Implications of mappings between International Classification of Diseases clinical diagnosis codes and Human Phenotype Ontology terms
Authors: Tan Amelia L M , Gonçalves Rafael S , Yuan William , Brat Gabriel A , Gentleman Robert , Kohane Isaac S ,   The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), Masino Aaron J , Makoudjou Adeline , Albayrak Adem , Gutiérrez-Sacristán Alba , Zambelli Alberto , Malovini Alberto , Carmona Aldo , Hoffmann Alexander , Gramfort Alexandre , Geva Alon , Blanco-Martínez Alvar , Tan Amelia L M , Terriza-Torres Ana I , Spiridou Anastasia , Prunotto Andrea , South Andrew M , Vallejos Andrew K , Atz Andrew , Burgun Anita , Alloni Anna , Cattelan Anna Maria , Jannot Anne Sophie , Neuraz Antoine , Bellasi Antonio , Maram Anupama , Dagliati Arianna , Sandrin Arnaud , Serret-Larmande Arnaud , Mensch Arthur , Pfaff Ashley C , Batugo Ashley , Krishnamurthy Ashok K , Adam Atif , Dionne Audrey , Devkota Batsal , Moal Bertrand , He Bing , Beaulieu-Jones Brendin R , Beaulieu-Jones Brett K , Ostasiewski Brian D , Aronow Bruce J , Tan Bryce W Q , Tan Byorn W L , Torti Carlo , Sáez Carlos , Neto Carlos Tadeu Breda , Sonday Charles , Caucheteux Charlotte , Mao Chengsheng , Zucco Chiara , Daniel Christel , Haverkamp Christian , Hong Chuan , Bonzel Clara-Lea , Moraleda Cinta , Leprovost Damien , Key Daniel A , Zöller Daniela , Pillion Danielle , Mowery Danielle L , Amendola Danilo F , Henderson Darren W , Hanauer David A , Taylor Deanne M , Wassermann Demian , Hazard Derek Y , Kraska Detlef , Mazzotti Diego R , Silvio Domenick , Bell Douglas S , Murad Douglas A , Salamanca Elisa , Bucholz Emily , Getzen Emily J , Pfaff Emily R , Schriver Emily R , Toh Emma M S , Parimbelli Enea , Trecarichi Enrico M , Ashraf Fatima , Vidorreta Fernando J Sanz , Bourgeois Florence T , Sperotto Francesca , Angoulvant François , Brat Gabriel A , Varoquaux Gael , Omenn Gilbert S , Agapito Giuseppe , Albi Giuseppe , Weber Griffin M , Verdy Guillaume , Lemaitre Guillaume , Roig-Domínguez Gustavo , Prokosch Hans U , Zhang Harrison G , Estiri Hossein , Krantz Ian D , Kohane Isaac S , Honerlaw Jacqueline P , Cruz-Rojo Jaime , Norman James B , Balshi James , Cimino James J , Aaron James R , Santos Janaina C C , Newburger Jane W , Zahner Janet J , Moore Jason H , Marwaha Jayson S , Craig Jean B , Klann Jeffrey G , Morris Jeffrey S , Obeid Jihad , Vie Jill-Jênn , Chen Jin , Son Jiyeon , Zachariasse Joany M , Booth John , Holmes John H , Bernal-Sobrino José Luis , Cruz-Bermúdez Juan Luis , Leblanc Judith , Schuettler Juergen , Dubiel Julien , Champ Julien , Olson Karen L , Moshal Karyn L , Kernan Kate F , Kirchoff Katie , Wagholikar Kavishwar B , Ngiam Kee Yuan , Cho Kelly , Mandl Kenneth D , Huling Kenneth M , Chen Krista Y , Lynch Kristine E , Sanchez-Pinto L Nelson , Garmire Lana X , Han Larry , Patel Lav P , Waitman Lemuel R , Lenert Leslie , Anthony Li L L J , Esteve Loic , Chiudinelli Lorenzo , Chiovato Luca , Scudeller Luigia , Samayamuthu Malarkodi Jebathilagam , Martins Marcelo R , Minicucci Marcos F , Menezes Maria Clara Saad , Vella Margaret E , Mazzitelli Maria , Savino Maria , Milano Marianna , Okoshi Marina P , Cannataro Mario , Alessiani Mario , Keller Mark S , Hilka Martin , Wolkewitz Martin , Boeker Martin , Raskin Maryna , Bucalo Mauro , Hutch Meghan R , Bernaux Mélodie , Beraghi Michele , Morris Michele , Vitacca Michele , Pedrera-Jiménez Miguel , Daniar Mohamad , Shah Mohsin A , Liu Molei , Maripuri Monika , Kainth Mundeep K , Yehya Nadir , Santhanam Nandhini , Palmer Nathan P , Loh Ne Hooi Will , Sebire Neil J , Romero-Garcia Nekane , Brown Nicholas W , Paris Nicolas , Griffon Nicolas , Gehlenborg Nils , Orlova Nina , García-Barrio Noelia , Grisel Olivier , Rojo Pablo , Serrano-Balazote Pablo , Sacchi Paolo , Tippmann Patric , Martel Patricia , Serre Patricia , Avillach Paul , Azevedo Paula S , Rubio-Mayo Paula , Schubert Petra , Guzzi Pietro H , Sliz Piotr , Das Priyam , Long Qi , Ramoni Rachel B , Goh Rachel S J , Badenes Rafael , Bruno Raffaele , Kavuluru Ramakanth , Bellazzi Riccardo , Issitt Richard W , Follett Robert W , Bradford Robert L , Prudente Robson A , Bey Romain , Griffier Romain , Duan Rui , Mahmood Sadiqa , Mousavi Sajad , Lozano-Zahonero Sara , Pizzimenti Sara , Maidlow Sarah E , Wong Scott , DuVall Scott L , Cossin Sébastien , L'Yi Sehi , Murphy Shawn N , Fan Shirley , Visweswaran Shyam , Rieg Siegbert , Bosari Silvano , Makwana Simran , Bréant Stéphane , Bhatnagar Surbhi , Tanni Suzana E , Cormont Sylvie , Ahooyi Taha Mohseni , Priya Tanu , Naughton Thomas P , Ganslandt Thomas , Colicchio Tiago K , Cai Tianxi , Gradinger Tobias , González Tomás González , Zuccaro Valentina , Tibollo Valentina , Jouhet Vianney , Quirós-González Víctor , Panickan Vidul Ayakulangara , Benoit Vincent , Njoroge Wanjiku F M , Bryant William A , Yuan William , Xiong Xin , Wang Xuan , Ye Ye , Luo Yuan , Ho Yuk-Lam , Strasser Zachary H , Abad Zahra Shakeri Hossein , Xia Zongqi , Kate Kernan F , Hernández-Arango Alejandro , Schwamm Eli L 
Published: November 2024

Objective

Integrating electronic health record (EHR) data with other resources is essential in rare disease research due to low disease prevalence. Such integration is dependent on the alignment of ontologies used for data annotation. The international classification of diseases (ICD) is used to annotate clinical diagnoses, while the human phenotype ontology (HPO) is used to annotate phenotypes. Although these ontologies overlap in the biomedical entities they describe, the extent to which they are interoperable is unknown. We investigate how well aligned these ontologies are and whether such alignments facilitate EHR data integration.

Materials and Methods

We conducted an empirical analysis of the coverage of mappings between ICD and HPO. We interpret this mapping coverage as a proxy for how easily clinical data can be integrated with research ontologies such as HPO. We quantify how exhaustively ICD codes are mapped to HPO by analyzing mappings in the unified medical language system (UMLS) Metathesaurus. We analyze the proportion of ICD codes mapped to HPO within a real-world EHR dataset.

Results and Discussion

Our analysis revealed that only 2.2% of ICD codes have direct mappings to HPO in UMLS. Within our EHR dataset, less than 50% of ICD codes have mappings to HPO terms. ICD codes that are used frequently in EHR data tend to have mappings to HPO; ICD codes that represent rarer medical conditions are seldom mapped.

Conclusion

We find that interoperability between ICD and HPO via UMLS is limited. While other mapping sources could be incorporated, there are no established conventions for what resources should be used to complement UMLS.