scholarly journals A “Clinician’s Probability Calculator” to convert pre-test to post-test probability of COVID-19 , based on method validation from each laboratory

2020 ◽  
Author(s):  
Zoe Brooks ◽  
Saswati Das ◽  
Tom Pliura
Keyword(s):  
Health Staff ◽  
Test Results ◽  
Likelihood Ratios ◽  
Percent Agreement ◽  
Public Health Staff ◽  
Post Test ◽  
Online Calculator ◽  
Test Result ◽  
Post Test Probability ◽  
Test Probability

During coronavirus pandemic testing and identifying the virus has been a unique and constant challenge for the scientific community. In this paper, we discuss a practical solution to help guide clinicians and public health staff with the interpretation of the probability that a positive, or negative, COVID-19 test result indicates an infected person, based on their clinical estimate of pre-test probability of infection. The LinkedIn survey confirmed that the pre-test probability of COVID-19 increases with patient age, known contact, and severity of symptoms, as well as prevalence of disease in the local population. PPA (Positive Percent Agreement, PPA) and NPA (Negative Percent Agreement, specificity), differ between individual methods. Results vary between laboratories and the manufacturer for the same method. The confidence intervals of results vary with the number of samples tested, often adding a large range of possibilities to the reported test result. The online calculator met the objective.The authors postulated that the clinical pre-test probability of COVID-19 increases relative to local prevalence of disease plus patient age, known contact, and severity of symptoms. We conducted a small survey on LinkedIn to confirm that hypothesis. We examined results of PPA (Positive Percent Agreement, sensitivity) and NPA (Negative Percent Agreement, specificity) from 73 individual laboratory experiments for molecular tests for SARS-CoV-2as reported to the FIND database,(1) and for selected methods in FDA EUA submissions (2,3). We calculated likelihood ratios to convert pre-test to post-test probability of disease, then further calculated the number of true and false results expected in every ten positive or negative test results, plus an estimate that one in ‘x’ test results is true. We designed an online calculator to create graphics and text to fulfill the objective. A positive or negative test result from one laboratory conveys a higher probability for the presence or absence of COVID-19 than the same result from another laboratory, depending on clinical pre-test probability of disease plus proven method PPA and NPA in each laboratory. Likelihood ratios and confidence intervals provide valuable information but are seldom used in clinical settings. We recommend that testing laboratories verify PPA and NPA, and utilize a tool such as the “Clinician’s Probability Calculator” to verify acceptable test performance and create reports to help guide clinicians and public health staff with estimation of post-test probability of COVID-19 .

scholarly journals “Clinician’s Probability Calculator” to Convert Pre-Test to Post-Test Probability of COVID-19, Based on Method Validation from Each Laboratory

2021 ◽  
Author(s):  
Zoe Brooks ◽  
Saswati Das ◽  
Tom Pliura
Keyword(s):  
Test Performance ◽  
False Negative ◽  
Likelihood Ratios ◽  
Unique Challenge ◽  
Molecular Tests ◽  
Percent Agreement ◽  
Post Test ◽  
Test Result ◽  
Post Test Probability ◽  
Test Probability

Identifying the SARS-CoV-2 virus has been a unique challenge for the scientific community. In this paper, we discuss a practical solution to help guide clinicians with interpretation of the probability that a positive, or negative, COVID-19 test result indicates an infected person, based on their clinical estimate of pre-test probability of infection.The authors conducted a small survey on LinkedIn to confirm that hypothesis that that the clinical pre-test probability of COVID-19 increases relative to local prevalence of disease plus patient age, known contact, and severity of symptoms. We examined results of PPA (Positive Percent Agreement, sensitivity) and NPA (Negative Percent Agreement, specificity) from 73 individual laboratory experiments for molecular tests for SARS-CoV-2 as reported to the FIND database 1, and for selected methods in FDA EUA submissions2,3. Authors calculated likelihood ratios to convert pre-test to post-test probability of disease and designed an online calculator to create graphics and text to report results. Despite best efforts, false positive and false negative Covid-19 test results are unavoidable4,5. A positive or negative test result from one laboratory has a different probability for the presence of disease than the same result from another laboratory. Likelihood ratios and confidence intervals can convert the physician or other healthcare professional’s clinical estimate of pre-test probability to post-test probability of disease. Ranges of probabilities differ depending on proven method PPA and NPA in each laboratory. We recommend that laboratories verify PPA and NPA and utilize a the “Clinician’s Probability Calculator” to verify acceptable test performance and create reports to help guide clinicians with estimation of post-test probability of COVID-19.

scholarly journals A Bayesian decision support sequential model for severity of illness predictors and intensive care admissions in pneumonia

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Amado Alejandro Baez ◽  
Laila Cochon ◽  
Jose Maria Nicolas
Keyword(s):  
Predictive Value ◽  
Severity Of Illness ◽  
Positive Test ◽  
Bayesian Decision ◽  
Likelihood Ratios ◽  
Negative Test ◽  
Significant Difference ◽  
Post Test ◽  
Post Test Probability ◽  
Test Probability

Abstract Background Community-acquired pneumonia (CAP) is one of the leading causes of morbidity and mortality in the USA. Our objective was to assess the predictive value on critical illness and disposition of a sequential Bayesian Model that integrates Lactate and procalcitonin (PCT) for pneumonia. Methods Sensitivity and specificity of lactate and PCT attained from pooled meta-analysis data. Likelihood ratios calculated and inserted in Bayesian/ Fagan nomogram to calculate posttest probabilities. Bayesian Diagnostic Gains (BDG) were analyzed comparing pre and post-test probability. To assess the value of integrating both PCT and Lactate in Severity of Illness Prediction we built a model that combined CURB65 with PCT as the Pre-Test markers and later integrated the Lactate Likelihood Ratio Values to generate a combined CURB 65 + Procalcitonin + Lactate Sequential value. Results The BDG model integrated a CUBR65 Scores combined with Procalcitonin (LR+ and LR-) for Pre-Test Probability Intermediate and High with Lactate Positive Likelihood Ratios. This generated for the PCT LR+ Post-test Probability (POSITIVE TEST) Posterior probability: 93% (95% CI [91,96%]) and Post Test Probability (NEGATIVE TEST) of: 17% (95% CI [15–20%]) for the Intermediate subgroup and 97% for the high risk sub-group POSITIVE TEST: Post-Test probability:97% (95% CI [95,98%]) NEGATIVE TEST: Post-test probability: 33% (95% CI [31,36%]) . ANOVA analysis for CURB 65 (alone) vs CURB 65 and PCT (LR+) vs CURB 65 and PCT (LR+) and Lactate showed a statistically significant difference (P value = 0.013). Conclusions The sequential combination of CURB 65 plus PCT with Lactate yielded statistically significant results, demonstrating a greater predictive value for severity of illness thus ICU level care.

scholarly journals Component-Resolved Diagnosis of Hazelnut Allergy in Children

Nutrients ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 640
Author(s):  
Carlo Caffarelli ◽  
Carla Mastrorilli ◽  
Angelica Santoro ◽  
Massimo Criscione ◽  
Michela Procaccianti
Keyword(s):  
Diagnostic Accuracy ◽  
Area Under The Curve ◽  
Food Challenge ◽  
Final Diagnosis ◽  
Childhood And Adolescence ◽  
Test Results ◽  
Post Test ◽  
Food Challenges ◽  
Post Test Probability ◽  
Test Probability

Hazelnuts commonly elicit allergic reactions starting from childhood and adolescence, with a rare resolution over time. The definite diagnosis of a hazelnut allergy relies on an oral food challenge. The role of component resolved diagnostics in reducing the need for oral food challenges in the diagnosis of hazelnut allergies is still debated. Therefore, three electronic databases were systematically searched for studies on the diagnostic accuracy of specific-IgE (sIgE) on hazelnut proteins for identifying children with a hazelnut allergy. Studies regarding IgE testing on at least one hazelnut allergen component in children whose final diagnosis was determined by oral food challenges or a suggestive history of serious symptoms due to a hazelnut allergy were included. Study quality was assessed by the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Eight studies enrolling 757 children, were identified. Overall, sensitivity, specificity, area under the curve and diagnostic odd ratio of Cor a 1 sIgE were lower than those of Cor a 9 and Cor a 14 sIge. When the test results were positive, the post-test probability of a hazelnut allergy was 34% for Cor a 1 sIgE, 60% for Cor a9 sIgE and 73% for Cor a 14 sIgE. When the test results were negative, the post-test probability of a hazelnut allergy was 55% for Cor a 1 sIgE, 16% for Cor a9 sIgE and 14% for Cor a 14 sIgE. Measurement of IgE levels to Cor a 9 and Cor a 14 might have the potential to improve specificity in detecting clinically tolerant children among hazelnut-sensitized ones, reducing the need to perform oral food challenges.

Stratum-specific likelihood ratios of two versions of the General Health Questionnaire

2001 ◽  
Vol 31 (3) ◽  
pp. 519-529 ◽  
Author(s):  
T. A. FURUKAWA ◽  
D. P. GOLDBERG, ◽  
S. RABE-HESKETH ◽  
T. B. ÜSTÜN
Keyword(s):  
General Health ◽  
Meta Analysis ◽  
General Health Questionnaire ◽  
Likelihood Ratios ◽  
Random Samples ◽  
Post Test ◽  
Meta Regression ◽  
Post Test Probability ◽  
Threshold Approach ◽  
Test Probability

Background. In other branches of epidemiology, stratum specific likelihood ratios (SSLRs) have been found to be preferable to fixed best threshold approaches to screening instruments. This paper presents SSLRs of GHQ-12 and GHQ-28 and compares the SSLR method with the traditional optimal threshold approach.Methods. Random effects meta-analysis and meta-regression were used to obtain pooled estimates of SSLRs of the two questionnaires for the 15 centres participating in the WHO study of Psychological Problems in General Health Care. We illustrated the use of SSLRs by applying them to random samples of patients from centres with different backgrounds.Results. For developed and urban centres, the estimates of SSLRs were homogeneous for 10 out of 12 strata of the GHQ-12 and GHQ-28. For other centres, the overall results, which were heterogeneous for six out of 12 strata, were deemed the currently available best estimates. When we applied these results to centres with different prevalences of mental disorders and backgrounds, the estimates matched the actually observed closely. These examples showed how the SSLR approach is more informative than the traditional threshold approach.Conclusions. Those working in developed urban settings can use the corresponding SSLRs with reasonable confidence. Those working in non-urban or developing areas may wish to use the overall results, while acknowledging that they must remain less certain until further research can explicate heterogeneity. These SSLRs have been incorporated into nomograms and spreadsheet programmes so that future researchers can swiftly derive the post-test probability for a patient or a group of patients from a pre-test probability and GHQ score.

A Ruler for Interpreting Diagnostic Test Results

2005 ◽  
Vol 44 (01) ◽  
pp. 124-126 ◽  
Author(s):  
W. Lehmacher ◽  
M. Hellmich
Keyword(s):  
Diagnostic Test ◽  
Likelihood Ratio ◽  
Bayes Rule ◽  
Test Results ◽  
Post Test ◽  
Log Odds ◽  
Additive Equation ◽  
Diagnostic Test Result ◽  
Test Result ◽  
Test Probability

Summary Objectives: Bayes’ rule formalizes how the pre-test probability of having a condition of interest is changed by a diagnostic test result to yield the post-test probability of having the condition. To simplify this calculation a geometric solution in form of a ruler is presented. Methods: Using odds and the likelihood ratio of a test result in favor of having the condition of interest, Bayes’ rule can succinctly be expressed as ”the post-test odds equals the pre-test odds times the likelihood ratio”. Taking logarithms of both sides yields an additive equation. Results: The additive log odds equation can easily be solved geometrically. We propose a ruler made of two scales to be adjusted laterally. A different, widely used solution in form of a nomogram was published by Fagan [2]. Conclusions: Whilst use of the nomogram seems more obvious, the ruler may be easier to operate in clinical practice since no straight edge is needed for precise reading. Moreover, the ruler yields more intuitive results because it shows the change in probability due to a given test result on the same scale.

Utility of Bone Marrow Biopsy, Bone Marrow Aspirate, Flow Cytometry, and Molecular Testing in the Clinical Staging of Lymphoma: An Institutional Experience.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 4596-4596
Author(s):  
Randall J. Dumont ◽  
Louis A.V. Fernandez ◽  
Ridas Juskevicius
Keyword(s):  
Bone Marrow ◽  
Predictive Value ◽  
Bone Marrow Aspirate ◽  
Bone Marrow Involvement ◽  
Performance Characteristics ◽  
Positive Test Result ◽  
Post Test ◽  
Test Result ◽  
Post Test Probability ◽  
Test Probability

Abstract Lymphomas are a diverse group of solid tumors of lymphoid cells that are broadly subdivided into Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL) categories. In the Western world, lymphoma is the most common hematologic malignancy. Complete and accurate staging of the patient with lymphoma is essential in determining the extent of initial disease. Bone marrow biopsy (BMB) remains the gold standard for assessing bone marrow involvement by lymphoma. It allows morphological diagnosis as well as the use of immunohistochemistry in difficult cases. At the time of BMB, other specimens are often gathered for use in ancillary investigations that can aid in diagnosis and/or staging. These investigations include bone marrow aspirate (BMA), flow cytometry (FC), and molecular studies (M) such as PCR. The objectives of this study were to evaluate the performance characteristics of BMA, FC, and M relative to BMB in lymphoma staging, to determine how frequently ancillary testing is positive for bone marrow involvement with lymphoma while BMB is negative, as well as to determine the clinical significance of this situation. Retrospective analysis was performed on 294 lymphoma cases from 1997 to 2002 at a single adult tertiary care center. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), pre-test probability (PreTP), post-test probability given a positive test result (PTP+), and post-test probability given a negative test result (PTP−) were determined. Chart review was performed to determine clinical significance of the situation when BMB is negative and one or more ancillary investigations are positive. The number of cases positive for lymphoma by BMB, BMA, M, and FC was 157,113, 109, and 111, respectively. The performance characteristics are indicated in Table I. Twelve cases in which one or more ancillary investigations were positive when BMB was negative were identified. Clinical management was not altered in these cases. In staging of lymphoma, BMB remains the gold standard for the determination of bone marrow involvement. When compared to BMB, ancillary investigations have a high specificity and PPV, but only moderate sensitivity and NPV. Ancillary bone marrow investigations appear to add little information to lymphoma staging, and may not be fiscally justified. Table I. Performance Characteristics Of Ancillary Bone Marrow Investigations In The Staging Of Lymphoma Parameter Bone Marrow Aspirate Molecular FlowCytometry Abbreviations: PPV = positive predictive value; NPV = negative predictive; PreTP = pre-test probability; PTP+ = post-test probability given a positive test result; PTP− = post-test probability given a negative test result Sensitivity 71% 64% 68% Specificity 98% 93% 96% PPV 97% 92% 95% PreTP 53% 53% 53% PTP+ 97% 92% 95% PTP− 26% 31% 28%

scholarly journals Diagnostic accuracy of a fully automated multiplex celiac disease antibody panel for serum and plasma

2019 ◽  
Vol 57 (8) ◽  
pp. 1207-1217
Author(s):  
Jeff Terryberry ◽  
Jani Tuomi ◽  
Subo Perampalam ◽  
Russ Peloquin ◽  
Eric Brouwer ◽  
...  
Keyword(s):  
Celiac Disease ◽  
Diagnostic Accuracy ◽  
Response To Treatment ◽  
Likelihood Ratios ◽  
Post Test ◽  
Biopsy Confirmation ◽  
Automated Platform ◽  
Disease Antibody ◽  
Post Test Probability ◽  
Test Probability

Abstract Background An automated multiplex platform using capillary blood can promote greater throughput and more comprehensive studies in celiac disease (CD). Diagnostic accuracy should be improved using likelihood ratios for the post-test probability of ruling-in disease. Methods The Ig_plex™ Celiac Disease Panel on the sqidlite™ automated platform measured IgA and IgG antibodies to tTG and DGP in n = 224 CD serum or plasma samples. Diagnostic accuracy metrics were applied to the combined multiplex test results for several CD populations and compared to conventional single antibody ELISA tests. Results With multiple positive antibody results, the post-test probability for ruling-in untreated and treated CD increased to over 90%. The number of samples positive for more than one antibody also increased in untreated CD to ≥90%. Measurement of all four CD antibodies generate cut-off dependent accuracy profiles that can monitor response to treatment with the gluten-free diet (GFD). Higher positive tTG and DGP antibodies are seen more frequently in confirmed CD without (81%–94%) than with GFD treatment (44%–64%). In CD lacking biopsy confirmation, overall agreement of plasma to serum was ≥98% for all antibodies, and 100% for venous to capillary plasma. Conclusions The Ig_plex Celiac Disease Panel increases the likelihood of confirming CD based on the post-test probability of disease results for multi-reactive markers. Specific positivity profiles and cut-off intervals can be used to monitor GFD treatment and likely disease progression. Using serum, venous and capillary plasma yield comparable and accurate results.

scholarly journals Atypical Endometrial Hyperplasia and Unexpected Cancers at Final Histology: A Study on Endometrial Sampling Methods and Risk Factors

Diagnostics ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 474 ◽  
Author(s):  
Luca Giannella ◽  
Giovanni Delli Carpini ◽  
Francesco Sopracordevole ◽  
Maria Papiccio ◽  
Matteo Serri ◽  
...  
Keyword(s):  
Risk Factors ◽  
Endometrial Hyperplasia ◽  
Probability Analysis ◽  
Secondary Outcome ◽  
Endometrial Sampling ◽  
Final Histology ◽  
Atypical Endometrial Hyperplasia ◽  
Post Test ◽  
Post Test Probability ◽  
Test Probability

Background: Up to 40% of women with atypical endometrial hyperplasia (AEH) can reveal endometrial cancer (EC) at hysterectomy. The pre-operative endometrial sampling method (ESM) and some independent cancer predictors may affect this outcome. The present study aimed to compare the rate of EC at hysterectomy in women with AEH undergoing dilation and curettage (D&C), hysteroscopically-guided biopsy (HSC-bio), or hysteroscopic endometrial resection (HSC-res). The secondary outcome was to compare the reliability of ESMs in women showing independent variables associated with EC. Methods: Two-hundred-and-eight consecutive women with AEH and undergoing hysterectomy between January 2000 and December 2017 were analyzed retrospectively. Based on pre- and post-test probability analysis for EC, three ESMs were compared: D&C, HSC-bio, and HSC-res. Univariate and multivariate analyses were performed to assess risk factors predicting cancer on final histology. Finally, the patient’s characteristics were compared between the three ESM groups. Results: D&C and HSC-bio included 75 women in each group, while HSC-res included 58 women. Forty-nine women (23.6%) revealed cancer at hysterectomy (pre-test probability). Post-test probability analysis showed that HSC-res had the lowest percentage of EC underestimation: HSC-res = 11.6%; HSC-bio = 19.5%; D&C = 35.3%. Patient characteristics showed no significant differences between the three ESMs. Multivariate analysis showed that body mass index ≥40 (Odds Ratio (OR) = 19.75; Confidence Intervals (CI) 2.193–177.829), and age (criterion > 60 years) (OR = 1.055, CI 1.002–1.111) associated significantly with EC. In women with one or both risk factors, post-test probability analysis showed that HSC-res was the only method with a lower EC rate at hysterectomy compared to a pre-test probability of 44.2%: HSC-res = 19.96%; HSC-bio = 53.81%; D&C = 63.12%. Conclusions: HSC-res provided the lowest rate of EC underestimation in AEH, also in women showing EC predictors. These data may be considered for better diagnostic and therapeutic planning of AEH.

scholarly journals Pengaruh Penggunaan Media Gambar Untuk Meningkatkan Hasil Belajar Bahasa Arab Siswa Kelas VII Di Madrasah Tsanawiyah Muhammadiyah 12 Palirangan

2021 ◽  
Vol 7 (1) ◽  
pp. 39-60
Author(s):  
Idzi' Layyinati
Keyword(s):  
Student Learning ◽  
Learning Outcomes ◽  
Human Life ◽  
Student Learning Outcomes ◽  
Arabic Language ◽  
Test Results ◽  
Correlation Test ◽  
Post Test ◽  
The Mean ◽  
Test Result

Education is a necessity for everyone. Educational activities are activities that are very important in human life and cannot be separated from their lives. With education, human needs regarding change and development can be fulfilled. The mean score of the pre-test results was 71.73, while the mean value of the post-test results was 79.61. From these data it can be seen that the pre-test result value is lower than the post-test score, so it can be interpreted that there are differences in student learning outcomes before and after using image media in the learning process. the completeness of the pre-test result value is 46.15%, while the post-test result value is 80.76%. From this data, it can be seen that using image media can affect student learning outcomes. Then from the hypothesis testing using the Product Moment Correlation test and the Paired Sample T-test with the help of SPSS 20 software in the sig (2-tailed) section, it isknown that 0.000 <0.05 The condition is if r count is smaller than r table, then H0 is accepted. and Ha is rejected and vice versa if r count is greater than r table (r count> r table) then H0 is rejected and Ha is accepted. In fact, r count (0.977) is greater than r table (0.404). Thus H0 is rejected and Ha is accepted. As the basis for decision making in the Product Moment Correlation test and decision guidelines based on the probability value, it can be concluded that H0 is rejected and Ha is accepted. This means that image media can affect the improvement of learning outcomes in Arabic language material for class VII at MTs. Muhammadiyah 12Palirangan

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