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Record W4414195773 · doi:10.7759/cureus.92385

Evaluation of the Efficacy of Remote Ischemic Preconditioning in Reducing Renal Injury in Patients Undergoing Partial Nephrectomy: A Systematic Review

2025· review· en· W4414195773 on OpenAlexaboutno aff
Abhinav Singhal, Sachin Yallappa

Bibliographic record

VenueCureus · 2025
Typereview
Languageen
FieldMedicine
TopicCardiac Ischemia and Reperfusion
Canadian institutionsnot available
Fundersnot available
KeywordsIschemic preconditioningRenal functionCohortAcute kidney injuryUrinary systemNephrectomyIschemiaRandomized controlled trialCochrane Library

Abstract

fetched live from OpenAlex

Partial nephrectomy often results in temporary renal ischemia, predisposing the kidney to ischemia-reperfusion injury. Remote ischemic preconditioning is a novel technique that has emerged as a potential strategy to attenuate renal ischemia-reperfusion injury. Remote ischemic preconditioning involves brief, controlled ischemia of a limb, which induces the development of systemic protective mechanisms against ischemia. This systematic review evaluates the efficacy of remote ischemic preconditioning in reducing renal injury post-partial nephrectomy, focusing on urinary biomarkers, renal function parameters and long-term kidney function outcomes. This review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and comprehensive literature searches were performed across PubMed, EMBASE, Cochrane and SCOPUS for studies published from 1st January 2015 to 15th June 2025. Eligible studies included randomized controlled trials, cohort and case-control studies examining remote ischemic preconditioning in adult patients undergoing partial nephrectomy, with outcomes involving urinary biomarkers such as neutrophil gelatinase-associated lipocalin (NGAL) and renal function parameters such as estimated glomerular filtration rate (eGFR) and serum creatinine. Risk of bias was assessed using frameworks such as Cochrane's RoB 2.0, ROBINS-I, and Newcastle-Ottawa Scale. The results were mixed with five studies meeting the inclusion criteria, comprising four randomised controlled trials and one cohort study. High-quality trials demonstrated significant short-term improvements in postoperative eGFR, reduced pain and shorter hospital stays when remote ischemic preconditioning was used as part of a multimodal strategy. Others showed reduced urinary NGAL or attenuated serum creatinine rise, but without consistent functional benefit. Variability in remote ischemic preconditioning protocols, outcome measures, and patient populations limited direct comparisons. Overall, studies were at low to moderate risk of bias. Remote ischemic preconditioning appears to be a safe and feasible intervention with potential short-term renal benefits following partial nephrectomy. However, evidence remains inconclusive due to heterogeneity and limited long-term data. Future large-scale, standardized trials incorporating sensitive biomarkers and robust renal function outcomes are needed to clarify the clinical utility of performing remote ischemic preconditioning and optimizing its application in renal surgery.

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)0.007
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0110.011
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.028
GPT teacher head0.348
Teacher spread0.320 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2025
Admission routes1
Has abstractyes

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