Prevention of cardio-renal syndromes: workgroup statements from the 7th ADQI Consensus Conference
Bibliographic record
Abstract
With the demographic changes in Western societies toward individuals who are older, more obese and with proneness towards Type 2 diabetes and hypertension, there is increasing interest in the effects of chronic kidney disease (CKD) on the cardiovascular system [1]. The numbers of individuals in the world who meet a definition of CKD based on a reduced estimated glomerular filtration rate (eGFR) or evidence of kidney damage by imaging studies or biomarkers are expected to increase sharply over the next several decades. In a systematic review of population-based studies, the median prevalence of CKD was 7.2% in persons aged 30 years or older and, in persons over age 64, the prevalence of CKD varied from 23.4 to 35.8% [2]. Because of the high degree of overlap, it has been recommended that patients with cardiovascular disease (CVD) be screened for CKD [3]. Since approximately half of all deaths in those with CKD are attributed to cardiovascular causes, there is rationale to explore CKD as a ‘cardiovascular risk state’ and understand the pathobiological evidence for changes in the vascular tree and the heart [4]. It should also be recognized that we are in the midst of a chronic heart failure (HF) epidemic [5]. There is increasing recognition that there is an overlap between CKD and HF [6] and, in fact, there is a complex bidirectional pathophysiologic state [7] that worsens the function of both organs as defined elsewhere in this issue. Unfortunately, guidelines do not specifically address this issue and are often not followed in clinical practice [8]. The rationale for the prevention of cardio-renal syndromes (CRS) is predicated on the concept that, once the syndrome begins, it is difficult to interrupt, is not completely reversible in all cases and is associated with serious adverse outcomes including hospitalization, need for dialysis and death. Because the pathophysiology of CRS is believed to be complex, it is likely that multimodality preventive strategies working via multiple therapeutic targets will be needed (see Table 1). We will approach prevention in line with the classification system proposed by Ronco and colleagues (see Figure 1) [9,10]. This discussion will utilize the Risk, Injury, Failure, Loss, and End Stage (RIFLE) staging system [11] for the acute kidney injury (AKI) components of CRS as shown in Figure 2 and the American College of Cardiology/American Heart Association (ACC/AHA) stages of HF depicted in Figure 3 [18].
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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 | 0.048 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".