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Impact of renal function on eligibility for chemotherapy and survival in patients who have undergone radical nephro‐ureterectomy

2013· article· en· W1484305797 on OpenAlexaff
Évanguelos Xylinas, Michael Rink, Vitaly Margulis, Thomas Clozel, Richard K. Lee, Evi Comploj, Giacomo Novara, Jay D. Raman, Yair Lotan, Alon Z. Weizer, Morgan Rouprêt, Armin Pycha, Douglas S. Scherr, Christian Seitz, Vincenzo Ficarra, Quoc‐Dien Trinh, Pierre I. Karakiewicz, Francesco Montorsi, Marc Zerbib, Shahrokh F. Shariat

Bibliographic record

VenueBritish Journal of Urology · 2013
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineRenal functionUrologyCisplatinInterquartile rangeChemotherapyKidney diseaseRetrospective cohort studyUrothelial carcinomaInternal medicineOncologyCancer

Abstract

fetched live from OpenAlex

UNLABELLED: What's known on the subject? and what does the study add?: Radical nephroureterectomy (RNU), the standard of care treatment for high-risk urothelial carcinoma of the upper tract (UTUC), results in loss of a renal unit. Loss of renal function decreases eligibility for systemic chemotherapies and results in decreased overall survival in various malignancies. The study shows that only a small proportion of patients had a preoperative renal function that would allow cisplatin-based chemotherapy. Moreover, eGFR significantly decreased after RNU, thereby lowering the rate of cisplatin eligibility to only 16 and 52% of patients based on the thresholds of 60 and 45 mL/min/1.73 m(2) , respectively. Taken together with the rest of the literature, the findings of the study support the use of cisplatin-based chemotherapy, when indicated, in the neoadjuvant rather than adjuvant setting. OBJECTIVE: To report (i) the estimated glomerular filtration rate (eGFR) changes in patients undergoing radical nephro-ureterectomy (RNU) for upper tract urothelial carcinoma (UTUC); (ii) the rate of change in eGFR in patients eligible for cisplatin-based chemotherapy; and (iii) the association of preoperative, postoperative and rate of change of renal function variables with survival outcomes. PATIENT AND METHODS: We performed a retrospective analysis of 666 patients treated with RNU for UTUC at seven international institutions from 1994 to 2007. The eGFR was calculated at baseline and at 3-6 months (Modification of Diet in Renal Disease formula (MDRD) and Chronic Kidney Disease Epidemiology Collaboration formula (CKD-EP) equations). RESULTS: The median (interquartile range) eGFR decreased by 18.2 (8-12)% after RNU. A total of 37% of patients had a preoperative eGFR ≥ 60 mL/min/1.73 m(2) , which decreased to 16% after RNU (P < 0.001); 72% of patients had a preoperative eGFR ≥ 45 mL/min/1.73 m(2) , which decreased to 52% after RNU (P < 0.001). The distributions were similar when analyses were restricted to patients with locally advanced disease (pT3-pT4) and/or lymph node metastasis. Patients older than the median age of 70 years were more likely to have a decrease in eGFR after RNU (P < 0.001). None of the renal function variables was associated with clinical outcomes such as disease recurrence, cancer-specific and overall mortality; however, when analyses were restricted to patients who had no adjuvant chemotherapy and did not experience disease recurrence (n = 431), a preoperative eGFR ≥ 60 mL/min/1.73 m(2) (P = 0.03) and a postoperative eGFR ≥ 45 mL/min/1.73 m(2) (P = 0.04) were associated with better overall survival in univariable analyses. CONCLUSIONS: In patients who had UTUC, eGFR was low and furthermore, it significantly decreased after RNU. Renal function did not affect cancer-specific outcomes after RNU.

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)0.003
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.013
GPT teacher head0.293
Teacher spread0.280 · 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".

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Citations165
Published2013
Admission routes1
Has abstractyes

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