Understand LPR

Structure, history and nuances — before you write code

Published

June 6, 2026

The National Patient Register (LPR) is the source of diagnoses and hospital contacts. It covers all public hospital admissions and outpatient contacts in Denmark.

LPR is more complex than most registers, because it changed format in 2019 and is split into somatic and psychiatric tables. This page explains the structure — the periods, the ICD codes, the diagnosis types and the pitfalls you need to know before you extract data. The concrete extraction recipes are in Phase 9b — Extract from LPR.

Tip

In short: LPR changed format in 2019 — use LPR2 (up to March 2019) and LPR3 (after) and combine them. All ICD codes carry a D prefix you usually strip, and you choose diagnosis types (A/B for outcomes, +G for baseline comorbidity).

Tip

Reading order: Read this page first to understand the data. Then go to Phase 9b for the runnable code examples. You build the cohort itself in Phase 10.

LPR is split into two periods

In March 2019 LPR changed format. Studies covering the period across 2019 must query both systems and combine them.

LPR2 somatic LPR2 psychiatric LPR3
Period up to March 2019 up to March 2019 March 2019 and onwards
Contact register lpr_adm t_psyk_adm lpr_a_kontakt
Diagnosis register lpr_diag t_psyk_diag lpr_a_diagnose
Covers psychiatry No Yes Yes (both combined)
Join key recnum k_recnum / v_recnum¹ dw_ek_kontakt
Date column d_inddto (Date) d_inddto (Date) kont_starttidspunkt (datetime)²
pnr column pnr v_cpr³ pnr
Diagnosis code c_diag c_diag diag_kode
Diagnosis type c_diagtype c_diagtype diag_kode_type
Contact type c_pattype ("0" = inpatient) c_pattype kont_type ("ALCA00" = inpatient)

¹ t_psyk_adm has k_recnum; t_psyk_diag has v_recnum — rename both to recnum before joining. ² datetime format — convert with as.Date(). ³ Rename: rename(pnr = v_cpr).

Note

Psychiatry: separate in LPR2, combined in LPR3 Before 2019, psychiatric diagnoses (F-codes: dementia, depression etc.) were stored in separate registers (t_psyk_adm, t_psyk_diag). The structure resembles somatic LPR2, but column names differ — see the table footnotes above. From March 2019, LPR3 combines both: somatic and psychiatric contacts and diagnoses are in the same tables, and no separate psychiatric query is needed.

gantt
    title LPR registers
    dateFormat  YYYY
    axisFormat  %Y
    section LPR2 somatic
    lpr_adm + lpr_diag               :done,   1977, 2019
    section LPR2 psychiatric
    t_psyk_adm + t_psyk_diag         :done,   1995, 2019
    section LPR3 combined
    lpr_a_kontakt + lpr_a_diagnose   :active, 2019, 2027

ICD codes and the D-prefix

ICD-10 (International Classification of Diseases, 10th revision) is the WHO’s international system for classifying diseases and conditions. All hospital diagnoses in Denmark are coded with ICD-10, e.g. G30 for Alzheimer’s disease and F00 for dementia in Alzheimer’s.

All ICD-10 codes in DST have a prepended "D": "DG30" (Alzheimer’s), "DF00" (dementia), "DI21" (acute myocardial infarction).

Strip the D-prefix before comparison — it makes code more readable and easier to reuse:

mutate(icd3 = substr(c_diag, 2, 4))   # "DG30" → "G30" (3-digit code)
mutate(icd4 = substr(c_diag, 2, 5))   # "DI219" → "I219" (4-digit code)

substr(x, start, stop) keeps characters from position start up to and including stop (counted from 1). substr(c_diag, 2, 4) skips position 1 (the D-prefix) and keeps characters 2, 3 and 4: "DG30" → "G30". Use 2–5 for 4-digit codes: "DI219" → "I219".

Diagnosis types: A, B and G

Code Meaning When to include
A Action diagnosis — primary reason for the contact Always for outcomes
B Secondary diagnosis — additional condition present Always for outcomes
G Underlying condition — background comorbidity Only for baseline comorbidity 
# For outcomes and exclusion diagnoses:
filter(c_diagtype %in% c("A", "B"))

# For baseline comorbidity (NMI, Charlson):
filter(c_diagtype %in% c("A", "B", "G"))

Retracted diagnoses in LPR3 (senere_afkraeftet)

LPR3 flags diagnoses that have been retracted. The standard filter:

filter(is.na(senere_afkraeftet) | senere_afkraeftet != "Ja")

The is.na() part is deliberate. R’s default behaviour is: NA != "Ja" returns NA — not TRUE. A filter treats NA as FALSE and drops the row. filter(senere_afkraeftet != "Ja") alone would therefore remove all diagnoses that have no retraction marker at all (i.e. NA fields) — even though they are definitely not retracted. is.na(...) fixes this: “keep the row if the field is NA OR if it is not "Ja"”. The filter thus retains uncategorised diagnoses, which is the safest assumption.

Next steps

You now know LPR’s structure and the most important pitfalls. The next step is to extract the diagnoses with code:

→ Phase 9b — Extract from LPR

See also

External resources on LPR3 and the 2019 transition

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