OBJECTIVES:

There has long been an association between congenital heart disease (CHD) and general neurodevelopmental delays. However, the association between CHD and autism spectrum disorders (AuSDs) is less well understood. Using administrative data, we sought to determine the association between CHD and AuSD and identify specific CHD lesions with higher odds of developing AuSD.

METHODS:

We performed a 1:3 case-control study of children enrolled in the US Military Health System from 2001 to 2013. Children with International Classification of Disease, Ninth Revision, Clinical Modification codes for AuSD were identified as cases and matched with controls on the basis of date of birth, sex, and enrollment time frame. Each child’s records were reviewed for CHD lesions and associated procedures. Conditional logistic regression determined odds ratios (ORs) and 95% confidence intervals (CIs) for comparative associations.

RESULTS:

There were 8760 cases with AuSD and 26 280 controls included in the study. After adjustment for genetic syndrome, maternal age, gestational diabetes, short gestation, newborn epilepsy, birth asphyxia, and low birth weight, there were increased odds of AuSD in patients with CHD (OR 1.32; 95% CI 1.10–1.59). Specific lesions with significant OR included atrial septal defects (n = 82; OR 1.72; 95% CI 1.07–2.74) and ventricular septal defects (n = 193; OR 1.65; 95% CI 1.21–2.25).

CONCLUSIONS:

Children with CHD have increased odds of developing AuSD. Specific lesions associated with increased risk include atrial septal defects and ventricular septal defects. These findings will be useful for counseling parents of children with CHD.

What’s Known on This Subject:

Congenital heart disease (CHD) has previously been associated with neurodevelopmental delays. Authors of previous studies have looked at single CHD lesions such as hypoplastic left heart syndrome or Tetralogy of Fallot rather than more common CHD lesions.

What This Study Adds:

In this study, we quantify the association between CHD and autism spectrum disorders, including an association with milder and more common forms of CHD such as atrial and ventricular septal defects.

As a result of tremendous advances in surgical technique, children born today with complex congenital heart disease (CHD) have improved survival over earlier surgical eras.1  This improved survival, however, has been accompanied by a greater appreciation for important comorbidities, including neurodevelopmental challenges. In 2012, the American Heart Association released a scientific statement on the evaluation and management of neurodevelopmental outcomes in children with CHD.2  In the statement, Marino et al2  specifically address the increased risk for autism spectrum disorders (AuSDs) and the need for screening in early development. Despite this strong recommendation, there is a paucity of research on AuSD as a developmental outcome of CHD. Our purpose with this study is to investigate and better quantify the association between CHD and AuSD.

Many studies have been used to look for associations between CHD and neurodevelopmental disorders; however, nearly all of these previous studies have limitations that fall short of illuminating the association between CHD and AuSD. Several studies have demonstrated a link between CHD, neurodevelopmental delays, and subsequent poor school performance.3,4  Although much of the etiology for the neurologic disorder is thought to stem from a combination of preoperative, operative, and postoperative factors, there is no certainty of the degree to which any specific factor contributes to the outcome. Authors of previous rigorous studies looking to identify factors contributing to neurodevelopmental delay in CHD have tended to focus on specific cardiac defects,5,6  look at structural brain anomalies rather than developmental outcomes,7  or otherwise be insufficiently powered to detect a link between rare disease and a specific developmental outcome such as AuSD.8  Surprisingly, few of these previous studies have measured AuSD as an outcome.

Some of the limitations of previous studies can be overcome by using a large, administrative database that collects information on both CHD and AuSD in children with sufficient power to detect an association between rare diseases. Hultman et al9  and Weir et al10  have attempted to study this association using administrative data with conflicting results. An excellent and more recent database study by Tsao et al11  revealed a hazard ratio of 1.97 (95% confidence interval [CI] 1.11–3.52) between AuSD and CHD; however, the authors stopped short of investigating subtypes of CHD. Razzaghi et al12  demonstrated a larger effect with a crude odds ratio (OR) of 4.6 (95% CI 1.9–11.0). In the study by Razzaghi et al,12  validity was limited by use of voluntary parental questionnaire responses with binary inputs (yes or no) for both AuSD and CHD diagnoses.

To overcome the limitations of previous studies, we used the Military Health System (MHS) administrative database, a large database that includes longitudinal billing for care of children across multiple geographically dispersed regions. Our objective with this study was to determine the association of CHD and CHD subtypes with the development of autism by using the MHS database and fill a gap in the medical literature. On the basis of previous research, we primarily hypothesized that AuSD would be associated with CHD and secondarily that more hemodynamically severe forms of CHD would be more highly associated with AuSD.

We performed a nested case-control study using the MHS database. The MHS is one of the largest single-payer health care systems in the United States, providing care to millions of military members and dependent beneficiaries, both in military facilities and through purchased care in civilian facilities. The MHS database was designed to capture demographic and billing data for all care within the system. This includes in- and outpatient care, diagnostic and procedure codes, dates of service, and prescriptions. As such, the MHS database had sufficient power to identify associations between rare conditions and outcomes. Several of our coauthors designed and compiled this nested case-control data set for a study of perinatal AuSD risk factors.13  We performed a secondary analysis on this data set to investigate an association between CHD and AuSD.

Cases were defined as children with a diagnosis of AuSD who had a birth record in the MHS between October 1, 2000, and September 30, 2011, and at least 2 years of follow-up from birth through study exit as defined by the last visit or the end of the study period on September 30, 2013. Cases were identified as children with International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) codes for diagnosis of AuSD at 2 separate clinical encounters. This method of using separate AuSD billing occurrences to identify AuSD in database studies has previously been validated.14,15  Exclusion criteria included ICD-9-CM diagnostic codes for childhood disintegrative disorder and degenerative conditions of childhood such as Rett syndrome, Alpers disease, infantile necrotizing encephalomyelopathy, Leigh disease, and subacute necrotizing encephalopathy.

We matched each AuSD case with 3 controls who also had a birth record in the MHS on the basis of age, sex, date of birth, and enrollment time frame. To ensure that cases and controls had sufficient time to demonstrate symptoms of AuSD, all children had to have at least 2 years of clinical follow-up for study inclusion. The enrollment time frame for controls was the same or longer than the enrollment time frame for the cases. The enrollment time frame for controls was then truncated to exactly match the cases for the analysis.

Case and control records were then reviewed for ICD-9-CM codes associated with CHD and for cardiac surgery International Classification of Disease, Ninth Revision, Common Procedural Terms codes. Records with billing for 1 of 54 different CHD diagnoses were included for analysis (Supplemental Table 4). Given concerns for hemodynamically insignificant patent foramen ovale being misclassified as atrial septal defect (ASD) before 3 months of age, records with ASD codes were included only if they were billed at >3 months of age, were accompanied by an ASD repair procedure code, or accompanied by another developmental grouping lesion.

Each record was sorted into CHD subgroups on the basis of diagnostic or procedure codes for any 1 of developmentally similar lesions (Supplemental Table 4). Developmental grouping has been used in the Baltimore-Washington Infant Study and other previous CHD studies to look for associations among developmentally and physiologically similar lesions.16  For example, hypoplastic left heart syndrome (HLHS) can be grouped with aortic stenosis and coarctation of the aorta under the developmental grouping left heart obstructive lesions (LeftObs). The developmental groups were not exclusive, and a child could have multiple groups where appropriate. Thus, a child with atrioventricular septal defects (AVSDs) and aortic stenosis would be analyzed with both the AVSD and LeftOb developmental groups. Contrarily, records were classified as the developmental group for the most unifying diagnosis when appropriate. That is, a child with ASD, ventricular septal defect (VSD), and AVSD was only categorized as an AVSD rather than ASD, VSD, and AVSD because ASD and VSD are components of AVSD. Similarly, VSD and conoventricular lesions were categorized only as conoventricular. Further review of the records indicated binary presence of International Classification of Diseases, Ninth Revision codes for genetic syndromes, term versus preterm birth, maternal prenatal factors including substance abuse and diabetes, and numerous other perinatal factors (Supplemental Table 5).

Univariate descriptive statistics were calculated for all variables of interest, including counts and percentages for categorical data, or median (interquartile range) for continuous data. Comparisons between children with AuSD and controls were made by using Cochran-Mantel-Haenszel tests for categorical data, and generalized estimating equations were used for continuous data to account for the matched sets. Unadjusted conditional logistic regression was performed to determine the ORs and associated 95% CIs between any CHD exposure and AuSD. To account for potential confounders, multivariable conditional logistic regression was then performed to quantify the association between any CHD exposure and AuSD after adjusting for presence of a genetic syndrome, maternal age, gestational diabetes, short gestation, newborn epilepsy, birth asphyxia, and low birth weight. We adjusted for these 7 factors because of previously published associations with AuSD.13,1721  Similarly, we performed the same unadjusted and adjusted conditional logistic regression on each subgroup of interest, specifically patients diagnosed with anomalous pulmonary venous return, ASD, AVSD, conoventricular defect, Ebstein anomaly, LeftOb, right heart obstructive lesion (RightOb), and VSD (Supplemental Table 4). When making these comparisons, we excluded children with CHD lesions other than that under consideration from our analysis (ie, children with ASD were not included in analysis of LeftOb). This was done to ensure we were not confounding our results with cases of CHD. Statistical analyses were performed by using SAS version 9.4 (SAS Institute, Inc, Cary, NC). Statistical significance was assessed for P values less than .05 (P < .05).

Finally, we performed a sensitivity analysis to address concerns of potential misclassification of CHD using administrative data.22  For this portion of the study, we narrowed our CHD case definition to include only cases for which there were ≥2 CHD diagnoses, CHD diagnoses were billed at separate encounters, or there were any surgical or interventional codes.23,24  We then repeated our analyses in a similar fashion as our initial analyses.

A total of 8760 children with AuSD and 26 280 controls were included in the study. The groups were similar with respect to the matching characteristics, with boys comprising 79.9% of each group. Cases and controls had an average of 7.01 years enrollment time frame in the study. Compared with controls, those with AuSD were more likely to have a genetic syndrome, preterm birth, neonatal epilepsy, birth asphyxia, low birth weight, maternal gestational diabetes, and younger maternal age at delivery (Table 1). We identified 1063 individuals with CHD: 401 of the 8760 children with AuSD and 662 of the 26 280 controls.

TABLE 1

Characteristics of Children With AuSDs and Matched Controls

CharacteristicsAuSD (n = 8760)Control (n = 26 280)P
CHD, n (%) 401 (4.6) 662 (2.5) <.001 
Genetic syndrome, n (%) 411 (4.7) 178 (0.7) <.001 
Short gestation, n (%) 1171 (13.4) 2302 (8.8) <.001 
Newborn epilepsy, n (%) 203 (2.3) 70 (0.3) <.001 
Birth asphyxia, n (%) 3715 (42.4) 9574 (36.4) <.001 
Low birth wt, n (%) 772 (8.8) 1453 (5.5) <.001 
Gestational diabetes, n (%) 1062 (12.1) 2916 (11.1) .009 
Maternal age, ya 28.0 (24.3–32.3) 29.1 (25.5–33.2) <.001 
Sex, male, n (%) 6995 (79.9) 20 985 (79.9) .9999b 
CharacteristicsAuSD (n = 8760)Control (n = 26 280)P
CHD, n (%) 401 (4.6) 662 (2.5) <.001 
Genetic syndrome, n (%) 411 (4.7) 178 (0.7) <.001 
Short gestation, n (%) 1171 (13.4) 2302 (8.8) <.001 
Newborn epilepsy, n (%) 203 (2.3) 70 (0.3) <.001 
Birth asphyxia, n (%) 3715 (42.4) 9574 (36.4) <.001 
Low birth wt, n (%) 772 (8.8) 1453 (5.5) <.001 
Gestational diabetes, n (%) 1062 (12.1) 2916 (11.1) .009 
Maternal age, ya 28.0 (24.3–32.3) 29.1 (25.5–33.2) <.001 
Sex, male, n (%) 6995 (79.9) 20 985 (79.9) .9999b 
a

Median (interquartile range).

b

Subjects were matched on the basis of sex, date of birth, and enrollment time frame.

Overall, those with AuSD had an OR of 1.85 (95% CI 1.63–2.10; n = 1063) of having CHD as compared with controls (Table 2). After adjustment for covariates, there remained increased odds of AuSD in patients with CHD. For any CHD, the OR was 1.33 (95% CI 1.16–1.52; n = 1063). This finding was largely driven by the ASD group (OR 1.97; 95% CI 1.48–2.61; n = 216), but it was also significant for the LeftOb group (OR 1.42; 95% CI 1.04–1.93; n = 202) and the VSD group (OR 1.28; 95% CI 1.00–1.63; n = 336) (Fig 1, Table 2).

TABLE 2

Unadjusted and Adjusted ORs for the Association of AuSD With CHD

CharacteristicsnUnadjustedAdjusteda
OR (95% CI)POR (95% CI)P
Any CHD 1063 1.85 (1.63–2.10) <.001 1.33 (1.16–1.52) <.001 
 TAPVR or PAPVR 18 3.75 (1.48–9.50) .011 2.42 (0.86–6.79) .09 
 ASD 216 2.45 (1.87–3.20) <.001 1.97 (1.48–2.61) <.001 
 AVSD 66 2.08 (1.27–3.39) .007 0.88 (0.51–1.54) .66 
 Conoventricular 82 2.47 (1.60–3.81) <.001 1.32 (0.81–2.15) .27 
 Ebstein 13 2.57 (0.86–7.65) .16 1.17 (0.32–4.27) .81 
 LeftOb 202 2.42 (1.92–3.20) <.001 1.42 (1.04–1.93) .027 
 RightOb 430 1.50 (1.22–1.83) <.001 1.01 (0.81–1.25) .97 
 VSD 336 1.74 (1.39–2.18) <.001 1.28 (1.00–1.63) .047 
CharacteristicsnUnadjustedAdjusteda
OR (95% CI)POR (95% CI)P
Any CHD 1063 1.85 (1.63–2.10) <.001 1.33 (1.16–1.52) <.001 
 TAPVR or PAPVR 18 3.75 (1.48–9.50) .011 2.42 (0.86–6.79) .09 
 ASD 216 2.45 (1.87–3.20) <.001 1.97 (1.48–2.61) <.001 
 AVSD 66 2.08 (1.27–3.39) .007 0.88 (0.51–1.54) .66 
 Conoventricular 82 2.47 (1.60–3.81) <.001 1.32 (0.81–2.15) .27 
 Ebstein 13 2.57 (0.86–7.65) .16 1.17 (0.32–4.27) .81 
 LeftOb 202 2.42 (1.92–3.20) <.001 1.42 (1.04–1.93) .027 
 RightOb 430 1.50 (1.22–1.83) <.001 1.01 (0.81–1.25) .97 
 VSD 336 1.74 (1.39–2.18) <.001 1.28 (1.00–1.63) .047 

Conoventricular defect includes tetralogy of Fallot, truncus arteriosus, aortopulmonary window; Ebstein represents Ebstein malformation; LeftOb includes HLHS, mitral stenosis, aortic stenosis, and coarctation of the aorta; RightOb includes tricuspid atresia, pulmonary atresia, pulmonary stenosis not including tetralogy of Fallot. PAPVR, partial anomalous pulmonary venous return; TAPVR, total anomalous pulmonary venous return.

a

Adjusted for maternal age, gestational diabetes, genetic syndrome, short gestation, newborn epilepsy, birth asphyxia, and low birth weight.

FIGURE 1

ORs for AuSD with associated CHD categorical diagnosis adjusted for maternal age, gestational diabetes, genetic syndrome, short gestation, newborn epilepsy, birth asphyxia, and low birth weight. AVSD represents AVSD and/or atrioventricular canal defect; conoventricular defect includes tetralogy of Fallot, truncus arteriosus, and aortopulmonary window; Ebstein represents Ebstein malformation; LeftOb includes HLHS, mitral stenosis, aortic stenosis, and coarctation of the aorta; RightOb includes tricuspid atresia, pulmonary atresia, and pulmonary stenosis not including tetralogy of Fallot. PAPVR, partial anomalous pulmonary venous return; TAPVR, total anomalous pulmonary venous return.

FIGURE 1

ORs for AuSD with associated CHD categorical diagnosis adjusted for maternal age, gestational diabetes, genetic syndrome, short gestation, newborn epilepsy, birth asphyxia, and low birth weight. AVSD represents AVSD and/or atrioventricular canal defect; conoventricular defect includes tetralogy of Fallot, truncus arteriosus, and aortopulmonary window; Ebstein represents Ebstein malformation; LeftOb includes HLHS, mitral stenosis, aortic stenosis, and coarctation of the aorta; RightOb includes tricuspid atresia, pulmonary atresia, and pulmonary stenosis not including tetralogy of Fallot. PAPVR, partial anomalous pulmonary venous return; TAPVR, total anomalous pulmonary venous return.

Close modal

In the sensitivity analysis, we identified 593 individuals with CHD: 246 of the 8760 children with AuSD and 347 of the 26 280 controls. Despite the smaller numbers, we found similar results for any CHD (OR 1.32; 95% CI 1.10–1.59; n = 593) as well as for ASD (OR 1.72; 95% CI 1.07–2.74; n = 82) and VSD (OR 1.65; 95% CI 1.21–2.25; n = 193) (Fig 2, Table 3). For the LeftOb group, we found a similar adjusted OR of 1.25 in the sensitivity analysis, but the finding was no longer significant (95% CI 0.88–1.77; n = 161).

FIGURE 2

ORs for AuSD with associated CHD categorical diagnosis in the sensitivity analysis adjusted for maternal age, gestational diabetes, genetic syndrome, short gestation, newborn epilepsy, birth asphyxia, and low birth weight. AVSD represents AVSD and/or atrioventricular canal defect; conoventricular defect includes tetralogy of Fallot, truncus arteriosus, and aortopulmonary window; Ebstein represents Ebstein malformation; LeftOb includes HLHS, mitral stenosis, aortic stenosis, and coarctation of the aorta; RightOb includes tricuspid atresia, pulmonary atresia, and pulmonary stenosis not including tetralogy of Fallot. PAPVR, partial anomalous pulmonary venous return; TAPVR, total anomalous pulmonary venous return.

FIGURE 2

ORs for AuSD with associated CHD categorical diagnosis in the sensitivity analysis adjusted for maternal age, gestational diabetes, genetic syndrome, short gestation, newborn epilepsy, birth asphyxia, and low birth weight. AVSD represents AVSD and/or atrioventricular canal defect; conoventricular defect includes tetralogy of Fallot, truncus arteriosus, and aortopulmonary window; Ebstein represents Ebstein malformation; LeftOb includes HLHS, mitral stenosis, aortic stenosis, and coarctation of the aorta; RightOb includes tricuspid atresia, pulmonary atresia, and pulmonary stenosis not including tetralogy of Fallot. PAPVR, partial anomalous pulmonary venous return; TAPVR, total anomalous pulmonary venous return.

Close modal
TABLE 3

Unadjusted and Adjusted ORs for the Association of AuSD With CHD in the Sensitivity Analysis

CharacteristicsnUnadjustedAdjusteda
OR (95% CI)POR (95% CI)P
Any CHD 593 2.15 (1.82–2.53) <.001 1.32 (1.10–1.59) .003 
 TAPVR or PAPVR 15 3.43 (1.24–9.46) .035 1.97 (0.62–6.24) .25 
 ASD 82 2.72 (1.76–4.20) <.001 1.72 (1.07–2.74) .024 
 AVSD 61 2.23 (1.34–3.70) .004 0.89 (0.50–1.58) .69 
 Conoventricular 74 2.69 (1.71–4.25) <.001 1.40 (0.84–2.33) .20 
 Ebstein 11 2.50 (0.76–8.19) .23 0.89 (0.22–3.67) .88 
 LeftOb 161 2.37 (1.73–3.23) <.001 1.25 (0.88–1.77) .22 
 RightOb 296 1.72 (1.36–2.18) <.001 1.03 (0.79–1.34) .86 
 VSD 193 2.41 (1.82–3.20) <.001 1.65 (1.21–2.25) .002 
CharacteristicsnUnadjustedAdjusteda
OR (95% CI)POR (95% CI)P
Any CHD 593 2.15 (1.82–2.53) <.001 1.32 (1.10–1.59) .003 
 TAPVR or PAPVR 15 3.43 (1.24–9.46) .035 1.97 (0.62–6.24) .25 
 ASD 82 2.72 (1.76–4.20) <.001 1.72 (1.07–2.74) .024 
 AVSD 61 2.23 (1.34–3.70) .004 0.89 (0.50–1.58) .69 
 Conoventricular 74 2.69 (1.71–4.25) <.001 1.40 (0.84–2.33) .20 
 Ebstein 11 2.50 (0.76–8.19) .23 0.89 (0.22–3.67) .88 
 LeftOb 161 2.37 (1.73–3.23) <.001 1.25 (0.88–1.77) .22 
 RightOb 296 1.72 (1.36–2.18) <.001 1.03 (0.79–1.34) .86 
 VSD 193 2.41 (1.82–3.20) <.001 1.65 (1.21–2.25) .002 

Conoventricular defect includes tetralogy of Fallot, truncus arteriosus, aortopulmonary window; Ebstein represents Ebstein malformation; LeftOb includes HLHS, mitral stenosis, aortic stenosis, and coarctation of the aorta; RightOb includes tricuspid atresia, pulmonary atresia, pulmonary stenosis not including tetralogy of Fallot. PAPVR, partial anomalous pulmonary venous return; TAPVR, total anomalous pulmonary venous return.

a

Adjusted for maternal age, gestational diabetes, genetic syndrome, short gestation, newborn epilepsy, birth asphyxia, and low birth weight.

In this study, the largest of its kind used to study the association of CHD with AuSD, we found that CHD is associated with increased odds of developing AuSD. Among CHD subtypes, significant associations with AuSD were found for ASDs and VSDs in both the initial analysis and the sensitivity analysis; for LeftObs, a significant association was found only in the initial analysis. There have been many previous studies that have revealed specific developmental delays associated with CHD; however, there are only a few studies that were used to specifically assess for an association between AuSD and CHD with sufficient statistical power.9,11,12  To our knowledge, this is the only study in which there has been a comparison between AuSD and multiple CHD subtypes.

Our findings are consistent with previous studies of CHD developmental outcomes, which have shown an increased risk of developmental and academic delay after CHD diagnosis and treatment.25  In a recent review, Wernovsky and Licht26  estimated that nearly half of all severe CHD survivors experience some degree of developmental delay on a spectrum from mild to severe. Authors of some well-designed studies of CHD developmental outcomes used multidomain assessments such as the Wechsler, Woodcock-Johnson, and Bayley scoring systems to measure developmental outcome in a subset of patients with CHD.6,27,28  Mahle et al27  demonstrated a median full scale IQ of 86 (range 50–116) for 28 Fontan surgery survivors with HLHS. Newburger et al6  and Ravishankar et al28  reported lower-than-normative development using the Bayley Scales of Infant Development in 14-month-old toddlers with palliated single-ventricle physiology. In none of the studies mentioned did authors directly measure AuSD as an outcome of CHD.

The association of CHD with AuSD has varied in previous smaller studies. Authors of 1 of the earliest such studies with 408 AuSD cases and 2040 healthy controls found increased odds of CHD (OR 2.5; 95% CI 1.1–5.8) for the cases.9  On the other hand, authors of a later study of similar size (417 AuSD cases; 2067 controls) and design failed to find an association.10  In a more recent study, Tsao et al11  used a large administrative database with 3500 CHD cases and 14 000 controls to demonstrate a hazard ratio of 1.97 (95% CI 1.11–3.52) for AuSD after CHD diagnosis. Despite the power in the study, Tsao et al11  noted that they were unable to further analyze the underlying CHD lesions “due to limitations inherent to the specific…dataset.”

The causal link between CHD and AuSD is unknown. In some cases, there may be a common genetic mechanism. For instance, much research has been done regarding AuSD and 22q11.2 deletion syndrome, frequently known as DiGeorge or velocardiofacial syndrome in which 75% to 85% of patients have a CHD, most frequently conotruncal defects involving the great arteries.29  Although mostly associated with mild cognitive impairment, 22q11.2 deletion has been associated with AuSD, with an estimated 14% of affected patients meeting Vineland Adaptive Behavior Scales AuSD diagnostic criteria.30,31  Niklasson et al32  studied this association, finding a nearly 20% incidence of AuSD with 22q11.2 deletion but no correlation between AuSD and CHD in the study sample. CHARGE syndrome comprises another congenital disorder with a high incidence of CHD.33,34  According to a recent meta-analysis, CHARGE syndrome has an AuSD pooled prevalence of 30% (range 13%–48%).35  Our data reveal that the association between CHD and AuSD, especially ASD and AuSD, persists even when controlling for known genetic associations of AuSD. This suggests pleiotropy of underlying genetic loci contributing to both conditions. Vorstman et al36  summarized this nicely, reporting on at least 9 chromosomal anomalies in which AuSD was associated with CHD. With >800 genes implicated in AuSD36  and at least 8 genes linked with ASD formation,3739  there is considerable opportunity for discovery of unifying genetic loci between the conditions.

In other cases, there may be potential environmental or clinical factors that serve as a causal link between CHD and AuSD. There has been a great deal of research into the risk factors for AuSD and comorbid conditions over the past 25 years, including multiple other studies using the MHS database and this data set in particular. Although the etiology of AuSD remains unclear, an observation pertinent to this study is that epilepsy has an estimated prevalence of 12% to 26% among children with AuSD.40,41  This finding is intriguing because early onset of seizures has been linked to poor neurodevelopmental outcomes after CHD surgery.26,27  The postulated mechanisms for seizures in surgically palliated HLHS is due to cerebral hypoperfusion both in utero and in the perioperative period. Given the association of both CHD and AuSD with seizure disorders, it is a plausible suggestion that CHD may be linked to the subsequent development of the AuSD phenotype by cerebral hypoperfusion and subsequent epilepsy syndrome. Our study was not designed to reveal such a link; therefore, this is an opportunity for further research.

The strengths of this study are drawn from the comprehensive billing data in the MHS database, which follows children as they move across geographic regions of the United States. We were able to conglomerate a large population of children who were diagnosed and treated at multiple centers around the country. The large population provided the statistical power necessary to assess for associations between 2 uncommon childhood conditions. In addition, there was reduced access to care bias because all enrolled children were recipients of a health care benefit with little to no financial burden to the family.

There are a number of limitations for our study. Foremost is that administrative database studies such as this have inherent weaknesses related to the completeness and accuracy of the data as they were recorded.24  To mitigate this weakness, we used a validated definition of AuSD and applied similar rules to check our CHD cases for internal consistency. Our sensitivity analysis demonstrates that the effects of CHD on AuSD persist even with more stringent criteria, although this likely underestimates the effect of CHD on AuSD. A second limitation is potential ascertainment bias within the study population. Children identified with CHD or AuSD tend to present for care more frequently, thus making diagnosis of additional conditions more likely than in the general population. Finally, even with our large sample size, the rarity of CHD events may have limited some of our analyses. This was especially true for Ebstein anomaly, AVSDs, and conoventricular defects, and it may explain why the association of LeftObs with AuSD was no longer significant in the sensitivity analysis.

On the basis of our data, children with CHD have increased odds of developing AuSD. This finding was particularly notable for those with ASDs and VSDs. We hope that this information will allow pediatricians and pediatric cardiologists to better care for children and counsel families regarding the expected developmental course for children with CHD. Directions for future research on this subject include further elucidating any potential causal mechanisms that could provide opportunities for intervention.

Drs Sigmon and Oster conceptualized and designed the study from the assembled data set, analyzed and interpreted the data, and drafted the initial manuscript; Mr Kelleman conducted statistical data analysis; Dr Nylund and Ms Susi conceptualized and designed the autism cohort and collected and organized the data set; and all authors critically reviewed and revised the manuscript, approved the final manuscript as submitted, and agree to be accountable for all aspects of the work.

The views expressed herein are those of the authors and do not reflect the official policy or position of the US Army, the US Air Force, the Department of Defense, or the US Government.

FUNDING: Supported by the Congressional Directed Medical Research Programs Autism Research Award, W81XWH-12-2-0066.

     
  • ASD

    atrial septal defect

  •  
  • AuSD

    autism spectrum disorder

  •  
  • AVSD

    atrioventricular septal defect

  •  
  • CHD

    congenital heart disease

  •  
  • CI

    confidence interval

  •  
  • HLHS

    hypoplastic left heart syndrome

  •  
  • ICD-9-CM

    International Classification of Disease Ninth Revision: Clinical Modification

  •  
  • LeftOb

    left heart obstructive lesion

  •  
  • MHS

    Military Health System

  •  
  • OR

    odds ratio

  •  
  • RightOb

    right heart obstructive lesion

  •  
  • VSD

    ventricular septal defect

1
Oster
ME
,
Lee
KA
,
Honein
MA
,
Riehle-Colarusso
T
,
Shin
M
,
Correa
A
.
Temporal trends in survival among infants with critical congenital heart defects
.
Pediatrics
.
2013
;
131
(
5
).
2
Marino
BS
,
Lipkin
PH
,
Newburger
JW
, et al;
American Heart Association Congenital Heart Defects Committee, Council on Cardiovascular Disease in the Young, Council on Cardiovascular Nursing, and Stroke Council
.
Neurodevelopmental outcomes in children with congenital heart disease: evaluation and management: a scientific statement from the American Heart Association
.
Circulation
.
2012
;
126
(
9
):
1143
1172
3
Mahle
WT
.
Neurologic and cognitive outcomes in children with congenital heart disease
.
Curr Opin Pediatr
.
2001
;
13
(
5
):
482
486
4
Mahle
WT
,
Wernovsky
G
.
Long-term developmental outcome of children with complex congenital heart disease
.
Clin Perinatol
.
2001
;
28
(
1
):
235
247
5
Kasmi
L
,
Calderon
J
,
Montreuil
M
, et al
.
Neurocognitive and psychological outcomes in adults with dextro-transposition of the great arteries corrected by the arterial switch operation
.
Ann Thorac Surg
.
2018
;
105
(
3
):
830
836
6
Newburger
JW
,
Sleeper
LA
,
Bellinger
DC
, et al;
Pediatric Heart Network Investigators
.
Early developmental outcome in children with hypoplastic left heart syndrome and related anomalies: the single ventricle reconstruction trial
.
Circulation
.
2012
;
125
(
17
):
2081
2091
7
De Asis-Cruz
J
,
Donofrio
MT
,
Vezina
G
,
Limperopoulos
C
.
Aberrant brain functional connectivity in newborns with congenital heart disease before cardiac surgery
.
Neuroimage Clin
.
2017
;
17
:
31
42
8
Brandlistuen
RE
,
Stene-Larsen
K
,
Holmstrøm
H
, et al
.
Occurrence and predictors of developmental impairments in 3-year-old children with congenital heart defects
.
J Dev Behav Pediatr
.
2011
;
32
(
7
):
526
532
9
Hultman
CM
,
Sparén
P
,
Cnattingius
S
.
Perinatal risk factors for infantile autism
.
Epidemiology
.
2002
;
13
(
4
):
417
423
10
Wier
ML
,
Yoshida
CK
,
Odouli
R
,
Grether
JK
,
Croen
LA
.
Congenital anomalies associated with autism spectrum disorders
.
Dev Med Child Neurol
.
2006
;
48
(
6
):
500
507
11
Tsao
PC
,
Lee
YS
,
Jeng
MJ
, et al
.
Additive effect of congenital heart disease and early developmental disorders on attention-deficit/hyperactivity disorder and autism spectrum disorder: a nationwide population-based longitudinal study
.
Eur Child Adolesc Psychiatry
.
2017
;
26
(
11
):
1351
1359
12
Razzaghi
H
,
Oster
M
,
Reefhuis
J
.
Long-term outcomes in children with congenital heart disease: National Health Interview Survey
.
J Pediatr
.
2015
;
166
(
1
):
119
124
13
Hisle-Gorman
E
,
Susi
A
,
Stokes
T
, et al
.
Prenatal, perinatal, and neonatal risk factors of autism spectrum disorder
.
Pediatr Res
.
2018
;
84
(
2
):
190
198
14
Burke
JP
,
Jain
A
,
Yang
W
, et al
.
Does a claims diagnosis of autism mean a true case?
Autism
.
2014
;
18
(
3
):
321
330
15
Coleman
KJ
,
Lutsky
MA
,
Yau
V
, et al
.
Validation of autism spectrum disorder diagnoses in large healthcare systems with electronic medical records
.
J Autism Dev Disord
.
2015
;
45
(
7
):
1989
1996
16
Oster
ME
,
Riehle-Colarusso
T
,
Alverson
CJ
,
Correa
A
.
Associations between maternal fever and influenza and congenital heart defects
.
J Pediatr
.
2011
;
158
(
6
):
990
995
17
Kolevzon
A
,
Gross
R
,
Reichenberg
A
.
Prenatal and perinatal risk factors for autism: a review and integration of findings
.
Arch Pediatr Adolesc Med
.
2007
;
161
(
4
):
326
333
18
Wang
C
,
Geng
H
,
Liu
W
,
Zhang
G
.
Prenatal, perinatal, and postnatal factors associated with autism: a meta-analysis
.
Medicine (Baltimore)
.
2017
;
96
(
18
):
e6696
19
Maimburg
RD
,
Vaeth
M
.
Perinatal risk factors and infantile autism
.
Acta Psychiatr Scand
.
2006
;
114
(
4
):
257
264
20
Gardener
H
,
Spiegelman
D
,
Buka
SL
.
Prenatal risk factors for autism: comprehensive meta-analysis
.
Br J Psychiatry
.
2009
;
195
(
1
):
7
14
21
Gardener
H
,
Spiegelman
D
,
Buka
SL
.
Perinatal and neonatal risk factors for autism: a comprehensive meta-analysis
.
Pediatrics
.
2011
;
128
(
2
):
344
355
22
Pasquali
SK
,
Peterson
ED
,
Jacobs
JP
, et al
.
Differential case ascertainment in clinical registry versus administrative data and impact on outcomes assessment for pediatric cardiac operations
.
Ann Thorac Surg
.
2013
;
95
(
1
):
197
203
23
Peacock
G
,
Amendah
D
,
Ouyang
L
,
Grosse
SD
.
Autism spectrum disorders and health care expenditures: the effects of co-occurring conditions
.
J Dev Behav Pediatr
.
2012
;
33
(
1
):
2
8
24
Cook
JA
,
Collins
GS
.
The rise of big clinical databases
.
Br J Surg
.
2015
;
102
(
2
):
e93
e101
25
Oster
ME
,
Watkins
S
,
Hill
KD
,
Knight
JH
,
Meyer
RE
.
Academic outcomes in children with congenital heart defects: a population-based cohort study
.
Circ Cardiovasc Qual Outcomes
.
2017
;
10
(
2
):
e003074
26
Wernovsky
G
,
Licht
DJ
.
Neurodevelopmental outcomes in children with congenital heart disease-what can we impact?
Pediatr Crit Care Med
.
2016
;
17
(
8
suppl 1
):
S232
S242
27
Mahle
WT
,
Clancy
RR
,
Moss
EM
, et al
.
Neurodevelopmental outcome and lifestyle assessment in school-aged and adolescent children with hypoplastic left heart syndrome
.
Pediatrics
.
2000
;
105
(
5
):
1082
1089
28
Ravishankar
C
,
Zak
V
,
Williams
IA
, et al;
Pediatric Heart Network Investigators
.
Association of impaired linear growth and worse neurodevelopmental outcome in infants with single ventricle physiology: a report from the Pediatric Heart Network Infant Single Ventricle Trial
.
J Pediatr
.
2013
;
162
(
2
):
250
256.e2
29
Ryan
AK
,
Goodship
JA
,
Wilson
DI
, et al
.
Spectrum of clinical features associated with interstitial chromosome 22q11 deletions: a European collaborative study
.
J Med Genet
.
1997
;
34
(
10
):
798
804
30
Kozma
C
.
On cognitive variability in velocardiofacial syndrome: profound mental retardation and autism
.
Am J Med Genet
.
1998
;
81
(
3
):
269
270
31
Fine
SE
,
Weissman
A
,
Gerdes
M
, et al
.
Autism spectrum disorders and symptoms in children with molecularly confirmed 22q11.2 deletion syndrome
.
J Autism Dev Disord
.
2005
;
35
(
4
):
461
470
32
Niklasson
L
,
Rasmussen
P
,
Oskarsdóttir
S
,
Gillberg
C
.
Autism, ADHD, mental retardation and behavior problems in 100 individuals with 22q11 deletion syndrome
.
Res Dev Disabil
.
2009
;
30
(
4
):
763
773
33
Graham
JM
 Jr.
.
A recognizable syndrome within CHARGE association: Hall-Hittner syndrome
.
Am J Med Genet
.
2001
;
99
(
2
):
120
123
34
Jongmans
MC
,
Admiraal
RJ
,
van der Donk
KP
, et al
.
CHARGE syndrome: the phenotypic spectrum of mutations in the CHD7 gene
.
J Med Genet
.
2006
;
43
(
4
):
306
314
35
Richards
C
,
Jones
C
,
Groves
L
,
Moss
J
,
Oliver
C
.
Prevalence of autism spectrum disorder phenomenology in genetic disorders: a systematic review and meta-analysis
.
Lancet Psychiatry
.
2015
;
2
(
10
):
909
916
36
Vorstman
JAS
,
Parr
JR
,
Moreno-De-Luca
D
, et al
.
Autism genetics: opportunities and challenges for clinical translation
.
Nat Rev Genet
.
2017
;
18
(
6
):
362
376
37
Posch
MG
,
Perrot
A
,
Berger
F
,
Ozcelik
C
.
Molecular genetics of congenital atrial septal defects
.
Clin Res Cardiol
.
2010
;
99
(
3
):
137
147
38
Stańczak
P
,
Witecka
J
,
Szydło
A
, et al
.
Mutations in mammalian tolloid-like 1 gene detected in adult patients with ASD
.
Eur J Hum Genet
.
2009
;
17
(
3
):
344
351
39
Lin
X
,
Huo
Z
,
Liu
X
, et al
.
A novel GATA6 mutation in patients with tetralogy of Fallot or atrial septal defect
.
J Hum Genet
.
2010
;
55
(
10
):
662
667
40
Viscidi
EW
,
Triche
EW
,
Pescosolido
MF
, et al
.
Clinical characteristics of children with autism spectrum disorder and co-occurring epilepsy
.
PLoS One
.
2013
;
8
(
7
):
e67797
41
Lai
MC
,
Lombardo
MV
,
Baron-Cohen
S
.
Autism
.
Lancet
.
2014
;
383
(
9920
):
896
910

Competing Interests

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

Supplementary data