Empirical assessment of the assumptions of ComBat with diffusion tensor imaging.

Purpose: Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides unique information about white matter microstructure in the brain but is susceptible to confounding effects introduced by scanner or acquisition differences. ComBat is a leading approach for addressing these site biases. However, despite its frequent use for harmonization, ComBat's robustness toward site dissimilarities and overall cohort size have not yet been evaluated in terms of DTI. Approach: As a baseline, we match N=358 participants from two sites to create a "silver standard" that simulates a cohort for multi-site harmonization. Across sites, we harmonize mean fractional anisotropy and mean diffusivity, calculated using participant DTI data, for the regions of interest defined by the JHU EVE-Type III atlas. We bootstrap 10 iterations at 19 levels of total sample size, 10 levels of sample size imbalance between sites, and 6 levels of mean age difference between sites to quantify (i) ßAGE, the linear regression coefficient of the relationship between FA and age; (ii) Î³/f*, the ComBat-estimated site-shift; and (iii) Î´/f*, the ComBat-estimated site-scaling. We characterize the reliability of ComBat by evaluating the root mean squared error in these three metrics and examine if there is a correlation between the reliability of ComBat and a violation of assumptions. Results: ComBat remains well behaved for ßAGE when N>162 and when the mean age difference is less than 4 years. The assumptions of the ComBat model regarding the normality of residual distributions are not violated as the model becomes unstable. Conclusion: Prior to harmonization of DTI data with ComBat, the input cohort should be examined for size and covariate distributions of each site. Direct assessment of residual distributions is less informative on stability than bootstrap analysis. We caution use ComBat of in situations that do not conform to the above thresholds.

DeepN4: Learning N4ITK Bias Field Correction for T1-weighted Images.

T1-weighted (T1w) MRI has low frequency intensity artifacts due to magnetic field inhomogeneities. Removal of these biases in T1w MRI images is a critical preprocessing step to ensure spatially consistent image interpretation. N4ITK bias field correction, the current state-of-the-art, is implemented in such a way that makes it difficult to port between different pipelines and workflows, thus making it hard to reimplement and reproduce results across local, cloud, and edge platforms. Moreover, N4ITK is opaque to optimization before and after its application, meaning that methodological development must work around the inhomogeneity correction step. Given the importance of bias fields correction in structural preprocessing and flexible implementation, we pursue a deep learning approximation / reinterpretation of the N4ITK bias fields correction to create a method which is portable, flexible, and fully differentiable. In this paper, we trained a deep learning network "DeepN4" on eight independent cohorts from 72 different scanners and age ranges with N4ITK-corrected T1w MRI and bias field for supervision in log space. We found that we can closely approximate N4ITK bias fields correction with naïve networks. We evaluate the peak signal to noise ratio (PSNR) in test dataset against the N4ITK corrected images. The median PSNR of corrected images between N4ITK and DeepN4 was 47.96 dB. In addition, we assess the DeepN4 model on eight additional external datasets and show the generalizability of the approach. This study establishes that incompatible N4ITK preprocessing steps can be closely approximated by naïve deep neural networks, facilitating more flexibility. All code and models are released at https://github.com/MASILab/DeepN4 .

MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal.

Data harmonization is necessary for removing confounding effects in multi-site diffusion image analysis. One such harmonization method, LinearRISH, scales rotationally invariant spherical harmonic (RISH) features from one site ("target") to the second ("reference") to reduce confounding scanner effects. However, reference and target site designations are not arbitrary and resultant diffusion metrics (fractional anisotropy, mean diffusivity) are biased by this choice. In this work we propose MidRISH: rather than scaling reference RISH features to target RISH features, we project both sites to a mid-space. We validate MidRISH with the following experiments: harmonizing scanner differences from 37 matched patients free of cognitive impairment, and harmonizing acquisition and study differences on 117 matched patients free of cognitive impairment. We find that MidRISH reduces bias of reference selection while preserving harmonization efficacy of LinearRISH. Users should be cautious when performing LinearRISH harmonization. To select a reference site is to choose diffusion metric effect-size. Our proposed method eliminates the bias-inducing site selection step.

Epidemiology of Postoperative Junctional Ectopic Tachycardia in Infants Undergoing Cardiac Surgery.

BACKGROUND: Junctional ectopic tachycardia (JET) complicates congenital heart surgery in 2% to 8.3% of cases. JET is associated with postoperative morbidity in single-center studies. We used the Pediatric Cardiac Critical Care Consortium data registry to provide a multicenter epidemiologic description of treated JET. METHODS: This is a retrospective study (February 2019-August 2022) of patients with treated JET. Inclusion criteria were (1) <12 months old at the index operation, and (2) treated for JET <72 hours after surgery. Diagnosis was defined by receiving treatment (pacing, cooling, and medications). A multilevel logistic regression analysis with hospital random effect identified JET risk factors. Impact of JET on outcomes was estimated by margins/attributable risk analysis using previous risk-adjustment models. RESULTS: Among 24,073 patients from 63 centers, 1436 (6.0%) were treated for JET with significant center variability (0% to 17.9%). Median time to onset was 3.4 hours, with 34% present on admission. Median duration was 2 days (interquartile range, 1-4 days). Tetralogy of Fallot, atrioventricular canal, and ventricular septal defect repair represented >50% of JET. Patient characteristics independently associated with JET included neonatal age, Asian race, cardiopulmonary bypass time, open sternum, and early postoperative inotropic agents. JET was associated with increased risk-adjusted durations of mechanical ventilation (incidence rate ratio, 1.6; 95% CI, 1.5-1.7) and intensive care unit length of stay (incidence rate ratio, 1.3; 95% CI, 1.2-1.3), but not mortality. CONCLUSIONS: JET is treated in 6% of patients with substantial center variability. JET contributes to increased use of postoperative resources. High center variability warrants further study to identify potential modifiable factors that could serve as targets for improvement efforts to ameliorate deleterious outcomes.

Evaluation of Mean Shift, ComBat, and CycleGAN for Harmonizing Brain Connectivity Matrices Across Sites.

Connectivity matrices derived from diffusion MRI (dMRI) provide an interpretable and generalizable way of understanding the human brain connectome. However, dMRI suffers from inter-site and between-scanner variation, which impedes analysis across datasets to improve robustness and reproducibility of results. To evaluate different harmonization approaches on connectivity matrices, we compared graph measures derived from these matrices before and after applying three harmonization techniques: mean shift, ComBat, and CycleGAN. The sample comprises 168 age-matched, sex-matched normal subjects from two studies: the Vanderbilt Memory and Aging Project (VMAP) and the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD). First, we plotted the graph measures and used coefficient of variation (CoV) and the Mann-Whitney U test to evaluate different methods' effectiveness in removing site effects on the matrices and the derived graph measures. ComBat effectively eliminated site effects for global efficiency and modularity and outperformed the other two methods. However, all methods exhibited poor performance when harmonizing average betweenness centrality. Second, we tested whether our harmonization methods preserved correlations between age and graph measures. All methods except for CycleGAN in one direction improved correlations between age and global efficiency and between age and modularity from insignificant to significant with p-values less than 0.05.

Identification of functional white matter networks in BOLD fMRI.

White matter signals in resting state blood oxygen level dependent functional magnetic resonance (BOLD-fMRI) have been largely discounted, yet there is growing evidence that these signals are indicative of brain activity. Understanding how these white matter signals capture function can provide insight into brain physiology. Moreover, functional signals could potentially be used as early markers for neurological changes, such as in Alzheimer's Disease. To investigate white matter brain networks, we leveraged the OASIS-3 dataset to extract white matter signals from resting state BOLD-FMRI data on 711 subjects. The imaging was longitudinal with a total of 2,026 images. Hierarchical clustering was performed to investigate clusters of voxel-level correlations on the timeseries data. The stability of clusters was measured with the average Dice coefficients on two different cross fold validations. The first validated the stability between scans, and the second validated the stability between subject populations. Functional clusters at hierarchical levels 4, 9, 13, 18, and 24 had local maximum stability, suggesting better clustered white matter. In comparison with JHU-DTI-SS Type-I Atlas defined regions, clusters at lower hierarchical levels identified well defined anatomical lobes. At higher hierarchical levels, functional clusters mapped motor and memory functional regions, identifying 50.00%, 20.00%, 27.27%, and 35.14% of the frontal, occipital, parietal, and temporal lobe regions respectively.

Characterizing patterns of DTI variance in aging brains.

Background: As large analyses merge data across sites, a deeper understanding of variance in statistical assessment across the sources of data becomes critical for valid analyses. Diffusion tensor imaging (DTI) exhibits spatially varying and correlated noise, so care must be taken with distributional assumptions. Purpose: We characterize the role of physiology, subject compliance, and the interaction of subject with the scanner in the understanding of DTI variability, as modeled in spatial variance of derived metrics in homogeneous regions. Methods: We analyze DTI data from 1035 subjects in the Baltimore Longitudinal Study of Aging (BLSA), with ages ranging from 22.4 to 103 years old. For each subject, up to 12 longitudinal sessions were conducted. We assess variance of DTI scalars within regions of interest (ROIs) defined by four segmentation methods and investigate the relationships between the variance and covariates, including baseline age, time from the baseline (referred to as "interval"), motion, sex, and whether it is the first scan or the second scan in the session. Results: Covariate effects are heterogeneous and bilaterally symmetric across ROIs. Inter-session interval is positively related (p ≪ 0.001) to FA variance in the cuneus and occipital gyrus, but negatively (p ≪ 0.001) in the caudate nucleus. Males show significantly (p ≪ 0.001) higher FA variance in the right putamen, thalamus, body of the corpus callosum, and cingulate gyrus. In 62 out of 176 ROIs defined by the Eve type-1 atlas, an increase in motion is associated (p < 0.05) with a decrease in FA variance. Head motion increases during the rescan of DTI (Δµ = 0.045 millimeters per volume). Conclusions: The effects of each covariate on DTI variance, and their relationships across ROIs are complex. Ultimately, we encourage researchers to include estimates of variance when sharing data and consider models of heteroscedasticity in analysis. This work provides a foundation for study planning to account for regional variations in metric variance.

Predicting Age from White Matter Diffusivity with Residual Learning.

Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI's advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are non-linearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.

Minimum Supervision Levels Required for Pediatric Cardiology Fellowship Graduation by Pediatric Cardiology Fellowship Program Directors.

Entrustable professional activities (EPAs) are "observable essential tasks expected to be performed by a physician for safe patient care in practice." Six Pediatric Cardiology (PC) EPAs and their level of supervision (LOS) scales were developed by medical educators in PC using a modified Delphi process and reviewed by the Subspecialty Pediatrics Investigator Network (SPIN). However, their general use in assessment for PC fellows for graduation requirements has yet to be studied. The objective of this study was to determine the minimum LOS required for PC fellows to graduate and compare it with the minimum LOS expected for safe and effective practice for the six PC EPAs, from the perspective of the PC Fellowship Program Directors(FPD). All Fellowship Program Directors(FPD) of ACGME-accredited PC fellowships were surveyed through SPIN between April 2017 and August 2017. For each of the PC EPAs, the FPDs were asked to indicate the minimum LOS expected for graduation and whether they would allow a fellow to graduate if this level was not achieved and the minimum LOS expected for a practicing pediatric cardiologist to provide safe and effective patient care. The minimum LOS was defined as the LOS for which no more than 20% of FPDs would want a lower level. The survey response rate was 80% (47/59). The majority of the FPDs did not require a minimum LOS of five corresponding to unsupervised practice in any of the six PC EPAs at graduation. For EPAs related to imaging, arrhythmia management, and management of cardiac problems, the minimum LOS for graduation was 3, corresponding to being "trusted to perform a task with indirect supervision for most simple and a few complex cases." For the EPAs related to interventional cardiology, heart failure pulmonary hypertension, and cardiac intensive care, the minimum LOS for graduation was 2, corresponding to being "trusted to perform a task only with direct supervision and coaching." The minimum LOS considered necessary for safe and effective practice for all but one EPA was 3. For the EPA related to the management of cardiac problems, the minimum LOS for safe practice was 4, corresponding to being "trusted to execute tasks independently except for few complex and critical cases." Most PC FPDs reported they would not require fellows to achieve the highest entrustment level for any of the six PC EPAs for graduation. It is crucial that educational programs evolve to address these essential activities during training better and that stakeholders ensure that graduating PC fellows have adequate resources and infrastructure to continue professional development as early career pediatric cardiologists.

DeepN4: Learning N4ITK Bias Field Correction for T1-weighted Images.

T1-weighted (T1w) MRI has low frequency intensity artifacts due to magnetic field inhomogeneities. Removal of these biases in T1w MRI images is a critical preprocessing step to ensure spatially consistent image interpretation. N4ITK bias field correction, the current state-of-the-art, is implemented in such a way that makes it difficult to port between different pipelines and workflows, thus making it hard to reimplement and reproduce results across local, cloud, and edge platforms. Moreover, N4ITK is opaque to optimization before and after its application, meaning that methodological development must work around the inhomogeneity correction step. Given the importance of bias fields correction in structural preprocessing and flexible implementation, we pursue a deep learning approximation / reinterpretation of the N4ITK bias fields correction to create a method which is portable, flexible, and fully differentiable. In this paper, we trained a deep learning network "DeepN4" on eight independent cohorts from 72 different scanners and age ranges with N4ITK-corrected T1w MRI and bias field for supervision in log space. We found that we can closely approximate N4ITK bias fields correction with naïve networks. We evaluate the peak signal to noise ratio (PSNR) in test dataset against the N4ITK corrected images. The median PSNR of corrected images between N4ITK and DeepN4 was 47.96 dB. In addition, we assess the DeepN4 model on eight additional external datasets and show the generalizability of the approach. This study establishes that incompatible N4ITK preprocessing steps can be closely approximated by naïve deep neural networks, facilitating more flexibility. All code and models are released at https://github.com/MASILab/DeepN4.

MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal.

Objective: Data harmonization is necessary for removing confounding effects in multi-site diffusion image analysis. One such harmonization method, LinearRISH, scales rotationally invariant spherical harmonic (RISH) features from one site ("target") to the second ("reference") to reduce confounding scanner effects. However, reference and target site designations are not arbitrary and resultant diffusion metrics (fractional anisotropy, mean diffusivity) are biased by this choice. In this work we propose MidRISH: rather than scaling reference RISH features to target RISH features, we project both sites to a mid-space. Methods: We validate MidRISH with the following experiments: harmonizing scanner differences from 37 matched patients free of cognitive impairment, and harmonizing acquisition and study differences on 117 matched patients free of cognitive impairment. Conclusion: MidRISH reduces bias of reference selection while preserving harmonization efficacy of LinearRISH. Significance: Users should be cautious when performing LinearRISH harmonization. To select a reference site is to choose diffusion metric effect-size. Our proposed method eliminates the bias-inducing site selection step.

Recombinant angiotensin II therapy in a child with cardiac dysfunction and Pandoraea and Candida sepsis.

Recombinant angiotensin II is an emerging drug therapy for refractory hypotension. Its use is relevant to patients with disruption of the renin-angiotensin-aldosterone system denoted by elevated direct renin levels. We present a child that responded to recombinant angiotensin II in the setting of right ventricular hypertension and multi-organism septic shock.

Implementation considerations for deep learning with diffusion MRI streamline tractography.

One area of medical imaging that has recently experienced innovative deep learning advances is diffusion MRI (dMRI) streamline tractography with recurrent neural networks (RNNs). Unlike traditional imaging studies which utilize voxel-based learning, these studies model dMRI features at points in continuous space off the voxel grid in order to propagate streamlines, or virtual estimates of axons. However, implementing such models is non-trivial, and an open-source implementation is not yet widely available. Here, we describe a series of considerations for implementing tractography with RNNs and demonstrate they allow one to approximate a deterministic streamline propagator with comparable performance to existing algorithms. We release this trained model and the associated implementations leveraging popular deep learning libraries. We hope the availability of these resources will lower the barrier of entry into this field, spurring further innovation.

Are Parent Discharge Readiness Scores Effective for Patients With Congenital Heart Disease After Cardiac Surgery?

OBJECTIVE: To assess discharge readiness and clinical engagement post-discharge in families of children undergoing congenital heart surgery. STUDY DESIGN: This prospective cross-sectional study was performed at a major tertiary pediatric cardiac referral center. Eligible parents and caregivers completed a discharge readiness tool, the Readiness for Hospital Discharge Scale for Parents of Hospitalized Children, via online survey on the day of discharge. Clinical engagement data included subsequent phone calls, clinic visits, emergency department visits, and hospital readmissions. Readiness for Hospital Discharge Scale for Parents of Hospitalized Children scores were measured as follows: very high (9-10), high (8-8.9), moderate (7-7.9), and low (<7). Descriptive statistics were used to describe demographic data. RESULTS: In total, 128 families enrolled between April and December 2021. Parent discharge readiness scores ranged from "high" to "very high." Families with lower socioeconomic status and younger patients (especially single-ventricle infants or "interstage") had a greater proportion of clinic visits, emergency department visits, and hospital readmissions within 30-days postdischarge compared with other groups. CONCLUSIONS: Discharge readiness scores were not associated with clinical engagement. We identified vulnerable populations as evidenced by a greater frequency of clinical engagement in the immediate postoperative period, particularly younger patients and first-time surgeries. Although these visits may be appropriate, novel programs could enhance education and emotional support to prevent delay in seeking care or creating excessive stress and anxiety after discharge.

Entrustable Professional Activities and Their Relevance to Pediatric Cardiology Training.

Entrustable professional activities (EPAs) have become a popular framework for medical trainee assessment and a supplemental component for milestone and competency assessment. EPAs were developed to facilitate assessment of competencies and furthermore to facilitate translation into clinical practice. In this review, we explore the rationale for the introduction of EPAs, examine whether they fulfill the promise expected of them, and contemplate further developments in their application with specific reference to training in pediatric cardiology.

Correlating surgical and pathological diagnoses in pediatric appendicitis.

BACKGROUND: The stratification of appendicitis into simple and complex variants has far-reaching implications. While the operative diagnosis made by the surgeon dictates clinical management, the pathologic diagnosis often differs and is frequently used for coding and reimbursement. The purpose of this study was to examine discrepancies between the operative and pathologic diagnoses with subsequent correlation to clinical outcomes. METHODS: Patients with acute appendicitis from July 2011 to July 2012 were identified. Diagnoses included simple (normal, acute, and suppurative) and complex (gangrenous and perforated). We evaluated the inter-rater reliability between pathologic and operative diagnoses in the five appendicitis categories. Clinical outcomes of deep and superficial surgical site infections were evaluated according to the pathologic and surgical diagnosis. RESULTS: During the study period, we identified 1166 patients with acute appendicitis. The surgeon and pathologist agreed on the specific diagnosis (acute, suppurative, gangrenous, perforated, normal) in 48% of patients (kappa 0.289, 95% CI 0.259-0.324, p=0.001). Agreement on disease severity (simple vs. complex) improved to 82%. The operative diagnosis more accurately predicted infectious complications than the pathologic diagnoses. CONCLUSION: Significant discordance exists between surgical and pathologic diagnoses. While the relevance of this discordance to clinical outcomes is still not clear, a potential for incorrect hospital coding and subsequent reimbursement exists. Future quality improvement projects should focus on standardizing the surgical and pathologic diagnoses.

Performance of CT examinations in children with suspected acute appendicitis in the community setting: a need for more education.

OBJECTIVE: Despite a recent focus on the preferential use of ultrasound over CT for pediatric appendicitis, most children transferred from community hospitals still undergo diagnostic CT scans. The purpose of this study was to evaluate CT techniques performed for children with acute appendicitis at nonpediatric treatment centers. MATERIALS AND METHODS: All patients treated for acute appendicitis at our tertiary-care pediatric hospital from July 1, 2011, through June 30, 2012, were identified. Patient demographics, imaging modality used to diagnoses appendicitis (CT or ultrasound), location (home or referral institution), and CT technique parameters were collected. The estimated mean organ radiation dose, number of imaging phases, and use of contrast media were evaluated at home and referral institutions. RESULTS: During the study period, 1215 patients underwent appendectomies after imaging, with 442 (36.4%) imaged at referral facilities. Most referral patients received a diagnosis by CT (n=384, 87%), compared with 73 of 773 (9.4%) who received a diagnosis by CT at the home institution. The estimated mean (±SD) organ radiation dose was not statistically significantly different between home and referral institutions (13.5±7.3 vs 12.9±6.4 mGy; p=0.58) for single-phase examinations. Of 384 referral patients, 344 had images available for review. In total, 40% (138/344) of patients from referral centers were imaged with suboptimal CT techniques: 50 delayed phase only, 52 dual phase (eight of which were imaged twice in delayed phase), eight triple phase, and 36 without IV contrast agent. CONCLUSION: CT parameters and radiation doses from single-phase examinations in children with appendicitis were similar at nonpediatric treatment centers and a tertiary care children's hospital. Future educational outreach should focus on optimizing other technical parameters.

Overuse of fluoroscopic gastrostomy studies in a children's hospital.

BACKGROUND: Gastrostomy tubes are often dislodged or exchanged in children. Indications for fluoroscopic examination of gastrostomy location include concern for malposition, dislodgement, leak, or gastric outlet obstruction. We hypothesized that most of the studies obtained at our institution were not ordered for one of the aforementioned indications and do not ultimately affect patient management. METHODS: All fluoroscopic gastrostomy studies performed from January 2011 to December 2012 were reviewed. Transgastric jejunostomy studies were excluded. Patient demographics, indications for the study, elapsed time since placement, imaging findings, and short-term outcomes were recorded. Chi-square analysis was used to evaluate relationships between categorical variables. RESULTS: During the study period, 337 patients who underwent fluoroscopic gastrostomy studies were identified; median age was 2.5 y (0.05-23.8). Sixty-two percent (208/337) of the studies were ordered in asymptomatic patients to confirm tube placement location after routine exchange or replacement. Symptomatic patients accounted for 38% of the studies. Ordering physicians were primarily nonsurgeons (72%, 242/337). Abnormal findings were observed in 4.8% (16/337) of patients, six (1.7%) of whom required an operative intervention. The 2.9% (6/208) abnormal study rate for asymptomatic patients was significantly lower than the 7.9% (10/129) rate in the patients who were evaluated for symptomatic indications (P = 0.03). CONCLUSIONS: Most of the fluoroscopic gastrostomy studies ordered at a tertiary care center did not appear to alter patient care. Development of a standardized management algorithm based on clinical indications is necessary to decrease the number of extraneous gastrostomy studies.

Fetal MRI lung volumes are predictive of perinatal outcomes in fetuses with congenital lung masses.

PURPOSE: The purpose of this study was to evaluate fetal magnetic resonance imaging (MRI) as a modality for predicting perinatal outcomes and lung-related morbidity in fetuses with congenital lung masses (CLM). METHODS: The records of all patients treated for CLM from 2002 to 2012 were reviewed retrospectively. Fetal MRI-derived lung mass volume ratio (LMVR), observed/expected normal fetal lung volume (O/E-NFLV), and lesion-to-lung volume ratio (LLV) were calculated. Multivariate regression and receiver operating characteristic analyses were applied to determine the predictive accuracy of prenatal imaging. RESULTS: Of 128 fetuses with CLM, 93% (n=118) survived. MRI data were available for 113 fetuses. In early gestation (<26weeks), MRI measurements of LMVR and LLV correlated with risk of fetal hydrops, mortality, and/or need for fetal intervention. In later gestation (>26weeks), LMVR, LLV, and O/E-NFLV correlated with neonatal respiratory distress, intubation, NICU admission and need for neonatal surgery. On multivariate regression, LMVR was the strongest predictor for development of fetal hydrops (OR: 6.97, 1.58-30.84; p=0.01) and neonatal respiratory distress (OR: 12.38, 3.52-43.61; p≤0.001). An LMVR >2.0 predicted worse perinatal outcome with 83% sensitivity and 99% specificity (AUC=0.94; p<0.001). CONCLUSION: Fetal MRI volumetric measurements of lung masses and residual normal lung are predictive of perinatal outcomes in fetuses with CLM. These data may assist in perinatal risk stratification, counseling, and resource utilization.

Identifying and reducing early complications of surgical central lines in infants and toddlers.

BACKGROUND: Hospitalized infants>1 y old often require central venous catheters (CVC) for prolonged therapy. There are limited data describing the complication profile for this young population. The purpose of this study was to review outcomes associated with CVC insertion in this high-risk group and compare them to those in older children to develop directed quality improvement projects. MATERIALS AND METHODS: Patients receiving their first CVC, a tunneled central line or port-a-cath, from 2007-2010 were included. Femoral, non-tunneled, and hemodialysis catheters were excluded. Patients aged 0-12.0 mo (infants) were compared with those 12.1-36 mo (toddlers). Complications (<30 d) included infection, malposition, malfunction, intraoperative, and the need for operative exchange. Statistical analysis included Student t-test, chi-square, and a Kaplan-Meier survival analysis. RESULTS: We identified 115 infants and 129 toddlers who underwent CVC insertion during the study period. Complication rates were higher in the infant group than in the toddler group, as was the operative exchange rate. Higher infection rates in the infant group appeared to contribute to the difference in early complications and exchange rates. A survival analysis indicated improved catheter duration in toddlers (P=0.001). CONCLUSIONS: In this cohort study, infants had a higher early complication rate, mostly attributable to infection, than their older counterparts. This difference could be explained by increased use of a tunneled central line for daily total parented nutrition in infants with gastrointestinal anomalies, as opposed to port-a-cath for chemotherapy infusion in older children. These data have prompted a number of targeted quality improvement initiatives to address relevant complications in this infant population.