1
|
Zheng J, Tang Y, Peng X, Zhao J, Chen R, Yan R, Peng Y, Zhang W. Indirect estimation of pediatric reference interval via density graph deep embedded clustering. Comput Biol Med 2024; 169:107852. [PMID: 38134750 DOI: 10.1016/j.compbiomed.2023.107852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 11/10/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023]
Abstract
Establishing reference intervals (RIs) for pediatric patients is crucial in clinical decision-making, and there is a critical gap of pediatric RIs in China. However, the direct sampling technique for establishing RIs is resource-intensive and ethically challenging. Indirect estimation methods, such as unsupervised clustering algorithms, have emerged as potential alternatives for predicting reference intervals. This study introduces deep graph clustering methods into indirect estimation of pediatric reference intervals. Specifically, we propose a Density Graph Deep Embedded Clustering (DGDEC) algorithm, which incorporates a density feature extractor to enhance sample representation and provides additional perspectives for distinguishing different levels of health status among populations. Additionally, we construct an adjacency matrix by computing the similarity between samples after feature enhancement. The DGDEC algorithm leverages the adjacency matrix to capture the interrelationships between patients and divides patients into different groups, thereby estimating reference intervals for the potential healthy population. The experimental results demonstrate that when compared to other indirect estimation techniques, our method ensures the predicted pediatric reference intervals in different age and gender groups are closer to the true values while maintaining good generalization performance. Additionally, through ablation experiments, our study confirms that the similarity between patients and the multi-scale density features of samples can effectively describe the potential health status of patients.
Collapse
Affiliation(s)
- Jianguo Zheng
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Yongqiang Tang
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Xiaoxia Peng
- Center for Clinical Epidemiology and Evidence-Based Medicine, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China.
| | - Jun Zhao
- Information Center, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China.
| | - Rui Chen
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Ruohua Yan
- Center for Clinical Epidemiology and Evidence-Based Medicine, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China.
| | - Yaguang Peng
- Center for Clinical Epidemiology and Evidence-Based Medicine, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China.
| | - Wensheng Zhang
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| |
Collapse
|
2
|
Blatter TU, Witte H, Fasquelle-Lopez J, Theodoros Naka C, Raisaro JL, Leichtle AB. The BioRef Infrastructure, a Framework for Real-Time, Federated, Privacy-Preserving, and Personalized Reference Intervals: Design, Development, and Application. J Med Internet Res 2023; 25:e47254. [PMID: 37851984 PMCID: PMC10620636 DOI: 10.2196/47254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 07/13/2023] [Accepted: 07/14/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND Reference intervals (RIs) for patient test results are in standard use across many medical disciplines, allowing physicians to identify measurements indicating potentially pathological states with relative ease. The process of inferring cohort-specific RIs is, however, often ignored because of the high costs and cumbersome efforts associated with it. Sophisticated analysis tools are required to automatically infer relevant and locally specific RIs directly from routine laboratory data. These tools would effectively connect clinical laboratory databases to physicians and provide personalized target ranges for the respective cohort population. OBJECTIVE This study aims to describe the BioRef infrastructure, a multicentric governance and IT framework for the estimation and assessment of patient group-specific RIs from routine clinical laboratory data using an innovative decentralized data-sharing approach and a sophisticated, clinically oriented graphical user interface for data analysis. METHODS A common governance agreement and interoperability standards have been established, allowing the harmonization of multidimensional laboratory measurements from multiple clinical databases into a unified "big data" resource. International coding systems, such as the International Classification of Diseases, Tenth Revision (ICD-10); unique identifiers for medical devices from the Global Unique Device Identification Database; type identifiers from the Global Medical Device Nomenclature; and a universal transfer logic, such as the Resource Description Framework (RDF), are used to align the routine laboratory data of each data provider for use within the BioRef framework. With a decentralized data-sharing approach, the BioRef data can be evaluated by end users from each cohort site following a strict "no copy, no move" principle, that is, only data aggregates for the intercohort analysis of target ranges are exchanged. RESULTS The TI4Health distributed and secure analytics system was used to implement the proposed federated and privacy-preserving approach and comply with the limitations applied to sensitive patient data. Under the BioRef interoperability consensus, clinical partners enable the computation of RIs via the TI4Health graphical user interface for query without exposing the underlying raw data. The interface was developed for use by physicians and clinical laboratory specialists and allows intuitive and interactive data stratification by patient factors (age, sex, and personal medical history) as well as laboratory analysis determinants (device, analyzer, and test kit identifier). This consolidated effort enables the creation of extremely detailed and patient group-specific queries, allowing the generation of individualized, covariate-adjusted RIs on the fly. CONCLUSIONS With the BioRef-TI4Health infrastructure, a framework for clinical physicians and researchers to define precise RIs immediately in a convenient, privacy-preserving, and reproducible manner has been implemented, promoting a vital part of practicing precision medicine while streamlining compliance and avoiding transfers of raw patient data. This new approach can provide a crucial update on RIs and improve patient care for personalized medicine.
Collapse
Affiliation(s)
- Tobias Ueli Blatter
- University Institute of Clinical Chemistry, University Hospital Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Harald Witte
- University Institute of Clinical Chemistry, University Hospital Bern, Bern, Switzerland
| | | | - Christos Theodoros Naka
- University Institute of Clinical Chemistry, University Hospital Bern, Bern, Switzerland
- Laboratory of Biometry, University of Thessaly, Volos, Greece
| | - Jean Louis Raisaro
- Biomedical Data Science Center, University Hospital Lausanne, Lausanne, Switzerland
| | - Alexander Benedikt Leichtle
- University Institute of Clinical Chemistry, University Hospital Bern, Bern, Switzerland
- Center for Artificial Intelligence in Medicine, University of Bern, Bern, Switzerland
| |
Collapse
|
3
|
Martinez-Sanchez L, Gabriel-Medina P, Villena-Ortiz Y, García-Fernández AE, Blanco-Grau A, Cobbaert CM, Bravo-Nieto D, Garriga-Edo S, Sanz-Gea C, Gonzalez-Silva G, López-Hellín J, Ferrer-Costa R, Casis E, Rodríguez-Frías F, den Elzen WPJ. Harmonization of indirect reference intervals calculation by the Bhattacharya method. Clin Chem Lab Med 2023; 61:266-274. [PMID: 36395007 DOI: 10.1515/cclm-2022-0439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 11/03/2022] [Indexed: 11/18/2022]
Abstract
OBJECTIVES The aim of this study was to harmonize the criteria for the Bhattacharya indirect method Microsoft Excel Spreadsheet for reference intervals calculation to reduce between-user variability and use these criteria to calculate and evaluate reference intervals for eight analytes in two different years. METHODS Anonymized laboratory test results from outpatients were extracted from January 1st 2018 to December 31st 2019. To assure data quality, we examined the monthly results from an external quality control program. Reference intervals were determined by the Bhattacharya method with the St Vincent's hospital Spreadsheet firstly using original criteria and then using additional harmonized criteria defined in this study. Consensus reference intervals using the additional harmonized criteria were calculated as the mean of four users' lower and upper reference interval results. To further test the operation criteria and robustness of the obtained reference intervals, an external user validated the Spreadsheet procedure. RESULTS The extracted test results for all selected laboratory tests fulfilled the quality criteria and were included in the present study. Differences between users in calculated reference intervals were frequent when using the Spreadsheet. Therefore, additional criteria for the Spreadsheet were proposed and applied by independent users, such as: to set central bin as the mean of all the data, bin size as small as possible, at least three consecutive bins and a high proportion of bins within the curve. CONCLUSIONS The proposed criteria contributed to the harmonization of reference interval calculation between users of the Bhattacharya indirect method Spreadsheet.
Collapse
Affiliation(s)
- Luisa Martinez-Sanchez
- Biochemistry Department, Clinical Laboratories, Vall d'Hebron University Hospital, Barcelona, Spain
- Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Spain
- Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Centre, Leiden, The Netherlands
- Clinical Biochemistry Research Team, Vall d'Hebron Institute of Research (VHIR), Barcelona, Spain
| | - Pablo Gabriel-Medina
- Biochemistry Department, Clinical Laboratories, Vall d'Hebron University Hospital, Barcelona, Spain
- Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Spain
- Clinical Biochemistry Research Team, Vall d'Hebron Institute of Research (VHIR), Barcelona, Spain
| | - Yolanda Villena-Ortiz
- Biochemistry Department, Clinical Laboratories, Vall d'Hebron University Hospital, Barcelona, Spain
- Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Spain
- Clinical Biochemistry Research Team, Vall d'Hebron Institute of Research (VHIR), Barcelona, Spain
| | - Alba E García-Fernández
- Biochemistry Department, Clinical Laboratories, Vall d'Hebron University Hospital, Barcelona, Spain
- Clinical Biochemistry Research Team, Vall d'Hebron Institute of Research (VHIR), Barcelona, Spain
| | - Albert Blanco-Grau
- Biochemistry Department, Clinical Laboratories, Vall d'Hebron University Hospital, Barcelona, Spain
- Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Spain
- Clinical Biochemistry Research Team, Vall d'Hebron Institute of Research (VHIR), Barcelona, Spain
| | - Christa M Cobbaert
- Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Centre, Leiden, The Netherlands
| | - Daniel Bravo-Nieto
- Biochemistry Department, Clinical Laboratories, Vall d'Hebron University Hospital, Barcelona, Spain
- Clinical Biochemistry Research Team, Vall d'Hebron Institute of Research (VHIR), Barcelona, Spain
| | - Sarai Garriga-Edo
- Biochemistry Department, Clinical Laboratories, Vall d'Hebron University Hospital, Barcelona, Spain
- Clinical Biochemistry Research Team, Vall d'Hebron Institute of Research (VHIR), Barcelona, Spain
| | - Clara Sanz-Gea
- Biochemistry Department, Clinical Laboratories, Vall d'Hebron University Hospital, Barcelona, Spain
- Clinical Biochemistry Research Team, Vall d'Hebron Institute of Research (VHIR), Barcelona, Spain
| | - Gonzalo Gonzalez-Silva
- Biochemistry Department, Clinical Laboratories, Vall d'Hebron University Hospital, Barcelona, Spain
- Clinical Biochemistry Research Team, Vall d'Hebron Institute of Research (VHIR), Barcelona, Spain
| | - Joan López-Hellín
- Biochemistry Department, Clinical Laboratories, Vall d'Hebron University Hospital, Barcelona, Spain
- Clinical Biochemistry Research Team, Vall d'Hebron Institute of Research (VHIR), Barcelona, Spain
| | - Roser Ferrer-Costa
- Biochemistry Department, Clinical Laboratories, Vall d'Hebron University Hospital, Barcelona, Spain
- Clinical Biochemistry Research Team, Vall d'Hebron Institute of Research (VHIR), Barcelona, Spain
| | - Ernesto Casis
- Clinical Laboratories, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Francisco Rodríguez-Frías
- Biochemistry Department, Clinical Laboratories, Vall d'Hebron University Hospital, Barcelona, Spain
- Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Spain
- Clinical Biochemistry Research Team, Vall d'Hebron Institute of Research (VHIR), Barcelona, Spain
| | - Wendy P J den Elzen
- Clinical Biochemistry Research Team, Vall d'Hebron Institute of Research (VHIR), Barcelona, Spain
- Department of Clinical Chemistry, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| |
Collapse
|