scholarly journals Clinicians' guide to evaluating diagnostic and screening tests in psychiatry

2004 ◽  
Vol 10 (6) ◽  
pp. 446-454 ◽  
Author(s):  
James Warner
Keyword(s):  
Diagnostic Tests ◽  
Critical Appraisal ◽  
Evidence Base ◽  
Psychiatric Practice ◽  
Screening Tests ◽  
Clinical Settings ◽  
Likelihood Ratios ◽  
Predictive Values ◽  
Incorrect Diagnosis ◽  
Sensitivity Specificity

The emphasis on the evidence base of treatments may diminish awareness that critical appraisal of research into other aspects of psychiatric practice is equally important. There is a risk that diagnostic tests may be inappropriate in some clinical settings or the results of a particular test may be over-interpreted, leading to incorrect diagnosis. This article outlines the method of critically evaluating the validity of articles about diagnostic and screening tests in psychiatry and discusses concepts of sensitivity, specificity and predictive values. The use of likelihood ratios in improving clinical certainty that a disease is present or absent is examined.

The Interpretation of Test Results

2000 ◽  
Vol 4 (1) ◽  
pp. 19-25 ◽  
Author(s):  
Michael Binder ◽  
Stephan Dreiseitl
Keyword(s):  
Decision Making ◽  
Receiver Operating Characteristic ◽  
Diagnostic Tests ◽  
Operating Characteristic ◽  
Roc Curves ◽  
Test Results ◽  
Likelihood Ratios ◽  
Clinical Routine ◽  
Predictive Values ◽  
Sensitivity Specificity

Background: Dermatologists need to interpret an increasing number of research studies and diagnostic tests. Understanding the techniques for interpreting test results and making decisions based upon those tests represent important tools for decision making for both clinicians and researchers. Objective: This article focuses briefly on the key parameters of diagnostic tests: sensitivity, specificity, prevalence, predictive values, likelihood ratios, and the concept of receiver-operating-characteristic (ROC) curves. A simple example is presented in a step-by-step manner. Conclusion: The principles of interpreting test results are easy to learn and applicable in daily clinical routine. Therefore, dermatologists should be familiar with the concepts outlined in this paper.

scholarly journals El dilema del vigilante. Valoración de pruebas diagnósticas.

2018 ◽  
Vol 10 (8) ◽  
pp. 4
Author(s):  
Manuel Molina
Keyword(s):  
Diagnostic Tests ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Likelihood Ratios ◽  
Predictive Values ◽  
Significant Parameter ◽  
Quantitative Results ◽  
Sensitivity Specificity

Existen una serie de parámetros que caracterizan las pruebas diagnósticas, como son la sensibilidad, la especificidad, los valores predictivos y los cocientes de probabilidad. Solo estos últimos nos permiten el cálculo de la probabilidad del paciente de presentar la enfermedad, con independencia de la prevalencia en la población. Para caracterizar las pruebas con resultado cuantitativo se utilizan las curvas ROC, cuyo parámetro más significativo es el área bajo la curva. ABSTRACT There are a series of parameters that characterize the diagnostic tests, such as sensitivity, specificity, predictive values and likelihood ratios. Only the latter allow us to calculate the patient's probability of presenting the disease, regardless of the prevalence in the population. To characterize the tests with quantitative results, the ROC curves are used, whose most significant parameter is the area under the curve.

scholarly journals Diagnostic testing: an emergency medicine perspective

CJEM ◽  
2002 ◽  
Vol 4 (05) ◽  
pp. 348-354 ◽  
Author(s):  
Andrew Worster ◽  
Grant Innes ◽  
Riyad B. Abu-Laban
Keyword(s):  
Diagnostic Tests ◽  
Diagnostic Testing ◽  
Test Results ◽  
Emergency Physicians ◽  
Likelihood Ratios ◽  
Predictive Values ◽  
The Core ◽  
Core Concepts ◽  
Sensitivity Specificity ◽  
Test Probability

ABSTRACT: Emergency physicians use diagnostic tests extensively, and the ability to order and interpret test results appropriately is a critical skill. An understanding of sensitivity, specificity, predictive values and likelihood ratios, as well as an awareness of the importance of pre-test probability, is essential. The purpose of this article is to explain, in a straightforward and clinically applicable manner, the core concepts related to diagnostic testing.

scholarly journals Value and Diagnostic Efficacy of Fetal Morphology Assessment Using Ultrasound in A Poor-Resource Setting

Diagnostics ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 109 ◽  
Author(s):  
Ukweh ◽  
Ugbem ◽  
Okeke ◽  
Ekpo
Keyword(s):  
Predictive Value ◽  
Performance Metrics ◽  
Area Under The Curve ◽  
Youden Index ◽  
Likelihood Ratios ◽  
Diagnostic Efficacy ◽  
Predictive Values ◽  
Resource Setting ◽  
Post Test ◽  
Sensitivity Specificity

Background: Ultrasound is operator-dependent, and its value and efficacy in fetal morphology assessment in a low-resource setting is poorly understood. We assessed the value and efficacy of fetal morphology ultrasound assessment in a Nigerian setting. Materials and Methods: We surveyed fetal morphology ultrasound performed across five facilities and followed-up each fetus to ascertain the outcome. Fetuses were surveyed in the second trimester (18th–22nd weeks) using the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) guideline. Clinical and surgical reports were used as references to assess the diagnostic efficacy of ultrasound in livebirths, and autopsy reports to confirm anomalies in terminated pregnancies, spontaneous abortions, intrauterine fetal deaths, and still births. We calculated sensitivity, specificity, positive and negative predictive values, Area under the curve (AUC), Youden index, likelihood ratios, and post-test probabilities. Results: In total, 6520 fetuses of women aged 15–46 years (mean = 31.7 years) were surveyed. The overall sensitivity, specificity, and AUC were 77.1 (95% CI: 68–84.6), 99.5 (95% CI: 99.3–99.7), and 88.3 (95% CI: 83.7–92.2), respectively. Other performance metrics were: positive predictive value, 72.4 (95% CI: 64.7–79.0), negative predictive value, 99.6 (95% CI: 99.5–99.7), and Youden index (77.1%). Abnormality prevalence was 1.67% (95% CI: 1.37–2.01), and the positive and negative likelihood ratios were 254 (95% CI: 107.7–221.4) and 0.23 (95% CI: 0.16–0.33), respectively. The post-test probability for positive test was 72% (95% CI: 65–79). Conclusion: Fetal morphology assessment is valuable in a poor economics setting, however, the variation in the diagnostic efficacy across facilities and the limitations associated with the detection of circulatory system anomalies need to be addressed.

Neutrophil CD64 Index as a superior biomarker for early diagnosis of infection in febrile patients in the hematology department

2017 ◽  
Vol 55 (1) ◽  
pp. 82-90 ◽  
Author(s):  
Shu-Dao Xiong ◽  
Lian-Fang Pu ◽  
Hui-Ping Wang ◽  
Lin-Hui Hu ◽  
Yang-Yang Ding ◽  
...  
Keyword(s):  
High Specificity ◽  
Likelihood Ratios ◽  
Predictive Values ◽  
Combined Analysis ◽  
Reactive Protein ◽  
Neutrophil Cd64 ◽  
Special Value ◽  
Diagnostic Values ◽  
Neutropenic Patients ◽  
Sensitivity Specificity

Abstract Background: In the hematology department, the availability of biomarkers for early detection of infection is difficult to obtain. The present study aimed to compare the diagnostic values of neutrophil CD64 Index, procalcitonin (PCT), interleukin-6 (IL-6) and C-reactive protein (CRP) and to determine whether the combined analysis of these biomarkers offer stronger predictive power in the diagnosis for the infection of febrile patients. Methods: Neutrophil CD64 Index, PCT, IL-6 and CRP levels were determined in 356 febrile patients in the hematology ward from May 2013 to May 2015. Sensitivity, specificity, positive and negative likelihood ratios, positive and negative predictive values, receiver operating characteristic (ROC) areas under the curve (AUC), and logistic regression analysis were determined to evaluate the diagnostic values of these biomarkers. Results: The levels of the four biomarkers were higher in the infection patients (p<0.001), and the PCT and IL-6 were higher in the patients with positive microbial blood culture (p<0.01). The neutrophil CD64 Index, PCT, IL-6, CRP had AUCs of 0.95, 0.83, 0.75 and 0.73, respectively. The best cut-off value of the neutrophil CD64 Index to detect infections was 5.06, with high specificity (87.5%) and sensitivity (88.4%). Furthermore, neutrophil CD64 Index, PCT and IL-6 offered the best combination of diagnosis with sensitivity of 93.9% and an AUC of 0.95. In addition, the neutrophil CD64 Index may have a special value to assist the physician to diagnose infection in the neutropenic patients with fever. Conclusions: The neutrophil CD64 Index is useful for early identification of infections in febrile patients in the hematology department. The combined analysis of the CD64 Index, PCT and IL-6 could further improve its sensitivity.

A unique antiphospholipid assay recognizing phospholipid mixture compared with criteria antiphospholipid immunoassays in lupus patients

Lupus ◽  
2016 ◽  
Vol 26 (6) ◽  
pp. 606-615 ◽  
Author(s):  
Y Zuo ◽  
R Willis ◽  
E Papalardo ◽  
M Petri ◽  
E N Harris ◽  
...  
Keyword(s):  
Lupus Erythematosus ◽  
Clinical Manifestations ◽  
Case Series ◽  
Likelihood Ratios ◽  
Serum Samples ◽  
Predictive Values ◽  
Pregnancy Morbidity ◽  
Commercial Kits ◽  
Sensitivity Specificity

Background While essential for the classification of antiphospholipid syndrome (APS), anticardiolipin (aCL) assays lack specificity and anti-β2glycoproteinI (anti-β2GPI) assays lack sensitivity in this regard. Our aim was to perform a comparative analysis of the APhL ELISA assay (IgG/IgM) and criteria antiphospholipid (aPL) immunoassays in identifying APS-related clinical manifestations in a large group of patients with systemic lupus erythematosus (SLE). Methods Serum samples from 1178 patients from the Hopkins ( n = 543), LUMINA ( n = 588) and Jamaican SLE cohorts ( n = 47) were examined for IgG/IgM positivity in aCL (in-house), anti-β2GPI (two commercial kits) and APhL (Louisville APL) ELISA assays. Correlation of assay positivity with clinical manifestations and sensitivity, specificity, positive and negative predictive values and likelihood ratios were evaluated. A case series analysis was also performed in patients for whom there was isolated positivity in the specific aPL assays. Results The prevalence of aCL positivity was 34.9%, anti-β2GPI kit A was 22.6%, APhL was 11.5% and anti-β2GPI kit B was 7.6% in the study population. Anti-β2GPI kit B, aCL and APhL assays were correlated with venous thrombosis, while only APhL was significantly correlated with arterial thrombosis and consistently correlated with pregnancy-related morbidity. No significant correlations were noted for anti-β2GPI kit A. Sensitivity was greatest for aCL assays followed by anti-β2GPI kit A, APhL and anti-β2GPI kit B, while specificity was greatest and equal for anti-β2GPI kit B and APhL assays. Conclusions Overall, APhL antibodies, especially IgG, represent a promising biomarker for the classification of APS patients in the context of autoimmunity and in risk assessment with regards to pregnancy morbidity and thrombotic manifestations.

scholarly journals Performance of Diagnostic Tests for Plasma Cell Myeloma

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 5570-5570
Author(s):  
Gilda Zagoya ◽  
Alejandro Ruiz-Argüelles ◽  
Guillermo J. Ruiz-Arguelles
Keyword(s):  
Plasma Cell ◽  
Diagnostic Tests ◽  
Laboratory Tests ◽  
Light Chains ◽  
Plasma Cell Myeloma ◽  
Immunoglobulin Light Chains ◽  
Predictive Values ◽  
Sensitivity Specificity ◽  
Total Immunoglobulin

Abstract Introduction. In vitro laboratory tests to diagnose of plasma cell myeloma vary considerably in sensitivity, specificity and positive and negative predictive values. We compared the performance of quantification of free immunoglobulin light chains with other methods used to detect a monoclonal protein in serum and/or urine. Objective. Compare sensitivity, specificity and positive and negative predictive values of several in vitro laboratory tests to detect monoclonal proteins in serum and urine in persons with plasma cell myeloma. Methods. 70 subjects with plasma cell myeloma and 50 controls were studied. Diagnostic tests included: (1) quantification of free and total immunoglobulin light-chains by immune assays; (2) immune fixation of heavy-and light-chains in serum and urine after gel electrophoresis; and (3) serum protein capillary electrophoresis. Diagnosis of plasma cell myeloma was based on clinical and radiological criteria, bone marrow examination and flow cytometric immune phenotyping with monoclonal antibodies to CD56, CD19, CD138 (CD38) and CD45. Sensitivity, specificity and positive and negative predictive values for each tests were estimated from contingency tables. Results. Quantification of free immmunoglobulin light-chains had the highest sensitivity and specificity and best positive and negative predictive values. Immune fixation of serum immunoglobulins was next best. Quantification of total immunoglobulin light-chains was the least sensitive and specific with the worst positive and negative predictive values. Quantitation of free light-chains had the additional advantage of objectivity (independence from observer bias). The immune fixation test was the most subject to observer bias. Conclusion. Quantification of free immunoglobulin light-chains had the best sensitivity, specificity and positive and negative predictive values for diagnosing plasma cell myeloma. (Table 1) Disclosures No relevant conflicts of interest to declare.

Clinical Epidemiology

2016 ◽  
pp. 285-290
Author(s):  
Scott C. Litin ◽  
John B. Bundrick
Keyword(s):  
Diagnostic Test ◽  
Diagnostic Tests ◽  
Clinical Decision Making ◽  
Clinical Epidemiology ◽  
Clinical Decision ◽  
Test Results ◽  
Specific Patient ◽  
Predictive Values ◽  
Test Sensitivity ◽  
Sensitivity Specificity

Diagnostic tests are tools that either increase or decrease the likelihood of disease. The sensitivity, specificity, and predictive values of normal and abnormal test results can be calculated with even a limited amount of information. Some physicians prefer interpreting diagnostic test results by using the likelihood ratio. This ratio takes properties of a diagnostic test (sensitivity and specificity) and makes them more helpful in clinical decision making. It helps the clinician determine the probability of disease in a specific patient after a diagnostic test has been performed.

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