scholarly journals Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis

2021 ◽  
Vol 12 ◽  
Author(s):  
Ying Luo ◽  
Ying Xue ◽  
Qun Lin ◽  
Liyan Mao ◽  
Guoxing Tang ◽  
...  
Keyword(s):  
Bacterial Meningitis ◽  
Cell Count ◽  
Tuberculous Meningitis ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Specific Antigen ◽  
Diagnostic Model ◽  
City Hospital ◽  
Roc Curve Analysis ◽  
New City

BackgroundThe differential diagnosis between tuberculous meningitis (TBM) and bacterial meningitis (BM) remains challenging in clinical practice. This study aimed to establish a diagnostic model that could accurately distinguish TBM from BM.MethodsPatients with TBM or BM were recruited between January 2017 and January 2021 at Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort). The detection for indicators involved in cerebrospinal fluid (CSF) and T-SPOT assay were performed simultaneously. Multivariate logistic regression was used to create a diagnostic model.ResultsA total of 174 patients (76 TBM and 98 BM) and another 105 cases (39 TBM and 66 BM) were enrolled from Qiaokou cohort and Caidian cohort, respectively. Significantly higher level of CSF lymphocyte proportion while significantly lower levels of CSF chlorine, nucleated cell count, and neutrophil proportion were observed in TBM group when comparing with those in BM group. However, receiver operating characteristic (ROC) curve analysis showed that the areas under the ROC curve (AUCs) produced by these indicators were all under 0.8. Meanwhile, tuberculosis-specific antigen/phytohemagglutinin (TBAg/PHA) ratio yielded an AUC of 0.889 (95% CI, 0.840–0.938) in distinguishing TBM from BM, with a sensitivity of 68.42% (95% CI, 57.30%–77.77%) and a specificity of 92.86% (95% CI, 85.98%–96.50%) when a cutoff value of 0.163 was used. Consequently, we successfully established a diagnostic model based on the combination of TBAg/PHA ratio, CSF chlorine, CSF nucleated cell count, and CSF lymphocyte proportion for discrimination between TBM and BM. The established model showed good performance in differentiating TBM from BM (AUC: 0.949; 95% CI, 0.921–0.978), with 81.58% (95% CI, 71.42%–88.70%) sensitivity and 91.84% (95% CI, 84.71%–95.81%) specificity. The performance of the diagnostic model obtained in Qiaokou cohort was further validated in Caidian cohort. The diagnostic model in Caidian cohort produced an AUC of 0.923 (95% CI, 0.867–0.980) with 79.49% (95% CI, 64.47%–89.22%) sensitivity and 90.91% (95% CI, 81.55%–95.77%) specificity.ConclusionsThe diagnostic model established based on the combination of four indicators had excellent utility in the discrimination between TBM and BM.

scholarly journals Combination of HLA-DR on Mycobacterium tuberculosis-Specific Cells and Tuberculosis Antigen/Phytohemagglutinin Ratio for Discriminating Active Tuberculosis From Latent Tuberculosis Infection

2021 ◽  
Vol 12 ◽  
Author(s):  
Ying Luo ◽  
Ying Xue ◽  
Guoxing Tang ◽  
Qun Lin ◽  
Huijuan Song ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Roc Curve ◽  
Latent Tuberculosis ◽  
Diagnostic Model ◽  
City Hospital ◽  
Roc Curve Analysis ◽  
Hla Dr ◽  
Tnf Α ◽  
Ifn Γ ◽  
Latent Tb Infection

BackgroundNovel approaches for tuberculosis (TB) diagnosis, especially for distinguishing active TB (ATB) from latent TB infection (LTBI), are urgently warranted. The present study aims to determine whether the combination of HLA-DR on Mycobacterium tuberculosis (MTB)-specific cells and TB antigen/phytohemagglutinin (TBAg/PHA) ratio could facilitate MTB infection status discrimination.MethodsBetween June 2020 and June 2021, participants with ATB and LTBI were recruited from Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort), respectively. The detection of HLA-DR on MTB-specific cells upon TB antigen stimulation and T-SPOT assay were simultaneously performed on all subjects.ResultsA total of 116 (54 ATB and 62 LTBI) and another 84 (43 ATB and 41 LTBI) cases were respectively enrolled from Qiaokou cohort and Caidian cohort. Both HLA-DR on IFN-γ+TNF-α+ cells and TBAg/PHA ratio showed discriminatory value in distinguishing between ATB and LTBI. Receiver operator characteristic (ROC) curve analysis showed that HLA-DR on IFN-γ+TNF-α+ cells produced an area under the ROC curve (AUC) of 0.886. Besides, TBAg/PHA ratio yield an AUC of 0.736. Furthermore, the combination of these two indicators resulted in the accurate discrimination with an AUC of 0.937. When the threshold was set as 0.36, the diagnostic model could differentiate ATB from LTBI with a sensitivity of 92.00% and a specificity of 81.82%. The performance obtained in Qiaokou cohort was further validated in Caidian cohort.ConclusionsThe combination of HLA-DR on MTB-specific cells and TBAg/PHA ratio could serve as a robust tool to determine TB disease states.

scholarly journals Presepsin values and prognostic nutritional index predict mortality in intensive care unit patients with sepsis: a pilot study

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Yuichiro Shimoyama ◽  
Osamu Umegaki ◽  
Noriko Kadono ◽  
Toshiaki Minami
Keyword(s):  
Multivariate Analysis ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Prognostic Nutritional Index ◽  
Early Mortality ◽  
Curve Analysis ◽  
Rank Test ◽  
Roc Curve Analysis ◽  
Log Rank Test ◽  
Nutritional Index

Abstract Objective Sepsis is a major cause of mortality for critically ill patients. This study aimed to determine whether presepsin values can predict mortality in patients with sepsis. Results Receiver operating characteristic (ROC) curve analysis, Log-rank test, and multivariate analysis identified presepsin values and Prognostic Nutritional Index as predictors of mortality in sepsis patients. Presepsin value on Day 1 was a predictor of early mortality, i.e., death within 7 days of ICU admission; ROC curve analysis revealed an AUC of 0.84, sensitivity of 89%, and specificity of 77%; and multivariate analysis showed an OR of 1.0007, with a 95%CI of 1.0001–1.0013 (p = 0.0320).

scholarly journals Development and validation of a prognostic model to predict clinical deterioration in hospitalized patients with COVID-19

2021 ◽  
Author(s):  
Javid Azadbakht ◽  
Sina Rashedi ◽  
Soheil Kooraki ◽  
Hamed Kowsari ◽  
Elnaz Tabibian
Keyword(s):  
Roc Curve ◽  
Scoring System ◽  
Prognostic Model ◽  
Operating Characteristic ◽  
Clinical Laboratory ◽  
External Validation ◽  
Clinical Deterioration ◽  
Roc Curve Analysis ◽  
Prospective Multicenter Study ◽  
Development And Validation

Abstract Objectives We aimed to develop and validate a prognostic model to predict clinical deterioration defined as either death or intensive care unit admission of hospitalized COVID-19 patients.Methods This prospective, multicenter study investigated 172 consecutive hospitalized COVID-19 patients who underwent a chest computed tomography (CT) scan between March 20 and April 30, 2020 (development cohort), as well as an independent sample of 40 consecutive patients for external validation (validation cohort). The clinical, laboratory, and radiologic data were gathered, and logistic regression along with receiver operating characteristic (ROC) curve analysis was performed.Results The overall clinical deterioration rates of the development and validation cohorts were 28.4% (49 of 172) and 30% (12 of 40), respectively. Seven predictors were included in the scoring system with a total score of 15: CT severity score\(\ge\)15 (Odds Ratio (OR)=6.34, 4 points), pleural effusion (OR = 6.80, 2 points), symptom onset to admission ≤ 6 days (OR = 2.44, 2 points), age\(\ge\)70 years (OR = 2.44, 2 points), diabetes mellitus (OR = 2.24, 2 points), dyspnea (OR = 2.17, 1.5 points), and abnormal leukocyte count (OR = 1.89, 1.5 points). The area under the ROC curve for the scoring system in the development and validation cohorts was 0.823 (CI [0.751–0.895]) and 0.558 (CI [0.340–0.775]), respectively.Conclusion This study provided a new easy-to-calculate scoring system with external validation for hospitalized COVID-19 patients to predict clinical deterioration based on a combination of seven clinical, laboratory, and radiologic parameters.

Serum diiodotyrosine—a biomarker to differentiate destructive thyroiditis from Graves’ disease

2021 ◽  
Author(s):  
Naoya Fujita ◽  
Yosuke Ono ◽  
Azusa Sano ◽  
Motohiro Kimata ◽  
Seigo Oyama ◽  
...  
Keyword(s):  
Roc Curve ◽  
Operating Characteristic ◽  
Cross Sectional Study ◽  
Curve Analysis ◽  
Diagnostic Methods ◽  
Graves Disease ◽  
Roc Curve Analysis ◽  
Cross Sectional ◽  
Chromatography Tandem Mass Spectrometry ◽  
Liquid Chromatography Tandem Mass

Objective: Conventional diagnostic methods are limited in their ability to differentiate destructive thyroiditis from Graves’ disease. We hypothesised that serum diiodotyrosine (DIT) and monoiodotyrosine (MIT) levels could be biomarkers for differentiating destructive thyroiditis from Graves’ disease. Design: Patients with destructive thyroiditis (n = 13) and Graves’ disease (n = 22) were enrolled in this cross-sectional study. Methods: We assayed the serum DIT and MIT levels using liquid chromatography-tandem mass spectrometry. A receiver operating characteristic (ROC) curve analysis was used to determine the sensitivity and specificity of the serum DIT and MIT levels as biomarkers for differentiating destructive thyroiditis from Graves’ disease. Results: The serum DIT and MIT levels were significantly higher in patients with destructive thyroiditis than in those with Graves’ disease. The ROC curve analysis showed that the serum DIT levels (≥ 359.9 pg/mL) differentiated destructive thyroiditis from Graves’ disease, significantly, with 100.0% sensitivity and 95.5% specificity (P < .001). The diagnostic accuracy of the serum MIT levels (≥119.4 pg/mL) was not as high as that of the serum DIT levels (sensitivity, 84.6%; specificity, 77.3%; P = .001). Conclusions: The serum DIT levels may serve as a novel diagnostic biomarker for differentiating destructive thyroiditis from Graves’ disease.

Evaluation of urinary γ-glutamyl transferase and serum creatinine in non-azotaemic hospitalised dogs

2019 ◽  
Vol 185 (2) ◽  
pp. 52-52 ◽  
Author(s):  
Francesca Perondi ◽  
Ilaria Lippi ◽  
Gianila Ceccherini ◽  
Veronica Marchetti ◽  
Grazia Guidi
Keyword(s):  
Serum Creatinine ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Intensive Care Unit Mortality ◽  
Kidney Injury ◽  
Blood Biomarkers ◽  
Urine Protein ◽  
Roc Curve Analysis ◽  
Significant Difference ◽  
Glutamyl Transferase

Urinary and blood biomarkers for diagnosis of acute kidney injury (AKI) in hospitalised dogs were evalueted. This prospective study included 97 dogs, classified according to the International Renal Interest Society classification into no AKI and AKI grade 1 (48-hour increase in serum creatinine≥0.3 mg/dl and/or urinary production <1 ml/kg/hour for at least six hours). A total of 62 of 97 dogs (64 per cent) were classified as AKI 1. A statistically significant difference was found between no AKI and AKI 1 in urine protein to creatinine ratio, urinary γ-glutamyl transferase (uGGT) and uGGT/cu (P<0.0001). Thirteen of 97 dogs (13.4 per cent) that developed increased creatinine and change in AKI grade showed high mortality (n=9/13; 69.2 per cent). The receiver operating characteristic (ROC) curve analysis of uGGT/cu index as a marker for AKI grade 1 had an area under the ROC curve of 0.78; optimal cut-off point was 57.50 u/g, with sensitivity and specificity of 75.4 per cent and 75.6 per cent, respectively. Overall intensive care unit mortality was 23.7 per cent (23/97), 13.4 per cent (13/97) of which died during hospitalisation and 10.3 per cent (10/97) within 28 days after discharge. uGGT is an acceptable marker for distinguishing between AKI 1 and no AKI.

Value of multimodality imaging in the diagnosis of breast lesions with calcification: A retrospective study

2020 ◽  
Vol 76 (1) ◽  
pp. 85-98 ◽  
Author(s):  
Wenjuan Tong ◽  
Xiaoling Zhang ◽  
Jia Luo ◽  
Fushun Pan ◽  
Jinyu Liang ◽  
...  
Keyword(s):  
Roc Curve ◽  
Operating Characteristic ◽  
Multimodality Imaging ◽  
Curve Analysis ◽  
Breast Lesions ◽  
Roc Curve Analysis ◽  
Diagnostic Efficacy ◽  
Contrast Enhanced ◽  
Conventional Ultrasound ◽  
Group A

PURPOSE: To assess the value of conventional ultrasound (US), contrast-enhanced ultrasound (CEUS) and mammography in the diagnosis of breast lesions with calcifications. METHODS: A total of 87 breast lesions with calcification were subjected to US, CEUS and mammography and divided into 3 groups: Group A (all cases), Group A1 (31 cases who underwent US and CEUS first followed by mammography), and Group A2 (56 cases who underwent mammography first followed by US and CEUS). A receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic efficacy of different methods in different groups. RESULTS: In Group A, the area under the ROC curve (AUROC) of CEUS were 0.937, which were significantly higher than that of mammography (p < 0.05). In Group A1, the AUROC of CEUS were 0.842, which were not significantly different from that of US and mammography (p > 0.05). In Group A2, the AUROC of CEUS were 0.987, which were significantly higher than that of mammography and US (p < 0.05). CONCLUSION: Based on the mammography results, the combination of US and CEUS might improve the diagnostic efficacy in breast lesions with calcification.

scholarly journals A Novel Inflammation-Based Prognostic Score: The Fibrinogen/Albumin Ratio Predicts Prognoses of Patients after Curative Resection for Hepatocellular Carcinoma

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qiaodong Xu ◽  
Yongcong Yan ◽  
Songgang Gu ◽  
Kai Mao ◽  
Jianlong Zhang ◽  
...  
Keyword(s):  
Roc Curve ◽  
Prognostic Value ◽  
Curative Resection ◽  
Operating Characteristic ◽  
Curve Analysis ◽  
Prognostic Scores ◽  
Roc Curve Analysis ◽  
Optimal Cutoff ◽  
Albumin Ratio ◽  
Cutoff Value

Background. Inflammation is an important hallmark of cancer. Fibrinogen and albumin are both vital factors in systemic inflammation. This study investigated the prognostic value of the fibrinogen/albumin ratio in HCC patients who underwent curative resection. Methods. HCC patients (n=151) who underwent curative resection were evaluated retrospectively. The optimal cutoff value for the fibrinogen/albumin ratio was selected by receiver operating characteristic (ROC) curve analysis. Correlations between preoperative fibrinogen/albumin ratios and clinicopathologic characteristics were analyzed by χ2 test. The area under the receiver operating characteristic curve (AUC) was calculated to compare the prognostic value of the fibrinogen/albumin ratio with other prognostic scores (neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and albumin-bilirubin (ALBI) score). The overall survival (OS) and time to recurrence (TTR) were assessed by the log-rank test and the Cox proportional hazard regression model. Results. An optimal cutoff value of the preoperative fibrinogen/albumin ratio (0.062) was determined for 151 patients who underwent curative resection for HCC via a ROC curve analysis. Fibrinogen/albumin ratio > 0.062 was significantly associated with microvascular invasion, an advanced BCLC stage, and ALBI grade. Multivariate analyses revealed that fibrinogen/albumin ratio was an independent predictor for OS (P=0.003) and TTR (P=0.035). The prognostic ability of fibrinogen/albumin ratio was comparable to other prognostic scores (NLR, PLR, and ALBI score) by AUC analysis. Patients with a fibrinogen/albumin ratio > 0.062 had lower 1-, 3-, and 5-year OS rates (66.0%, 41.8%, and 28.2% versus 81.9%, 69.3%, and 56.1%, resp., P<0.001) and higher 1-, 3-, and 5-year recurrence rates (60.9%, 79.2%, and 90.5% versus 49.5%, 69.1%, and 77.1%, resp., P=0.008) compared with patients with fibrinogen/albumin ratio ≤ 0.062. Conclusion. The preoperative fibrinogen/albumin ratio is an effective prognostic factor for HCC patients who underwent curative resection. An elevated fibrinogen/albumin ratio significantly correlates with poorer survival and a higher risk of recurrence in HCC patients.

Regular papers / Articles ordinairesReceiver operating characteristic (ROC) curve analysis of the effectiveness of construction planning efforts in Australia and the United Kingdom

2003 ◽  
Vol 30 (1) ◽  
pp. 159-167 ◽  
Author(s):  
O O Faniran ◽  
D G Proverbs
Keyword(s):  
United Kingdom ◽  
Roc Curve ◽  
Construction Projects ◽  
Operating Characteristic ◽  
Curve Analysis ◽  
Construction Planning ◽  
Roc Curve Analysis ◽  
Building Projects ◽  
The United Kingdom ◽  
Planning Project

This paper presents the results of a receiver operating characteristic (ROC) curve analysis of the effectiveness of construction planning efforts, based on samples of building projects in Australia and the United Kingdom. The results of the study provide an evaluation of the extent of the effort that must be invested in planning and control activities to achieve success in the performance of construction projects. The study also illustrates the potential application of ROC curve analysis in construction engineering and management research. Planning efforts in a sample of 52 building projects in Australia and 37 building projects in the United Kingdom were evaluated and compared. This study builds on work done in an earlier study in which the concept of optimal planning of construction projects was explored. The ROC curve analysis offers several advantages over the regression methodology employed in the previous optimal planning study. The graphical representation of the relationship between sensitivity and specificity over all possible diagnostic cutoff points provides an insight into the interactions of the variables that was not apparent in the original methodology.Key words: construction planning, project planning, project management, ROC curve analysis, Australia, United Kingdom.

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