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

Objective: To determine the ability of the ratio of platelet to lymphocyte (PLR) for predicting extubation failure in septic patients receiving invasive mechanical ventilation (IMV). Methods: The retrospective cohort study was conducted in ICU at Beijing Chao-Yang Hospital Shijingshan District, Capital Medical University in China from January, 2010 to December, 2019, including patients with sepsis who received IMV. 180 patients were enrolled in the study, including 111 male and 69 female, with the age range of 23-93 years and the median age of 76 years, and with an average age of 71.22 years. The medical records were reviewed, such as age, sex, acute physiology and chronic health evaluation II (APACHEII), sequential organ failure assessment (SOFA), spontaneous breathing trial (SBT) outcome, weaning outcome, complete blood count before SBT. According to weaning outcome, patients were divided into weaning failure and weaning success group. The difference of PLR, white blood cell(WBC), C-reaction protein (CRP) and procalcitonin (PCT) were compared between weaning failure and success group. Receiver-operating characteristics (ROC) curves and multivariate logistical regression analysis were employed to analyze the performance of these inflammatory markers for predicting weaning failure in patients with sepsis. Results: 180 patients with sepsis were included in the study and 37 patients (20.5%) experienced weaning failure (31 SBT failure and 6 extubation failure after successful SBT). PLR was higher in weaning failure group than that in weaning success group (Z=-5.793,P<0.001). Other inflammation biomarkers such as WBC, CRP and PCT were also higher in weaning failure group than that in weaning success group(Z=-4.356, -3.118 and -2.743, P<0.001, 0.002 and 0.006, respectively). According to ROC curves, PLR has a better predictive ability for weaning failure (AUC=0.809,95%CI 0.733-0.885) when compared to WBC (AUC=0.773,95%CI 0.648-0.817), CRP (AUC=0.666,95%CI 0.577-0.755) and PCT (AUC=0.603,95%CI 0.508-0.698). The cutoff value of PLR for predicting weaning failure was 257.69 with sensitivity 78.38%, specificity 76.22%, and diagnostic accuracy 71.66%. According to multivariate logistic regression analyses, PLR>257.69 was an independent risk factor for predicting weaning failure in patients with sepsis. Conclusions: PLR may be a valuable biomarker for predicting weaning failure in septic patients receiving IMV, and the patients with higher PLR should be handled with caution since they are at higher risk of weaning failure, and some more effective treatment should be in consideration after extubation.
Humans ; Male ; Female ; Aged ; Young Adult ; Adult ; Middle Aged ; Aged, 80 and over ; Respiration, Artificial ; Retrospective Studies ; Sepsis/diagnosis* ; Procalcitonin ; C-Reactive Protein ; Biomarkers ; ROC Curve ; Lymphocytes

Objective: Compare and analyze the results of the domestic Lanyi AH600 glycated hemoglobin analyzer and other different detection systems to understand the comparability of the detection results of different detectors, and establish the best cut point of Lanyi AH600 determination of haemoglobin A1c (HbA1c) in the diagnosis of diabetes. Methods: Multi center cohort study was adopted. The clinical laboratory departments of 18 medical institutions independently collected test samples from their respective hospitals from March to April 2022, and independently completed comparative analysis of the evaluated instrument (Lanyi AH600) and the reference instrument HbA1c. The reference instruments include four different brands of glycosylated hemoglobin meters, including Arkray, Bio-Rad, DOSOH, and Huizhong. Scatter plot was used to calculate the correlation between the results of different detection systems, and the regression equation was calculated. The consistency analysis between the results of different detection systems was evaluated by Bland Altman method. Consistency judgment principles: (1) When the 95% limits of agreement (95% LoA) of the measurement difference was within 0.4% HbA1c and the measurement score was≥80 points, the comparison consistency was good; (2) When the measurement difference of 95% LoA exceeded 0.4% HbA1c, and the measurement score was≥80 points, the comparison consistency was relatively good; (3) The measurement score was less than 80 points, the comparison consistency was poor. The difference between the results of different detection systems was tested by paired sample T test or Wilcoxon paired sign rank sum test; The best cut-off point of diabetes was analyzed by receiver operating characteristic curve (ROC). Results: The correlation coefficient R² of results between Lanyi AH600 and the reference instrument in 16 hospitals is≥0.99; The Bland Altman consistency analysis showed that the difference of 95% LoA in Nanjing Maternity and Child Health Care Hospital in Jiangsu Province (reference instrument: Arkray HA8180) was -0.486%-0.325%, and the measurement score was 94.6 points (473/500); The difference of 95% LoA in the Tibetan Traditional Medical Hospital of TAR (reference instrument: Bio-Rad Variant II) was -0.727%-0.612%, and the measurement score was 89.8 points; The difference of 95% LoA in the People's Hospital of Chongqing Liang Jiang New Area (reference instrument: Huizhong MQ-2000PT) was -0.231%-0.461%, and the measurement score was 96.6 points; The difference of 95% LoA in the Taihe Hospital of traditional Chinese Medicine in Anhui Province (reference instrument: Huizhong MQ-2000PT) was -0.469%-0.479%, and the measurement score was 91.9 points. The other 14 hospitals, Lanyi AH600, were compared with 4 reference instrument brands, the difference of 95% LoA was less than 0.4% HbA1c, and the scores were all greater than 95 points. The results of paired sample T test or Wilcoxon paired sign rank sum test showed that there was no statistically significant difference between Lanyi AH600 and the reference instrument Arkray HA8180 (Z=1.665,P=0.096), with no statistical difference. The mean difference between the measured values of the two instruments was 0.004%. The comparison data of Lanyi AH600 and the reference instrument of all other institutions had significant differences (all P<0.001), however, it was necessary to consider whether it was within the clinical acceptable range in combination with the results of the Bland-Altman consistency analysis. The ROC curve of HbA1c detected by Lanyi AH600 in 985 patients with diabetes and 3 423 patients with non-diabetes was analyzed, the area under curve (AUC) was 0.877, the standard error was 0.007, and the 95% confidence interval 95%CI was (0.864, 0.891), which was statistically significant (P<0.001). The maximum value of Youden index was 0.634, and the corresponding HbA1c cut point was 6.235%. The sensitivity and specificity of diabetes diagnosis were 76.2% and 87.2%, respectively. Conclusion: Among the hospitals and instruments currently included in this study, among these four hospitals included Nanjing Maternity and Child Health Care Hospital in Jiangsu Province (reference instrument: Arkray HA8180), Tibetan Traditional Medical Hospital of TAR (reference instrument: Bio-Rad Variant Ⅱ), the People's Hospital of Chongqing Liang Jiang New Area (reference instrument: Huizhong MQ-2000PT), and the Taihe Hospital of traditional Chinese Medicine in Anhui Province (reference instrument: Huizhong MQ-2000PT), the comparison between Lanyi AH600 and the reference instruments showed relatively good consistency, while the other 14 hospitals involved four different brands of reference instruments: Arkray, Bio-Rad, DOSOH, and Huizhong, Lanyi AH600 had good consistency with its comparison. The best cut point of the domestic Lanyi AH600 for detecting HbA1c in the diagnosis of diabetes is 6.235%.
Pregnancy ; Child ; Humans ; Female ; Glycated Hemoglobin ; Cohort Studies ; Diabetes Mellitus/diagnosis* ; Sensitivity and Specificity ; ROC Curve

Humans ; Hospital Mortality ; ROC Curve ; Emergency Service, Hospital ; Heart Failure ; Renal Insufficiency, Chronic ; Retrospective Studies

To investigate the γ pass rate limit of plan verification equipment for volumetric modulated arc therapy (VMAT) plan verification and its sensitivity on the opening and closing errors of multi-leaf collimator (MLC), 50 cases of nasopharyngeal carcinoma VMAT plan with clockwise and counterclockwise full arcs were randomly selected. Eight kinds of MLC opening and closing errors were introduced in 10 cases of them, and 80 plans with errors were generated. Firstly, the plan verification was conducted in the form of field-by-field measurement and true composite measurement. The γ analysis with the criteria of 3% dose difference, distance to agreement of 2 mm, 10% dose threshold, and absolute dose global normalized conditions were performed for these fields. Then gradient analysis was used to investigate the sensitivity of field-by-field measurement and true composite measurement on MLC opening and closing errors, and the receiver operating characteristic curve (ROC) was used to investigate the optimal threshold of γ pass rate for identifying errors. Tolerance limits and action limits for γ pass rates were calculated using statistical process control (SPC) method for another 40 cases. The error identification ability using the tolerance limit calculated by SPC method and the universal tolerance limit (95%) were compared with using the optimal threshold of ROC. The results show that for the true composite measurement, the clockwise arc and the counterclockwise arc, the descent gradients of the γ passing rate with per millimeter MLC opening error are 10.61%, 7.62% and 6.66%, respectively, and the descent gradients with per millimeter MLC closing error are 9.75%, 7.36% and 6.37%, respectively. The optimal thresholds obtained by the ROC method are 99.35%, 97.95% and 98.25%, respectively, and the tolerance limits obtained by the SPC method are 98.98%, 97.74% and 98.62%, respectively. The tolerance limit calculated by SPC method is close to the optimal threshold of ROC, both of which could identify all errors of ±2 mm, while the universal tolerance limit can only partially identify them, indicating that the universal tolerance limit is not sensitive on some large errors. Therefore, considering the factors such as ease of use and accuracy, it is suggested to use the true composite measurement in clinical practice, and to formulate tolerance limits and action limits suitable for the actual process of the institution based on the SPC method. In conclusion, it is expected that the results of this study can provide some references for institutions to optimize the radiotherapy plan verification process, set appropriate pass rate limit, and promote the standardization of plan verification.
Humans ; Radiotherapy, Intensity-Modulated ; Immune Tolerance ; Nasopharyngeal Carcinoma ; ROC Curve ; Nasopharyngeal Neoplasms/radiotherapy*

Objective: To explore the influencing factors of abnormal pulmonary function in dust-exposed workers and establish the risk prediction model of abnormal pulmonary function. Methods: In April 2021, a total of 4255 dust exposed workers from 47 enterprises in 2020 were included in the study. logistic regression was used to analyze the influencing factors of abnormal pulmonary function in dust-exposed workers, and the corresponding nomogram prediction model was established. The model was evaluated by ROC curve, Calibrationpolt and decision analysis curve. Results: logistic regression analysis showed that age (OR=1.03, 95%CI=1.02~1.05, P<0.001) , physical examination type (OR=4.52, 95%CI=1.69~12.10, P=0.003) , dust type (Comparison with coal dust, Cement dust, OR=3.45, 95%CI=1.45~8.18, P=0.005, Silica dust (OR=2.25, 95%CI=1.01~5.03, P=0.049) , blood pressure (OR=1.63, 95%CI=1.22~2.18, P=0.001) , creatinine (OR=0.08, 95%CI=0.05~0.12, P<0.001) , daily exposure time (OR=1.06, 95%CI=1.10~1.12, P=0.034) and total dust concentration (OR=1.29, 95%CI=1.08~1.54, P=0.005) were the influencing factors of abnormal pulmonary function. The area under the ROC curve of risk prediction nomogram model was 0.764. The results of decision analysis curve showed that the nomogram model had reference value in the prevention and intervention of abnormal pulmonary function when the threshold probability exceeded 0.05. Conclusion: The accuracy ofthe nomogram model constructed by logistic regression werewell in predicting the risk of abnormal lung function of dust-exposed workers.
Humans ; Dust/analysis* ; Lung ; Nomograms ; Risk Factors ; ROC Curve

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