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.

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 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 “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.

392Systematic review and meta-analysis on the diagnostic accuracy for the detection of relevant coronary artery stenosis of vasodilator myocardial perfusion CMR and dobutamine stress echocardiography

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
S M Haberkorn ◽  
S I Bueter ◽  
M Kelm ◽  
G Hopkin ◽  
S E Peterson
Keyword(s):  
Coronary Artery ◽  
Myocardial Perfusion ◽  
Stress Echocardiography ◽  
Dobutamine Stress Echocardiography ◽  
Coronary Artery Stenosis ◽  
Meta Analysis ◽  
Dobutamine Stress ◽  
Post Test ◽  
Post Test Probability ◽  
Test Probability

Abstract Background Relevance of coronary artery stenosis in patients with stabile coronary artery disease (SCAD) is defined by myocardial ischemia due to flow limitation. While FFR-guided treatment of SCAD is a class IA recommendation. The initial risk stratification with detection of relevant CAD can be facilitated by several myocardial imaging methods without any preference mentioned in current guidelines. Objectives This study aimed to systematically assess and to compare the diagnostic accuracy of vasodilator myocardial perfusion cardiovascular magnetic resonance imaging (pCMR) and dobutamine stress echocardiography (DSE) for the non-invasive detection of relevant SCAD through a meta-analysis, to enable an evidential preference in risk stratification. In contrast to previously published work, this meta-analysis explicitly included only studies with rigorous eligibility criteria and a narrowly prespecified definition of their invasive reference tests. Selection criteria A study was included if (1) CCA or FFR was used as a reference standard for diagnosing relevant SCAD, defined as >70% stenosis or a value <0.80 on FFR recordings, respectively; (2) sufficient data to permit analysis and to reconstruct contingency tables (explicitly true-positive, false-positive, false-negative and true-negative findings) was provided; (3) there was a minimal sample size of 20 patients; (4) assessment of myocardial perfusion reserve was performed using vasodilators adenosine or regadenoson for pCMR, and dobutamine used for echocardiography; and (5) the studies were of prospective design. Data collection and analysis: From the 5,634 studies identified, 1,306 relevant articles were selected after title screening. Just 47 fulfilled all inclusion criteria on full-text review, resulting in a total sample size of 4,742 patients. Data extraction was performed for each study by two reviewers independently.Pooled analysis was performed based on a random effects models. Results The sensitivity, specificity and diagnostic odds ratio (DOR) for pCMR were 0.88 (95% confidence interval (CI): 0.85–0.90), 0.84 (95% CI: 0.81–0.87), and 38 (95% CI: 29–49), and for DSE 0.72 (95% CI: 0.61–0.81), 0.89 (95% CI: 0.83–0.93), and 20 (95% CI: 9–46), respectively. Post-test probability was augmented by positive (likelihood ratio) LR of 5.5 (95% CI: 4.7–6.5) and negative LR of 0.14 (95% CI: 0.12–0.18) based on Bayes' theorem, as compared to LR of 6.3 (95% CI: 3.8, 10.4) and negative LR of 0.31 (95% CI: 0.21, 0.46) for DSE. The size of the prediction region on the hierarchical summary receiver operating characteristic (HSROC) plot for pCMR (0.29; 95% CI 0.11–0.77) was significantly smaller compared to the one of DSE (1.07; 95% CI 0.27–4.19; p<0.01). Forrest plot pCMR Conclusion The results of this systematic review and meta-analysis show that pCMR is characterized by a superior diagnostic test accuracy of relevant SCAD compared to DSE and that it can refine the post-test probability of SCAD. Acknowledgement/Funding European Heart Academy of the European Society of Cardiology

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.

Impact of thyroid nodule size on prevalence and post-test probability of malignancy: A systematic review

2014 ◽  
Vol 125 (1) ◽  
pp. 263-272 ◽  
Author(s):  
Jennifer J. Shin ◽  
Diana Caragacianu ◽  
Gregory W. Randolph
Keyword(s):  
Systematic Review ◽  
Thyroid Nodule ◽  
Nodule Size ◽  
Post Test ◽  
Post Test Probability ◽  
Test Probability

scholarly journals Ultrasound diagnosis of molar pregnancy

Ultrasound ◽  
2018 ◽  
Vol 26 (3) ◽  
pp. 153-159 ◽  
Author(s):  
Jackie A. Ross ◽  
Alina Unipan ◽  
Jackie Clarke ◽  
Catherine Magee ◽  
Jemma Johns
Keyword(s):  
Likelihood Ratio ◽  
Gestational Trophoblastic Disease ◽  
Ultrasound Diagnosis ◽  
Histopathological Diagnosis ◽  
Molar Pregnancy ◽  
Diagnostic Challenge ◽  
Trophoblastic Disease ◽  
Post Test ◽  
Post Test Probability ◽  
Test Probability

Introduction The primary aims of this study were to establish what proportion of ultrasonically suspected molar pregnancies were proven on histological examination and what proportion of histologically diagnosed molar pregnancies were identified by ultrasound pre-operatively. The secondary aim was to review the features of these scans to help identify criteria that may improve ultrasound diagnosis. Methods This was a retrospective observational study conducted in the Early Pregnancy Unit at King’s College Hospital London over an 11-year period. Cases of ultrasonically suspected molar pregnancy or other gestational trophoblastic disease were identified and compared with the final histopathological diagnosis. In addition, cases which were diagnosed on histopathology that were not suspected on ultrasound were also examined. In discrepant cases, the images were reviewed unblinded by two senior sonographers. Statistical analysis for likelihood ratio and post-test probabilities was performed. Results One hundred eighty-two women had gestational trophoblastic disease suspected on ultrasound examination (1:360, 0.3%); 106/182 (58.2%, 95% CI 51.0 to 65.2%) had histologically confirmed gestational trophoblastic disease. The likelihood ratio for gestational trophoblastic disease after a positive ultrasound was 607.27, with a post-test probability of 0.628.The sensitivity of ultrasound for gestational trophoblastic disease was 70.7% (95% CI 62.9% to 77.4%) with an estimated specificity of 99.88% (95% CI 99.85% to 99.91%); 102/143 (71.3%, 95% CI 63.4 to 78.1%) molar pregnancies were suspected on pre-op ultrasound; 60/68 (88.2%, 95% CI 78.2 to 94.2%) of complete moles were suspected on pre-op ultrasound, compared with 42/75 (56.0%, 95% CI 44.7 to 66.7%) of partial moles. On retrospective review of the pre-op ultrasound images, there were cases that could have been suspected prior to surgery. Conclusion Detecting molar pregnancy by ultrasound remains a diagnostic challenge, particularly for partial moles. These data suggest that there has been an increase in both the predictive value and the sensitivity of ultrasound over time, with a high LR and post-test probability; however, the diagnostic criteria remain ill-defined and could be improved.

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