ICD-11 extension codes support detailed clinical abstraction and comprehensive classification

Drösler, Saskia E.; Weber, Stefanie; Chute, Christopher G.

doi:10.1186/s12911-021-01635-2

Cited by 24 publications

(21 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Since ICD-10 has only one code, D35.2, for describing PA, we expanded this code into several other clinical modified codes and terms, i.e., D35.202 for nonfunctioning pituitary adenoma and D35.203 for recurrent pituitary adenoma. The ICD-11 system, released in May 2018, better supports detailed clinical abstraction and comprehensive classification and better adapts to the needs of the physician in the era of mega data than the previous version [ 30 ]. The subtypes of PAs according to ICD-11 include non-secreting pituitary adenoma (2F37.0) and other secretary adenomas (2F37.Y), which more completely include detailed information on the PA, such as the endocrine status and recurrence, via postcoordination.…”

Section: Discussionmentioning

confidence: 99%

Moving toward a standardized diagnostic statement of pituitary adenoma using an information extraction model: a real-world study based on electronic medical records

Zhou

Guo

Duan

et al. 2022

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Purpose Diagnostic statements for pituitary adenomas (PAs) are complex and unstandardized. We aimed to determine the most commonly used elements contained in the statements and their combination patterns and variations in real-world clinical practice, with the ultimate goal of promoting standardized diagnostic recording and establishing an efficient element extraction process. Methods Patient medical records from 2012 to 2020 that included PA among the first three diagnoses were included. After manually labeling the elements in the diagnostic texts, we obtained element types and training sets, according to which an information extraction model was constructed based on the word segmentation model “Jieba” to extract information contained in the remaining diagnostic texts. Results A total of 576 different diagnostic statements from 4010 texts of 3770 medical records were enrolled in the analysis. The first ten diagnostic elements related to PA were histopathology, tumor location, endocrine status, tumor size, invasiveness, recurrence, diagnostic confirmation, Knosp grade, residual tumor, and refractoriness. The automated extraction model achieved F1-scores that reached 100% for all ten elements in the second round and 97.3–100.0% in the test set consisting of an additional 532 diagnostic texts. Tumor location, endocrine status, histopathology, and tumor size were the most commonly used elements, and diagnoses composed of the above elements were the most frequent. Endocrine status had the greatest expression variability, followed by Knosp grade. Among all the terms, the percentage of loss of tumor size was among the highest (21%). Among statements where the principal diagnoses were PAs, 18.6% did not have information on tumor size, while for those with other diagnoses, this percentage rose to 48% (P < 0.001). Conclusion Standardization of the diagnostic statement for PAs is unsatisfactory in real-world clinical practice. This study could help standardize a structured pattern for PA diagnosis and establish a foundation for research-friendly, high-quality clinical information extraction.

show abstract

Section: Discussionmentioning

confidence: 99%

Moving toward a standardized diagnostic statement of pituitary adenoma using an information extraction model: a real-world study based on electronic medical records

Zhou

Guo

Duan

et al. 2022

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…The target ICD-11 list was composed by a subset of the Extension Codes (9), namely the “Anatomical Detail” branch, taken from the Foundation layer. It was not extracted in advance, but dynamically obtained through the ICD-11 API (10) when needed, as explained in the next paragraphs.…”

Section: Methodsmentioning

confidence: 99%

Harmonization of ICF Body Structures and ICD-11 Anatomic Detail: one foundation for two classifications

Mea

Almborg

Martinuzzi

et al. 2023

Preprint

View full text Add to dashboard Cite

The Family of International Classifications of the World Health Organization currently includes three reference classifications, namely International Classification of Diseases (ICD), International Classification of Functioning, Disability, and Health (ICF), and International Classification of Health Interventions (ICHI). Each of them serves a specific classification need. However, they share some common concepts that are present, in different forms, in two or all of them. One important set of shared concepts is the representation of human anatomy entities, which are not always modeled in the same way and with the same level of detail. To understand the relationships among the three anatomical representations, an effort is needed to compare them, identifying common areas, gaps, and compatible and incompatible modeling. The work presented here contributes to this effort, focusing on the anatomy representations in ICF and ICD-11. For this aim, three experts were asked to identify, for each entity in the ICF Body Structures, one or more entities in the ICD-11 Anatomic Detail that could be considered identical, broader or narrower. To do this, they used a specifically developed web application, which also automatically identified the most obvious equivalences. A total of 631 maps were independently identified by the three mappers for 218 ICF Body Structures, with an interobserver agreement of 93.5%. Together with 113 maps identified by the software, they were then consolidated into 434 relations. The results highlight some differences between the two classifications: in general, ICF is less detailed than ICD-11; ICF favors lumping of structures; in very few cases, the two classifications follow different anatomic models. For these issues, solutions have to be found that are compliant with the WHO approach to classification modeling and maintenance.

show abstract

“…The addition of Chapter X, called ‘Extension Codes’, which contains multidimensional information on concepts including laterality, anatomy, histopathology, stage and grade details, supports detailed clinical abstraction and allows for multiaxial coding. Through postcoordination, extension codes have the potential to improve coding granularity [ 6 ], which has been proven by many studies [ 7 – 9 ]. In addition, its flexibility should not be ignored [ 10 ].…”

Section: Coding Frameworkmentioning

confidence: 99%

The novel and powerful ICD-11 classification system for neoplasm coding: a comparative study with the ICD-O

Zhou

Wang

2022

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background The International Classification of Diseases (ICD) and the International Classification of Diseases for Oncology (ICD-O) are both widely used global classification systems. In 2018, the initial release of the ICD-11 was published by the World Health Organization (WHO), integrating the morphology section of the ICD-O. Methods This paper aims to provide potential ICD-11 users with a profound understanding of the neoplasm classifications of the ICD-11 by analysing the differences and relationships between the ICD-11 and ICD-O in terms of the coding framework, compatibility and intelligence level. Results The ICD-11 and ICD-O have remarkable differences in coding structure. Compared to the ICD-O, the ICD-11 has the following advantages: adding histopathology to the stem codes, obtaining a meaningful minimum amount of information through stem codes for statistics, supporting the usage of ICD-O morphology categories and capturing clinical details via extension codes for multiaxial coding. In addition, the rich Foundation Component, linearization derived from the Foundation Component and updating mechanism all support the compatibility of the ICD-11 with other classification systems. Notably, the WHO provides terminology coding with a smart coding tool, and coding in the ICD-11 can draw on statistical codes and uniform resource identifiers (URIs) simultaneously. Conclusions The ICD-11 represents a novel classification system with distinguishing features that include facilitating statistics, multiaxial coding, coding granularity, compatibility and intelligence. These features enable the ICD-11 to be more powerful for neoplasm coding than the ICD-O and basically meet the needs of stakeholders.

show abstract

ICD-11 extension codes support detailed clinical abstraction and comprehensive classification

Cited by 24 publications

References 8 publications

Moving toward a standardized diagnostic statement of pituitary adenoma using an information extraction model: a real-world study based on electronic medical records

Moving toward a standardized diagnostic statement of pituitary adenoma using an information extraction model: a real-world study based on electronic medical records

Harmonization of ICF Body Structures and ICD-11 Anatomic Detail: one foundation for two classifications

The novel and powerful ICD-11 classification system for neoplasm coding: a comparative study with the ICD-O

Contact Info

Product

Resources

About