Secondary use of routine data employing a common data model:
towards a formalized description for endotyping of asthma/COPD
Bor Ditewig
b.q.ditewig@amc.uva.nlDaily tutor
PD dr. med. Martin Boeker Supervisor
dr. ir. Ronald Cornet
Hosting institution
University of Freiburg, Faculty of Medicine, Institute for Medical Biometry and Statistics, Freiburg
Home institution
University of Amsterdam, Academic Medical Centre, Department of Medical Informatics, Amsterdam
Abstract
IntroductionEndotypes of asthma/COPD could be indicated using a predictive model. In this work, to train a predictive model, routine data originating from different, isolated
information systems is used. The latter brings a uniformity problem. In a process model for developing a harmonized data model, a dataset to describe asthma/COPD endotypes is determined and described in the OMOP CDM.
Methods
This work covers the clinical considerations of in- and excluding data items and the development of the semantic model. Literature research, surveys and expert meetings with physicians are held to determine the relevance and availability of data items. Then, the description of the dataset the OMOP CDM is made using a Delphi method variant.
Results
A dataset of 125 data items is established, defined, and coded in LOINC. A
representation of this set is described in the OMOP CDM using 8 standardized tables. Doing the former, a process model is demonstrated.
Conclusion
Secondary use of data narrows but does not eliminate establishing a dataset to
represent a complex clinical entity. To serialize the dataset, a description of the use of the OMOP CDM is given.
Introduction
Today, healthcare data is largely stored in isolated information silos, such as hospital
information systems. Each system has its own data model and uses its own terminologies, the health informatics structure is highly fragmented1. This makes exchange of information
between healthcare organizations challenging and use of data for (clinical) research purposes hard, which leads to the development of research-specific multi-centre data stores. This results in a costly and time-consuming infrastructure and hampers the improvement of the quality of clinical data. To overcome this, several approaches for data integration have been developed world-wide. One of these is OHDSI’s Observational Medical Outcomes
Partnership (OMOP) Common Data Model (CDM)2, which will be elaborated on later.
The German MIRACUM consortium3, as part of the national Medical Informatics
Inititative4, unifies multiple university medical centres, aimed to foster IT innovations for
healthcare research and medical care. One of the objectives of MIRACUM is the implementation of data integration centres (DIC) at each partner site. Besides the
establishment of the DIC, MIRACUM thrives for sharing their applicability in use cases in which the DIC are used in real-world research scenarios. For “Use Case 2 From Data to Knowledge – Clinico-molecular Predictive Knowledge Tool”, the objective is (1) to describe the patient populations at the sites of the consortium, and, based on the former, (2) to develop, train and evaluate predictive models for, among other major medical conditions, asthma and chronic obstructive pulmonary disease (COPD). Within these medical conditions endotypes exist5-7. Anderson defines an endotype as “a subtype of disease defined functionally and
pathologically by a molecular mechanism or by treatment response”5. Asthma/COPD are
conditions with pathophysiological similarities and differences for which patients need to be characterized better to define subtypes8. When a predictive model is deployed, cluster
membership of asthma/COPD patients can be indicated automatically. Through this, patients’ clinical care can be adjusted to their needs. As the prevalence of airway diseases in the western world is increasing, its impact could be reduced by better adjusted therapies9.
However, recent treatments are based on an old definition of the diseases9, in which
endotypes have not been defined in an unambiguous, generally accepted way.
To train this predictive model, MIRACUM gathers data of all participating university medical centres. As not all sites use the same routine information technology (IT) systems and representations of relevant data, a uniformity problem arises. Data not stored in a common data model and with a fixed semantic cannot be used for distributed analysis across sites. As a harmonized target model, MIRACUM will use the OMOP CDM. The underlying data and their representation in the common data model need to be formally described and defined. As we want to create a harmonized dataset for the endotyping of asthma and COPD the
objectives of this work are
(1) to determine what data are needed to distinguish endotypes,
(2) to investigate which data are available at the different partner sites, and
(3) to evaluate how OHDSI’s OMOP CDM can be optimally used to represent the data from within the MIRACUM consortium.
Methods
SettingThis study is conducted in Freiburg as part of the MIRACUM consortium. Other partner sites are located in Erlangen, Frankfurt, Gießen, Magdeburg, Mainz, Marburg, and Mannheim3.
MIRACUM is one of the consortia funded by the German Federal Ministry for Education and Research (BMBF)4. The medical informatics initiatives scope is to provide access to clinical
information for patients, doctors and researchers.
The initial model for the phenotype of asthma/COPD to build upon is set up by a laboratory clinician. The first version contained 7 categories, Demographics and Vital signs (11), Laboratory results (422, of which 350 allergens), Lung function (39), Cardiac function (4), Questionnaires (4), Diagnosis and Medication (5), and Imaging (2), adding up to 487 items. Conceptual model for asthma and COPD
Starting with the initial dataset, a qualitative literature study is done to complete the dataset using other points of view and experience from previous studies. Also, the individual data items are described and defined to create a good comprehension for all non-clinical team members. Scientific literature is searched in the Google Scholar and the PubMed search engines. Both engines are searched for asthma (resp. “asthma”[MeSH Terms]), COPD (resp. “pulmonary disease, chronic obstructive”[MeSH Terms]), endotype and endotypes in
different compositions using Boolean operators. The collected literature is assessed on its goals and reasoning based on which we decided to in- or exclude variables. All used literature aimed to determine heterogeneity in asthma or COPD based on physically measurable data. Further quantitative and qualitative evaluation of dataset and its availability
To create a quantitative assessment of the proposed dataset, a survey is distributed across all partner sites. The preliminary dataset is assessed by pneumologists to assess on which
subjects consensus is still missing. In the survey, clinicians will be asked to assess every data item on its relevance in the description and diagnosis of asthma or COPD on a Likert scale with items Keine Bedeutung (no meaning), Weniger wichtig (less important), Wichtig
(important), and Entscheidend für Diagnose (crucial for diagnosis). Also, an assessment of the availability of data items is made by asking the clinicians to indicate, with a Boolean yes or no, whether an item is measured regularly.
Next, the dataset is discussed in expert meetings with experts from all partner sites. The results of the survey are presented to start a discussion about the composition of the
dataset. The discussion is led to elaborate on multiple subjects: in- and exclusion, current availability, potential availability, missing items, differentiation between importance of items, and methods.
As routine data is used, not all items appropriate for endotyping asthma/COPD are available. The expert meetings and questionnaires are leading in the determination of data-availability. Figure 3 summarizes the above process of determining a valid dataset.
Common data model
To provide a harmonized data model, OHDSI’s OMOP CDM v5.3 used10. This common data
model is developed to store medical data and is optimized for data analysis and predictive modelling2. The CDM contains built-in standardized vocabularies such as ICD-10 for
conditions11 CPT4 for procedures12 and the Logical Observation Identifiers Names and Codes
(LOINC) for measurements13. All included standardized terminologies are accessible via
Athena14. In this tool, many terminologies are mapped to OHDSI’s standard vocabulary. The
OMOP CDM consists of 39 tables of which 2 are obligatory to fill in every implementation of the model. The model is person-centric and leaves room for customization, notwithstanding the fact that the ways of storing clinical data, vocabularies, provider data and health
economics are all predefined15.
To prepare the implementation of the CDM, source data is observed. Anonymized sample test results of lung function tests and coding schemas of the Medical Centre of the University Freiburg are necessary to create a correct mapping to LOINC. The Regenstrief institute offers the tool, RELMA (version 2.63), to map from local mappings to LOINC, taking names, units and specimen into consideration16.
Lastly, to define our implementation of the CDM, a method based on the Delphi method is used (see Figure 2). To optimize the use of the provided CDM, a description is written, then an expert can comment on this whereafter changes can be applied. This process iterates until the model description is satisfactory. In this paper, the specification of a common data model is limited to the domains Demographics and Vital signs and Lung function.
Figure 2: Delphi-based method for developing a custom OMOP CDM implementation
Evaluate requirements (Re)formulate proposal OMOP experts comment Reformulate proposal
The practical implementation of the model in this consortium is described in Figure 1. Talend Open Studio for Data Integration 6.5.117 is used to extract the data from their source systems,
e.g. HL7v2 OBX messages for lung function measurements and CSV files for lab data. The data warehousing tool i2b218 functions as a staging area in which the data are consolidated.
This provides the possibility to adjust the CDM without having to re-extract the data from their source systems. At last, the data will be transferred to OMOP CDM in which analysis can be performed.
Results
DataThe key results of the process of determining a workable dataset for classifying
asthma/COPD endotypes are shown in Figure 3. Appendix B shows an overview of data items used in six recent studies. In the literature, the most frequently used factor in differentiating between endotypes, is Th2-inflammation5-7,19-21, a type of inflammation characterized by the
cytokines produced by CD4 positive T helper cells22 which leads to consequences such as
B-cell IgE production, eosinophilic inflammation and mast B-cell growth5,22. Also, the ratio and
quantity of eosinophils, neutrophils and granulocytes in general is frequently taken into consideration5-7,19,21. Furthermore, granulocyte type distribution has been used7,20,21. Studies
also report that treatment effectiveness can mark endotypes5-7,19,21.
Figure 3: Process of determining a valid dataset and the main results
The resulting dataset which was presented to the physicians as a survey and its
complete results can be found in Appendices C and D. Six of the eight partner sites responded and the main results are displayed in Table 1. A cut-off point of 40% is taken, to include items based on their relevance according to physicians. Consequently, 86,7% of the proposed items sufficed. Also, an availability of 50% or more is necessary to guarantee a sufficient populated working set. This prerequisite was not met by 24,8%. Mandatory items by OMOP, for
example ethnicity, race and birth year, are included irrespective of survey outcomes. The expert meetings raised several new topics for discussion. These include non-pathophysiological information which will be discussed later. Important data, such as
bronchial challenge testing, initially were missing and are added to the dataset. The complete final dataset can be found in Appendix E.
Conceptual model for asthma
and COPD
•487 items •7 categories •Compiled with one
clinician Definition and completion of data items •Literature based •Add items •Assign standardized coding Consolidation and prioritization of data items •Physicians from participating hospitals cooperate in surveys and expert meetings •Detect unavailable
items
•Detect missing items
Valid dataset •125 items •9 categories •Supported and defined by literature and experts
Table 1: relevance and availability of data items according to physiologists from partner sites #items (%items) <40% relevance >=40% relevance #items (%items) 165 (100%) 22 (13,3%) 143 (86,7%) <50% availability 41 (24,8%) 19 (11,5%) 22 (13,3%) >50% availability 124 (75,2%) 3 (1,8%) 121 (73,3%) Data model
In the OMOP CDM, the PERSON and OBSERVATION_PERIOD tables are obligatory. Clinical data tables are linked to PERSON as pictured in the model in Figure 4. Observation period is the time in which a Person is at risk of having clinical events recorded within the source systems. On these source systems, queries will run on J44.*, Sonstige chronische obstruktive Lungenkrankheit (Other chronic obstructive pulmonary disease), and J45.*, Asthma bronchiale (bronchial asthma), of ICD-10-GM – German Modification, the German variation on 10 - to extract data from the right patients. Types already defined in ICD-10-GM classification are identified in CONDITION_OCCURRENCE. Measurements are stored in MEASUREMENT and can be categorised in the field
“measurement_type_concept_id”. The latter can be relevant for query purposes. Other results, including surveys and cardiac testing, are stored in OBSERVATION. To be able to identify measurements such as lung function before and after bronchodilation,
PROCEDURE_OCCURRENCE, and DRUG_EXPOSURE are used because not all items measured before and after bronchodilation are differentiated by codes in the standardized vocabulary. The administration of a bronchodilator is stored in
PROCEDURE_OCCURRENCE with CPT4-code 94060, which is linked to the
measurements before and after this exposure by using the “Occurs before” and “Occurs after” concepts in FACT_RELATIONSHIP. To specify the bronchodilator DRUG_EXPOSURE is applied and linked to the measurement and procedure.
Discussion
In this work, we aimed to define a valid dataset for the clinical description of asthma/COPD and the identification of their endotypes as well as the final representation of these data with a standardized terminology in the OMOP CDM.
The initial conceptual model of physiological characteristics which could indicate asthma/COPD endotypes, is extended based on literature research. The considered literature predominantly executed research specific to asthma/COPD endotypes. This facilitated a broad spectrum of possible biomarkers. Next, experts from partner sites assessed the dataset in expert meetings and discussed the dataset, which provided multiple changes. Missing items were added esp. parts of lung function tests. Furthermore, the realistic view on the availability of data items re-shaped the dataset drastically because only commonly used data can be obtained. For clinical assessment, not only physiological data items (biomarkers) are important, but signs and symptoms (conditions) are crucial as well. Factors such as clinical signs, living area, pollution, disease phase during measurements, comorbidities, mortality and exacerbations are important. Also the type of and response to medication are considered as foundational indicators for the description of the diseases and thus of their endotypes. Therefore, it is essential to include functional test outcomes and exacerbation severity combined with administered medications. A strength of this work is the participation of technical experts as well as clinical experts, pneumologists and allergologists, with broad experience in the diagnosis of pulmonary conditions.
The final dataset is based on literature and local knowledge which reduces the risk of a delimited or biased initial view. Yet, local availability of data has had a leading influence on the exclusion of several items. Therefore, research in other regions should consider following
the procedure in Figure 3 in their respective area. Cut-off points for the rating of relevance of data items need to be considered carefully. In this work, an open approach has been chosen. Data items are included with consent of 40% of experts to facilitate a broad spectrum of yet unrecognized factors affecting asthma/COPD. Only 13,3% of the proposed items are considered inadequate after consulting the physicians. Even items are kept in the dataset which are not available at all partner sites.
In previous similar research, the OMOP CDM has been used successfully23. It turned
out to be an adaptable reference model. The wide range of integrated standardized
terminologies creates an adequate vocabulary and saves the effort of mapping between several terminologies. In our work LOINC is used, where this did not satisfy our needs, local terms are mapped to OMOP’s standard vocabulary which does not cover all data items either24. But,
Figure 4: Data model for storing lung function data in the MIRACUM consortium, based on the OMOP CDM10
if OMOP’s standard vocabulary does not cover an item, the obligatory field in which the code is stored, is given value ‘0’ and a custom code can be assigned in the intended provided field. Although the FACT_RELATIONSHIP table is not yet explicitly defined in OHDSI’s open source software tool, Atlas, we have chosen to incorporate it extensively in our proposal because of the relational structure that has to be defined for a proper description of clinical conditions. Lung function tests before and after bronchodilation are measured in short temporal succession. Therefore, the timestamps of measurements might not be documented sufficiently fine-grained enough to guarantee a clear differentiation between them.
Additionally, causality and other relations between clinical observations (e.g. drugs
administered through a certain procedure, or biomarkers observed under certain conditions) are documented to create a coherent semantical network.
Limitations and prospects
The goal of this work is to re-use routine data. This limits the composition of the dataset to items used in contemporary diagnostics, hence there is no possibility to assess relationships between diseases and items that are not regularly measured. However, with already 9000 patients within the target population in one hospital (Medical Centre of the University Freiburg), the available patient set is significantly larger than possible in cohort studies with the same financial resources. Also, by using data from only academic hospitals in Germany, the available data is limited to inpatient visits. Whilst COPD and especially asthma patients visit smaller practices like general practitioners, a next step in using routine data for
secondary clinical use, should pursuit the inclusion of outpatient data.
Concluding, we demonstrated a process model to compile a custom dataset and used it to create a dataset for endotyping asthma/COPD. Secondary use of clinical data narrows but does not eliminate the availability of relevant data. Based on this work, a recommendation for the use of OHDSI’s OMOP CDM has been given for all partner sites to serialize the defined dataset. In this way the work presented here, is the foundation for a consortium-wide
harmonized dataset is available for distributed analysis of asthma and COPD.
Acknowledgement
Special thanks to Martin Boeker and Ronald Cornet for the extensive guidance while conducting and writing this work. MIRACUM is funded by the BMBF under the Funding Number FKZ 01ZZ1606H.
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Appendix A: list of abbreviations used in this paper
6MWT 6-minute walk testA1AT alpha 1 antitrypsin ABG arterial blood gas test ACT asthma control test ANA Anti-nuclear antibody
ANCA Anti-neutrophil cytoplasmic antibody BALF bronchoalveolar lavage fluid
BMI body mass index
BODE body-mass index, airflow obstruction, dyspnea, and exercise BMBF German ministry for education and research
BSG erythrocyte sedimentation rate CAT COPD assessment test
CCP cyclic citrullinated peptide CD4 cluster of differentiation 4 CDM common data model
COPD chronic obstructive pulmonary disease CPT4 current procedural terminology, 4th edition CRP c-reactive protein
CT computed tomography DIC data integration centre
DLCOc diffusing capacity of the lung for carbon monoxide DSP distance saturation product
EKG electrocardiogram
FeNO fractional exhaled nitric oxide
FVC forced vital capacity HER electronic health record ETL extract, transform, load GM German modification HBA1c glycated haemoglobin HL7 health level 7
IC inspiratory capacity
ICD international statistical classification of diseases and related health problems IgE immunoglobin E
IgG immunoglobin G
IgM immunoglobin M
LTA4H leukotriene A4 hydrolase LTE4 leukotriene E4
LZ-EKG Holter monitor
IL interleukin
IT information technology
J44.* Sonstige chronische obstruktive Lungenkrankheit (Other chronic obstructive pulmonary disease),
J45.* Asthma bronchiale (bronchial asthma)
LOINC logical observational identifiers names and codes MCH mean corpuscular haemoglobin
MCHC mean corpuscular haemoglobin concentration MCV mean corpuscular volume
MEF 25/50/75 mid expiratory volume at 25, 50 an 75% of exhalation mMRC modified British medical research council
MPO myeloperoxidase
OMOP observational medical outcomes partnership PEF peak expiratory volume
pCO2 partial pressure of carbon dioxide in blood pO2 partial pressure of oxygen in blood
SGRQ St. George´s respiratory questionnaire SO2 oxygen saturation in arterial blood Srtot specific airway resistance
RF rheumatoid factor
RL vs. O2 by room air vs. oxygen administered
Rö x-ray
Rtot airway resistance RR Blood pressure RV residual volume Temp temperature Th2 T helper cell 2 TLC total lung capacity
TNF-alpha tumour necrosis factor alpha UKG echocardiography
VA alveolar volume Vcin vital capacity
Appendix B: high granular overview of data-items used in recent studies to
differentiate asthma and COPD endotypes, not all granularity levels are
mentioned
Study ® 5 6 19 7 20 21 ¯ Data-item 6MWT X A1AT X X ABG X Airflow obstruction X X BALF (as a substance) X X BMI /nutrition X X XBODE (as an overall
index) X CAT X CRP X X Eosinophils X X X X X X Exacerbations (in stadardfragebogen) X X X X X X Exercise X X X FeNO X X X Fibrinogen X
Genetic risk factors X X X X
IgE X X X X X
IgE, mold specific X
IgG X
IL1, 17, 23 X X
IL1, IL6, IL8 X X X
IL4, IL13 X LTE4 X LTA4H X Lymphocytes X X X Matrix metalloproteinase 9 X Medical history X X X mMRC X Mucus X Myeloperoxidase X Neutrophils X X X X X X Neutrophil elastase X Operational lung functioning X X X X X X Periostin X X Prolil endopeptidase X Proline glycine proline X Radiology X
Sputum
(cytometry/volume) X X X X
Th2-inflammation X X X X X X
TNF-alpha X X X
Appendix D: inclusion (green) and exclusion (red) of data items based on
expert information (points overlap)
Appendix E: complete final dataset
Definition LOINC code LOINC LongName OMOP DOMAIN OMOP ID
Patient and
demographics
person_id Person field
Date of birth 21112-8 Birth date Person field
Nationality 68329-2 Country of origin Person field
Length 8302-2 Body height Measurement 3036277
Mass 29463-7 Body weight Measurement 3025315
BMI 39156-5 Body mass index Measurement 3038553
Gender 46098-0 Sex Person field
Ethnicity 74694-1 Ethnicity [AHRQ] Person field
Race 32624-9 Race Person field
Vital signs
Body temperature 8310-5 Body temperature Measurement 3020891
Blood pressure 8478-0 Mean blood pressure Measurement 3027598
Heart rate 8867-4 Heart rate Measurement 3027018
Blood oxygen
saturation 59408-5
Oxygen saturation in Arterial blood
by Pulse oximetry Measurement 40762499
Respiratory rate 9279-1 Respiratory rate Measurement 3024171
Laboratory Leukocytes 26464-8 Leukocytes [#/volume] in Blood Measurement 3010813 Erythrocytes 26453-1 Erythrocytes [#/volume] in Blood Measurement 3026361 Thrombocytes 26515-7 Platelets [#/volume] in Blood Measurement 3007461 Hemoglobin 718-7 Hemoglobin [Mass/volume] in Blood Measurement 3000963 Hematocrit 20570-8 Hematocrit [Volume Fraction] of Blood Measurement 3009542 Creatinine in serum 2160-0 Creatinine [Mass/volume] in
Serum or Plasma Measurement 3016723
C-reactive protein 1988-5
C reactive protein [Mass/volume] in
Serum or Plasma Measurement 3020460
Serum or Plasma by High sensitivity method Alpha-1 antitrypsin 9407-8 Alpha 1 antitrypsin [Mass/volume] in Stool Measurement 3014717 1825-9 Alpha 1 antitrypsin [Mass/volume] in
Serum or Plasma Measurement 3026285
Cholesterol 2093-3
Cholesterol [Mass/volume] in
Serum or Plasma Measurement 3027114
Blood glucose 2339-0 Glucose [Mass/volume] in Blood Measurement 3000483 Anti-nuclear antibody 27200-5 Nuclear Ab [Units/volume] in Serum Measurement 3021587
29953-7 Nuclear Ab [Titer] in Serum Measurement 3037522
Anti-neutrophil cytoplasmic antibody 5128-4 Neutrophil cytoplasmic Ab [Units/volume] in Serum Measurement 3001334 21023-7 Neutrophil cytoplasmic Ab
[Titer] in Serum Measurement 3026712
complete blood count 58410-2 Complete blood count (hemogram) panel - Blood by
Automated count Measurement 40761511
small blood count 55429-5 Short blood count panel - Blood Measurement 40758558
white cells 26464-8 Leukocytes [#/volume] in Blood Measurement 3010813 red cells 26453-1 Erythrocytes [#/volume] in Blood Measurement 3026361 hemoglobin 718-7 Hemoglobin [Mass/volume] in Blood Measurement 3000963 hematocrit 20570-8 Hematocrit [Volume Fraction] of Blood Measurement 3009542 mean corpuscular
mean corpuscular
hemoglobin 28539-5 MCH [Entitic mass] Measurement 3035941
mean corpusular hemoglobin
concentration 28540-3 MCHC [Mass/volume] Measurement 3003338
neutrophil 23761-0 Neutrophils/100 leukocytes in Blood by Manual count Measurement 3027368 770-8 Neutrophils/100 leukocytes in Blood by
Automated count Measurement 3008342
lymphocyte 737-7 Lymphocytes/100 leukocytes in Blood by Manual count Measurement 3038058 736-9 Lymphocytes/100 leukocytes in Blood by
Automated count Measurement 3037511
monocyte 744-3 Monocytes/100 leukocytes in Blood by Manual count Measurement 3022407 5905-5 Monocytes/100 leukocytes in Blood by
Automated count Measurement 3011948
eosinophil 714-6 Eosinophils/100 leukocytes in Blood by Manual count Measurement 3015956 713-8 Eosinophils/100 leukocytes in Blood by
Automated count Measurement 3010457
basophil 707-0 Basophils/100 leukocytes in Blood by Manual count Measurement 3009797 706-2 Basophils/100 leukocytes in Blood by
Automated count Measurement 3013869
thrombocytes 778-1 Platelets [#/volume] in Blood by Manual count Measurement 3010834 777-3 Platelets [#/volume] in Measurement 3024929
Blood by Automated count neutrophil 753-4 Neutrophils [#/volume] in Blood by Manual count Measurement 3017501 26499-4 Neutrophils [#/volume] in Blood Measurement 3017732 lymphocyte 732-8 Lymphocytes [#/volume] in Blood by Manual count Measurement 3003215 731-0 Lymphocytes [#/volume] in Blood by
Automated count Measurement 3004327
monocyte 743-5 Monocytes [#/volume] in Blood by Manual count Measurement 3034107 742-7 Monocytes [#/volume] in Blood by
Automated count Measurement 3033575
eosinophil 712-0 Eosinophils [#/volume] in Blood by Manual count Measurement 3009932 711-2 Eosinophils [#/volume] in Blood by
Automated count Measurement 3028615
basophil 705-4 Basophils [#/volume] in Blood by Manual count Measurement 3027651 704-7 Basophils [#/volume] in Blood by
Automated count Measurement 3013429
brain natriuretic
peptide 30934-4
Natriuretic peptide B
[Mass/volume] in
Serum or Plasma Measurement 3011960
C-reactive protein 30522-7 C reactive protein [Mass/volume] in Serum or Plasma by High sensitivity method Measurement 3010156
immunoglobulin E 19113-0 IgE [Units/volume] in Serum or Plasma Measurement 3005322 immunoglobulin G 2465-3 IgG [Mass/volume] in
Serum or Plasma Measurement 3005719
immunoglobulin
M 2472-9
IgM
[Mass/volume] in
Serum or Plasma Measurement 3028026
Allergens custom allergen panel - - - - Lung function Before bronchodilatation vital capacity, maximum exhaled air volume after maximum inhalation 81440-0 Vital capacity [Volume] Respiratory system --pre bronchodilation Measurement 21493437 forced vital capacity, maximum forced exhaled air volume after maximum inhalation 19877-0 Foreced vital capacity [Volume] Respiratory system --pre bronchodilation Measurement 3037879 forced expiratory volume 20157-4 FEV1 --pre bronchodilation Measurement 3005025 inspiratory capacity, maximum inhaled air volume after
normal exhalation 19848-1 Inspiratory capacity Measurement 3009911
specific resistance of airflow - - Measurement 0 resistance of airflow 81443-4 Airway resistance --pre bronchodilation Measurement 21493440
total lung capacity 81450-9
Total lung capacity --pre bronchodilation Measurement 21493124 residual volume 81452-5 Residual volume --pre bronchodilation Measurement 21493126 diffusing capacity of the lung for
carbon monoxide 19911-7
Diffusion capacity.carbon
diffusing capacity of the lung for carbon monoxide divided by the alveolar volume 82619-8 Diffusion capacity/Alveolar volume --pre bronchodilation Measurement 42528789 peak expiratory volume 69975-1 Maximum expiratory gas flow Respiratory system airway --pre bronchodilation Measurement 42868464 mid expiratory volume 75 - - Measurement 0 mid expiratory volume 50 - - Measurement 0 mid expiratory volume 25 - - Measurement 0 After bronchodilatation vital capacity, maximum exhaled air volume after maximum inhalation 81441-8 Vital capacity [Volume] Respiratory system --pre bronchodilation Measurement 21493438 forced vital capacity, maximum forced exhaled air volume after maximum inhalation 19875-4 Foreced vital capacity [Volume] Respiratory system --pre bronchodilation Measurement 3001668 forced expiatory volume 20155-8 FEV1 --pre bronchodilation Measurement 3023550 inspiratory capacity, maximum inhaled air volume after
normal exhalation 19848-1 Inspiratory capacity Measurement 3009911
specific resistance of airflow - - Measurement 0 resistance of airflow 81442-6 Airway resistance --pre bronchodilation Measurement 21493439
total lung capacity 81451-7
Total lung capacity --pre bronchodilation Measurement 21493125 residual volume 81453-3 Residual volume --pre bronchodilation Measurement 21493445
diffusing capacity of the lung for
carbon monoxide 19911-7
Diffusion capacity.carbon
monoxide Measurement 3008905
diffusing capacity of the lung for carbon monoxide divided by the alveolar volume 82620-6 Diffusion capacity/Alveolar volume --pre bronchodilation Measurement 42528790 peak expiratory volume 69976-9 Maximum expiratory gas flow Respiratory system airway --post bronchodilation Measurement 42868465 mid expiratory volume 75 - - Measurement 0 mid expiratory volume 50 - - Measurement 0 mid expiratory volume 25 - - Measurement 0
Arterial blood gas
test
BGA bei room air vs. administered
oxygen 59410-1
Oxygen saturation in Arterial blood by Pulse oximetry
--on room air Measurement 40762501
pH value 2744-1 pH of Arterial blood Measurement 3019977
partial pressure of
oxygen 2703-7
Oxygen [Partial pressure] in
Arterial blood Measurement 3027801
partial pressure of
carbon dioxide 2019-8
Carbon dioxide [Partial pressure]
in Arterial blood Measurement 3027946
extent to which oxygen saturates the hemoglobin molecules in
erythrocytes 2708-6 Oxygen saturation in arterial blood Measurement 3016502 level of
bicarbonate 1960-4
Bicarbonate [Moles/volume] in
Arterial blood Measurement 3008152
amount of base present in blood 1925-7 Base excess in Arterial blood by calculation Measurement 3003396 6-minute walk test
extent to which oxygen saturates the hemoglobin molecules in erythrocytes during activity 59412-7 Oxygen saturation in Arterial blood by Pulse oximetry
--post exercise Measurement 40762503
rate of perceived exertion before
activity 82289-0
Rating of perceived
exertion [Score] Measurement 21493652
rate of perceived exertion after
activity 82289-0
Rating of perceived
exertion [Score] Measurement 21493652
ABG after activity
type of activity 73985-4 Exercise activity Measurement 44786643
pH value 2744-1 pH of Arterial blood Measurement 3019977
partial pressure of
oxygen 2703-7
Oxygen [Partial pressure] in
Arterial blood Measurement 3027801
partial pressure of
carbon dioxide 2019-8
Carbon dioxide [Partial pressure]
in Arterial blood Measurement 3027946
extent to which oxygen saturates the hemoglobin molecules in
erythrocytes 2708-6 Oxygen saturation in arterial blood Measurement 3016502 level of
bicarbonate 1960-4
Bicarbonate [Moles/volume] in
Arterial blood Measurement 3008152
amount of base present in blood 1925-7 Base excess in Arterial blood by calculation Measurement 3003396 Fractional exhaled nitric oxide 76093-4 Nitric oxide [VFr/Ppres]
Airway adaptor Measurement 21490806
Bronchial
challenge testing
forced expiratory
volume - pre met. 43253-4 FEV1 --pre dose methacholine Measurement 3043478 forced expiratory
volume - post
met. 43255-9 FEV1 --post dose methacholine Measurement 3045045
mass of
metacholine 65866-6 Methacholine [Mass] of Dose Measurement 40768552
forced expiratory volume - post met. 43260-9 FEV1 --post 0.025 mg/mL methacholine Measurement 3046800
forced expiratory volume - post met. 43259-1 FEV1 --post 0.25 mg/mL methacholine Measurement 3043285 forced expiratory volume - post met. 43257-5 FEV1 --post 2.5 mg/mL methacholine Measurement 3046754 forced expiratory volume - post met. 43258-3 FEV1 --post 10 mg/mL methacholine Measurement 3043291 forced expiratory volume - post met. 43256-7 FEV1 --post 25 mg/mL methacholine Measurement 3046522 Cardial diagnostics electrocardiograp hy 28010-7 EKG Report (narrative) Observation 3004283
echocardiography 42148-7 US Heart Measurement 3033702
Questionnaires chronic obstructive pulmonary disease modified british medical research council, short-of-breath-grouping - - Observation 42872763 copd assessment test, patient-completed assessing impact of copd - - Observation 44802479 asthma asthma control test, patient self-administred tool for identifying those with poorly
controlled asthma 82674-3
Asthma Control
Test [ACT] Observation 42528834
Diagnosis and
medication
complete list of
diagnosis 29308-4 Diagnosis Condition
[ICD-10-GM: J44.*; J45.*]
main diagnosis 18630-4 Primary diagnosis Condition
[ICD-10-GM: J44.*; J45.*] ambulant vs
complete list of
medication 18605-6 Medication current Set Drug table
professional anamnesis 11340-7 History of Occupation Narrative Measurement 3010546 Imaging
x-ray thorax 24983-9 XR Thoracic spine Measurement 3018634
