Prediction of Difficult Tracheal Videolaryngoscopic Intubation Using El-Ganzouri Risk Index

Summary Introduction. Unanticipated difficult intubation still exists as a significant problem. El-Ganzouri risk index is a bedside difficult airway evaluation method, which can help determine risk of difficult airways. However, evidence of using of El-Ganzouri risk index in difficult laryngeal visualisation using videolaryngoscopes is limited. Aim of the Study. Determine the specificity and sensitivity of El-Ganzouri multivariate risk index to predict difficult laryngeal exposure using Storz C-MAC videolaryngoscope equipped with D type blade. Material and methods. We conducted a study of 29 patients, which were examined using El-Ganzouri risk index before induction of anaesthesia. After induction of anaesthesia videolaryngoscopy was performed using Storz C-MAC videolaryngoscope equipped with D-blade and laryngeal visualisation was graded using Cormack-Lehane scale. After laryngeal visualisation grading endotracheal tube insertion was performed and numbers of attempts as well as complications during intubation were recorded. Sensitivity, specificity, positive and negative predictive values were calculated, receiver operating characteristic curve and area under curve was obtained. Results. Sensitivity and specificity were 54.2% and 80.0% at El-Ganzouri risk index cut-off value of 2 points. Calculated positive predictive value was 26.7% and negative predictive value was 92.9%. Calculated AUC was 78.3%. Conclusions. El-Ganzouri risk index shows moderate sensitivity and specificity when used with Storz C-MAC videolaryngoscope. It can be used to predict difficult laryngeal visualization during videolaryngoscopic intubation.

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A New Evaluation Method for Lumbar Spinal Instability: Passive Lumbar Extension Test

Physical Therapy ◽

10.2522/ptj.20050281 ◽

2006 ◽

Vol 86 (12) ◽

pp. 1661-1667 ◽

Cited By ~ 38

Author(s):

Yuichi Kasai ◽

Koichiro Morishita ◽

Eiji Kawakita ◽

Tetsushi Kondo ◽

Atsumasa Uchida

Keyword(s):

Likelihood Ratio ◽

Sensitivity And Specificity ◽

Evaluation Method ◽

Positive Likelihood Ratio ◽

Spinal Instability ◽

Predictive Values ◽

Flexion Extension ◽

Lumbar Extension ◽

Lumbar Spinal ◽

Sensitivity Specificity

Abstract Background and PurposeAlthough many studies have described clinical examination measures for the diagnosis of lumbar spinal instability, few of them have investigated the sensitivity and specificity of the measures that were used. The authors devised a passive lumbar extension (PLE) test for assessing lumbar spinal instability. The purpose of this study was to investigate the sensitivity, specificity, and positive likelihood ratio of this test. Subjects and Methods. The PLE test as well as the instability catch sign, painful catch sign, and apprehension sign tests were done for 122 subjects with lumbar degenerative diseases. The subjects were divided into 2 groups—instability positive and instability negative—on the basis of findings on flexion-extension films of the lumbar spine. The sensitivity, specificity, predictive values, and positive likelihood ratio of each test were investigated. Results. The sensitivity and specificity of the PLE test were 84.2% and 90.4%, respectively. These values were higher than those of other signs. The positive likelihood ratio of the PLE test was 8.84 (95% confidence interval=4.51–17.33). Discussion and Conclusion. The PLE test is an effective method for examining patients for lumbar spinal instability and can be performed easily in an outpatient clinic.

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Predictive Value of Matrix Metalloproteinases and Their Inhibitors for Mortality in Septic Patients: A Cohort Study

Journal of Intensive Care Medicine ◽

10.1177/0885066617732284 ◽

2017 ◽

Vol 35 (1) ◽

pp. 95-103 ◽

Cited By ~ 3

Author(s):

Sergio Serrano-Gomez ◽

Gabriel Burgos-Angulo ◽

Daniela Camila Niño-Vargas ◽

María Eugenia Niño ◽

María Eugenia Cárdenas ◽

...

Keyword(s):

Cohort Study ◽

Matrix Metalloproteinases ◽

Positive Predictive Value ◽

Negative Predictive Value ◽

Predictive Value ◽

Characteristic Curve ◽

Population Sample ◽

Tissue Inhibitor Of Metalloproteinase ◽

Predictive Values ◽

Sensitivity Specificity

Purpose: Over 170 biomarkers are being investigated regarding their prognostic and diagnostic accuracy in sepsis in order to find new tools to reduce morbidity and mortality. Matrix metalloproteinases (MMPs) and their inhibitors have been recently studied as promising new prognostic biomarkers in patients with sepsis. This study is aimed at determining the utility of several cutoff points of these biomarkers to predict mortality in patients with sepsis. Materials and Methods: A multicenter, prospective, analytic cohort study was performed in the metropolitan area of Bucaramanga, Colombia. A total of 289 patients with sepsis and septic shock were included. MMP-9, MMP-2, tissue inhibitor of metalloproteinase 1 (TIMP-1), TIMP-2, TIMP-1/MMP-9 ratio, and TIMP-2/MMP-2 ratio were determined in blood samples. Value ranges were correlated with mortality to estimate sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiving operating characteristic curve. Results: Sensitivity ranged from 33.3% (MMP-9/TIMP-1 ratio) to 60.6% (TIMP-1) and specificity varied from 38.8% (MMP-2/TIMP-2 ratio) to 58.5% (TIMP-1). As for predictive values, positive predictive value range was from 17.5% (MMP-9/TIMP-1 ratio) to 70.4% (MMP-2/TIMP-2 ratio), whereas negative predictive values were between 23.2% (MMP-2/TIMP-2 ratio) and 80.9% (TIMP-1). Finally, area under the curve scores ranged from 0.31 (MMP-9/TIMP-1 ratio) to 0.623 (TIMP-1). Conclusion: Although TIMP-1 showed higher sensitivity, specificity, and negative predictive value, with a representative population sample, we conclude that none of the evaluated biomarkers had significant predictive value for mortality.

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Monofrequency electrical impedance mammography (EIM) diagnostic system in breast cancer screening

BMC Cancer ◽

10.1186/s12885-020-07283-5 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Blanca Murillo-Ortiz ◽

Abraham Hernández-Ramírez ◽

Talia Rivera-Villanueva ◽

David Suárez-García ◽

Mario Murguía-Pérez ◽

...

Keyword(s):

Breast Cancer ◽

Positive Predictive Value ◽

Negative Predictive Value ◽

Sensitivity And Specificity ◽

Predictive Value ◽

Electrical Impedance ◽

Screening Method ◽

Future Research ◽

Predictive Values ◽

Sensitivity Specificity

Abstract Background Some evidence has shown that malignant breast tumours have lower electrical impedance than surrounding normal tissues. Electrical impedance could be used as an indicator for breast cancer detection. The purpose of our study was to analyse the sensitivity and specificity of electrical impedance mammography (EIM) and its implementation for the differential diagnosis of pathological lesions of the breast, either alone or in combination with mammography/ultrasound, in 1200 women between 25 and 70 years old. Methods This study is a prospective, cross-sectional epidemiological observational study of serial screening. The women were invited to participate and signed a consent letter. Impedance imaging of the mammary gland was evaluated with the computerized mammography equipment of MEIK electroimpedance v.5.6. (0.5 mA, 50 kHz), developed and manufactured by PKF SIM-Technika®. The successful identification of breast cancer along with the sensitivity, specificity, and positive and negative predictive values of EIM were determined as follows: % sensitivity; % specificity; % positive predictive value (PPV); and % negative predictive value (NPV). Results EIM had a sensitivity of 85% and a specificity of 96%; the positive predictive value was 12%, and the negative predictive value was 99%. Seven cases were biopsy confirmed cancers. Significant correlations between the electrical conductivity index and body mass index (BMI) (p = 0.04) and patient age were observed (p = 0.01). We also observed that the average conductivity distribution increased according to age group (p = 0.001). We used the chi-squared test to assess the interactions between percent density and BMI (normal < 25 kg/m2 (n = 310), overweight 25–29.9 kg/m2 (n = 418) and obese ≥30 (n = 437)) (p < 0.05). The patients with a diagnosis of mammary carcinoma had a BMI of 35.51 kg/m2. Conclusions Our results demonstrate that the use of monofrequency electrical impedance mammography (EIM) in the detection of breast cancer had a sensitivity and specificity of 85 and 96%, respectively. These findings may support future research in the early detection of breast cancer. EIM is a non-radiation method that may also be used as a screening method for young women with dense breasts and a high risk of developing breast cancer.

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Designing and Interpreting COVID-19 Diagnostics: Mathematics, Visual Logistics, and Low Prevalence

Archives of Pathology & Laboratory Medicine ◽

10.5858/arpa.2020-0443-sa ◽

2020 ◽

Author(s):

Gerald J. Kost

Keyword(s):

Sensitivity And Specificity ◽

Predictive Value ◽

Clinical Performance ◽

Geometric Mean ◽

High Sensitivity ◽

False Positives ◽

Predictive Values ◽

Minimum Requirements ◽

Sensitivity Specificity ◽

Low Prevalence

ABSTRACT Context. Coronavirus infectious disease-19 (COVID-19) diagnostics require understanding of how predictive values depend on sensitivity, specificity, and especially, low prevalence. Clear expectations, high sensitivity and specificity, and manufacturer disclosure will facilitate excellence of tests. Objectives. To derive mathematical equations for designing and interpreting COVID-19 tests, assess Food and Drug Administration (FDA) Emergency Use Authorization and Health Canada minimum requirements, establish sensitivity and specificity tiers, and enhance clinical performance in low prevalence settings. Design. PubMed and other sources generated articles on COVID-19 testing and prevalence. EndNote X9.1 consolidated references. Mathematica and open access software helped prove equations, perform recursive calculations, graph multivariate relationships, and visualize patterns, including a new relationship, predictive value geometric mean-squared. Results. Derived equations were used to illustrate shortcomings of COVID-19 diagnostics in low prevalence. Visual logistics helped establish sensitivity/specificity tiers. FDA/Canada's 90% sensitivity, 95% specificity minimum requirements generate excessive false positives at low prevalence. False positives exceed true positives at <5.3% prevalence, or if sensitivity is improved to 100% and specificity to 98%, at <2% prevalence. Recursive testing improves predictive value. Three tiers emerged from these results. With 100% sensitivity, physicians can select desired predictive values, then input local prevalence, to determine suitable specificity. Conclusions. Understanding low prevalence impact will help healthcare providers meet COVID-19 needs for effective testing. Laypersons should receive clinical performance disclosure when submitting specimens. Home testing needs to meet the same high standards as other tests. In the long run, it will be more cost-effective to improve COVID-19 POC tests rather than repeat testing multiple times.

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Evaluation of the Mini Nutritional Assessment in the elderly, Tehran, Iran

Public Health Nutrition ◽

10.1017/s1368980010000303 ◽

2010 ◽

Vol 13 (9) ◽

pp. 1373-1379 ◽

Cited By ~ 16

Author(s):

Bahareh Amirkalali ◽

Farshad Sharifi ◽

Hossein Fakhrzadeh ◽

Mojde Mirarefin ◽

Maryam Ghaderpanahi ◽

...

Keyword(s):

Positive Predictive Value ◽

Serum Albumin ◽

Negative Predictive Value ◽

Predictive Value ◽

Nutritional Assessment ◽

Characteristic Curve ◽

Mini Nutritional Assessment ◽

Youden Index ◽

Predictive Values ◽

Sensitivity Specificity

AbstractObjectiveTo determine whether the Mini Nutritional Assessment (MNA) can screen and diagnose for malnutrition in the Iranian elderly.DesignThe MNA was administered to all volunteers. Each patient underwent anthropometric and serum albumin measurements. Reliability, validity, sensitivity, specificity, positive- and negative-predictive values were estimated. To identify optimal threshold values for predicting malnutrition, receiver-operating characteristic curve analysis was performed for MNA scores.SettingKahrizak Charity Foundation (Tehran, Iran).SubjectsTwo hundred and twenty-one consecutive elderly patients entered into the cross-sectional study. Amputees and patients with liver or renal disorders, oedema or any end-stage diseases were excluded.ResultsAccording to MNA score, 3·2 % were malnourished, 43·4 % were at risk of malnutrition and 53·4 % were well nourished. The proportions in these categories according to ideal body weight and serum albumin were 2·3 %, 17·1 % and 80·6 %, respectively. Cronbach’s α coefficient (reliability) was 0·61. The correlations between total MNA score, anthropometric values and serum albumin (criterion-related validity) were all significant. There were significant differences in total MNA score between two BMI groups but not between two categories according to serum albumin and skin ulcers (construct validity). The sensitivity and specificity of the MNA according to its established cut-off points were 82 % and 63 %, respectively. Positive-predictive value was 35 % and negative-predictive value was 93 %. By using the best cut-off point (MNA score of 22 according to Youden index), the sensitivity, specificity, positive-predictive value and negative-predictive value were 88 %, 62 %, 57 % and 89 %, respectively.ConclusionsThe MNA with its established cut-off points may not be a good fit for Asian populations, including Iranian elderly.

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Relations among sensitivity, specificity and predictive values of medical tests based on biomarkers

General Psychiatry ◽

10.1136/gpsych-2020-100453 ◽

2021 ◽

Vol 34 (2) ◽

pp. e100453

Author(s):

Hongyue Wang ◽

Bokai Wang ◽

Xunan Zhang ◽

Changyong Feng

Keyword(s):

Sensitivity And Specificity ◽

Predictive Value ◽

Health Management ◽

Risk Function ◽

Disease Status ◽

Test Results ◽

Predictive Values ◽

Test Sensitivity ◽

Sensitivity Specificity

Diagnostic tests are usually based on some quantitative biomarkers. Two key parameters used to characterise the quality of a test are test sensitivity and specificity. Predictive values of the disease status based on test results are also of interest in medical research and public health management. In this paper, we study the relations among sensitivity, specificity and predictive values of the test. The core concept is risk function, which is assumed to be an increasing function of the biomarker. Our results show that test sensitivity and specificity change in opposite directions. The positive predictive value and the sensitivity also change in opposite directions. Likewise, the negative predictive value and the specificity change in opposite directions.

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Discriminating Malignancy in Thyroid Nodules: The Nomogram Versus the Kwak and ACR TI-RADS

Otolaryngology ◽

10.1177/0194599820939071 ◽

2020 ◽

Vol 163 (6) ◽

pp. 1156-1165

Author(s):

Juan Xiao ◽

Qiang Xiao ◽

Wei Cong ◽

Ting Li ◽

Shouluan Ding ◽

...

Keyword(s):

Thyroid Nodules ◽

Characteristic Curve ◽

Area Under The Curve ◽

Diagnostic Study ◽

Diagnostic Efficiency ◽

Training Set ◽

Multivariable Logistic Regression Model ◽

Predictive Values ◽

Validation Set ◽

Sensitivity Specificity

Objective To develop an easy-to-use nomogram for discrimination of malignant thyroid nodules and to compare diagnostic efficiency with the Kwak and American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TI-RADS). Study Design Retrospective diagnostic study. Setting The Second Hospital of Shandong University. Subjects and Methods From March 2017 to April 2019, 792 patients with 1940 thyroid nodules were included into the training set; from May 2019 to December 2019, 174 patients with 389 nodules were included into the validation set. Multivariable logistic regression model was used to develop a nomogram for discriminating malignant nodules. To compare the diagnostic performance of the nomogram with the Kwak and ACR TI-RADS, the area under the receiver operating characteristic curve, sensitivity, specificity, and positive and negative predictive values were calculated. Results The nomogram consisted of 7 factors: composition, orientation, echogenicity, border, margin, extrathyroidal extension, and calcification. In the training set, for all nodules, the area under the curve (AUC) for the nomogram was 0.844, which was higher than the Kwak TI-RADS (0.826, P = .008) and the ACR TI-RADS (0.810, P < .001). For the 822 nodules >1 cm, the AUC of the nomogram was 0.891, which was higher than the Kwak TI-RADS (0.852, P < .001) and the ACR TI-RADS (0.853, P < .001). In the validation set, the AUC of the nomogram was also higher than the Kwak and ACR TI-RADS ( P < .05), each in the whole series and separately for nodules >1 or ≤1 cm. Conclusions When compared with the Kwak and ACR TI-RADS, the nomogram had a better performance in discriminating malignant thyroid nodules.

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Development of Machine Learning Models to Predict Probabilities and Types of Stroke at Prehospital Stage: the Japan Urgent Stroke Triage Score Using Machine Learning (JUST-ML)

Translational Stroke Research ◽

10.1007/s12975-021-00937-x ◽

2021 ◽

Author(s):

Kazutaka Uchida ◽

Junichi Kouno ◽

Shinichi Yoshimura ◽

Norito Kinjo ◽

Fumihiro Sakakibara ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Random Forests ◽

Prediction Models ◽

Characteristic Curve ◽

Predictive Performance ◽

Vessel Occlusion ◽

Predictive Values ◽

Training Cohort ◽

Sensitivity Specificity

AbstractIn conjunction with recent advancements in machine learning (ML), such technologies have been applied in various fields owing to their high predictive performance. We tried to develop prehospital stroke scale with ML. We conducted multi-center retrospective and prospective cohort study. The training cohort had eight centers in Japan from June 2015 to March 2018, and the test cohort had 13 centers from April 2019 to March 2020. We use the three different ML algorithms (logistic regression, random forests, XGBoost) to develop models. Main outcomes were large vessel occlusion (LVO), intracranial hemorrhage (ICH), subarachnoid hemorrhage (SAH), and cerebral infarction (CI) other than LVO. The predictive abilities were validated in the test cohort with accuracy, positive predictive value, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and F score. The training cohort included 3178 patients with 337 LVO, 487 ICH, 131 SAH, and 676 CI cases, and the test cohort included 3127 patients with 183 LVO, 372 ICH, 90 SAH, and 577 CI cases. The overall accuracies were 0.65, and the positive predictive values, sensitivities, specificities, AUCs, and F scores were stable in the test cohort. The classification abilities were also fair for all ML models. The AUCs for LVO of logistic regression, random forests, and XGBoost were 0.89, 0.89, and 0.88, respectively, in the test cohort, and these values were higher than the previously reported prediction models for LVO. The ML models developed to predict the probability and types of stroke at the prehospital stage had superior predictive abilities.

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Screening for aneurysms of the abdominal aorta using a simple screening device

The Ultrasound Journal ◽

10.1186/s13089-020-00192-5 ◽

2020 ◽

Vol 12 (1) ◽

Author(s):

J. W. Brakel ◽

T. A. Berendsen ◽

P. M. C. Callenbach ◽

J. van der Burgh ◽

R. J. Hissink ◽

...

Keyword(s):

Negative Predictive Value ◽

Sensitivity And Specificity ◽

Abdominal Aorta ◽

Predictive Value ◽

Cost Effective ◽

Abdominal Ultrasound ◽

Predictive Values ◽

Bladder Scan ◽

Health Council ◽

Screening Device

Abstract Introduction Several countries advocate screening for aneurysms of the abdominal aorta (AAA) in selected patients. In the Netherlands, routine screening is currently under review by the National Health Council. In any screening programme, cost-efficiency and accuracy are key. In this study, we evaluate the Aorta Scan (Verathon, Amsterdam, Netherlands), a cost-effective and easy-to-use screening device based on bladder scan technology, which enables untrained personnel to screen for AAA. Methods We subjected 117 patients to an Aorta Scan and compared the results to the gold standard (abdominal ultrasound). We used statistical analysis to determine sensitivity and specificity of the Aorta Scan, as well as the positive and negative predictive values, accuracy, and inter-test agreement (Kappa). Results Sensitivity and specificity were 0.86 and 0.98, respectively. Positive predictive value was 0.98 and negative predictive value was 0.88. Accuracy was determined at 0.92 and the Kappa value was 0.85. When waist–hip circumferences (WHC) of > 115 cm were excluded, sensitivity raised to 0.96, specificity stayed 0.98, positive and negative predictive value were 0.98 and 0.96, respectively, accuracy to 0.97, and Kappa to 0.94. Conclusion Herein, we show that the Aorta Scan is a cost-effective and very accurate screening tool, especially in patients with WHC below 115 cm, which makes it a suitable candidate for implementation into clinical practice, specifically in the setting of screening selected populations for the presence of AAA.

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Foundational Statistical Principles in Medical Research: Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value

Medicina ◽

10.3390/medicina57050503 ◽

2021 ◽

Vol 57 (5) ◽

pp. 503

Author(s):

Thomas F. Monaghan ◽

Syed N. Rahman ◽

Christina W. Agudelo ◽

Alan J. Wein ◽

Jason M. Lazar ◽

...

Keyword(s):

Positive Predictive Value ◽

Negative Predictive Value ◽

Predictive Value ◽

Medical Literature ◽

Disease Prevalence ◽

Positive Test Result ◽

Predictive Values ◽

Sensitivity Specificity ◽

Test Result ◽

Test Probability

Sensitivity, which denotes the proportion of subjects correctly given a positive assignment out of all subjects who are actually positive for the outcome, indicates how well a test can classify subjects who truly have the outcome of interest. Specificity, which denotes the proportion of subjects correctly given a negative assignment out of all subjects who are actually negative for the outcome, indicates how well a test can classify subjects who truly do not have the outcome of interest. Positive predictive value reflects the proportion of subjects with a positive test result who truly have the outcome of interest. Negative predictive value reflects the proportion of subjects with a negative test result who truly do not have the outcome of interest. Sensitivity and specificity are inversely related, wherein one increases as the other decreases, but are generally considered stable for a given test, whereas positive and negative predictive values do inherently vary with pre-test probability (e.g., changes in population disease prevalence). This article will further detail the concepts of sensitivity, specificity, and predictive values using a recent real-world example from the medical literature.

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