Biomedical subjects

John A Phillips

Publications and source records attributed to John A Phillips.

4 recordsLinked to original sources

PubMed2026

An EHR-based framework for modeling growth curves and constructing growth centile charts for genetic disorders.

Growth modeling is central to human genetics, as deviations from typical growth can signal an underlying disorder. In this cohort study, we developed a generalizable framework for generating growth charts across genetic conditions using electronic health records (EHR). Leveraging 22 years of longitudinal EHR data from 452,470 patients across 15 genetic conditions and unaffected individuals, we generated sex- and condition-specific growth charts using Generalized Additive Models for Location, Scale, and Shape, and quantified differences in size, timing, and intensity using SuperImposition by Translation and Rotation (SITAR). SITAR-derived growth parameters showed strong concordance with established annotations in OMIM and Orphanet, and identified previously unreported growth patterns. We stratified cystic fibrosis by CFTR functional class and observed greater growth impairment in individuals with homozygous minimal-function variants compared to those with residual function. This framework provides a generalizable approach for leveraging EHR data to refine genotype-phenotype relationships and enable continuous updating of growth charts across genetic conditions.

Journal Article↗

PubMed2026

Coagulopathy in Neonates With Classic Galactosemia: A Life-Threatening Yet Underrecognized Complication.

INTRODUCTION: Classic galactosemia (CG) is a rare metabolic disorder caused by galactose-1-phosphate uridylyltransferase deficiency, leading to toxic metabolite accumulation and life-threatening complications such as failure to thrive, sepsis, and acute liver failure. We hypothesize that coagulopathy is an underrecognized complication of CG and that this gap is reflected by limited documentation in the medical literature. METHODS: A PubMed literature review was conducted to identify articles describing coagulopathy in CG. We filtered for guidelines, meta-analyses, reviews, and systematic reviews. Our article screening followed PRISMA guidelines. RESULTS: Of 49 identified articles, 26/49 (53%) met inclusion criteria. Only 6/49 (12%) explicitly described coagulopathy in CG, and only 1/49 (2%) discussed management. DISCUSSION: These data supports our hypothesis that coagulopathy may be an underrecognized complication of CG by clinicians and identifies a gap in current medical literature. Improved early recognition of coagulopathy in neonates with CG could prevent delays in treatment and improve outcomes.

Humans↗

PubMed2026

Evaluating pregnancy and neonatal outcomes in mothers with genetic disease using electronic health care records.

PURPOSE: The effect of Mendelian disorders on pregnancy and neonatal outcomes is poorly understood because of their rarity and the challenge of compiling complete prenatal and postnatal records. METHODS: Using electronic health records from a single academic center, we developed a retrospective cohort of maternal-infant dyads. Cases were mothers with molecularly confirmed Mendelian disorders paired with live-born infants; controls had no documented genetic disease. Outcomes were evaluated overall, by organ system, and by individual disorder. RESULTS: The cohort included 58,912 dyads, 241 with genetic diagnoses and 58,671 controls. Although overall outcomes were generally favorable, mothers with genetic disorders had higher rates of cesarean delivery and neonatal intensive care unit admission, earlier gestational age, and lower Apgar scores. Neonatal risks were greatest among neurological and cardiovascular disorders. Known associations were replicated, including increased neonatal intensive care unit admission in 22q11.2 deletion syndrome, cesarean delivery in Turner syndrome, and gestational diabetes in cystic fibrosis. We also provided descriptive electronic-health-record-based case reports and case series for 35 disorders previously lacking published pregnancy outcome data. CONCLUSION: This study identifies elevated perinatal risks in specific Mendelian disease groups and demonstrates how electronic-health-record-linked data can support prenatal counseling, risk stratification, and individualized care for individuals with genetic disorders.

Humans↗

PubMed2025

Characterizing trends in clinical genetic testing: A single-center analysis of EHR data from 1.8 million patients over two decades.

A lack of structural data in electronic health records (EHRs) makes assessing the impact of genetic testing on clinical practice challenging. We extracted clinical genetic tests from the EHRs of more than 1.8 million patients seen at Vanderbilt University Medical Center from 2002 to 2022. With these data, we quantified the use of clinical genetic testing in healthcare and described how testing patterns and results changed over time. We assessed trends in types of genetic tests, tracked usage across medical specialties, and introduced a new measure, the genetically attributable fraction (GAF), to quantify the proportion of observed phenotypes attributable to a genetic diagnosis over time. We identified 104,392 tests and 19,032 molecularly confirmed diagnoses. The proportion of patients with genetic testing in their EHRs increased from 1.0% in 2002 to 6.1% in 2022, and testing became more comprehensive with the growing use of multi-gene panels. The number of unique diseases diagnosed with genetic testing increased from 51 in 2002 to 509 in 2022, and there was a rise in the number of variants of uncertain significance. The phenome-wide GAF for 6,505,620 diagnoses made in 2022 was 0.46%, and the GAF was greater than 5% for 74 phenotypes, including pancreatic insufficiency (67%), chorea (64%), atrial septal defect (24%), microcephaly (17%), paraganglioma (17%), and ovarian cancer (6.8%). Our study provides a comprehensive quantification of the increasing role of genetic testing at a major academic medical institution and demonstrates its growing utility in explaining the observed medical phenome.

Humans↗