Manual for using the LABKA database for

research projects

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Table of Content

Preface ............................................................................................................................................................... 4

1. Purpose ...................................................................................................................................................... 5

2. Introduction ............................................................................................................................................... 5

3. Database Content ...................................................................................................................................... 6

3.1. Variables ............................................................................................................................................ 6

3.2. NPU terminology and the DNK codes ................................................................................................ 6

3.3. National short names (NKN) .............................................................................................................. 8

3.4. Analysis codes .................................................................................................................................... 8

4. Project guide .............................................................................................................................................. 9

4.1. Contact group and collaboration ....................................................................................................... 9

4.2. Documentation .................................................................................................................................. 9

4.3. Initiation .......................................................................................................................................... 10

4.4. Identifying biomarkers in the LABKA database ............................................................................... 15

4.5. Data management ........................................................................................................................... 17

4.5.1. Errors ....................................................................................................................................... 17

4.5.2. Completeness .......................................................................................................................... 18

4.5.3. Traceability .............................................................................................................................. 18

4.5.4. Comparability .......................................................................................................................... 19

4.6. Data analysis .................................................................................................................................... 20

4.7. Interpretation .................................................................................................................................. 22

5. References and links ................................................................................................................................ 23

5.1. NPU terminology and NKN .............................................................................................................. 23

5.2. Links for lists of analyses ................................................................................................................. 23

5.3. Guides for data management and analysis ..................................................................................... 24

5.4. References ....................................................................................................................................... 24

5.5. Previous KEA studies with LABKA .................................................................................................... 24

Appendices ...................................................................................................................................................... 26

Appendix A. Codes for municipalities and regions ...................................................................................... 26

Appendix B. List of contact persons ............................................................................................................ 33

Appendix C. Checklist for data management .............................................................................................. 34

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Preface

The LABKA database is one of many Danish health databases available for research. When combining it to

the other existing health databases in Denmark, endless opportunities for high-quality and high-impact

research are available. Nevertheless, the actual work with the LABKA database can be cumbersome and

difficult. This was a general conception among researchers and statisticians at the Dept. of Clinical

Epidemiology.

Therefore, a process was undertaken to identify the actual problems with LABKA data and how they were

dealt with. From these experiences came a request for a manual on how to approach projects using LAKBA

data. This is the first version of this manual.

The Sundhedsdatastyrelsen is expected to open a nationwide database for routine biomarkers. At current,

it is not known what the actual content of the database will be, e.g. which variables it contains,

completeness etc.. Therefore, it is uncertain whether this manual will satisfy the needs of researchers and

statisticians using nationwide data, and the manual will most likely need to be updated when more

experience with the nationwide database has been gathered.

All contributors to this manual are greatly acknowledged. For readers, please report back with any

suggestions for improvements and comments to the authors. Thank you.

Fall 2016, Aarhus, Denmark.

Johan Frederik Håkonsen Arendt

Contributors: Trine Frøslev , Kirstine Kobberøe Søgaard, Vera Ehrenstein, Anil Mor, Louise Bill, Christian

Fynbo Christiansen, Thomas Bøjer Rasmussen, Sinna Pilgaard Ulrichsen, Henrik Gammelager, Uffe Heide-

Jørgensen, Priscila Corraini , Helle Schleicher Kjær, Jesper Smit, Elisabeth Svensson, Jonathan Montomoli,

Lars Pedersen, Henrik Toft Sørensen. Anette Tarp Hansen, Jurgita Janukonyté, Søren Andreas Ladefoged.

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1. Purpose

The purpose of this manual is to introduce researchers at KEA and their collaborators to the LABKA

database, its content and how to approach a project using LABKA data. This manual is not a fulfilling A-Z

manual that can eliminate all problems or challenges encountered when working with LABKA data. It

should be considered a systematic guide, from which the steps in a project using LABKA data are addressed,

including which things to consider in each step of a project, how to identify problems and also suggestions

for solving the problems at these steps.

For non-Danish speakers, please note that a lot of the information on LABKA, e.g. codes and biomarkers

and most of the references and links provided here are in Danish and are adjusted to Danish standards. For

you, it’s advisable to consult Danish speaking collaborators.

If you are in doubt about how to approach LABKA data or the collaboration with clinical biochemist (see

4.1.), then contact Johan Frederik Håkonsen Arendt, jfba@clin.au.dk.

2. Introduction

The LABKA database is short for the “Clinical Laboratory Information System Research Database”. The

name “LABKA” comes from the IT software providing integration of information from various parts of the

software used in hospital laboratories. The database holds information on hospital laboratory analyses

performed at departments of clinical biochemistry on blood samples, urine, cerebrospinal fluid and other

bodily fluids. The geographical area covered corresponds to the current Regions of Northern and Central

Denmark (The Danish Regions were formed in 2007), the same area as the former Counties of Aarhus,

Viborg, Ringkøbing and North Jutland (Denmark was divided in Counties until 2007) plus the area

corresponding to the municipality of Horsens and Brædstrup (formerly located in Vejle County, now in the

region of Central Denmark). For former and current municipalities with corresponding codes, please see

appendix A.

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The analyses were requested from physicians at both public and private hospitals and general and special

practitioner’s offices. The requests were done as a part of routine clinical practice in the process of

diagnosis and monitoring disease progress and treatment effects. This in turn means that all persons in the

database are to be considered patients. The LABKA database is also described in detail by Grann et al (see

5.4. References).

3. Database Content

3.1. Variables

All records in all LABKA datasets hold information on CPR number, date and time of analysis, NPU code

and/or analysis code and a result. Most records also hold a NPU name and/or NKN, (Nationale kortnavne,

NKN; National short names), unit and reference range. The NPU/NKN name and the NPU codes and analysis

codes are used to identify the biomarkers (see outline in 4.3. Identifying codes). Note that not all database

outputs are equal and therefore do not contain all the same variables.

Also note, that some of these variables can be erroneous and that results from the same biomarker are not

always directly comparable. Also, you can find component names and codes that do not fit into the NPU

terminology. These issues are addressed in the following sections.

3.2. NPU terminology and the DNK codes

The NPU terminology is an international classification system for medical laboratory analyses. The

terminology is designed to give information on a current state of a relevant biological system at a specified

time point. The terminology does not describe processes or specific phases of analytical procedures. The

NPU codes are not assigned to specific analytical principles, methods or platforms, and therefore, results

are not always directly comparable. The terminology assigns a unique five digit NPU number and

corresponding name for each biomarker with the following syntax (please note the many Danish

abbreviations used – these are the ones you will find in the LABKA database and other relevant sources):

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NPU12345 System—Component; kind-of-property = ? (and sometimes) unit.

The system corresponds to the biological system in question, e.g. plasma P, cerebrospinal fluid Csv (“v” for

“væske”, fluid in Danish), secretion Sekr. In parentheses after the System can sometimes be found

specifications such as P(vB)— corresponding to plasma from venous blood or Sekr(Trachea) corresponding

to secretion from trachea. Since serum is an artefact created in the laboratory, the abbreviation S is not

found in the NPU terminology. Serum is used to approximate the state in the patient´s plasma and

therefore P is used. The component is the biomarker in question – it is the part of the system in question.

The component can be further specified using parentheses. After the semicolon is the kind-of-property.

This corresponds to concentration, number, color, sequence variation etc. Very often, the kind-of-property

is used to specify the use of arbitrary units, e.g. referring to international standard preparations, scales etc.

also in parentheses. The kind-of-property differs a lot depending on the biomarker and type of analysis. The

? is the space for the actual result, and sometimes the NPU code comes with a unit.

The Danish version of the NPU terminology, called DNK, is translated using Kemisk Ordbog, Klinisk Ordbog

and Dansk Retskrivningsordbog. Not all words/terms are translated. The Danish version is updated monthly

at www.labterm.dk, and all updates are approved by designated groups under the different scientific

societies of laboratory medicine that use NPU codes. The administration and communication of the Danish

NPU terminology is done by the National Health IT (NIS). The Danish version of the NPU terminology allows

for extension into codes with only a Danish national relevance or that can´t fit into the NPU terminology.

These codes are called DNK codes, and they have the same syntax as the NPU codes, but the prefix DNK

instead of NPU before the five digit code. Also, other prefixes, such as AAA, AAB, etc. can be found. A list of

their meaning is given in 5.1. References and links.

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3.3. National short names (NKN)

The National short names (Natonale Kortnavne, NKN) have the purpose of giving the clinician a meaningful

name for the biomarker in question, and are developed to display biomarker results at www.sundhed.dk.

The results of biomarker analyses are transferred to the electronic medical files and other electronic

platforms (e.g. general practitioners’ systems) with NKN as the biomarker identifier. They are the

administered and published by National Health IT, and are assigned and approved by designated working

groups within the relevant medical specialist areas in laboratory medicine. The NKN list is updated together

with the update of the Danish version of the NPU terminology. Each NKN can cover several NPU

codes/names, but NPU codes/names only refer to one NKN. The NKN have a different syntax than the NPU:

Component; System = ? and sometimes unit.

The NKN often leaves out the kind-of-property and has a limit of 35 characters. If a kind-of-property is

included it comes after the component (and component specifications) in the syntax. Danish translations

are used when meaningful, , e.g. “Sten” for “Calculus”. Otherwise, the terminology of the NPU is sustained.

The NKN also allows for using clinical initialisms in square brackets, e.g. [APTT] and [CDT]. Note, that by

October 6 2014, the NKN names are used in the laboratory list (“Analysefortegnelser”) from the

departments of Clinical Biochemistry in the Region of Central Denmark (link found in 5.2.)

3.4. Analysis codes The use of analysis codes was phased out in 2006/2007, and NPU/DNK codes were phased in. There is some

overlap in the period, so you may find biomarkers with both analysis codes and NPU/DNK codes. These

codes were allocated to specific biomarkers, just like the NPU codes. However, there are no available

sources to identify which corresponds to which biomarkers. Only an incomprehensive list is available (see

5.1. References and links). These codes have a varying number of digits, but no letters. Unlike NPU codes,

you may find a system called “S” for serum.

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4. Project guide

4.1. Contact group and collaboration In collaboration with the specialist council in clinical biochemistry in Region of Central Denmark, a contact

group has been established. It consists of three medical doctors specialized in clinical biochemistry, who all

show great interest in the research potentials of the LABKA database. Their contact information can be

found in Appendix B.

The agreement between KEA and the contact group was established to enhance the possibilities to

collaborate between epidemiologist and clinical biochemists, and to improve the research involving LABKA

data. In recognition that LABKA data have different properties than other types of registry data and that

each new biomarker give rise to new question, the contact group will work as consultants or collaborators

on specific projects, or facilitate contact with other clinical biochemists with the relevant expertise.

Therefore, project investigators using LABKA data are strongly encouraged to involve the contact group.

Depending on the type and amount of expertise needed, the number and type of biomarkers used and the

experience with those biomarkers, the level of collaboration or consultation will differ from one project to

the other. This manual will cover more general issues for consideration and problem solving.

4.2. Documentation

The use of LABKA data allows for the use of hundreds of biomarkers, and therefore, a great variety of

projects. Like all other types of research, the knowledge and experience with LABKA data accumulate from

different projects. To gain the most out of the LABKA data and the experiences gathered, researchers and

statisticians are strongly encouraged to document the knowledge and experience they gather during their

work with the specific biomarkers. This means, that for each individual project, the researchers should

write a summary of all the steps performed. To make this easier, a template for biomarker documentation

is available at O:\HE_KEA-Klinisk\4 Grupper\LABKA\Biomarkers studied. Also, macros for SAS are available

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for identifying and tabulating biomarker codes. Please read this template before starting your search for

the biomarker of interest. This will make the documentation a lot easier.

Likewise, statisticians and researchers should consider including syntaxes, macros and other things of

relevance from the statistical work done in the individual project.

By documenting the biomarkers that have been studied it will improve the possibilities for future projects

and the quality of those projects. This does not mean that new projects should blindly follow the work of

others, but documentation from previous projects should form a good platform for new projects, and new

projects are encouraged to follow the outline in this manual. However, be aware that LABKA records hold

sensitive data and should be treated accordingly.

Documentation should be done at O:\HE_KEA-Klinisk\4 Grupper\LABKA\Biomarkers studied. An Excel sheet

will provide an updated overview of the content of this folder. To give the best overview for others, please

update the Excel sheet when you have used a new biomarker and added the documentation using the

template provided.

4.3. Initiation

When initiating a project, the crucial element is always to construct a relevant hypothesis. To construct a

hypothesis you need to have a reasonable amount of background knowledge on the topic of interest. For

example, when you want to study the effect of a drug A on the risk or prognosis of a disease B, you need to

know something about A, B, and when using registry-based data, how the data on A and B are collected

into the registries. For the drug A you need to know something about the indications for treatment, the

administration, the effects, side effects and interactions, the epidemiology and how data on A is collected

in the database. However, drugs often come in different doses, preparations, and with the same overall

group effect, but different specific effects. For the disease B, you need to something about risk factors,

etiology, pathogenesis, diagnostic criteria, epidemiology and how data on B is collected in the registry. This

is no different when initiating projects using LABKA data. Analogous to the example above, for biomarkers

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you need to know the indication for requesting the test, how the test result can affect the patient´s

probability of diagnosis and treatment, that is, you need some clinical background knowledge of the use of

the biomarker. You also need to know about how, when and where the biomarker measurement was

performed. And biomarkers of the same name and NPU code do not always have comparable results across

calendar periods and hospital labs. Therefore, knowledge on the quality of the LABKA database content is

needed. The output from the LABKA software corresponds well to the type of data that you would want to

have in a database, so the output is more or less directly transferable from the laboratory to the database.

However, we are now aware that this does not preclude errors, missing data and data of dubious quality to

occur in the LABKA database. As in other studies, consider consulting a specialist – in this case, a laboratory

physician specialized in clinical biochemistry. Just like consulting or including a cardiologist in your project

when studying heart diseases, consider consulting a clinical biochemist in order to perform a project of high

scientific quality and validity. Information on the contact group of clinical biochemist and the agreed

procedure is found above and in Appendix B.

A good start is to go to “Analysefortegnelsen” from the Dept. of Clinical Biochemistry, Aarhus University

Hospital. The list of analyses is alphabetically sorted (and so are the lists from other labs) and has a search

function.

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Search tool Alphabetical order

Biomarker names

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Here, many biomarkers in the LABKA database can be found with NPU/DNK code and name, and often with

a short description on indication, interpretation, reference ranges and measurement ranges, type of

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analysis and its imprecision (see marked with red). All of this information can be relevant for finding codes

and names, and for later use in data management and analysis. Other departments of cIinical biochemistry

have similar lists available online (see 5.2.), and it is recommended to search those as well as previous

papers and even Google to try to get an impression of the type of biomarkers available. Often you will have

a clue of this when initiating your project. From this, you can also get an impression of the complexity in

gathering the correct codes for your project (see 4.4.). Note, that some biomarkers in the

“Analysefortegnelsen” (and similar lists) are not performed at the laboratory hosting the online list. In that

case, there will often be a link and/or name for the performing laboratory.

Since finding the correct codes can be difficult, the statistician, epidemiologist and often also the clinical

biochemist should work very closely together to identify the relevant search terms and codes. Otherwise,

you risk doing the same work twice and also of missing measurement in the LABKA database.

Not all of this information is readily available from e.g. online sources, published articles or textbooks.

Therefore, in order to gain knowledge about the biomarker in question, you often need to consult project

collaborators or consultants in clinical biochemistry (see above and Appendix B).

In line with 4.2., document your considerations, your discussions and who was involved in these parts of

the projects. Then others can benefit and learn from it.

Next is the need for the actual data and the expertise to handle, analyze and interpret the data. So when

using LABKA data, you need to know which kind of data you need (which biomarker(s)), how they can be

handled prior to data analysis, how these data should be included (single measures, repeated measures),

how they should be analyzed (exposure, outcome, covariate) and how to interpret the results from the

analytical phase. In order to plan all these steps you need to write a project protocol, and involve the

collaborators relevant to execute the protocol. The following sections will help you to create the parts of a

protocol that involves LABKA data.

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4.4. Identifying biomarkers in the LABKA database

Once you have identified the biomarker(s) relevant for your project, you need to identify them in the

LABKA database. This is done by basically searching for the relevant names and codes for the biomarker in

question in all available resources (see 5.2.), and then cross check codes and names with the content in the

LABKA database. If this has already been done for the biomarker of interest, you can find the codes and

names at: V:\4 Grupper\LABKA\Biomarkers studied. If you use codes and names already identified and

documented, please still double check and see if you have identified the relevant codes yourself. You can’t

expect the work from previous projects to be fully comprehensive for your new project. So try and go

through the documentation of the previous project that you can be inspired from.

Start off with using “Analysefortegnelsen” from Aarhus University Hospital. From that, you can get the

name(s) and code(s) used for the biomarker(s) of interest at present. Do the same with the other lists of

analyses from the other hospitals, and make a spreadsheet/document of the relevant biomarkers you find.

Then, search in the database www.labterm.dk. Below, is found a screenshot of the search tool that’s in

Danish. Always check the box “Alle poster” to include outdated codes. In the example below, the word

“albumin” is searched for. Consider using broader searches, e.g. “alb”, and remember to search for all

synonyms, Danish, English and international. Searching for specific NPU codes can also be done, and can be

helpful if to see of the NPU code(s) are/have been implemented in Denmark.

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Then a list of hits is shown:

Download or copy this list into your spreadsheet/document, and in case of multiple searches, make a full

list of candidate hits. Crosscheck the codes and names found in the different lists from departments of

clinical biochemistry, previous projects and papers and/or Google.

Perform a similar search in the descriptive dataset of the LABKA database that was made at KEA, and use

the exact same names and codes as found in the online sources for searching (usually the statisticians do

this, since they have the access to the LABKA database). Go through all the codes found, and select the

ones appropriate for your study. If in doubt, consult collaborators and/or consultants from clinical

biochemistry.

In this phase, you can often get a first impression of the work load of data management. If the unit has

changed, you can often find that in the list of hits from www.labterm.dk, and the use of different reference

intervals or cut-offs should be obtained from the different “Analysefortegnelser” from the different

hospital labs. So be aware of these issues during this phase, and you the next phase of data management is

more likely to be successful.

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4.5. Data management

Once you have found the relevant codes, the next phase is data management. During this phase, you

should be able to uncover any errors in the records, the completeness, traceability and comparability of

your biomarker(s), and consider the need for corrections, stratified analyses or exclusion. It can sometimes

be advisable to exclude codes/names where relatively few patients are recorded, especially when sampling

large cohorts. A check list of issues can be found in appendix C.

4.5.1. Errors

It is important to be aware of errors in the LABKA records. They can occur at three levels:

1. The laboratory level: Most biomarkers will have relatively few results showing a text string like the

following: “Uegnet til analyse”, “Hæmolyse”, “Ikke nok prøvemateriale”, “Prøven bortkommet” and others.

If you find such results, consider if they should be included or if the better option is to search for another

record for the individual patient(s) close to the same date as the erroneous record. The biomarker will

often be tested shortly after, if the clinician finds that the indication is upheld.

2. The database level: The most straight-forward approach to identify errors at database level is to tabulate

each of the variables relevant to your project, and look for clusters and outliers. The errors that have been

found most often are on the time variables (hours, minutes, and seconds) and for biomarkers that are

answered with a text string. For some of the erroneous time records, the error is obvious and can be

corrected, if necessary. For example, a typical error is the misplacing of hours into minutes and minutes

into seconds, leaving hours to 00 or 12. In principle, other variables from a LABKA record can also be

erroneous.

3. Text strings: some biomarkers are answered using a text string. Usually these are functional test or

genetic analyses. They are not always included in the LABKA database, but you will find text strings as the

ones stated above. Be aware that some records will be with a “<” or “>”, meaning that the measured

biomarker was below or above a certain cut-off or measurement range. They can be coded as text strings,

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but consider including them as “normal” or “abnormal” (for cut-offs) or according to their numerical value

(for outside a measurement range).

4.5.2. Completeness

The completeness of the LABKA database should both be assessed geographically and chronologically, if

relevant to your project. This can be hard to assess because there is no identifier for the specific hospital

labs. Instead, different approaches can be taken. First, if a visit to the general practitioners within a

reasonably short time span of the blood sample is recorded within Sygesikringsregistret (5.4. References:

Andersen el al), it is reasonable to identify the requester as the general practitioner. In particular, a specific

code for blood sampling (2601, 2101) will imply that the general practitioner is the requester. The same

goes for a hospital admission or outpatient clinic visit recorded in the Danish National Registry of Patients.

Then if you compare the list from Sygesikringsregistret and DNRP to the list of general practitioners and

hospitals in the geographical area, you can get an idea of the completeness of your data. Third, to assess

chronological completeness, consider tabulating results identified to specific labs or areas by year. Then

you can see if results are missing from specific areas or labs in different years. Be aware though, that

hospital departments can request a blood sample that will then be taken by the general practitioner.

4.5.3. Traceability

Traceability refers to two things. 1. Tracing the requesting physician or department. 2. Tracing the hospital

lab performing the test you identified as a LABKA record. As stated above, no identifier for specific labs or

requesting physician/department is found in the LABKA database. This makes it difficult to trace both the

requester and the performing lab at a specific hospital. Again, the following approaches can be considered.

First, some biomarkers are hardly ever used by general practitioners or only used in highly specific

diagnostic procedures. To get an idea about this, go to “Analysefortegnelsen” from Aarhus University

Hospital. It has separate lists for general practitioners and hospitals. Second, if a visit to the general

practitioners within a reasonably short time span of the analysis is recorded within Sygesikringsregistret, it

can be considered as requested from a general practitioner. For some, the Sygesikringsregistret will also

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have recorded that a blood sample was taken at the general practitioner. Likewise, if a hospital admission

or outpatient visit is recorded in the National Patient Register within a reasonable time span from the

analysis, it can be considered as requested from a hospital department. Note that some general

practitioners sent their patients to the hospital lab for blood sample collection. And, for some types of

analyses, the sample can’t be taken at by the general practitioners because of laboratory standards

precluding this. Go to “Analysefortegnelsen” from Aarhus University Hospital. In the list for general

practitioners you can collect the information given to the general practitioners on which samples should be

taken exclusively at the hospital labs. Further, hospital departments can request a blood sample that will

then be taken by the general practitioner. When the requesting physician/department has been identified

you can approximate at which hospital lab the sample was analyzed. This can be very helpful if you have

problems with comparability and want to consult specific labs that you have identified to have analyzed the

biomarker and produced the results. Also, if you are interested in specific subgroups, e.g. patients treated

by the general practitioners, the traceability is an important issue.

4.5.4. Comparability

Comparability should always be assessed. The same biomarker with the same NPU code and name will not

necessarily give comparable results if the sample is analyzed at different laboratories on different analytical

platforms. So when generating large cohorts, as often done in registry-based research, results for one

specific biomarker can have different levels, units and reference ranges or cut-offs. Therefore, obtaining

comparability is crucial when using LABKA data and should always be assessed. Before assessing the

comparability of results you should already have selected your codes so that you do not pool data of

biomarkers that are difficult or impossible to compare, e.g. B-Glucose and P-Glucose. If you have pooled

such data, you need to go back to 4.4. Identification in the LABKA database and reconsider the identified

codes. If in doubt on which biomarker codes/names that can be considered comparable, consult your

collaborator/consultant in clinical biochemístry.

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This part can perhaps be considered the trickiest part of data management. Since the NPU terminology and

the corresponding Danish modification don’t allocate codes and names to specific laboratory analyses, the

comparability across labs and codes can be difficult. Especially, in the early periods of the database where

accreditation standards and national/international quality control programs were not fully implemented.

Further, the suboptimal traceability to specific labs can complicate any attempt to correct results from

different labs and across different years with the purpose of increasing comparability.

At present, the best option is to tabulate individually all codes used and then stratify by calendar year. If

differences in patient’s median, mean, minimum, and maximum values are seen it could indicate

differences between codes or across years (see example below). Also from 4.4. Identifying biomarkers in

the LABKA database, you can get a clue of the potential for obtaining comparable results by looking for

changes in biomarker units and reference ranges. If incomparable results are suspected, it is recommended

to try to track the hospital lab using the specific code(s) and ask them to track the analytical changes for the

biomarker and ask for advice on comparability. Not always, can results be converted with standard

conversion factors and exclusion or stratification of results is the best option. Exclusion and/or stratification

can have an impact on sample size, statistical power and generalizability of your results.

4.6. Data analysis

There a many ways of applying LABKA results to your analyses. Many biomarkers are continuous results

and often have reference ranges or cut-offs. And depending on whether the biomarker is the exposure or

outcome of interest or a covariate, the options for using LABKA data in projects are numerous. This section

is not an outline of different study designs or different statistical models. It is some advice on how to take

earlier steps in this manual into consideration in the analytical phase of a project.

In the data management phase, you should have gained insight into the completeness, traceability and

comparability of LABKA records. So if “your” LABKA data are not geographically complete you should

consider restricting your analyses to an area that is complete on all data. Further, if you have not managed

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to obtain comparability for your LABKA results, consider either excluding the results considered

incomparable or dividing your analyses using strata of comparable results. Multiple imputation is also a

potential tool for handling missing data.

For some biomarkers, results outside the reference range can represent very sick patients. That is, they die

or experience outcomes that make analyses of the outcome of interest infeasible. To solve this, consider a

time period or a range of results for further exclusion to obtain a more homogenous study population. Or

consider identifying a population of more “healthy” patients, e.g. patients with blood samples taken at

their general practitioner’s request.

In order to validate your results, consider making them stratified by covariates, such as sex, calendar year,

follow-up strata, comorbidities etc. Then you can assess whether relevant covariates mark a difference in

indication for requesting a biomarker among different subgroups of patients and across the study period.

Another approach is to consider making sensitivity analyses, outlining relevant alternative scenarios and

repeating the analyses.

Because a blood sample is requested in diagnosis and treatment of diseases, patients with LABKA records

will have a both prior and subsequent higher risk of diseases than people in the background. Prior because

they consult the health care system because of symptoms (“doctor-seaking behavior”), and the health

professional finds indication for requesting a blood sample. And subsequent because depending on the

biomarker result(s) their risk of disease is modified. This in turn means that using standardization or

matching a comparison population to your patient population may be confounded by both the indication

for requesting and the results of the biomarker. Therefore, consider making analyses comparing the risk of

outcome(s) within your patient population with LABKA records, e.g. within and outside the reference range

or in different levels of the biomarker in question. Assuming that the indication for requesting the

measurement is the same (which is of cause debatable), the prior risk of disease is then comparable

between patients, and you will then only assess the subsequent risk – the risk of disease given the result(s)

of the biomarker.

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4.7. Interpretation

The interpretation of results is naturally different for each specific project. Some general issues for

consideration are outlined below. They are not essentially different from projects not using LABKA data.

As stated above, the indication of a biomarker request and the disease risk related to the biomarker result

can confound the results. This warrants consideration when interpreting results. Further, if including many

biomarkers in your study and restricting the analyses to patients with complete data on these biomarkers,

the confounding by indication is likely to increase.

On the other hand, if results are robust within strata, e.g. strata of incomparable biomarker results or of

other covariates, the association is strengthened. The same goes for robust results in sensitivity analyses.

Like for any other project, the better the project is planned and executed, the more valid the results can be

considered.

Also in this phase of a study, a laboratory physician can be helpful when interpreting results from your

study. They will often have a good overview of which diseases/outcomes are associated with the biomarker

of interest, and that may be potentially relevant for some studies.

23

5. References and links

5.1. NPU terminology and NKN

NPU terminologien – Brugermanual. Principper, administration og brug. Sektor for National Sundheds-IT.

Statens Serum Institut. 2014

Retningslinjer for udarbejdelse og administration af nationale korte navne (’NKN’) til skærmvisning af

laboratorieinformation. Redigeret 10.10.2012.

Can be found in O:\HE_KEA-Klinisk \Biostatistik & Data Management\Stat Journal Club\2015-02-16 LABKA -

Introduction and example of use\articles

List of translation from NPU names to NKN names “Analysefortegnelser” in Region of Central Denmark. The

NKN names replaced the NPU names in the “Analysefortegnelser” in 2014 October 6 . See: V:\4

Grupper\LABKA

List of analysis codes and odd prefixes: O:\HE_KEA-Klinisk\4 Grupper\LABKA\Biomarkers studied\analysis

codes NB not complete; O:\HE_KEA-Klinisk\4 Grupper\LABKA\Biomarkers studied\prefix codes

5.2. Links for lists of analyses

Database on all NPU and other codes used in Denmark: www.labterm.dk

Analysefortegnelsen from the Dept. of Clinical Biochemistry, Aarhus University Hospital:

http://www.auh.dk/om+auh/afdelinger/klinisk+biokemisk+afdeling/for+sundhedsfaglige/analysefortegnels

e/nbg

Analysefortegnelsen from the Dept. of Clinical Biochemistry, Randers Hospital:

http://www.regionshospitalet-randers.dk/afdelinger/klinisk+biokemisk+afdeling/analysefortegnelse

Laboratorievejledning from the Region of Northern Denmark:

http://www.laboratorievejledning.dk/prog/view.aspx

Analysehåndbog from Dept. of Clinical Biochemistry, Hospital unit West:

http://e-dok.rm.dk/symfonidms/d_hove_labkba.nsf/AabnKapitel/B58EF601F6C4B218C1257044001E2F8F

24

5.3. Guides for documentation, data management and analysis

O:\HE_KEA-Klinisk\4 Grupper\LABKA\Biomarkers studied\Template Documentation of LABKA biomarkers

O:\HE_KEA-Klinisk \Biostatistik & Data Management\Stat Journal Club\2015-02-16 LABKA - Introduction

and example of use

O:\HE_KEA-Klinisk \1.4 Ansættelse, arbejdstilrettelæggelse og kompetenceudvikling\5 Uddannelse og

kompetenceudvikling\Journal Club\02.12.13 Use of LABKA data

5.4. References

Grann AF, Erichsen R, Nielsen AG, Frøslev T, Thomsen RW. Existing data sources for clinical epidemiology:

The clinical laboratory information system (LABKA) research database at Aarhus University, Denmark. Clin

Epidemiol 2011:3;133-138

Schmidt M1, Pedersen L, Sørensen HT. The Danish Civil Registration System as a tool in epidemiology. Eur J

Epidemiol. 2014;29(8):541-9.

Andersen JS, Olivarius NdeF, Krasnik A.The Danish National Health Service Register. Scand J Public Health.

2011:39(Suppl 7): 34-37.

5.5. Previous KEA studies with LABKA

(list not comprehensive)

Schmidt SA, Heide-Jørgensen U, Manthripragada AD, Ehrenstein V. Prevalence and characteristics of

patients with low levels of low-density lipoprotein cholesterol in northern Denmark: a descriptive study.

Clin Epidemiol. 2015:7;201-212.

Arendt JF, Pedersen L, Nexo E, Sørensen HT. Elevated Plasma Vitamin B12 Levels as a Marker for Cancer: A

Population-Based Cohort Study. J Natl Cancer Inst. 2013;105(23):1799-1805.

Holland-Bill L1, Christiansen CF, Ulrichsen SP, Ring T, Jørgensen JO, Sørensen HT. Validity of the

International Classification of Diseases, 10th revision discharge diagnosis codes for hyponatraemia in the

Danish National Registry of Patients. BMJ Open. 2014;4(4).

25

Gammelager H, Christiansen CF, Johansen MB, Tønnesen E, Jespersen B, Sørensen HT. One-year mortality

among Danish intensive care patients with acute kidney injury: a cohort study. Crit Care. 2012;16(4).

Ben Ghezala I, Arendt JF, Erichsen R, Zalfani J, Gammelager H, Frøslev T, Olsen M. Positive predictive value

of the diagnosis coding for vitamin B12 deficiency anemia in the Danish National Patient Register. Clin

Epidemiol. 2012;4:333-8.

Zalfani J, Frøslev T, Olsen M, Ben Ghezala I, Gammelager H, Arendt JF, Erichsen R. Positive predictive value

of the International Classification of Diseases, 10th edition diagnosis codes for anemia caused by bleeding

in the Danish National Registry of Patients. Clin Epidemiol. 2012;4:327-31.

Gammelager H, Christiansen CF, Johansen MB, Tønnesen E, Jespersen B, Sørensen HT. Three-year risk of

cardiovascular disease among intensive care patients with acute kidney injury: a population-based cohort

study. Crit Care. 2014;18(5).

Hansen MK, Gammelager H, Mikkelsen MM, Hjortdal VE, Layton JB, Johnsen SP, Christiansen CF. Post-

operative acute kidney injury and five-year risk of death, myocardial infarction, and stroke among elective

cardiac surgical patients: a cohort study. Crit Care. 2013;17(6).

Gradel KO, Thomsen RW, Lundbye-Christensen S, Nielsen H, Schønheyder HC. Baseline C-reactive protein

level as a predictor of mortality in bacteraemia patients: A population-based cohort study. Clin Microbiol

Infect. 2011;17(4);627-32.

Freundlich M, Thomsen RW, Pedersen L, et al. Aminoglycoside treatment and mortality after bacteraemia

in patients given appropriate empirical therapy: A Danish hospital-based cohort study. J Antimicrob

Chemother. 2007;60:1115–1123.

Kornum JB, Thomsen RW, Riis A, et al. Diabetes, glycemic control, and risk of hospitalization with

pneumonia: A population-based casecontrol study. Diabetes Care. 2008;31:1541–1545.

Kornum JB, Thomsen RW, Riis A, et al. Type 2 diabetes and pneumonia outcomes: A population-based

cohort study. Diabetes Care. 2007;30: 2251–2257..

26

Appendices

Appendix A. Codes for municipalities and regions

Codes for the municipalities (kommune) in Denmark before and after the formation of the Danish Regions, January 1st 2007. Codes are only for

municipalities in the Regions of Central and Northern Denmark

New

Kommune

code

New

Kommune name

Old

Kommune

code

Old

Kommune name

County

number County (amt) name

Region

number

Region

name

615 Horsens 601 Brædstrup (del af) 60 Vejle Amt 082 Midtjylland

615 Horsens 609 Gedved 60 Vejle Amt 082 Midtjylland

615 Horsens 615 Horsens 60 Vejle Amt 082 Midtjylland

657 Herning 651 Aulum-Haderup 65 Ringkjøbing Amt 082 Midtjylland

657 Herning 657 Herning 65 Ringkjøbing Amt 082 Midtjylland

657 Herning 677 Trehøje 65 Ringkjøbing Amt 082 Midtjylland

657 Herning 685 Åskov 65 Ringkjøbing Amt 082 Midtjylland

661 Holstebro 661 Holstebro 65 Ringkjøbing Amt 082 Midtjylland

661 Holstebro 679 Ulfborg-Vemb 65 Ringkjøbing Amt 082 Midtjylland

661 Holstebro 683 Vinderup 65 Ringkjøbing Amt 082 Midtjylland

27

665 Lemvig 665 Lemvig 65 Ringkjøbing Amt 082 Midtjylland

665 Lemvig 673 Thyborøn-Harboøre 65 Ringkjøbing Amt 082 Midtjylland

671 Struer 671 Struer 65 Ringkjøbing Amt 082 Midtjylland

671 Struer 675 Thyholm 65 Ringkjøbing Amt 082 Midtjylland

706 Syddjurs 701 Ebeltoft 70 Århus Amt 082 Midtjylland

706 Syddjurs 721 Midtdjurs 70 Århus Amt 082 Midtjylland

706 Syddjurs 733 Rosenholm 70 Århus Amt 082 Midtjylland

706 Syddjurs 739 Rønde 70 Århus Amt 082 Midtjylland

707 Norddjurs 707 Grenå 70 Århus Amt 082 Midtjylland

707 Norddjurs 725 Nørre Djurs 70 Århus Amt 082 Midtjylland

707 Norddjurs 735 Rougsø 70 Århus Amt 082 Midtjylland

707 Norddjurs 747 Sønderhald (del af) 70 Århus Amt 082 Midtjylland

710 Favrskov 709 Hadsten 70 Århus Amt 082 Midtjylland

710 Favrskov 711 Hammel 70 Århus Amt 082 Midtjylland

710 Favrskov 713 Hinnerup 70 Århus Amt 082 Midtjylland

710 Favrskov 717 Langå (del af) 70 Århus Amt 082 Midtjylland

710 Favrskov 767 Hvorslev 76 Viborg Amt 082 Midtjylland

28

727 Odder 727 Odder 70 Århus Amt 082 Midtjylland

730 Randers 717 Langå (del af) 70 Århus Amt 082 Midtjylland

730 Randers 719 Mariager (del af) 70 Århus Amt 082 Midtjylland

730 Randers 723 Nørhald 70 Århus Amt 082 Midtjylland

730 Randers 729 Purhus 70 Århus Amt 082 Midtjylland

730 Randers 731 Randers 70 Århus Amt 082 Midtjylland

730 Randers 747 Sønderhald (del af) 70 Århus Amt 082 Midtjylland

740 Silkeborg 705 Gjern 70 Århus Amt 082 Midtjylland

740 Silkeborg 743 Silkeborg 70 Århus Amt 082 Midtjylland

740 Silkeborg 749 Them 70 Århus Amt 082 Midtjylland

740 Silkeborg 771 Kjellerup 76 Viborg Amt 082 Midtjylland

741 Samsø 741 Samsø 70 Århus Amt 082 Midtjylland

746 Skanderborg 601 Brædstrup (del af) 60 Vejle Amt 082 Midtjylland

746 Skanderborg 703 Galten 70 Århus Amt 082 Midtjylland

746 Skanderborg 715 Hørning 70 Århus Amt 082 Midtjylland

746 Skanderborg 737 Ry 70 Århus Amt 082 Midtjylland

746 Skanderborg 745 Skanderborg 70 Århus Amt 082 Midtjylland

29

751 Århus 751 Århus 70 Århus Amt 082 Midtjylland

756 Ikast-Brande 625 Nørre-Snede 60 Vejle Amt 082 Midtjylland

756 Ikast-Brande 653 Brande 65 Ringkjøbing Amt 082 Midtjylland

756 Ikast-Brande 663 Ikast 65 Ringkjøbing Amt 082 Midtjylland

760 Ringkøbing-Skjern 655 Egvad 65 Ringkjøbing Amt 082 Midtjylland

760 Ringkøbing-Skjern 659 Holmsland 65 Ringkjøbing Amt 082 Midtjylland

760 Ringkøbing-Skjern 667 Ringkøbing 65 Ringkjøbing Amt 082 Midtjylland

760 Ringkøbing-Skjern 669 Skjern 65 Ringkjøbing Amt 082 Midtjylland

760 Ringkøbing-Skjern 681 Videbæk 65 Ringkjøbing Amt 082 Midtjylland

766 Hedensted 613 Hedensted 60 Vejle Amt 082 Midtjylland

766 Hedensted 619 Juelsminde 60 Vejle Amt 082 Midtjylland

766 Hedensted 627 Tørring-Uldum (del af) 60 Vejle Amt 082 Midtjylland

773 Morsø 773 Morsø 76 Viborg Amt 081 Nordjylland

779 Skive 777 Sallingsund 76 Viborg Amt 082 Midtjylland

779 Skive 779 Skive 76 Viborg Amt 082 Midtjylland

779 Skive 781 Spøttrup 76 Viborg Amt 082 Midtjylland

779 Skive 783 Sundsøre 76 Viborg Amt 082 Midtjylland

30

787 Thisted 765 Hanstholm 76 Viborg Amt 081 Nordjylland

787 Thisted 785 Sydthy 76 Viborg Amt 081 Nordjylland

787 Thisted 787 Thisted 76 Viborg Amt 081 Nordjylland

791 Viborg 761 Bjerringbro 76 Viborg Amt 082 Midtjylland

791 Viborg 763 Fjends 76 Viborg Amt 082 Midtjylland

791 Viborg 769 Karup 76 Viborg Amt 082 Midtjylland

791 Viborg 775 Møldrup 76 Viborg Amt 082 Midtjylland

791 Viborg 789 Tjele 76 Viborg Amt 082 Midtjylland

791 Viborg 791 Viborg 76 Viborg Amt 082 Midtjylland

791 Viborg 793 Aalestrup (del af) 76 Viborg Amt 082 Midtjylland

810 Brønderslev-Dronninglund 805 Brønderslev 80 Nordjyllands Amt 081 Nordjylland

810 Brønderslev-Dronninglund 807 Dronninglund 80 Nordjyllands Amt 081 Nordjylland

813 Frederikshavn 813 Frederikshavn 80 Nordjyllands Amt 081 Nordjylland

813 Frederikshavn 841 Skagen 80 Nordjyllands Amt 081 Nordjylland

813 Frederikshavn 847 Sæby 80 Nordjyllands Amt 081 Nordjylland

820 Vesthimmerlands 793 Aalestrup (del af) 76 Viborg Amt 081 Nordjylland

820 Vesthimmerlands 809 Farsø 80 Nordjyllands Amt 081 Nordjylland

31

820 Vesthimmerlands 827 Løgstør 80 Nordjyllands Amt 081 Nordjylland

820 Vesthimmerlands 861 Aars 80 Nordjyllands Amt 081 Nordjylland

825 Læsø 825 Læsø 80 Nordjyllands Amt 081 Nordjylland

840 Rebild 833 Nørager (del af) 80 Nordjyllands Amt 081 Nordjylland

840 Rebild 843 Skørping 80 Nordjyllands Amt 081 Nordjylland

840 Rebild 845 Støvring 80 Nordjyllands Amt 081 Nordjylland

846 Mariagerfjord 719 Mariager (del af) 70 Århus Amt 081 Nordjylland

846 Mariagerfjord 793 Aalestrup (del af) 76 Viborg Amt 081 Nordjylland

846 Mariagerfjord 801 Arden 80 Nordjyllands Amt 081 Nordjylland

846 Mariagerfjord 815 Hadsund 80 Nordjyllands Amt 081 Nordjylland

846 Mariagerfjord 823 Hobro 80 Nordjyllands Amt 081 Nordjylland

846 Mariagerfjord 833 Nørager (del af) 80 Nordjyllands Amt 081 Nordjylland

849 Jammerbugt 803 Brovst 80 Nordjyllands Amt 081 Nordjylland

849 Jammerbugt 811 Fjerritslev 80 Nordjyllands Amt 081 Nordjylland

849 Jammerbugt 835 Pandrup 80 Nordjyllands Amt 081 Nordjylland

849 Jammerbugt 849 Aabybro 80 Nordjyllands Amt 081 Nordjylland

851 Aalborg 817 Hals 80 Nordjyllands Amt 081 Nordjylland

32

851 Aalborg 831 Nibe 80 Nordjyllands Amt 081 Nordjylland

851 Aalborg 837 Sejlflod 80 Nordjyllands Amt 081 Nordjylland

851 Aalborg 851 Aalborg 80 Nordjyllands Amt 081 Nordjylland

860 Hjørring 819 Hirtshals 80 Nordjyllands Amt 081 Nordjylland

860 Hjørring 821 Hjørring 80 Nordjyllands Amt 081 Nordjylland

860 Hjørring 829 Løkken-Vrå 80 Nordjyllands Amt 081 Nordjylland

860 Hjørring 839 Sindal 80 Nordjyllands Amt 081 Nordjylland

33

Appendix B. List of contact persons

Department of Clinical Epidemiology

Johan Frederik Håkonsen Arendt, jfba@clin.au.dk. LABKA manual

Lars Pedersen, lap@clin.au.dk. LABKA database content

Hanne Kjeldahl Schlosser, hks@clin.au.dk. Access to LABKA database

Contact group from specialist council in clinical biochemistry in Region of Central Denmark:

Søren Andreas Ladefoged, soerlade@rm.dk

Anette Tarp Hansen, Anette.Tarp.Hansen@skejby.rm.dk

Jurgita Janukonyté, jurgjanu@rm.dk

34

Appendix C. Checklist for data management

Have you looked for?:

1. Missing measurements: if you fewer patients than expected with measurements of the biomarker in question, consider if you have find the right

codes for that biomarker OR if the database is chronologically and/or geographically complete. Although difficult, you should from clinical

knowledge be able to judge the expected number of patients with a biomarker measured.

2. Data errors: typically either laboratory errors so sample was not analyzed (expected to be very few) OR errors in the database. Database errors

have most often been seen in relation to time variables for sample collection. Plot and tabulate the variables of relevance, and look for outliers,

clusters and missing values. Remember to check for answers with “<” and “>”.

3. Completeness:

3.1. Chronological: Tabulate proportions of missing biomarker records per year in your study population

3.2. Geographical: Tabulate proportions of missing biomarker records for each hospital (or department) in your study population

4. Traceability: Note that the LABKA database does not have requester or lab identification codes. So in case you need to identify the requester look

for either: A. a hospital contact within a reasonable timeframe before the biomarker record; B. a code for “consultation” or “blood sampling” in

“Sygesikringsregistret”, corresponding to a fee given to the patient’s general practitioner.

5. Comparability: Although standardization has improved in recent years, biomarker results have an inherent challenge of comparability. Therefore,

tabulate units, reference ranges, and values of minimum, maximum, mean, median and possible other distribution characteristics for each

biomarker code (NPU, local Danish code, analysis code) and for each year of the study period.

You are strongly encouraged to work closely with the statistician and the contact group (see appendix B).

Reports/PhD theses from Department of Clinical Epidemiology

1. Ane Marie Thulstrup: Mortality, infections and operative risk in patients with liver cirrhosis in Denmark. Clinical epidemiological studies. PhD thesis. 2000.

2. Nana Thrane: Prescription of systemic antibiotics for Danish children. PhD thesis. 2000.

3. Charlotte Søndergaard. Follow-up studies of prenatal, perinatal and postnatal risk factors in infantile colic. PhD thesis. 2001.

4. Charlotte Olesen: Use of the North Jutland Prescription Database in epidemiological studies of drug use and drug safety during pregnancy. PhD thesis. 2001.

5. Yuan Wei: The impact of fetal growth on the subsequent risk of infectious disease and asthma in childhood. PhD thesis. 2001.

6. Gitte Pedersen. Bacteremia: treatment and prognosis. PhD thesis. 2001.

7. Henrik Gregersen: The prognosis of Danish patients with monoclonal gammopathy of undertermined significance: register-based studies. PhD thesis. 2002.

8. Bente Nørgård: Colitis ulcerosa, coeliaki og graviditet; en oversigt med speciel reference til forløb og sikkerhed af medicinsk behandling. PhD thesis. 2002.

9. Søren Paaske Johnsen: Risk factors for stroke with special reference to diet, Chlamydia pneumoniae, infection, and use of non-steroidal anti-inflammatory drugs. PhD thesis. 2002.

10. Elise Snitker Jensen: Seasonal variation of meningococcal disease and factors associated with its outcome. PhD thesis. 2003.

11. Andrea Floyd: Drug-associated acute pancreatitis. Clinical epidemiological studies of selected drugs. PhD thesis. 2004.

12. Pia Wogelius: Aspects of dental health in children with asthma. Epidemiological studies of dental anxiety and caries among children in North Jutland County, Denmark. PhD thesis. 2004.

13. Kort-og langtidsoverlevelse efter indlæggelse for udvalgte kræftsygdomme i Nordjyllands, Viborg og Århus amter 1985-2003. 2004.

14. Reimar W. Thomsen: Diabetes mellitus and community-acquired bacteremia: risk and prognosis. PhD thesis. 2004.

15. Kronisk obstruktiv lungesygdom i Nordjyllands, Viborg og Århus amter 1994-2004. Forekomst og prognose. Et pilotprojekt. 2005.

16. Lungebetændelse i Nordjyllands, Viborg og Århus amter 1994-2004. Forekomst og prognose. Et pilotprojekt. 2005.

17. Kort- og langtidsoverlevelse efter indlæggelse for nyre-, bugspytkirtel- og leverkræft i Nordjyllands, Viborg, Ringkøbing og Århus amter 1985-2004. 2005.

18. Kort- og langtidsoverlevelse efter indlæggelse for udvalgte kræftsygdomme i Nordjyllands, Viborg, Ringkøbing og Århus amter 1995-2005. 2005.

19. Mette Nørgaard: Haematological malignancies: Risk and prognosis. PhD thesis. 2006.

20. Alma Becic Pedersen: Studies based on the Danish Hip Arthroplastry Registry. PhD thesis. 2006.

Særtryk: Klinisk Epidemiologisk Afdeling - De første 5 år. 2006.

21. Blindtarmsbetændelse i Vejle, Ringkjøbing, Viborg, Nordjyllands og Århus Amter. 2006.

22. Andre sygdommes betydning for overlevelse efter indlæggelse for seks kræftsygdomme i Nordjyllands, Viborg, Ringkjøbing og Århus amter 1995-2005. 2006.

23. Ambulante besøg og indlæggelser for udvalgte kroniske sygdomme på somatiske hospitaler i Århus, Ringkjøbing, Viborg, og Nordjyllands amter. 2006.

24. Ellen M Mikkelsen: Impact of genetic counseling for hereditary breast and ovarian cancer disposition on psychosocial outcomes and risk perception: A population-based follow-up study. PhD thesis. 2006.

25. Forbruget af lægemidler mod kroniske sygdomme i Århus, Viborg og Nordjyllands amter 2004-2005. 2006.

26. Tilbagelægning af kolostomi og ileostomi i Vejle, Ringkjøbing, Viborg, Nordjyllands og Århus Amter. 2006.

27. Rune Erichsen: Time trend in incidence and prognosis of primary liver cancer and liver cancer of unknown origin in a Danish region, 1985-2004. Research year report. 2007.

28. Vivian Langagergaard: Birth outcome in Danish women with breast cancer, cutaneous malignant melanoma, and Hodgkin’s disease. PhD thesis. 2007.

29. Cynthia de Luise: The relationship between chronic obstructive pulmonary disease, comorbidity and mortality following hip fracture. PhD thesis. 2007.

30. Kirstine Kobberøe Søgaard: Risk of venous thromboembolism in patients with liver disease: A nationwide population-based case-control study. Research year report. 2007.

31. Kort- og langtidsoverlevelse efter indlæggelse for udvalgte kræftsygdomme i Region Midtjylland og Region Nordjylland 1995-2006. 2007.

32. Mette Skytte Tetsche: Prognosis for ovarian cancer in Denmark 1980-2005: Studies of use of hospital discharge data to monitor and study prognosis and impact of comorbidity and venous thromboembolism on survival. PhD thesis. 2007.

33. Estrid Muff Munk: Clinical epidemiological studies in patients with unexplained chest and/or epigastric pain. PhD thesis. 2007.

34. Sygehuskontakter og lægemiddelforbrug for udvalgte kroniske sygdomme i Region Nordjylland. 2007.

35. Vera Ehrenstein: Association of Apgar score and postterm delivery with neurologic morbidity: Cohort studies using data from Danish population registries. PhD thesis. 2007.

36. Annette Østergaard Jensen: Chronic diseases and non-melanoma skin cancer. The impact on risk and prognosis. PhD thesis. 2008.

37. Use of medical databases in clinical epidemiology. 2008.

38. Majken Karoline Jensen: Genetic variation related to high-density lipoprotein metabolism and risk of coronary heart disease. PhD thesis. 2008.

39. Blodprop i hjertet - forekomst og prognose. En undersøgelse af førstegangsindlæggelser i Region Nordjylland og Region Midtjylland. 2008.

40. Asbestose og kræft i lungehinderne. Danmark 1977-2005. 2008.

41. Kort- og langtidsoverlevelse efter indlæggelse for udvalgte kræftsygdomme i Region Midtjylland og Region Nordjylland 1996-2007. 2008.

Sandra Kruchov Thygesen. Atrial fibrillation in patients with ischemic stroke: A population-based study. Research year report. 2008.

42. Akutte indlæggelsesforløb og skadestuebesøg på hospiter i Region Midtjylland og Region Nordjylland 2003-2007. Et pilotprojekt. Not published.

43. Peter Jepsen: Prognosis for Danish patients with liver cirrhosis. PhD thesis. 2009.

44. Lars Pedersen: Use of Danish health registries to study drug-induced birth defects – A review with special reference to methodological issues and maternal use of non-steroidal anti-inflammatory drugs and Loratadine. PhD thesis. 2009.

45. Steffen Christensen: Prognosis of Danish patients in intensive care. Clinical epidemiological studies on the impact of preadmission cardiovascular drug use on mortality. PhD thesis. 2009.

46. Morten Schmidt: Use of selective cyclooxygenase-2 inhibitors and nonselective nonsteroidal antiinflammatory drugs and risk of cardiovascular events and death after intracoronary stenting. Research year report. 2009.

47. Jette Bromman Kornum: Obesity, diabetes and hospitalization with pneumonia. PhD thesis. 2009.

48. Theis Thilemann: Medication use and risk of revision after primary total hip arthroplasty. PhD thesis. 2009.

49. Operativ fjernelse af galdeblæren. Region Midtjylland & Region Nordjylland. 1998-2008. 2009.

50. Mette Søgaard: Diagnosis and prognosis of patients with community-acquired bacteremia. PhD thesis. 2009.

51. Marianne Tang Severinsen. Risk factors for venous thromboembolism: Smoking, anthropometry and genetic susceptibility. PhD thesis. 2010.

52. Henriette Thisted: Antidiabetic Treatments and ischemic cardiovascular disease in Denmark: Risk and outcome. PhD thesis. 2010.

53. Kort- og langtidsoverlevelse efter indlæggelse for udvalgte kræftsygdomme. Region Midtjylland og Region Nordjylland 1997-2008. 2010.

54. Prognosen efter akut indlæggelse på Medicinsk Visitationsafsnit på Nørrebrogade, Århus Sygehus. 2010.

55. Kaare Haurvig Palnum: Implementation of clinical guidelines regarding acute treatment and secondary medical prophylaxis among patients with acute stroke in Denmark. PhD thesis. 2010.

56. Thomas Patrick Ahern: Estimating the impact of molecular profiles and prescription drugs on breast cancer outcomes. PhD thesis. 2010.

57. Annette Ingeman: Medical complications in patients with stroke: Data validity, processes of care, and clinical outcome. PhD thesis. 2010.

58. Knoglemetastaser og skeletrelaterede hændelser blandt patienter med prostatakræft i Danmark. Forekomst og prognose 1999-2007. 2010.

59. Morten Olsen: Prognosis for Danish patients with congenital heart defects - Mortality, psychiatric morbidity, and educational achievement. PhD thesis. 2010.

60. Knoglemetastaser og skeletrelaterede hændelser blandt kvinder med brystkræft i Danmark. Forekomst og prognose 1999-2007. 2010.

61. Kort- og langtidsoverlevelse efter hospitalsbehandlet kræft. Region Midtjylland og Region Nordjylland 1998-2009. 2010.

62. Anna Lei Lamberg: The use of new and existing data sources in non-melanoma skin cancer research. PhD thesis. 2011.

63. Sigrún Alba Jóhannesdóttir: Mortality in cancer patients following a history of squamous cell skin cancer – A nationwide population-based cohort study. Research year report. 2011.

64. Martin Majlund Mikkelsen: Risk prediction and prognosis following cardiac surgery: the EuroSCORE and new potential prognostic factors. PhD thesis. 2011.

65. Gitte Vrelits Sørensen: Use of glucocorticoids and risk of breast cancer: a Danish population-based case-control study. Research year report. 2011.

66. Anne-Mette Bay Bjørn: Use of corticosteroids in pregnancy. With special focus on the relation to congenital malformations in offspring and miscarriage. PhD thesis. 2012.

67. Marie Louise Overgaard Svendsen: Early stroke care: studies on structure, process, and outcome. PhD thesis. 2012.

68. Christian Fynbo Christiansen: Diabetes, preadmission morbidity, and intensive care: population-based Danish studies of prognosis. PhD thesis. 2012.

69. Jennie Maria Christin Strid: Hospitalization rate and 30-day mortality of patients with status asthmaticus in Denmark – A 16-year nationwide population-based cohort study. Research year report. 2012.

70. Alkoholisk leversygdom i Region Midtjylland og Region Nordjylland. 2007-2011. 2012.

71. Lars Jakobsen: Treatment and prognosis after the implementation of primary percutaneous coronary intervention as the standard treatment for ST-elevation myocardial infarction. PhD thesis. 2012.

72. Anna Maria Platon: The impact of chronic obstructive pulmonary disease on intensive care unit admission and 30-day mortality in patients undergoing colorectal cancer surgery: a Danish population-based cohort study. Research year report. 2012.

73. Rune Erichsen: Prognosis after Colorectal Cancer - A review of the specific impact of comorbidity, interval cancer, and colonic stent treatment. PhD thesis. 2013.

74. Anna Byrjalsen: Use of Corticosteroids during Pregnancy and in the Postnatal Period and Risk of Asthma in Offspring - A Nationwide Danish Cohort Study. Research year report. 2013.

75. Kristina Laugesen: In utero exposure to antidepressant drugs and risk of attention deficit hyperactivity disorder (ADHD). Research year report. 2013.

76. Malene Kærslund Hansen: Post-operative acute kidney injury and five-year risk of death, myocardial infarction, and stroke among elective cardiac surgical patients: A cohort study. Research year report. 2013.

77. Astrid Blicher Schelde: Impact of comorbidity on the prediction of first-time myocardial infarction, stroke, or death from single-photon emission computed tomography myocardial perfusion imaging: A Danish cohort study. Research year report. 2013.

78. Risiko for kræft blandt patienter med kronisk obstruktiv lungesygdom (KOL) i Danmark. (Online publication only). 2013.

79. Kirurgisk fjernelse af milten og risikoen for efterfølgende infektioner, blodpropper og død. Danmark 1996-2005. (Online publication only). 2013.

Jens Georg Hansen: Akut rhinosinuitis (ARS) – diagnostik og behandling af voksne i almen praksis. 2013.

80. Henrik Gammelager: Prognosis after acute kidney injury among intensive care patients. PhD thesis. 2014.

81. Dennis Fristrup Simonsen: Patient-Related Risk Factors for Postoperative Pneumonia following Lung Cancer Surgery and Impact of Pneumonia on Survival. Research year report. 2014.

82. Anne Ording: Breast cancer and comorbidity: Risk and prognosis. PhD thesis. 2014.

83. Kristoffer Koch: Socioeconomic Status and Bacteremia: Risk, Prognosis, and Treatment. PhD thesis. 2014.

84. Anne Fia Grann: Melanoma: the impact of comorbidities and postdiagnostic treatments on prognosis. PhD thesis. 2014.

85. Michael Dalager-Pedersen: Prognosis of adults admitted to medical departments with community-acquired bacteremia. PhD thesis. 2014.

86. Henrik Solli: Venous thromboembolism: risk factors and risk of subsequent arterial thromboembolic events. Research year report. 2014.

87. Eva Bjerre Ostenfeld: Glucocorticoid use and colorectal cancer: risk and postoperative outcomes. PhD thesis. 2014.

88. Tobias Pilgaard Ottosen: Trends in intracerebral haemorrhage epidemiology in Denmark between 2004 and 2012: Incidence, risk-profile and case-fatality. Research year report. 2014.

89. Lene Rahr-Wagner: Validation and outcome studies from the Danish Knee Ligament Reconstruction Registry. A study in operatively treated anterior cruciate ligament injuries. PhD thesis. 2014.

90. Marie Dam Lauridsen: Impact of dialysis-requiring acute kidney injury on 5-year mortality after myocardial infarction-related cardiogenic shock - A population-based nationwide cohort study. Research year report. 2014.

91. Ane Birgitte Telén Andersen: Parental gastrointestinal diseases and risk of asthma in the offspring. A review of the specific impact of acid-suppressive drugs, inflammatory bowel disease, and celiac disease. PhD thesis. 2014.

Mikkel S. Andersen: Danish Criteria-based Emergency Medical Dispatch – Ensuring 112 callers the right help in due time? PhD thesis. 2014.

92. Jonathan Montomoli: Short-term prognosis after colorectal surgery: The impact of liver disease and serum albumin. PhD thesis. 2014.

93. Morten Schmidt: Cardiovascular risks associated with non-aspirin non-steroidal anti-inflammatory drug use: Pharmacoepidemiological studies. PhD thesis. 2014.

94. Betina Vest Hansen: Acute admission to internal medicine departments in Denmark - studies on admission rate, diagnosis, and prognosis. PhD thesis. 2015.

95. Jacob Gamst: Atrial Fibrillation: Risk and Prognosis in Critical Illness. PhD thesis. 2015.

96. Søren Viborg: Lower gastrointestinal bleeding and risk of gastrointestinal cancer. Research year report. 2015.

97. Heidi Theresa Ørum Cueto: Folic acid supplement use in Danish pregnancy planners: The impact on the menstrual cycle and fecundability. PhD thesis. 2015.

98. Niwar Faisal Mohamad: Improving logistics for acute ischaemic stroke treatment: Reducing system delay before revascularisation therapy by reorganisation of the prehospital visitation and centralization of stroke care. Research year report. 2015.

99. Malene Schou Nielsson: Elderly patients, bacteremia, and intensive care: Risk and prognosis. PhD thesis. 2015.

100. Jens Tilma: Treatment Injuries in Danish Public Hospitals 2006-2012. Research year report. 2015.

101. Thomas Lyngaa: Intensive care at the end-of-life in patients dying of cancer and non-cancer chronic diseases: A nationwide study. Research year report. 2015.

102. Lone Winther Lietzen: Markers of immune competence and the clinical course of breast cancer. PhD thesis. 2015.

103. Anne Høy Seemann Vestergaard: Geographical Variation in Use of Intensive Care in Denmark: A Nationwide Study. Research year report. 2015.

104. Cathrine Wildenschild Nielsen: Fecundability among Danish pregnancy planners. Studies on birth weight, gestational age and history of miscarriage. PhD thesis. 2015.

105. Kathrine Dyhr Lycke: Preadmission use of antidepressants and quality of care, intensive care admission and mortality of colorectal cancer surgery – a nationwide population-based cohort study. Research year report. 2015.

106. Louise Bill: Hyponatremia in acute internal medicine patients: prevalence and prognosis. PhD thesis. 2015.

107. Kirstine Kobberøe Søgaard: Risk and prognosis of venous thromboembolism in patients with liver disease. PhD thesis. 2015.

108. Rikke Nørgaard Pedersen: Reoperation due to surgical bleeding in breast cancer patients and breast cancer recurrence: A Danish population-based cohort study. Research year report. 2015.

109. Thomas Deleuran: Cirrhosis of the liver and diseases of the large joints. PhD Thesis. 2016.

110. Anne Mette Falstie-Jensen: Hospital accreditation – what’s in it for the patients? PhD thesis. 2016.

111. Sandra Kruchov Thygesen: Respiratory distress syndrome in moderately late and late preterm infants and selected long-term neurodevelopmental outcomes. PhD thesis. 2016.

112. Alma Bečić Pedersen: Total hip replacement surgery - occurrence and prognosis. Doctoral dissertation. 2016.

113. Anil Mor: Type 2 Diabetes and Risk of Infections. PhD thesis. 2016.

114. Aske Hess Rosenquist: Behavioral Development Following Early Life Organochlorine Exposure. Research year report. 2016.

115. Simon Ramsdal Sørensen: Anti-platelet and Anti-coagulant Prescriptions and Breast Cancer Recurrence: a Danish Nationwide Prospective Cohort Study. Research year report. 2016.

116. Regional Differences in Treatment of Patients with Inflammatory Bowel Disease in Denmark

117. Clara Reece Medici: Impact of preadmission anti-inflammatory drug use on risk of depression and anxiety after intensive care requiring mechanical ventilation. Research year report. 2016.

118. Johan Frederik Håkonsen Arendt. Clinical implications and biochemical understanding of high plasma vitamin B12 levels. PhD thesis. 2016.