Literature DB >> 32344400

Systemic Inflammation Score as a Predictor of Pneumonia after Radical Resection of Gastric Cancer: Analysis of a Multi-Institutional Dataset.

Michita Shoka1, Mitsuro Kanda2, Seiji Ito3, Yoshinari Mochizuki4, Hitoshi Teramoto5, Kiyoshi Ishigure6, Toshifumi Murai7, Takahiro Asada8, Akiharu Ishiyama9, Hidenobu Matsushita10, Chie Tanaka1, Daisuke Kobayashi1, Michitaka Fujiwara1, Kenta Murotani11, Yasuhiro Kodera1.   

Abstract

BACKGROUND: Curative treatment for gastric cancer (GC) comprising gastrectomy with systematic lymph node dissection can result in postoperative complications. Postoperative pneumonia is sometimes fatal, like surgery-related complications such as anastomotic leakage. In this retrospective study, we analyzed a multi-institutional collaborative dataset with the aim of identifying predictors of postgastrectomy pneumonia.
METHODS: From a retrospective database of 3,484 patients who had undergone gastrectomy for GC at nine Japanese institutions between 2010 and 2014, 1,415 patients who met all eligibility criteria were identified as eligible for analysis. Predictive values of 31 candidate variables for postoperative pneumonia were assessed.
RESULTS: Forty-two patients (3.0%) had grade II or higher postoperative pneumonia. Preoperative systemic inflammation score (SIS) had the greatest area under the curve (0.655) for predicting postoperative pneumonia (optimal cutoff value = 2). The odds ratio (OR) of high SISs associated with postoperative pneumonia was 3.10 (95% confidence interval [CI], 1.54-6.07; p < 0.001). Multivariate binomial logistic analysis identified high SIS as an independent risk factor for postoperative pneumonia (OR, 2.31; 95% CI, 1.19-4.48; p = 0.013). A forest plot revealed that ORs of high SISs were highest in female patients.
CONCLUSIONS: Our findings indicate that the preoperative SIS may serve as a simple predictor of postgastrectomy pneumonia, assisting physicians' efforts to take preventive measures against this complication.
© 2020 S. Karger AG, Basel.

Entities:  

Keywords:  Gastric cancer; Pneumonia; Predictor; Systemic inflammation score

Mesh:

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Year:  2020        PMID: 32344400     DOI: 10.1159/000506940

Source DB:  PubMed          Journal:  Dig Surg        ISSN: 0253-4886            Impact factor:   2.588


  4 in total

1.  Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation.

Authors:  Chaojin Chen; Dong Yang; Shilong Gao; Yihan Zhang; Liubing Chen; Bohan Wang; Zihan Mo; Yang Yang; Ziqing Hei; Shaoli Zhou
Journal:  Respir Res       Date:  2021-03-31

2.  Predictive value of modified systemic inflammation score for postoperative unplanned ICU admission in patients with NSCLC.

Authors:  Zhulin Wang; Hua Zhang; Chunyao Huang; Kaiyuan Li; Wenqing Luo; Guoqing Zhang; Xiangnan Li
Journal:  Front Surg       Date:  2022-08-03

3.  Machine Learning-based Correlation Study between Perioperative Immunonutritional Index and Postoperative Anastomotic Leakage in Patients with Gastric Cancer.

Authors:  Xuanyu Liu; Su Lei; Qi Wei; Yizhou Wang; Haibin Liang; Lei Chen
Journal:  Int J Med Sci       Date:  2022-07-04       Impact factor: 3.642

4.  ELK4 promotes the development of gastric cancer by inducing M2 polarization of macrophages through regulation of the KDM5A-PJA2-KSR1 axis.

Authors:  Lei Zheng; Hongmei Xu; Ya Di; Lanlan Chen; Jiao Liu; Liying Kang; Liming Gao
Journal:  J Transl Med       Date:  2021-08-09       Impact factor: 5.531

  4 in total

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