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Record W2962776364 · doi:10.1002/lt.25612

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2019· letter· en· W2962776364 on OpenAlexaff
Florence Wong, K. Rajender Reddy, Jacqueline G. O’Leary, Guadalupe García–Tsao, Patrick S. Kamath, Jasmohan S. Bajaj

Bibliographic record

VenueLiver Transplantation · 2019
Typeletter
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineCirrhosisMallinckrodtHepatorenal syndromeAscitesRenal functionKidney diseasePopulationIntensive care medicineInternal medicineGastroenterologyFamily medicine

Abstract

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Florence Wong receives research grants from Mallinckrodt Pharmaceuticals. Jasmohan S. Bajaj receives research grant support from Grifols USA. K. Rajender Reddy advises for Merck and Co., Gilead Sciences, Shiongi Inc., Dova Pharmaceuticals, and Spark Therapeutics and receives unrestricted research grants from Merck and Co., Gilead Sciences, Intercept Pharmaceuticals, Mallinckrodt Pharmaceuticals, Conatus Pharmaceuticals, and Exact Sciences (paid to the University of Pennsylvania). Jacqueline G. O’Leary consults for Mallinckrodt Pharmaceuticals. To the Editor: Thank you to Dr. Kumar for his interest in our recent publication on the prevalence of chronic kidney disease (CKD) in patients with cirrhosis admitted into the hospital.1 It is indeed true that most efforts for assessing renal function in cirrhosis have been concentrated on studying acute kidney injury (AKI) because this condition has an immediate negative impact on the prognosis of these patients. It is for this reason that we wanted to report on the emerging problems that CKD is going to pose to the population of patients with cirrhosis, especially in sick patients with decompensated cirrhosis admitted into the hospital. Chronic renal failure in cirrhosis used to be defined by a serum creatinine of >1.5 mg/dL2 and was synonymous with hepatorenal syndrome type 2 (HRS2), which is the form of functional renal failure most commonly observed in patients with cirrhosis and ascites in whom renal function slowly deteriorates over weeks to months. However, this is no longer the case because clinicians caring for patients with liver cirrhosis recognized that the rising prevalence of obesity and nonalcoholic steatohepatitis and the association of diabetes among these patients have led to a significant increase in patients with renal impairment that is not necessarily related to HRS2. This finding has led the International Club of Ascites to propose a change in the definition of renal failure in cirrhosis,3 with CKD encompassing both functional and structural renal diseases that produce a chronic reduction in glomerular filtration rate (GFR). CKD was defined as a chronic reduction in GFR of <60 mL/minute for >3 months calculated using the Modification of Diet in Renal Disease (MDRD)–4 variable formula.3 The group recognized that the MDRD4 formula tends to overestimate the GFR and, therefore, may falsely provide reassurance to the clinicians. However, they also acknowledged that it was the most practical formula to use. Although the definition of AKI in cirrhosis has been updated,4 the definition for CKD has not been modified since its original publication. Therefore, we wholeheartedly agree with Dr. Kumar that there is an urgent need to perform studies to delineate what proportion of CKD in cirrhosis is due to HRS2 and what proportion is related to structural renal damage due to, for example, conditions such as diabetes. By accepting that CKD is a generic term that is inclusive of all cases of reduced renal function, be it functional or structural, it is not surprising that the prevalence is 46.8%. Because the MDRD formula overestimates the GFR, the actual prevalence of CKD may be even higher. There may be many reasons for this result. First, this study reports on the prevalence of CKD among hospitalized patients with cirrhosis with complications, who, by virtue of the fact that they had more advanced liver disease and were sicker, were more likely to have renal dysfunction. Second, the definition of CKD has changed from using a threshold of serum creatinine of 1.5 mg/dL to that of a GFR of <60 mL/minute for more than 3 months. This new definition would include many patients with a serum creatinine within the normal laboratory range5 due to their cachexia and low muscle mass and, therefore, low serum creatinine levels, thereby inflating the prevalence of CKD. Third, the prevalence of nonalcoholic fatty liver disease is significantly higher in North America than in the rest of the world,6 and one would expect the prevalence of CKD to increase in parallel given the association with diabetes and, therefore, diabetic nephropathy. It should be pointed out that a similar high prevalence of CKD has been reported in other populations of patients with cirrhosis: 31% of patients wait‐listed for liver transplant7 and 45%‐46% of outpatients with cirrhosis regardless of whether the MDRD4, the MDRD–6 variable formula, or the Chronic Kidney Disease Epidemiology Collaboration formula was used to calculate the GFR.8 The definition of CKD using a GFR of <60 mL/minute for >3 months is to alert clinicians that there is reduction in renal function, and we believe that this definition should remain. The appropriate action is not to redefine CKD. Rather, it is important to delineate the various phenotypes that make up this condition called CKD because each phenotype will require a different treatment plan. The Kidney Disease Improving Global Outcomes (KDIGO) organization has subdivided CKD patients into those with or without kidney damage, as indicated by either pathological abnormalities, or blood or urine tests or imaging confirmation of kidney damage. Those patients with or without kidney damage are further subdivided according to the presence or absence of systemic hypertension.9 Clearly, these subdivisions of CKD will not work for patients with cirrhosis. Therefore, it is incumbent upon hepatologists to further study the various phenotypes of CKD in cirrhosis so that we can devise appropriate treatment strategies for these patients.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.016
GPT teacher head0.238
Teacher spread0.222 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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