Bibliographic record
Abstract
See Clinical Research on Page 1424 in Volume 3, Issue 6 See Clinical Research on Page 1424 in Volume 3, Issue 6 Acute kidney injury (AKI) is defined by Kidney Disease: Improving Global Outcome (KDIGO) consensus criteria based on increase in serum creatinine (SCr) and decrease in urine output, which aim to capture an abrupt drop in glomerular filtration rate (GFR).1Kidney Disease: Improving Global Outcomes (KDIGO) Acute Kidney Injury Work GroupKDIGO Clinical Practice Guideline for Acute Kidney Injury.Kidney Int Suppl. 2012; 2: 1-138Abstract Full Text Full Text PDF Scopus (1987) Google Scholar Although most instances of injury to kidney tubules are accompanied by a drop in GFR and a subsequent increase in SCr, there are settings in which there is a discordance between the two. For example, during intensive blood pressure control or therapy with inhibitors of the renin−angiotensin−aldosterone system, AKI defined by SCr increase is not accompanied by kidney injury and causes false alarm.2Moledina D.G. Parikh C.R. Phenotyping of acute kidney injury: beyond serum creatinine.Semin Nephrol. 2018; 38: 3-11Abstract Full Text Full Text PDF PubMed Scopus (83) Google Scholar, 3Zhang W.R. Craven T.E. Malhotra R. et al.Kidney damage biomarkers and incident chronic kidney disease during blood pressure reduction: a case-control study.Ann Intern Med. 2018; 169: 610-618Crossref PubMed Scopus (48) Google Scholar Perhaps a bigger problem in sepsis-related AKI is that SCr might not increase despite injury to the kidneys. This could be due to a decrease in SCr production or its dilution from i.v. fluid administration. It could also be due to unfavorable kinetics of SCr, which does not increase for 24 to 48 hours after kidney injury has occurred. This is problematic, as patients could be exposed to nephrotoxins that exacerbate tubular injury while SCr has not yet detected AKI. Many biomarkers that directly measure injury to kidney tubules are being tested to overcome these limitations of SCr. More than 1000 articles evaluating biomarkers in AKI were published in the past decade.4Parikh C.R. Mansour S.G. Perspective on clinical application of biomarkers in AKI.J Am Soc Nephrol. 2017; 28: 1677-1685Crossref PubMed Scopus (50) Google Scholar Prominent among these are neutrophil gelatinase−associated lipocalin (NGAL), kidney injury molecule−1, interleukin-18, and, most recently, the product of tissue inhibitor of metalloproteinase−2 and insulin-like growth factor-binding protein−7 (TIMP2*IGFBP7). These biomarkers aim to overcome the limitations of SCr by directly measuring tubular injury or function rather than GFR (Figure 1). Among these, only TIMP-2*IGFBP-7 is approved for diagnostic use in the United States, whereas NGAL is available in the European Union, Canada, and various Asian and South American countries. In the study published in the November 2018 issue of Kidney International Reports, Hollinger et al.5Hollinger A. Wittebole X. François B. et al.Proenkephalin A 119-159 (Penkid) Is an Early Biomarker of Septic Acute Kidney Injury: The Kidney in Sepsis and Septic Shock (Kid-SSS) Study.Kidney Int Rep. 2018; 3: 1424-1433Abstract Full Text Full Text PDF PubMed Scopus (34) Google Scholar present results of a novel biomarker proenkephalin A 119-159 (penkid) in patients admitted to the intensive care unit (ICU) with sepsis and septic shock. Penkid is a 5-kDa, stable breakdown product of enkephalins, accumulates in the blood in settings of reduced GFR, and is associated with AKI and mortality in patients with sepsis and heart failure.6Ng L.L. Squire I.B. Jones D.J.L. et al.Proenkephalin, renal dysfunction, and prognosis in patients with acute heart failure: a GREAT Network study.J Am Coll Cardiol. 2017; 69: 56-69Crossref PubMed Scopus (49) Google Scholar, 7Kim H. Hur M. Lee S. et al.Proenkephalin, neutrophil gelatinase-associated lipocalin, and estimated glomerular filtration rates in patients with sepsis.Ann Lab Med. 2017; 37: 388-397Crossref PubMed Scopus (40) Google Scholar In this study, the authors evaluated penkid in two distinct ICU cohorts: the Kid-SSS study (n = 583) and the FROG-ICU study (n = 536). In the Kid-SSS cohort, 60% of participants were on mechanical ventilation, 58% were on vasopressors, and 28-day mortality among study participants was 22%. Penkid was measured in blood samples collected within 24 hours of ICU admission. The primary outcome was occurrence of major adverse kidney events (MAKE) at 7 days, which was a composite of death, dialysis, or persistent renal dysfunction. Penkid was 3-fold higher in those who experienced MAKE, with a standardized odds ratio of 3.3 (1.8–6) after controlling for key confounders such as age, sex, and estimated GFR (eGFR), but not for changes in SCr or urine output. Penkid showed an area under the receiver operating characteristic curve (AUC) for MAKE of 0.84 (95% confidence interval, 0.80–0.87), which was similar to the AUC of SCr (0.83 [0.80–0.87]). In 212 (37%) patients with a renal component of a Sequential Organ Failure Assessment (SOFA) score of 0 (SCr ≤ 1.2 mg/dl), the authors demonstrated that penkid had a better AUC than SCr (0.78 vs. 0.64, P < 0.001). Because patient harm could occur from failure to identify AKI using SCr-based definition in sepsis, penkid could help risk stratify this subgroup missed by SCr. For example, in those with admission SCr ≤ 1.2 mg/dl, <5% of those with penkid values <84.2 experienced MAKE, whereas about 20% of those with penkid value >84.2 experienced this outcome. This study has several strengths. First, the authors show consistent association of penkid with MAKE in 2 separate cohorts. However, it should be noted that none of the predictions (such as AUC) of derivation cohort were tested in the validation cohort. Second, the authors present detailed results comparing the discriminative ability of penkid to existing biomarkers. The most important limitation is that penkid is a marker of GFR, not tubular injury. Thus, it is expected to have many of the same limitations of other markers of GFR such as SCr and cystatin C. Not surprisingly, the overall performance of SCr and penkid for AKI was similar in this and prior studies.7Kim H. Hur M. Lee S. et al.Proenkephalin, neutrophil gelatinase-associated lipocalin, and estimated glomerular filtration rates in patients with sepsis.Ann Lab Med. 2017; 37: 388-397Crossref PubMed Scopus (40) Google Scholar, 8Shah K.S. Taub P. Patel M. et al.Proenkephalin predicts acute kidney injury in cardiac surgery patients.Clin Nephrol. 2015; 83: 29-35Crossref PubMed Scopus (41) Google Scholar The authors identified a subset of patients with low SCr in whom penkid outperformed SCr, but its performance in this subgroup was much lower than in the overall cohort (AUC, 0.78). In this subgroup, only 1 in 5 participants with a penkid level above the cut-off experienced MAKE outcomes. Thus, 4 in 5 patients will incorrectly be classified as at risk for MAKE with this biomarker. Second, the authors did not compare penkid to any of the other biomarkers of AKI such as neutrophil gelatinase-associated lipocalin (NGAL) or TIMP2*IGFBP7. However, in a recent study, penkid showed a higher AUC as compared with TIMP2*IGFBP7 for AKI (AUC, 0.91 vs. 0.67) and for renal replacement therapy (0.78 vs. 0.68).9Gayat E. Touchard C. Hollinger A. Vieillard-Baron A. Mebazaa A. Legrand M. Back-to-back comparison of PenKid with NephroCheck(R) to predict acute kidney injury at admission in intensive care unit: a brief report.Crit Care. 2018; 22: 24Crossref PubMed Scopus (16) Google Scholar Another study showed that, in septic patients, penkid had a higher AUC than NGAL for AKI (0.73 vs. 0.68) and for renal replacement therapy (0.87 vs. 0.74).7Kim H. Hur M. Lee S. et al.Proenkephalin, neutrophil gelatinase-associated lipocalin, and estimated glomerular filtration rates in patients with sepsis.Ann Lab Med. 2017; 37: 388-397Crossref PubMed Scopus (40) Google Scholar In patients with sepsis in the emergency department, penkid had a similar AUC to that of NGAL (see Supplementary Reference). No AKI biomarker has emerged as a reliable diagnostic test for clinical use in human AKI. Despite excellent performance in animal studies and small human studies, these biomarkers showed underwhelming performance in large human AKI cohorts. This could be due to the heterogeneity of human AKI, in which no single biomarker could capture all the different subtypes of AKI, or due to comparison of novel biomarkers to serum creatinine, which itself is an imperfect marker of AKI, as noted above. Moreover, in the absence of drug therapies for AKI and only minor improvements in prediction above SCr, the cost of these novel biomarkers is perhaps too burdensome for widespread clinical use. Implementation studies of AKI biomarkers, that is, demonstration that integration of these biomarkers in clinical care improves care or reduces cost, are also lacking. It is unclear whether penkid has overcome any of these limitations of AKI biomarkers. Penkid is also a marker of GFR similar to SCr, and its clinical utility over this existing and widely available biomarker remains to be demonstrated. The author declared no competing interests. DGM is supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under Award Number K23DK117065. The content is solely the responsibility of the author and does not necessarily represent the official views of the National Institutes of Health. Download .docx (.01 MB) Help with docx files Supplementary Reference Proenkephalin A 119-159 (Penkid) Is an Early Biomarker of Septic Acute Kidney Injury: The Kidney in Sepsis and Septic Shock (Kid-SSS) StudyKidney International ReportsVol. 3Issue 6PreviewSepsis is the leading cause of acute kidney injury (AKI) in critically ill patients. The Kidney in Sepsis and Septic Shock (Kid-SSS) study evaluated the value of proenkephalin A 119-159 (penkid)—a sensitive biomarker of glomerular function, drawn within 24 hours upon intensive care unit (ICU) admission and analyzed using a chemiluminescence immunoassay—for kidney events in sepsis and septic shock. Full-Text PDF Open Access
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".