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IRIS
A collaboration among 157 newborn screening programs in 47 countries has lead to the
creation of a database of 705,333 discrete analyte concentrations from 11,462 cases affected with
57 metabolic disorders, and from 631 heterozygotes for 12 conditions. This evidence was first
applied to establish disease ranges for amino acids and acylcarnitines, and clinically validate 114
cutoff target ranges.
Objective: To improve quality and performance with an evidence-based approach, multivariate
pattern recognition software has been developed to aid in the interpretation of complex analyte
profiles. The software generates tools that convert multiple clinically significant results into a
single numerical score based on overlap between normal and disease ranges, penetration within
the disease range, differences between specific conditions, and weighted correction factors.
Design: Eighty-five on-line tools target either a single condition or the differential diagnosis
between two or more conditions. Scores are expressed as a numerical value and as the percentile
rank among all cases with the condition chosen as primary target, and are compared to
interpretation guidelines. Tools are updated automatically after any new data submission (2009-
2011: 5.2 new cases added per day on average).
Main outcome measures: Retrospective evaluation of past cases suggest that these tools could
have avoided at least half of 277 false positive outcomes caused by carrier status for fatty acid
oxidation disorders, and could have prevented 88% of false negative events caused by cutoff
7
values set inappropriately. In Minnesota, their prospective application has been a major
contributing factor to the sustained achievement of a false positive rate below 0.1% and a
positive predictive value above 60%.
Conclusions: Application of this computational approach to raw data could make cutoff values
for single analytes effectively obsolete. This paradigm is not limited to newborn screening and is
applicable to the interpretation of diverse multi-analyte profiles utilized in laboratory medicine.
Abstract word
Enhanced interpretation of newborn screening results without analyte cutoff values
Gregg Marquardt 1;Robert Currier;PhD 2;David M. S. McHugh 1;Dimitar Gavrilov;MD;PhD 1;Mark J. Magera 1;Dietrich Matern;MD 1;Devin Oglesbee;PhD 1;Kimiyo Raymond;MD 1;Piero Rinaldo;MD;PhD 1;Emily H. Smith;PhD 1;Silvia Tortorelli;MD;PhD 1;Coleman T. Turgeon 1;Fred Lorey;PhD 2;Bridget Wilcken;MD 3;Veronica Wiley;PhD 3;Lawrence C. Greed;BSc 4;Barry Lewis;MD 4;François Boemer;PharmD PhD 5;Roland Schoos;PhD 5;Sandrine Marie;PhD 6;Marie Françoise Vincent;MD;PhD 6;Yuri Cleverthon Sica;Msc 7;Mouseline Torquado Domingos 7;Khalid Al Thihli;MD 8;Graham Sinclair;PhD 8;Osama Y. Al Dirbashi;PhD 9;Pranesh Chakraborty;MD 9;Mark Dymerski 10;Cory Porter 10;Adrienne Manning;11;Margretta R. Seashore;MD 12;Jonessy Quesada;MD 13;Alejandra Reuben 13;Petr Chrastina;MSc 14;Petr Hornik;PhD 14;Iman Atef Mandour;MD 15;Sahar Abdel Atty Sharaf;MD 15;Olaf Bodamer;MD;PhD 16;Bonifacio Dy;MD 17;Jasmin Torres 17;Roberto Zori;MD 18;David Cheillan;PhD 19;Christine Vianey Saban;PhD 19;David Ludvigson 20;Adrya Stembridge 21;Jim Bonham;PhD 22;Melanie Downing;Msc 22;Yannis Dotsikas;PhD 23;Yannis L. Loukas;PhD 23;Vagelis Papakonstantinou;PhD 24;Georgios S. A. Zacharioudakis;PhD 24;Ákos Baráth;PhD 25;Eszter Karg;MD;PhD 25;Leifur Franzson;PhD 26;Jon J. Jonsson;MD;PhD 26;Nancy N. Breen 27;Barbara G. Lesko 27;Stanton L. Berberich;PhD 28;Kimberley Turner;RN 29;RUOPPOLO, MARGHERITA;MD 30;Emanuela Scolamiero 30;Italo Antonozzi;MD 31;Claudia Carducci;MS 31;Ubaldo Caruso 32;Michela Cassanello 32;Giancarlo la Marca;Pharm Sc 33;Elisabetta Pasquini;MD 34;Iole Maria Di Gangi;PhD 35;Giuseppe Giordano;PhD 35;Marta Camilot;PhD 36;Francesca Teofoli 36;Shawn M. Manos;BS 37;Colleen K. Peterson;BS 37;Stephanie K. Mayfield Gibson;MD 38;Darrin W. Sevier 38;Soo Youn Lee;MD;PhD 39;Hyung2 Doo Park;MD;PhD 39;Issam Khneisser;MS 40;Phaidra Browning 41;Fizza Gulamali Majid;PhD 42;Michael S. Watson;PhD 43;Roger B. Eaton;PhD 44;Inderneel Sahai;MD 44;Consuelo Ruiz 45;Rosario Torres 45;Mary A. Seeterlin;PhD 46;Eleanor L. Stanley 46;Amy Hietala 47;Mark McCann 47;Carlene Campbell 48;Patrick V. Hopkins 48;Monique G. de Sain Van der Velden;PhD 49;Bert Elvers 50;Mark A. Morrissey;PhD 51;Sherlykutty Sunny 51;Detlef Knoll;MSc 52;Dianne Webster;PhD 52;Dianne M. Frazier;PhD 53;Julie D. McClure;MPH 53;David E. Sesser 54;Sharon A. Willis 54;Hugo Rocha;Msc 55;Laura Vilarinho;PhD 55;Catharine John;PhD 56;James Lim;PhD 56;S. Graham Caldwell 57;Kathy Tomashitis;MNS 57;Daisy E. Castiñeiras Ramos 58;Jose Angel Cocho de Juan;PhD 58;Inmaculada Rueda Fernández;MD 59;Raquel Yahyaoui Macías;MD 59;José María Egea Mellado 60;Inmaculada González Gallego;PhD 60;Carmen Delgado Pecellin;PhD 61;Maria Sierra García Valdecasas Bermejo;PhD 61;Yin Hsiu Chien;MD;PhD 62;Wuh Liang Hwu;MD;PhD 62;Thomas Childs;MT 63;Christine D. McKeever 63;Tijen Tanyalcin;MD;PhD 64;Mahera Abdulrahman;MD;PhD 65;Cecilia Queijo;PhD 66;Aída Lemes;MD 66;Tim Davis 67;William Hoffman 67;Mei Baker;MD 68;Gary L. Hoffman 6.8.
2012
Abstract
A collaboration among 157 newborn screening programs in 47 countries has lead to the
creation of a database of 705,333 discrete analyte concentrations from 11,462 cases affected with
57 metabolic disorders, and from 631 heterozygotes for 12 conditions. This evidence was first
applied to establish disease ranges for amino acids and acylcarnitines, and clinically validate 114
cutoff target ranges.
Objective: To improve quality and performance with an evidence-based approach, multivariate
pattern recognition software has been developed to aid in the interpretation of complex analyte
profiles. The software generates tools that convert multiple clinically significant results into a
single numerical score based on overlap between normal and disease ranges, penetration within
the disease range, differences between specific conditions, and weighted correction factors.
Design: Eighty-five on-line tools target either a single condition or the differential diagnosis
between two or more conditions. Scores are expressed as a numerical value and as the percentile
rank among all cases with the condition chosen as primary target, and are compared to
interpretation guidelines. Tools are updated automatically after any new data submission (2009-
2011: 5.2 new cases added per day on average).
Main outcome measures: Retrospective evaluation of past cases suggest that these tools could
have avoided at least half of 277 false positive outcomes caused by carrier status for fatty acid
oxidation disorders, and could have prevented 88% of false negative events caused by cutoff
7
values set inappropriately. In Minnesota, their prospective application has been a major
contributing factor to the sustained achievement of a false positive rate below 0.1% and a
positive predictive value above 60%.
Conclusions: Application of this computational approach to raw data could make cutoff values
for single analytes effectively obsolete. This paradigm is not limited to newborn screening and is
applicable to the interpretation of diverse multi-analyte profiles utilized in laboratory medicine.
Abstract word
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/464388
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simulazione ASN
Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2023-2025 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
La presente simulazione è stata realizzata sulla base delle specifiche raccolte sul tavolo ER del Focus Group IRIS coordinato dall’Università di Modena e Reggio Emilia e delle regole riportate nel DM 589/2018 e allegata Tabella A. Cineca, l’Università di Modena e Reggio Emilia e il Focus Group IRIS non si assumono alcuna responsabilità in merito all’uso che il diretto interessato o terzi faranno della simulazione. Si specifica inoltre che la simulazione contiene calcoli effettuati con dati e algoritmi di pubblico dominio e deve quindi essere considerata come un mero ausilio al calcolo svolgibile manualmente o con strumenti equivalenti.