Pre- and post-gastrectomy anemia in gastric cancer patients

Jung, Myung-jae; Kim, Hyoung-Il; Cho, Hyoung won; Yoon, Hyong Seo; Kim, Choong Bai

doi:10.14216/kjco.11024

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…In the preoperative setting, a retrospective review by Jung et al reported anemia in 43.6% (99/227) of GC patients. Of those, 24.2% (24/99) developed IDA ( 38 ). Recognition of warning signs was associated with anticipating faster help-seeking for potential symptoms of cancer ( 27 ).…”

Section: Discussionmentioning

confidence: 99%

High-risk population's knowledge of risk factors and warning symptoms and their intention toward gastric cancer screening in Southeastern China

Huang

Liu

Marzo

et al. 2022

Front. Public Health

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BackgroundAs the incidence of gastric cancer (GC) increases sharply in adults aged over 40 years, screening of this high-risk population is important. This study aimed to explore knowledge level of GC related risk factors and symptoms, and to identify influencing factors associated with intention toward GC screening among people aged 40 years old and above in China.MethodsA cross-sectional, web-based survey was conducted among people aged 40 years old and above between October 2021 and March 2022 in Southeastern China. The participants' knowledge was assessed by a series of questions about risk factors (24-item scale) and warning symptoms (14-item scale).ResultsA total of 2547 complete responses were received. The mean age was 47.72 (±7.20) years and near 60% were male. Respondents had a moderate level of knowledge about risk factors and warning symptoms of GC. The total mean knowledge score was 23.9 (±9.8) out of a possible score of 38. Majority (80%) of respondents reported intention to be screened for GC in the next 5 years. The most influential predictors of screening intention were income level (OR = 2.13, 95% CI: 1.36–3.32), perceived benefits (OR = 1.99, 95% CI: 1.33–2.73), perceived severity (OR = 1.68, 95% CI: 1.20–2.34), ever took GC screening (OR = 1.63, 95% CI: 1.28–2.08), perceived poor overall health (OR = 1.59, 95% CI: 1.19–2.11), and perceived barriers (OR = 1.56, 95% CI: 1.17–2.09). Other significant factors were ever diagnosed with chronic gastric diseases, total knowledge score, and cues-to-action. The major reasons for not willing to take screening were “endoscopy is uncomfortable” (29.6%), “worry about screening results” (23.6%), and “have no symptoms” (21.3%).ConclusionHigh-risk population aged 40 years and above expressed high intention to receive GC screening. Intervention to improve health promotion and reduce the barriers to uptake of GC screening among high-risk populations in China is warranted.

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Section: Discussionmentioning

confidence: 99%

High-risk population's knowledge of risk factors and warning symptoms and their intention toward gastric cancer screening in Southeastern China

Huang

Liu

Marzo

et al. 2022

Front. Public Health

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“… 23 Prior studies in Korea have simply reported the prevalence of IDA in patients with GC before and after gastrectomy. 24 Despite the potential link, specifically how this particular ICD code stood out as the top influencing variable outperforming other variables is unclear. GERD is most closely related to esophageal adenocarcinoma and to a lesser extent to GC arising from the cardia of the stomach.…”

Section: Discussionmentioning

confidence: 99%

Identifying Disease of Interest With Deep Learning Using Diagnosis Code

et al. 2023

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Background Autoencoder (AE) is one of the deep learning techniques that uses an artificial neural network to reconstruct its input data in the output layer. We constructed a novel supervised AE model and tested its performance in the prediction of a co-existence of the disease of interest only using diagnostic codes. Methods Diagnostic codes of one million randomly sampled patients listed in the Korean National Health Information Database in 2019 were used to train, validate, and test the prediction model. The first used AE solely for a feature engineering tool for an input of a classifier. Supervised Multi-Layer Perceptron (sMLP) was added to train a classifier to predict a binary level with latent representation as an input (AE + sMLP). The second model simultaneously updated the parameters in the AE and the connected MLP classifier during the learning process (End-to-End Supervised AE [EEsAE]). We tested the performances of these two models against baseline models, eXtreme Gradient Boosting (XGB) and naïve Bayes, in the prediction of co-existing gastric cancer diagnosis. Results The proposed EEsAE model yielded the highest F1-score and highest area under the curve (0.86). The EEsAE and AE + sMLP gave the highest recalls. XGB yielded the highest precision. Ablation study revealed that iron deficiency anemia, gastroesophageal reflux disease, essential hypertension, gastric ulcers, benign prostate hyperplasia, and shoulder lesion were the top 6 most influential diagnoses on performance. Conclusion A novel EEsAE model showed promising performance in the prediction of a disease of interest.

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