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Prognostic effect of preoperative systemic immune-inflammation index in patients treated with cytoreductive nephrectomy for metastatic renal cell carcinoma

2022· article· en· W3137527432 on OpenAlexaff
Ekaterina Laukhtina, Benjamin Pradère, David D’Andrea, Giuseppe Rosiello, Stefano Luzzago, Angela Pecoraro, Carlotta Palumbo, Sophie Knipper, Pierre I. Karakiewicz, Vitaly Margulis, Fahad Quhal, Reza Sari Motlagh, Hadi Mostafaei, Keiichiro Mori, Victor M. Schuettfort, Dmitry Enikeev, Shahrokh F. Shariat

Bibliographic record

VenueMinerva Urology and Nephrology · 2022
Typearticle
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineRenal cell carcinomaNephrectomyInternal medicineConcordanceProportional hazards modelKidney cancerPopulationUrologyYouden's J statisticGastroenterologyOncologySurgeryPredictive valueKidney

Abstract

fetched live from OpenAlex

BACKGROUND: Identifying those of patients with metastatic renal cell carcinoma (mRCC) who are most likely to benefit from cytoreductive nephrectomy (CN) is challenging. We tested the association between preoperative value of Systemic Immune-Inflammation Index (SII) and overall survival (OS) as well as cancer-specific survival (CSS) in mRCC patients treated with CN. METHODS: mRCC patients treated with CN at different institutions were included. After assessing for the optimal pretreatment SII cut‑off value, we found 710 to have the maximum Youden Index value. The overall population was therefore divided into two SII groups using this cut‑off (low, <710 vs. high, ≥710). Univariable and multivariable Cox regression analyses tested the association SII and OS as well as CSS. The discrimination of the model was evaluated with the Harrel's Concordance Index (C-Index). The clinical value of the SII was evaluated with decision curve analysis (DCA). RESULTS: Among 613 mRCC patients, 298 (49%) patients had a SII≥710. Median follow-up was 31 (IQR 16-58) months. On univariable analysis, high preoperative serum SII was significantly associated with worse OS (HR: 1.28, 95% CI: 1.07-1.54, P=0.01) and CSS (HR: 1.29, 95% CI: 1.08-1.55, P=0.01). On multivariable analysis, which adjusted for the effect of established clinicopathologic features, SII≥710 was associated with OS (HR: 1.25, 95% CI: 1.04-1.50, P=0.02) and CSS (HR: 1.26, 95% CI: 1.05-1.52, P=0.01). The addition of SII only slightly improved the discrimination of a base model that included established clinicopathologic features (C-index: 0.637 vs. 0.629). On DCA, the inclusion of SII did not improve the net-benefit of the prognostic model. On multivariable analyses, SII≥710 remained independently associated with the worse OS and CSS in IMDC intermediate risk group (both: HR: 1.31, 95% CI: 1.02-1.67, P=0.03). In the subgroup analyses based on the BMI, among patients with BMI ≥ 25, SII was significantly associated with OS (HR: 1.29, 95% CI: 1.04-1.61, P=0.02) and CSS (HR: 1.31, 95% CI: 1.05-1.63, P=0.02). CONCLUSIONS: We found an independent association of high SII prior to CN with unfavorable clinical outcomes, particularly in patients with intermediate risk mRCC and patients with increased BMI. Despite these results, it does not seem to add any prognostic or clinical benefit beyond that obtained by currently available clinicopathologic characteristics as sole worker.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models agreeAgreement compares identical category sets and study designs across arms.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

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

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 head0.005
GPT teacher head0.219
Teacher spread0.214 · 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

Labeled directly by 2 models reading the full record.

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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Citations11
Published2022
Admission routes1
Has abstractyes

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