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Background@#Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. @*Methods@#This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO 2 /FIO 2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine).The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley’s additive explanations (SHAP). @*Results@#Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756–0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626–0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. @*Conclusion@#Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.
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Background/Aims@#Sepsis-3 criteria and quick Sequential Organ Failure Assessment (qSOFA) have been advocated to be used in defining sepsis in the general population. We aimed to compare the Sepsis-3 criteria and Chronic Liver Failure-SOFA (CLIF-SOFA) scores as predictors of in-hospital mortality in cirrhotic patients admitted to the emergency department (ED) for infections. @*Methods@#A total of 1,622 cirrhosis patients admitted at the ED for infections were assessed retrospectively. We analyzed their demographic, laboratory, and microbiological data upon diagnosis of the infection. The primary endpoint was inhospital mortality rate. The predictive performances of baseline CLIF-SOFA, Sepsis-3, and qSOFA scores for in-hospital mortality were evaluated. @*Results@#The CLIF-SOFA score proved to be significantly better in predicting in-hospital mortality (area under the receiver operating characteristic curve [AUROC], 0.80; 95% confidence interval [CI], 0.78–0.82) than the Sepsis-3 (AUROC, 0.75; 95% CI, 0.72–0.77, P10%; this is the cutoff point for the definition of sepsis. @*Conclusions@#Among cirrhosis patients presenting with infections at the ED, CLIF-SOFA scores showed a better predictive performance for mortality than both Sepsis-3 criteria and qSOFA scores, and can be a useful tool of risk stratification in cirrhotic patients requiring timely intervention for infection.
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Background@#Although patients with chronic obstructive pulmonary disease (COPD) experience high morbidity and mortality worldwide, few biomarkers are available for COPD.Here, we analyzed potential biomarkers for the diagnosis of COPD by using word embedding. @*Methods@#To determine which biomarkers are likely to be associated with COPD, we selected respiratory disease-related biomarkers. Degrees of similarity between the 26 selected biomarkers and COPD were measured by word embedding. And we infer the similarity with COPD through the word embedding model trained in the large-capacity medical corpus, and search for biomarkers with high similarity among them. We used Word2Vec, Canonical Correlation Analysis, and Global Vector for word embedding. We evaluated the associations of selected biomarkers with COPD parameters in a cohort of patients with COPD. @*Results@#Cytokeratin 19 fragment (Cyfra 21-1) was selected because of its high similarity and its significant correlation with the COPD phenotype. Serum Cyfra 21-1 levels were determined in patients with COPD and controls (4.3 ± 5.9 vs. 3.9 ± 3.6 ng/mL, P = 0.611). The emphysema index was significantly correlated with the serum Cyfra 21-1 level (correlation coefficient = 0.219,P = 0.015). @*Conclusion@#Word embedding may be used for the discovery of biomarkers for COPD and Cyfra 21-1 may be used as a biomarker for emphysema. Additional studies are needed to validate Cyfra 21-1 as a biomarker for COPD.
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Background@#Chronic obstructive pulmonary disease (COPD) is a common chronic respiratory disease with increased prevalence in the elderly. Telomeres are repetitive DNA sequences found at the end of the chromosome, which progressively shorten as cells divide. Telomere length is known to be a molecular marker of aging. This study aimed to assess the relationship between telomere length and the risk of COPD, lung function, respiratory symptoms, and emphysema index in Chronic Obstructive Pulmonary Disease in Dusty Areas (CODA) cohort. @*Methods@#We extracted DNA from the peripheral blood samples of 446 participants, including 285 COPD patients and 161 control participants. We measured absolute telomere length using quantitative real-time polymerase chain reaction. All participants underwent spirometry and quantitative computed tomography scan. Questionnaires assessing respiratory symptoms and the COPD Assessment Test was filled by all the participants. @*Results@#The mean age of participants at the baseline visit was 72.5±7.1 years. Males accounted for 72% (321 participants) of the all participants. The mean telomere length was lower in the COPD group compared to the non-COPD group (COPD, 16.81±13.90 kb; non-COPD, 21.97±14.43 kb). In COPD patients, 112 (75.7%) were distributed as tertile 1 (shortest), 91 (61.1%) as tertile 2 and 82 (55%) as tertile 3 (longest). We did not find significant associations between telomere length and lung function, exacerbation, airway wall thickness, and emphysema index after adjusting for sex, age, and smoking status. @*Conclusion@#In this study, the relationship between various COPD phenotypes and telomere length was analyzed, but no significant statistical associations were shown.
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Background@#Although patients with chronic obstructive pulmonary disease (COPD) experience high morbidity and mortality worldwide, few biomarkers are available for COPD.Here, we analyzed potential biomarkers for the diagnosis of COPD by using word embedding. @*Methods@#To determine which biomarkers are likely to be associated with COPD, we selected respiratory disease-related biomarkers. Degrees of similarity between the 26 selected biomarkers and COPD were measured by word embedding. And we infer the similarity with COPD through the word embedding model trained in the large-capacity medical corpus, and search for biomarkers with high similarity among them. We used Word2Vec, Canonical Correlation Analysis, and Global Vector for word embedding. We evaluated the associations of selected biomarkers with COPD parameters in a cohort of patients with COPD. @*Results@#Cytokeratin 19 fragment (Cyfra 21-1) was selected because of its high similarity and its significant correlation with the COPD phenotype. Serum Cyfra 21-1 levels were determined in patients with COPD and controls (4.3 ± 5.9 vs. 3.9 ± 3.6 ng/mL, P = 0.611). The emphysema index was significantly correlated with the serum Cyfra 21-1 level (correlation coefficient = 0.219,P = 0.015). @*Conclusion@#Word embedding may be used for the discovery of biomarkers for COPD and Cyfra 21-1 may be used as a biomarker for emphysema. Additional studies are needed to validate Cyfra 21-1 as a biomarker for COPD.
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Background@#Chronic obstructive pulmonary disease (COPD) is a common chronic respiratory disease with increased prevalence in the elderly. Telomeres are repetitive DNA sequences found at the end of the chromosome, which progressively shorten as cells divide. Telomere length is known to be a molecular marker of aging. This study aimed to assess the relationship between telomere length and the risk of COPD, lung function, respiratory symptoms, and emphysema index in Chronic Obstructive Pulmonary Disease in Dusty Areas (CODA) cohort. @*Methods@#We extracted DNA from the peripheral blood samples of 446 participants, including 285 COPD patients and 161 control participants. We measured absolute telomere length using quantitative real-time polymerase chain reaction. All participants underwent spirometry and quantitative computed tomography scan. Questionnaires assessing respiratory symptoms and the COPD Assessment Test was filled by all the participants. @*Results@#The mean age of participants at the baseline visit was 72.5±7.1 years. Males accounted for 72% (321 participants) of the all participants. The mean telomere length was lower in the COPD group compared to the non-COPD group (COPD, 16.81±13.90 kb; non-COPD, 21.97±14.43 kb). In COPD patients, 112 (75.7%) were distributed as tertile 1 (shortest), 91 (61.1%) as tertile 2 and 82 (55%) as tertile 3 (longest). We did not find significant associations between telomere length and lung function, exacerbation, airway wall thickness, and emphysema index after adjusting for sex, age, and smoking status. @*Conclusion@#In this study, the relationship between various COPD phenotypes and telomere length was analyzed, but no significant statistical associations were shown.
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BACKGROUND@#The development of lung cancer results from the interaction between genetic mutations and dynamic epigenetic alterations, although the exact mechanisms are not completely understood. Changes in DNA methylation may be a promising biomarker for early detection and prognosis of lung cancer. We evaluated the serial changes in genome-wide DNA methylation patterns in blood samples of lung cancer patients.@*METHODS@#Blood samples were obtained for three consecutive years from three patients (2 years before, 1 year before, and after lung cancer detection) and from three control subjects (without lung cancer). We used the MethylationEPIC BeadChip method, which covers the 850,000 bp cytosine-phosphate-guanine (CpG) site, to conduct an epigenome-wide analysis. Significant differentially methylated regions (DMRs) were identified using p-values <0.05 in a correlation test identifying serial methylation changes and serial increase or decrease in β value above 0.1 for three consecutive years.@*RESULTS@#We found three significant CpG sites with differentially methylated β values and 7,105 CpG sites with significant correlation from control patients without lung cancer. However, there were no significant DMRs. In contrast, we found 11 significant CpG sites with differentially methylated β values and 10,562 CpG sites with significant correlation from patients with lung cancer. There were two significant DMRs: cg21126229 (RNF212) and cg27098574 (BCAR1).@*CONCLUSION@#This study revealed DNA methylation changes that might be implicated in lung cancer development. The DNA methylation changes may be the possible candidate target regions for the early detection and prevention of lung cancer.
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BACKGROUND: The development of lung cancer results from the interaction between genetic mutations and dynamic epigenetic alterations, although the exact mechanisms are not completely understood. Changes in DNA methylation may be a promising biomarker for early detection and prognosis of lung cancer. We evaluated the serial changes in genome-wide DNA methylation patterns in blood samples of lung cancer patients. METHODS: Blood samples were obtained for three consecutive years from three patients (2 years before, 1 year before, and after lung cancer detection) and from three control subjects (without lung cancer). We used the MethylationEPIC BeadChip method, which covers the 850,000 bp cytosine-phosphate-guanine (CpG) site, to conduct an epigenome-wide analysis. Significant differentially methylated regions (DMRs) were identified using p-values <0.05 in a correlation test identifying serial methylation changes and serial increase or decrease in β value above 0.1 for three consecutive years. RESULTS: We found three significant CpG sites with differentially methylated β values and 7,105 CpG sites with significant correlation from control patients without lung cancer. However, there were no significant DMRs. In contrast, we found 11 significant CpG sites with differentially methylated β values and 10,562 CpG sites with significant correlation from patients with lung cancer. There were two significant DMRs: cg21126229 (RNF212) and cg27098574 (BCAR1). CONCLUSION: This study revealed DNA methylation changes that might be implicated in lung cancer development. The DNA methylation changes may be the possible candidate target regions for the early detection and prevention of lung cancer.
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Humanos , Biomarcadores , Metilación de ADN , ADN , Epigenómica , Neoplasias Pulmonares , Pulmón , Métodos , Metilación , PronósticoRESUMEN
Gastric cancer mainly spreads to the liver, peritoneum, and lymph nodes and rarely metastasizes to the gallbladder. The prognosis of gastric cancer with metastasis to the gallbladder is reported to be very poor, and presentation with cholecystitis is a prognostic factor. Herein, we present a case of gastric cancer with metastasis to the gallbladder, accompanied by acute cholecystitis.