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Record W4317932188 · doi:10.1177/20543581221150556

Management of Type 2 Diabetic Kidney Disease in 2022: A Narrative Review for Specialists and Primary Care

2023· review· en· W4317932188 on OpenAlexafffundabout
David Z.I. Cherney, Alan Bell, Louis Girard, Philip A. McFarlane, Louise Moist, Sharon J. Nessim, Steven D. Soroka, Sara Stafford, Andrew Steele, Navdeep Tangri, Jordan Weinstein

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2023
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of ManitobaFraser HealthUniversity of British ColumbiaLakeridge HealthNova Scotia Health AuthorityHealth Sciences CentreUniversity of TorontoWestern UniversityMcGill UniversityUniversity of CalgaryToronto General HospitalSt. Michael's HospitalJewish General Hospital
FundersJanssen PharmaceuticalsBayer HealthCareAstraZeneca Canada
KeywordsMedicinePrimary careIntensive care medicineKidney diseaseDiabetic nephropathyDiseaseType 2 diabetesKidneyInternal medicineDiabetes mellitusFamily medicineEndocrinology

Abstract

fetched live from OpenAlex

Purpose of review: Kidney disease is present in almost half of Canadian patients with type 2 diabetes (T2D), and it is also the most common first cardiorenal manifestation of T2D. Despite clear guidelines for testing, opportunities are being missed to identify kidney diseases, and many Canadians are therefore not receiving the best available treatments. This has become even more important given recent clinical trials demonstrating improvements in both kidney and cardiovascular (CV) endpoints with sodium-glucose cotransporter 2 (SGLT2) inhibitors and a nonsteroidal mineralocorticoid receptor antagonist, finerenone. The goal of this document is to provide a narrative review of the current evidence for the treatment of diabetic kidney disease (DKD) that supports this new standard of care and to provide practice points. Sources of information: An expert panel of Canadian clinicians was assembled, including 9 nephrologists, an endocrinologist, and a primary care practitioner. The information the authors used for this review consisted of published clinical trials and guidelines, selected by the authors based on their assessment of their relevance to the questions being answered. Methods: Panelists met virtually to discuss potential questions to be answered in the review and agreed on 10 key questions. Two panel members volunteered as co-leads to write the summaries and practice points for each of the identified questions. Summaries and practice points were distributed to the entire author list by email. Through 2 rounds of online voting, a second virtual meeting, and subsequent email correspondence, the authors reached consensus on the contents of the review, including all the practice points. Key findings: It is critical that DKD be identified as early as possible in the course of the disease to optimally prevent disease progression and associated complications. Patients with diabetes should be routinely screened for DKD with assessments of both urinary albumin and kidney function. Treatment decisions should be individualized based on the risks and benefits, patients' needs and preferences, medication access and cost, and the degree of glucose lowering needed. Patients with DKD should be treated to achieve targets for A1C and blood pressure. Renin-angiotensin-aldosterone system blockade and treatment with SGLT2 inhibitors are also key components of the standard of care to reduce the risk of kidney and CV events for these patients. Finerenone should also be considered to further reduce the risk of CV events and chronic kidney disease progression. Education of patients with diabetes prescribed SGLT2 inhibitors and/or finerenone is an important component of treatment. Limitations: No formal guideline process was used. The practice points are not graded and are not intended to be viewed as having the weight of a clinical practice guideline or formal consensus statement. However, most practice points are well aligned with current clinical practice guidelines.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.344
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2023
Admission routes3
Has abstractyes

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