Preterm congenital heart disease and neurodevelopment: the importance of looking beyond the initial hospitalization

Katz, Jenna A.; Levy, Paul C.; Butler, Samantha; Sadhwani, Anjali; Lakshminrusimha, Satyanarayana; Morton, Sarah U.

doi:10.1038/s41372-023-01687-4

Cited by 6 publications

(3 citation statements)

References 56 publications

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“…Full-term infants with severe CHD are at high risk of brain injury and impaired brain maturity. This hit to the brain is “doubled” in premature infants according to previous reports ( 33 , 34 ). Our study showed a 3-fold increase in the risk of PVL in premature infants with severe CHD in comparison to those without CHD after adjusting for GA, BW, and gender.…”

Section: Discussionmentioning

confidence: 72%

Neonatal outcomes in preterm infants with severe congenital heart disease: a national cohort analysis

Aly,

Qattea,

Kattea

et al. 2024

Front. Pediatr.

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BackgroundPrematurity and congenital heart disease (CHD) are the leading causes of neonatal mortality and morbidity. Limited data are available about the outcomes of premature infants with severe CHD.MethodsWe queried The National Inpatient Database using ICD-10 codes for premature patients (<37 weeks) with severe CHD from 2016 to 2020. Severe CHDs were grouped into three categories: A. left-sided lesions with impaired systemic output, B. Cyanotic CHD, and C. Shunt lesions with pulmonary overcirculation. Patients with isolated atrial or ventricular septal defects and patent ductus arteriosus were excluded. We also excluded patients with chromosomal abnormalities and major congenital anomalies. Patients' demographics, clinical characteristics, and outcomes were evaluated by comparing premature infants with vs. without CHD adjusting for gestational age (GA), birth weight, and gender.ResultsA total of 27710 (1.5%) out of 1,798,245 premature infants had severe CHD. This included 27%, 58%, and 15% in groups A, B, and C respectively. The incidence of severe CHD was highest between 25 and 28 weeks of gestation and decreased significantly with increasing GA up to 36 weeks (p < 0.001). Premature infants with severe CHD had a significantly higher incidence of neonatal morbidities including necrotizing enterocolitis (NEC) [OR = 4.88 (4.51–5.27)], interventricular hemorrhage [OR = 6.22 (5.57–6.95)], periventricular leukomalacia [OR = 3.21 (2.84–3.64)] and bronchopulmonary dysplasia [OR = 8.26 (7.50–10.06) compared to preterm infants of similar GA without CHD. Shunt lesions had the highest incidence of NEC (8.5%) compared to 5.3% in cyanotic CHD and 3.7% in left-sided lesions (p < 0.001). Mortality was significantly higher in premature infants with CHD compared to control [11.6% vs. 2.5%, p < 0.001]. Shunt lesions had significantly higher mortality (11.0%) compared to those with left-sided lesions (8.3%) and cyanotic CHD (6.4%), p < 0.001.ConclusionPremature infants with severe CHD are at high risk of neonatal morbidity and mortality. Morbidity remains increased across all GA groups and in all CHD categories. This significant risk of adverse outcomes is important to acknowledge when managing this patient population and when counseling their families. Future research is needed to examine the impact of specific rather than categorized congenital heart defects on neonatal outcomes.

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Section: Discussionmentioning

confidence: 72%

Neonatal outcomes in preterm infants with severe congenital heart disease: a national cohort analysis

Aly,

Qattea,

Kattea

et al. 2024

Front. Pediatr.

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“…Finally, morbidity and longer-term neurologic outcomes are outside the scope of this work. They are, however, critically important considerations and have been previously reported to be poorer in infants with CHD [4,24].…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Double Jeopardy: A Distinct Mortality Pattern Among Preterm Infants with Congenital Heart Disease

Higgins,

Levy,

Ball

et al. 2024

Pediatr Cardiol

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Contemporary United States (US) data on the survival of preterm infants with congenital heart disease (CHD) are unavailable despite the over-representation of CHD and improving surgical outcomes in the preterm population. The aim of this study is to use population-based data to compare 1-year survival and early mortality (< 3 days) by gestational age (GA) between preterm infants with and without cyanotic CHD (CCHD) in the US. This national retrospective cohort included all liveborn, preterm infants between 21 and 36 weeks GA with a birth certificate indicating the presence or absence of CCHD (n = 2,654,253) born between 2014 and 2019 in the US. Data were provided by the US Center for Disease Control database linking birth and death certificates. Of liveborn preterm infants, 0.13% (n = 3619) had CCHD. 1-year survival was significantly lower in infants 23–36 weeks with CCHD compared to those without. The greatest survival gap occurred between 28 and 31 weeks (28 weeks adjusted risk difference 37.5%; 95% CI 28.4, 46.5; 31 weeks 37.9%; 30.5, 45.3). Early mortality accounted for more than half of deaths among infants 23–31 weeks with CCHD (23 weeks-68%, CI 46.7, 83.7; 31 weeks-63.9%, 52.9, 73.6). Survival trends demonstrated worsened 1-year survival in infants 35–36 weeks with CCHD over the study period. The pattern of mortality for preterm infants with CCHD is distinct from those without. The significant survival gap in the very preterm population and notably high rate of early death in the infants with CCHD calls for renewed attention to early neonatal intensive care for this dually affected population.

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“…In this work, the researchers examine preterm birth rates in CHD infants, emphasize their complex medical needs, and stress the importance of evaluating outcomes beyond survival. They explore shared mechanisms of neurodevelopmental impairment in CHD and prematurity, proposing future directions for improved outcomes [41]. A total of 12,926,083 individuals (ages 3-18) from 86 studies had a CHD prevalence of 4.69 per 1000 children.…”

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confidence: 99%

A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart Disease

et al. 2023

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Congenital heart disease (CHD) is a critical global public health concern, particularly when it comes to newborn mortality. Low- and middle-income countries face the highest mortality rates due to limited resources and inadequate healthcare access. To address this pressing issue, machine learning presents an opportunity to develop accurate predictive models that can assess the risk of death from CHD. These models can empower healthcare professionals by identifying high-risk infants and enabling appropriate care. Additionally, machine learning can uncover patterns in the risk factors associated with CHD mortality, leading to targeted interventions that prevent or reduce mortality among vulnerable newborns. This paper proposes an innovative machine learning approach to minimize newborn mortality related to CHD. By analyzing data from infants diagnosed with CHD, the model identifies key risk factors contributing to mortality. Armed with this knowledge, healthcare providers can devise customized interventions, including intensified care for high-risk infants and early detection and treatment strategies. The proposed diagnostic model utilizes maternal clinical history and fetal health information to accurately predict the condition of newborns affected by CHD. The results are highly promising, with the proposed Cardiac Deep Learning Model (CDLM) achieving remarkable performance metrics, including a sensitivity of 91.74%, specificity of 92.65%, positive predictive value of 90.85%, negative predictive value of 55.62%, and a miss rate of 91.03%. This research aims to make a significant impact by equipping healthcare professionals with powerful tools to combat CHD-related newborn mortality, ultimately saving lives and improving healthcare outcomes worldwide.

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Preterm congenital heart disease and neurodevelopment: the importance of looking beyond the initial hospitalization

Cited by 6 publications

References 56 publications

Neonatal outcomes in preterm infants with severe congenital heart disease: a national cohort analysis

Neonatal outcomes in preterm infants with severe congenital heart disease: a national cohort analysis

Double Jeopardy: A Distinct Mortality Pattern Among Preterm Infants with Congenital Heart Disease

A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart Disease

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