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.

Midterm Outcomes and Cardiac Magnetic Resonance Imaging following Multisystem Inflammatory Syndrome in Children.

At midterm follow-up visits performed at a median of 7 months (IQR 6.0-8.4 months), 16 patients with multisystem inflammatory syndrome in children had resolution of left ventricular dysfunction and most had resolution of coronary aneurysms. On cardiovascular magnetic resonance imaging, no patients had late gadolinium enhancement.

The role of fetal echocardiogram after detection of extracardiac anomalies in utero (fetal echocardiogram for extracardiac malformations).

OBJECTIVE: We aimed to investigate the utility of comprehensive screening fetal echocardiography (FE) for patients diagnosed with any type of fetal extracardiac malformation (ECM) at a single multidisciplinary fetal center. METHODS: We retrospectively reviewed all patients presenting to our referral center for FE due to a prenatal diagnosis of ECM (January 2013-December 2018). RESULTS: Among 641 patients with ≥1 ECM referred for FE, 78 (12.2%) had CHD diagnosed at 25.6 ± 0.5 weeks. The frequency of CHD by type of ECM ranged from 35.1% for craniofacial to 9.8% for thoracic. Increasing number of fetal ECMs was strongly associated with CHD: odds ratio 2.01 (95% confidence interval: 1.06-3.69) for two ECMs, 9.57 (2.00-49.05) for three ECMs, and 11.68 (3.84-37.15) for more than three ECMs. Of fetuses with ECM and an abnormal genetic finding, 33.3% had CHD as compared to 10.9% of those without (p < 0.0001). Obstetric anatomy sonogram detected 43.6% of CHD. CONCLUSION: CHD was commonly diagnosed among fetuses with any type of ECM at our center but was not always detected on obstetric sonogram. As the presence of CHD may impact decision-making and perinatal care, patients with a diagnosis of any fetal ECM should be considered for FE.

Detection of circulating tumor DNA in patients with osteosarcoma.

Identification and quantification of somatic alterations in plasma-derived, circulating tumor DNA (ctDNA) is gaining traction as a non-invasive and cost effective method of disease monitoring in cancer patients, particularly to evaluate response to treatment and monitor for disease recurrence. To our knowledge, genetic analysis of ctDNA in osteosarcoma has not yet been studied. To determine whether somatic alterations can be detected in ctDNA and perhaps applied to patient management in this disease, we collected germline, tumor, and serial plasma samples from pediatric, adolescent, and young adult patients with osteosarcoma and used targeted Next Generation Sequencing (NGS) to identify somatic single nucleotide variants (SNV), insertions and deletions (INDELS), and structural variants (SV) in 7 genes commonly mutated in osteosarcoma. We demonstrate that patient-specific somatic alterations identified through comparison of tumor-germline pairs can be detected and quantified in cell-free DNA of osteosarcoma patients.

Targeting Glycoprotein NMB With Antibody-Drug Conjugate, Glembatumumab Vedotin, for the Treatment of Osteosarcoma.

BACKGROUND: Cure rates for children and young adults with osteosarcoma have remained stagnant over the past three decades. Targeting glycoprotein non-metastatic b (GPNMB) with the antibody-drug conjugate glembatumumab vedotin has improved outcomes for patients with melanoma and breast cancer. The potential utility of targeting GPNMB in osteosarcoma was explored. METHODS: GPNMB protein expression was evaluated by immunohistochemistry in human osteosarcoma tumor samples and by enzyme-linked immunosorbent assay (ELISA) in osteosarcoma cell lines. mRNA expression was measured by quantitative PCR in primary osteosarcoma samples and cell lines. Surface GPNMB expression was evaluated by flow cytometry and correlated with in vitro and in vivo cytotoxicity of glembatumumab vedotin. RESULTS: Sixty seven human osteosarcoma samples were evaluated by immunohistochemistry, including 12 samples from initial biopsy, 38 samples from definitive surgery, and 17 from the time of disease recurrence. GPNMB was expressed in 92.5% (62/67) of osteosarcoma samples. All primary osteosarcoma samples expressed high levels of GPNMB mRNA. Glembatumumab induced cytotoxic effects in 74% (14/19) of osteosarcoma cell lines, and GPNMB protein levels correlated with glembatumumab in vitro cytotoxicity (r = -0.46, P = 0.04). All osteosarcoma cell lines demonstrated surface GPNMB expression. CONCLUSIONS: GPNMB is expressed in osteosarcoma and targeting GPNMB with the antibody-drug conjugate glembatumumab vedotin demonstrates osteosarcoma cytotoxic activity. Clinical trials are indicated to assess the efficacy of targeting GPNMB in patients with osteosarcoma.

The cervical microbiome over 7 years and a comparison of methodologies for its characterization.

BACKGROUND: The rapidly expanding field of microbiome studies offers investigators a large choice of methods for each step in the process of determining the microorganisms in a sample. The human cervicovaginal microbiome affects female reproductive health, susceptibility to and natural history of many sexually transmitted infections, including human papillomavirus (HPV). At present, long-term behavior of the cervical microbiome in early sexual life is poorly understood. METHODS: The V6 and V6-V9 regions of the 16S ribosomal RNA gene were amplified from DNA isolated from exfoliated cervical cells. Specimens from 10 women participating in the Natural History Study of HPV in Guanacaste, Costa Rica were sampled successively over a period of 5-7 years. We sequenced amplicons using 3 different platforms (Sanger, Roche 454, and Illumina HiSeq 2000) and analyzed sequences using pipelines based on 3 different classification algorithms (usearch, RDP Classifier, and pplacer). RESULTS: Usearch and pplacer provided consistent microbiome classifications for all sequencing methods, whereas RDP Classifier deviated significantly when characterizing Illumina reads. Comparing across sequencing platforms indicated 7%-41% of the reads were reclassified, while comparing across software pipelines reclassified up to 32% of the reads. Variability in classification was shown not to be due to a difference in read lengths. Six cervical microbiome community types were observed and are characterized by a predominance of either G. vaginalis or Lactobacillus spp. Over the 5-7 year period, subjects displayed fluctuation between community types. A PERMANOVA analysis on pairwise Kantorovich-Rubinstein distances between the microbiota of all samples yielded an F-test ratio of 2.86 (p<0.01), indicating a significant difference comparing within and between subjects' microbiota. CONCLUSIONS: Amplification and sequencing methods affected the characterization of the microbiome more than classification algorithms. Pplacer and usearch performed consistently with all sequencing methods. The analyses identified 6 community types consistent with those previously reported. The long-term behavior of the cervical microbiome indicated that fluctuations were subject dependent.