Estimation Of Pretest Probability Research Articles

A New Clinical Model to Estimate the Pre-Test Probability of Cushing's Syndrome: The Cushing Score.

BackgroundHypercortisolism accounts for relevant morbidity and mortality and is often a diagnostic challenge for clinicians. A prompt diagnosis is necessary to treat Cushing’s syndrome as early as possible.ObjectiveThe aim of this study was to develop and validate a clinical model for the estimation of pre-test probability of hypercortisolism in an at-risk population.DesignWe conducted a retrospective multicenter case-control study, involving five Italian referral centers for Endocrinology (Turin, Messina, Naples, Padua and Rome). One hundred and fifty patients affected by Cushing’s syndrome and 300 patients in which hypercortisolism was excluded were enrolled. All patients were evaluated, according to current guidelines, for the suspicion of hypercortisolism.ResultsThe Cushing score was built by multivariable logistic regression, considering all main features associated with a clinical suspicion of hypercortisolism as possible predictors. A stepwise backward selection algorithm was used (final model AUC=0.873), then an internal validation was performed through ten-fold cross-validation. Final estimation of the model performance showed an average AUC=0.841, thus reassuring about a small overfitting effect. The retrieved score was structured on a 17.5-point scale: low-risk class (score value: ≤5.5, probability of disease=0.8%); intermediate-low-risk class (score value: 6-8.5, probability of disease=2.7%); intermediate-high-risk class (score value: 9-11.5, probability of disease=18.5%) and finally, high-risk class (score value: ≥12, probability of disease=72.5%).ConclusionsWe developed and internally validated a simple tool to determine pre-test probability of hypercortisolism, the Cushing score, that showed a remarkable predictive power for the discrimination between subjects with and without a final diagnosis of Cushing’s syndrome.

Development and Internal Validation of a Predictive Model for Adult GH Deficiency Prior to Stimulation Tests.

BackgroundThe diagnosis of adult GH deficiency (GHD) relies on a reduced GH response to provocative tests. Their diagnostic accuracy, however, is not perfect, and a reliable estimation of pre-test GHD probability could be helpful for a better interpretation of their results.MethodsEighty patients showing concordant GH response to two provocative tests, i.e. the insulin tolerance test and the GHRH + arginine test, were enrolled. Data on IGF-I values and on the presence/absence of other pituitary deficits were collected and integrated for the estimation of GHD probability prior to stimulation tests.ResultsAn independent statistically significant association with the diagnosis of GHD was found both for IGF-I SDS (OR 0.34, 95%-CI 0.18-0.65, p=0.001) and for the presence of other pituitary deficits (OR 6.55, 95%-CI 2.06-20.83, p=0.001). A low (<25%) pre-test GHD probability could be predicted when IGF-I SDS > +0.91 in the presence of other pituitary deficits or IGF-I SDS > -0.52 in the absence of other pituitary deficits. A high (>75%) pre-test GHD probability could be predicted when IGF-I SDS < -0.82 in the presence of other pituitary deficits or IGF-I SDS < -2.26 in the absence of other pituitary deficits.ConclusionThis is the first study that proposes a quantitative estimation of GHD probability prior to stimulation tests. Our risk class stratification represents a simple tool that could be adopted for a Bayesian interpretation of stimulation test results, selecting patients who may benefit from a second stimulation test and possibly reducing the risk of wrong GHD diagnosis.

Incorporating coronary calcification into pretest assessment of the likelihood of coronary artery disease: validation and recalibration of a new diagnostic tool

Abstract Funding Acknowledgements Type of funding sources: None. Background A new clinical tool was recently proposed to improve the estimation of pre-test probability of obstructive coronary artery disease (CAD) by incorporating coronary artery calcium score (CACS) with clinical risk factors. This new model (Clinical + CACS) showed improved prediction when compared to the method recommended by the 2019 ESC guidelines on chronic coronary syndromes, but was never tested or adjusted for use in our population. The aim of this study was to assess the performance of this new method in a Portuguese cohort of symptomatic patients referred for coronary computed tomography angiography (CCTA), and to recalibrate it if necessary. Methods We conducted a two-center cross-sectional study assessing symptomatic patients who underwent CCTA for suspected CAD. Key exclusion criteria were age &amp;lt; 30 years, known CAD, suspected acute coronary syndrome, or symptoms other than chest pain or dyspnea. Obstructive CAD was defined as any luminal stenosis ≥50% on CCTA. The Clinical + CACS prediction model was assessed for discrimination and calibration. A logistical recalibration of the model was conducted in a random sample of 50% of the patients and subsequently validated in the other half. Results A total of 1910 patients (mean age 60 ± 11 years, 60% women) were included in the analysis. Symptom characteristics were: 39% non-anginal chest pain, 30% atypical angina, 19% dyspnea and 12% typical angina. The observed prevalence of obstructive CAD was 12.9% (n = 247). Patients with obstructive CAD were more often male, were significantly older, had higher prevalence of typical angina and cardiovascular risk factors, and higher CACS values. The new Clinical + CACS tool showed greater discriminative power than the ESC 2019 prediction model, with a C-statistic of 0.83 (CI 95% 0.81-0.86) versus 0.67 (CI 95% 0.64-0.71), respectively (p-value for comparison &amp;lt; 0.001). Before recalibration, the Clinical + CACS model underestimated the likelihood of CAD in our population across all quartiles of pretest probability (mean relative underestimation of 49%), which was subsequently corrected by the recalibration procedure - Figure. Conclusions In a Portuguese cohort of symptomatic patients undergoing CCTA for suspected CAD, the new Clinical + CACS model showed better discrimination power than the 2019 ESC method. The underestimation of the Clinical + CACS model was corrected by recalibrating it for our population. This new tool might prove useful for guiding decisions on the need for further testing.

Incorporating Coronary Calcification Into Pretest Assessment Of The Likelihood Of Coronary Artery Disease - Validation And Recalibration Of A New Diagnostic Tool

Introduction: A new clinical tool was recently proposed to improve the estimation of pre-test probability of obstructive coronary artery disease (CAD) by incorporating coronary artery calcium score (CACS) with clinical risk factors. This new model (Clinical+CACS) showed improved prediction when compared to the method recommended by the 2019 ESC guidelines on chronic coronary syndromes, but was never tested or adjusted for use in our population. The aim of this study was to assess the performance of this new method in a Portuguese cohort of symptomatic patients referred for coronary computed tomography angiography (CCTA), and to recalibrate it if necessary.

Using body temperature and variables commonly available in the EHR to predict acute infection: a proof-of-concept study showing improved pretest probability estimates for acute COVID-19 infection among discharged emergency department patients.

Obtaining body temperature is a quick and easy method to screen for acute infection such as COVID-19. Currently, the predictive value of body temperature for acute infection is inhibited by failure to account for other readily available variables that affect temperature values. In this proof-of-concept study, we sought to improve COVID-19 pretest probability estimation by incorporating covariates known to be associated with body temperature, including patient age, sex, comorbidities, month, and time of day. For patients discharged from an academic hospital emergency department after testing for COVID-19 in March and April of 2020, we abstracted clinical data. We reviewed physician documentation to retrospectively generate estimates of pretest probability for COVID-19. Using patients' COVID-19 PCR test results as a gold standard, we compared AUCs of logistic regression models predicting COVID-19 positivity that used: (1) body temperature alone; (2) body temperature and pretest probability; (3) body temperature, pretest probability, and body temperature-relevant covariates. Calibration plots and bootstrap validation were used to assess predictive performance for model #3. Data from 117 patients were included. The models' AUCs were: (1) 0.69 (2) 0.72, and (3) 0.76, respectively. The absolute difference in AUC was 0.029 (95% CI-0.057 to 0.114, p=0.25) between model 2 and 1 and 0.038 (95% CI-0.021 to 0.097, p=0.10) between model 3 and 2. By incorporating covariates known to affect body temperature, we demonstrated improved pretest probability estimates of acute COVID-19 infection. Future work should be undertaken to further develop and validate our model in a larger, multi-institutional sample.

Declining Accuracy of the Traditional Diamond-Forrester Estimates of Pretest Probability of Coronary Artery Disease

This Viewpoint summarizes the evidence for a decline in the accuracy of Diamond-Forrester pretest probability estimates, indicates which estimation method is most reliable, and explains why these estimates are clinically useful.

Interval Likelihood Ratios and the Diagnostic Process.

In this issue of Pediatrics, Liang et al1 apply the concept of interval likelihood ratios (ILRs) to the diagnosis of urinary tract infections (UTIs) in children under the age of 2 years. The authors conducted a cross-sectional study of a cohort of children <2 years of age who had a urine culture and urine analysis (UA) simultaneously obtained from their emergency department. Their analysis, in contrast to most previous reports, derives ILRs for the scaled results of the UA relative to the diagnosis of UTIs.Most readers will be familiar with the terms sensitivity and specificity. ILRs are closely related to sensitivity and specificity but have at least 2 advantages. First, a separate ILR can be derived for each value of a test result when there can be more than just positive or negative results. For example, a urine dipslide can quantify the presence of leukocyte esterase (LE) as trace, 1+, 2+, or 3+. Liang et al1 showed 3+ LE increases the probability a child has a UTI compared with an LE of 1+ or 2+. This result may not be surprising, but by describing the relationship between LE and UTIs in ILR terms, the diagnostician can better refine her estimate of the probability of UTI, adjusting the probability more for 3+ LE than for 1+ LE.2A second advantage of ILR is that the diagnostician can use relatively simple arithmetic to estimate the probability of a diagnosis (eg, UTI) given a laboratory result (eg, 3+ LE). Starting with a previous probability of disease, she converts this to the odds of disease (probability/1 − probability) and multiplies it by the appropriate ILR (eg, 38 for 3+ LE) to generate the posterior odds. She can then convert the posterior odds back to a probability (probability = posterior odds/[1 + odds]). If the pretest probability of UTI is 5%,3 the odds are 0.05/(1 − 0.05) or (rounding) 0.05. Next, 0.05 × 38 is ∼2 (I rounded the ILR to 40). Finally, the posttest probability is 2/(2 + 1) or 0.67. With a little rounding (and practice), this calculation is easily done in your head.Of course, the probability estimate resulting from this calculation depends on the probability you start with. Liang et al1 found a pretest probability of UTI of 9.2%, which is somewhat higher than that in other large studies of UTI risk in febrile infants,3 probably as a result of some selection bias. That is why the posttest probability reported by Liang et al1 after a 3+ LE is 79%, not 67%. However, ILRs are relatively resistant to selection bias. For this reason, it behooves the diagnostician to know how to apply ILRs to her own estimates of pretest probability to calculate a posttest probability.It is tempting to take this posttest probability of UTI and apply another ILR because, after all, the UA gives us many results. For example, if microscopy showed there were many bacteria in the urine, we might take the posttest odds (2) after the LE and apply the ILR for many bacteria (14), generating a new posttest odds of 28. The corresponding probability would be 28 of 29 or over 96%! This approach of multiplying all of the applicable ILR together by the pretest odds is a technique called sequential Bayes’, and it has been used in computer-based diagnostic systems for decades.4 But the approach is fraught with risk because it assumes the ILRs are independent of one another, given the presence of UTI. We know this cannot always be true. Surely, the probability of seeing white cells on microscopy is not independent of the likelihood of seeing LE on a dipslide.Accommodating these interdependencies among ILRs requires more sophisticated statistical modeling, measuring the associations among different tests, and calculating the diagnostic value of different combinations of results.5 Liang et al1 have not conducted these analyses, but truthfully, these approaches generally require larger sample sizes than were available in this study (198 cases of UTI).Nonetheless, Liang et al1 have taken a step toward helping us be more quantitative in our diagnostic reasoning. This is one important strategy to reduce diagnostic errors, which represent “a major public health problem likely to affect every one of us at least once in our lifetime, sometimes with devastating consequences.”6 If we can learn to apply basic quantitative diagnostic tools like ILR to our medical practice, we are likely to reduce important causes of diagnostic errors (faulty data collection or interpretation, flawed reasoning, or incomplete knowledge) and their downstream impact on our patients.7

Validation of the European Society of Cardiology pre-test probability model for obstructive coronary artery disease.

Estimation of pre-test probability (PTP) of disease in patients with suspected coronary artery disease (CAD) is a common challenge. Due to decreasing prevalence of obstructive CAD in patients referred for diagnostic testing, the European Society of Cardiology suggested a new PTP (2019-ESC-PTP) model. The aim of this study was to validate that model. Symptomatic patients referred for coronary computed tomography angiography (CTA) due to suspected CAD in a geographical uptake area of 3.3 million inhabitants were included. The reference standard was a combined endpoint of CTA and invasive coronary angiography (ICA) with obstructive CAD defined at ICA as a ≥50% diameter stenosis or fractional flow reserve ≤0.80 when performed. The 2019-ESC-PTP, 2013-ESC-PTP, and CAD Consortium basic PTP scores were calculated based on age, sex, and symptoms. Of the 42328 identified patients, coronary stenosis was detected in 8.8% using the combined endpoint. The 2019-ESC-PTP and CAD Consortium basic scores classified substantially more patients into the low PTP groups (PTP < 15%) than did the 2013-ESC-PTP (64% and 65% vs. 16%, P < 0.001). Using the combined endpoint as reference, calibration of the 2019-ESC-PTP model was superior to the 2013-ESC-PTP and CAD Consortium basic score. The new 2019-ESC-PTP model is well calibrated and superior to the previously recommended models in predicting obstructive stenosis detected by a combined endpoint of CTA and ICA.

Outcomes of anatomical vs. functional testing for coronary artery disease : Lessons from the major trials.

Management of patients presenting with suspected stable coronary artery disease (CAD) are challenging because estimation of pretest probability for obstructive CAD remains difficult. In addition, identification of those who benefit from coronary revascularization remains ineffective regardless of the wide array of noninvasive testing alternatives available. Functional testing, which has long been considered to be the test of choice to risk stratify these patients, shows modest agreement with CAD severity detected by invasive coronary angiography and has been reported to be ineffective in settings of low prevalence of obstructive CAD. Agrowing body of evidence demonstrates the excellent diagnostic accuracy as well as prognostic value of coronary computed tomography (CT) angiography especially in conjunction with noninvasive fractional flow reserve (FFR) testing, challenging the primary role of functional testing especially in patients without prior or known CAD. Landmark trials, including the Prospective Multicenter Imaging Study for Evaluation of chest pain (PROMISE) and Scottish Computed Tomography of the Heart (SCOT-HEART), have contributed to abetter understanding of how coronary CT angiography may play arole in more efficient management and even improved health outcomes. The emerging role of coronary CT has been acknowledged by the 2019 Guidelines of the European Society of Cardiology recommending the use of CT as afirst-line tool for the evaluation of patients with stable chest pain with aclassI, level of evidenceB recommendation. The purpose of this article is to provide an overview on existing evidence, clinical implication, limitations of available data, and remaining questions to be answered by future research.

Diagnosis of peanut allergy using continuous likelihood ratios

BackgroundPeanut allergy (PA) is associated with an economic and psychological burden on patients and families. Its diagnosis includes tests for peanut specific immunoglobulin E (sIgE), the values of which usually are categorized as positive or negative using a predefined cutoff (usually 0.35 kU/L). With the use of Bayes' theorem, this categorization can be replaced with a continuous interpretation of sIgE, which accounts for the prevalence of PA and history of ingestion. ObjectiveTo evaluate a method for estimating the likelihood ratio (LR) for each value of sIgE by performing a pilot investigation with the results of oral food challenges. The LR could be used to estimate the probability of PA. MethodsThe outcomes of oral food challenges and serum IgE values from 117 children seen in an allergy clinic between January 2017 and November 2019 were obtained. Polynomial regression of the receiver operation characteristics curve was used to determine an LR for each value of sIgE. Linear regression was used to estimate an LR for each value of sIgE. ResultssIgE ranged from less than 0.1 kU/L to 35 kU/L. Bayes' theorem and a receiver operation characteristics curve were used to estimate LRs for each value of peanut sIgE. The value of IgE associated with an LR of 1 was 0.22 kU/L, which is comparable to other studies that used a value of 0.35 kU/L to separate positive from negative results. ConclusionWhen combined with estimates of pretest probability, this method should permit the development of computerized decision-making algorithms to estimate the probability that a patient has PA.

Validation of European Society of Cardiology pre-test probabilities for obstructive coronary artery disease in suspected stable angina.

Aims To assess contemporary pre-test probability estimates for obstructive coronary artery disease in patients with stable chest pain.Methods and results In this substudy of a multicentre randomized controlled trial, we compared 2019 European Society of Cardiology (ESC)-endorsed pre-test probabilities with observed prevalence of obstructive coronary artery disease on computed tomography coronary angiography (CTCA). We assessed associations between pre-test probability, 5-year coronary heart disease death or non-fatal myocardial infarction and study intervention (standard care vs. CTCA). The study population consisted of 3755 patients (30–75 years, 46% women) with a median pre-test probability of 11% of whom 1622 (43%) had a pre-test probability of >15%. In those who underwent CTCA (n = 1613), the prevalence of obstructive disease was 22%. When divided into deciles of pre-test probability, the observed disease prevalence was similar but higher than the corresponding median pre-test probability [median difference 2.3 (1.3–5.6)%]. There were more clinical events in patients with a pre-test probability >15% compared to those at 5–15% and <5% (4.1%, 1.5%, and 1.4%, respectively, P < 0.001). Across the total cohort, fewer clinical events occurred in patients who underwent CTCA, with the greatest difference in those with a pre-test probability >15% (2.8% vs. 5.3%, log rank P = 0.01), although this interaction was not statistically significant on multivariable modelling.Conclusion The updated 2019 ESC guideline pre-test probability recommendations tended to slightly underestimate disease prevalence in our cohort. Pre-test probability is a powerful predictor of future coronary events and helps select those who may derive the greatest absolute benefit from CTCA.

Highlights From the Circulation Family of Journals.

Highlights From the Circulation Family of Journals.

Performance of Traditional Pretest Probability Estimates in Stable Patients Undergoing Myocardial Perfusion Imaging.

The yield of myocardial perfusion imaging is low in contemporary patients with suspected coronary artery disease (CAD) selected based on American College of Cardiology Foundation/American Heart Association pretest probability estimate. We compared traditional pretest estimates of CAD probability with the prevalence of abnormal myocardial perfusion single-photon emission computed tomography (MPS). This was a cohort study from a single academic center. Consecutive stable patients without known CAD referred for stress MPS for suspected CAD between 2004 and 2011 were identified (n=15 777). Angina typicality was determined using standard criteria. Abnormal MPS perfusion was defined as a summed stress score ≥4, ischemia as summed stress score ≥4 and summed difference score ≥2, and extensive ischemia as summed difference score ≥8 using a standard, 17-segment model of the left ventricle. The pretest probability of CAD was determined using the American College of Cardiology Foundation/American Heart Association criteria. Overall, 14% (n=2177) of patients had abnormal MPS of whom 11% (n=1698) had ischemia and 4% (n=684) extensive ischemia. In patients with chest pain who underwent treadmill MPS (n=4764), only 27% reported angina on the treadmill. Typical angina was associated with the highest prevalence for positive MPS (33% in men and 14% in women), ischemia (30% in men and 12% in women), and extensive ischemia (22% in men and 4% in women) when compared with other symptom categories. Prevalence of MPS abnormality was substantially lower than expected based on pretest probability estimates across most sex and age groups. In multivariable analysis, the pretest probability estimate was not an independent predictor of abnormal MPS. Traditional estimates of pretest probability of CAD are not predictive of MPS perfusion abnormality and overestimate its prevalence in stable patients.

Impact of Patient Affect on Physician Estimate of Probability of Serious Illness and Test Ordering.

The authors hypothesize patient facial affect may influence clinician pretest probability (PTP) estimate of cardiopulmonary emergency (CPE) and desire to order a computerized tomographic pulmonary angiogram (CTPA). This prospective study was conducted at three Indiana University-affiliated hospitals in two parts: collecting videos of patients undergoing CTPA for suspected acute pulmonary embolism watching a humorous video (August 2014-April 2015) and presenting the medical histories and videos to clinicians to determine the impact of patient facial affect on physicians' PTP estimate of CPE and desire to order a CTPA (June-November 2015). Patient outcomes were adjudicated as CPE+ or CPE- by three independent reviewers. Physicians completed a standardized test of facial affect recognition, read standardized medical histories, then viewed videos of the patients' faces. Clinicians marked their PTP estimate of CPE and desire for a CTPA before and after seeing the video on a visual analog scale (VAS). Fifty physicians completed all 73 videos. Seeing the patient's face produced a > 10% absolute change in PTP estimate of CPE in 1,204/3,650 (33%) cases and desire for a CTPA in 1,095/3,650 (30%) cases. The mean area under the receiver operating characteristic curve for CPE estimate was 0.55 ± 0.15, and the change in CPE VAS was negatively correlated with physicians' standardized test scores (r = -0.23). Clinicians may use patients' faces to make clinically important inferences about presence of serious illness and need for diagnostic testing. However, these inferences may fail to align with actual patient outcomes.

90: METHODS OF ESTIMATING PRETEST PROBABILITY IN PRIMARY CARE SETTING

Proper estimating of pretest probability (PTP) by primary care physicians is the main precondition for applying Evidence Based Medicine in clinical setting. PTP of a patient is not equal to...

Bayesian pretest probability estimation for primary malignant bone tumors based on the Surveillance, Epidemiology and End Results Program (SEER) database

In the diagnostic process of primary bone tumors, patient age, tumor localization and to a lesser extent sex affect the differential diagnosis. We therefore aim to develop a pretest probability calculator for primary malignant bone tumors based on population data taking these variables into account. We access the SEER (Surveillance, Epidemiology and End Results Program of the National Cancer Institute, 2015 release) database and analyze data of all primary malignant bone tumors diagnosed between 1973 and 2012. We record age at diagnosis, tumor localization according to the International Classification of Diseases (ICD-O-3) and sex. We take relative probability of the single tumor entity as a surrogate parameter for unadjusted pretest probability. We build a probabilistic (naïve Bayes) classifier to calculate pretest probabilities adjusted for age, tumor localization and sex. We analyze data from 12,931 patients (647 chondroblastic osteosarcomas, 3659 chondrosarcomas, 1080 chordomas, 185 dedifferentiated chondrosarcomas, 2006 Ewing's sarcomas, 281 fibroblastic osteosarcomas, 129 fibrosarcomas, 291 fibrous malignant histiocytomas, 289 malignant giant cell tumors, 238 myxoid chondrosarcomas, 3730 osteosarcomas, 252 parosteal osteosarcomas, 144 telangiectatic osteosarcomas). We make our probability calculator accessible at http://ebm-radiology.com/bayesbone/index.html . We provide exhaustive tables for age and localization data. Results from tenfold cross-validation show that in 79.8% of cases the pretest probability is correctly raised. Our approach employs population data to calculate relative pretest probabilities for primary malignant bone tumors. The calculator is not diagnostic in nature. However, resulting probabilities might serve as an initial evaluation of probabilities of tumors on the differential diagnosis list.

Pathogenesis, Diagnosis, and Treatment of Venous Thromboembolism in Older Adults

Older adults have a significantly greater risk of venous thromboembolism (VTE), including deep vein thrombosis and pulmonary embolism, than younger adults. The cause of this greater risk is thought to be multifactorial, including age-related changes in hemostatic factors and greater comorbid conditions and hospitalizations, but is not completely understood. Moreover, VTE remains underrecognized in older adults and may present atypically. Thus, a low index of clinical suspicion is essential when evaluating older adults with possible VTE. Despite this underrecognition in older adults, the diagnostic approach remains similar for all age groups and includes estimation of pretest probability, measurement of the D-dimer, and imaging. Antithrombotic agents are the mainstay of VTE treatment and, when used appropriately, substantially reduce VTE recurrence and complications. The approval of novel oral anticoagulants (NOACs), including dabigatran, rivaroxaban, apixaban, and edoxaban, provide clinicians with new therapeutic options. In some individuals, NOACs may offer advantages over warfarin, including fewer drug interactions, more-predictable anticoagulation, and lower risk of bleeding. Nevertheless, anticoagulation of VTE in older adults should always be performed cautiously, because age is a risk factor for bleeding complications. Identifying modifiable bleeding risk factors and balancing the risks of VTE recurrence with hemorrhage are important considerations when using anticoagulants in older adults.

Is D-dimer used according to clinical algorithms in the diagnostic work-up of patients with suspicion of venous thromboembolism? A study in six European countries

IntroductionClinical algorithms consisting of pre-test probability estimation and D-dimer testing are recommended in diagnostic work-up for suspected venous thromboembolism (VTE). The aim of this study was to explore how physicians working in emergency departments investigated patients suspected to have VTE. Materials and methodsA questionnaire with two case histories related to the diagnosis of suspected pulmonary embolism (PE) (Case A) and deep venous thrombosis (DVT) (Case B) were sent to physicians in six European countries. The physicians were asked to estimate pre-test probability of VTE, and indicate their clinical actions. ResultsIn total, 487 physicians were included. Sixty percent assessed pre-test probability of PE to be high in Case A, but 7% would still request only D-dimer and 11% would exclude PE if D-dimer was negative, which could be hazardous. Besides imaging, a D-dimer test was requested by 41%, which is a “waste of resources” (extra costs and efforts, no clinical benefit). For Case B, 92% assessed pre-test probability of DVT to be low. Correctly, only D-dimer was requested by 66% of the physicians, while 26% requested imaging, alone or in addition to D-dimer, which is a “waste of resources”. ConclusionsThese results should encourage scientific societies to improve the dissemination and knowledge of the current recommendations for the diagnosis of VTE.

Estimation of frequency and pretest probability of CAD in patients presenting with recent onset chest pain by multi-detector CT angiography

BackgroundCoronary artery disease (CAD) is the single most common cause of death in the developed world, responsible for about 1 in every 5 deaths. The morbidity, mortality, and socioeconomic importance of this disease make timely accurate diagnosis and cost-effective management of CAD of the utmost importance (Cassar et al., 2009; Russo et al., 2010; Dedic et al., 2011). ObjectivesThe aim of this study was to estimate the frequency and pretest probability of CAD in patients presenting with recent onset chest pain by multi-detector CT coronary angiography as a non-invasive imaging tool. MethodsThis study included 40 patients, presenting with recent onset chest pain showing initial negative ECG and troponins enzyme, and they were scheduled for elective multislice CT coronary angiography between March 2013 and September 2013 in Cairo Radiology Center (Cairo Scan). ResultsAmong 40 patients there were 31(77.5%) had CAD (37.5% significant and 40% non-significant CAD). 29(72.5%) were hypertensive, 27(67.5%) were diabetic, 23(57.5%) were dyslipidemic, 13(32.5%) were smokers, and 10(25%) were with positive family history of IHD. Significant CAD seen in 60% of hypertensive, 70% of diabetic, 20% of those with positive family history, 17% in those with low pretest probability group, 37% in intermediate probability group, and 57% in high probability group. Hypertensive patients showed similar CACS compared to normotensive (275.73 vs. 237, P value>0.05), and diabetic patients showed higher CACS compared to non-diabetic (328 vs. 179.9, P value<0.05). Diabetic patients showed higher incidence of multi-vessel affection than non-diabetic (85.7% vs. 75%, P value<0.05). Patients ⩾60years old had higher CACS than those <60years (mean 444.4 vs. 66.2, P value<0.05). Male patients <60years also had higher CACS compared to females of the same age group (mean 105.47 in male vs. 26.9 in female, P value<0.05), while no such significance was seen between males and females >60years. ConclusionCAD was seen in 77.5% in those with recent onset chest pain. Higher incidence of significant CAD was seen in hypertensive and diabetic patients. Family history is still not a reliable factor in our society. Male patient <60years old has higher incidence of CAD than female in same age group. Pretest probability for CAD is a simple and important test as all patients in higher probability have CAD and most of them have significant disease so it is better to direct referral of them to conventional angiography to gain a benefit from direct intervention, radiation and cost benefit.

Correlation between clinicians-assigned weights to findings and their diagnostic odd ratio; case of congestive heart failure.

BackgroundIncorrect estimation of pretest probability and misinterpretation of test results can change post-test probability in medical decision making. The aim of this study was to evaluate how physicians assess weight of findings of congestive heart failure (CHF) and how much their estimation is correlated with findings’ Diagnostic Odd Ratio (DOR).MethodsThe participants were asked to answer a questionnaire based on a scenario of a patient having dyspnea. Eighteen findings in 3 categories including: history, examination and radiographic findings were inserted along a column and a row as a matrix. The respondents had to compare each finding in the column with all other findings in the row and insert a mark in boxes below the findings of the row that had greater weight compared to the finding in the column. The weight of each finding was considered as total number of “marked boxes” in front of that finding. DOR of findings was calculated using their positive and negative likelihood ratios (LRs) based on current best evidence. Findings ranked in the order of their DOR and were compared with the ranking in the order of participants-assigned weights. We examined correlation between average weights assigned by physicians and DOR of findings. In subgroup analysis correlations between average weights assigned by physicians and DOR of history, examination and radiographic findings were examined.ResultsSeventy five physicians completed the questionnaire. Correlation between ranking in the order of findings’ DOR and ranking in the order of clinicians-assigned weights was significant (p-value = 0.005 r = 0.64). In contrast correlations between participants-assigned weights and DOR of history, examination and radiographic findings were positive but non- significant (r = 0.181, p-value = 0.7, r = 0.343, p-value = 0.506 and r = 0.219, p-value = 0.723 respectively).ConclusionOur result show that although correlation between clinicians-assigned weights and DOR of entire findings was significant, correlations between clinicians-assigned weights to the different categories of findings and their DOR were not significant. Reevaluating probabilistic reasoning by emphasis on using LRs can make pretest probability estimating and interpretation of test results more objective and would ultimate in more precise and homogenous post-test probabilities.