Are there lost opportunities in chronic kidney disease? A region-wide cohort study

Objectives Identify the windows of opportunity for the diagnosis of chronic kidney disease (CKD) and the prevention of its adverse outcomes and quantify the potential population gains of such prevention. Design and setting Observational, population-wide study of residents in the Stockholm and Skåne regions of Sweden between 1 January 2015 and 31 December 2020. Participants All patients who did not yet have a diagnosis of CKD in healthcare but had CKD according to laboratory measurements of CKD biomarkers available in electronic health records. Outcome measures We assessed the proportions of the patient population that received a subsequent diagnosis of CKD in healthcare, that used guideline-directed pharmacological therapy (statins, renin-angiotensin aldosterone system inhibitors (RAASi) and/or sodium-glucose cotransporter-2 inhibitors (SGLT2i)) and that experienced adverse outcomes (all-cause mortality, cardiovascular mortality or major adverse cardiovascular events (MACE)). The potential to prevent adverse outcomes in CKD was assessed using simulations of guideline-directed pharmacological therapy in untreated subsets of the study population. Results We identified 99 382 patients with undiagnosed CKD during the study period. Only 33% of those received a subsequent diagnosis of CKD in healthcare after 5 years. The proportion that used statins or RAASi was of similar size to the proportion that didn’t, regardless of how advanced their CKD was. The use of SGLT2i was negligible. In simulations of optimal treatment, 22% of the 21 870 deaths, 27% of the 14 310 cardiovascular deaths and 39% of the 22 224 MACE could have been avoided if every patient who did not use an indicated medication for their laboratory-confirmed CKD was treated with guideline-directed pharmacological therapy for CKD. Conclusions While we noted underdiagnosis and undertreatment of CKD in this large contemporary population, we also identified a substantial realisable potential to improve CKD outcomes and reduce its burden by treating patients early with guideline-directed pharmacological therapy.


Figure S2
. Multi-state models presenting the proportions of patients diagnosed or not diagnosed with chronic kidney disease (CKD) in healthcare who received or didn't receive guidelinedirected pharmacological therapy, including the terminal, absorbing outcome, all-cause death .
Figure S3.Multi-state models presenting the proportions of patients diagnosed or not diagnosed with chronic kidney disease (CKD) in healthcare who received or didn't receive guidelinedirected pharmacological therapy, including the terminal, absorbing outcomes, major adverse cardiovascular events (MACE) and non-cardiovascular disease (CVD) death ............................ Figure S4.Multi-state models presenting the proportions of patients diagnosed or not diagnosed with chronic kidney disease (CKD) in healthcare who received or didn't receive guidelinedirected pharmacological therapy, including the terminal, absorbing outcome, all-cause death .

Supplemental Methods. The CELOSIA database
The CELOSIA database, from which this present study's sample was extracted, includes all Swedish residents aged ≥18 years who were diagnosed with chronic kidney disease (CKD), heart failure or diabetes mellitus between January 1 st , 2000, and December 31 st , 2020.
Patients were identified from several sources with national coverage, including the Swedish Prescribed Drug Registry, the National Patient Registry, and the National Death Registry.The Swedish Prescribed Drug Registry logs each resident's filled drug prescriptions according to the Anatomical Therapeutic Chemical [ATC] classification system. 1 The National Patient Registry and the National Death Registry records diagnoses, surgical procedures, and causes of death using the International Classification of Diseases [ICD] 2 system and the Nordic Medico-Statistical Committee Classification of Surgical Procedure system. 3 Patients were also identified from several data sources with regional coverage, including the Healthcare Data Warehouse of Region Stockholm, the Healthcare Data Warehouse of Region Skåne Melior, and the Profdoc Medical Office of Region Skåne.Additionally, the CELOSIA database utilized electronic health records from Region Stockholm and Region Skåne to diagnose patients with CKD using laboratory measurements.
A complete list of the ATC codes, ICD-10 codes, procedure codes, and laboratory values used to identify these patients is available in Supplemental Table 2. Patients were required to have a 12-digit personal identity number, an identity number unique to all Swedish residents, 4 enabling data to be linked between the different sources.
The total population with data available for the present study was ~3.8 million people, corresponding to 36% of the Swedish population.
Table S1.Meta-analyses that assessed the effect of first-line pharmacological treatments on the risk of major adverse cardiovascular events in patients with chronic kidney disease, with or without diabetes CKD denotes chronic kidney disease; CVD, cardiovascular disease; MACE, major cardiovascular events.ICD-10 codes for "CKD (broad)" were used to search for diagnoses of CKD prior to the index date, which would exclude them from the analyses.ICD-10 codes for "CKD (narrow) were used to search for diagnoses of CKD during follow-up.
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) Data for baseline demographics and prevalent comorbidities were collected for each patient on the date this study identified their CKD (index date) using eGFR and UACR measurements according to the KDIGO clinical practice guidelines.The most recent clinical measurements and laboratory values within the one year prior to the index date are reported.For patients residing in Region Stockholm, the most recent socioeconomic data within the 10 years prior to the index date was collected from the Mosaic system, which applies the principles of geodemography to consumer household and individual data to categorize patients into one of the following three levels: 1) Highest income/education; 2) Medium income/education; and 3) Lowest income/education.AMI denotes acute myocardial infarction; BMI, body mass index; BP, blood pressure; CHD, coronary heart disease; eGFR, estimated glomerular filtration rate; HbA1c, glycated hemoglobin; IQR, interquartile range; PAD, peripheral artery disease; RAASi, renin-angiotensin aldosterone system inhibitors; SGLT2i, sodium-glucose cotransporter-2 inhibitors; UACR, urinary albumin-creatinine ratio.
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s)

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Figure S3.Multi-state models presenting the proportions of patients diagnosed or not diagnosed with chronic kidney disease (CKD) in healthcare who received or didn't receive guideline-directed pharmacological therapy, including the terminal, absorbing outcomes, major adverse cardiovascular events (MACE) and non-cardiovascular disease (CVD) death

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Figure S4.Multi-state models presenting the proportions of patients diagnosed or not diagnosed with chronic kidney disease (CKD) in healthcare who received or didn't receive guideline-directed pharmacological therapy, including the terminal, absorbing outcome, all-cause death

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Figure S5.Multi-state models presenting the proportions of patients diagnosed or not diagnosed with chronic kidney disease (CKD) in healthcare who received or didn't receive guideline-directed pharmacological therapy, including the terminal, absorbing outcomes, major adverse cardiovascular events (MACE) and non-cardiovascular disease (CVD) death

BMJ
Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance

Table S3 .
Stages of chronic kidney disease diagnosed using measurements of eGFR and UACR according to the KDIGO clinical practice guidelines

Table S4 .
International Classification of Diseases (ICD)-10 codes used to search for diagnoses of CKD, prevalent comorbidities, and outcomes of interest

Table S5 .
Anatomical Therapeutic Chemical (ATC) classification system codes used to search for treatments of interest in this study's population : 10.1136/bmjopen-2023-

Table S6 .
Baseline characteristics of the total study cohort stratified by age group

Table S7 .
Proportions of patients diagnosed with chronic kidney disease in healthcare within five years of the development of the disease