Introduction

Patient registries with complete nationwide coverage and individual-level linkage potential are rare.1 The Danish National Patient Registry (DNPR), established in 1977, is renowned for its longitudinal data registration and is, therefore, a commonly used data source for population-based research.2 It encompasses personal and admission data, and information on diagnoses, treatments, and examinations.2 However, the increasing use of routinely collected health data for research puts great demands on data quality. Variables recorded in the DNPR are not automatically validated; consequently, the assessment of data quality relies on ad hoc validation studies. Although an increasing number of such validation studies have been published, the information is scattered and has not been systematically reviewed since 2015.2

The reporting of the cardiovascular data quality in registry-based research is often insufficient. Not uncommonly, papers only cite a single validation study, typically the most recent one (in terms of publication year) or the one reporting the highest positive predictive value (PPV). Optimally, referencing should reflect a summary of the evidence available from all existing validation studies for a specific variable in the study period. In addition to prioritizing larger over smaller validation studies, such summaries should consider the components of the algorithm used to identify a study variable and to what extent these components align with previous validation studies. Thus, the diagnosis code is only one component of an algorithm used to identify a disease in the DNPR. Other components include admission data (eg admission type, patient contact, and department specialty), other diagnostic specifications (such as primary vs secondary diagnoses), procedures, in-hospital medical treatment, previous medical history (to identify incident events), time since first diagnosis (to identify recurrent events), and calendar year.2 The importance of individual algorithm components for the validity of a variable has not been examined.

To provide an overview of the cardiovascular data quality in the DNPR and to examine key determinants of validity, we reviewed all validated cardiovascular variables in the DNPR from 1977 through 2024.

Methods Setting

The Danish healthcare system is universal and tax-supported, providing all Danish residents equal access to health care.2,3 Thus, access to general practitioners, private practicing specialists, hospitals, outpatient specialty clinics, and partial reimbursement of prescribed medication is covered by taxes.2,3 Self-payment covers the remaining costs related to medication and dental care.2 Referral to hospitals or specialists is initiated by the general practitioner, excluding emergency-related hospital contacts and contacts to ophthalmologists and ear, nose, and throat specialists.2,3

The ten-digit Civil Personal Register (CPR) number, assigned to all persons residing in Denmark at birth or immigration,4 allows individual-level linkage of the DNPR to other Danish registries.4

The Danish National Patient Registry Coverage

The primary aim of the DNPR is to monitor hospital and health services utilization.2 Since 1978, the DNPR has had complete nationwide coverage of inpatient contacts. From 1995 onwards, all outpatient, psychiatric, and emergency department contacts have been included.

Data types

The DNPR records administrative data, diagnoses, treatments, and examinations.2Administrative data include personal and admission data, eg hospital and department codes, admission type, patient contact type (inpatient [IN], outpatient [OUT], or emergency department [ED]), and dates of admission and discharge.2 For each hospital contact, one primary (A) and optional secondary (B) diagnoses are registered in the DNPR.2,3 The diagnoses are assigned at discharge, at transfer to another department, or at the end of an outpatient visit (before 2019 the diagnosis was assigned at the end of an outpatient course).2 According to the classification systems used (see below), treatments are categorized as surgery, other treatments, anesthesia, and intensive care. To provide cardiological context, we focused on cardiac surgery and subcategorized “other treatments” into invasive procedures (eg radiofrequency ablation and percutaneous coronary intervention), in-hospital medical treatments, pacemakers, and mechanical circulatory support. Examinations include both non-invasive (eg cardiac CT angiography) and invasive examinations (eg coronary angiogram) (Figure 1).

Figure 1 Overview of the range of positive predictive values reported for individual cardiovascular treatments and examinations in the Danish National Patient Registry (1977–2024).

Notes: The figure includes one PPV per validated variable. Thus, in cases where several PPVs were reported for a variable, we used the highest PPV. All PPVs for each validated variable are listed in Table 1.

Classification systems

The classifications used in the DNPR are provided in the Health Care Classification System (Danish, Sundhedsvæsenets Klassifikations System [SKS]).2 The SKS is a collection of international, Nordic, and Danish classifications.2 SKS codes contain up to ten alphanumeric characters, the first being a letter representing a primary group, following a monohierarchical classification system.2 Thus, diagnoses are registered under “D”, surgery under “K”, other treatments under “B”, anesthesia and intensive care under “N”, and examinations under “U” or “ZZ”.2 Until the end of 1993, diagnoses were reported according to the World Health Organization’s International Classification of Diseases (ICD), eight revision (ICD-8), and since 1994 according to the tenth revision (ICD-10).2 From 1977–1995 surgeries were reported according to the Danish Classification of Surgical Procedures and Therapies, and since 1996 according to the Danish version of the Nordic Medico-Statistical Committee Classification of Surgical Procedures (NOMESCO).2

All hospitals are legally required to upload their data to the DNPR at least monthly. In practice this is, however, often done on a weekly or daily basis.2 Since 2003, private hospitals have been obliged to report to the DNPR.2

Measures of data quality

Data quality covers accuracy and completeness. Measures for accuracy include the PPV and the negative predictive value (NPV).2,3 The PPV is the most often used measure and is defined as the proportion of patients registered with a given disease who truly have the disease. The NPV refers to the proportion of people without a given registration of a disease who truly do not have the disease. Measures of completeness, include sensitivity and specificity. Sensitivity is the proportion of true cases with a given disease who are correctly registered with that disease in the DNPR (true positive). Specificity is the proportion of people without a given disease who are correctly classified as unaffected in the registry (true negative). Of note, the NPV and specificity of cardiovascular variables in the DNPR are rarely assessed as they require a sample of people without diagnosis/procedure codes.

Systematic review Search strategy

Figure 2 presents an overview of the review process, including the search string. To provide an overview of the data quality of cardiovascular variables in the DNPR, we performed a systematic literature search of MEDLINE (PubMed) and the Danish Medical Journal (http://ugeskriftet.dk/udgivelser). Both databases were searched until 2023. In practice, we performed two searches, the first in 2015 (covering 1977–2015), and the second in 2023 (covering 2013–2023). We used identical search strings (in 2015 and 2023) as developed and published in 2015.2 It included the Danish name (“Landspatientregisteret”) as well as commonly used English terms for the DNPR. Validation may be a secondary aim not highlighted in the title or abstract of an article, potentially leading to incompleteness of the search string. Further, we used a two-year overlap with the previous search to increase the completeness. We therefore also retrieved relevant papers from reference lists, citations in screened papers, e-mail notifications from the journal Clinical Epidemiology (known to publish many validation studies), and colleagues. To provide the most updated overview, we included such additional papers through October 2024. The literature review was conducted by MS and SAJS for the 1977–2015 period and by KHL and CHF for the 2013–2024 period.

Figure 2 Flow-chart of the systematic review of studies validating cardiovascular variables in the Danish National Patient Registry (1977–2024).

Notes: The literature search was performed using the following search string in 1) PubMed: “Danish National Patient Registry” OR “Danish National Registry of Patients” OR “Danish National Hospital Register” OR “Danish National Health Registry” OR “Danish National Patient Register” OR “Danish Hospital Discharge Registry” OR “Danish National Hospital Registry” OR “Danish Hospital Registers”; and 2) the Danish Medical Journal: “Landspatientregisteret”.

Eligibility

Titles, abstracts, and, if necessary, the full text of all retrieved papers were screened for eligibility. A study was eligible for inclusion if it was published during 1977–2024 and reported any information on data quality for cardiovascular diseases within the ICD chapter I00–I99: Diseases of the circulatory system, and cardiovascular conditions outside the ICD I00–I99 chapter eg cardiac tumors (C00–D48: Neoplasms) or congenital cardiac malformations (Q00–Q99: Congenital malformations, deformations, and chromosomal abnormalities). We also included validation studies of cardiovascular treatments, ie, surgery (K codes) or other treatments (B codes), and examinations (U codes).

Extracted information

All authors independently extracted relevant information from eligible papers (MS/SAJS in 2015, and KHL/CHF in 2024). For each study, we extracted patient contact type (IN/OUT/ED), diagnosis type (A/B), occurrence type (first-time/readmission), codes/algorithms used, measures of accuracy (PPV/NPV), measure of completeness (sensitivity/specificity), the reference standard used, and results (absolute numbers, proportions, and confidence intervals [Cls]). In case of missing information, we requested additional details from the corresponding author. As CIs can be calculated in several ways, we recalculated proportions using Wilson’s score method based on the absolute numbers provided in the articles, as it ensured comparability across studies.5 If no absolute numbers were available in the article, we reported the proportions as stated by the authors. Any disagreements during the review were resolved by discussions.

Results Literature search

We identified 1,408 papers in PubMed and 675 papers in the Danish Medical Journal. After removal of duplicates a total of 2,049 papers were screened, and among these 1,848 papers were excluded because they did not validate variables in the DNPR. Additionally, 78 papers were identified from reference lists, citations, journal e-mail notifications, or colleagues. We reviewed 279 validation studies of which 63 papers assessed cardiovascular variables (34 additional papers since the first 2015 search). These 63 papers included a total of 229 cardiovascular variables covering a broad range of cardiovascular diseases, treatments, and examinations (Figure 2).

Bibliography of cardiovascular variables

A complete bibliography of all validated cardiovascular variables is presented in Table 1. The bibliography includes detailed information on time period, patient contact type, type of diagnosis, occurrence type, specified patient subgroup, measurement(s) of validity, and the reference standard used. When we describe the validity of a disease/treatment in the following sections, we refer to the validity of the algorithm used to identify the disease/treatment in the DNPR.

Table 1 Bibliography of All Validated Cardiovascular Variables in the Danish National Patient Registry (1977–2024)

To supplement Table 1 and to provide an overview of key findings, we have summarized the PPVs according to the coding classification systems (Table 1S and 2S) and clinical categorization (Figures 1 and 3). Table 1S presents a summary of the results for the validated cardiovascular diseases including ICD code, number of validation studies/variables, study period range, and PPV range. Table 2S presents a similar summary of treatments, categorized as surgeries, procedures, and examinations. Variables presented in Tables 1, 1S, and 2S are listed chronologically according to the coding classification systems. Figures 1 and 3 provide a visual overview of the PPVs for cardiovascular diseases and treatments according to clinical areas. If a study reported more than one PPV for the same variable (ie using different algorithms), we only included one of the reported PPVs in the figure. All reported PPVs for each validated variable are listed in Table 1.

Figure 3 Overview of the range of positive predictive values reported for individual cardiovascular diagnoses in the Danish National Patient Registry (1997–2024).

Abbreviation: ICD=implantable cardiac defibrillator; CRT=cardiac resynchronization therapy.

Notes: The figure includes one PPV per validated variable. Thus, in cases where several PPVs were reported for a variable, we used the highest PPV. All PPVs for each validated variable are listed in Table 1.

Cardiovascular variables overall

Among the 229 validated cardiovascular variables, 200 variables assessed diagnoses and 29 assessed procedures, including 10 surgeries, 14 other treatments, and 5 examinations (Tables 1S and 2S). The information stated in the medical record was most commonly used as the reference standard for validation. Most often one cardiovascular diagnosis, treatment, examination, or procedure was validated in each paper, but two recent studies validated 29 cardiovascular diagnoses and 14 procedures, respectively.6,67 Overall, the PPV was ≥90% for 83 (36%) variables, 80–89% for 59 (26%) variables, 70–79% for 36 (16%) variables, 60–69% for 17 (7%) variables, 50–59% for 9 (4%) variables, and <50% for 25 (11%) variables (Table 1). The data quality was generally higher for treatments (92% had PPVs ≥95%) and examinations (100% had PPVs ≥95%) than diagnoses (71% had PPVs ≥80%) (Table 1).

Cardiovascular diagnoses

Although many different diagnoses have been validated (Table 1), some remain to be assessed. For many diagnoses, eg, ischemic heart disease, the PPV improved over time. Thirty variables assessed the validity of a diagnosis of ischemic heart disease. There was an increase in the rate of validated variables over time. Eighteen variables describing acute myocardial infarction (MI) were validated. For MI, the PPVs increased over time from 92% (1979–1980) to >97% (1996–2012).6,14,16–23 However, lower PPVs were also reported in MI subgroups.16,21 Acute coronary syndrome was validated four times during 1993–2007. The PPV increased from 66% (1993–2003) to 92% (2007).14,15 Likewise, the PPV for unstable angina pectoris increased from 28% (1993–2003) to 88% (2010–2012).6,14 The PPV for angina pectoris ranged from 4 to 93% with the lowest estimates observed in subgroups, eg in breast cancer patients (PPV 47%).6,15,16

Treatment and examinations

Four studies have validated cardiac treatments and examinations (Table 1),39,65,67,68 the majority validated during 2010–2012.67 Most variables had PPVs >95% (24/29 variables). The PPV range for examinations (including echocardiography, cardiac CT angiography, and coronary angiogram) was 96–100%. For invasive procedures (including percutaneous coronary intervention, radiofrequency ablation, and right heart catheterization), the PPV range was 96–100%, while the PPV range was 89–98% for in-hospital medical treatment (including DC cardioversion, thrombolysis, and inotrope/vasopressor treatment). The PPV range was 98–100% for cardiac surgery (including mitral valve surgery, aortic valve surgery, coronary artery bypass grafting, and heart transplantation). Except for impella (PPV 38%) and intra-aortic balloon pump (PPV 43%), the PPV was also high for mechanical circulatory support (PPV 100%), including left ventricular assist device and cardiopulmonary support. Finally, the PPV was between 83 and 100% for cardiac devices, including cardiac pacemaker, cardiac resynchronization therapy pacemaker, implantable cardiac defibrillator, implantable cardioverter defibrillator, and cardiac resynchronization therapy defibrillator.

Determinants of validity

We found that the validity of a diagnosis was not solely based on the ICD code, but a combination of the ICD code and administrative data (patient contact type, type of diagnosis, and occurrence type),39,45 setting, and time period.53 For most diagnoses or treatment codes, only one overall PPV was reported. In some cases, PPVs were also reported for subgroups, eg breast cancer patients or patients with diabetes mellitus.16,21 Overall, NPVs, sensitivity, and specificity were only reported for a few validated variables.22,26,35,38

Discussion

We identified 63 papers, which validated a total of 229 variables in the DNPR during 1977–2024. The variables covered the most common cardiovascular diagnoses, treatments, and examinations. Of these, 200 variables assessed diagnoses, 24 assessed treatments (10 surgeries and 14 other treatments), and 5 assessed examinations. We observed a considerable increase in the number of validation studies45,46 and PPVs6,13, for many diagnoses over time.6,17,18,20,22,35,39,45,46 The data quality varied substantially between variables. The predictive value was generally higher for treatments (PPV≥95% for 92%) and examinations (PPV≥95% for 100%) than for diagnoses (PPV≥80% for 71%). Key determinants for the validity of diagnoses were patient contact type (inpatient vs outpatient), diagnosis type (primary vs secondary), setting (university vs regional hospitals), and calendar year.

Variations in predictive values

Predictive values depend on disease prevalence, which in turn may vary according to the setting. Thus, some studies only included diagnoses/treatments recorded at university hospitals,37 where higher PPVs are expected owing to the higher prevalence in these specialized settings. The diagnostic process for a disease may also affect the PPV. For example, low PPVs may be seen in diseases that can be challenging to diagnose, such as myocarditis (PPV=64% during 2010–2012).6 A condition, which is difficult to diagnose may likewise be challenging to validate. Some studies excluded patients with insufficient information in their medical records, which may result in lower PPVs. Similarly, PPV improvements over time may be explained by the implementation of diagnostic guidelines (eg new diagnostic criteria) or modalities to confirm or reject a disease. For instance, the use of troponin measurements for diagnosing acute myocardial infarction or updated definitions of myocardial infarction and myocardial injury.69,70 Increased awareness of correct coding among clinicians may also play a role. Finally, until 2019, the distribution of finances to the Danish hospitals was based on the use of ICD-codes according to the Diagnosis Related Groups system (DRG), which may have motivated more detailed or comprehensive coding, eg coding B diagnoses or treatments.70

Researchers should be aware that because of these possible time trends in data quality, the results may not be extrapolated outside the validated calendar periods. For instance, aortic dissection was validated during 1996–2016 while types of aortic dissection (type A and B) were only validated during the latter part of 2006–2016; thus, the validity stratified by type of dissection may not generalize to times before 2006.53 This is particularly a limitation for diagnoses and treatments with sparse validated data.

Strengths and limitation

Although the most common cardiovascular diseases, treatments, and examinations registered in DNPR have been validated, several variables remain to be examined. Nevertheless, variables related to cardiovascular disease have been more thoroughly validated than other medical specialties, eg diseases of the eye and ear (ICD-10 codes: H00–H95) or skin and subcutaneous tissue diseases (ICD-10 codes: L00–L99).2 Furthermore, there has been an increase in the rate of validated cardiovascular variables over time.2 Despite using a systematic search to identify all relevant papers, our search string may have missed some validation studies within cardiovascular diseases.

Some papers did not provide information on eg patient contact type, type of diagnosis, occurrence type, and numbers for recalculation. Although we contacted authors in case of uncertainty, we were not always able to obtain the relevant missing information. Any missing information is listed as not available (Table 1). In a few cases, the paper was excluded due to sparse information.

Perspectives and implications

This paper elucidates that the validity of diagnoses registered in the DNPR depends on components in the algorithm used for validation. Therefore, we recommend that researchers specify variable definitions according to such characteristics and strongly advise against using superficial and imprecise wordings for data quality such as “The validity of cardiovascular diagnoses in the DNPR is high” to imply that it is therefore also high for the given study variable. In contrast, we have shown that the PPV varies considerably between individual diagnoses and depends on the algorithm used to define them.

This overview of validated cardiovascular variables provides researchers with an opportunity to assess and report the validity of a diagnosis, treatment, or examination according to their study population based on several validated variables instead of a single validation study. Based on these key factors, researchers can avoid reporting a single PPV, which may give overconfidence to one or few selected validation studies, and instead provide an interval of most likely PPVs to summarize the available evidence.

Conclusions

The predictive values for cardiovascular variables in the DNPR were overall high for treatments and examinations, supporting their use for cardiovascular registry-based research. For diagnoses, the validity varied considerably between individual variables and depended on the components of the algorithm used to define them. Such components must, therefore, be considered when designing and interpreting a study using cardiovascular data from the DNPR. Importantly, not all cardiovascular variables have been validated and the data quality may change over time. The ongoing need for conducting validation studies to assess the data quality of cardiovascular variables in the DNPR, therefore, remains.

Data sharing statement

The study is based on published papers. The search string and all papers included in the study are cited in the paper.

Data permission and ethics approval

The study is based on published papers; thus this study does not need ethical approval.

Author contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

MS is supported by the Novo Nordisk Foundation (grant NNF19OC0054908). The funding source had no influence in the conduct of the study. This research received no specific grant from any funding agency in the public, commericial or non-for-profit sector.

Disclosure

The authors report no conflicts of interest in this work.

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