Patient Sociodemographic and Provider Characteristic Predictors of Echocardiogram Ordering in Pediatric Patients: A Retrospective Analysis

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Brown, Alicia H. Chaves This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9408883/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background: Implicit bias and socioeconomic factors may influence diagnostic testing in pediatrics, but their effects on echocardiogram (echo) ordering in outpatient pediatric cardiology are not well understood. We evaluated whether patient demographic factors and provider characteristics were associated with appropriate or inappropriate echo ordering for initial outpatient evaluation of pediatric chest pain. Methods: We conducted a retrospective study of 299 pediatric patients undergoing initial outpatient evaluation for chest pain between 2018 and 2024 at the University of Maryland Children's Hospital outpatient cardiology clinics. Echocardiogram appropriateness was determined using previously published appropriate use criteria (AUC) and structured clinical management and assessment plan (SCAMP) criteria. Associations between patient demographics, insurance type, neighborhood Childhood Opportunity Index (COI), provider characteristics, and echo appropriateness were assessed. Results: Echocardiogram ordering was concordant with AUC/SCAMP recommendations in 232 encounters (77.6%). White patients were more likely than non-White patients to receive guideline-concordant ordering (83.8% vs. 72.8%, p = .025). Discordance between echo ordering and recommendations was primarily due to providers ordering an echo when not recommended, which was seen more often in non-White patients and patients from lower COI neighborhoods. Providers with less than 10 years of experience ordered fewer echocardiograms (51.6% vs. 81.9%, p < .001) but were more likely to order them in concordance with AUC/SCAMP criteria (86.9% vs. 71.2%, p < .001). Conclusions: Most echocardiogram ordering was guideline-concordant; however, disparities were associated with patient race, neighborhood opportunity, and provider experience. These findings identify opportunities to improve equitable, guideline-concordant care in outpatient pediatric cardiology. Pediatrics Cardiology Bias Echocardiogram Chest Pain Introduction Implicit bias refers to unconscious attitudes or stereotypes that influence understanding, actions, and decisions in clinical care. In healthcare, implicit bias has been shown to contribute to inequities in diagnostic testing, treatment, and clinical outcomes [ 10 ]. When two patients present with similar symptoms but undergo different diagnostic evaluations based on factors unrelated to clinical presentation, disparities in care quality and health outcomes may result. Several studies have demonstrated racial and socioeconomic differences in the use of diagnostic imaging in pediatric emergency departments. Marin et al. found that Black and Hispanic children received fewer computed tomography (CT), magnetic resonance imaging (MRI), and other radiographic studies compared with White children presenting with similar clinical symptoms [ 1 ]. Similarly, Hambrook et al. found that pediatric patients who were White or had private insurance and presented with chest pain in an Emergency Room setting had a greater likelihood of receiving any form of testing (labs, imaging, etc.) compared to non-White and publicly insured patients [ 2 ]. A 2012 study from Natale et al. describes how across 25 emergency departments across the United States, patients of minoritized races had lower odds of having a CT head compared to their White counterparts for minor head trauma [ 3 ]. A 2013 study from Payne et al. did not find differences in laboratory or radiological testing in patients with head injury but did demonstrate overall disparities in laboratory and radiologic testing in the pediatric emergency room setting (including in subgroups of patients with fever and upper respiratory tract infection) [ 4 ]. Evidence of bias extends beyond emergency care settings. Nyman et al. demonstrated that neonatal nurses evaluating infants for Neonatal Opioid Withdrawal Syndrome in standardized scenarios had bias against Black patients [ 5 ]. Presented with identical clinical scenarios along with a picture of a White or Black infant, the providers rated Black infants as having lower scores on the Modified Finnegan score (i.e., less signs of withdrawal). Additionally, a 2024 study from Kemp et al. found that racial and ethnic minority patients from a Pediatric IBD clinic had an older average age of diagnosis of IBD compared to white patients even when adjusted for other sociodemographic variables [ 6 ]. These findings highlight the potential influence of unconscious implicit bias in clinical decision-making across a variety of pediatric settings. Chest pain is a common reason for referral to pediatric cardiology. Echocardiograms (echo) may be indicated when specific symptoms, red flags, or risk factors are present. In 2014, Appropriate Use Criteria (AUC) were developed to guide decision-making for ordering of initial outpatient pediatric echos [ 7 ]. The recommendations in this document are based on expert opinion and review of the literature on indications for echocardiography, such as chest pain, and indicates whether an echo is considered “appropriate”, “may be appropriate”, or “rarely appropriate” for a set of clinical scenarios. Use of a standardized clinical assessment and management plan for pediatric chest pain has been shown to guide appropriate use of echos while reducing inappropriate use of echos [ 9 ]. While neither replaces clinical judgement, they can reduce variation in care and provide a way to compare care between patients with similar clinical findings. Prior research suggests gaps in the appropriate evaluation of chest pain in the emergency room setting [ 2 ]. To date, no studies have examined whether similar disparities exist in the outpatient pediatric cardiology setting, particularly in the ordering of echos for chest pain. Understanding whether race, ethnicity, insurance type, or socioeconomic status (SES) influence adherence to AUC in echocardiogram ordering is essential for promoting equitable care. The purpose of this study is to evaluate whether demographic or socioeconomic factors are associated with the ordering of echocardiograms for pediatric patients presenting with chest pain. Specifically, we aim to: Determine whether race or ethnicity is associated with appropriateness of echocardiogram ordering based on AUC criteria. Based on previous findings on this topic, we hypothesize that minoritized racial and ethnic groups will be less likely to receive an echocardiogram when clinically indicated and less likely to receive an echocardiogram when not indicated compared with White patients. Examine whether socioeconomic status and insurance type are associated with appropriateness of echocardiogram ordering. We hypothesize that children living in higher-SES neighborhoods will be more likely to undergo echocardiography—both appropriately and inappropriately—than children living in lower-SES neighborhoods. This study builds on prior literature by addressing diagnostic equity in a population and clinical setting that has received little prior investigation. Identifying potential disparities in cardiac testing may inform educational interventions, guideline implementation, and system-level efforts to promote equitable care. Methods The study was reviewed by the University of Maryland School of Medicine Institutional Review Board and was determined to be exempt. We conducted a retrospective cohort study of pediatric patients evaluated for chest pain at the University of Maryland Children’s Hospital outpatient cardiology clinics between 2018 and 2024. Eligible participants included all children undergoing a first-time outpatient evaluation for a chief complaint of chest pain. Patients were excluded if their chest pain was attributable to a known cardiac condition such as congenital heart defects, if race or ethnicity data were missing, if residential address information was unavailable for socioeconomic status (SES) assessment, or if they had undergone any prior cardiology evaluations before the study visit. Primary exposure variables included patient/family-reported race and ethnicity as documented in the electronic medical record, neighborhood SES determined using the Child Opportunity Index (COI) for the patient’s zip code, and insurance payor type (private, public, or other). The outcome variable was the appropriateness of echocardiogram (echo) ordering, categorized into four mutually exclusive groups: an echo ordered when considered appropriate, an echo ordered when considered inappropriate, an echo not ordered when considered appropriate, and an echo not ordered when considered inappropriate. Appropriateness was determined by comparing each patient’s clinical presentation and documented symptoms with established AUC criteria for pediatric transthoracic echocardiography and with the 2012 quality improvement initiative termed Standardized Clinical Assessment and Management Plans (SCAMP) recommendations [ 7 – 8 ]. Data were abstracted from electronic medical records, including demographic characteristics, clinical presentation and history, specifically for cardiac risk factors, physical exam, other testing results (including electrocardiography) and echo utilization. Zip codes were used to determine geocodes based on the US Census Bureau data (geocoding.geo.census.gov/geocoder). The neighborhood z-score (a weighted average of all COI domain z-scores, nationally-normalized) and neighborhood level (nationally-normalized overall COI score in categories of “very low”, “low”, “moderate”, “high”, or “very high”) were determined based on the patient’s geocode and year of visit. Analysis Descriptive statistics were used to summarize demographic and clinical characteristics. Associations between the primary independent variables (race/ethnicity and insurance type), the dependent variable (echocardiogram ordering concordance with AUC/SCAMP recommendations), as well as potential confounders were evaluated using Chi-square tests or Fisher’s exact analysis based on subgroup size and number of subgroups. Logistic regression models were used to evaluate associations between race/ethnicity, insurance payor, and the appropriateness of echocardiogram ordering, adjusting for potential clinical confounders. Since there were only 13 total providers and thus even smaller numbers of providers in race/sex categories, we were concerned that analysis by provider race and sex might be biased due to unmeasured provider factors. We chose to limit analysis to provider-patient race and sex concordance. We also identified that several providers’ encounters had been selected much more frequently than others (likely due to time with practice, volume of outpatient clinics). We repeated the analysis of a subset of data in which the number of encounters for each provider was limited to 30. This analysis showed similar results to the primary analysis. Our initial analysis revealed that there were very few encounters in which an echocardiogram was not ordered when the AUC/SCAMP guidelines considered it appropriate. Since much of the discordance between provider ordering and AUC/SCAMP appropriateness was due to physicians ordering echocardiograms when not considered appropriate, we did a sub-analysis of the visits in which an echocardiogram was ordered. Statistical significance was defined as a two-sided p-value of < 0.05. Analysis was done using IBM SPSS Statistics Version 28 (IBM Corp., Armonk, New York, United States of America) The study was reviewed and approved (or exempted) by the University of Maryland School of Medicine Institutional Review Board, and all data were de-identified prior to analysis. Appropriate Use Criteria [ 7 ]: Chest Pain Appropriate Use Ratings Rarely Appropriate (R) Chest pain with no other symptoms or signs of cardiovascular disease, a benign family history, and a normal ECG Non-exertional chest pain with no recent ECG Non-exertional chest pain with normal ECG Reproducible chest pain with palpation or deep inspiration May Be Appropriate (M) Chest pain with no other symptoms or signs of cardiovascular disease, a benign family history, and an abnormal ECG Chest pain with family history of premature coronary artery disease Chest pain with recent onset of fever Chest pain with recent illicit drug use Appropriate (A) Exertional chest pain Non-exertional chest pain with abnormal ECG Chest pain with family history of sudden unexplained death or cardiomyopathy Results Baseline Characteristics of study participants: A total of 368 subjects were screened, of whom 299 (81.3%) met the inclusion criteria and were enrolled. The mean age of the cohort was 11.8 years (SD 3.51), and 50.8% were female. Most patients identified as White (45.2%) or Black (38.8%), with smaller proportions identifying as Asian (2.3%) or other/multiracial (13.7%). Approximately half of subjects had private insurance (52.5%), and the majority spoke English as their primary language (93.3%). Of the cohort, 101 subjects (33.8%) had their visit at the main office at UMMC while the remaining subjects had their visits at satellite locations. A summary of the subject’s independent variables is seen in Table 1 . Table 1 Subject variables (n = 299) Female sex 152 (50.8) Race White Black Asian Other or multiple Ethnicity Hispanic 135 (45.2) 116 (38.8) 7 (2.3) 41 (13.7) 31 (10.4) Language English Spanish Other 279 (93.3) 16 (5.4) 4 (1.3) Insurance Private Public Self-pay 157 (52.5) 141 (47.2) 1 (0.3) Clinic location Main office Satellite office 101 (33.8) 198 (66.2) Childhood opportunity index Category Very Low Low Moderate High Very High Z-score 35 (19.1) 16(5.4) 40(13.4) 61(20.4) 90(30.1) 0.60 (SD, 0.921) Thirteen providers (11 physicians, 2 nurse practitioners) conducted the selected visits. Six providers were female and seven were male; eight identified as White, two as Black, and three as Asian. Overall, 37.8% of encounters involved race-concordant provider–patient pairs and 47.2% involved sex-concordant pairs. Providers with more than 10 years of experience conducted 177 encounters (59.2%). Exertional chest pain was reported in 31.1% of patients, family history of cardiac disease in 11%, and fever or recent infection in 9.7%. Physical examination revealed abnormal physical exam findings in 6.0% of patients, and electrocardiograms were abnormal in 11.7%. Based on the AUC criteria and the documented clinical information, 46.2% of the encounters were classified as “rarely appropriate”, 12.4% of the encounters were classified as “may be appropriate”, and 41.5% of the encounters were classified as “appropriate” for ordering of an echo. Similarly, 43.8% of the encounters met criteria for echocardiography indicated by the SCAMP algorithm. A summary of the encounters is seen in Table 2 . Table 2 Encounter summary Echocardiograms ordered 208 (69.6) History Exertional chest pain Past medical history Family history Fever or infection Additional symptoms Any history red flags 93 (31.1) 5 (1.7) 33 (11) 29 (9.7) 86 (28.8) 176 (58.9) Physical exam Abnormal finding(s) Reproducible chest pain 18 (6) 57 (19.1) Electrocardiogram Abnormal 35 (11.7) AUC category Rarely appropriate May be appropriate Appropriate 138 (46.2) 37 (12.4) 124 (41.5) SCAMP algorithm Echo indicated 131 (43.8) Echo indicated by either algorithm 160 (53.5) Overall, the echo utilization was considered appropriate in 232 encounters (77.6%) by the AUC or SCAMP criteria. Provider decision about echo ordering was discordant with AUC/SCAMP recommendations in 67 encounters (22.4%), including 59 (19.7%) cases in which an echo was ordered despite not meeting criteria and 8 (2.7%) cases in which an echo was not ordered when it might be considered appropriate. Results of analysis of the association of patient, visit, and provider variables with concordance between echo ordering and AUC/SCAMP recommendations are shown in Table 3 . There was more likely to be concordance with echocardiogram ordering and AUC/SCAMP recommendations in patients who identified as White/non-Hispanic compared to other races/ethnicities (83.8% vs. 72.8%, p = .025). This difference appeared to be primarily due to “overordering” in the non-White or Hispanic patients, i.e., an echocardiogram was ordered when AUC/SCAMP did not consider it appropriate. Patients with private insurance were slightly less likely to have an echo ordered compared to subjects without private insurance (65% vs. 75.2%, p = .059), but concordance of provider’s decision about echo ordering with AUC/SCAMP guidelines was not significantly different between privately insured vs. publicly insured (79.6% vs. 75.2%, p = .405). When analyzing the concordance of AUC/SCAMP recommendations with a patient’s Child Opportunity Index, it was found that patients that had “High” or “Very High” COI had concordant echo ordering in greater than 80% of encounters. Patients in the “Very Low”, “Low”, and “Moderate” categories had lower rates of concordance at 80%, 62.5%, and 67.5% respectively. Patients with “Moderate”, “Low”, or “Very Low” COI were more likely to receive an echo when not indicated by either AUC or SCAMP criteria (27.5%, 31.3%, and 20%) compared to patients with “High” or “Very High” COI (16.4% and 14.4%) (p=.019). Providers with less than 10 years of experience were less likely to order echocardiograms compared to their peers with greater than 10 years of experience (51.6% vs. 81.9%, p < .001), but their echo ordering was considered appropriate more often (86.9% vs 71.2%, p = 0.002). There was no difference in overall concordance between echo ordering and AUC/SCAMP recommendations between race-concordant and race-discordant provider-patient pairs (82.3% vs 74.7%, p = 0.153) or sex-concordant and sex-discordant provider-patient pairs (80.9% vs 74.7%, p = 0.214). Logistic regression analysis found that echocardiogram ordering was more likely to be concordant with AUC/SCAMP recommendations in encounters with White/non-Hispanic patients and providers with less than 10 years of experience. Since there were very few encounters in which an echocardiogram was not ordered when AUC/SCAMP considered an echocardiogram appropriate, we repeated the analysis on the subgroup of encounters in which an echocardiogram was ordered. In that subgroup, of the inappropriately ordered echocardiograms, more were done in the non-White or Hispanic patients (69.5%, p = 0.056). Additionally, 83.1% of the inappropriately ordered echo's were ordered by providers with more than 10 years of experience (p = 0.008). Multiple logistic regression in this subgroup showed that in encounters with non-White or Hispanic patients and providers with more than 10 years of experience an echocardiogram was more likely to be ordered when not considered appropriate by AUC/SCAMP guidelines. Table 3 Echocardiogram ordering to AUC/SCAMP recommendation concordance by patient, visit, and provider variables Patient, visit or provider variable Echo ordering concordant with AUC/SCAMP recommendation Echo ordered/AUC or SCAMP recommended Echo ordering concordant with AUC/SCAMP recommendation Echo ordering discordant with AUC/SCAMP recommendation N (%) p-value Yes/yes N (%) No/no N (%) Yes/no N (%) No/yes N (%) p-value Patient Race/ethnicity White and non-Hispanic Non-White or Hispanic 109(83.8) 123(72.8) 0.025* 67(51.5) 82(48.5) 42(32.3) 41(24.3) 18(13.8) 41(24.3) 3(2.3) 5(3) 0.111 Patient sex Female Male 117(77) 115(78.2) 0.794 75(49.3) 74(50.3) 42(27.6) 41(27.9) 32(21.1) 27(18.4) 3(2) 5(3.4) 0.835 Preferred Language English Non-English 218(78.1) 14(70) 0.40 142(50.9)7(35) 76(27.2) 7(35) 53(19) 6(30) 8(2.9) 0(0) 0.381 Insurance Private Public 125(79.6) 106(75.2) 0.405 77(49) 72(51.1) 48(30.6) 34(24.1) 25(15.9)34(24.1) 7(4.5) 1(0.7) 0.055 Childhood Opportunity Index Very Low Low Moderate High Very High 28(80) 10(62.5) 27(67.5) 49(80.3) 75(83.3) 0.246 21(60) 9(56.3) 12(30) 27(44.3) 43(47.8) 7(20) 1(6.3) 15(37.5) 22(36.1) 32(35.6) 7(20) 5(31.3) 11(27.5) 10(16.4) 13(14.4) 0(0) 1(6.3) 2(5) 2(3.3) 2(2.2) 0.019 Clinic location Satellite Main office 152(76.8) 80(79.2) 0.663 93(47) 56(55.4) 59(29.8) 24(23.8) 38(19.2)21(20.8) 8(4) 0(0) 0.111 Provider years of practice 10 106 (86.9) 126(71.2) 0.002* 53(43.4) 96(54.2) 53(43.4) 30(16.9) 10(8.2) 49(27.7) 6(4.9) 2(1.1) < 0.001* Patient and provider race Concordant Discordant 93(82.3) 139(74.7) 0.153 47(41.6) 102(55) 46(40.7) 37 (20) 17(15) 42 (22.6) 3(2.7) 5(2.7) 0.001* Patient and provider sex Concordant Discordant 114(80.9) 118(74.7) 0.214 71(50.4) 78(49.4) 43(30.5) 40(25.3) 24(17) 35(22.2) 3(2.1) 5(3.2) 0.567 Table 4 Multiple Logistic Regression Analysis for outcome of echo ordering discordant with AUC/SCAMP recommendation Variable Odds ratio 95% CI p-value All encounters Provider > 10 years 2.795 1.49,5.23 0.001 Patient non-White or Hispanic 2.02 1.12,3.64 0.02 All encounters in which an echocardiogram was ordered Provider > 10 years 2.827 1.31,6.08 0.008 Patient non-White or Hispanic 1.965 1.02,3.78 0.043 Discussion In this retrospective analysis of pediatric patients presenting with chest pain to outpatient cardiology clinics, we found that the majority of echo utilization decisions aligned with established AUC or SCAMP criteria, with 77.6% of encounters classified as physician decision making as concordant with AUC/SCAMP recommendations. However, nearly one-quarter of encounters involved discordance in physician decision making with AUC/SCAMP recommendations, most commonly ordering an echo when criteria were not met. These findings highlight the variation in outpatient pediatric cardiology practices despite the availability of evidence-based guidelines. Several demographic factors were associated with differences in care. White patients were significantly more likely than non-White patients to have concordance between physician decision making and AUC/SCAMP recommendations (83.8% vs. 72.8%, p = .025), indicating potential disparities in adherence to clinical guidelines. Although non-White patients did not have significantly higher overall rates of echo ordering, they constituted the majority (69.5%) of inappropriately ordered studies. While this difference approached statistical significance (p = .056), the trend raises concern that minoritized race children may be more likely to have erroneous evaluations by providers, as prior work identifying racial disparities in diagnostic testing across pediatric settings [ 1 – 4 ]. However, unlike in these previous studies, in this case, patients of minoritized race were more likely to receive a test when it may be unnecessary than to not get the test. Diagnostics is not the only area in medicine where there are differences in evaluating and treating patients. In a secondary analysis of a 2006–2009 National Hospital Ambulatory Medical Care Survey, it was determined that non-Hispanic, Black patients were less likely to receive analgesic treatment for abdominal pain than non-Hispanic White patients [ 11 ]. Another study analyzing long-bone fractures in children younger than 18 years old, non-White and Hispanic patients were more likely to receive any form of analgesic but less likely to receive opioids or have optimal pain reduction compared to white patients [ 12 ]. Socioeconomic indicators also demonstrated notable associations. White patients were substantially more likely to have private insurance than non-White patients (73% vs. 37%, p < .001). However, insurance status itself was not associated with appropriateness of echocardiogram ordering (79.6% vs. 75.2%, p = .405). Children with private insurance were slightly less likely to undergo echo compared with those with public insurance (65% vs. 75.2%), though this difference did not meet statistical significance (p = .059). These findings differ from prior studies reporting lower odds of receiving diagnostic testing among publicly insured children and highlight the potential influence of setting-specific practice patterns [ 13 ]. When analyzing patient’s COI with the concordance of providers ordering echocardiograms with the AUC/SCAMP criteria, patients with “Very Low”, “Low”, or “Moderate” COI were more likely to discordantly receive an echo (20%, 31.3%, and 27.5%) compared to patients with “High” or “Very High” COI (16.4% and 14.4%) (p=.019). The reasons for the type of disparity seen in this study – more ordering of studies in patients of minoritized races or lower SES – is unclear. It is possible that if providers evaluate a patient from a “Low” or “Very Low” COI in their office for a cardiac complaint, they may inappropriately order imaging that may not be indicated to minimize future emergency department utilization in the future for similar complaints. Additionally, children with “Very Low” and “Low” COI have decreased odds of attending well child visits compared to “Very High” COI [ 17 ]. Given that children with lower COI are less likely to be seen consistently in primary care, providers may feel inclined to have a more expansive evaluation even if not indicated by guidelines. Providers may order more testing in order to avoid undertreating or missing disease in patient populations, such as those in the “Low” or “Very Low” COI, that historically experience disparities. They may also be aware of higher levels of mistrust in medical providers within minority populations and think that additional testing will reassure families [ 18 ]. Provider-level characteristics demonstrated strong associations with echo ordering patterns. Providers with less than 10 years of experience ordered significantly fewer echocardiograms compared with those with more experience (51.6 vs 81.9%, p < .001) yet were more likely to order them more in concordance with the AUC/SCAMP recommendations (86.9% vs. 71.2%, p = .002). Additionally, the majority (83.1%) of inappropriately ordered echocardiograms were ordered by providers with more than 10 years of experience (p = .008). These findings may reflect habitual practice patterns, differences in risk perception, or decreased adherence to evolving guideline recommendations among more experienced clinicians which may put them at risk for providing lower quality care [ 14 ]. Conversely, this discrepancy could possibly result from providers with less than 10 years of experience learning only one set of guidelines (AUC published in 2014) compared to senior clinicians who have had to utilize many more recommendations throughout their career. Younger or less experienced physicians may have the tendency to follow guidelines more closely than their older and more experienced peers [ 16 ]. Further investigation is warranted to better understand why discordance in echo ordering was concentrated among this group and if this trend is similar in other areas of the United States or in other specialties. A study published in 2017 examined how an educational intervention could improve the appropriateness of ordering echo's by pediatric cardiologists. The study analyzed 6 centers and the effects that a multifaceted educational intervention (pre-educational intervention analysis, PowerPoint lectures, providers assigning indications prior to ordering echos, and audit/feedback) had on the appropriateness ratings for echo’s. Overall, the study showed that across all centers, there was an increase in the proportion of studies ordered for appropriate AUC indications (72.5% to 76.2%) and a decrease in the proportion of studies ordered for rarely appropriate AUC indications (9.6% to 7.4%) after the educational intervention [ 15 ]. On an analysis of each center, 3 of the 6 centers had significantly reduced the proportion of echos ordered for rarely appropriate AUC indications [ 15 ]. This study demonstrates that physician education and feedback on AUC criteria can lead to a more uniform and systematic approach to ordering echos resulting in more concordance. Several limitations should be considered when interpreting the findings of this study. This study was retrospective and relied on electronic medical record documentation from providers. The documentation for each patient encounter could have been incomplete or variable (depending on provider) which could have affected the classification of clinical features and echocardiogram appropriateness based on AUC and SCAMP criteria that we retrospectively assigned. The assessment of appropriateness was dependent on documented symptoms and risk factors, and unrecorded clinical details may have influenced clinical decision-making that may not be apparent retrospectively. Additionally, as a single-center study conducted within an outpatient pediatric cardiology practice, the findings may not be generalizable to other clinical settings or populations. Race and ethnicity were obtained from the medical record and, while presumably patient-reported, cannot be verified to capture patient or family self-identification. Furthermore, provider-level analyses may have been influenced by unequal encounter volumes and unmeasured factors such as patient complexity or clinical context. Finally, the observational design limits the ability to draw causal conclusions regarding the observed associations. Taken together, the results demonstrate that both patient-level and provider-level factors were associated with differences in echo ordering and appropriateness in this outpatient pediatric population. Although the majority of echo ordering decisions aligned with either AUC or SCAMP guidelines, disparities—particularly those related to race and provider characteristics—were evident. These findings highlight opportunities for targeted provider level education, reinforcement of guideline-based practice, and evaluation of structural contributors to variation in care [ 15 ]. Future studies should evaluate the underlying drivers of these disparities, including provider perceptions of risk, implicit bias, and clinic-level workflow differences. Interventions such as clinical decision support tools, standardized intake processes, and provider training may help promote more equitable and guideline-concordant care. Expanding this work to include multicenter data and prospective evaluations would further clarify the generalizability and causal mechanisms behind the disparities identified. Declarations The authors declare that they have no financial or non-financial competing interests related to this work. Author Contribution C.B. and A.C. wrote the main manuscript text and prepared all figures and tables. All authors reviewed the manuscript. References Marin JR, Rodean J, Hall M et al (2021) Racial and Ethnic Differences in Emergency Department Diagnostic Imaging at US Children’s Hospitals, 2016–2019. JAMA Netw Open 4(1):e2033710. 10.1001/jamanetworkopen.2020.33710 Hambrook JT, Kimball TR, Khoury P, Cnota J (2010) Disparities Exist in the Emergency Department Evaluation of Pediatric Chest Pain. 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J Pediatr Gastroenterol Nutr 78(3):634–643. 10.1002/jpn3.12131 Epub 2024 Jan 29. PMID: 38284647; PMCID: PMC11181309 Campbell R, Douglas P, Eidem B ACC/AAP/AHA/, ASE/HRS/SCAI/SCCT/SCMR/SOPE, American Heart Association (2014) Appropriate Use Criteria for Initial Transthoracic Echocardiography in Outpatient Pediatric Cardiology: A Report of the American College of Cardiology Appropriate Use Criteria Task Force, American Academy of Pediatrics, American Society of Echocardiography, Heart Rhythm Society, Society for Cardiovascular Angiography and Interventions, Society of Cardiovascular Computed Tomography, Society for Cardiovascular Magnetic Resonance, and Society of Pediatric Echocardiography. JACC. 2014 Nov, 64 (19) 2039–2060. https://doi.org/10.1016/j.jacc.2014.08.003 Verghese GR, Friedman KG, Rathod RH, Meiri A, Saleeb SF, Graham DA, Geggel RL, Fulton DR (2012) Resource Utilization Reduction for Evaluation of Chest Pain in Pediatrics Using a Novel Standardized Clinical Assessment and Management Plan (SCAMP). J Am Heart Assoc 1(2):jah3–e000349 Epub 2012 Apr 24. PMID: 23130120; PMCID: PMC3487367 Friedman KG, Kane DA, Rathod RH, Renaud A, Farias M, Geggel R, Fulton DR, Lock JE, Saleeb SF (2011) Management of pediatric chest pain using a standardized assessment and management plan. Pediatrics 128(2):239–245. 10.1542/peds.2011-0141 Epub 2011 Jul 11. PMID: 21746719; PMCID: PMC9923781 FitzGerald C, Hurst S (2017) Implicit bias in healthcare professionals: a systematic review. BMC Med Ethics 18(1):19. 10.1186/s12910-017-0179-8 PMID: 28249596; PMCID: PMC5333436 Johnson TJ, Weaver MD, Borrero S, Davis EM, Myaskovsky L, Zuckerbraun NS, Kraemer KL (2013) Association of race and ethnicity with management of abdominal pain in the emergency department. Pediatrics 132(4):e851–e858. 10.1542/peds.2012-3127 Epub 2013 Sep 23. PMID: 24062370; PMCID: PMC4074647 Goyal MK, Johnson TJ, Chamberlain JM, Cook L, Webb M, Drendel AL, Alessandrini E, Bajaj L, Lorch S, Grundmeier RW, Alpern ER, PEDIATRIC EMERGENCY CARE APPLIED RESEARCH NETWORK (PECARN) (2020) Racial and Ethnic Differences in Emergency Department Pain Management of Children With Fractures. Pediatrics 145(5):e20193370. 10.1542/peds.2019-3370 Epub 2020 Apr 20. PMID: 32312910; PMCID: PMC7193974 Mannix R, Chiang V, Stack AM (2012) Insurance status and the care of children in the emergency department. J Pediatr. ;161(3):536–541.e3. 10.1016/j.jpeds.2012.03.013 . Epub 2012 May 11. PMID: 22578580 Choudhry NK, Fletcher RH, Soumerai SB (2005) Systematic review: the relationship between clinical experience and quality of health care. Ann Intern Med. ;142(4):260 – 73. 10.7326/0003-4819-142-4-200502150-00008 . PMID: 15710959 Sachdeva R, Douglas PS, Kelleman MS, McCracken CE, Lopez L, Stern KWD, Eidem BW, Benavidez OJ, Weiner RB, Welch E, Campbell RM, Lai WW (2017) Educational intervention for improving the appropriateness of transthoracic echocardiograms ordered by pediatric cardiologists. Congenit Heart Dis 12(3):373–381. 10.1111/chd.12455 Epub 2017 Feb 22. PMID: 28225219 Francke AL, Smit MC, de Veer AJ, Mistiaen P (2008) Factors influencing the implementation of clinical guidelines for health care professionals: a systematic meta-review. BMC Med Inf Decis Mak 8:38. 10.1186/1472-6947-8-38 PMID: 18789150; PMCID: PMC2551591 Tyris J, Putnick DL, Parikh K, Lin TC, Sundaram R, Yeung EH (2024) Nov-Dec;24(8):1220–1228 Place-Based Opportunity and Well Child Visit Attendance in Early Childhood. Acad Pediatr. 10.1016/j.acap.2024.06.012 . Epub 2024 Jun 25. PMID: 38936606; PMCID: PMC11513235 Tekeste R, Grant M, Newton P et al (2025) Prevalence of Medical Mistrust and Its Impact on Patient Satisfaction in Pediatric Caregivers. J Racial Ethnic Health Disparities 12:3648–3654. https://doi.org/10.1007/s40615-024-02165-z Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 05 May, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers agreed at journal 26 Apr, 2026 Reviewers invited by journal 16 Apr, 2026 Editor assigned by journal 16 Apr, 2026 Submission checks completed at journal 16 Apr, 2026 First submitted to journal 13 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9408883","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627636469,"identity":"c19a8e7d-6e17-41ac-a0b0-2f148abd3659","order_by":0,"name":"Casey A. Brown","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABIUlEQVRIiWNgGAWjYPCCAyCC8QFCoAAiKEFAC7MBQsCAOC1sSArwaOFvP/7wMQ/DnXx+idxjlT8qtjEYXDv87DOPgV0e3wHmg7d5MLVInMkxNuZheGY5c0Ze2m2eM7cZDG6nGc/mMUguljzAlmyNRYsBQw6bNA/DYQOD2zlmtxnbQFoSjJl5DJgTNxzgMZPGpoX/+fPfMC2FP8Fa0j8DtdQDtfB/w6pFIsGMGaaFgResJQdky2GQLWzYtEjceGMsOcfgmYHk/DfG0kC/8EjezilmnGNwPHHmYTZjyzlYQqw//eGHNxV3DPh5zhh+/FFxW47vdvpmhjcV1Yl9x5sf3niDJZSBgIkHKQ55ICIgkhm7chBg/EFYZBSMglEwCkYyAAA06mdPYnSZ0QAAAABJRU5ErkJggg==","orcid":"","institution":"University of Maryland School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Casey","middleName":"A.","lastName":"Brown","suffix":""},{"id":627636470,"identity":"385680f2-f3e9-4363-bb03-f5174b72466d","order_by":1,"name":"Alicia H. Chaves","email":"","orcid":"","institution":"University of Maryland School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Alicia","middleName":"H.","lastName":"Chaves","suffix":""}],"badges":[],"createdAt":"2026-04-14 00:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9408883/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9408883/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108976583,"identity":"d269a502-b649-4dbc-9de8-202e2623289e","added_by":"auto","created_at":"2026-05-11 11:25:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":342059,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9408883/v1/062071d3-9d96-4011-bcaf-4f3c7eac8640.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Patient Sociodemographic and Provider Characteristic Predictors of Echocardiogram Ordering in Pediatric Patients: A Retrospective Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eImplicit bias refers to unconscious attitudes or stereotypes that influence understanding, actions, and decisions in clinical care. In healthcare, implicit bias has been shown to contribute to inequities in diagnostic testing, treatment, and clinical outcomes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. When two patients present with similar symptoms but undergo different diagnostic evaluations based on factors unrelated to clinical presentation, disparities in care quality and health outcomes may result.\u003c/p\u003e \u003cp\u003eSeveral studies have demonstrated racial and socioeconomic differences in the use of diagnostic imaging in pediatric emergency departments. Marin et al. found that Black and Hispanic children received fewer computed tomography (CT), magnetic resonance imaging (MRI), and other radiographic studies compared with White children presenting with similar clinical symptoms [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Similarly, Hambrook et al. found that pediatric patients who were White or had private insurance and presented with chest pain in an Emergency Room setting had a greater likelihood of receiving any form of testing (labs, imaging, etc.) compared to non-White and publicly insured patients [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. A 2012 study from Natale et al. describes how across 25 emergency departments across the United States, patients of minoritized races had lower odds of having a CT head compared to their White counterparts for minor head trauma [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. A 2013 study from Payne et al. did not find differences in laboratory or radiological testing in patients with head injury but did demonstrate overall disparities in laboratory and radiologic testing in the pediatric emergency room setting (including in subgroups of patients with fever and upper respiratory tract infection) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEvidence of bias extends beyond emergency care settings. Nyman et al. demonstrated that neonatal nurses evaluating infants for Neonatal Opioid Withdrawal Syndrome in standardized scenarios had bias against Black patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Presented with identical clinical scenarios along with a picture of a White or Black infant, the providers rated Black infants as having lower scores on the Modified Finnegan score (i.e., less signs of withdrawal). Additionally, a 2024 study from Kemp et al. found that racial and ethnic minority patients from a Pediatric IBD clinic had an older average age of diagnosis of IBD compared to white patients even when adjusted for other sociodemographic variables [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These findings highlight the potential influence of unconscious implicit bias in clinical decision-making across a variety of pediatric settings.\u003c/p\u003e \u003cp\u003eChest pain is a common reason for referral to pediatric cardiology. Echocardiograms (echo) may be indicated when specific symptoms, red flags, or risk factors are present. In 2014, Appropriate Use Criteria (AUC) were developed to guide decision-making for ordering of initial outpatient pediatric echos [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The recommendations in this document are based on expert opinion and review of the literature on indications for echocardiography, such as chest pain, and indicates whether an echo is considered \u0026ldquo;appropriate\u0026rdquo;, \u0026ldquo;may be appropriate\u0026rdquo;, or \u0026ldquo;rarely appropriate\u0026rdquo; for a set of clinical scenarios. Use of a standardized clinical assessment and management plan for pediatric chest pain has been shown to guide appropriate use of echos while reducing inappropriate use of echos [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While neither replaces clinical judgement, they can reduce variation in care and provide a way to compare care between patients with similar clinical findings.\u003c/p\u003e \u003cp\u003ePrior research suggests gaps in the appropriate evaluation of chest pain in the emergency room setting [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. To date, no studies have examined whether similar disparities exist in the outpatient pediatric cardiology setting, particularly in the ordering of echos for chest pain. Understanding whether race, ethnicity, insurance type, or socioeconomic status (SES) influence adherence to AUC in echocardiogram ordering is essential for promoting equitable care.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe purpose of this study is to evaluate whether demographic or socioeconomic factors are associated with the ordering of echocardiograms for pediatric patients presenting with chest pain.\u003c/b\u003e Specifically, we aim to:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDetermine whether race or ethnicity is associated with appropriateness of echocardiogram ordering\u003c/b\u003e based on AUC criteria. Based on previous findings on this topic, we hypothesize that minoritized racial and ethnic groups will be \u003cb\u003eless likely\u003c/b\u003e to receive an echocardiogram when clinically indicated and \u003cb\u003eless likely\u003c/b\u003e to receive an echocardiogram when not indicated compared with White patients.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eExamine whether socioeconomic status and insurance type are associated with appropriateness of echocardiogram ordering.\u003c/b\u003e We hypothesize that children living in higher-SES neighborhoods will be \u003cb\u003emore likely\u003c/b\u003e to undergo echocardiography\u0026mdash;both appropriately and inappropriately\u0026mdash;than children living in lower-SES neighborhoods.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThis study builds on prior literature by addressing diagnostic equity in a population and clinical setting that has received little prior investigation. Identifying potential disparities in cardiac testing may inform educational interventions, guideline implementation, and system-level efforts to promote equitable care.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe study was reviewed by the University of Maryland School of Medicine Institutional Review Board and was determined to be exempt. We conducted a retrospective cohort study of pediatric patients evaluated for chest pain at the University of Maryland Children\u0026rsquo;s Hospital outpatient cardiology clinics between 2018 and 2024. Eligible participants included all children undergoing a first-time outpatient evaluation for a chief complaint of chest pain. Patients were excluded if their chest pain was attributable to a known cardiac condition such as congenital heart defects, if race or ethnicity data were missing, if residential address information was unavailable for socioeconomic status (SES) assessment, or if they had undergone any prior cardiology evaluations before the study visit.\u003c/p\u003e \u003cp\u003ePrimary exposure variables included patient/family-reported race and ethnicity as documented in the electronic medical record, neighborhood SES determined using the Child Opportunity Index (COI) for the patient\u0026rsquo;s zip code, and insurance payor type (private, public, or other). The outcome variable was the appropriateness of echocardiogram (echo) ordering, categorized into four mutually exclusive groups: an echo ordered when considered appropriate, an echo ordered when considered inappropriate, an echo not ordered when considered appropriate, and an echo not ordered when considered inappropriate. Appropriateness was determined by comparing each patient\u0026rsquo;s clinical presentation and documented symptoms with established AUC criteria for pediatric transthoracic echocardiography and with the 2012 quality improvement initiative termed Standardized Clinical Assessment and Management Plans (SCAMP) recommendations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eData were abstracted from electronic medical records, including demographic characteristics, clinical presentation and history, specifically for cardiac risk factors, physical exam, other testing results (including electrocardiography) and echo utilization. Zip codes were used to determine geocodes based on the US Census Bureau data (geocoding.geo.census.gov/geocoder). The neighborhood z-score (a weighted average of all COI domain z-scores, nationally-normalized) and neighborhood level (nationally-normalized overall COI score in categories of \u0026ldquo;very low\u0026rdquo;, \u0026ldquo;low\u0026rdquo;, \u0026ldquo;moderate\u0026rdquo;, \u0026ldquo;high\u0026rdquo;, or \u0026ldquo;very high\u0026rdquo;) were determined based on the patient\u0026rsquo;s geocode and year of visit.\u003c/p\u003e \u003cp\u003eAnalysis\u003c/p\u003e \u003cp\u003eDescriptive statistics were used to summarize demographic and clinical characteristics. Associations between the primary independent variables (race/ethnicity and insurance type), the dependent variable (echocardiogram ordering concordance with AUC/SCAMP recommendations), as well as potential confounders were evaluated using Chi-square tests or Fisher\u0026rsquo;s exact analysis based on subgroup size and number of subgroups. Logistic regression models were used to evaluate associations between race/ethnicity, insurance payor, and the appropriateness of echocardiogram ordering, adjusting for potential clinical confounders.\u003c/p\u003e \u003cp\u003eSince there were only 13 total providers and thus even smaller numbers of providers in race/sex categories, we were concerned that analysis by provider race and sex might be biased due to unmeasured provider factors. We chose to limit analysis to provider-patient race and sex concordance. We also identified that several providers\u0026rsquo; encounters had been selected much more frequently than others (likely due to time with practice, volume of outpatient clinics). We repeated the analysis of a subset of data in which the number of encounters for each provider was limited to 30. This analysis showed similar results to the primary analysis.\u003c/p\u003e \u003cp\u003e Our initial analysis revealed that there were very few encounters in which an echocardiogram was not ordered when the AUC/SCAMP guidelines considered it appropriate. Since much of the discordance between provider ordering and AUC/SCAMP appropriateness was due to physicians ordering echocardiograms when not considered appropriate, we did a sub-analysis of the visits in which an echocardiogram was ordered.\u003c/p\u003e \u003cp\u003eStatistical significance was defined as a two-sided p-value of \u0026lt;\u0026thinsp;0.05. Analysis was done using IBM SPSS Statistics Version 28 (IBM Corp., Armonk, New York, United States of America) The study was reviewed and approved (or exempted) by the University of Maryland School of Medicine Institutional Review Board, and all data were de-identified prior to analysis.\u003c/p\u003e \u003cp\u003eAppropriate Use Criteria [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eChest Pain Appropriate Use Ratings\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRarely Appropriate (R)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChest pain with no other symptoms or signs of cardiovascular disease, a benign family history, and a normal ECG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-exertional chest pain with no recent ECG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-exertional chest pain with normal ECG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReproducible chest pain with palpation or deep inspiration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMay Be Appropriate (M)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChest pain with no other symptoms or signs of cardiovascular disease, a benign family history, and an abnormal ECG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChest pain with family history of premature coronary artery disease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChest pain with recent onset of fever\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChest pain with recent illicit drug use\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAppropriate (A)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExertional chest pain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-exertional chest pain with abnormal ECG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChest pain with family history of sudden unexplained death or cardiomyopathy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline Characteristics of study participants:\u003c/h2\u003e\n \u003cp\u003eA total of 368 subjects were screened, of whom 299 (81.3%) met the inclusion criteria and were enrolled. The mean age of the cohort was 11.8 years (SD 3.51), and 50.8% were female. Most patients identified as White (45.2%) or Black (38.8%), with smaller proportions identifying as Asian (2.3%) or other/multiracial (13.7%). Approximately half of subjects had private insurance (52.5%), and the majority spoke English as their primary language (93.3%). Of the cohort, 101 subjects (33.8%) had their visit at the main office at UMMC while the remaining subjects had their visits at satellite locations. A summary of the subject\u0026rsquo;s independent variables is seen in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSubject variables (n\u0026thinsp;=\u0026thinsp;299)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e152 (50.8)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003cp\u003eOther or multiple\u003c/p\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e135 (45.2)\u003c/p\u003e\n \u003cp\u003e116 (38.8)\u003c/p\u003e\n \u003cp\u003e7 (2.3)\u003c/p\u003e\n \u003cp\u003e41 (13.7)\u003c/p\u003e\n \u003cp\u003e31 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLanguage\u003c/p\u003e\n \u003cp\u003eEnglish\u003c/p\u003e\n \u003cp\u003eSpanish\u003c/p\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e279 (93.3)\u003c/p\u003e\n \u003cp\u003e16 (5.4)\u003c/p\u003e\n \u003cp\u003e4 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsurance\u003c/p\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003cp\u003ePublic\u003c/p\u003e\n \u003cp\u003eSelf-pay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e157 (52.5)\u003c/p\u003e\n \u003cp\u003e141 (47.2)\u003c/p\u003e\n \u003cp\u003e1 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClinic location\u003c/p\u003e\n \u003cp\u003eMain office\u003c/p\u003e\n \u003cp\u003eSatellite office\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e101 (33.8)\u003c/p\u003e\n \u003cp\u003e198 (66.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChildhood opportunity index\u003c/p\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003cp\u003eVery Low\u003c/p\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003cp\u003eVery High\u003c/p\u003e\n \u003cp\u003eZ-score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e35 (19.1)\u003c/p\u003e\n \u003cp\u003e16(5.4)\u003c/p\u003e\n \u003cp\u003e40(13.4)\u003c/p\u003e\n \u003cp\u003e61(20.4)\u003c/p\u003e\n \u003cp\u003e90(30.1)\u003c/p\u003e\n \u003cp\u003e0.60 (SD, 0.921)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThirteen providers (11 physicians, 2 nurse practitioners) conducted the selected visits. Six providers were female and seven were male; eight identified as White, two as Black, and three as Asian. Overall, 37.8% of encounters involved race-concordant provider\u0026ndash;patient pairs and 47.2% involved sex-concordant pairs. Providers with more than 10 years of experience conducted 177 encounters (59.2%).\u003c/p\u003e\n \u003cp\u003eExertional chest pain was reported in 31.1% of patients, family history of cardiac disease in 11%, and fever or recent infection in 9.7%. Physical examination revealed abnormal physical exam findings in 6.0% of patients, and electrocardiograms were abnormal in 11.7%. Based on the AUC criteria and the documented clinical information, 46.2% of the encounters were classified as \u0026ldquo;rarely appropriate\u0026rdquo;, 12.4% of the encounters were classified as \u0026ldquo;may be appropriate\u0026rdquo;, and 41.5% of the encounters were classified as \u0026ldquo;appropriate\u0026rdquo; for ordering of an echo. Similarly, 43.8% of the encounters met criteria for echocardiography indicated by the SCAMP algorithm. A summary of the encounters is seen in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEncounter summary\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEchocardiograms ordered\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e208 (69.6)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistory\u003c/p\u003e\n \u003cp\u003eExertional chest pain\u003c/p\u003e\n \u003cp\u003ePast medical history\u003c/p\u003e\n \u003cp\u003eFamily history\u003c/p\u003e\n \u003cp\u003eFever or infection\u003c/p\u003e\n \u003cp\u003eAdditional symptoms\u003c/p\u003e\n \u003cp\u003eAny history red flags\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e93 (31.1)\u003c/p\u003e\n \u003cp\u003e5 (1.7)\u003c/p\u003e\n \u003cp\u003e33 (11)\u003c/p\u003e\n \u003cp\u003e29 (9.7)\u003c/p\u003e\n \u003cp\u003e86 (28.8)\u003c/p\u003e\n \u003cp\u003e176 (58.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhysical exam\u003c/p\u003e\n \u003cp\u003eAbnormal finding(s)\u003c/p\u003e\n \u003cp\u003eReproducible chest pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18 (6)\u003c/p\u003e\n \u003cp\u003e57 (19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrocardiogram\u003c/p\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e35 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUC category\u003c/p\u003e\n \u003cp\u003eRarely appropriate\u003c/p\u003e\n \u003cp\u003eMay be appropriate\u003c/p\u003e\n \u003cp\u003eAppropriate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e138 (46.2)\u003c/p\u003e\n \u003cp\u003e37 (12.4)\u003c/p\u003e\n \u003cp\u003e124 (41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCAMP algorithm\u003c/p\u003e\n \u003cp\u003eEcho indicated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e131 (43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEcho indicated by either algorithm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e160 (53.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eOverall, the echo utilization was considered appropriate in 232 encounters (77.6%) by the AUC or SCAMP criteria. Provider decision about echo ordering was discordant with AUC/SCAMP recommendations in 67 encounters (22.4%), including 59 (19.7%) cases in which an echo was ordered despite not meeting criteria and 8 (2.7%) cases in which an echo was not ordered when it might be considered appropriate.\u003c/p\u003e\n \u003cp\u003eResults of analysis of the association of patient, visit, and provider variables with concordance between echo ordering and AUC/SCAMP recommendations are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. There was more likely to be concordance with echocardiogram ordering and AUC/SCAMP recommendations in patients who identified as White/non-Hispanic compared to other races/ethnicities (83.8% vs. 72.8%, p = .025). This difference appeared to be primarily due to \u0026ldquo;overordering\u0026rdquo; in the non-White or Hispanic patients, i.e., an echocardiogram was ordered when AUC/SCAMP did not consider it appropriate. Patients with private insurance were slightly less likely to have an echo ordered compared to subjects without private insurance (65% vs. 75.2%, p = .059), but concordance of provider\u0026rsquo;s decision about echo ordering with AUC/SCAMP guidelines was not significantly different between privately insured vs. publicly insured (79.6% vs. 75.2%, p = .405).\u003c/p\u003e\n \u003cp\u003eWhen analyzing the concordance of AUC/SCAMP recommendations with a patient\u0026rsquo;s Child Opportunity Index, it was found that patients that had \u0026ldquo;High\u0026rdquo; or \u0026ldquo;Very High\u0026rdquo; COI had concordant echo ordering in greater than 80% of encounters. Patients in the \u0026ldquo;Very Low\u0026rdquo;, \u0026ldquo;Low\u0026rdquo;, and \u0026ldquo;Moderate\u0026rdquo; categories had lower rates of concordance at 80%, 62.5%, and 67.5% respectively. Patients with \u0026ldquo;Moderate\u0026rdquo;, \u0026ldquo;Low\u0026rdquo;, or \u0026ldquo;Very Low\u0026rdquo; COI were more likely to receive an echo when not indicated by either AUC or SCAMP criteria (27.5%, 31.3%, and 20%) compared to patients with \u0026ldquo;High\u0026rdquo; or \u0026ldquo;Very High\u0026rdquo; COI (16.4% and 14.4%) (p=.019).\u003c/p\u003e\n \u003cp\u003eProviders with less than 10 years of experience were less likely to order echocardiograms compared to their peers with greater than 10 years of experience (51.6% vs. 81.9%, p \u0026lt; .001), but their echo ordering was considered appropriate more often (86.9% vs 71.2%, p\u0026thinsp;=\u0026thinsp;0.002). There was no difference in overall concordance between echo ordering and AUC/SCAMP recommendations between race-concordant and race-discordant provider-patient pairs (82.3% vs 74.7%, p\u0026thinsp;=\u0026thinsp;0.153) or sex-concordant and sex-discordant provider-patient pairs (80.9% vs 74.7%, p\u0026thinsp;=\u0026thinsp;0.214). Logistic regression analysis found that echocardiogram ordering was more likely to be concordant with AUC/SCAMP recommendations in encounters with White/non-Hispanic patients and providers with less than 10 years of experience.\u003c/p\u003e\n \u003cp\u003eSince there were very few encounters in which an echocardiogram was not ordered when AUC/SCAMP considered an echocardiogram appropriate, we repeated the analysis on the subgroup of encounters in which an echocardiogram was ordered. In that subgroup, of the inappropriately ordered echocardiograms, more were done in the non-White or Hispanic patients (69.5%, p\u0026thinsp;=\u0026thinsp;0.056). Additionally, 83.1% of the inappropriately ordered echo\u0026apos;s were ordered by providers with more than 10 years of experience (p\u0026thinsp;=\u0026thinsp;0.008). Multiple logistic regression in this subgroup showed that in encounters with non-White or Hispanic patients and providers with more than 10 years of experience an echocardiogram was more likely to be ordered when not considered appropriate by AUC/SCAMP guidelines.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEchocardiogram ordering to AUC/SCAMP recommendation concordance by patient, visit, and provider variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003ePatient, visit or provider variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eEcho ordering concordant with AUC/SCAMP recommendation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003eEcho ordered/AUC or SCAMP recommended\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eEcho ordering concordant with AUC/SCAMP recommendation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eEcho ordering discordant with AUC/SCAMP recommendation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes/yes\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo/no\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes/no\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo/yes\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatient Race/ethnicity\u003c/p\u003e\n \u003cp\u003eWhite and non-Hispanic\u003c/p\u003e\n \u003cp\u003eNon-White or Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e109(83.8)\u003c/p\u003e\n \u003cp\u003e123(72.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.025*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e67(51.5)\u003c/p\u003e\n \u003cp\u003e82(48.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e42(32.3)\u003c/p\u003e\n \u003cp\u003e41(24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18(13.8)\u003c/p\u003e\n \u003cp\u003e41(24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3(2.3)\u003c/p\u003e\n \u003cp\u003e5(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatient sex\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e117(77)\u003c/p\u003e\n \u003cp\u003e115(78.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e75(49.3)\u003c/p\u003e\n \u003cp\u003e74(50.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e42(27.6)\u003c/p\u003e\n \u003cp\u003e41(27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e32(21.1)\u003c/p\u003e\n \u003cp\u003e27(18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3(2)\u003c/p\u003e\n \u003cp\u003e5(3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreferred Language\u003c/p\u003e\n \u003cp\u003eEnglish\u003c/p\u003e\n \u003cp\u003eNon-English\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e218(78.1)\u003c/p\u003e\n \u003cp\u003e14(70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e142(50.9)7(35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e76(27.2)\u003c/p\u003e\n \u003cp\u003e7(35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e53(19)\u003c/p\u003e\n \u003cp\u003e6(30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8(2.9)\u003c/p\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsurance\u003c/p\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003cp\u003ePublic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e125(79.6)\u003c/p\u003e\n \u003cp\u003e106(75.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e77(49)\u003c/p\u003e\n \u003cp\u003e72(51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e48(30.6)\u003c/p\u003e\n \u003cp\u003e34(24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25(15.9)34(24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7(4.5)\u003c/p\u003e\n \u003cp\u003e1(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChildhood Opportunity Index\u003c/p\u003e\n \u003cp\u003eVery Low\u003c/p\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003cp\u003eVery High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e28(80)\u003c/p\u003e\n \u003cp\u003e10(62.5)\u003c/p\u003e\n \u003cp\u003e27(67.5)\u003c/p\u003e\n \u003cp\u003e49(80.3)\u003c/p\u003e\n \u003cp\u003e75(83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e21(60)\u003c/p\u003e\n \u003cp\u003e9(56.3)\u003c/p\u003e\n \u003cp\u003e12(30)\u003c/p\u003e\n \u003cp\u003e27(44.3)\u003c/p\u003e\n \u003cp\u003e43(47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7(20)\u003c/p\u003e\n \u003cp\u003e1(6.3)\u003c/p\u003e\n \u003cp\u003e15(37.5)\u003c/p\u003e\n \u003cp\u003e22(36.1)\u003c/p\u003e\n \u003cp\u003e32(35.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7(20)\u003c/p\u003e\n \u003cp\u003e5(31.3)\u003c/p\u003e\n \u003cp\u003e11(27.5)\u003c/p\u003e\n \u003cp\u003e10(16.4)\u003c/p\u003e\n \u003cp\u003e13(14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003cp\u003e1(6.3)\u003c/p\u003e\n \u003cp\u003e2(5)\u003c/p\u003e\n \u003cp\u003e2(3.3)\u003c/p\u003e\n \u003cp\u003e2(2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClinic location\u003c/p\u003e\n \u003cp\u003eSatellite\u003c/p\u003e\n \u003cp\u003eMain office\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e152(76.8)\u003c/p\u003e\n \u003cp\u003e80(79.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e93(47)\u003c/p\u003e\n \u003cp\u003e56(55.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e59(29.8)\u003c/p\u003e\n \u003cp\u003e24(23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e38(19.2)21(20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8(4)\u003c/p\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProvider years of practice\u003c/p\u003e\n \u003cp\u003e\u0026lt;10\u003c/p\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e106 (86.9)\u003c/p\u003e\n \u003cp\u003e126(71.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e53(43.4)\u003c/p\u003e\n \u003cp\u003e96(54.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e53(43.4)\u003c/p\u003e\n \u003cp\u003e30(16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10(8.2)\u003c/p\u003e\n \u003cp\u003e49(27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6(4.9)\u003c/p\u003e\n \u003cp\u003e2(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatient and provider race\u003c/p\u003e\n \u003cp\u003eConcordant\u003c/p\u003e\n \u003cp\u003eDiscordant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e93(82.3)\u003c/p\u003e\n \u003cp\u003e139(74.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e47(41.6)\u003c/p\u003e\n \u003cp\u003e102(55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e46(40.7)\u003c/p\u003e\n \u003cp\u003e37 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17(15)\u003c/p\u003e\n \u003cp\u003e42 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3(2.7)\u003c/p\u003e\n \u003cp\u003e5(2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatient and provider sex\u003c/p\u003e\n \u003cp\u003eConcordant\u003c/p\u003e\n \u003cp\u003eDiscordant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e114(80.9)\u003c/p\u003e\n \u003cp\u003e118(74.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e71(50.4)\u003c/p\u003e\n \u003cp\u003e78(49.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e43(30.5)\u003c/p\u003e\n \u003cp\u003e40(25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24(17)\u003c/p\u003e\n \u003cp\u003e35(22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3(2.1)\u003c/p\u003e\n \u003cp\u003e5(3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultiple Logistic Regression Analysis for outcome of echo ordering discordant with AUC/SCAMP recommendation\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eAll encounters\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProvider\u0026thinsp;\u0026gt;\u0026thinsp;10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.49,5.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatient non-White or Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12,3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eAll encounters in which an echocardiogram was ordered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProvider\u0026thinsp;\u0026gt;\u0026thinsp;10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31,6.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatient non-White or Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02,3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective analysis of pediatric patients presenting with chest pain to outpatient cardiology clinics, we found that the majority of echo utilization decisions aligned with established AUC or SCAMP criteria, with 77.6% of encounters classified as physician decision making as concordant with AUC/SCAMP recommendations. However, nearly one-quarter of encounters involved discordance in physician decision making with AUC/SCAMP recommendations, most commonly ordering an echo when criteria were not met. These findings highlight the variation in outpatient pediatric cardiology practices despite the availability of evidence-based guidelines.\u003c/p\u003e \u003cp\u003eSeveral demographic factors were associated with differences in care. White patients were significantly more likely than non-White patients to have concordance between physician decision making and AUC/SCAMP recommendations (83.8% vs. 72.8%, p = .025), indicating potential disparities in adherence to clinical guidelines. Although non-White patients did not have significantly higher overall rates of echo ordering, they constituted the majority (69.5%) of inappropriately ordered studies. While this difference approached statistical significance (p = .056), the trend raises concern that minoritized race children may be more likely to have erroneous evaluations by providers, as prior work identifying racial disparities in diagnostic testing across pediatric settings [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, unlike in these previous studies, in this case, patients of minoritized race were more likely to receive a test when it may be unnecessary than to not get the test.\u003c/p\u003e \u003cp\u003eDiagnostics is not the only area in medicine where there are differences in evaluating and treating patients. In a secondary analysis of a 2006\u0026ndash;2009 National Hospital Ambulatory Medical Care Survey, it was determined that non-Hispanic, Black patients were less likely to receive analgesic treatment for abdominal pain than non-Hispanic White patients [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Another study analyzing long-bone fractures in children younger than 18 years old, non-White and Hispanic patients were more likely to receive any form of analgesic but less likely to receive opioids or have optimal pain reduction compared to white patients [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSocioeconomic indicators also demonstrated notable associations. White patients were substantially more likely to have private insurance than non-White patients (73% vs. 37%, p \u0026lt; .001). However, insurance status itself was not associated with appropriateness of echocardiogram ordering (79.6% vs. 75.2%, p = .405). Children with private insurance were slightly less likely to undergo echo compared with those with public insurance (65% vs. 75.2%), though this difference did not meet statistical significance (p = .059). These findings differ from prior studies reporting lower odds of receiving diagnostic testing among publicly insured children and highlight the potential influence of setting-specific practice patterns [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. When analyzing patient\u0026rsquo;s COI with the concordance of providers ordering echocardiograms with the AUC/SCAMP criteria, patients with \u0026ldquo;Very Low\u0026rdquo;, \u0026ldquo;Low\u0026rdquo;, or \u0026ldquo;Moderate\u0026rdquo; COI were more likely to discordantly receive an echo (20%, 31.3%, and 27.5%) compared to patients with \u0026ldquo;High\u0026rdquo; or \u0026ldquo;Very High\u0026rdquo; COI (16.4% and 14.4%) (p=.019).\u003c/p\u003e \u003cp\u003eThe reasons for the type of disparity seen in this study \u0026ndash; more ordering of studies in patients of minoritized races or lower SES \u0026ndash; is unclear. It is possible that if providers evaluate a patient from a \u0026ldquo;Low\u0026rdquo; or \u0026ldquo;Very Low\u0026rdquo; COI in their office for a cardiac complaint, they may inappropriately order imaging that may not be indicated to minimize future emergency department utilization in the future for similar complaints. Additionally, children with \u0026ldquo;Very Low\u0026rdquo; and \u0026ldquo;Low\u0026rdquo; COI have decreased odds of attending well child visits compared to \u0026ldquo;Very High\u0026rdquo; COI [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Given that children with lower COI are less likely to be seen consistently in primary care, providers may feel inclined to have a more expansive evaluation even if not indicated by guidelines. Providers may order more testing in order to avoid undertreating or missing disease in patient populations, such as those in the \u0026ldquo;Low\u0026rdquo; or \u0026ldquo;Very Low\u0026rdquo; COI, that historically experience disparities. They may also be aware of higher levels of mistrust in medical providers within minority populations and think that additional testing will reassure families [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProvider-level characteristics demonstrated strong associations with echo ordering patterns. Providers with less than 10 years of experience ordered significantly fewer echocardiograms compared with those with more experience (51.6 vs 81.9%, p \u0026lt; .001) yet were more likely to order them more in concordance with the AUC/SCAMP recommendations (86.9% vs. 71.2%, p = .002). Additionally, the majority (83.1%) of inappropriately ordered echocardiograms were ordered by providers with more than 10 years of experience (p = .008). These findings may reflect habitual practice patterns, differences in risk perception, or decreased adherence to evolving guideline recommendations among more experienced clinicians which may put them at risk for providing lower quality care [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Conversely, this discrepancy could possibly result from providers with less than 10 years of experience learning only one set of guidelines (AUC published in 2014) compared to senior clinicians who have had to utilize many more recommendations throughout their career. Younger or less experienced physicians may have the tendency to follow guidelines more closely than their older and more experienced peers [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Further investigation is warranted to better understand why discordance in echo ordering was concentrated among this group and if this trend is similar in other areas of the United States or in other specialties.\u003c/p\u003e \u003cp\u003eA study published in 2017 examined how an educational intervention could improve the appropriateness of ordering echo's by pediatric cardiologists. The study analyzed 6 centers and the effects that a multifaceted educational intervention (pre-educational intervention analysis, PowerPoint lectures, providers assigning indications prior to ordering echos, and audit/feedback) had on the appropriateness ratings for echo\u0026rsquo;s. Overall, the study showed that across all centers, there was an increase in the proportion of studies ordered for appropriate AUC indications (72.5% to 76.2%) and a decrease in the proportion of studies ordered for rarely appropriate AUC indications (9.6% to 7.4%) after the educational intervention [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. On an analysis of each center, 3 of the 6 centers had significantly reduced the proportion of echos ordered for rarely appropriate AUC indications [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This study demonstrates that physician education and feedback on AUC criteria can lead to a more uniform and systematic approach to ordering echos resulting in more concordance.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered when interpreting the findings of this study. This study was retrospective and relied on electronic medical record documentation from providers. The documentation for each patient encounter could have been incomplete or variable (depending on provider) which could have affected the classification of clinical features and echocardiogram appropriateness based on AUC and SCAMP criteria that we retrospectively assigned. The assessment of appropriateness was dependent on documented symptoms and risk factors, and unrecorded clinical details may have influenced clinical decision-making that may not be apparent retrospectively. Additionally, as a single-center study conducted within an outpatient pediatric cardiology practice, the findings may not be generalizable to other clinical settings or populations. Race and ethnicity were obtained from the medical record and, while presumably patient-reported, cannot be verified to capture patient or family self-identification. Furthermore, provider-level analyses may have been influenced by unequal encounter volumes and unmeasured factors such as patient complexity or clinical context. Finally, the observational design limits the ability to draw causal conclusions regarding the observed associations.\u003c/p\u003e \u003cp\u003eTaken together, the results demonstrate that both patient-level and provider-level factors were associated with differences in echo ordering and appropriateness in this outpatient pediatric population. Although the majority of echo ordering decisions aligned with either AUC or SCAMP guidelines, disparities\u0026mdash;particularly those related to race and provider characteristics\u0026mdash;were evident. These findings highlight opportunities for targeted provider level education, reinforcement of guideline-based practice, and evaluation of structural contributors to variation in care [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFuture studies should evaluate the underlying drivers of these disparities, including provider perceptions of risk, implicit bias, and clinic-level workflow differences. Interventions such as clinical decision support tools, standardized intake processes, and provider training may help promote more equitable and guideline-concordant care. Expanding this work to include multicenter data and prospective evaluations would further clarify the generalizability and causal mechanisms behind the disparities identified.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare that they have no financial or non-financial competing interests related to this work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.B. and A.C. wrote the main manuscript text and prepared all figures and tables. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMarin JR, Rodean J, Hall M et al (2021) Racial and Ethnic Differences in Emergency Department Diagnostic Imaging at US Children\u0026rsquo;s Hospitals, 2016\u0026ndash;2019. 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PMID: 38936606; PMCID: PMC11513235\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTekeste R, Grant M, Newton P et al (2025) Prevalence of Medical Mistrust and Its Impact on Patient Satisfaction in Pediatric Caregivers. J Racial Ethnic Health Disparities 12:3648\u0026ndash;3654. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40615-024-02165-z\u003c/span\u003e\u003cspan address=\"10.1007/s40615-024-02165-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"pediatric-cardiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pedc","sideBox":"Learn more about [Pediatric Cardiology](http://link.springer.com/journal/246)","snPcode":"246","submissionUrl":"https://submission.nature.com/new-submission/246/3","title":"Pediatric Cardiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Pediatrics, Cardiology, Bias, Echocardiogram, Chest Pain","lastPublishedDoi":"10.21203/rs.3.rs-9408883/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9408883/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eImplicit bias and socioeconomic factors may influence diagnostic testing in pediatrics, but their effects on echocardiogram (echo) ordering in outpatient pediatric cardiology are not well understood. We evaluated whether patient demographic factors and provider characteristics were associated with appropriate or inappropriate echo ordering for initial outpatient evaluation of pediatric chest pain.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective study of 299 pediatric patients undergoing initial outpatient evaluation for chest pain between 2018 and 2024 at the University of Maryland Children's Hospital outpatient cardiology clinics. Echocardiogram appropriateness was determined using previously published appropriate use criteria (AUC) and structured clinical management and assessment plan (SCAMP) criteria. Associations between patient demographics, insurance type, neighborhood Childhood Opportunity Index (COI), provider characteristics, and echo appropriateness were assessed.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eEchocardiogram ordering was concordant with AUC/SCAMP recommendations in 232 encounters (77.6%). White patients were more likely than non-White patients to receive guideline-concordant ordering (83.8% vs. 72.8%, p = .025). Discordance between echo ordering and recommendations was primarily due to providers ordering an echo when not recommended, which was seen more often in non-White patients and patients from lower COI neighborhoods. Providers with less than 10 years of experience ordered fewer echocardiograms (51.6% vs. 81.9%, p \u0026lt; .001) but were more likely to order them in concordance with AUC/SCAMP criteria (86.9% vs. 71.2%, p \u0026lt; .001).\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003e Most echocardiogram ordering was guideline-concordant; however, disparities were associated with patient race, neighborhood opportunity, and provider experience. These findings identify opportunities to improve equitable, guideline-concordant care in outpatient pediatric cardiology.\u003c/p\u003e","manuscriptTitle":"Patient Sociodemographic and Provider Characteristic Predictors of Echocardiogram Ordering in Pediatric Patients: A Retrospective Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-24 11:30:09","doi":"10.21203/rs.3.rs-9408883/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-05T15:16:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316295702052035655273882269324164103080","date":"2026-04-27T14:52:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45621103987761938685471686914726940088","date":"2026-04-26T18:28:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-17T01:01:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-16T13:13:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-16T13:12:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Pediatric Cardiology","date":"2026-04-13T23:57:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"pediatric-cardiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pedc","sideBox":"Learn more about [Pediatric Cardiology](http://link.springer.com/journal/246)","snPcode":"246","submissionUrl":"https://submission.nature.com/new-submission/246/3","title":"Pediatric Cardiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f27fcd3d-ecc8-48a2-b081-c76714211448","owner":[],"postedDate":"April 24th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-05T15:16:44+00:00","index":35,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-24T11:30:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-24 11:30:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9408883","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9408883","identity":"rs-9408883","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}