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• W4283025587 abstract "Chromosomal microarray (CMA) is a testing modality frequently used in pediatric patients; however, published data on its utilization are limited to the genetic setting. We performed a database search for all CMA testing performed from 2010 to 2020, and delineated the diagnostic yield based on patient characteristics, including sex, age, clinical specialty of providers, indication of testing, and pathogenic finding. The indications for testing were further categorized into Human Phenotype Ontology categories for analysis. This study included a cohort of 14,541 patients from 29 different medical specialties, of whom 30% were from the genetics clinic. The clinical indications for testing suggested that neonatology patients demonstrated the greatest involvement of multiorgan systems, involving the most Human Phenotype Ontology categories, compared with developmental behavioral pediatrics and neurology patients being the least. The top pathogenic findings for each specialty differed, likely due to the varying clinical features and indications for testing. Deletions involving the 22q11.21 locus were the top pathogenic findings for patients presenting to genetics, neonatology, cardiology, and surgery. Our data represent the largest pediatric cohort published to date. This study is the first to demonstrate the diagnostic utility of this assay for patients seen in the setting of different specialties, and it provides normative data of CMA results among a general pediatric population referred for testing because of variable clinical presentations. Chromosomal microarray (CMA) is a testing modality frequently used in pediatric patients; however, published data on its utilization are limited to the genetic setting. We performed a database search for all CMA testing performed from 2010 to 2020, and delineated the diagnostic yield based on patient characteristics, including sex, age, clinical specialty of providers, indication of testing, and pathogenic finding. The indications for testing were further categorized into Human Phenotype Ontology categories for analysis. This study included a cohort of 14,541 patients from 29 different medical specialties, of whom 30% were from the genetics clinic. The clinical indications for testing suggested that neonatology patients demonstrated the greatest involvement of multiorgan systems, involving the most Human Phenotype Ontology categories, compared with developmental behavioral pediatrics and neurology patients being the least. The top pathogenic findings for each specialty differed, likely due to the varying clinical features and indications for testing. Deletions involving the 22q11.21 locus were the top pathogenic findings for patients presenting to genetics, neonatology, cardiology, and surgery. Our data represent the largest pediatric cohort published to date. This study is the first to demonstrate the diagnostic utility of this assay for patients seen in the setting of different specialties, and it provides normative data of CMA results among a general pediatric population referred for testing because of variable clinical presentations. Pediatric patients with suspected genetic diseases often undergo multiple biochemical and genetic testing procedures during the diagnostic odyssey.1Michaels-Igbokwe C. McInnes B. MacDonald K.V. Currie G.R. Omar F. Shewchuk B. Bernier F.P. Marshall D.A. (Un)standardized testing: the diagnostic odyssey of children with rare genetic disorders in Alberta, Canada.Genet Med. 2021; 23: 272-279Abstract Full Text Full Text PDF PubMed Scopus (11) Google Scholar,2Shashi V. McConkie-Rosell A. Rosell B. Schoch K. Vellore K. McDonald M. Jiang Y.H. Xie P. Need A. Goldstein D.B. The utility of the traditional medical genetics diagnostic evaluation in the context of next-generation sequencing for undiagnosed genetic disorders.Genet Med. 2014; 16: 176-182Abstract Full Text Full Text PDF PubMed Scopus (207) Google Scholar Depending on clinical features, a spectrum of tests may be ordered until the molecular diagnosis is reached. In general, chromosomal microarray (CMA) is among the first genetic tests ordered as part of clinical investigation, for as many as 79% of patients.1Michaels-Igbokwe C. McInnes B. MacDonald K.V. Currie G.R. Omar F. Shewchuk B. Bernier F.P. Marshall D.A. (Un)standardized testing: the diagnostic odyssey of children with rare genetic disorders in Alberta, Canada.Genet Med. 2021; 23: 272-279Abstract Full Text Full Text PDF PubMed Scopus (11) Google Scholar Copy number variant (CNV) analysis at the genomic level has been recommended as a first-tier testing approach for the assessment of individuals with autism spectrum disorders, developmental delays, and multiple congenital abnormalities in the postnatal setting.3Miller D.T. Adam M.P. Aradhya S. Biesecker L.G. Brothman A.R. Carter N.P. Church D.M. Crolla J.A. Eichler E.E. Epstein C.J. Faucett W.A. Feuk L. Friedman J.M. Hamosh A. Jackson L. Kaminsky E.B. Kok K. Krantz I.D. Kuhn R.M. Lee C. Ostell J.M. Rosenberg C. Scherer S.W. Spinner N.B. Stavropoulos D.J. Tepperberg J.H. Thorland E.C. Vermeesch J.R. Waggoner D.J. Watson M.S. Martin C.L. Ledbetter D.H. Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies.Am J Hum Genet. 2010; 86: 749-764Abstract Full Text Full Text PDF PubMed Scopus (2018) Google Scholar, 4Riggs E.R. Andersen E.F. Cherry A.M. Kantarci S. Kearney H. Patel A. Raca G. Ritter D.I. South S.T. Thorland E.C. Pineda-Alvarez D. Aradhya S. Martin C.L. Technical standards for the interpretation and reporting of constitutional copy-number variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen).Genet Med. 2020; 22: 245-257Abstract Full Text Full Text PDF PubMed Scopus (587) Google Scholar, 5Waggoner D. Wain K.E. Dubuc A.M. Conlin L. Hickey S.E. Lamb A.N. Martin C.L. Morton C.C. Rasmussen K. Schuette J.L. Schwartz S. Miller D.T. ACMG Professional Practice and Guidelines CommitteeYield of additional genetic testing after chromosomal microarray for diagnosis of neurodevelopmental disability and congenital anomalies: a clinical practice resource of the American College of Medical Genetics and Genomics (ACMG).Genet Med. 2018; 20: 1105-1113Abstract Full Text Full Text PDF PubMed Scopus (37) Google Scholar Over the last decade, CMA has been broadly implemented for investigating submicroscopic genomic gains and losses at higher resolution than that offered by conventional G-banding karyotype, reaching a diagnostic rate of 15% to 20%.3Miller D.T. Adam M.P. Aradhya S. Biesecker L.G. Brothman A.R. Carter N.P. Church D.M. Crolla J.A. Eichler E.E. Epstein C.J. Faucett W.A. Feuk L. Friedman J.M. Hamosh A. Jackson L. Kaminsky E.B. Kok K. Krantz I.D. Kuhn R.M. Lee C. Ostell J.M. Rosenberg C. Scherer S.W. Spinner N.B. Stavropoulos D.J. Tepperberg J.H. Thorland E.C. Vermeesch J.R. Waggoner D.J. Watson M.S. Martin C.L. Ledbetter D.H. Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies.Am J Hum Genet. 2010; 86: 749-764Abstract Full Text Full Text PDF PubMed Scopus (2018) Google Scholar Early studies demonstrated variable diagnostic rates, depending on disease phenotypes. One study reported a diagnostic rate of 8% for patients with neurodevelopmental disorders, whereas another study on patients with intellectual disability and autism spectrum disorders had a diagnostic rate of 19% using CMA.6Battaglia A. Doccini V. Bernardini L. Novelli A. Loddo S. Capalbo A. Filippi T. Carey J.C. Confirmation of chromosomal microarray as a first-tier clinical diagnostic test for individuals with developmental delay, intellectual disability, autism spectrum disorders and dysmorphic features.Eur J Paediatr Neurol. 2013; 17: 589-599Abstract Full Text Full Text PDF PubMed Scopus (150) Google Scholar, 7Clark M.M. Stark Z. Farnaes L. Tan T.Y. White S.M. Dimmock D. Kingsmore S.F. Meta-analysis of the diagnostic and clinical utility of genome and exome sequencing and chromosomal microarray in children with suspected genetic diseases.NPJ Genom Med. 2018; 3: 16Crossref PubMed Scopus (309) Google Scholar, 8Stavropoulos D.J. Merico D. Jobling R. Bowdin S. Monfared N. Thiruvahindrapuram B. et al.Whole genome sequencing expands diagnostic utility and improves clinical management in pediatric medicine.NPJ Genom Med. 2016; 1: 15012Crossref PubMed Scopus (247) Google Scholar However, the scope of previous studies was narrow, and the effect of varying disease phenotypes on diagnostic yield using CMA has not been explored. Moreover, data surrounding diagnostic rates among different specialty clinics do not appear to have been systematically analyzed. This study summarizes our findings from 11 years of CMA clinical testing, and evaluates various factors that may have influenced the diagnostic yield. It further delineates the correlation of clinical phenotypic features and clinical provider specialties with various pathogenic CNV findings in our patient cohort. The research protocol was approved by the Institutional Review Board at Nationwide Children's Hospital (Columbus, OH; STUDY00001490). Clinical microarray results from Nationwide Children's Hospital CoPath (Sunquest Information Systems, Tucson, AZ) laboratory reporting software from January 1, 2010, to December 31, 2020, were selected for review. Age, indication(s) for study, pathogenic finding(s), and date of accession were collected. All proficiency testing and in-house competency testing were excluded from this study. Pediatric cases were defined as those aged ≤18 years at the time of testing. The ordering provider for each case was manually assigned to a medical specialty. Other was assigned for those with undetermined specialties. Chromosome bands corresponding to each pathogenic finding were extracted from the reports. Variants of unknown significance were not included in this study. Interpretation of CNV finding was based on literature search, database review, and our internally validated copy number variation databases. Before April 2013, microarray testing was performed using the NimbleGen 135K oligonucleotide array (Roche NimbleGen Inc., Pleasanton, CA). In April 2013, our laboratory switched to Agilent 180k CGH + SNP array (Agilent Technologies, Santa Clara, CA). All results were analyzed using Genoglyphix software (Perkin Elmer, Waltham, MA). CNV calls were made when five consecutive probes were flagged by Genoglyphix software. Indications for study were provided by the ordering clinician on the requisition form. If not provided, laboratory staff obtained the information by contacting the ordering provider/external hospital. For tests ordered on inpatients, genetics laboratory staff reviewed the Problem List (list of patient's current medical problems) in the patient's electronic medical records (Epic; Epic Systems, Verona, WI) and selected medical problems most relevant for microarray testing. Clinical indications were mapped to the Human Phenotype Ontology (HPO) using three Natural Language Processing tools: ClinPhen and two models available in PhenoTagger.9Deisseroth C.A. Birgmeier J. Bodle E.E. Kohler J.N. Matalon D.R. Nazarenko Y. Genetti C.A. Brownstein C.A. Schmitz-Abe K. Schoch K. Cope H. Signer R. Undiagnosed Diseases N. Martinez-Agosto J.A. Shashi V. Beggs A.H. Wheeler M.T. Bernstein J.A. Bejerano G. ClinPhen extracts and prioritizes patient phenotypes directly from medical records to expedite genetic disease diagnosis.Genet Med. 2019; 21: 1585-1593Abstract Full Text Full Text PDF PubMed Scopus (43) Google Scholar, 10Kohler S. Gargano M. Matentzoglu N. Carmody L.C. Lewis-Smith D. Vasilevsky N.A. et al.The human phenotype ontology in 2021.Nucleic Acids Res. 2021; 49: D1207-D1217Crossref PubMed Scopus (326) Google Scholar, 11Luo L. Yan S. Lai P.-T. Veltri D. Oler A. Xirasagar S. Ghosh R. Similuk M. Robinson P.N. Lu Z. PhenoTagger: a hybrid method for phenotype concept recognition using human phenotype ontology.Bioinformatics. 2021; 37: 1884-1890Crossref Scopus (6) Google Scholar ClinPhen detects HPO terms by comparing their names and synonyms with words and phrases contained in the input text provided by the user.9Deisseroth C.A. Birgmeier J. Bodle E.E. Kohler J.N. Matalon D.R. Nazarenko Y. Genetti C.A. Brownstein C.A. Schmitz-Abe K. Schoch K. Cope H. Signer R. Undiagnosed Diseases N. Martinez-Agosto J.A. Shashi V. Beggs A.H. Wheeler M.T. Bernstein J.A. Bejerano G. ClinPhen extracts and prioritizes patient phenotypes directly from medical records to expedite genetic disease diagnosis.Genet Med. 2019; 21: 1585-1593Abstract Full Text Full Text PDF PubMed Scopus (43) Google Scholar PhenoTagger combines a dictionary-based approach with a machine learning approach and allows users to substitute various machine learning models.11Luo L. Yan S. Lai P.-T. Veltri D. Oler A. Xirasagar S. Ghosh R. Similuk M. Robinson P.N. Lu Z. PhenoTagger: a hybrid method for phenotype concept recognition using human phenotype ontology.Bioinformatics. 2021; 37: 1884-1890Crossref Scopus (6) Google Scholar For this task, PhenoTagger was used with an included convolutional neural network model, as well as with the model BioBERT.12Lee J. Yoon W. Kim S. Kim D. Kim S. So C.H. Kang J. BioBERT: a pre-trained biomedical language representation model for biomedical text mining.Bioinformatics. 2020; 36: 1234-1240Crossref PubMed Scopus (1876) Google Scholar Given a clinical indication string as input, each of these tools outputs a list of HPO identifiers detected; in addition, PhenoTagger models include a confidence rating between 0 and 1 (a decision threshold of 0.8 was fixed for this study). The phenotypes present in each clinical indication were collected by recording any HPO terms detected by ClinPhen or by PhenoTagger with either machine learning model, and then the detected terms were replaced by any of the 23 classes that are immediate descendants of HP:0000118 Phenotypic Abnormality (eg, HP:0000707 Abnormality of the Nervous System and HP:0033127 Abnormality of the Musculoskeletal System). The chromosome bands were extracted from patients' clinical reports, and were used to indicate the pathogenic findings in this study. The genomic coordinates of each band across the entire hg19 reference genome (downloaded from University of California, Santa Cruz, Genome Browser) were used to determine the boundaries of each pathogenic finding (Supplemental Table S1). The frequency of the gains and losses for every region observed across the genome in the study cohort was calculated using dplyr 1.0.6 R package (https://dplyr.tidyverse.org). The frequencies in the study cohort were then plotted along with the karyotype using the circlize 0.4.12 R package (https://jokergoo.github.io/circlize_book/book).13Gu Z. Gu L. Eils R. Schlesner M. Brors B. Circlize implements and enhances circular visualization in R.Bioinformatics. 2014; 30: 2811-2812Crossref PubMed Scopus (1702) Google Scholar Bar graphs, box plots, and violin plots were generated using GraphPad Prism Version 9.0.0 for Windows (GraphPad Software, San Diego, CA). Bubble scatterplots were graphed using Plotly (Plotly Technologies Inc. Collaborative Data Science, Montreal, QC, Canada). Heat maps were generated using Microsoft Excel (Microsoft Corp., Redmond, WA). Weighted regression was used to calculate the trend in diagnostic when compared with year of testing and number of HPO categories present in patients (Figures 1C and 2F).Figure 2Cohort stratification by medical specialties and clinical presentations. A: The top eight specialties of which chromosomal microarray testing was ordered at ≥200 cases are represented in this bubble scatterplot. Diagnostic rate (y axis) is plotted against the number of cases (x axis; in log10 scale). Size of the bubbles represents the number of solved cases. Red dashed lines represent the medians. B: The top five Human Phenotype Ontology (HPO) categories present at ≥10% in our cohort are represented in this bubble scatterplot. Diagnostic rate (y axis) is plotted against the number of cases (x axis; in log10 scale). Size of the bubbles represents the number of solved cases. Red dashed lines represent the medians. C: Heat map illustrates the frequencies of HPO categories present for top eight specialties. Black boxed areas indicate >50%. Dark gray boxed areas indicate 10% to 50%. Light gray boxed areas indicate 2.5% to 9.99%. White boxed areas indicate <2.5%. D: The age distribution of each specialty is illustrated in this violin plot. The black lines illustrate the medians. E: The distribution of the number of HPO categories present in each age group is illustrated in this box plot. The box extends from 25th to 75th percentile, and the whiskers are drawn down to the 5th percentile and up to the 95th percentile. The points above and below the whiskers represent individual samples. F: Diagnostic rate is stratified on the basis of the number of HPO terms present in patients.View Large Image Figure ViewerDownload Hi-res image Download (PPT) Our patient cohort consisted of 14,541 pediatric cases (9359 male; 5173 female; 9 unknown), of whom 10.3% (n = 1501) were reported to have pathogenic CNV findings (Figure 1A and Table 1). Diagnostic rates were 12.3% (n = 634) and 9.2% (n = 864) in female and male patients, respectively (Figure 1A). Patients aged <1 year accounted for one-quarter of the study cohort [25.4% (n = 3689)], with 12.8% (n = 471) having an associated pathogenic finding by CMA (Figure 1B and Table 1). On average, 1321.9 cases were tested per year (range, 1046 to 1559 cases), with an upward trend for the diagnostic rate from 2010 to 2020 (weighted regression, R2 = 0.593; P = 0.006) (Figure 1C). Two different array testing platforms were utilized during the time frame of this study. Most cases [68.3% (n = 9936)] were profiled using the Agilent 180k CGH + SNP array platform, with the remainder of cases [31.7% (n = 4605)] assessed by the NimbleGen 135K oligonucleotide array platform (Figure 1D). The diagnostic rates are 9.3% (n = 427) and 10.8% (n = 1074) for NimbleGen and Agilent, respectively (Figure 1D).Table 1Baseline Demographics and Characteristics of the Patient CohortCharacteristicsPatients, n (%) (N = 14,541)Sex Male9359 (64) Female5173 (36) Undetermined9Age category, years 03689 (25) 1–34449 (31) 4–93905 (27) 10–182498 (17)Medical specialty (top 8) Genetics4467 Devevelopmental behavioral4095 Neurology1885 Neonatology1262 General pediatrics949 Psychiatry480 Cardiology239 Surgery224Clinical presentation category (top 5) Abnormality of the nervous system10,527 Abnormality of the musculoskeletal system3084 Abnormality of head or neck2192 Growth abnormality1640 Abnormality of the cardiovascular system1631 Open table in a new tab CMA testing was ordered by 957 clinical providers from 29 medical specialty clinics. Genetics and developmental behavioral pediatrics (referred to as developmental pediatrics hereafter) represented the top two ordering specialties, and accounted for 30.7% (4467/14,541) and 28.2% (4095/14,541), respectively, with the top eight specialties collectively representing 93.5% (13,601/14,541) of the cohort (Table 1; Figure 2A provides the top eight specialties with at least 200 cases; Supplemental Figure S1 provides all specialties). When comparing the diagnostic rates of the top eight most frequent specialties, surgery and genetics had the highest at 14.7% (33/224) and 13.8% (618/4467), respectively, whereas developmental pediatrics had the lowest at 6.2% (253/4095) (Figure 2A). The variety and distribution of clinical indications were also examined for microarray testing. The top five testing indications listed in at least 10% of our cohort included abnormalities involving the nervous system, musculoskeletal system, head and neck, growth, and cardiovascular system, which represented 72.4% (10,527/14,541), 21.2% (3084/14,541), 15.1% (2192/14,541), 11.3% (1640/14,541), and 11.2% (1631/14,541) of cases, respectively (Figure 2B). The remainder of the abnormality categories was noted in <10% of patients (Supplemental Figure S2). Among patients with the top five abnormalities, our data demonstrated that abnormalities involving the musculoskeletal system demonstrated the highest diagnostic rate of 14.8% (457/3084), whereas those with nervous system indications had the lowest diagnostic rate at 9.2% (972/10,527) (Figure 2B). To systematically assess the clinical presentations for patients sent for microarray testing, the HPO categories were quantified from each of the eight specialties that included ≥200 cases. Patients from neonatology exhibited the most syndromic clinical features, with 16 HPO categories present at >2.5% of neonatology patients, of which 11 were present at >10% (Figure 2C and Supplemental Table S2). In contrast, patients from developmental pediatrics and neurology only exhibited three to four categories at a frequency of ≥2.5%, respectively. Among the HPO categories, abnormality of the nervous system was noted in all nine specialties, with five (developmental pediatrics, neurology, psychiatry, general pediatrics, and genetics) being >50% of their respective patients (Figure 2C and Supplemental Table S2). For patients from cardiology and surgery, abnormalities of the cardiovascular system were present in 97.1% and 77.2%, respectively (Figure 2C and Supplemental Table S2). When assessing the age distribution to each of the top nine specialties, neonatology, cardiology, and surgery patients were most heavily represented at the age of <1 year (Figure 2D and Supplemental Table S3). For genetics and neurology, 50% of patients were aged <3 years (Figure 2D). Most developmental pediatrics patients were between the ages of 2 and 5 years. In contrast, the age distribution for psychiatry patients was enriched in the adolescent population (Figure 2D). The number of HPO categories represented within our cohort was further assessed. Although there were 23 categories under phenotypic abnormalities, the most HPO categories attributed to any one patient was nine (n = 4). A total of 8855 patients in our cohort had only one HPO category present. When correlating the number of HPO categories with age groups, those aged <1 year have clinical indications that map to more HPO categories than older patients (Figure 2E). Notably, the diagnostic rate trended upward with increasing numbers of HPO categories (weighted regression, R2 = 0.0.921; P = 1.08 × 10–5); cases with only one HPO category demonstrated a diagnostic rate of 7.8%, whereas cases with eight or nine categories had diagnostic rates of 57.1% and 25%, respectively (Figure 2F). Next, the chromosome bands of the pathogenic findings were used from the clinical reports of our cohort to examine the CNVs reported as deletions and duplications. Among all pathogenic CNVs in our cohort, the most frequent findings were 22q11.21 deletion (7.1%), 15q11.2 deletion (5.2%), 16p11.2 deletion (4.4%), 16p11.2 duplication (3.8%), 22q11.21 duplication (2.8%), and 16p13.11 duplication (2.8%), which cumulatively accounted for 26% of all findings (Figure 3A). Deletions were more common compared with duplications (1004 versus 621). When delineated by specialty, 22q11.2 deletion syndrome was the most frequent finding for genetics (6.0%), neonatology (12.9%), cardiology (36%), and surgery (25.7%) (Figure 3B). The 15q11.2 deletion syndrome was the most frequent finding for neurology (8%) and the second most frequent finding for genetics (5.3%). The 16p11.2 duplication and 16p11.2 deletion were the most frequent findings (both at 5.9%) for developmental pediatrics patients, followed by 16p12.2 deletion (5.5%), 15q11.2 deletion (5.2%), and 16p13.11 duplication (4.8%). When stratifying the pathogenic finding based on the top five most frequently noted clinical features, 15q11.2 deletion was the most frequent for abnormalities of the nervous system. The 22q11.21 deletion constituted the most frequent finding for those patients with abnormalities of musculoskeletal system, cardiovascular system, and head/neck. The 16p11.2 deletion was the most frequent finding among patients with growth abnormalities. Given that 22q11.2 deletion was the most common finding in our cohort, the characteristics of patients who exhibited pathogenic deletions involving 22q11.2 were further studied. Of 115 patients having a 22q11.2 deletion, 47.0% (54/115) were aged <1 year, 22.0% (25/114) were aged between 1 and 3 years, 15.8% (18/114) were aged 4 to 9 years, and 15.8% (18/114) were aged 10 to 18 years (Figure 4A). When comparing the clinical features across the age groups, patients aged <1 year had the most HPO categories present (Figure 4B and Supplemental Table S4). The frequency of commonly reported clinical features in our cohort was comparable to those referenced in the Campbell et al14Campbell I.M. Sheppard S.E. Crowley T.B. McGinn D.E. Bailey A. McGinn M.J. Unolt M. Homans J.F. Chen E.Y. Salmons H.I. Gaynor J.W. Goldmuntz E. Jackson O.A. Katz L.E. Mascarenhas M.R. Deeney V.F.X. Castelein R.M. Zur K.B. Elden L. Kallish S. Kolon T.F. Hopkins S.E. Chadehumbe M.A. Lambert M.P. Forbes B.J. Moldenhauer J.S. Schindewolf E.M. Solot C.B. Moss E.M. Gur R.E. Sullivan K.E. Emanuel B.S. Zackai E.H. McDonald-McGinn D.M. What is new with 22q? An update from the 22q and you center at the Children's Hospital of Philadelphia.Am J Med Genet A. 2018; 176: 2058-2069Crossref PubMed Scopus (77) Google Scholar 2018 study (Figure 4C). Notably, abnormalities of the nervous system were present in 80%, 66.7%, and 77.8% of the older age groups, respectively, whereas they were only present at 7.4% in the <1-year age group (Figure 4B and Supplemental Table S4). Conversely, 72.2% of the patients aged <1 year had abnormalities of the cardiovascular system, whereas only 11.1% to 16% for other age groups had cardiovascular abnormalities (Figure 4B and Supplemental Table S4). As deletions of the 22q11.2 region comprised the top findings for genetics, neonatology, cardiology, and surgery, the differences in the landscape of clinical features were further assessed across specialties. Patients presenting to genetics (n = 40) and neonatology (n = 20) demonstrated a wider spectrum of clinical features, compared with cardiology (n = 9) and surgery (n = 9) (Figure 4D and Supplemental Table S5). For the past decade, CMA testing has become deeply integrated into the diagnostic workup, and is frequently ordered for pediatric patients with suspected genetic disorders. Its prevalence as a genetic test can be attributed to the more refined resolution of CNV detection when compared with chromosome analysis. From 2010 to 2020, our institution performed CMA on >14,000 pediatric patients. Given the breadth of our study cohort, which reflects a wide spectrum of age, sex, clinical features, and specialty clinics, the diagnostic rates and pathogenic findings could be further stratified against patient characteristics. As molecular-based genetic testing continues to evolve, particularly in the context of next-generation sequencing (NGS)–based assays, this study reiterates the critical role of CNV resolution as a part of the diagnostic odyssey, and reveals multiple determinants that influence the diagnostic utility of CNV detection. As a reference testing laboratory for our tertiary-care hospital and surrounding community hospitals and clinics, we provide genetic testing to a wide array of patients through ordering providers. By categorizing each provider into a medical specialty, the data could be further stratified and the origin of the ordering provider determined. Although genetics represented the most frequent ordering specialty (30%), a significant number of CMAs was ordered from a range of other clinics, spanning 28 different specialties (Supplemental Figure S1). Because CMA is recommended as the first-tier test for patients with neurodevelopmental disorders and multiple congenital anomalies, it is reasonable to assume that the ordering of CMA frequently marks the beginning of a genetic diagnostic workup.3Miller D.T. Adam M.P. Aradhya S. Biesecker L.G. Brothman A.R. Carter N.P. Church D.M. Crolla J.A. Eichler E.E. Epstein C.J. Faucett W.A. Feuk L. Friedman J.M. Hamosh A. Jackson L. Kaminsky E.B. Kok K. Krantz I.D. Kuhn R.M. Lee C. Ostell J.M. Rosenberg C. Scherer S.W. Spinner N.B. Stavropoulos D.J. Tepperberg J.H. Thorland E.C. Vermeesch J.R. Waggoner D.J. Watson M.S. Martin C.L. Ledbetter D.H. Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies.Am J Hum Genet. 2010; 86: 749-764Abstract Full Text Full Text PDF PubMed Scopus (2018) Google Scholar This study quantifies the extent to which the diagnostic path may not necessarily begin in the genetics clinic, but rather may involve that of many diverse specialties that have incorporated CMA as a frequent tool in diagnostic evaluations. Furthermore, this suggests that nongenetic specialties have gained proficiency in CMA ordering and a level of familiarity in understanding the return of genetic data reported from CMA. Our study also revealed that the diagnostic utility of CMA differs among medical specialties. For the top eight specialties with ≥200 cases in our cohort, the diagnostic rate ranges from 6.2% to 14.7%, with surgery and genetics having the highest diagnostic rate, and developmental pediatrics having the lowest (Figure 2A). The low diagnostic yield in developmental pediatrics may" @default.
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• W4283025587 title "A Decade's Experience in Pediatric Chromosomal Microarray Reveals Distinct Characteristics Across Ordering Specialties" @default.
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