Upload
others
View
2
Download
0
Embed Size (px)
Citation preview
Manual for using the LABKA database for
research projects
2
3
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
4
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.
5
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, [email protected].
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.
6
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):
7
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.
8
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.
9
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
10
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
11
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.
12
Search tool Alphabetical order
Biomarker names
13
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
14
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.
15
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.
16
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.
17
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,
18
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
19
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.
20
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
21
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.
22
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, [email protected]. LABKA manual
Lars Pedersen, [email protected]. LABKA database content
Hanne Kjeldahl Schlosser, [email protected]. Access to LABKA database
Contact group from specialist council in clinical biochemistry in Region of Central Denmark:
Søren Andreas Ladefoged, [email protected]
Anette Tarp Hansen, [email protected]
Jurgita Janukonyté, [email protected]
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.