Objective: To identify the predisposing factors, clinical characteristics, and risk factors of disease progression to establish a novel predictive survival model and evaluate its application value for hepatitis B virus-related acute-on-chronic liver failure. Methods: 153 cases of HBV-ACLF were selected according to the guidelines for the diagnosis and treatment of liver failure (2018 edition) of the Chinese Medical Association Hepatology Branch. Predisposing factors, the basic liver disease stage, therapeutic drugs, clinical characteristics, and factors affecting survival status were analyzed. Cox proportional hazards regression analysis was used to screen prognostic factors and establish a novel predictive survival model. The receiver operating characteristic curve (ROC) was used to evaluate predictive value with the Model for End-Stage Liver Disease (MELD) and the Chronic Liver Failure Consortium Acute-on-Chronic Liver Failure score (CLIF-C ACLF). Results: 80.39% (123/153) based on hepatitis B cirrhosis had developed ACLF. HBV-ACLF's main inducing factors were the discontinuation of nucleos(t)ide analogues (NAs) and the application of hepatotoxic drugs, including Chinese patent medicine/Chinese herbal medicine, non-steroidal anti-inflammatory drugs, anti-tuberculosis drugs, central nervous system drugs, anti-tumor drugs, etc. 34.64% of cases had an unknown inducement. The most common clinical symptoms at onset were progressive jaundice, poor appetite, and fatigue. The short-term mortality rate was significantly higher in patients complicated with hepatic encephalopathy, upper gastrointestinal hemorrhage, hepatorenal syndrome, and infection (P < 0.05). Lactate dehydrogenase, albumin, the international normalized ratio, the neutrophil-to-lymphocyte ratio, hepatic encephalopathy, and upper gastrointestinal bleeding were the independent predictors for the survival status of patients. The LAINeu model was established. The area under the curve for evaluating the survival of HBV-ACLF was 0.886, which was significantly higher than the MELD and CLIF-C ACLF scores (P < 0.05), and the prognosis was worse when the LAINeu score ≥ -3.75. Conclusion: Discontinuation of NAs and the application of hepatotoxic drugs are common predisposing factors for HBV-ACLF. Hepatic decompensation-related complications and infection accelerate the disease's progression. The LAINeu model can predict patient survival conditions more accurately.
Humans ; Hepatitis B virus ; Hepatic Encephalopathy/complications* ; Acute-On-Chronic Liver Failure/diagnosis* ; End Stage Liver Disease/complications* ; Severity of Illness Index ; Risk Factors ; ROC Curve ; Prognosis ; Retrospective Studies

This study investigated whether free prostate-specific antigen (fPSA) performs better than total PSA (tPSA) in predicting prostate volume (PV) in Chinese men with different PSA levels. A total of 5463 men with PSA levels of <10 ng ml^-1 and without prostate cancer diagnosis were included in this study. Patients were classified into four groups: PSA <2.5 ng ml^-1, 2.5-3.9 ng ml^-1, 4.0-9.9 ng ml^-1, and 2.5-9.9 ng ml^-1. Pearson/Spearman's correlation coefficient (r) and receiver operating characteristic (ROC) curves were used to evaluate the ability of tPSA and fPSA to predict PV. The correlation coefficient between tPSA and PV in the PSA <2.5 ng ml^-1 cohort (r = 0.422; P < 0.001) was markedly higher than those of the cohorts with PSA levels of 2.5-3.9 ng ml^-1, 4.0-9.9 ng ml^-1, and 2.5-9.9 ng ml^-1 (r = 0.114, 0.167, and 0.264, respectively; all P ≤ 0.001), while fPSA levels did not differ significantly among different PSA groups. Area under ROC curve (AUC) analyses revealed that the performance of fPSA in predicting PV ≥40 ml (AUC: 0.694, 0.714, and 0.727) was better than that of tPSA (AUC = 0.545, 0.561, and 0.611) in men with PSA levels of 2.5-3.9 ng ml^-1, 4.0-9.9 ng ml^-1, and 2.5-9.9 ng ml^-1, respectively, but not at PSA levels of <2.5 ng ml^-1 (AUC: 0.713 vs 0.720). These findings suggest that the relationship between tPSA and PV may vary with PSA level and that fPSA is more powerful at predicting PV only in the ''gray zone'' (PSA levels of 2.5-9.9 ng ml^-1), but its performance was similar to that of tPSA at PSA levels of <2.5 ng ml^-1.
Male ; Humans ; Prostate-Specific Antigen ; Prostate ; East Asian People ; Prostatic Neoplasms/diagnosis* ; ROC Curve

This study aimed to compare the predictive value of six selected anthropometric indicators for benign prostatic hyperplasia (BPH). Males over 50 years of age who underwent health examinations at the Health Management Center of the Second Xiangya Hospital, Central South University (Changsha, China) from June to December 2020 were enrolled in this study. The characteristic data were collected, including basic anthropometric indices, lipid parameters, six anthropometric indicators, prostate-specific antigen, and total prostate volume. The odds ratios (ORs) with 95% confidence intervals (95% CIs) for all anthropometric parameters and BPH were calculated using binary logistic regression. To assess the diagnostic capability of each indicator for BPH and identify the appropriate cutoff values, receiver operating characteristic (ROC) curves and the related areas under the curves (AUCs) were utilized. All six indicators had diagnostic value for BPH (all P ≤ 0.001). The visceral adiposity index (VAI; AUC: 0.797, 95% CI: 0.759-0.834) had the highest AUC and therefore the highest diagnostic value. This was followed by the cardiometabolic index (CMI; AUC: 0.792, 95% CI: 0.753-0.831), lipid accumulation product (LAP; AUC: 0.766, 95% CI: 0.723-0.809), waist-to-hip ratio (WHR; AUC: 0.660, 95% CI: 0.609-0.712), waist-to-height ratio (WHtR; AUC: 0.639, 95% CI: 0.587-0.691), and body mass index (BMI; AUC: 0.592, 95% CI: 0.540-0.643). The sensitivity of CMI was the highest (92.1%), and WHtR had the highest specificity of 94.1%. CMI consistently showed the highest OR in the binary logistic regression analysis. BMI, WHtR, WHR, VAI, CMI, and LAP all influence the occurrence of BPH in middle-aged and older men (all P ≤ 0.001), and CMI is the best predictor of BPH.
Middle Aged ; Male ; Humans ; Aged ; Prostatic Hyperplasia ; Obesity/epidemiology* ; Body Mass Index ; China/epidemiology* ; Waist-Height Ratio ; ROC Curve ; Waist Circumference ; Risk Factors

OBJECTIVES: To explore a new method for electroencephalography (EEG) background analysis in neonates with hypoxic-ischemic encephalopathy (HIE) and its relationship with clinical grading and head magnetic resonance imaging (MRI) grading. METHODS: A retrospective analysis was performed for the video electroencephalography (vEEG) and amplitude-integrated electroencephalography (aEEG) monitoring data within 24 hours after birth of neonates diagnosed with HIE from January 2016 to August 2022. All items of EEG background analysis were enrolled into an assessment system and were scored according to severity to obtain the total EEG score. The correlations of total EEG score with total MRI score and total Sarnat score (TSS, used to evaluate clinical gradings) were analyzed by Spearman correlation analysis. The total EEG score was compared among the neonates with different clinical gradings and among the neonates with different head MRI gradings. The receiver operating characteristic (ROC) curve and the area under thecurve (AUC) were used to evaluate the value of total EEG score in diagnosing moderate/severe head MRI abnormalities and clinical moderate/severe HIE, which was then compared with the aEEG grading method. RESULTS: A total of 50 neonates with HIE were included. The total EEG score was positively correlated with the total head MRI score and TSS (r_s=0.840 and 0.611 respectively, P<0.001). There were significant differences in the total EEG score between different clinical grading groups and different head MRI grading groups (P<0.05). The total EEG score and the aEEG grading method had an AUC of 0.936 and 0.617 respectively in judging moderate/severe head MRI abnormalities (P<0.01) and an AUC of 0.887 and 0.796 respectively in judging clinical moderate/severe HIE (P>0.05). The total EEG scores of ≤6 points, 7-13 points, and ≥14 points were defined as mild, moderate, and severe EEG abnormalities respectively, which had the best consistency with clinical grading and head MRI grading (P<0.05). CONCLUSIONS The new EEG background scoring method can quantitatively reflect the severity of brain injury and can be used for the judgment of brain function in neonates with HIE.
Infant, Newborn ; Humans ; Hypoxia-Ischemia, Brain/diagnostic imaging* ; Retrospective Studies ; Brain Injuries ; Electroencephalography ; ROC Curve

OBJECTIVE: To investigate the value of lymphatic contrast-enhanced ultrasound (LCEUS) with intra-glandular injection of contrast agent for diagnosis of central compartment lymph node metastasis of thyroid cancer. METHODS: From November, 2020 to May, 2022, the patients suspected of having thyroid cancer and scheduled for biopsy at our center received both conventional ultrasound and LCEUS examinations of the central compartment lymph nodes before surgery. All the patients underwent surgical dissection of the lymph nodes. The perfusion features in LCEUS were classified as homogeneous enhancement, heterogeneous enhancement, regular/irregular ring, and non-enhancement. With pathological results as the gold standard, we compared the diagnostic ability of conventional ultrasound and LCEUS for identifying metastasis in the central compartment lymph nodes. RESULTS: Forty-nine patients with 60 lymph nodes were included in the final analysis. Pathological examination reported metastasis in 34 of the lymph nodes, and 26 were benign lymph nodes. With ultrasound findings of heterogeneous enhancement, irregular ring and non-enhancement as the criteria for malignant lesions, LCEUS had a diagnostic sensitivity, specificity and accuracy of 97.06%, 92.31% and 95% for diagnosing metastatic lymph nodes, respectively, demonstrating its better performance than conventional ultrasound (P < 0.001). Receiver-operating characteristic curve analysis showed that LCEUS had a significantly greater area under the curve than conventional ultrasound for diagnosing metastatic lymph nodes (94.7% [0.856-0.988] vs 78.2% [0.656-0.878], P=0.003). CONCLUSION LCEUS can enhance the display and improve the diagnostic accuracy of the central compartment lymph nodes to provide important clinical evidence for making clinical decisions on treatment of thyroid cancer.
Humans ; Lymphatic Metastasis/diagnostic imaging* ; Thyroid Neoplasms/pathology* ; Ultrasonography/methods* ; Lymph Nodes/pathology* ; ROC Curve

OBJECTIVE: To develop a few-shot learning (FSL) approach for classifying optical coherence tomography (OCT) images in patients with inherited retinal disorders (IRDs). METHODS: In this study, an FSL model based on a student-teacher learning framework was designed to classify images. 2,317 images from 189 participants were included. Of these, 1,126 images revealed IRDs, 533 were normal samples, and 658 were control samples. RESULTS: The FSL model achieved a total accuracy of 0.974-0.983, total sensitivity of 0.934-0.957, total specificity of 0.984-0.990, and total F1 score of 0.935-0.957, which were superior to the total accuracy of the baseline model of 0.943-0.954, total sensitivity of 0.866-0.886, total specificity of 0.962-0.971, and total F1 score of 0.859-0.885. The performance of most subclassifications also exhibited advantages. Moreover, the FSL model had a higher area under curves (AUC) of the receiver operating characteristic (ROC) curves in most subclassifications. CONCLUSION This study demonstrates the effective use of the FSL model for the classification of OCT images from patients with IRDs, normal, and control participants with a smaller volume of data. The general principle and similar network architectures can also be applied to other retinal diseases with a low prevalence.
Humans ; Tomography, Optical Coherence ; Deep Learning ; Retinal Diseases/diagnostic imaging* ; Retina/diagnostic imaging* ; ROC Curve

OBJECTIVES: This study aims to predict the risk of deep caries exposure in radiographic images based on the convolutional neural network model, compare the prediction results of the network model with those of senior dentists, evaluate the performance of the model for teaching and training stomatological students and young dentists, and assist dentists to clarify treatment plans and conduct good doctor-patient communication before surgery. METHODS: A total of 206 cases of pulpitis caused by deep caries were selected from the Department of Stomatological Hospital of Tianjin Medical University from 2019 to 2022. According to the inclusion and exclusion criteria, 104 cases of pulpitis were exposed during the decaying preparation period and 102 cases of pulpitis were not exposed. The 206 radiographic images collected were randomly divided into three groups according to the proportion: 126 radiographic images in the training set, 40 radiographic images in the validation set, and 40 radiographic images in the test set. Three convolutional neural networks, visual geometry group network (VGG), residual network (ResNet), and dense convolutional network (DenseNet) were selected to analyze the rules of the radiographic images in the training set. The radiographic images of the validation set were used to adjust the super parameters of the network. Finally, 40 radiographic images of the test set were used to evaluate the performance of the three network models. A senior dentist specializing in dental pulp was selected to predict whether the deep caries of 40 radiographic images in the test set were exposed. The gold standard is whether the pulp is exposed after decaying the prepared hole during the clinical operation. The prediction effect of the three network models (VGG, ResNet, and DenseNet) and the senior dentist on the pulp exposure of 40 radiographic images in the test set were compared using receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score to select the best network model. RESULTS: The best network model was DenseNet model, with AUC of 0.97. The AUC values of the ResNet model, VGG model, and the senior dentist were 0.89, 0.78, and 0.87, respectively. Accuracy was not statistically different between the senior dentist (0.850) and the DenseNet model (0.850)(P>0.05). Kappa consistency test showed moderate reliability (Kappa=0.6>0.4, P<0.05). CONCLUSIONS Among the three convolutional neural network models, the DenseNet model has the best predictive effect on whether deep caries are exposed in imaging. The predictive effect of this model is equivalent to the level of senior dentists specializing in dental pulp.
Humans ; Deep Learning ; Neural Networks, Computer ; Pulpitis/diagnostic imaging* ; Reproducibility of Results ; ROC Curve ; Random Allocation

Background A simple measurement of central venous pressure (CVP)-mean by the digital monitor display has become increasingly popular. However, the agreement between CVP-mean and CVP-end (a standard method of CVP measurement by analyzing the waveform at end-expiration) is not well determined. This study was designed to identify the relationship between CVP-mean and CVP-end in critically ill patients and to introduce a new parameter of CVP amplitude (ΔCVP= CVPmax - CVPmin) during the respiratory period to identify the agreement/disagreement between CVP-mean and CVP-end.Methods In total, 291 patients were included in the study. CVP-mean and CVP-end were obtained simultaneously from each patient. CVP measurement difference (|CVP-mean - CVP-end|) was defined as the difference between CVP-mean and CVP-end. The ΔCVP was calculated as the difference between the peak (CVPmax) and the nadir value (CVPmin) during the respiratory cycle, which was automatically recorded on the monitor screen. Subjects with |CVP-mean - CVP-end|≥ 2 mmHg were divided into the inconsistent group, while subjects with |CVP-mean - CVP-end| < 2 mmHg were divided into the consistent group.Results ΔCVP was significantly higher in the inconsistent group [7.17(2.77) vs.5.24(2.18), P<0.001] than that in the consistent group. There was a significantly positive relationship between ΔCVP and |CVP-mean - CVP-end| (r=0.283, P <0.0001). Bland-Altman plot showed the bias was -0.61 mmHg with a wide 95% limit of agreement (-3.34, 2.10) of CVP-end and CVP-mean. The area under the receiver operating characteristic curves (AUC) of ΔCVP for predicting |CVP-mean - CVP-end| ≥ 2 mmHg was 0.709. With a high diagnostic specificity, using ΔCVP<3 to detect |CVP-mean - CVP-end| lower than 2mmHg (consistent measurement) resulted in a sensitivity of 22.37% and a specificity of 93.06%. Using ΔCVP>8 to detect |CVP-mean - CVP-end| >8 mmHg (inconsistent measurement) resulted in a sensitivity of 31.94% and a specificity of 91.32%.Conclusions CVP-end and CVP-mean have statistical discrepancies in specific clinical scenarios. ΔCVP during the respiratory period is related to the variation of the two CVP methods. A high ΔCVP indicates a poor agreement between these two methods, whereas a low ΔCVP indicates a good agreement between these two methods.
Humans ; Central Venous Pressure ; Respiration ; ROC Curve

OBJECTIVES: To study the changes in cell free-DNA (cf-DNA), a marker of neutrophil extracellular traps (NETs), in neonates with acute respiratory distress syndrome (ARDS), and to evaluate its relationship with the severity and early diagnosis of ARDS. METHODS: The neonates diagnosed with ARDS in the Affiliated Hospital of Jiangsu University from January 2021 to June 2022 were enrolled in the prospective study. The neonates were divided into mild, moderate, and severe ARDS groups based on the oxygen index (OI) (4≤OI<8, 8≤OI<16, and OI≥16, respectively). The control group was selected from jaundice neonates who were observed in the neonatal department of the hospital during the same period, and they had no pathological factors causing neonatal jaundice. Peripheral blood samples were collected on day 1, day 3, and day 7 after admission for the ARDS group, and on the day of admission for the control group. Serum cf-DNA levels were measured using a fluorescence enzyme-linked immunosorbent assay. Serum interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) levels were measured using enzyme-linked immunosorbent assay. A Pearson correlation analysis was used to evaluate the correlation of serum cf-DNA levels with serum IL-6 and TNF-α levels. RESULTS: A total of 50 neonates were enrolled in the ARDS group, including 15 neonates with mild ARDS, 25 with moderate ARDS, and 10 with severe ARDS. Twenty-five neonates were enrolled in the control group. Compared with the control group, the serum levels of cf-DNA, IL-6, and TNF-α in all ARDS groups were significantly increased (P<0.05). Compared with the mild ARDS group, the serum levels of cf-DNA, IL-6, and TNF-α in the moderate and severe ARDS groups were significantly increased (P<0.05), and the increase was more significant in the severe ARDS group (P<0.05). The serum levels of cf-DNA, IL-6, and TNF-α in all ARDS groups were significantly increased on day 3 after admission and significantly decreased on day 7 after admission compared with those on day 1 after admission (P<0.05). The Pearson correlation analysis showed that there was a positive correlation between serum cf-DNA levels and IL-6 levels as well as TNF-α levels in 50 neonates with ARDS (P<0.05). CONCLUSIONS There is an excessive expression of NETs in neonates with ARDS, and dynamic monitoring of serum cf-DNA levels has certain clinical value in evaluating the severity and early diagnosis of ARDS in neonates.
Infant, Newborn ; Humans ; Extracellular Traps ; Prospective Studies ; Tumor Necrosis Factor-alpha ; Interleukin-6 ; Prognosis ; ROC Curve ; Respiratory Distress Syndrome ; DNA

UNLABELLED: AbstractObjective: To analysis the clinical data of patients after single-center hematopoietic stem cell transplantation (HSCT) and construct a predictive model for metabolic syndrome (MS) diagnosis. METHODS: Ninety-three hematology patients who underwent HSCT at the First Hospital of Lanzhou University from July 2015 to September 2022 were selected to collect basic data, transplantation status and postoperative data, the clinical characteristics of patients with and without MS after transplantation were compared and analyzed. Logistic regression model was used to analyze the influence fators of MS after transplantation, and a predictive model of HSCT-MS diagnosis was constructed under the influence of independent influence factors. The model was evaluated using the ceceiver operating characteristic curve (ROC curve). RESULTS: Metabolic syndrome occurred in 36 of 93 HSCT patients and did not occur in 57. Compared with non-HSCT-MS group, HSCT-MS had significantly higher fasting blood glucose (FBG) levels before transplantation, shorter course before transplantation, and higher bilirubin levels after transplantation (P<0.05). The statistically significant clinical indicators were subjected to multi-factor logistic regression analysis, and the results showed that pre-transplant high FBG, pre-transplant short disease course and post-transplant high bilirubin were independent influence factors for HSCT-MS. The standard error of predicting the occurrence of HSCT-MS based on the clinical model was 0.048, the area under the curve AUC=0.776, 95% CI :0.683-0.869, the optimal threshold was 0.58 based on the Jorden index at maximum, the sensitivity was 0.694, and the specificity was 0.772, which has certain accuracy. CONCLUSION A clinical prediction model for HSCT-MS based on logistic regression analysis is constructed through the analysis of clinical data, which has certain clinical value.
Humans ; Metabolic Syndrome ; Prognosis ; Models, Statistical ; Hematopoietic Stem Cell Transplantation ; ROC Curve ; Retrospective Studies