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Record W3038021344 · doi:10.1159/000508088

Misleading Numbers: Is the Risk of Acute Kidney Injury with COVID-19 Truly This Low?

2020· letter· en· W3038021344 on OpenAlexafffundabout
Samuel A. Silver, Edward G. Clark, Swapnil Hiremath

Bibliographic record

VenueAmerican Journal of Nephrology · 2020
Typeletter
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of OttawaQueen's University
FundersUniversity of Ottawa
KeywordsMedicineAcute kidney injuryPopulationCohortCoronavirus disease 2019 (COVID-19)Intensive care medicineComorbidityKidney diseaseDiseaseCohort studySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Internal medicineEmergency medicinePediatricsInfectious disease (medical specialty)

Abstract

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Dear Editor,The case series from Wang et al. [1] of 116 patients from Remnin Hospital with coronavirus disease 2019 (COVID-19) reports that none developed acute kidney injury (AKI). This finding and several other aspects of this study cause us concern. In particular, 0% of patients with AKI is strikingly inconsistent with the reported literature so far. In addition, two other case series published as preprints from the same institution reported that 23–32% of deaths were accompanied by AKI [2, 3]. We speculate that this discrepancy may have occurred for several reasons.First, as it is unclear how the authors assembled this cohort, it is not possible to determine which fraction of patients admitted to the institution was included. Was it a specialized COVID-19 ward for a selected patient population? Without more details, it is possible that this case series consists of a highly selected group of patients, and hence is not representative of the general patient population. This is suggested by the small number of patients with any preexisting comorbidities. Although 5 patients had end-stage kidney disease at baseline, none of the others had CKD despite a definition of CKD which was quite broad. Another potential source of selection bias is that it is unclear where patients were captured with respect to the trajectory of their COVID-19 infections. Were they all new admissions? Were some patients already hospitalized at the time of their COVID-19 diagnosis? Were some already recovering? The patients most at risk for AKI would be those captured during the acute phase of their illness. Without having more details about how patients were identified for inclusion, the results are difficult to interpret.Second, no data are provided regarding the number of patients who had a baseline serum Cr available. These definitions result in bidirectional misclassification of AKI incidence, and so clear numbers are needed [4, 5].Third, outcome ascertainment for AKI is unclear. It is unclear how often serum Cr and urine output were measured to ascertain AKI. To some extent, the availability of renal replacement therapy could have affected ascertainment if patients died with (or recovered from) AKI before having had follow-up kidney function testing. Table 2 [1] from the study suggests that testing was done weekly, which may not have been sufficiently frequent in certain instances. Additionally, it is unclear how to interpret what is meant by serum Cr values and kidney deterioration for the 5 patients with end-stage kidney disease who already were on dialysis.Last, given that AKI affects between 10 and 25% of hospitalized patients, we would expect patients with COVID-19 to at least fall somewhere within this range or above it if specifically assessing a critically ill population.For the reasons stated above, we believe that the results reported by Wang et al. [1] should be taken with caution. We suggest that clinicians should continue to closely monitor for AKI in patients with COVID-19 as they would in other hospitalized patients and while we await further evidence of any potentially unique AKI risks in this population.E.G.C. and S.H. would like to acknowledge research salary support from the Department of Medicine, University of Ottawa.The authors have no conflicts of interest to declare.The authors did not receive any funding.The letter was drafted by all three authors.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, 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 score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.008
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.320
Teacher spread0.303 · 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".

Quick stats

Citations2
Published2020
Admission routes3
Has abstractyes

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