Evidence map›Paper›PMID 38375202›Full record

ArticleFrontiers in oncology2023

The value of a risk model combining specific risk factors for predicting postoperative severe morbidity in biliary tract cancer.

BaoLong Ye, JunFeng Xie, KeXing Xi, ZhiShun Huang, YanNian Liao, ZiWen Chen, Wu Ji

Abstract read
In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Clinical and pathologic insights into early and late breast cancer relapses: a real-world analysis from the El Álamo IV registry.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

BaoLong YeDepartment of Gastrointestinal and Hernia Surgery, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, China.
JunFeng XieDepartment of Gastrointestinal and Hernia Surgery, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, China.
KeXing XiDepartment of Gastrointestinal and Hernia Surgery, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, China.
ZhiShun HuangDepartment of Gastrointestinal and Hernia Surgery, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, China.
YanNian LiaoResearch Institute of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
ZiWen ChenDepartment of Gastrointestinal and Hernia Surgery, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, China.
Wu JiDepartment of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Several surgical risk models are widely utilized in general surgery to predict postoperative morbidity. However, no studies have been undertaken to examine the predictive efficacy of these models in biliary tract cancer patients, and other perioperative variables can also influence morbidity. As a result, the study's goal was to examine these models alone, as well as risk models combined with disease-specific factors, in predicting severe complications. Methods: A retrospective study of 129 patients was carried out. Data on demographics, surgery, and outcomes were gathered. These model equations were used to determine the morbidity risks. Severe morbidity was defined as the complication comprehensive index ≥ 40. Results: Severe morbidity was observed in 25% (32/129) patients. Multivariate analysis demonstrated that four parameters [comprehensive risk score ≥1, T stage, albumin decrease value, and international normalized ratio (INR)] had a significant influence on the probability of major complications. The area under the curve (AUC) of combining the four parameters was assessed as having strong predictive value and was superior to the Estimation of Physiologic Ability and Surgical Stress System (E-PASS) alone (the AUC value was 0.858 Conclusion: The mE-PASS and POSSUM models are ineffective in predicting postoperative morbidity in patients above the age of 70. In biliary tract cancer (BTC) patients undergoing radical operation, a combination of E-PASS and perioperative parameters generates a reasonable prediction value for severe complications.

Indexed as

biliary tract cancermorbidityrisk prediction modelsrisk scorespecific risk factors

Identifiers

PMID38375202
PMCPMC10876292

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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.