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Objective:To establish a nomogram based on features under endoscopic ultrasonography (EUS) for predicting the diagnosis of small gastric stromal tumors.Methods:The clinicopathological data of 189 patients with gastric submucosal tumors (diameter less than 2 cm) who underwent endoscopic resection at the Department of Gastroenterology, Tongji Hospital of Tongji University from June 2015 to August 2021 were retrospectively collected. All patients were divided into the modeling group ( n=126) and the validation group ( n=63) at 2∶1 by random function of software R. Independent influencing factors for the diagnosis of small gastric stromal tumors under EUS screened by univariable and multivariable logistic regression analysis were used to establish the diagnostic prediction nomogram. The receiver operator characteristic (ROC) curves were drawn to evaluate the discrimination of the model both in the modeling group and the validation group. Hosmer-Lemeshow test and calibration curve were used to evaluate the calibration of the model in both groups. Results:The age of patients >60 years ( OR=2.815, 95% CI:1.148-6.900, P=0.024), the lesions located in cardia/fundus ( OR=5.210, 95% CI:1.225-22.165, P=0.025), originated in muscularis propria ( OR=6.404, 95% CI:2.262-18.135, P<0.001) and of external growth ( OR=6.024, 95% CI:1.252-28.971, P=0.025) were independent influencing factors for the diagnosis of small gastric stromal tumors under EUS. The diagnostic prediction nomogram was established based on the four factors above. The areas under ROC curve of the modeling group and validation group were 0.834 (95% CI:0.765-0.903) and 0.780 (95% CI:0.667-0.893). Hosmer-Lemeshow test indicated that this model fit the data well ( χ2=10.23, P=0.176 in the modeling group; χ2=2.62, P=0.918 in the validation group). Calibration charts of the model drawn by Bootstrap method showed that the calibration curves fit well with the standard curves in both groups. Conclusion:The nomogram based on features under EUS for predicting the diagnosis of small gastric stromal tumors provides a visual reference for endoscopists to diagnose small gastric stromal tumors under EUS with good discrimination and calibration.
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Objective:To evaluate the predictive value of anthropometric indicators in predicting cardiovascular risk in the population with metabolic syndrome(MS).Methods:A cross-sectional study was used to analyze the correlation between anthropometric measures and cardiovascular risk in subjects with MS. Cardiometabolic risk was assessed with cardiometabolic risk index(CMRI). Receiver operating characteristic(ROC) curve analysis was used to assess the predictive power of anthropometric measures for cardiometabolic risk.Results:(1) The anthropometric measures [body mass index(BMI), waist-hip ratio(WHR), waist-to-height ratio(WtHR), body fat percentage(BFP), visceral fat index(VFI), conicity index(CI), a body shape index(ABSI), body roundness index(BRI), abdominal volume index(AVI)] in the MS group were significantly higher than those in the non-MS group( P<0.05). Moreover, there were significant differences in CMRI score and vascular risk between the two groups( P<0.05). (2) Logistic regression analysis showed that the cardiovascular risk was increased with the increases of BMI, VFI, WHR, WtHR, CI, BRI, and AVI after adjusting for confounding factors in the overall population, the non-MS population, and the MS population( P<0.05). (3) In the ROC analysis, the AUC values of BMI, VFI, and AVI were 0.767, 0.734, and 0.770 in the overall population; 0.844, 0.816, and 0.795 in the non-MS population; 0.701, 0.666, and 0.702 in the MS population, respectively. For the overall population and non-MS population, the optimal cut points of BMI to diagnose high cardiovascular risk were 26.04 kg/m 2 and 24.36 kg/m 2; the optimal cut points of VFI were 10.25 and 9.75; the optimal cut points of AVI were 17.3 cm 2 and 15.53 cm 2, respectively. In the MS population, the optimal cut point as a predictor of high cardiovascular risk in young and middle-aged men with MS was 27.63 kg/m 2, and the optimal cut point of AVI in women was 18.08 cm 2. Conclusion:BMI, VFI, and AVI can be used as predictors of cardiovascular risk in the general population. BMI can be used as a predicator of high cardiovascular risk in young and middle-age men with MS. AVI can be used as a predicator of high cardiovascular risk in women with MS.
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Objective:To investigate the expression and correlation of serum trefoil factor 3 (TFF3), serum secreted frizzled-related protein 5 (SFRP5), galectin-3 (Gal-3), and nesfatin-1 (NES-1) in patients with type 2 diabetes(T2DM), diabetic nephropathy(DN), chronic kidney disease (CKD), and healthy controls. To explore the relationship between the above factors and the diagnosis of DN and to establish a diagnostic formula for the diagnosis of DN combined with the above four factors.Methods:In each group 36 patients hospitalized in Tianjin Third Central Hospital from April 2017 to June 2019 were enrolled. 36 healthy volunteers were also chosen as the healthy control group. After 8 to 10 hours of fasting, the venous blood of the subjects in each group was centrifuged, the serum was collected for detection, the serum levels of TFF3, SFRP5, Gal-3, and NES-1 were compared, and the Pearson correlation analysis was performed. According to whether the diagnosis of DN was repeated, the subjects were divided into the DN group and the non-DN group (including a healthy control group, T2DM group, and CKD group). The four datasets were analyzed by binary logistic regression, and the diagnostic prediction model of DN was established, which was further verified by receiver operating characteristic (ROC).Results:The serum levels of TFF3, Gal-3 and NES-1 in DN groups were significantly higher than those in healthy control group, T2DM group and CKD group (all P<0.05), but the serum level of SFRP5 in DN group was significantly lower than that in healthy control group, T2DM group and CKD group (all P<0.05). The differences between the four groups in the four aforementioned indicators were all statistically significant (all P<0.001). The Pearson correlation analysis showed that there was a significant correlation between the above four indicators (all P<0.01). The area under the ROC curve of TFF3, SFRP5, Gal-3, and NES-1 was 0.849, 0.807, 0.882, and 0.841 respectively. The area under the curve diagnosed by the combination of four indicators (0.986) was significantly higher than that of a single indicator, and the difference was statistically significant ( Z=3.75, 4.08, 3.63, 4.06, all P<0.05). Conclusions:The joint prediction model based on serum TFF3, SFRP5, Gal-3, and NES-1 can effectively improve the diagnostic accuracy of DN and provide an important basis for clinical diagnosis of DN.
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Objective@#To establish a diagnostic prediction model for esophageal squamous cell carcinoma (ESCC) and search the potential biomarkers of ESCC. @*Methods@#Serum samples from 59 patients with ESCC and 57 healthy controls were collected, and randomly divided into the training group (44 patients and 42 healthy controls) and validation group (15 patients and 15 healthy controls). Serum proteins/peptides were extracted and purified with weak cation-exchange chromatography Magnetic Beads (WCX-MB), and detected by the matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF MS). Then the differentially expressed proteins/peptides were screened out, and a diagnostic prediction model for ESCC was established and preliminarily validated. @*Results@#The ClinProTools software identified 31 differential peptide peaks (P＜0.05), among which 18 peaks had significant difference (P＜0.01). Compared with healthy controls, 8 peaks were up-regulated in ESCC patients, while 10 peaks were down-regulated. Among them, the areas under the receiver operating characteristics (ROC) curve (AUC ROC ) of m/z 2 660.84 and m/z 5 336.49 peaks were 0.95 and 0.91, respectively, and their expressions were up-regulated in ESCC patients. The validation results showed that the accuracy, sensitivity and specificity of the diagnostic prediction model established by the genetic algorithm (GA) were 93.10%, 92.90% and 93.30%, respectively. @*Conclusion@#The established diagnostic prediction model may be used for the auxiliary diagnosis of ESCC. Two peptide peaks of m/z 2 660.84 and m/z 5 336.49 may be the potential biomarkers of ESCC.
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OBJECTIVES: This study aimed at investigating the diagnostic predictability of Cognitive Impairment Diagnosing Instrument(CIDI) in diagnosing dementia of elderly people aged 60 years or more. METHODS: The subjects were 129 patients with other mental diseases than dementia whose ages were more than 60 years and 86 patients with dementia. Psychiatric diagnoses were made by according to the DSM-IV criteria. Converted age(chronological age minus 59) was employed for the statistical reason. The length of education was classified into 4 intervals:1 for less than one year schooling, 2 for grade schooling, 3 for junior or senior high schooling and 4 for college or more schooling and each number expresses ordinal scale. The converted age, length of education expressed by one of 4 interval scores, and total CIDI score were independent variables while the diagnosis(dementia vs nondementia) was dependent variable in the logistic regression analysis. RESULTS: -2 log likelihood was 102.773 when the length of education, converted age and total CIDI score were included while it was 289.395 when only the constant was included(K 2=186.622, df=3, p=0.000). The goodness-of-fit statistic was 156.798(K 2=6.5843, df=8, p=0.5821), and the overall concordance of diagnostic classification was 90.2%. The logistic regression equation for the diagnosis of dementia was generated as follows:y=7.5752+0.0940*X 1+0.9820*X 2-0.1811*X 3(y=ln{pai/(1-pai)}, X 1:converted age, X 2:education intervals, X 3:total CIDI score, pai:possibility of dementia, > OR =0.50 indicating dementia and <0.50 indicating nondementia). The e bs(95% C.I.) for the converted age, education interval and total CIDI score were 1.0985(1.0107-1.1940), 2.6699(1.4134-5.0436), 0.8344(0.7898-0.8815), respectively. CONCLUSIONS: The CIDI could be considered as a useful diagnostic tool for dementia using the logistic regression analysis.