Machine Learning Quantification of Pulmonary Regurgitation Fraction from Echocardiography

Assessment of pulmonary regurgitation (PR) guides treatment for patients with congenital heart disease. Quantitative assessment of PR fraction (PRF) by echocardiography is limited. Cardiac MRI (cMRI) is the reference-standard for PRF quantification. We created an algorithm to predict cMRI-quantified PRF from echocardiography using machine learning (ML). We retrospectively performed echocardiographic measurements paired to cMRI within 3 months in patients with ≥ mild PR from 2009 to 2022. Model inputs were vena contracta ratio, PR index, PR pressure half-time, main and branch pulmonary artery diastolic flow reversal (BPAFR), and transannular patch repair. A gradient boosted trees ML algorithm was trained using k-fold cross-validation to predict cMRI PRF by phase contrast imaging as a continuous number and at > mild (PRF ≥ 20%) and severe (PRF ≥ 40%) thresholds. Regression performance was evaluated with mean absolute error (MAE), and at clinical thresholds with area-under-the-receiver-operating-characteristic curve (AUROC). Prediction accuracy was compared to historical clinician accuracy. We externally validated prior reported studies for comparison. We included 243 subjects (median age 21 years, 58% repaired tetralogy of Fallot). The regression MAE = 7.0%. For prediction of > mild PR, AUROC = 0.96, but BPAFR alone outperformed the ML model (sensitivity 94%, specificity 97%). The ML model detection of severe PR had AUROC = 0.86, but in the subgroup with BPAFR, performance dropped (AUROC = 0.73). Accuracy between clinicians and the ML model was similar (70% vs. 69%). There was decrement in performance of prior reported algorithms on external validation in our dataset. A novel ML model for echocardiographic quantification of PRF outperforms prior studies and has comparable overall accuracy to clinicians. BPAFR is an excellent marker for > mild PRF, and has moderate capacity to detect severe PR, but more work is required to distinguish moderate from severe PR. Poor external validation of prior works highlights reproducibility challenges.

Local large language models for privacy-preserving accelerated review of historic echocardiogram reports

OBJECTIVES

The study developed framework that leverages an open-source Large Language Model (LLM) to enable clinicians to ask plain-language questions about a patient's entire echocardiogram report history. This approach is intended to streamline the extraction of clinical insights from multiple echocardiogram reports, particularly in patients with complex cardiac diseases, thereby enhancing both patient care and research efficiency.

MATERIALS AND METHODS

Data from over 10 years were collected, comprising echocardiogram reports from patients with more than 10 echocardiograms on file at the Mount Sinai Health System. These reports were converted into a single document per patient for analysis, broken down into snippets and relevant snippets were retrieved using text similarity measures. The LLaMA-2 70B model was employed for analyzing the text using a specially crafted prompt. The model's performance was evaluated against ground-truth answers created by faculty cardiologists.

RESULTS

The study analyzed 432 reports from 37 patients for a total of 100 question-answer pairs. The LLM correctly answered 90% questions, with accuracies of 83% for temporality, 93% for severity assessment, 84% for intervention identification, and 100% for diagnosis retrieval. Errors mainly stemmed from the LLM's inherent limitations, such as misinterpreting numbers or hallucinations.

CONCLUSION

The study demonstrates the feasibility and effectiveness of using a local, open-source LLM for querying and interpreting echocardiogram report data. This approach offers a significant improvement over traditional keyword-based searches, enabling more contextually relevant and semantically accurate responses; in turn showing promise in enhancing clinical decision-making and research by facilitating more efficient access to complex patient data.

Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling

BACKGROUND

Artificial intelligence-enhanced ECG analysis shows promise to detect ventricular dysfunction and remodeling in adult populations. However, its application to pediatric populations remains underexplored.

METHODS

A convolutional neural network was trained on paired ECG-echocardiograms (≤2 days apart) from patients ≤18 years of age without major congenital heart disease to detect human expert-classified greater than mild left ventricular (LV) dysfunction, hypertrophy, and dilation (individually and as a composite outcome). Model performance was evaluated on single ECG-echocardiogram pairs per patient at Boston Children's Hospital and externally at Mount Sinai Hospital using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC).

RESULTS

The training cohort comprised 92 377 ECG-echocardiogram pairs (46 261 patients; median age, 8.2 years). Test groups included internal testing (12 631 patients; median age, 8.8 years; 4.6% composite outcomes), emergency department (2830 patients; median age, 7.7 years; 10.0% composite outcomes), and external validation (5088 patients; median age, 4.3 years; 6.1% composite outcomes) cohorts. Model performance was similar on internal test and emergency department cohorts, with model predictions of LV hypertrophy outperforming the pediatric cardiologist expert benchmark. Adding age and sex to the model added no benefit to model performance. When using quantitative outcome cutoffs, model performance was similar between internal testing (composite outcome: AUROC, 0.88, AUPRC, 0.43; LV dysfunction: AUROC, 0.92, AUPRC, 0.23; LV hypertrophy: AUROC, 0.88, AUPRC, 0.28; LV dilation: AUROC, 0.91, AUPRC, 0.47) and external validation (composite outcome: AUROC, 0.86, AUPRC, 0.39; LV dysfunction: AUROC, 0.94, AUPRC, 0.32; LV hypertrophy: AUROC, 0.84, AUPRC, 0.25; LV dilation: AUROC, 0.87, AUPRC, 0.33), with composite outcome negative predictive values of 99.0% and 99.2%, respectively. Saliency mapping highlighted ECG components that influenced model predictions (precordial QRS complexes for all outcomes; T waves for LV dysfunction). High-risk ECG features include lateral T-wave inversion (LV dysfunction), deep S waves in V1 and V2 and tall R waves in V6 (LV hypertrophy), and tall R waves in V4 through V6 (LV dilation).

CONCLUSIONS

This externally validated algorithm shows promise to inexpensively screen for LV dysfunction and remodeling in children, which may facilitate improved access to care by democratizing the expertise of pediatric cardiologists.

Derivation, External Validation and Clinical Implications of a deep learning approach for intracranial pressure estimation using non-cranial waveform measurements

Importance

Increased intracranial pressure (ICP) is associated with adverse neurological outcomes, but needs invasive monitoring.

Objective

Development and validation of an AI approach for detecting increased ICP (aICP) using only non-invasive extracranial physiological waveform data.

Design

Retrospective diagnostic study of AI-assisted detection of increased ICP. We developed an AI model using exclusively extracranial waveforms, externally validated it and assessed associations with clinical outcomes.

Setting

MIMIC-III Waveform Database (2000-2013), a database derived from patients admitted to an ICU in an academic Boston hospital, was used for development of the aICP model, and to report association with neurologic outcomes. Data from Mount Sinai Hospital (2020-2022) in New York City was used for external validation.

Participants

Patients were included if they were older than 18 years, and were monitored with electrocardiograms, arterial blood pressure, respiratory impedance plethysmography and pulse oximetry. Patients who additionally had intracranial pressure monitoring were used for development (N=157) and external validation (N=56). Patients without intracranial monitors were used for association with outcomes (N=1694).

Exposures

Extracranial waveforms including electrocardiogram, arterial blood pressure, plethysmography and SpO2.

Main Outcomes and Measures

Intracranial pressure > 15 mmHg. Measures were Area under receiver operating characteristic curves (AUROCs), sensitivity, specificity, and accuracy at threshold of 0.5. We calculated odds ratios and p-values for phenotype association.

Results

The AUROC was 0.91 (95% CI, 0.90-0.91) on testing and 0.80 (95% CI, 0.80-0.80) on external validation. aICP had accuracy, sensitivity, and specificity of 73.8% (95% CI, 72.0%-75.6%), 99.5% (95% CI 99.3%-99.6%), and 76.9% (95% CI, 74.0-79.8%) on external validation. A ten-percentile increment was associated with stroke (OR=2.12; 95% CI, 1.27-3.13), brain malignancy (OR=1.68; 95% CI, 1.09-2.60), subdural hemorrhage (OR=1.66; 95% CI, 1.07-2.57), intracerebral hemorrhage (OR=1.18; 95% CI, 1.07-1.32), and procedures like percutaneous brain biopsy (OR=1.58; 95% CI, 1.15-2.18) and craniotomy (OR = 1.43; 95% CI, 1.12-1.84; P < 0.05 for all).

Conclusions and Relevance

aICP provides accurate, non-invasive estimation of increased ICP, and is associated with neurological outcomes and neurosurgical procedures in patients without intracranial monitoring.

Quantitative Prediction of Right Ventricular Size and Function From the ECG

BACKGROUND

Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep learning-enabled ECG analysis for estimation of right ventricular (RV) size or function is unexplored.

METHODS AND RESULTS

We trained a deep learning-ECG model to predict RV dilation (RVEDV >120 mL/m2), RV dysfunction (RVEF ≤40%), and numerical RVEDV and RVEF from a 12-lead ECG paired with reference-standard cardiac magnetic resonance imaging volumetric measurements in UK Biobank (UKBB; n=42 938). We fine-tuned in a multicenter health system (MSHoriginal [Mount Sinai Hospital]; n=3019) with prospective validation over 4 months (MSHvalidation; n=115). We evaluated performance with area under the receiver operating characteristic curve for categorical and mean absolute error for continuous measures overall and in key subgroups. We assessed the association of RVEF prediction with transplant-free survival with Cox proportional hazards models. The prevalence of RV dysfunction for UKBB/MSHoriginal/MSHvalidation cohorts was 1.0%/18.0%/15.7%, respectively. RV dysfunction model area under the receiver operating characteristic curve for UKBB/MSHoriginal/MSHvalidation cohorts was 0.86/0.81/0.77, respectively. The prevalence of RV dilation for UKBB/MSHoriginal/MSHvalidation cohorts was 1.6%/10.6%/4.3%. RV dilation model area under the receiver operating characteristic curve for UKBB/MSHoriginal/MSHvalidation cohorts was 0.91/0.81/0.92, respectively. MSHoriginal mean absolute error was RVEF=7.8% and RVEDV=17.6 mL/m2. The performance of the RVEF model was similar in key subgroups including with and without left ventricular dysfunction. Over a median follow-up of 2.3 years, predicted RVEF was associated with adjusted transplant-free survival (hazard ratio, 1.40 for each 10% decrease; P=0.031).

CONCLUSIONS

Deep learning-ECG analysis can identify significant cardiac magnetic resonance imaging RV dysfunction and dilation with good performance. Predicted RVEF is associated with clinical outcome.

Neighborhood Childhood Opportunity, Race/Ethnicity, and Surgical Outcomes in Children With Congenital Heart Disease

BACKGROUND

Racial and ethnic disparities in outcomes for children with congenital heart disease (CHD) coexist with disparities in educational, environmental, and economic opportunity.

OBJECTIVES

We sought to determine the associations between childhood opportunity, race/ethnicity, and pediatric CHD surgery outcomes.

METHODS

Pediatric Health Information System encounters aged <18 years from 2016 to 2022 with International Classification of Diseases-10th edition codes for CHD and cardiac surgery were linked to ZIP code-level Childhood Opportunity Index (COI), a score of neighborhood educational, environmental, and socioeconomic conditions. The associations of race/ethnicity and COI with in-hospital surgical death were modeled with generalized estimating equations and formal mediation analysis. Neonatal survival after discharge was modeled by Cox proportional hazards.

RESULTS

Of 54,666 encounters at 47 centers, non-Hispanic Black (Black) (OR: 1.20; P = 0.01), Asian (OR: 1.75; P < 0.001), and Other (OR: 1.50; P < 0.001) groups had increased adjusted mortality vs non-Hispanic Whites. The lowest COI quintile had increased in-hospital mortality in unadjusted and partially adjusted models (OR: 1.29; P = 0.004), but not fully adjusted models (OR: 1.14; P = 0.13). COI partially mediated the effect of race/ethnicity on in-hospital mortality between 2.6% (P = 0.64) and 16.8% (P = 0.029), depending on model specification. In neonatal multivariable survival analysis (n = 13,987; median follow-up: 0.70 years), the lowest COI quintile had poorer survival (HR: 1.21; P = 0.04).

CONCLUSIONS

Children in the lowest COI quintile are at risk for poor outcomes after CHD surgery. Disproportionally increased mortality in Black, Asian, and Other populations may be partially mediated by COI. Targeted investment in low COI neighborhoods may improve outcomes after hospital discharge. Identification of unmeasured factors to explain persistent risk attributed to race/ethnicity is an important area of future exploration.

Quantitative prediction of right ventricular and size and function from the electrocardiogram

Background

Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep-learning enabled 12-lead electrocardiogram analysis (DL-ECG) for estimation of RV size or function is unexplored.

Methods

We trained a DL-ECG model to predict RV dilation (RVEDV>120 mL/m2), RV dysfunction (RVEF≤40%), and numerical RVEDV/RVEF from 12-lead ECG paired with reference-standard cardiac MRI (cMRI) volumetric measurements in UK biobank (UKBB; n=42,938). We fine-tuned in a multi-center health system (MSHoriginal; n=3,019) with prospective validation over 4 months (MSHvalidation; n=115). We evaluated performance using area under the receiver operating curve (AUROC) for categorical and mean absolute error (MAE) for continuous measures overall and in key subgroups. We assessed association of RVEF prediction with transplant-free survival with Cox proportional hazards models.

Results

Prevalence of RV dysfunction for UKBB/MSHoriginal/MSHvalidation cohorts was 1.0%/18.0%/15.7%, respectively. RV dysfunction model AUROC for UKBB/MSHoriginal/MSHvalidation cohorts was 0.86/0.81/0.77, respectively. Prevalence of RV dilation for UKBB/MSHoriginal/MSHvalidation cohorts was 1.6%/10.6%/4.3%. RV dilation model AUROC for UKBB/MSHoriginal/MSHvalidation cohorts 0.91/0.81/0.92, respectively. MSHoriginal MAE was RVEF=7.8% and RVEDV=17.6 ml/m2. Performance was similar in key subgroups including with and without left ventricular dysfunction. Over median follow-up of 2.3 years, predicted RVEF was independently associated with composite outcome (HR 1.37 for each 10% decrease, p=0.046).

Conclusions

DL-ECG analysis can accurately identify significant RV dysfunction and dilation both overall and in key subgroups. Predicted RVEF is independently associated with clinical outcome.

Criteria for Early Pacemaker Implantation in Patients With Postoperative Heart Block After Congenital Heart Surgery

BACKGROUND:

Guidelines recommend observation for atrioventricular node recovery until postoperative days (POD) 7 to 10 before permanent pacemaker placement (PPM) in patients with heart block after congenital cardiac surgery. To aid in surgical decision-making for early PPM, we established criteria to identify patients at high risk of requiring PPM.

METHODS:

We reviewed all cases of second degree and complete heart block (CHB) on POD 0 from August 2009 through December 2018. A decision tree model was trained to predict the need for PPM amongst patients with persistent CHB and prospectively validated from January 2019 through March 2021. Separate models were developed for all patients on POD 0 and those without recovery by POD 4.

RESULTS:

Of the 139 patients with postoperative heart block, 68 required PPM. PPM was associated with older age (3.2 versus 1.0 years; P =0.018) and persistent CHB on POD 0 (versus intermittent CHB or second degree heart block; 87% versus 58%; P =0.001). Median days [IQR] to atrioventricular node recovery was 2 [0–5] and PPM was 9 [6–11]. Of the 100 cases of persistent CHB (21 in the validation cohort), 59 (59%) required PPM. A decision tree model identified 4 risk factors for PPM in patients with persistent CHB: (1) aortic valve replacement, subaortic stenosis repair, or Konno procedure; (2) ventricular L-looping; (3) atrioventricular valve replacement; (4) and absence of preoperative antiarrhythmic agent (in POD 0 model only). The POD 4 model specificity was 0.89 [0.67–0.99] and positive predictive value was 0.94 [95% CI 0.81–0.98], which was stable in prospective validation (positive predictive value 1.0).

CONCLUSIONS:

A data-driven analysis led to actionable criteria to identify patients requiring PPM. Patients with left ventricular outflow tract surgery, atrioventricular valve replacement, or ventricular L-Looping could be considered for PPM on POD 4 to reduce risks of temporary pacing and improve care efficiency.

Identification of patients at risk of new onset heart failure: Utilizing a large statewide health information exchange to train and validate a risk prediction model

BACKGROUND

New-onset heart failure (HF) is associated with poor prognosis and high healthcare utilization. Early identification of patients at increased risk incident-HF may allow for focused allocation of preventative care resources. Health information exchange (HIE) data span the entire spectrum of clinical care, but there are no HIE-based clinical decision support tools for diagnosis of incident-HF. We applied machine-learning methods to model the one-year risk of incident-HF from the Maine statewide-HIE.

METHODS AND RESULTS

We included subjects aged ≥ 40 years without prior HF ICD9/10 codes during a three-year period from 2015 to 2018, and incident-HF defined as assignment of two outpatient or one inpatient code in a year. A tree-boosting algorithm was used to model the probability of incident-HF in year two from data collected in year one, and then validated in year three. 5,668 of 521,347 patients (1.09%) developed incident-HF in the validation cohort. In the validation cohort, the model c-statistic was 0.824 and at a clinically predetermined risk threshold, 10% of patients identified by the model developed incident-HF and 29% of all incident-HF cases in the state of Maine were identified.

CONCLUSIONS

Utilizing machine learning modeling techniques on passively collected clinical HIE data, we developed and validated an incident-HF prediction tool that performs on par with other models that require proactively collected clinical data. Our algorithm could be integrated into other HIEs to leverage the EMR resources to provide individuals, systems, and payors with a risk stratification tool to allow for targeted resource allocation to reduce incident-HF disease burden on individuals and health care systems.

Impact of institutional routine surveillance endomyocardial biopsy frequency in the first year on rejection and graft survival in pediatric heart transplantation

BACKGROUND

Routine surveillance biopsy (RSB) is performed to detect asymptomatic acute rejection (AR) after heart transplantation (HT). Variation in pediatric RSB across institutions is high. We examined center-based variation in RSB and its relationship to graft loss, AR, coronary artery vasculopathy (CAV), and cost of care during the first year post-HT.

METHODS

We linked the Pediatric Health Information System (PHIS) and Scientific Registry of Transplant Recipients (SRTR, 2002-2016), including all primary-HT aged 0-21 years. We characterized centers by RSB frequency (defined as median biopsies performed among recipients aged ≥12 months without rejection in the first year). We adjusted for potential confounders and center effects with mixed-effects regression analysis.

RESULTS

We analyzed 2867 patients at 29 centers. After adjusting for patient and center differences, increasing RSB frequency was associated with diagnosed AR (OR 1.15 p = 0.004), a trend toward treated AR (OR 1.09 p = 0.083), and higher hospital-based cost (US$390 315 vs. $313 248, p < 0.001) but no difference in graft survival (HR 1.00, p = 0.970) or CAV (SHR 1.04, p = 0.757) over median follow-up 3.9 years. Center RSB-frequency threshold of ≥2/year was associated with increased unadjusted rates of treated AR, but no association was found at thresholds greater than this.

CONCLUSION

Center RSB frequency is positively associated with increased diagnosis of AR at 1 year post-HT. Graft survival and CAV appear similar at medium-term follow-up. We speculate that higher frequency RSB centers may have increased detection of clinically less important AR, though further study of the relationship between center RSB frequency and differences in treated AR is necessary.

Prediction of the 1-Year Risk of Incident Lung Cancer: Prospective Study Using Electronic Health Records from the State of Maine

BACKGROUND

Lung cancer is the leading cause of cancer death worldwide. Early detection of individuals at risk of lung cancer is critical to reduce the mortality rate.

OBJECTIVE

The aim of this study was to develop and validate a prospective risk prediction model to identify patients at risk of new incident lung cancer within the next 1 year in the general population.

METHODS

Data from individual patient electronic health records (EHRs) were extracted from the Maine Health Information Exchange network. The study population consisted of patients with at least one EHR between April 1, 2016, and March 31, 2018, who had no history of lung cancer. A retrospective cohort (N=873,598) and a prospective cohort (N=836,659) were formed for model construction and validation. An Extreme Gradient Boosting (XGBoost) algorithm was adopted to build the model. It assigned a score to each individual to quantify the probability of a new incident lung cancer diagnosis from October 1, 2016, to September 31, 2017. The model was trained with the clinical profile in the retrospective cohort from the preceding 6 months and validated with the prospective cohort to predict the risk of incident lung cancer from April 1, 2017, to March 31, 2018.

RESULTS

The model had an area under the curve (AUC) of 0.881 (95% CI 0.873-0.889) in the prospective cohort. Two thresholds of 0.0045 and 0.01 were applied to the predictive scores to stratify the population into low-, medium-, and high-risk categories. The incidence of lung cancer in the high-risk category (579/53,922, 1.07%) was 7.7 times higher than that in the overall cohort (1167/836,659, 0.14%). Age, a history of pulmonary diseases and other chronic diseases, medications for mental disorders, and social disparities were found to be associated with new incident lung cancer.

CONCLUSIONS

We retrospectively developed and prospectively validated an accurate risk prediction model of new incident lung cancer occurring in the next 1 year. Through statistical learning from the statewide EHR data in the preceding 6 months, our model was able to identify statewide high-risk patients, which will benefit the population health through establishment of preventive interventions or more intensive surveillance.

Increased mortality, morbidities, and costs after heart transplantation in heterotaxy syndrome and other complex situs arrangements

OBJECTIVES

Identify pediatric heart transplant (HT) recipients with heterotaxy and other complex arrangements of cardiac situs (heterotaxy/situs anomaly) and compare mortality, morbidities, length of stay (LOS), and costs to recipients with congenital heart disease without heterotaxy/situs anomaly.

METHODS

Using linked registry data (2001-2016), we identified 186 HT recipients with heterotaxy/situs anomaly and 1254 with congenital heart disease without heterotaxy/situs anomaly. We compared post-HT outcomes in univariable and multivariable time-to-event analyses. LOS and cost from HT to discharge were compared using Wilcoxon rank-sum tests. Sensitivity analyses were performed using stricter heterotaxy/situs anomaly group inclusion criteria and through propensity matching.

RESULTS

HT recipients with heterotaxy/situs anomaly were older (median age, 5.1 vs 1.6 years; P < .001) and more often black, Asian, Hispanic, or "other" nonwhite (54% vs 32%; P < .001). Heterotaxy/situs anomaly was independently associated with increased mortality (hazard ratio, 1.58; 95% confidence interval, 1.19-2.09; P = .002), even among 6-month survivors (hazard ratio, 1.86; 95% confidence interval, 1.09-3.16; P = .021). Heterotaxy/situs anomaly recipients more commonly required dialysis (odds ratio, 2.58; 95% confidence interval, 1.51-4.42; P = .001) and cardiac reoperation (odds ratio, 1.91; 95% confidence interval, 1.17-3.11; P = .010) before discharge. They had longer ischemic times (19.2 additional minutes [range, 10.9-27.5 minutes]; P < .001), post-HT intensive care unit LOS (16 vs 13 days; P = .012), and hospital LOS (26 vs 23 days; P = .005). Post-HT hospitalization costs were also greater ($447,604 vs $379,357; P = .001).

CONCLUSIONS

Heterotaxy and other complex arrangements of cardiac situs are associated with increased mortality, postoperative complications, LOS, and costs after HT. Although increased surgical complexity can account for many of these differences, inferior late survival is not well explained and deserves further study.

Pediatric heart transplantation at adult-specialty centers in the United States: A multicenter registry analysis

Recent Organ Procurement and Transplantation Network bylaw revisions mandate that US transplant programs have an "approved pediatric component" in order to perform heart transplantation (HT) in patients <18 years old. The impact of this change on adolescents, a group known to be at high risk for graft loss and nonadherence, is unknown. We studied all US primary pediatric (age <18 years) HT from 2000 to 2015 to compare graft survival between centers organized mainly for adult versus pediatric care. Centers were designated as pediatric- or adult-specialty care according to the ratio of pediatric:adult HT performed and minimum age of HT (pediatric-specialty defined as ratio>0.7; adult-specialty ratio<0.05 and minimum age >8 years). In propensity score-matched cohorts, we observed no difference in graft loss by center type (median survival: adult 12.4 years vs pediatric 9.2 years, P = .174). Compared to the matched pediatric cohort, adult-specialty center recipients lived closer to their transplant center (31 vs 45 miles, P = .012), and trended toward fewer out-of-state transplants (15 vs 25%, P = .082). Our data suggest that select adolescents can achieve similar midterm graft survival at centers organized primarily for adult HT care. Regardless of post-HT setting, the development of care models that demonstrably improve adherence may be of greatest benefit to improving survival of this high-risk population.

Association Between Thiopurine S-Methyltransferase (TPMT) Genetic Variants and Infection in Pediatric Heart Transplant Recipients Treated With Azathioprine: A Multi-Institutional Analysis

OBJECTIVES

Bone marrow suppression is a common adverse effect of the immunosuppressive drug azathioprine. Polymorphisms in the gene encoding thiopurine S-methyltransferase (TPMT) can alter the metabolism of azathioprine, resulting in marrow toxicity and life-threatening infection. In a multicenter cohort of pediatric heart transplant (HT) recipients, we determined the frequency of TPMT genetic variation and assessed whether azathioprine-treated recipients with TPMT variants were at increased risk of infection.

METHODS

We genotyped TPMT in 264 pediatric HT recipients for the presence of the TPMT*2, TPMT*3A, and TPMT*3C variant alleles. Data on infection episodes and azathioprine use were collected as part of each patient's participation in the Pediatric Heart Transplant Study. We performed unadjusted Kaplan-Meier analyses comparing infection outcomes between groups.

RESULTS

TPMT variants were identified in 26 pediatric HT recipients (10%): *3A (n = 17), *3C (n = 8), and *2 (n = 1). Among those with a variant allele, *3C was most prevalent in black patients (4 of 5) and *3A most prevalent among white and Hispanic patients (16 of 20). Among 175 recipients (66%) who received azathioprine as part of the initial immunosuppressive regimen, we found no difference in the number of infections at 1 year after HT (0.7 ± 1.3; range, 0-6 versus 0.5 ± 0.9; range, 0-3; p = 0.60) or in freedom from infection and bacterial infection between non-variant and variant carriers. There was 1 infection-related death in each group.

CONCLUSIONS

In this multicenter cohort of pediatric HT recipients, the prevalence of TPMT variants was similar across racial/ethnic groups to what has been previously reported in non-pediatric HT populations. We found no association between variant alleles and infection in the first year after HT. Because clinically detected cytopenia could have prompted dose adjustment or cessation, we recommend future studies assess the relationship of genotype to leukopenia/neutropenia in the pediatric transplantation population.

Transition from brand to generic tacrolimus is associated with a decrease in trough blood concentration in pediatric heart transplant recipients

There are limited data available on the bioequivalence of generic and brand-name tacrolimus in pediatric and heart transplant patients. We characterized changes in 12-hour trough concentrations and clinical outcomes after transition from brand to generic tacrolimus in pediatric thoracic organ transplant recipients. Patients with a pharmacy-confirmed date of switch between generic and brand tacrolimus were identified, as well as a matched control group that did not switch for comparison. We identified 18 patients with a confirmed date of switch, and in 12 patients that remained on the same dose, trough concentrations were 14% less than when they were on brand (p = 0.037). The average change was -1.15 ± 1.76 ng/mL (p = 0.045). The control group did not experience a change in trough concentration and was different than the switched group (p = 0.005). There were no differences in dosage changes or kidney or liver function. In the year after switch, 24% of patients who were switched to generic experienced a rejection event vs. 18% in the patients on brand. We suggest a strategy of monitoring around the time of transition, and education of the patient/family to notify the care team when changes from brand to generic or between generics occur.

Loss of Id3 increases VCAM-1 expression, macrophage accumulation, and atherogenesis in Ldlr-/- mice

OBJECTIVE

Inhibitor of differention-3 (Id3) promotes B cells homing to the aorta and atheroprotection in Apoe(-/-) mice. We sought to determine the impact of loss of Id3 in the Ldlr((-/-)) mouse model of diet-induced atherosclerosis and identify novel Id3 targets in the vessel wall.

METHODS AND RESULTS

Ex vivo optical imaging confirmed that Id3((-/-)) Ldlr((-/-)) mice have significantly fewer aortic B cells than Id3((+/+)) Ldlr(-/-) mice. After 8 and 16 weeks of Western diet, Id3((-/-)) Ldlr((-/-)) mice developed significantly more atherosclerosis than Id3((+/+)) Ldlr((-/-)) mice, with Id3(+/-) Ldlr(-/-) mice demonstrating an intermediate phenotype. There were no differences in serum lipid levels between genotypes. Immunostaining demonstrated that aortas from Id3((-/-)) Ldlr((-/-)) mice had greater intimal macrophage density and C-C chemokine ligand 20 and vascular cell adhesion molecule 1 (VCAM-1) expression compared with Id3((+/+)) Ldlr(-/-) mice. Real-time polymerase chain reaction demonstrated increased VCAM-1 mRNA levels in the aortas of Id3(-/-) Ldlr(-/-) mice. Primary vascular smooth muscle cells from Id3((-/-)) mice expressed greater amounts of VCAM-1 protein compared with control. Gain and loss of function studies in primary vascular smooth muscle cells identified a role for Id3 in repressing VCAM-1 promoter activation. Chromatin immunoprecipitation demonstrated interaction of E12 with the VCAM-1 promoter, which is inhibited by Id3.

CONCLUSIONS

Id3 is an atheroprotective transcription regulator with targets in both B cells and vessel wall cells leading to reduced macrophage accumulation and reduced atherosclerosis formation.

Nocturnin regulates circadian trafficking of dietary lipid in intestinal enterocytes

BACKGROUND

Efficient metabolic function in mammals depends on the circadian clock, which drives temporal regulation of metabolic processes. Nocturnin is a clock-regulated deadenylase that controls its target mRNA expression posttranscriptionally through poly(A) tail removal. Mice lacking nocturnin (Noc(-/-) mice) are resistant to diet-induced obesity and hepatic steatosis yet are not hyperactive or hypophagic.

RESULTS

Here we show that nocturnin is expressed rhythmically in the small intestine and is induced by olive oil gavage and that the Noc(-/-) mice have reduced chylomicron transit into the plasma following the ingestion of dietary lipids. Genes involved in triglyceride synthesis and storage and chylomicron formation have altered expression, and large cytoplasmic lipid droplets accumulate in the apical domains of the Noc(-/-) enterocytes. The physiological significance of this deficit in absorption is clear because maintenance of Noc(-/-) mice on diets that challenge the chylomicron synthesis pathway result in significant reductions in body weight, whereas diets that bypass this pathway do not.

CONCLUSIONS

Therefore, we propose that nocturnin plays an important role in the trafficking of dietary lipid in the intestinal enterocytes by optimizing efficient absorption of lipids.

Microdomain organization and frequency-dependence of CREB-dependent transcriptional signaling in heart cells

Voltage-gated Ca(v)1.2 calcium channels couple membrane depolarization to cAMP response-element-binding protein (CREB)-dependent transcriptional activation. To investigate the spatial and temporal organization of CREB-dependent transcriptional nuclear microdomains, we combined perforated patch-clamp technique and FRET microscopy for monitoring CREB and CREB-binding protein interaction in the nuclei of live cells. The experimental approach to the quantitative assessment of CREB-dependent transcriptional signaling evoked by cAMP- and Ca(v)1.2-dependent mechanisms was devised in COS1 cells expressing recombinant Ca(v)1.2 calcium channels. Using continuous 2-dimensional wavelet transform and time series analyses, we found that nuclear CREB-dependent transcriptional signaling is organized differentially in spatially and temporally separated microdomains of 4 distinct types. In rat neonatal cardiomyocytes, CREB-dependent transcription is mediated by the cAMP-initiated CaMKII-sensitive and Ca(v)1.2-initiated CaMKII-insensitive mechanisms. The latter microdomains show a tendency to exhibit periodic behavior correlated with spontaneous contraction of myocytes suggestive of frequency-dependent CREB-dependent transcriptional regulation in the heart.

Effect of Ca(v)beta subunits on structural organization of Ca(v)1.2 calcium channels

BACKGROUND

Voltage-gated Ca(v)1.2 calcium channels play a crucial role in Ca(2+) signaling. The pore-forming alpha(1C) subunit is regulated by accessory Ca(v)beta subunits, cytoplasmic proteins of various size encoded by four different genes (Ca(v)beta(1)-beta(4)) and expressed in a tissue-specific manner.

METHODS AND RESULTS

Here we investigated the effect of three major Ca(v)beta types, beta(1b), beta(2d) and beta(3), on the structure of Ca(v)1.2 in the plasma membrane of live cells. Total internal reflection fluorescence microscopy showed that the tendency of Ca(v)1.2 to form clusters depends on the type of the Ca(v)beta subunit present. The highest density of Ca(v)1.2 clusters in the plasma membrane and the smallest cluster size were observed with neuronal/cardiac beta(1b) present. Ca(v)1.2 channels containing beta(3), the predominant Ca(v)beta subunit of vascular smooth muscle cells, were organized in a significantly smaller number of larger clusters. The inter- and intramolecular distances between alpha(1C) and Ca(v)beta in the plasma membrane of live cells were measured by three-color FRET microscopy. The results confirm that the proximity of Ca(v)1.2 channels in the plasma membrane depends on the Ca(v)beta type. The presence of different Ca(v)beta subunits does not result in significant differences in the intramolecular distance between the termini of alpha(1C), but significantly affects the distance between the termini of neighbor alpha(1C) subunits, which varies from 67 A with beta(1b) to 79 A with beta(3).

CONCLUSIONS

Thus, our results show that the structural organization of Ca(v)1.2 channels in the plasma membrane depends on the type of Ca(v)beta subunits present.