Evidence map›Paper›PMID 36923226›Full record

ArticleFrontiers in endocrinology2023

A novel prognostic model for patients with colon adenocarcinoma.

Chengliang Yin, Wanling Wang, Wenzhe Cao, Yuanyuan Chen, Xiaochun Sun, Kunlun He

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Article in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

4 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Chengliang YinMedical Big Data Research Center, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, China.
Wanling WangMedical Big Data Research Center, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, China.
Wenzhe CaoInstitute of Geriatrics, The Second Medical Center & National Clinical Research Center for Geriatrics Diseases, Beijing Key Laboratory of Research on Aging and Related Diseases, State Key Laboratory of Kidney Disease, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China.
Yuanyuan ChenMedical Big Data Research Center, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, China.
Xiaochun SunMedical Big Data Research Center, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, China.
Kunlun HeMedical Big Data Research Center, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Colon adenocarcinoma (COAD) is a highly heterogeneous disease, which makes its prognostic prediction challenging. The purpose of this study was to investigate the clinical epidemiological characteristics, prognostic factors, and survival outcomes of patients with COAD in order to establish and validate a predictive clinical model (nomogram) for these patients. Methods: Using the SEER (Surveillance, Epidemiology, and End Results) database, we identified patients diagnosed with COAD between 1983 and 2015. Disease-specific survival (DSS) and overall survival (OS) were assessed using the log-rank test and Kaplan-Meier approach. Univariate and multivariate analyses were performed using Cox regression, which identified the independent prognostic factors for OS and DSS. The nomograms constructed to predict OS were based on these independent prognostic factors. The predictive ability of the nomograms was assessed using receiver operating characteristic (ROC) curves and calibration plots, while accuracy was assessed using decision curve analysis (DCA). Clinical utility was evaluated with a clinical impact curve (CIC). Results: A total of 104,933 patients were identified to have COAD, including 31,479 women and 73,454 men. The follow-up study duration ranged from 22 to 88 months, with an average of 46 months. Multivariate Cox regression analysis revealed that age, gender, race, site_recode_ICD, grade, CS_tumor_size, CS_extension, and metastasis were independent prognostic factors. Nomograms were constructed to predict the probability of 1-, 3-, and 5-year OS and DSS. The concordance index (C-index) and calibration plots showed that the established nomograms had robust predictive ability. The clinical decision chart (from the DCA) and the clinical impact chart (from the CIC) showed good predictive accuracy and clinical utility. Conclusion: In this study, a nomogram model for predicting the individualized survival probability of patients with COAD was constructed and validated. The nomograms of patients with COAD were accurate for predicting the 1-, 3-, and 5-year DSS. This study has great significance for clinical treatments. It also provides guidance for further prospective follow-up studies.

Indexed as

AdenocarcinomaColonic NeoplasmsFemaleFollow-Up StudiesHumansMaleNomogramsPrognosiscolon adenocarcinomanomogramprognosisrisk factorsSEER

Identifiers

PMID36923226
PMCPMC10009111

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