at 12 months.Conclusion This study showed high rates of acceptance of a user-centered electronic AKI alert over time by clinicians taking care of patients with AKI.

In this survey study of institutions across the US, marked variability in evaluation, treatment, and follow-up of adolescents 12 through 18 years of age with mRNA coronavirus disease 2019 (COVID-19) vaccine-associated myopericarditis was noted. Only one adolescent with life-threatening complications was reported, with no deaths at any of the participating institutions.

Background During the COVID-19 pandemic, telemedicine has provided an alternative to in-person visits for patients practicing social distancing and undergoing quarantine. During this time, there has been a rapid expansion of telemedicine and its implementation in various clinical specialties and settings. In this observational study we aim to examine the utility of telemedicine in a pediatric rheumatology clinic, for 3 months during the COVID-19 pandemic. Methods A review of outpatient pediatric rheumatology telemedicine encounters were conducted from April-June 2020. Telemedicine visits (n = 75) were compared to patients seen in practice over the prior year in office-based visits (March 2019-March 2020) (n = 415). Patient characteristics, information on no-show visits, completed visits, new patient or follow-up status, and if new patients had received a visit within 2 weeks of calling to schedule an appointment were analyzed by chart review. An independent sample t-test and Chi Square statistic was used to determine statical significance between the two groups. A two-proportion z-test was used to compare visit metrics. Results The percentage of new patients utilizing telemedicine (60%) was lower and statistically significant compared to the percentage of new patient office visits (84%) the previous year (p < 0.0001). There was no change in no-show rate between groups and patient characteristics were similar. Conclusions This study demonstrates a statistically significant decrease in new patient visits during the pandemic with telemedicine-only appointments compared to in-office visits over the previous year. This suggests a possible hesitation to seek care during this time. However, there was no significant difference among patient characteristics between telemedicine visits during the pandemic and during in-office visits in the previous year. In our experience, patient visits were able to be conducted via telemedicine with a limited physical exam using caregiver's help during the pandemic. However, further studies will need to ascertain patient satisfaction and preference for telemedicine in the future.

Modern machine learning techniques (such as deep learning) offer immense opportunities in the field of human biological aging research. Aging is a complex process, experienced by all living organisms. While traditional machine learning and data mining approaches are still popular in aging research, they typically need feature engineering or feature extraction for robust performance. Explicit feature engineering represents a major challenge, as it requires significant domain knowledge. The latest advances in deep learning provide a paradigm shift in eliciting meaningful knowledge from complex data without performing explicit feature engineering. In this article, we review the recent literature on applying deep learning in biological age estimation. We consider the current data modalities that have been used to study aging and the deep learning architectures that have been applied. We identify four broad classes of measures to quantify the performance of algorithms for biological age estimation and based on these evaluate the current approaches. The paper concludes with a brief discussion on possible future directions in biological aging research using deep learning. This study has significant potentials for improving our understanding of the health status of individuals, for instance, based on their physical activities, blood samples and body shapes. Thus, the results of the study could have implications in different health care settings, from palliative care to public health.

Background The United States, and especially West Virginia, have a tremendous burden of coronary artery disease (CAD). Undiagnosed familial hypercholesterolemia (FH) is an important factor for CAD in the U.S. Identification of a CAD phenotype is an initial step to find families with FH. Objective We hypothesized that a CAD phenotype detection algorithm that uses discrete data elements from electronic health records (EHRs) can be validated from EHR information housed in a data repository. Methods We developed an algorithm to detect a CAD phenotype which searched through discrete data elements, such as diagnosis, problem lists, medical history, billing, and procedure (International Classification of Diseases [ICD]-9/10 and Current Procedural Terminology [CPT]) codes. The algorithm was applied to two cohorts of 500 patients, each with varying characteristics. The second (younger) cohort consisted of parents from a school child screening program. We then determined which patients had CAD by systematic, blinded review of EHRs. Following this, we revised the algorithm by refining the acceptable diagnoses and procedures. We ran the second algorithm on the same cohorts and determined the accuracy of the modification. Results CAD phenotype Algorithm I was 89.6% accurate, 94.6% sensitive, and 85.6% specific for group 1. After revising the algorithm (denoted CAD Algorithm II) and applying it to the same groups 1 and 2, sensitivity 98.2%, specificity 87.8%, and accuracy 92.4; accuracy 93% for group 2. Group 1F1 score was 92.4%. Specific ICD-10 and CPT codes such as "coronary angiography through a vein graft" were more useful than generic terms. Conclusion We have created an algorithm, CAD Algorithm II, that detects CAD on a large scale with high accuracy and sensitivity (recall). It has proven useful among varied patient populations. Use of this algorithm can extend to monitor a registry of patients in an EHR and/or to identify a group such as those with likely FH.

BACKGROUND: The Coronary Artery Risk Detection in Appalachian Communities (CARDIAC) Project is a state-wide risk factor screening program that operated in West Virginia for 19 years and screened more than 100,000 5th graders for obesity, hypertension, and dyslipidemia. OBJECTIVES: We investigated siblings in the CARDIAC Project to assess whether cardiometabolic risk factors (CMRFs) correlate in siblings. METHODS: We identified 12,053 children from 5752 families with lipid panel, blood pressure, and anthropometric data. A linkage application (LinkPlus from the U.S. Centers for Disease Control and Prevention) matched siblings based on parent names, addresses, telephone numbers, and school to generate a linkage probability curve. Graphical and statistical analyses demonstrate the relationships between CMRFs in siblings. RESULTS: Siblings showed moderate intraclass correlation coefficient of 0.375 for low-density lipoprotein cholesterol (LDL-C), 0.34 for high-density lipoprotein cholesterol (HDL-C), and 0.22 for triglyceride levels. The body mass index (BMI) intraclass correlation coefficient (0.383) is slightly better (2%) than LDL-C or HDL-C, but the standardized beta values from linear regression suggest a 3-fold impact of sibling LDL-C over the child's own BMI. The odds ratio of a second sibling having LDL-C < 110 mg/dL with a first sibling at that level is 3.444:1 (Confidence Limit 3.031-3.915, P .05). The odds ratio of a sibling showing an LDL-C $ 160 mg/dL, given a first sibling with that degree of elevated LDL-C is 29.6:1 (95% Confidence Limit: 15.54-56.36). The individual LDL-C level correlated more strongly with sibling LDL-C than with the individual's own BMI. Seventy-eight children with LDL-C 160 mg/dL and negative family history would have been missed, which represents more than half of those with LDL-C > 160 mg/dL (78 vs 67 or 54%). CONCLUSIONS: Sibling HDL-C levels, LDL-C levels, and BMIs correlate within a family. Triglyceride and blood pressure levels are less well correlated. The identified CMRF relationships strengthen the main findings of the overall CARDIAC Project: an elevated BMI is not predictive of elevated LDL-C and family history of coronary artery disease poorly predicts cholesterol abnormality at screening. Family history does not adequately identify children who should be screened for cholesterol abnormality. Elevated LDL-C (>160 mg/dL) in a child strongly suggests that additional siblings and parents be screened if universal screening is not practiced. (C) 2020 National Lipid Association. All rights reserved.

In starting a new pediatric rheumatology service in a rural state, we designed the practice to focus on patient access, patient quality, and patient experience. We created a clinical experience that starts with an intake call to optimize the face-to-face visit. A team-based care approach is used. Weekend appointments are offered to avoid school and work absence. The social determinants of health are addressed. In our first year, our patients have reported their appreciation for a high-touch, patient-centered experience.

A typical presentation of a foreign body aspiration (FBA) in a child includes witnessed choking, respiratory distress, cyanosis, coughing, wheezing, diminished breath sounds, and/or altered mental status. Following an extensive literature review, we found pneumothorax occurring secondary to FBA is a rare occurrence and should elicit prompt treatment. This 17-month-old female was admitted for respiratory syncytial virus (RSV) bronchiolitis and developed a subsequent pneumothorax during her hospital stay, consequent to aspiration of a cashew fragment two weeks before presentation. In light of the National Institute of Allergy and Infectious Diseases (NIAID)-sponsored expert panel’s addended guidelines, published and endorsed by the American Academy of Pediatrics (AAP) in 2017, we highlight a potential complication of increasing encouragement of peanut consumption in children as young as four months.

Background: Few states have published statewide epidemiology of abusive head trauma (AHT). Objective: To examine the statewide epidemiology of AHT in West Virginia (WV), with the primary objective of establishing AHT incidence for comparison to national data, and to use as a baseline for comparison to incidence post-implementation of a statewide AHT prevention program. Participants and setting: AHT cases in children less than 2 years old were identified from the 3 tertiary pediatric centers in WV. Methods: Cases were identified by using ICD-9 codes for initially identifying those with injuries which might be consistent with AHT, followed by medical record review to determine which of these met the criteria for inclusion as a case. Medical examiner data was used to find additional cases of AHT. Using the number of cases identified along with relevant census data, incidence of AHT was calculated. Results: There were 120 cases of AHT treated in WV hospitals from 2000 to 2010, 100 of which were WV residents. The incidence was 36.1/100,000 children < 1 year of age and was 21.9 cases per 100,000 children < 2 years of age. Incidence in infants increased during the latter years (2006-2010) of the study to 51.8/100,000 compared to the incidence during 2000-2005, which was 24.0/100,000 (p < .01). Conclusions: Compared to US national, state and regional figures, the WV incidence of AHT was among the highest. In addition, the incidence of AHT increased significantly over the study period. Possible factors contributing to the rise in incidence are discussed.

Introduction: Coronary Artery Risk Detection in Appalachian Communities is a school-based cross-sectional screening program to detect significant cardiometabolic disease risk factors (CMRFs) in WV children. Fifth graders have undergone school screening for family history, anthropometrics and lipid panel values since 1999 including screening for Acanthosis Nigricans (AN Pos or Neg). Our hypothesis is that presence of AN can predict need to screen for CMRFs. Methods: Parental consent and child assent were obtained before screening day using an IRB-approved protocol. Height and weight were used to calculate BMI and BMI%ile. Systolic blood pressure over 95%ile, triglyceride over 150 mg/dl, HDL under 40 mg/dl and BMI over 95%ile were tabulated and correlated. Glucose and HOMA IR were only checked for AN Pos subjects and thus not considered in analysis. Results: 102,930 5 th graders participated in CARDIAC up to 2017. 101,101 had BMI determination, 100,000 blood pressures, 67,710 lipid panels were checked. 27.9% of children (28715) were obese, defined as BMI over 95%ile and 18.4% (18,931 subjects) were overweight. 10.8% of AN pos children vs. 1.9% of all children exhibited 4 CMRFs (Obese BMI, Htn, high TG, low HDL, OR 7.31, 95% CL 6.4-8.35). Conclusion: AN correlated to CMRF clustering but many CMRF pos subjects were AN Neg (79.5% or 1236/1555 of those with 4 CMRFs). Multifactoral screening of lipids, BMI, blood pressure are indicated to identify risk for cardiovascular disease.

Introduction: Electronic Health Records (EHRs) benefit record keeping, information collation, error prevention, and charge capture. They provide a large database of clinical information that can be used for research. Sorting vast amounts of data manually is inefficient, hence, an effectual, validated method is required to uncover information from large sets of data and generate knowledge. The U.S., and especially West Virginia, has a tremendous burden of cardiovascular disease (CVD). Undiagnosed Familial Hypercholesterolemia (FH) is an important factor for CVD in the U.S. FH results in elevated levels of LDL from childhood and early atherosclerotic disease. We are interested in better screening processes for FH. One method is to detect adults with coronary artery disease (CAD) and determine if their lipid levels are indicative of FH. Relatives and children can then be screened for FH and treated. Efficient identification of a CAD phenotype from EHRs is an important initial step in this screening process. Hypothesis: We hypothesized that a CAD phenotype detection algorithm that uses discrete data elements from EHRs can be validated as a precursor to detection of FH. Methods: We developed an algorithm to detect a CAD phenotype, which searched through discrete data elements, such as diagnoses lists (ICD-10) and procedure (CPT) codes. Direct inspection of EHR discrete data avoided the need for artificial intelligence, such as natural language processing. The algorithm was applied to a cohort of 1,000 patients with varying characteristics. We then determined which patients had CAD by systematically going through EHRs. Following this, we revised the algorithm by refining the constraints under which it operated. We ran the algorithm again on the same 1,000 patients, and determined the accuracy of the modified algorithm. Results: Manual validation of the 1,000 patients resulted in 413 with CAD and 587 without. The original algorithm distinguished 488 CAD positive patients and 512 CAD negative patients. This was 89% accurate, 96% sensitive, and 85% specific. After revising the algorithm and applying it to the same cohort, it determined that there were 474 CAD patients and 526 without CAD. This was 93% accurate, 99% sensitive, and 89% specific. Conclusion: EHR usage has created a large pool of minable clinical data. However, without an efficient method to obtain inferences from it, the information cannot be effectually utilized. We have created an algorithm that detects CAD on a large scale with high accuracy. It has proven to be useful among a varied patient population. Since the constraints that are used, such as ICD codes and CPT codes, are universal, it can be utilized across many hospital systems; although, local validation is prudent. Using this algorithm can select a population with a propensity for FH, thereby allowing us to screen and manage patients with undiagnosed FH or other familial dyslipidemias.

Introduction: West Virginia exhibits pervasive cardiovascular disease (CVD) that may relate to a combination of ancestry and shared environment in families, including vulnerabilities related to diet, physical activity and tobacco use. Coronary Artery Risk Detection in Appalachian Communities (CARDIAC) is a school-based child risk factor screening program that has evaluated over 90,000 WV fifth graders in the past 20 years. Reverse Cascade screening for Familial Hypercholesterolemia (FH) has been difficult with the CARDIAC population. The WVU CTSI Integrated Data Repository (IDR) includes over 2 million records. Hypothesis: Linkage of child CARDIAC data to parent IDR data will allow new information discovery to inform management of CVD. Methods: We used direct demographic data linkage via Oracle with Soundex conversion of names, in the IDR, to find parents of the CARDIAC participants. Data was analyzed in the VMWare SSL environment. Results: 4759 children have a parent(s) identified. 959 mothers and 524 fathers have an LDL level from IDR. Race, BMI and gender was recorded from CARDIAC. 6.8 % of children, 40% of mothers and 44.8% of fathers have an abnormal LDL level >130 mg/dl in IDR. Positive predictive value of the abnormal child lipid level (≥130 mg/dl) is 17% for some parent (56/325) to be abnormal. 4 parents had LDL >190 mg/dl with child > 160, indicating likely FH in the pair (2.7% of pairs or 1 in 371 pairs). Conclusion: Formation of a virtual cohort of CARDIAC children and parents allows Virtual Reverse Cascade Screening to find FH. This project highlights the importance of familial tendency to hyperlipidemia that can aid detection of early lipid abnormality and cardiovascular risk in children and their young parents to promote wellness and potentially avoid early coronary artery disease. We are constructing a virtual longitudinal cohort to study CVD in WV as a part of a Learning Health System in which data management is at the forefront of healthcare improvement.

Screening for risk of unintentional falls remains low in the primary care setting because of the time constraints of brief office visits. National studies suggest that physicians caring for older adults provide recommended fall risk screening only 30 to 37 percent of the time. Given prior success in developing methods for repurposing electronic health record data for the identification of fall risk, this study involves building a model in which electronic health record data could be applied for use in clinical decision support to bolster screening by proactively identifying patients for whom screening would be beneficial and targeting efforts specifically to those patients. The final model, consisting of priority and extended measures, demonstrates moderate discriminatory power, indicating that it could prove useful in a clinical setting for identifying patients at risk of falls. Focus group discussions reveal important contextual issues involving the use of fall-related data and provide direction for the development of health systems-level innovations for the use of electronic health record data for fall risk identification.

Introduction: The Coronary Artery Risk Detection in Appalachian Communities (CARDIAC) Project gathers anthropometric, BP and lipid data from fifth graders in West Virginia in the past 18 y. 60,403 children had LDL cholesterol and we found 5259 sets of siblings by direct match on mothers first and last name. The suggestion that more sibships could be identified prompted evaluation of Link Plus software from Centers for Disease Control (CDC) to improve matching. Methods: LinkPlus generates potential matches via a probabilistic algorithm that allows relative weighting of multiple factors such as first and last name. For our purposes the deduplication rather than matching algorithm was run using mother’s first and last name using the NYSIIS (New York State Identification and Intelligence System) phonetic schema to avoid creating multiple many-to-many relationships that were difficult to analyze. Additional variables considered included county, street address, telephone, fathers first and last name, school. Subject last name was used as a blocking variable. Results: 7602 matched siblings were generated by the program that determined a probability score ranging from 61.3 to cut off at 15; few matches were observed below this level. The figure demonstrates exponential decay beginning at a probability score of 26 with 95% accuracy at 25.5. 6827 pairs were included at that level including 6824 matched pairs and only 3 false positive pairs. Partial matches (n = 61) likely are half sibs including exact match of telephone and/or street but only one parent matching. Child surname was not used in the algorithm. Typographical errors were accounted by Link Plus. Lipid correlations were similar to those found with excel but more robust. Conclusion: The Link Plus record matching program from CDC is able to successfully determine sibships with increased sensitivity compared with a direct match from a sorted excel file. The program was able to identify likely sibs and half-sibs plus avoid non-match due to minor typo errors in the analyzed fields.

Short stature is associated with increased LDL-cholesterol levels and coronary artery disease in adults. We investigated the relationship of stature to LDL levels in children in the West Virginia Coronary Artery Risk Detection in Appalachian Communities (CARDIAC) Project to determine whether the genetically determined inverse relationship observed in adults would be evident in fifth graders. A cross-sectional survey of schoolchildren was assessed for cardiovascular risk factors. Data collected at school screenings over 18 years in WV schools were analyzed for 63,152 fifth-graders to determine relationship of LDL to stature with consideration of age, gender, and BMI. The first (shortest) quartile showed an LDL level of 93.6 mg/dl compared with an LDL level of 89.7 mg/dl for the fourth (tallest) quartile. Each incremental increase of 1 SD of height lowered LDL by 0.049 mg/dl (P < 0.0001). Multivariate analysis showed LDL to vary inversely as a function of the first (lowest) quartile of height after controlling for gender, median age, BMI percentile for age and gender, and year of screening. The odds ratio for LDL ≥ 130 mg/dl for shortest versus tallest quartile is 1.266 (95% CL 1.162–1.380). The odds ratio for LDL ≥ 160 mg/dl is 1.456 (95% CL 1.163–1.822). The relationship between short stature and LDL, noted in adults, is confirmed in childhood.