Evidence map›Paper›PMID 41364248›Full record

ArticleJournal of robotic surgery2025

The prognostic value of preoperative Neutrophil-to-Albumin ratio (NPAR) for postoperative complications and survival in colorectal cancer patients undergoing robot-assisted surgery: a retrospective cohort study.

Jing Wang, Fang Chen, Tao Hu, Nanhui Yu

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Article in Journal of robotic surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Jing WangDepartment of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, China.
Fang ChenEmergency Medicine Department, The First Affiliated Hospital of Hunan Normal University, Hunan Provincial People's Hospital, Changsha, China.
Tao HuDepartment of Anesthesiology, The First Affiliated Hospital of Hunan Normal University, Hunan Provincial People's Hospital, Changsha, China.
Nanhui YuDepartment of Gastrointestinal Surgery, The Second Xiangya Hospital, Central South University, No.139 Renmin Middle Road, Furong District, Changsha, Hunan Province, China. yunanhui@csu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to evaluate the prognostic value of the preoperative neutrophil percentage-to-albumin ratio (NPAR) in predicting postoperative complications and long-term survival in patients undergoing Da Vinci robot-assisted laparoscopic radical resection for colorectal cancer. A retrospective cohort study was conducted, including 230 patients with stage I-III colorectal cancer who underwent robotic-assisted radical surgery at Second Xiangya Hospital of Central South University.from January 2016 to October 2020. Preoperative laboratory parameters within 7 days before surgery were collected to calculate NPAR. Restricted cubic spline (RCS) regression was used to assess the nonlinear association between NPAR and complication risk, with the optimal cutoff value (14.649) determined via the Youden index and validated through 5-fold cross-validation. Postoperative complications were defined as Clavien-Dindo grade ≥ II events within 30 days post-surgery. Firth's penalized logistic regression and ridge regression models, coupled with internal validation, evaluated predictive performance, while Cox proportional hazards and Kaplan-Meier analyses assessed long-term survival. RCS analysis revealed a significant nonlinear positive relationship between NPAR and postoperative complication risk (P < 0.001, P for nonlinearity = 0.002). Patients with NPAR ≥ 14.649 exhibited significantly higher odds of complications (adjusted OR = 4.00, 95% CI: 2.10-7.61, P < 0.001). The ridge-penalized model demonstrated excellent discrimination (AUC = 0.913; optimism-corrected AUC = 0.903) and calibration (Brier score = 0.086; slope = 1.020). Subgroup and sensitivity analyses confirmed robustness across age, sex, tumor location, and comorbidity strata. Elevated NPAR independently predicted poor overall survival (HR = 1.60, 95% CI: 1.21-2.12, P < 0.001), with Kaplan-Meier analysis showing reduced survival in the high-NPAR group (P < 0.001). Preoperative NPAR is a robust and accessible biomarker for predicting short-term complications and long-term outcomes in robotic colorectal cancer surgery. Its integration into preoperative risk assessment can facilitate individualized management and optimize postoperative care.

Indexed as

Colorectal NeoplasmsNeutrophilsPostoperative ComplicationsRobotic Surgical ProceduresSerum AlbuminAdultAgedFemaleHumansMaleMiddle AgedPreoperative PeriodPrognosisRetrospective StudiesSerum AlbuminAlbuminColorectal cancerNeutrophil percentageNPARPostoperative complicationsPrognosisRobotic surgery

Identifiers

PMID41364248

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.