scholarly journals Risk Factors to Predict Ocular Metastasis in Older Adult Patients With Gastric Cancer:LDL, ApoA1, and CA724

2022 ◽  
Vol 21 ◽  
pp. 153303382110658
Author(s):  
Wen-Qing Shi ◽  
Shi-Nan Wu ◽  
Tie Sun ◽  
Hui-Ye Shu ◽  
Qi-Chen Yang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Roc Curve ◽  
Area Under The Curve ◽  
Density Lipoprotein ◽  
Demographic Characteristics ◽  
Curve Analysis ◽  
Apolipoprotein A1 ◽  
Roc Curve Analysis ◽  
Significant Difference ◽  
Ocular Metastasis

Objective: The purpose of this study was to explore the risk factors for Ocular metastasis (OM) of Gastric cancer (GC). Methods: This is a retrospective cohort study. A total of 1165 patients with GC were enrolled in this study 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 < .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 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 < .001, P = .033, and P = .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 < .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.

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.

scholarly journals An Adjunct Indicator for the Diagnosis of Fracture-Related Infections: Platelet Count to Mean Platelet Volume Ratio

2020 ◽  
Vol 5 (2) ◽  
pp. 54-59
Author(s):  
John Strony ◽  
Taylor Paziuk ◽  
Brianna Fram ◽  
Kyle Plusch ◽  
Gerard Chang ◽  
...  
Keyword(s):  
Platelet Count ◽  
Roc Curve ◽  
Mean Platelet Volume ◽  
Area Under The Curve ◽  
Volume Ratio ◽  
Curve Analysis ◽  
Platelet Volume ◽  
Roc Curve Analysis ◽  
Significant Difference ◽  
Fracture Nonunion

Abstract. Introduction: Fracture-related infection (FRI) is a common complication associated with orthopaedic fracture care. Diagnosing these complications in the preoperative setting is difficult. Platelets are a known acute phase reactant with indices that change in accordance with infection and inflammation. The purpose of our study was to assess the diagnostic utility of platelet indices at assessing FRI.Methods: A retrospective review performed for all patients who underwent revision surgery for fracture nonunion between 2013 and 2018. Radiographs were employed to define nonunion. Intraoperative cultures were used to define FRI. Receiver operator characteristic (ROC) curve analysis was used to assess the diagnostic ability of preoperative erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and the platelet count/mean platelet volume ratio (P/V) at recognizing FRI.Results: Of the 53 revision surgeries that were performed for fracture nonunion, 17 (32.1%) were identified as FRI. There were no significant demographic differences between the two cohorts. Patients with FRIs exhibited higher values for ESR (54.82 vs. 19.16, p<0.001), CRP (0.90 vs. 0.35, p=0.003), and P/V (37.4 vs. 22.8, p<0.001) as compared to those within the aseptic nonunion cohort. ROC curve analysis for P/V demonstrated that at an optimal ratio of 23, area under the curve (AUC) is 0.814, specificity is 55.6%, and sensitivity is 100.0%. There was no significant difference in the diagnostic performance of the serum biomarkers but only ESR and P/V had an AUC greater than 0.80. The negative predictive value (NPV) for P/V, ESR, and CRP was 100.0%, 84.6%, and 78.6%, respectively.Conclusion: The P/V ratio may serve as a reliable screening test for FRI.

Abstract 12875: Serum Neutrophil Gelatinase-associated Lipocalin Has a Predictive Value for the Neurological Outcome of Post Cardiac Arrest Victims

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Tadashi Kaneko ◽  
Motoki Fujita ◽  
Yasuaki Ogino ◽  
Hiroki Irie ◽  
Shinsuke Iwashita ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Roc Curve ◽  
Predictive Value ◽  
Neurological Outcome ◽  
Neuron Specific Enolase ◽  
Area Under The Curve ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Significant Difference ◽  
Neutrophil Gelatinase

Introduction: Neutrophil gelatinase-associated lopocalin (NGAL) is well known as the biomarker of acute kidney injury. Currently, serum NGAL is reported as the biomarker which is associated with mortality and multiple organ dysfunction syndrome in septic patients. In this study, we measured serum NGAL in post cardiac arrest syndrome (PCAS) patients to investigate the predictive value of neurological outcome. Hypothesis: Serum NGAL is associated with favorable neurological outcome of PCAS patients. Methods: 43 cases of PCAS patients were measured serum NGAL on day 1 and 2, and serum neuron specific enolase (NSE) was also measured on day 2. Patients were divided into two group (group G: CPC 1-2 and group P: CPC 3-5, CPC: cerebral performance categories). Serum NGAL and NSE were compared between group G and P. Receiver operation characteristic (ROC) curve analysis were performed of both NGAL and NSE. Results: Group G (n = 20) and P (n = 23) were analyzed. Serum NGAL showed significant difference in day 2 (G: 282±116 ng/mL v.s. P: 299±87 ng/mL in day1: P = 0.609, G: 185±124 ng/mL v.s. P: 353±110 ng/mL in day 2: P<0.001). The ROC curve analysis showed area under the curve of NGAL in day 2 was 0.871, and the area of NSE in day2 showed 0.918. Conclusions: Serum NGAL in PCAS patients had the predictive value for neurological outcome which is comparable to serum NSE. (This study was supported by grants from Japanese Ministry of Health, Labour and Welfare: No. 25462824)

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 Combine preoperative estimation of serum thyroglobulin and ACR-thyroid imaging reporting and data system scoring could accurately define malignant thyroid nodules

2020 ◽  
Vol 8 (1) ◽  
pp. 32
Author(s):  
Waseem A. Shoda
Keyword(s):  
Roc Curve ◽  
Predictive Value ◽  
Thyroid Nodules ◽  
Curve Analysis ◽  
Data System ◽  
Operating Characteristics ◽  
Roc Curve Analysis ◽  
Serum Thyroglobulin ◽  
Thyroid Imaging ◽  
Significant Difference

Background: Evaluation of diagnostic ability of preoperative estimation of serum thyroglobulin (TG) to detect malignant thyroid nodules (TN) in comparison to the American College of Radiology, Thyroid imaging reporting and data system (ACR-TIRADS), fine needle aspiration cytology (FNAC) and intraoperative frozen section (IO-FS).Methods: 34 patients with ACR-TIRADS 2-4 TN were evaluated preoperatively for identification of malignancy and all underwent total thyroidectomy with bilateral neck block dissection if indicated. Results of preoperative investigations were statistically analyzed using the Receiver operating characteristics (ROC) curve analysis as predictors for malignancy in comparison to postoperative paraffin sections.Results: Preoperative serum TG levels had 100% sensitivity and negative predictive value, while ACR-TIRADS scoring had 100% specificity and positive predictive value with accuracy rates of 95.35% and 97.67% for TG and TIRADS, respectively. ROC curve analysis defined preoperative ACR-TIRADS class and serum TG as highly diagnostic than FNAC for defining malignancy with non-significant difference between areas under curve for TIRADS and TG. For cases had intermediate risk of malignancy on TIRADS, IO-FS had missed 3, FNAC missed 4, while serum TG levels were very high in the 13 cases and were defined by ROC curve as the only significant predictor for malignancy.Conclusions: Preoperative estimation of serum TG showed higher diagnostic validity than FNAC, high predictability of cancer and ability to verify the intermediate findings on TIRADS. Combined preoperative TIRADS and TG estimation could accurately discriminate malignant TN with high accuracy and spare the need for preoperative FNAC or IO-FS. 

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 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 Evaluation of serum AFP and DCP levels in the diagnosis of early-stage HBV-related HCC under different backgrounds

2020 ◽  
Vol 48 (10) ◽  
pp. 030006052096908
Author(s):  
Ting Song ◽  
Lili Wang ◽  
Ruopei Xin ◽  
Liping Zhang ◽  
Yun Tian
Keyword(s):  
Roc Curve ◽  
Diagnostic Performance ◽  
Operating Characteristic ◽  
Early Stage ◽  
Area Under The Curve ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Chronic Hbv Infection ◽  
B Virus ◽  
Chronic Hbv

Objective This study compared the diagnostic performance of alpha-fetoprotein (AFP) and des-gamma-carboxyprothrombin (DCP) in early-stage hepatitis B virus-related hepatocellular carcinoma (HBV-HCC) under different backgrounds. Methods Patients were enrolled and divided in four groups: chronic HBV infection (CHB), liver cirrhosis (LC), early-stage CHB-HCC, and early-stage LC-HCC. Serum AFP and DCP levels were measured. Receiver-operating characteristic (ROC) curve and area under the curve (AUC) analyses were applied to compare the diagnostic performance of DCP and AFP for HCC. Results In total, 200 patients were enrolled, including 48, 64, 33, and 55 patients with CHB, LC, CHB-HCC, and LC-HCC, respectively. ROC curve analysis revealed that the AUCs of AFP, DCP, and their combination in differentiating early-stage LC-HCC from LC were 0.776, 0.758, and 0.786, respectively. The values of these markers in discriminating early-stage CHB-HCC from CHB were 0.828, 0.731, and 0.862, respectively. Conclusions DCP was inferior to AFP in differentiating early-stage CHB-HCC from CHB. However, AFP and DCP displayed similar performance in distinguishing early-stage LC-HCC and LC.

scholarly journals Impact of comorbidity burden on mortality in patients with COVID-19 using the Korean health insurance database

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Soo Ick Cho ◽  
Susie Yoon ◽  
Ho-Jin Lee
Keyword(s):  
Risk Factors ◽  
Health Insurance ◽  
Roc Curve ◽  
Significant Risk ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Comorbidity Burden ◽  
Comorbidity Index ◽  
The Impact ◽  
Age And Sex

AbstractWe aimed to investigate the impact of comorbidity burden on mortality in patients with coronavirus disease (COVID-19). We analyzed the COVID-19 data from the nationwide health insurance claims of South Korea. Data on demographic characteristics, comorbidities, and mortality records of patients with COVID-19 were extracted from the database. The odds ratios of mortality according to comorbidities in these patients with and without adjustment for age and sex were calculated. The predictive value of the original Charlson comorbidity index (CCI) and the age-adjusted CCI (ACCI) for mortality in these patients were investigated using the receiver operating characteristic (ROC) curve analysis. Among 7590 patients, 227 (3.0%) had died. After age and sex adjustment, hypertension, diabetes mellitus, congestive heart failure, dementia, chronic pulmonary disease, liver disease, renal disease, and cancer were significant risk factors for mortality. The ROC curve analysis showed that an ACCI threshold > 3.5 yielded the best cut-off point for predicting mortality (area under the ROC 0.92; 95% confidence interval 0.91–0.94). Our study revealed multiple risk factors for mortality in patients with COVID-19. The high predictive power of the ACCI for mortality in our results can support the importance of old age and comorbidities in the severity of COVID-19.

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