Chronic Kidney Disease in Congenital Heart Disease Patients: A Narrative Review of Evidence
Bibliographic record
Abstract
PURPOSE OF REVIEW: Patients with congenital heart disease have a number of risk factors for the development of chronic kidney disease (CKD). It is well known that CKD has a large negative impact on health outcomes. It is important therefore to consider that patients with congenital heart disease represent a population in whom long-term primary and secondary prevention strategies to reduce CKD occurrence and progression could be instituted and significantly change outcomes. There are currently no clear guidelines for clinicians in terms of renal assessment in the long-term follow up of patients with congenital heart disease. Consolidation of knowledge is critical for generating such guidelines, and hence is the purpose of this view. This review will summarize current knowledge related to CKD in patients with congenital heart disease, to highlight important work that has been done to date and set the stage for further investigation, development of prevention strategies, and re-evaluation of appropriate renal follow-up in patients with congenital heart disease. SOURCES OF INFORMATION: The literature search was conducted using PubMed and Google Scholar. FINDINGS: Current epidemiological evidence suggests that CKD occurs in patients with congenital heart disease at a higher frequency than the general population and is detectable early in follow-up (i.e. during childhood). Best evidence suggests that approximately 30 to 50 % of adult patients with congenital heart disease have significantly impaired renal function. The risk of CKD is higher with cyanotic congenital heart disease but it is also present with non-cyanotic congenital heart disease. Although significant knowledge gaps exist, the sum of the data suggests that patients with congenital heart disease should be followed from an early age for the development of CKD. IMPLICATIONS: There is an opportunity to mitigate CKD progression and negative renal outcomes by instituting interventions such as stringent blood pressure control and reduction of proteinuria. There is a need to invest time, thought and money to fill existing knowledge gaps to improve health outcomes in this population. This review should serve as an impetus for generation of follow-up guidelines of kidney health evaluation in patients with congenital heart disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".