scholarly journals Right choice of a method for determination of cut-off values: A statistical tool for a diagnostic test

2014 ◽  
Vol 5 (3) ◽  
pp. 30-34 ◽  
Author(s):  
Balkishan Sharma ◽  
Ravikant Jain
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Diagnostic Test ◽  
Roc Curve ◽  
Traditional Method ◽  
Statistical Technique ◽  
Curve Analysis ◽  
Operating Characteristics ◽  
Roc Curve Analysis

Objective: The clinical diagnostic tests are generally used to identify the presence of a disease. The cutoff value of a diagnostic test should be chosen to maximize the advantage that accrues from testing a population of human and others. When a diagnostic test is to be used in a clinical condition, there may be an opportunity to improve the test by changing the cutoff value. To enhance the accuracy of diagnosis is to develop new tests by using a proper statistical technique with optimum sensitivity and specificity. Method: Mean±2SD method, Logistic Regression Analysis, Receivers Operating Characteristics (ROC) curve analysis and Discriminant Analysis (DA) have been discussed with their respective applications. Results: The study highlighted some important methods to determine the cutoff points for a diagnostic test. The traditional method is to identify the cut-off values is Mean±2SD method. Logistic Regression Analysis, Receivers Operating Characteristics (ROC) curve analysis and Discriminant Analysis (DA) have been proved to be beneficial statistical tools for determination of cut-off points.Conclusion: There may be an opportunity to improve the test by changing the cut-off value with the help of a correctly identified statistical technique in a clinical condition when a diagnostic test is to be used. The traditional method is to identify the cut-off values is Mean ± 2SD method. It was evidenced in certain conditions that logistic regression is found to be a good predictor and the validity of the same can be confirmed by identifying the area under the ROC curve. Abbreviations: ROC-Receiver operating characteristics and DA-Discriminant Analysis. Asian Journal of Medical Science, Volume-5(3) 2014: 30-34 http://dx.doi.org/10.3126/ajms.v5i3.9296      

Acute heart failure: predicting early in-hospital outcomes

2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
M Santos ◽  
S Paula ◽  
I Almeida ◽  
H Santos ◽  
H Miranda ◽  
...  
Keyword(s):  
Heart Failure ◽  
Logistic Regression ◽  
Acute Heart Failure ◽  
Roc Curve ◽  
Real Life ◽  
Study Data ◽  
Curve Analysis ◽  
Older Age ◽  
Invasive Ventilation ◽  
Roc Curve Analysis

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Patients (P) with acute heart failure (AHF) are a heterogeneous population. Risk stratification at admission may help predict in-hospital complications and needs. The Get With The Guidelines Heart Failure score (GWTG-HF) predicts in-hospital mortality (M) of P admitted with AHF. ACTION ICU score is validated to estimate the risk of complications requiring ICU care in non-ST elevation acute coronary syndromes. Objective To validate ACTION-ICU score in AHF and to compare ACTION-ICU to GWTG-HF as predictors of in-hospital M (IHM), early M [1-month mortality (1mM)] and 1-month readmission (1mRA), using real-life data. Methods Based on a single-center retrospective study, data collected from P admitted in the Cardiology department with AHF between 2010 and 2017. P without data on previous cardiovascular history or uncompleted clinical data were excluded. Statistical analysis used chi-square, non-parametric tests, logistic regression analysis and ROC curve analysis. Results Among the 300 P admitted with AHF included, mean age was 67.4 ± 12.6 years old and 72.7% were male. Systolic blood pressure (SBP) was 131.2 ± 37.0mmHg, glomerular filtration rate (GFR) was 57.1 ± 23.5ml/min. 35.3% were admitted in Killip-Kimball class (KKC) 4. ACTION-ICU score was 10.4 ± 2.3 and GWTG-HF was 41.7 ± 9.6. Inotropes’ usage was necessary in 32.7% of the P, 11.3% of the P needed non-invasive ventilation (NIV), 8% needed invasive ventilation (IV). IHM rate was 5% and 1mM was 8%. 6.3% of the P were readmitted 1 month after discharge. Older age (p < 0.001), lower SBP (p = 0,035) and need of inotropes (p < 0.001) were predictors of IHM in our population. As expected, patients presenting in KKC 4 had higher IHM (OR 8.13, p < 0.001). Older age (OR 1.06, p = 0.002, CI 1.02-1.10), lower SBP (OR 1.01, p = 0.05, CI 1.00-1.02) and lower left ventricle ejection fraction (LVEF) (OR 1.06, p < 0.001, CI 1.03-1.09) were predictors of need of NIV. None of the variables were predictive of IV. LVEF (OR 0.924, p < 0.001, CI 0.899-0.949), lower SBP (OR 0.80, p < 0.001, CI 0.971-0.988), higher urea (OR 1.01, p < 0.001, CI 1.005-1.018) and lower sodium (OR 0.92, p = 0.002, CI 0.873-0.971) were predictors of inotropes’ usage. Logistic regression showed that GWTG-HF predicted IHM (OR 1.12, p < 0.001, CI 1.05-1.19), 1mM (OR 1.10, p = 1.10, CI 1.04-1.16) and inotropes’s usage (OR 1.06, p < 0.001, CI 1.03-1.10), however it was not predictive of 1mRA, need of IV or NIV. Similarly, ACTION-ICU predicted IHM (OR 1.51, p = 0.02, CI 1.158-1.977), 1mM (OR 1.45, p = 0.002, CI 1.15-1.81) and inotropes’ usage (OR 1.22, p = 0.002, CI 1.08-1.39), but not 1mRA, the need of IV or NIV. ROC curve analysis revealed that GWTG-HF score performed better than ACTION-ICU regarding IHM (AUC 0.774, CI 0.46-0-90 vs AUC 0.731, CI 0.59-0.88) and 1mM (AUC 0.727, CI 0.60-0.85 vs AUC 0.707, CI 0.58-0.84). Conclusion In our population, both scores were able to predict IHM, 1mM and inotropes’s usage.

scholarly journals Predictive CT features for the diagnosis of primary pulmonary mucoepidermoid carcinoma: comparison with squamous cell carcinomas and adenocarcinomas

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaohua Ban ◽  
Xinping Shen ◽  
Huijun Hu ◽  
Rong Zhang ◽  
Chuanmiao Xie ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Roc Curve ◽  
Multinomial Logistic Regression ◽  
Univariate Analysis ◽  
Ct Imaging ◽  
Squamous Cell Carcinomas ◽  
Curve Analysis ◽  
Imaging Features ◽  
Roc Curve Analysis ◽  
Ct Features

Abstract Background To determine the predictive CT imaging features for diagnosis in patients with primary pulmonary mucoepidermoid carcinomas (PMECs). Materials and methods CT imaging features of 37 patients with primary PMECs, 76 with squamous cell carcinomas (SCCs) and 78 with adenocarcinomas were retrospectively reviewed. The difference of CT features among the PMECs, SCCs and adenocarcinomas was analyzed using univariate analysis, followed by multinomial logistic regression and receiver operating characteristic (ROC) curve analysis. Results CT imaging features including tumor size, location, margin, shape, necrosis and degree of enhancement were significant different among the PMECs, SCCs and adenocarcinomas, as determined by univariate analysis (P < 0.05). Only lesion location, shape, margin and degree of enhancement remained independent factors in multinomial logistic regression analysis. ROC curve analysis showed that the area under curve of the obtained multinomial logistic regression model was 0.805 (95%CI: 0.704–0.906). Conclusion The prediction model derived from location, margin, shape and degree of enhancement can be used for preoperative diagnosis of PMECs.

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 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 Association Between D-Dimer Level and In-Hospital Death of Pulmonary Embolism Patients

Dose-Response ◽  
2020 ◽  
Vol 18 (4) ◽  
pp. 155932582096843
Author(s):  
Zi-Kai Song ◽  
Haidi Wu ◽  
Xiaoyan Xu ◽  
Hongyan Cao ◽  
Qi Wei ◽  
...  
Keyword(s):  
Pulmonary Embolism ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Roc Curve ◽  
Curve Analysis ◽  
Hospital Death ◽  
Roc Curve Analysis ◽  
Survival Group ◽  
Death Group ◽  
D Dimer

To investigate whether D-dimer level could predict pulmonary embolism (PE) severity and in-hospital death, a total of 272 patients with PE were divided into a survival group (n = 249) and a death group (n = 23). Comparisons of patient characteristics between the 2 groups were performed using Mann-Whitney U test. Significant variables in univariate analysis were entered into multivariate logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was performed to determine the predictive value of D-dimer level alone or together with the simplified Pulmonary Embolism Severity Index (sPESI) for in-hospital death. Results showed that patients in the death group were significantly more likely to have hypotension (P = 0.008), tachycardia (P = 0.000), elevated D-dimer level (P = 0.003), and a higher sPESI (P = 0.002) than those in the survival group. Multivariable logistic regression analysis showed that D-dimer level was an independent predictor of in-hospital death (OR = 1.07; 95% CI, 1.003-1.143; P = 0.041). ROC curve analysis showed that when D-dimer level was 3.175 ng/ml, predicted death sensitivity and specificity were 0.913 and 0.357, respectively; and when combined with sPESI, specificity (0.838) and area under the curve (0.740) were increased. Thus, D-dimer level is associated with in-hospital death due to PE; and the combination with sPESI can improve the prediction level.

Abstract 19569: High-sensitive Troponin T Cut-off Value for Myocardial Infarction After Vascular Surgery

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Danielle M Gualandro ◽  
Gisela B Llobet ◽  
Pai C Yu ◽  
Daniela Calderaro ◽  
Andre C Marques ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Vascular Surgery ◽  
Roc Curve ◽  
Troponin T ◽  
Curve Analysis ◽  
Operating Characteristics ◽  
Roc Curve Analysis ◽  
Cutoff Value ◽  
Coronary Syndrome ◽  
High Sensitive Troponin

Introduction: Isolated high sensitive cardiac troponin T (hsTnT) elevations after vascular surgery are frequent and may lead to over diagnosis of myocardial infarction (MI). The aim of our study was to determine the accuracy of the current hsTnT cut-off value in the setting of acute coronary syndrome (ACS) after vascular surgery. Methods: Between August 2012 and March 2014, we included 337 consecutive patients submitted to arterial vascular surgery for which cardiac perioperative evaluation was requested. Perioperative surveillance included 12-lead electrocardiogram and hsTnT measurements on the three days following surgery. Patients were followed-up by cardiologists until hospital discharge and monitored for ACS. A receiver operating characteristics (ROC) curve analysis was performed to determine the hsTnT cut-off value with better accuracy for the diagnosis of perioperative ACS. Results: Of the 337 patients included, 240 (71.2%) presented hsTnT elevation above the manufacturer-provided cut-off value (0.014 ng/ml), whereas 22 (6.5%) fulfilled criteria for ACS. Median post-operative peak hsTnT of ACS patients were 0.215 ng/ml (IQR 0.043-0.493 ng/ml), versus 0.02 ng/ml (IQR 0.012-0.038 ng/ml) in patients that did not have events (P<0.001). After performing a ROC curve analysis (AUC = 0.876), we found that the manufacture-provided cutoff hsTnT value yielded a sensitivity of 100% and specificity of only 35% for diagnosis of perioperative ACS. A new hsTnT cutoff value of 0.0415 ng/ml was obtained with 86.4% sensitivity and 77% specificity for the diagnosis of perioperative ACS. Ninety-two patients (27.3%) had hsTnT elevations above the proposed new cutoff. Conclusion: A different hsTnT cutoff value of 0.0415 ng/ml is proposed and could be more useful for the diagnosis of perioperative ACS.

Sex estimation from the mastoid process using Micro-CT among Brazilians: Discriminant analysis and ROC curve analysis

2018 ◽  
Vol 14 ◽  
pp. 1-7 ◽  
Author(s):  
Nitiely Arrais Kramer ◽  
Thais Torralbo Lopez-Capp ◽  
Edgard Michel-Crosato ◽  
Maria Gabriela Haye Biazevic
Keyword(s):  
Discriminant Analysis ◽  
Roc Curve ◽  
Curve Analysis ◽  
Mastoid Process ◽  
Sex Estimation ◽  
Micro Ct ◽  
Roc Curve Analysis

scholarly journals Preoperative Albumin-to-Fibrinogen Ratio Predicts Postoperative Pneumonia in Patients with Esophageal Squamous Cell Carcinoma: A Retrospective Study

2020 ◽  
Author(s):  
Peng Zhang ◽  
Song Zhao
Keyword(s):  
Risk Factors ◽  
Esophageal Cancer ◽  
Logistic Regression ◽  
Roc Curve ◽  
Laboratory Tests ◽  
Curve Analysis ◽  
Postoperative Pneumonia ◽  
Function Parameter ◽  
Multivariate Logistic Regression ◽  
Roc Curve Analysis

Abstract Background: Postoperative pneumonia is the most common postoperative complication in patients with esophageal cancer. Prediction of postoperative pneumonia 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, fibrinogen, albumin-to-fibrinogen ratio(AFR) , and other preoperative laboratory tests and postoperative pneumonia in patients with esophageal cancer after esophagectomy.Methods: Retrospective analysis was performed on 177 consecutive patients who underwent esophagectomy in the Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University from December 2018 to December 2019.Postoperative pneumonia was defined according to the revised Uniform Pneumonia Score(rUPS).Patients were divided into pneumonia and non-pneumonia 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 and other laboratory tests data. Univariate and multivariate logistic regression analyses were used to assess the risk factors for postoperative pneumoniaResults: Of the 177 patients, 32 (18%) developed postoperative pneumonia. The AUC value predicted by AFR using ROC curve analysis was 0.767, 65.6% sensitivity and 83.4% specificity. Multivariate logistic regression analysis showed that albumin (P=0.013), creatinine (P=0.01), and AFR (P=0.016) were independent risk factors for postoperative pneumonia.Conclusion: Preoperative AFR can effectively predict the occurrence of postoperative pneumonia in patients with esophageal cancer

scholarly journals The Value of Preoperative Alpha-L-Fucosidase Levels in Evaluation of Malignancy and Differential Diagnosis of Urothelial Neoplasms

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Dongshan Chen ◽  
Naidong Xing ◽  
Zhanwu Cui ◽  
Cong Zhang ◽  
Zhao Zhang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Differential Diagnosis ◽  
Regression Analysis ◽  
Urothelial Carcinoma ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Curve Analysis ◽  
Clinicopathological Parameters ◽  
Roc Curve Analysis ◽  
Urothelial Tumors

Purpose. To evaluate the role of Alpha-L-fucosidase (AFU) in diagnosis and differential diagnosis of pure urothelial carcinoma (UC), urothelial carcinoma with squamous differentiation (UCSD), and squamous cell carcinoma (SqCC). Methods. A retrospective study was performed for 599 patients who were histologically confirmed with urothelial tumor. Preoperative AFU levels were compared across the distinct subgroups with different clinicopathological parameters. ROC curve analysis and logistic regression analysis were performed to further evaluate the clinical application value of serum AFU levels in diagnosis and differential diagnosis of urothelial tumors. Results. There were no statistically significant differences in the AFU levels between different groups with different malignant degrees (UC versus papilloma and papillary urothelial neoplasm of low malignant potential [PUNLMP], high-grade UC versus low-grade UC, invasive versus noninvasive malignant uroepithelial tumor) and different pathological types (UC, UCSD, and SqCC) (all P>0.05). ROC curve analysis and logistic regression analysis showed that there was no statistically significant association between AFU levels and the tumor characteristics (all P>0.05). Conclusions. Preoperative AFU levels cannot serve as a reliable predictor for malignant degree and differential diagnosis, including pure UC, UCSD, and SqCC of urothelial tumors.

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