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Record W3037575240 · doi:10.1136/jitc-2019-000467

Acute kidney injury associated with immune checkpoint inhibitor therapy: incidence, risk factors and outcomes

2020· article· en· W3037575240 on OpenAlexafffund
Alejandro Meraz-Muñoz, Eitan Amir, Pamela Ng, Carmen Ávila-Casado, Claire Ragobar, Christopher T. Chan, S. Joseph Kim, Ron Wald, Abhijat Kitchlu

Bibliographic record

VenueJournal for ImmunoTherapy of Cancer · 2020
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsUniversity Health Network
FundersUniversity Health Network
KeywordsMedicineAcute kidney injuryInternal medicineCreatinineIncidence (geometry)Kidney diseaseAdverse effectCancerGastroenterologyOncology

Abstract

fetched live from OpenAlex

BACKGROUND: Immune checkpoint inhibitors (ICPi) are a novel and promising anti-cancer therapy. There are limited data on the incidence, risk factors and outcomes of acute kidney injury (AKI) in patients receiving ICPi. METHODS: We conducted a cohort study of patients receiving ICPi at our center between 2010 and 2017 via electronic health record. The primary outcome was AKI (increase of >50% from baseline serum creatinine (sCr)). Risk factors for AKI were assessed using logistic regression. Survival among those with and without AKI was compared using the Kaplan-Meier method. RESULTS: Among 309 patients on ICPi, 51 (16.5%) developed AKI (Kidney Disease Improving Global Outcomes (KDIGO) stages 1: 53%, 2: 22%, 3: 25%). AKI was associated with other immune-related adverse events (IRAE) (OR 3.2, 95% CI 1.6 to 6; p<0.001), hypertension (OR 4.3, 95% CI 1.8 to 6.1; p<0.001) and cerebrovascular disease (OR 9.2; 95% CI 2.1 to 40; p<0.001). Baseline sCr, cancer, and ICPi type was not associated with AKI. Use of angiotensin-converting enzyme inhibitors/angiotensin-receptor blockers (OR 2.9; 95% CI 1.5 to 5.7; p=0.002), diuretics (OR 4.3; 95% CI 1.9 to 9.8; p<0.001), and corticosteroid treatment (OR 1.9; 95% CI 1.1 to 3.6; p=0.03) were associated with AKI. In the multivariable analysis, AKI was associated only with other IRAE (OR 2.82; 95% CI 1.45 to 5.48; p=0.002) and hypertension (OR 2.96; 95% CI 1.33 to 6.59; p=0.008). AKI was not associated with increased risk of mortality (HR 1.1; 95% CI: 0.8 to 1.6; p=0.67). ICPi nephrotoxicity was attributed via biopsy or nephrologist assessment in 12 patients (six interstitial nephritis, two membranous nephropathy, two minimal change disease, and two thrombotic microangiopathy). Subsequent doses of ICPi were administered to 12 patients with prior AKI, with one (8.3%) having recurrent AKI. CONCLUSION: AKI is a common complication in patients receiving ICPi treatment. The development of other IRAE and previous diagnosis of hypertension were associated with increased AKI risk. AKI was not associated with worse survival. Distinguishing kidney IRAE from other causes of AKI will present a frequent challenge to oncology and nephrology practitioners. Kidney biopsy should be considered to characterize kidney lesions and guide potential therapy.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.307
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations193
Published2020
Admission routes2
Has abstractyes

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