Knowledge bases and software support for variant interpretation in precision oncology.

Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.

Risk Factors for Re-treatment After Keratorefractive Lenticule Extraction (KLEx) for Myopia and Myopic Astigmatism.

PURPOSE: To identify potential risk factors that increase the likelihood of re-treatment following keratorefractive lenticule extraction (KLEx) for myopia and myopic astigmatism. METHODS: This was a retrospective study of patients with myopia and myopic astigmatism who underwent KLEx using the VisuMax 500 laser (Carl Zeiss Meditec) between April 2015 and December 2020. Patients were assigned to one of two groups: the control group and the re-treatment group (if they had additional refractive surgery within 2 years of the primary treatment). The effect of different preoperative, intraoperative, and postoperative parameters on the re-treatment rate was analyzed. RESULTS: Overall 1,822 eyes of 938 patients were analyzed. In total, 2.96% of eyes (n = 54) underwent re-treatment. The re-treated patients were more likely to be women and have high myopia, high astigmatism, steep corneas, higher ocular residual astigmatism, and residual myopic and/or astigmatic refractive error. In contrast, no significant correlation was found between re-treatment rate and age, chord µ, type of astigmatism, and corneal thickness. CONCLUSIONS: Factors associated with higher rates of retreatment after KLEx included female gender, manifest refractive high myopia (> -5.00 diopters [D]), astigmatism (> 2.00 D), spherical equivalent (> 6.00 D), ocular residual astigmatism, steeper corneas, and postoperative residual myopic and astigmatic refractive errors. This study may help to preoperatively detect patients at risk for re-treatment, improve preoperative patient counseling, and optimize patient selection to reduce future re-treatment rates. [J Refract Surg. 2024;40(6):e362-e370.].

Breaking Barriers for Interoperability: A Reference Implementation of CSV-FHIR Transformation Using Open-Source Tools.

FHIR is a widely accepted interoperability standard for exchanging medical data, but data transformation from the primary health information systems into FHIR is usually challenging and requires advanced technical skills and infrastructure. There is a critical need for low-cost solutions, and using Mirth Connect as an open-source tool provides this opportunity. We developed a reference implementation to transform data from CSV (the most common data format) into FHIR resources using Mirth Connect without any advanced technical resources or programming skills. This reference implementation is tested successfully for both quality and performance, and it enables reproducing and improving the implemented approach by healthcare providers to transform raw data into FHIR resources. For ensuring replicability, the used channel, mapping, and templates are available publicly on GitHub (https://github.com/alkarkoukly/CSV-FHIR-Transformer).

An OpenEHR Template with the Integrated German LOINC Terms.

An OpenEHR template based on LOINC terms in German language (LOINC-DE) has been created for the structured clinical data capture. The resulting template includes all terms available in LOINC-DE, which can be selected from the drop-down menu for clinical data capture. The template can be used as an independent laboratory form or it can be customized for local needs. This approach presents the possibility to include terminologies in EHR when capturing patient data.

An openEHR Virtual Patient Template for Pancreatic Cancer.

openEHR is an open-source technology for e-health, aims to build data models for interoperable Electronic Health Records (EHRs) and to enhance semantic interoperability. openEHR architecture consists of different building blocks, among them is the "template" which consists of different archetypes and aims to collect the data for a specific use-case. In this paper, we created a generic data model for a virtual pancreatic cancer patient, using the openEHR approach and tools, to be used for testing and virtual environments. The data elements for this template were derived from the "Oncology minimal data set" of HiGHmed project. In addition, we generated virtual data profiles for 10 patients using the template. The objective of this exercise is to provide a data model and virtual data profiles for testing and experimenting scenarios within the openEHR environment. Both of the template and the 10 virtual patient profiles are available publicly.