Dataset Information

The validity of pathology codes for biopsy-confirmed kidney disease in the Danish National Patobank.

ABSTRACT:

Background

This study validates the application of Systematized Nomenclature of Medicine second edition (SNOMED II) codes used to describe medical kidney biopsies in Denmark in encoded form, aiming to support robust epidemiological research on the causes, treatments and prognosis of kidney diseases.

Methods

Kidney biopsy reports from 1 January 1998 to 31 December 2018 were randomly extracted from the Danish National Patobank, using SNOMED codes. A 5% sample was selected, and nephrologists assessed the corresponding medical records, assigning each case the applied clinical diagnoses. Sensitivity, specificity, positive predictive values (PPV), negative predictive values and Cohen's kappa coefficient for the retrieved SNOMED codes were calculated.

Results

A total of 613 kidney biopsies were included. The primary clinical disease groups were glomerular disease (n = 368), tubulointerstitial disease (n = 67), renal vascular disease (n = 51), diabetic nephropathy (n = 51) and various renal disorders (n = 40). Several SNOMED codes were used to describe each clinical disease group and PPV for the combined SNOMED codes were high for glomerular disease (94%), diabetic nephropathy (85%) and systemic diseases affecting the kidney (96%). Conversely, tubulointerstitial disease (62%), renal vascular disease (60%) and other renal disorders (17%) showed lower PPV.

Conclusions

SNOMED codes have a high PPV for glomerular diseases, diabetic nephropathy and systemic diseases affecting the kidney, in which they could be applied for future epidemiological research.

SUBMITTER: Moller M

PROVIDER: S-EPMC11316393 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Publications

The validity of pathology codes for biopsy-confirmed kidney disease in the Danish National Patobank.

Møller Marie M Bressendorff Iain I Borg Rikke R Dieperink Hans H Gregersen Jon W JW Hansen Helle H Hommel Kristine K Hornum Mads M Ivarsen Per P Jensen Karina H KH Jørgensen Morten B MB Kristensen Tilde T Krustrup Dorrit D Mose Frank H FH Rossing Peter P Otte Kjeld E KE Persson Frederik F Schandorff Kristine D KD Hansen Ditte D

Clinical kidney journal 20240709 8

<h4>Background</h4>This study validates the application of Systematized Nomenclature of Medicine second edition (SNOMED II) codes used to describe medical kidney biopsies in Denmark in encoded form, aiming to support robust epidemiological research on the causes, treatments and prognosis of kidney diseases.<h4>Methods</h4>Kidney biopsy reports from 1 January 1998 to 31 December 2018 were randomly extracted from the Danish National Patobank, using SNOMED codes. A 5% sample was selected, and nephr ...[more]

PMID: 39131077

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Project description:ObjectiveTo examine the validity of the International Classification of Diseases, 10th revision (ICD-10) codes for hyponatraemia in the nationwide population-based Danish National Registry of Patients (DNRP) among inpatients of all ages.DesignPopulation-based validation study.SettingAll somatic hospitals in the North and Central Denmark Regions from 2006 through 2011.ParticipantsPatients of all ages admitted to hospital (n=819 701 individual patients) during the study period. The patient could be included in the study more than once, and our study did not restrict to patients with serum sodium measurements (total of n=2 186 642 hospitalisations).Main outcome measureWe validated ICD-10 discharge diagnoses of hyponatraemia recorded in the DNRP, using serum sodium measurements obtained from the laboratory information systems (LABKA) research database as the gold standard. One sodium value <135 mmol/L measured at any time during hospitalisation confirmed the diagnosis. We estimated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for ICD-10 codes for hyponatraemia overall and for cut-off points for increasing hyponatraemia severity.ResultAn ICD-10 code for hyponatraemia was recorded in the DNRP in 5850 of the 2 186 642 hospitalisations identified. According to laboratory measurements, however, hyponatraemia was present in 306 418 (14%) hospitalisations. Sensitivity of hyponatraemia diagnoses was 1.8% (95% CI 1.7% to 1.8%). For sodium values <115 mmol/L, sensitivity was 34.3% (95% CI 32.6% to 35.9%). The overall PPV was 92.5% (95% CI 91.8% to 93.1%) and decreased with increasing hyponatraemia severity. Specificity and NPV were high for all cut-off points (≥99.8% and ≥86.2%, respectively). Patients with hyponatraemia without a corresponding ICD-10 discharge diagnosis were younger and had higher Charlson Comorbidity Index scores than patients with hyponatraemia with a hyponatraemia code in the DNRP.ConclusionsICD-10 codes for hyponatraemia in the DNRP have high specificity but very low sensitivity. Laboratory test results, not discharge diagnoses, should be used to ascertain hyponatraemia.

Project description:Malnutrition is associated with adverse outcomes in patients with diabetic kidney disease (DKD). However, it is uncertain which nutritional assessment tools are most effective in predicting the adverse outcomes of DKD. This retrospective study was conducted at a single center and included 367 patients diagnosed with DKD based on biopsy results between August 2009 and December 2018. Four nutritional assessment indices, namely the Prognostic Nutritional Index (PNI), Geriatric Nutritional Risk Index (GNRI), Triglycerides (TG) × Total Cholesterol (TC) × Body Weight (BW) Index (TCBI), and Controlling Nutritional Status (CONUT) score, were selected and calculated. We aimed to assess the association between these nutritional scores and adverse outcomes, including progression to end-stage kidney disease (ESKD), cardiovascular diseases events (CVD), and all-cause mortality. Univariate and multivariate Cox regression analyses, Kaplan-Meier analysis, along with Restricted cubic spline analysis were used to examine the relationship between nutritional scores and adverse outcomes. Furthermore, the area under the curve (AUC) was calculated using time-dependent receiver operating characteristics to determine the predictive value of the four nutritional scores alone and some combinations. Lastly, ordered logistic regression analysis was conducted to explore the correlation between the four nutritional scores and different renal histologic changes. The incidence of ESKD, CVD, and all-cause mortality was significantly higher in patients with DKD who had a lower PNI, lower GNRI, and higher CONUT score. Additionally, The TCBI performed the worst in terms of grading and risk assessment. The PNI offer the highest predictive value for adverse outcomes and a stronger correlation with renal histologic changes compared to other nutritional scores. Patients diagnosed with DKD who have a worse nutritional status are more likely to experience higher rates of adverse outcomes. The PNI might offer more valuable predictive values and a stronger correlation with different renal histologic changes compared to other nutritional scores.

Dataset Information

The validity of pathology codes for biopsy-confirmed kidney disease in the Danish National Patobank.

Background

Methods

Results

Conclusions

Publications

The validity of pathology codes for biopsy-confirmed kidney disease in the Danish National Patobank.

Similar Datasets

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