scholarly journals Ocular Metastasis in Elderly Male Bladder Cancer Patients: Potential Risk Factors

2020 ◽  
Vol 14 (2) ◽  
pp. 155798832090899 ◽  
Author(s):  
Qian-Hui Xu ◽  
Qing Yuan ◽  
Yu-Qing Zhang ◽  
Biao Li ◽  
You-Lan Min ◽  
...  
Keyword(s):  
Risk Factors ◽  
Bladder Cancer ◽  
Logistic Regression ◽  
Cancer Patients ◽  
Roc Curve ◽  
Binary Logistic Regression ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Elderly Male ◽  
Male Patients

Bladder cancer is a common type of tumor among elderly male population; it causes intraocular metastasis (IOM). The study investigated the differences between elderly male bladder cancer patients with and without IOM, and identified risk factors for IOM. In this study, 749 elderly male patients (aged ≥50 years) with bladder cancer were included from November 2003 to December 2016. Differences between the IOM and non-IOM (NIOM) groups were evaluated by chi-square test and Student’s t-test. The binary logistic regression analysis calculates the risk factors. Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic value of IOM in elderly male patients with bladder cancer. The incidence of IOM in patients with bladder cancer was 1.7%. No significant differences were detected in age and histopathology between the IOM and NIOM groups. According to the study, the IOM group had higher ALP and Cyfra21-1. Binary logistic regression indicated that ALP and Cyfra21-1 were risk factors for IOM in elderly male bladder cancer patients ( p < .05). ROC curve analysis revealed area under the curve values for ALP and Cyfra21-1 of 0.913 and 0.814, using cutoff values of 9.65 and 83.5 U/L, respectively. The sensitivity and specificity values for ALP were 61.5% and 95.8%, respectively, while those for Cyfra21-1 were 84.6% and 73.3%. The investigation indicates that ALP and Cyfra21-1 are risk factors for IOM in elderly male patients with bladder cancer and ALP is more reliable at distinguishing IOM from NIOM in elderly male patients with bladder cancer.

scholarly journals Title: Preoperative albumin-to-fibrinogen ratio predicts postoperative pulmonary complications in patients after minimally invasive esophagectomy

2021 ◽  
Author(s):  
Zhang Peng ◽  
Zhao Song
Keyword(s):  
Risk Factors ◽  
Esophageal Cancer ◽  
Logistic Regression ◽  
Roc Curve ◽  
Training Group ◽  
Pulmonary Complications ◽  
Curve Analysis ◽  
Postoperative Pulmonary Complications ◽  
Multivariate Logistic Regression ◽  
Roc Curve Analysis

Abstract Background Postoperative pulmonary complications (PPCs) are the most common postoperative complications in patients with esophageal cancer. Prediction of PPCs by establishing a preoperative physiological function parameter model can help patients make adequate preoperative preparation, reduce treatment costs, and improve prognosis and quality of life. The purpose of this study was to investigate the relationship between albumin-to-fibrinogen ratio (AFR), prognostic nutritional index (PNI), albumin-to-globulin ratio (AGR), neutrophils-to-lymphocyte ratio (NLR), platelet-to-lymphocyte (PLR), and monocyte-to -lymphocyte ratio (MLR) and other preoperative laboratory tests and PPCs in patients after esophagectomy. Methods Retrospective analysis was performed on total 712 consecutive patients who underwent esophagectomy in the Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University from July 2018 to December 2020. Patients were divided into training (535 patients) and validation (177) groups for comparison of baseline data, perioperative indicators, and laboratory examination data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the efficacy, sensitivity and specificity of AFR, and Youden’s index was used to calculate the cut-off values of AFR. Univariate and multivariate logistic regression analyses were used to assess the risk factors for PPCs in training group. Results 112 (20.9%) in training group and 36 (20.3%) in validation group developed PPCs. The AUC value predicted by AFR using ROC curve analysis was 0.817, sensitivity 76.2% and specificity 78.7% in training group while AUC 0.803, sensitivity 69.4% and specificity 85.8%. Multivariate logistic regression analysis showed that smoking index, American Society of Anesthesiologists (ASA), AFR, and recurrent laryngeal nerve palsy were independent risk factors for PPCs. Conclusion Preoperative AFR can effectively predict the occurrence of PPCs in patients with esophageal cancer

scholarly journals The Prediction Model of Risk Factors for COVID-19 Developing into Severe Illness Based on 1046 Patients with COVID-19

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhichuang Lian ◽  
Yafang Li ◽  
Wenyi Wang ◽  
Wei Ding ◽  
Zongxin Niu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Curve Analysis ◽  
Severe Illness ◽  
Roc Curve Analysis ◽  
Underlying Diseases

This study analyzed the risk factors for patients with COVID-19 developing severe illnesses and explored the value of applying the logistic model combined with ROC curve analysis to predict the risk of severe illnesses at COVID-19 patients’ admissions. The clinical data of 1046 COVID-19 patients admitted to a designated hospital in a certain city from July to September 2020 were retrospectively analyzed, the clinical characteristics of the patients were collected, and a multivariate unconditional logistic regression analysis was used to determine the risk factors for severe illnesses in COVID-19 patients during hospitalization. Based on the analysis results, a prediction model for severe conditions and the ROC curve were constructed, and the predictive value of the model was assessed. Logistic regression analysis showed that age (OR = 3.257, 95% CI 10.466–18.584), complications with chronic obstructive pulmonary disease (OR = 7.337, 95% CI 0.227–87.021), cough (OR = 5517, 95% CI 0.258–65.024), and venous thrombosis (OR = 7322, 95% CI 0.278–95.020) were risk factors for COVID-19 patients developing severe conditions during hospitalization. When complications were not taken into consideration, COVID-19 patients’ ages, number of diseases, and underlying diseases were risk factors influencing the development of severe illnesses. The ROC curve analysis results showed that the AUC that predicted the severity of COVID-19 patients at admission was 0.943, the optimal threshold was −3.24, and the specificity was 0.824, while the sensitivity was 0.827. The changes in the condition of severe COVID-19 patients are related to many factors such as age, clinical symptoms, and underlying diseases. This study has a certain value in predicting COVID-19 patients that develop from mild to severe conditions, and this prediction model is a useful tool in the quick prediction of the changes in patients’ conditions and providing early intervention for those with risk factors.

scholarly journals Preoperative Albumin-to-Fibrinogen Ratio Predicts Postoperative Pneumonia in Patients with Esophageal Squamous Cell Carcinoma: A Retrospective Study

2020 ◽  
Author(s):  
Peng Zhang ◽  
Song Zhao
Keyword(s):  
Risk Factors ◽  
Esophageal Cancer ◽  
Logistic Regression ◽  
Roc Curve ◽  
Laboratory Tests ◽  
Curve Analysis ◽  
Postoperative Pneumonia ◽  
Function Parameter ◽  
Multivariate Logistic Regression ◽  
Roc Curve Analysis

Abstract Background: Postoperative pneumonia is the most common postoperative complication in patients with esophageal cancer. Prediction of postoperative pneumonia by establishing a preoperative physiological function parameter model can help patients make adequate preoperative preparation, reduce treatment costs, and improve prognosis and quality of life. The purpose of this study was to investigate the relationship between albumin, fibrinogen, albumin-to-fibrinogen ratio(AFR) , and other preoperative laboratory tests and postoperative pneumonia in patients with esophageal cancer after esophagectomy.Methods: Retrospective analysis was performed on 177 consecutive patients who underwent esophagectomy in the Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University from December 2018 to December 2019.Postoperative pneumonia was defined according to the revised Uniform Pneumonia Score(rUPS).Patients were divided into pneumonia and non-pneumonia groups for comparison of baseline data, perioperative indicators, and laboratory examination data.(Receiver operating characteristic)ROC curve analysis was used to evaluate the efficacy, sensitivity and specificity of AFR, and Youden’s index was used to calculate the cut-off values of AFR and other laboratory tests data. Univariate and multivariate logistic regression analyses were used to assess the risk factors for postoperative pneumoniaResults: Of the 177 patients, 32 (18%) developed postoperative pneumonia. The AUC value predicted by AFR using ROC curve analysis was 0.767, 65.6% sensitivity and 83.4% specificity. Multivariate logistic regression analysis showed that albumin (P=0.013), creatinine (P=0.01), and AFR (P=0.016) were independent risk factors for postoperative pneumonia.Conclusion: Preoperative AFR can effectively predict the occurrence of postoperative pneumonia in patients with esophageal cancer

Acute heart failure: predicting early in-hospital outcomes

2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
M Santos ◽  
S Paula ◽  
I Almeida ◽  
H Santos ◽  
H Miranda ◽  
...  
Keyword(s):  
Heart Failure ◽  
Logistic Regression ◽  
Acute Heart Failure ◽  
Roc Curve ◽  
Real Life ◽  
Study Data ◽  
Curve Analysis ◽  
Older Age ◽  
Invasive Ventilation ◽  
Roc Curve Analysis

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Patients (P) with acute heart failure (AHF) are a heterogeneous population. Risk stratification at admission may help predict in-hospital complications and needs. The Get With The Guidelines Heart Failure score (GWTG-HF) predicts in-hospital mortality (M) of P admitted with AHF. ACTION ICU score is validated to estimate the risk of complications requiring ICU care in non-ST elevation acute coronary syndromes. Objective To validate ACTION-ICU score in AHF and to compare ACTION-ICU to GWTG-HF as predictors of in-hospital M (IHM), early M [1-month mortality (1mM)] and 1-month readmission (1mRA), using real-life data. Methods Based on a single-center retrospective study, data collected from P admitted in the Cardiology department with AHF between 2010 and 2017. P without data on previous cardiovascular history or uncompleted clinical data were excluded. Statistical analysis used chi-square, non-parametric tests, logistic regression analysis and ROC curve analysis. Results Among the 300 P admitted with AHF included, mean age was 67.4 ± 12.6 years old and 72.7% were male. Systolic blood pressure (SBP) was 131.2 ± 37.0mmHg, glomerular filtration rate (GFR) was 57.1 ± 23.5ml/min. 35.3% were admitted in Killip-Kimball class (KKC) 4. ACTION-ICU score was 10.4 ± 2.3 and GWTG-HF was 41.7 ± 9.6. Inotropes’ usage was necessary in 32.7% of the P, 11.3% of the P needed non-invasive ventilation (NIV), 8% needed invasive ventilation (IV). IHM rate was 5% and 1mM was 8%. 6.3% of the P were readmitted 1 month after discharge. Older age (p &lt; 0.001), lower SBP (p = 0,035) and need of inotropes (p &lt; 0.001) were predictors of IHM in our population. As expected, patients presenting in KKC 4 had higher IHM (OR 8.13, p &lt; 0.001). Older age (OR 1.06, p = 0.002, CI 1.02-1.10), lower SBP (OR 1.01, p = 0.05, CI 1.00-1.02) and lower left ventricle ejection fraction (LVEF) (OR 1.06, p &lt; 0.001, CI 1.03-1.09) were predictors of need of NIV. None of the variables were predictive of IV. LVEF (OR 0.924, p &lt; 0.001, CI 0.899-0.949), lower SBP (OR 0.80, p &lt; 0.001, CI 0.971-0.988), higher urea (OR 1.01, p &lt; 0.001, CI 1.005-1.018) and lower sodium (OR 0.92, p = 0.002, CI 0.873-0.971) were predictors of inotropes’ usage. Logistic regression showed that GWTG-HF predicted IHM (OR 1.12, p &lt; 0.001, CI 1.05-1.19), 1mM (OR 1.10, p = 1.10, CI 1.04-1.16) and inotropes’s usage (OR 1.06, p &lt; 0.001, CI 1.03-1.10), however it was not predictive of 1mRA, need of IV or NIV. Similarly, ACTION-ICU predicted IHM (OR 1.51, p = 0.02, CI 1.158-1.977), 1mM (OR 1.45, p = 0.002, CI 1.15-1.81) and inotropes’ usage (OR 1.22, p = 0.002, CI 1.08-1.39), but not 1mRA, the need of IV or NIV. ROC curve analysis revealed that GWTG-HF score performed better than ACTION-ICU regarding IHM (AUC 0.774, CI 0.46-0-90 vs AUC 0.731, CI 0.59-0.88) and 1mM (AUC 0.727, CI 0.60-0.85 vs AUC 0.707, CI 0.58-0.84). Conclusion In our population, both scores were able to predict IHM, 1mM and inotropes’s usage.

scholarly journals Predictive CT features for the diagnosis of primary pulmonary mucoepidermoid carcinoma: comparison with squamous cell carcinomas and adenocarcinomas

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaohua Ban ◽  
Xinping Shen ◽  
Huijun Hu ◽  
Rong Zhang ◽  
Chuanmiao Xie ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Roc Curve ◽  
Multinomial Logistic Regression ◽  
Univariate Analysis ◽  
Ct Imaging ◽  
Squamous Cell Carcinomas ◽  
Curve Analysis ◽  
Imaging Features ◽  
Roc Curve Analysis ◽  
Ct Features

Abstract Background To determine the predictive CT imaging features for diagnosis in patients with primary pulmonary mucoepidermoid carcinomas (PMECs). Materials and methods CT imaging features of 37 patients with primary PMECs, 76 with squamous cell carcinomas (SCCs) and 78 with adenocarcinomas were retrospectively reviewed. The difference of CT features among the PMECs, SCCs and adenocarcinomas was analyzed using univariate analysis, followed by multinomial logistic regression and receiver operating characteristic (ROC) curve analysis. Results CT imaging features including tumor size, location, margin, shape, necrosis and degree of enhancement were significant different among the PMECs, SCCs and adenocarcinomas, as determined by univariate analysis (P < 0.05). Only lesion location, shape, margin and degree of enhancement remained independent factors in multinomial logistic regression analysis. ROC curve analysis showed that the area under curve of the obtained multinomial logistic regression model was 0.805 (95%CI: 0.704–0.906). Conclusion The prediction model derived from location, margin, shape and degree of enhancement can be used for preoperative diagnosis of PMECs.

scholarly journals Right choice of a method for determination of cut-off values: A statistical tool for a diagnostic test

2014 ◽  
Vol 5 (3) ◽  
pp. 30-34 ◽  
Author(s):  
Balkishan Sharma ◽  
Ravikant Jain
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Diagnostic Test ◽  
Roc Curve ◽  
Traditional Method ◽  
Statistical Technique ◽  
Curve Analysis ◽  
Operating Characteristics ◽  
Roc Curve Analysis

Objective: The clinical diagnostic tests are generally used to identify the presence of a disease. The cutoff value of a diagnostic test should be chosen to maximize the advantage that accrues from testing a population of human and others. When a diagnostic test is to be used in a clinical condition, there may be an opportunity to improve the test by changing the cutoff value. To enhance the accuracy of diagnosis is to develop new tests by using a proper statistical technique with optimum sensitivity and specificity. Method: Mean±2SD method, Logistic Regression Analysis, Receivers Operating Characteristics (ROC) curve analysis and Discriminant Analysis (DA) have been discussed with their respective applications. Results: The study highlighted some important methods to determine the cutoff points for a diagnostic test. The traditional method is to identify the cut-off values is Mean±2SD method. Logistic Regression Analysis, Receivers Operating Characteristics (ROC) curve analysis and Discriminant Analysis (DA) have been proved to be beneficial statistical tools for determination of cut-off points.Conclusion: There may be an opportunity to improve the test by changing the cut-off value with the help of a correctly identified statistical technique in a clinical condition when a diagnostic test is to be used. The traditional method is to identify the cut-off values is Mean ± 2SD method. It was evidenced in certain conditions that logistic regression is found to be a good predictor and the validity of the same can be confirmed by identifying the area under the ROC curve. Abbreviations: ROC-Receiver operating characteristics and DA-Discriminant Analysis. Asian Journal of Medical Science, Volume-5(3) 2014: 30-34 http://dx.doi.org/10.3126/ajms.v5i3.9296      

Factors predictive of cystoid macular oedema following endothelial keratoplasty: a single-centre review of 2233 cases

2021 ◽  
pp. bjophthalmol-2020-318076
Author(s):  
James Myerscough ◽  
Harry William Roberts ◽  
Angeli Christy Yu ◽  
Michael Mimouni ◽  
Luca Furiosi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Roc Curve ◽  
Macular Oedema ◽  
Curve Analysis ◽  
Cystoid Macular Oedema ◽  
Continuous Variables ◽  
Endothelial Keratoplasty ◽  
Roc Curve Analysis ◽  
Close Observation ◽  
Independent Risk Factors

AimsTo describe the incidence of postoperative cystoid macular oedema (CMO) after endothelial keratoplasty (EK) and to identify its contributory risk factors.Methods2233 patients undergoing EK at Ospedali Privati Forlì ‘Villa Igea’, between January 2005 to October 2018 for Descemet stripping automated endothelial keratoplasty (DSAEK) and June 2014 to August 2018 for Descemet membrane endothelial keratoplasty (DMEK) with a minimum follow-up of 18 months were evaluated. Univariate and multivariate analyses were conducted to identify and quantify contributory risk factors. Receiver operating characteristic (ROC) curve analysis were performed to determine ideal cut-off points of continuous variables.ResultsCMO was identified in 2.82% (n=63) of the cases. CMO occurred in 2.36% of DSAEK eyes and in 5.56% of DMEK eyes (p=0.001). Average onset of CMO was 4.27±6.63 months (range: 1–34 months) postoperatively. Compared with those who did not develop CMO, a higher proportion of patients in the CMO group had diabetes (24.2% vs 9.8%, p<0.001) (OR=3.16, 95% CI: 1.72 to 5.81, p<0.001), a higher proportion of patients who underwent DMEK rather than DSAEK (28.6% vs 14.1%, p=0.001) (OR=2.42, 95% CI: 1.35 to 4.33, p=0.003) and were older (70.5±10.0 vs 67.1±14.3 years, p=0.01). Using the cut-off of 67 years as identified by ROC curve analysis, subjects aged >67 years (OR=2.35, 95% CI: 1.30 to 4.26, p=0.005) were more likely to develop CMO. There were no other significant differences between the groups.ConclusionsOlder age (>67 years), diabetes mellitus and DMEK have been identified as independent risk factors for postoperative CMO following EK. Close observation is necessary during the first postoperative year after EK, particularly in patients with risk factors.

scholarly journals Analysis of fungal bloodstream infection in intensive care units in the Meizhou region of China: species distribution and resistance and the risk factors for patient mortality

2019 ◽  
Author(s):  
Guang-Wen Xiao ◽  
Wan-qing Liao ◽  
Yuenong Zhang ◽  
Xiaodong Luo ◽  
Cailing Zhang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care ◽  
Serum Albumin ◽  
Roc Curve ◽  
Bloodstream Infections ◽  
Mortality Rates ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Candida Spp ◽  
Icu Patients

Abstract Background : Fungal bloodstream infections (FBI) among intensive care unit (ICU) patients are increasing. Our objective was to characterize the fungal pathogens that cause bloodstream infections and determine the epidemiology and risk factors for patient mortality among ICU patients in Meizhou, China. Methods Eighty-one ICU patients with FBI during their stays were included in the study conducted from January 2008 to December 2017. Blood cultures were performed and the antimicrobial susceptibility profiles of the resulting isolates were determined. Logistic multiple regression and receiver operating characteristics (ROC) curve analysis were used to assess the risk factors for mortality among the cases. Results The prevalence of FBI in ICU patients was 0.38% (81/21,098) with a mortality rate of 35.8% (29/81). Ninety-eight strains of bloodstream-infecting fungi, mainly Candida spp., were identified from these patients. Candida albicans was most common (42.9%). Two strains of C. parapsilosis were no-sensitive to caspofungin, C. glabrata were less than 80% sensitive to azole drugs.. Logistic multiple regression showed that age, serum albumin, Acute Physiology and Chronic Health Evaluation (APACHE) II score, three or more underlying diseases, and length of stay in ICU were independent risk factors for mortality in FBI. ROC curve analysis showed that APACHE II scores > 19 and serum albumin ≤ 25g/L were the best predictors of mortality. Conclusion Candida spp. predominated with high mortality rates among cases of FBI in ICU. Thus, clinical staff should enhance overall patient monitoring and especially monitor fungal susceptibility to reduce mortality rates.

scholarly journals Construction on of a Ferroptosis-Related lncRNA-Based Model to Improve the Prognostic Evaluation of Gastric Cancer Patients Based on Bioinformatics

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiahui Pan ◽  
Xinyue Zhang ◽  
Xuedong Fang ◽  
Zhuoyuan Xin
Keyword(s):  
Gastric Cancer ◽  
Cancer Patients ◽  
Roc Curve ◽  
Cox Regression ◽  
Risk Group ◽  
Survival Curve ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Gastric Cancer Patients

BackgroundGastric cancer is one of the most serious gastrointestinal malignancies with bad prognosis. Ferroptosis is an iron-dependent form of programmed cell death, which may affect the prognosis of gastric cancer patients. Long non-coding RNAs (lncRNAs) can affect the prognosis of cancer through regulating the ferroptosis process, which could be potential overall survival (OS) prediction factors for gastric cancer.MethodsFerroptosis-related lncRNA expression profiles and the clinicopathological and OS information were collected from The Cancer Genome Atlas (TCGA) and the FerrDb database. The differentially expressed ferroptosis-related lncRNAs were screened with the DESeq2 method. Through co-expression analysis and functional annotation, we then identified the associations between ferroptosis-related lncRNAs and the OS rates for gastric cancer patients. Using Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) algorithm, we constructed a prognostic model based on 17 ferroptosis-related lncRNAs. We also evaluated the prognostic power of this model using Kaplan–Meier (K-M) survival curve analysis, receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA).ResultsA ferroptosis-related “lncRNA–mRNA” co-expression network was constructed. Functional annotation revealed that the FOXO and HIF-1 signaling pathways were dysregulated, which might control the prognosis of gastric cancer patients. Then, a ferroptosis-related gastric cancer prognostic signature model including 17 lncRNAs was constructed. Based on the RiskScore calculated using this model, the patients were divided into a High-Risk group and a low-risk group. The K-M survival curve analysis revealed that the higher the RiskScore, the worse is the obtained prognosis. The ROC curve analysis showed that the area under the ROC curve (AUC) of our model is 0.751, which was better than those of other published models. The multivariate Cox regression analysis results showed that the lncRNA signature is an independent risk factor for the OS rates. Finally, using nomogram and DCA, we also observed a preferable clinical practicality potential for prognosis prediction of gastric cancer patients.ConclusionOur prognostic signature model based on 17 ferroptosis-related lncRNAs may improve the overall survival prediction in gastric cancer.

scholarly journals Risk factors to predict ocular metastasis in older adult patients with gastric cancer:LDL, ApoA1, and CA724

2021 ◽  
Author(s):  
Wenqing Shi ◽  
Shinan Wu ◽  
Tie Sun ◽  
Huiye Shu ◽  
Qichen Yang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Older Adult ◽  
Roc Curve ◽  
Demographic Characteristics ◽  
Curve Analysis ◽  
Adult Patients ◽  
Apolipoprotein A1 ◽  
Roc Curve Analysis ◽  
Significant Difference ◽  
Ocular Metastasis

Abstract Background: Gastric cancer (GC) is one of the most common malignancies in the population. Although the incidence of GC has reduced, patient prognosis remains poor. Ocular metastases (OM) from GC are rare, and the occurrence of OM is often indicative of severe disease. The purpose of this study was to explore the risk factors for OM of GC.Methods: A total of 1165 older adult patients with GC were enrolled in this study from June 2003 to May 2019 and divided into OM and non-ocular metastasis (NOM) groups. Chi-square and independent samples t tests were used to determine whether differences in demographic characteristics and serological indicators (SI) between the two groups were significant. In addition, binary logistic regression was used to analyze the value of various SI as risk factors for OM in patients with GC. The statistical threshold was set as P < 0.05. Finally, receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic value of various SI in differentiating the occurrence of OM in patients with GC. Results: The incidence of OM in older adults with GC was 1.1%. Adenocarcinoma was the most common type of GC in both groups, and there was no significant difference in demographic characteristics, including sex and age between the groups. Low-density lipoprotein (LDL), carbohydrate antigen-724 (CA724), and carcinoembryonic antigen levels were significantly higher in the OM group than the NOM group, while those of apolipoprotein A1 (ApoA1) were significantly lower in the OM than the NOM group. Binary logistic analysis showed that LDL, ApoA1, and CA724 were independent risk factors for OM in patients with GC (P < 0.001,P = 0.033, and P = 0.008, respectively). ROC curve analysis generated area under the curve (AUC) values of 0.881, 0.576, and 0.906 for LDL, ApoA1, and CA724, respectively. In addition, combined analysis of LDL, ApoA1, and CA724 generated the highest AUC value of 0.924 (P < 0.001).Conclusion: Among SI, LDL, ApoA1, and CA724 have predictive value for the occurrence of OM in GC, with the three factors combined having the highest value.

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