Using Real-World Electronic Health Record Data to Assess Chronic Disease Screening in Children: A Case Study of Non-Alcoholic Fatty Liver Disease

Abstract

Background:

Data sources for assessing pediatric chronic diseases and associated screening practices are rare. One example is non-alcoholic fatty liver disease (NAFLD), a common chronic liver disease prevalent among children with overweight and obesity. If undetected, NAFLD can cause liver damage. Guidelines recommend screening for NAFLD using alanine aminotransferase (ALT) tests in children ≥9 years with obesity or those with overweight and cardiometabolic risk factors. This study explores how real-world data from electronic health records (EHRs) can be used to study NAFLD screening and ALT elevation.

Research Design:

Using IQVIA's Ambulatory Electronic Medical Record database, we studied patients 2–19 years of age with body mass index ≥85th percentile. Using a 3-year observation period (January 1, 2019 to December 31, 2021), ALT results were extracted and assessed for elevation (≥1 ALT result ≥22.1 U/L for females and ≥25.8 U/L for males). Patients with liver disease (including NAFLD) or receiving hepatotoxic medications during 2017–2018 were excluded.

Results:

Among 919,203 patients 9–19 years of age, only 13% had ≥1 ALT result, including 14% of patients with obesity and 17% of patients with severe obesity. ALT results were identified for 5% of patients 2–8 years of age. Of patients with ALT results, 34% of patients 2–8 years of age and 38% of patients 9–19 years of age had ALT elevation. Males 9–19 years of age had a higher prevalence of ALT elevation than females (49% vs. 29%).

Conclusions:

EHR data offered novel insights into NAFLD screening: despite screening recommendations, ALT results among children with excess weight were infrequent. Among those with ALT results, ALT elevation was common, underscoring the importance of screening for early disease detection.

Introduction

Few robust data sources exist to study obesity-related co-occurring conditions in children.¹ One such condition is non-alcoholic fatty liver disease (NAFLD), the most common chronic liver disease in children with excess weight.^2–4 Although often asymptomatic in childhood, NAFLD can be progressive and cause severe liver disease in adulthood.^3,5–7 NAFLD is most prevalent among children with obesity,^2,4 but when detected, can be treatable through lifestyle modification and weight management.⁸ However, because many conditions affect the liver, NAFLD is a diagnosis of exclusion, which makes it harder to detect.

Pediatric NAFLD prevalence estimates range widely from <2% to >50% in those with obesity, likely due to differences in study design, cohort size, and case identification methodology, among other factors.^2,4,9–11 For chronic conditions like NAFLD, understanding how many people are screened and have elevated screening results is important to inform prevention and treatment efforts. Electronic health records (EHRs) are an ideal data source to examine screening because provider actions are captured alongside clinically detailed data from a large patient population.

To measure screening for a health condition using EHR data, clinical practice guidelines that inform providers on recommended screening actions are translated into an algorithm for implementation within an EHR dataset. In 2017, the North American Society for Pediatric Gastroenterology, Hepatology, and Nutrition (NASPGHAN) recommended NAFLD screening in children 9–11 years of age with obesity or with overweight and risk factors, using alanine aminotransferase (ALT) tests, and when indicated, confirming NAFLD with a liver biopsy.⁸ Three studies found low rates of pediatric NAFLD screening (9% of children with obesity in 2018/2019), but reflected small samples or observed screening before the 2017 guidelines.^12–14

As repositories of EHR data become increasingly available for secondary analysis, public health researchers are exploring how this novel data source can be used to examine understudied conditions like NAFLD at a population level. Because limited guidance exists for using EHR data to study screening, exploratory analyses like ours can introduce analytic approaches and findings from real-world data into the literature. Our study is the first to use a large population of children with overweight or obesity to study NAFLD and one of a few conducted since the NAFLD screening guidelines were updated. We aimed to describe NAFLD screening by estimating the frequency of ALT testing and ALT elevation among >1 million children with overweight and obesity between 2019 and 2021.

Methods

Data Source

We used IQVIA's Ambulatory Electronic Medical Record database (AEMR-US, OMOP version February 5, 2022, data release).^15,16 IQVIA AEMR contains structured data, including laboratory results, vitals, diagnoses, and procedures from ≈82 million patients, including 9 million children, from all 50 states recorded by >100,000 health care providers.

Study Population

Our study population included children 2–19 years of age with ≥1 valid measure of body mass index (BMI) in the overweight or obesity category captured between January 1, 2019, and December 31, 2021. We included children who were younger than the recommended screening age (<9) due to a paucity of data about this age group and the desire to inform future NAFLD screening recommendations.⁸

To generate the study population, height and weight observations were obtained from AEMR, cleaned using growthcleanr (based on the Daymont algorithm),^17,18 and used to calculate BMI and BMI percentile (BMI%) based on CDC's BMI-for-age growth charts¹⁹; implausible or extreme values were excluded (Supplementary Appendix SA1).²⁰ To assign each patient to a BMI category and select patients with overweight or obesity for the study population, the earliest BMI from the observation period was selected for each patient and categorized as underweight (<5th percentile), healthy weight (≥5th to <85th percentile), overweight (≥85th to <95th percentile), obesity (≥95th percentile), and severe obesity (≥120% of the 95th percentile); severe obesity was a subset of obesity.

Exclusions

Patients were excluded from the study population if they had ≥1 diagnostic code for 27 exclusion conditions that can impact liver function and cause ALT elevation (e.g., hepatitis or celiac disease), ≥1 diagnostic code for NAFLD or non-alcoholic steatohepatitis (NASH) (Supplementary Appendix SA2), or ≥1 medication order for hepatotoxic medications (Supplementary Appendix SA3) during the 2 years preceding the observation period (January 1, 2017, to December 31, 2018).²¹ Patients diagnosed with NAFLD or NASH were excluded because the presence of an ALT result reflects condition management rather than screening. Diagnostic codes were identified by keyword search; medications were identified by ingredient.

ALT Test Results

ALT results were extracted from AEMR and included in this analysis; ALT results with a zero value were excluded as biologically implausible. We calculated NAFLD screening as the percent of patients who had ≥1 ALT result among those who had a BMI.

ALT results were assessed for elevation based on the 2017 NASPGHAN guidelines and a 2010 ALT threshold sensitivity study.^8,22 ALT elevation was defined as ≥1 ALT result ≥22.1 U/L for females and ≥25.8 U/L for males. Based on expert advice (co-author M Vos), ALT results ≥1000 U/L were excluded as results that reflect non-NAFLD liver injury.

Statistical Analysis

The analytic dataset included patient age, gender, racial/ethnic group, BMI category, and ALT results. Descriptive statistics for patients were summarized by age group (2–8 years and 9–19 years), gender (female or male), racial/ethnic group (Asian, Black, Hispanic, White, Other, or Unknown), and BMI category (overweight, obesity, or severe obesity). We estimated patient age based on the earliest BMI date during the observation period and midpoint of the patient's birth year (i.e., July 2). All analyses were conducted using SAS v9.4 (SAS Institute, Inc., Cary, NC).

Results

Between 2019 and 2021, 4,012,721 patients 2–19 years of age had a valid BMI in IQVIA AEMR; 4% had underweight, 62% had healthy weight, 15% had overweight, and 18% had obesity, including 6% with severe obesity. Among the 1,352,854 with overweight or obesity, 9277 (0.7%) were excluded based on hepatotoxic medications and liver-related conditions, yielding a study population of 1,343,577. Table 1 summarizes the study population and proportions of patients with ≥1 ALT result. A total of 136,849 patients with overweight or obesity (10%) had ≥1 ALT result. Thirteen percent of patients of screening-eligible age (9–19 years) had ≥1 ALT result, compared to 5% among those 2–8 years of age.

Table 1.

Description of Study Population and Patterns in Alanine Aminotransferase Test Results by BMI Category and Demographic Group, IQVIA AEMR 2019–2021

	Overall		BMI category
			Overweight	Obesity	Severe obesity
			n = 617,542	n = 726,035	n = 247,756
	No. (Prevalence %)
	Total	Patients with ≥1 ALT result
Total	1,343,577	136,849 (10)	49,530 (8)	87,319 (12)	39,390 (16)
Children aged 2–8 years	424,374	21,115 (5)	7328 (4)	13,787 (6)	5428 (11)
Gender
Female	201,662	10,438 (5)	3634 (4)	6804 (7)	2587 (11)
Male	222,712	10,677 (5)	3694 (3)	6983 (6)	2841 (10)
Racial/ethnic group^a
Asian	8837	431 (5)	167 (4)	264 (6)	93 (13)
Black	50,116	2766 (6)	849 (4)	1917 (7)	886 (12)
Hispanic	3497	191 (5)	62 (4)	129 (6)	53 (8)
Other	23,964	2005 (8)	651 (6)	1354 (10)	525 (15)
Unknown	99,123	4251 (4)	1425 (3)	2826 (5)	1138 (9)
White	238,837	11,471 (5)	4174 (3)	7297 (6)	2733 (10)
Children aged 9–19 years^b	919,203	115,734 (13)	42,202 (10)	73,532 (14)	33,962 (17)
Gender
Female	474,283	66,537 (14)	26,784 (12)	39,753 (16)	17,675 (19)
Male	444,920	49,197 (11)	15,418 (8)	33,779 (13)	16,287 (16)
Racial/ethnic group^a
Asian	15,203	2,368 (16)	1151 (13)	1217 (18)	368 (22)
Black	105,733	13,347 (13)	3718 (9)	9629 (15)	5376 (18)
Hispanic	8982	872 (10)	289 (8)	583 (11)	256 (12)
Other	47,323	7946 (17)	2666 (14)	5280 (19)	2519 (22)
Unknown	217,236	24,000 (11)	8916 (9)	15,084 (13)	6811 (15)
White	524,726	67,201 (13)	25,462 (11)	41,739 (15)	18,632 (18)

BMI category was assigned based on the earliest BMI during the observation period. BMI categories were based on sex-specific BMI-for-age percentiles and defined as overweight (BMI ≥85th to <95th percentile), obesity (BMI ≥95th percentile), and severe obesity (BMI ≥120% of the 95th percentile). Severe obesity is a subset of obesity.

IQVIA combines race and ethnicity in one field and does not specify if White, Black, and Asian are non-Hispanic, which may cause loss of true racial/ethnic identity data at the patient level.

Expert committee guidelines recommend NAFLD screening using ALT tests in children 9–11 years of age, who have obesity or overweight and risk factors.

ALT, alanine aminotransferase; BMI, body mass index.

Among patients 9–19 years of age, ALT results were less common among patients with overweight (10%) compared to patients with obesity (14%) and severe obesity (17%); and a larger proportion of females had ALT results compared to males in all BMI categories: overweight (12% vs. 8%); obesity (16% vs. 13%); and severe obesity (19% vs. 16%). Among patients 9–19 years of age, the presence of ALT results varied by racial/ethnic group: 16% among Asian patients, 13% among White and Black patients, and 10% among Hispanic patients.

Table 2 describes ALT elevation among the 21,115 patients 2–8 years of age and 115,734 patients 9–19 years of age with overweight or obesity, who had ≥1 ALT result. Among patients with ≥1 ALT result, ALT elevation was present in 34% of patients 2–8 years of age and 38% of patients 9–19 years of age. Across age groups, ALT elevation was more prevalent among patients with obesity (44%) and severe obesity (51%) than overweight (26%). Among patients 9–19 years of age, ALT elevation was more common among males than females (49% vs. 29%) with larger differences observed among patients with severe obesity (64% vs. 40%), and more common in Hispanic patients (45%) compared to White patients (39%), Asian patients (38%), and Black patients (25%).

Table 2.

Patterns in Alanine Aminotransferase Test Elevation by BMI Category and Demographic Group, IQVIA AEMR 2019–2021

	Overall		BMI category
	Overall		Overweight	Obesity	Severe obesity
	Patients with ≥1 ALT result	Patients with ≥1 elevated^a ALT result No. (Prevalence %)
Total	136,849	50,654 (37)	12,648 (26)	38,006 (44)	20,137 (51)
Children aged 2–8 years	21,115	7162 (34)	1720 (23)	5442 (39)	2618 (48)
Gender
Female	10,438	3656 (35)	923 (25)	2733 (40)	1212 (47)
Male	10,677	3506 (33)	797 (22)	2709 (39)	1406 (49)
Racial/ethnic group^b
Asian	431	122 (28)	36 (22)	86 (33)	41 (44)
Black	2766	531 (19)	131 (15)	400 (21)	219 (25)
Hispanic	191	76 (40)	22 (35)	54 (42)	25 (47)
Other	2005	835 (42)	199 (31)	636 (47)	287 (55)
Unknown	4251	1559 (37)	331 (23)	1228 (43)	627 (55)
White	11,471	4039 (35)	1001 (24)	3038 (42)	1419 (52)
Children aged 9–19 years^c	115,734	43,492 (38)	10,928 (26)	32,564 (44)	17,519 (52)
Gender
Female	66,537	19,441 (29)	5718 (21)	13,723 (35)	7103 (40)
Male	49,197	24,051 (49)	5210 (34)	18,841 (56)	10,416 (64)
Racial/ethnic group^b
Asian	2368	901 (38)	312 (27)	589 (48)	219 (60)
Black	13,347	3312 (25)	654 (18)	2658 (28)	1703 (32)
Hispanic	872	396 (45)	86 (30)	310 (53)	162 (63)
Other	7946	3425 (43)	788 (30)	2637 (50)	1429 (57)
Unknown	24,000	9140 (38)	2287 (26)	6853 (45)	3627 (53)
White	67,201	26,318 (39)	6801 (27)	19,517 (47)	10,379 (56)

ALT elevation was assessed among patients with ≥1 nonzero ALT result and defined as ≥1 ALT result ≥22.1 for females and ≥25.8 for males.

IQVIA combines race and ethnicity in one field and does not specify if White, Black, and Asian are non-Hispanic, which may cause loss of true racial/ethnic identity data at the patient level.

Expert committee guidelines recommend NAFLD screening using ALT tests in children 9–11 years of age, who have obesity or overweight and risk factors.

Discussion

The IQVIA AEMR dataset yielded a study population of 1.3 M pediatric patients with excess weight in 2019–2021, from which we identified >135,000 with potential NAFLD screening events. Clinical practice guidelines were translated into an algorithm and applied to EHR data to examine NAFLD screening and ALT elevation, a sign of NAFLD. Despite screening recommendations, 1 in 7 screening-eligible patients had ≥1 ALT result (9–19 years of age with obesity).^1,8 However, among patients with ALT results, ALT elevation was common (more than 1 in 3) and increased with BMI category and age. Creating an algorithm to assess NAFLD screening highlighted general complexities of measuring screening in EHR datasets as well as NAFLD-specific considerations, for which guidance was unavailable in the literature.

Based on NAFLD screening recommendations, we expected to find ALT results for a large proportion of patients ≥9 years of age with overweight or obesity. However, in patients 9–19 years of age, only 10% of those with overweight and 14% with obesity had ≥1 ALT result; this was similar to findings from the Morkem's study (9% of children with obesity were screened), but lower than the estimate from Sahota's study (54% of children with obesity were screened).^12,14 As expected, more patients 9–19 years of age had ALT results than patients 2–8 years of age, likely reflecting the recommendation to begin screening for NAFLD at age 9.⁸ Nonetheless, 86% of children 9–19 years of age with obesity, for whom NAFLD screening was indicated, had no documented ALT results over a 3-year period. Underscreening for NAFLD may be due, in part, to limited knowledge of or access to NAFLD treatment. NASPHGAN recommends that children with excess weight and NAFLD should be offered “lifestyle intervention counseling,” a key component of several existing evidence-based pediatric weight management interventions.²³ Clinicians should be aware that NAFLD screening is universally low and that underscreening contributes to under diagnosis, undertreatment, and underestimates of NAFLD burden in high-risk populations.

This study offers novel findings on the presence of ALT results and ALT elevation in a large cohort of patients with severe obesity (n = 247,756). ALT elevation was most common among those with severe obesity in both the screening-eligible population (9–19 years) and in younger patients 2–8 years of age (52% vs. 48% respectively), which suggests that NAFLD might be present in children many years before screening is recommended. Future analysis of ALT elevation in younger children could explore this concerning finding.

The National Health and Nutrition Examination Survey (NHANES) is the gold standard data source for estimates of ALT elevation, referred to as suspected NAFLD.^9,10 Although EHR-based analyses and NHANES are poor comparators because of differing methods (i.e., random sampling adjusted to be nationally representative vs. census from a large care-seeking population), NHANES is potentially the only validation data source for these NAFLD findings. We conducted a subanalysis of patients 12–19 years of age to approximate a comparison between IQVIA AEMR and NHANES. Both data sources have similar BMI category distributions, and among patients with BMI ≥85%, we observed similarities in the frequency of ALT elevation.^24–26 Using 2007–2010 NHANES data, Welsh et al. observed ALT elevation in 44% of patients 12–19 years of age with BMI ≥85%.⁹ We observed ALT elevation in 38% of patients 12–19 years of age with BMI ≥85% and ≥1 ALT result. Although the time periods are misaligned, these similarities are encouraging and suggest that EHR data may be a valid data source to characterize pediatric NAFLD and expand upon the limited insights about real-world obesity care that NHANES can provide.

This analysis highlighted the difficulties of measuring screening using EHR data. The lack of consensus around recommended frequency of NAFLD screening limited our ability to construct an observation window when screening “should” occur. In addition, examining screening based on laboratory test results (ALT) that are used for many different conditions (e.g., hepatitis) made it impossible to confirm that an ALT result was intended for NAFLD screening.

This is not unique to NAFLD; using glucose measurements to study diabetes would be similarly challenging. Further, describing NAFLD, a condition that is a diagnosis of exclusion, based on a laboratory measure that can be elevated by many different conditions was challenging. While we addressed this issue by excluding >20 conditions that can warrant an ALT test or cause ALT elevation, future analyses within a health system could leverage additional EHR data such as notes to definitively classify ALT test results as an NAFLD screening event and expand the chronic disease literature on this important detail.

Limitations

This study has at least four limitations. First, this analysis relied entirely on laboratory test results, which might be incomplete because the provider did not contribute data to IQVIA AEMR, the test result was unstructured or nonstandard and could not be captured in AEMR, or other reason. Second, ALT results may reflect screening for NAFLD or other conditions involving the liver; however, we excluded patients with pre-existing NAFLD and other liver conditions to minimize this possibility. Third, guidelines do not recommend NAFLD screening for patients with overweight and zero cardiometabolic risk factors; their presence in our study population could impact screening prevalence.

Identifying cardiometabolic risk factors for screening eligibility was beyond the scope of this analysis; however, two studies estimate that 50%–75% of children with overweight have ≥1 cardiometabolic risk factor, suggesting that many patients with overweight in our study population were likely screening eligible.^27,28 Fourth, analysis of EHR data from an aggregator like IQVIA has many general challenges. For example, race and ethnicity are combined into one field without specifying whether White, Black, and Asian are Hispanic, and one third of patients had unknown or other racial/ethnic group. In addition, for validation, patient charts, including provider notes, were unavailable; chart review validation could enhance future analyses. Finally, results from EHR-based analyses have limited generalizability as EHRs capture data on a health care-seeking population.

Summary

This analysis demonstrated that screening recommendations can be applied to EHR data to generate powerful chronic disease insights. An EHR-based algorithm to quantify pediatric NAFLD screening and ALT elevation can help increase understanding of NAFLD in youth with excess weight and might inform efforts to improve adoption of NAFLD screening. Infrequent use of ALT tests among children with excess weight suggests that NAFLD screening is not occurring in accordance with expert committee recommendations. Despite infrequent screening, ALT elevation, a key marker for NAFLD, was present in >40% of children with obesity and >50% of children with severe obesity who were screened. Information on the frequency of ALT measurement and ALT elevation can inform efforts to improve clinical quality, health service delivery, and population health.

Impact Statement

Despite recommended screening, alanine aminotransferase (ALT) results were available for only 13% of children 9–19 years of age with overweight and obesity in an ambulatory electronic health record database; among those, 38% had ≥1 elevated ALT result. Notably, 34% of children 2–8 years of age (for whom screening is not yet indicated) with ALT results had ≥1 elevated ALT result. Our findings suggest that screening for non-alcoholic fatty liver disease is infrequent, and that ALT elevation is common among youth with excess weight.

Footnotes

Acknowledgment

This work would not have been possible without the support of Drs. Brook Belay and Jennifer Chevinsky.

Authors' Contributions

Dr. E.M.K. conceptualized the study design, executed data curation and the analysis, and drafted the article. Mrs. S.L.P. and Dr. L.K. supported conceptualizing the study design, extracted data from IQVIA, supported data curation and analysis activities, drafted sections of the article, and reviewed drafts of the article. Mrs. R.P. provided clinical subject matter expertise, conducted a literature review, aided in the study design, and drafted and reviewed the article. Dr. R.J.K. aided in conceptualizing the study design, extracted data from IQVIA, supported data curation, and reviewed the article. Dr. A.B.G. aided in writing the article, provided clinical subject matter expertise, supporting the conceptualization of the study design, and reviewed the article. Dr. M.B.V. provided subject matter expertise, aided in the study design, and reviewed the article. Dr. H.M.B. provided domain subject matter expertise, aided in the study design, and reviewed the article. All authors approved the final article as submitted and agree to be accountable for all aspects of the work.

Funding Information

No funding was received for this article.

Author Disclosure Statement

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

Supplementary Material

References

Barlow SE & Expert Committee. Expert Committee Recommendations Regarding the Prevention, Assessment, and Treatment of Child and Adolescent Overweight and Obesity: Summary Report. Pediatrics, 2007; 120(Supplement_4):S164–S192; doi: 10.1542/peds.2007-2329C %J Pediatrics

Schwimmer

, Deutsch

, Kahen

, et al. Prevalence of fatty liver in children and adolescents. Pediatrics, 2006; 118(4):1388–1393; doi: 10.1542/peds.2006-1212

Patton

, Sirlin

, Behling

, et al. Pediatric nonalcoholic fatty liver disease: A critical appraisal of current data and implications for future research. J Pediatr Gastroenterol Nutr, 2006; 43(4):413–427; doi: 10.1097/01.mpg.0000239995.58388.56

Shapiro

, Noon

, JB

. Recent advances in the epidemiology of nonalcoholic fatty liver disease in children. Pediatr Obes, 2021; 16(11):e12849; doi: 10.1111/ijpo.12849

Schwimmer

. Definitive diagnosis and assessment of risk for nonalcoholic fatty liver disease in children and adolescents. Semin Liver Dis, 2007; 27(3):312–318; doi: 10.1055/s-2007-985075

Schwimmer

, McGreal

, Deutsch

, et al. Influence of gender, race, and ethnicity on suspected fatty liver in obese adolescents. Pediatrics, 2005; 115(5):e561–e565; doi: 10.1542/peds.2004-1832

Simon

, Roelstraete

, Hartjes

, et al. Non-alcoholic fatty liver disease in children and young adults is associated with increased long-term mortality. J Hepatol, 2021; 75(5):1034–1041.

Vos

, Abrams

, Barlow

, et al. NASPGHAN Clinical Practice Guideline for the Diagnosis and Treatment of Nonalcoholic Fatty Liver Disease in Children: Recommendations from the Expert Committee on NAFLD (ECON) and the North American Society of Pediatric Gastroenterology, Hepatology and Nutrition (NASPGHAN). J Pediatr Gastroenterol Nutr, 2017; 64(2):319–334; doi: 10.1097/MPG.0000000000001482

Welsh

, Karpen

, Vos

. Increasing prevalence of nonalcoholic fatty liver disease among United

States adolescents

, 1988–1994 to 2007–2010. Pediatrics, 2013;162(3):496–500. e1.

10.

Fraser

, Longnecker

, Lawlor

. Prevalence of elevated alanine aminotransferase among US adolescents and associated factors: NHANES 1999–2004. Gastroenterology, 2007; 133(6):1814–1820.

11.

, Golshan

, Harlow

, et al. Prevalence of nonalcoholic fatty liver disease in children with obesity. J Pediatr, 2019; 207(2019):64–70; doi: 10.1016/j.jpeds.2018.11.021

12.

Sahota

, Shapiro

, Newton

, et al. Incidence of nonalcoholic fatty liver disease in children: 2009–2018. Pediatrics, 2020; 146(6):e20200771; doi: 10.1542/peds.2020-0771

13.

Riley

, Bass

, Rosenthal

, et al. Underdiagnosis of pediatric obesity and underscreening for fatty liver disease and metabolic syndrome by pediatricians and pediatric subspecialists. J Pediatr, 2005; 147(6):839–842; doi: 10.1016/j.jpeds.2005.07.020

14.

Morkem

, Theal

, Barber

, et al. Screening patterns of nonalcoholic fatty liver disease in children with obesity in Canadian primary care: A cross-sectional study. Can J Gastroenterol Hepatol, 2022; 2022:8435581; doi: 10.1155/2022/8435581

15.

IQVIA E360TM SaaS Platform. Available from: https://www.iqvia.com/solutions/real-world-evidence/platforms/e360-saas-platform

16.

IQVIA e360 Fact Sheet. IQVIA. Updated 2021; 2022. Available from: https://www.iqvia.com/-/media/iqvia/pdfs/library/publications/iqvia-e360-double.pdf?_=1645718358710 [Last accessed: February 24, 2022].

17.

Growthcleanr Algorithm. MITRE Corporation; 2021. Available from: github.com/mitre/growthcleanr [Last accessed: May 18, 2021].

18.

Daymont

, Ross

, Russell Localio

, et al. Automated identification of implausible values in growth data from pediatric electronic health records. J Am Med Inform Assoc, 2017; 24(6):1080–1087; doi: 10.1093/jamia/ocx037

19.

Clinical Growth Charts. National Center for Health Statistics, Centers for Disease Control and Prevention. Available from: https://www.cdc.gov/growthcharts/clinical_charts.htm [Last accessed: January, 16, 2023].

20.

A SAS Program to calculate Body Mass Index for Children based on the 2000 CDC Growth Charts. Division of Nutrition, Physical Activity, and Obesity, Centers for Disease Control and Prevention; Available from: https://www.cdc.gov/nccdphp/dnpao/growthcharts/resources/sas.htm

21.

Non-alcoholic Fatty Liver Disease (NAFLD) & Alcoholic Fatty Liver Disease (ALD). CCHMC; 2016. Available from: https://phekb.org/phenotype/non-alcoholic-fatty-liver-disease-nalfd-alcoholic-fatty-liver-disease-ald

22.

Schwimmer

, Dunn

, Norman

, et al. SAFETY study: Alanine aminotransferase cutoff values are set too high for reliable detection of pediatric chronic liver disease. Gastroenterology, 2010; 138(4):1357–1364, 1364 e1–e2; doi: 10.1053/j.gastro.2009.12.052

23.

Hampl

, Hassink

, Skinner

, et al. Clinical Practice Guideline for the evaluation and treatment of children and adolescents with obesity. Pediatrics, 2023; 151(2):e2022060640.

24.

Freedman

, Goodman

, King

, et al. Tracking of obesity among 2- to 9-year-olds in an electronic heath record database from 2006 to 2018. Obes Sci Pract, 2020; 6(3):1–7.

25.

Freedman

, Goodman

, King

, et al. The longitudinal relation of childhood height to subsequent obesity in a large electronic health record database. Obesity, 2020; 28(9):1742–1749; doi: 10.1002/oby.22901

26.

Skinner

, Ravanbakht

, Skelton

, et al. Prevalence of obesity and severe obesity in US children, 1999–2016. Pediatrics, 2018; 141(3):e20173459; doi: 10.1542/peds.2017-3459

27.

l'Allemand-Jander

. Clinical diagnosis of metabolic and cardiovascular risks in overweight children: Early development of chronic diseases in the obese child. Int J Obes, 2010; 34 Suppl 2(2):S32–S36; doi: 10.1038/ijo.2010.237

28.

Lambert

, Delvin

, Levy

, et al. Prevalence of cardiometabolic risk factors by weight status in a population-based sample of Quebec children and adolescents. Can J Cardiol, 2008; 24(7):575–583; doi: 10.1016/s0828-282x(08)70639-1

Supplementary Material

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