Development of the "chronic condition measurement guide": a new tool to measure chronic conditions in older people based on ICD-10 and ATC-codes

Use of drugs with pharmacogenomics (PGx)-based dosing guidelines in a Danish cohort of persons with chronic kidney disease, both on dialysis and not on dialysis: Perspectives for prescribing optimization

AIM

The objective of this registry study is to assess the utilization of pharmacogenomic (PGx) drugs among patients with chronic kidney disease (CKD).

METHODS

This study was a retrospective study of patients affiliated with the Department of Nephrology, Aalborg University Hospital, Denmark in 2021. Patients diagnosed with CKD were divided into CKD without dialysis and CKD with dialysis. PGx prescription drugs were retrieved from the Patient Administration System. Actionable dosing guidelines (AG) for specific drug-gene pairs for CYP2D6, CYP2C9, CYP2C19 and SLCO1B1 were retrieved from the PharmGKB homepage.

RESULTS

Out of 1241 individuals, 25.5% were on dialysis. The median number of medications for each patient was 9 within the non-dialysis group and 16 within the dialysis group. Thirty-one distinct PGx drugs were prescribed. Altogether, 76.0% (943 individuals) were prescribed at least one PGx drug and the prevalence of prescriptions of PGx drugs was higher in the dialysis group compared to the non-dialysis group. The most frequently prescribed drugs with AG were metoprolol, pantoprazole, atorvastatin, simvastatin and warfarin.

CONCLUSION

This study demonstrated that a substantial proportion of patients with CKD are exposed to drugs or drug combinations for which there exists AG related to PGx of CYP2D6, CYP2C19, CYP2C9 and SLCO1B1.

Multimorbidity measures from health administrative data using ICD system codes: A systematic review

BACKGROUND

We aimed to identify and characterize adult population-based multimorbidity measures using health administrative data and the International Classification of Diseases (ICD) codes for disease identification.

METHODS

We performed a narrative systematic review of studies using or describing development or validation of multimorbidity measures. We compared the number of diseases included in the measures, the process of data extraction (case definition) and the validation process. We assessed the methodological robustness using eight criteria, five based on general criteria for indicators (AIRE instrument) and three multimorbidity-specific criteria.

RESULTS

Twenty-two multimorbidity measures were identified. The number of diseases they included ranged from 5 to 84 (median = 20), with 19 measures including both physical and mental conditions. Diseases were identified using ICD codes extracted from inpatient and outpatient data (18/22) and sometimes including drug claims (10/22). The validation process relied mainly on the capacity of the measures to predict health outcome (5/22), or on the validation of each individual disease against a gold standard (8/22). Six multimorbidity measures met at least six of the eight robustness criteria assessed.

CONCLUSION

There is significant heterogeneity among the measures used to assess multimorbidity in administrative databases, and about a third are of low to moderate quality. A more consensual approach to the number of diseases or groups of diseases included in multimorbidity measures may improve comparison between regions, and potentially provide better control for multimorbidity-related confounding in studies.