Estimation Of Pretest Probability Research Articles

Screening, risk stratification and CT imaging for assessment of coronary artery disease

Early treatment of cardiovascular risk factors and characterization of coronary plaques is essential to collect prognostic information about coronary artery disease (CAD) and prevent cardiovascular events. Discussion of the most important risk factors of CAD, basic diagnostic of CAD, prevention, and prognostic factors of CAD with focus on cardiac computed tomography (CT). Prevalence and prognostic impact of CAD risk factors; description of specific assessment of risk profiles; estimation of pretest probability; conventional prevention of CAD; prognostic assessment of CAD using the Calcium Scoring and coronary CT angiography. Assessment of risk profiles and estimation of pretest probability for obstructive coronary stenosis necessitates athorough evaluation of medical history and laboratory values. The composition and extent of calcified and noncalcified plaques in CT exams based on the criteria of the Coronary Artery Disease-Reporting and Data System give important prognostic information about the risk of cardiovascular events, which increases with high plaque burden and vice versa. Initial imaging with CT for evaluation of CAD leads to areduction of invasive coronary angiographies and catheter-associated complications. Besides early detection of cardiovascular risk factors, the additional assessment of plaque burden and significant stenosis in CT gives further prognostic information to facilitate effective therapies to prevent cardiovascular events and in the case of low plaque burden avoid invasive coronary angiography. However, systmatic screening using Calcium Scoring is not established yet due to insufficient data, although it could potentially be used for an early risk stratification in patients with multiple risk factors.

Predicting the presence of coronary plaques featuring high-risk characteristics using polygenic risk scores and targeted proteomics in patients with suspected coronary artery disease

BackgroundThe presence of coronary plaques with high-risk characteristics is strongly associated with adverse cardiac events beyond the identification of coronary stenosis. Testing by coronary computed tomography angiography (CCTA) enables the identification of high-risk plaques (HRP). Referral for CCTA is presently based on pre-test probability estimates including clinical risk factors (CRFs); however, proteomics and/or genetic information could potentially improve patient selection for CCTA and, hence, identification of HRP. We aimed to (1) identify proteomic and genetic features associated with HRP presence and (2) investigate the effect of combining CRFs, proteomics, and genetics to predict HRP presence.MethodsConsecutive chest pain patients (n = 1462) undergoing CCTA to diagnose obstructive coronary artery disease (CAD) were included. Coronary plaques were assessed using a semi-automatic plaque analysis tool. Measurements of 368 circulating proteins were obtained with targeted Olink panels, and DNA genotyping was performed in all patients. Imputed genetic variants were used to compute a multi-trait multi-ancestry genome-wide polygenic score (GPSMult). HRP presence was defined as plaques with two or more high-risk characteristics (low attenuation, spotty calcification, positive remodeling, and napkin ring sign). Prediction of HRP presence was performed using the glmnet algorithm with repeated fivefold cross-validation, using CRFs, proteomics, and GPSMult as input features.ResultsHRPs were detected in 165 (11%) patients, and 15 input features were associated with HRP presence. Prediction of HRP presence based on CRFs yielded a mean area under the receiver operating curve (AUC) ± standard error of 73.2 ± 0.1, versus 69.0 ± 0.1 for proteomics and 60.1 ± 0.1 for GPSMult. Combining CRFs with GPSMult increased prediction accuracy (AUC 74.8 ± 0.1 (P = 0.004)), while the inclusion of proteomics provided no significant improvement to either the CRF (AUC 73.2 ± 0.1, P = 1.00) or the CRF + GPSMult (AUC 74.6 ± 0.1, P = 1.00) models, respectively.ConclusionsIn patients with suspected CAD, incorporating genetic data with either clinical or proteomic data improves the prediction of high-risk plaque presence.Trial registrationhttps://clinicaltrials.gov/ct2/show/NCT02264717 (September 2014).

Safe Deferral of Coronary Computed Tomography Angiography for Patients With a Low Pretest Probability of Coronary Artery Disease in 2019 European Society of Cardiology Guidelines.

Coronary computed tomography angiography is increasingly used as the first-line test for suspected coronary artery disease. Its overuse in a low pretest probability (PTP) population may lead to low diagnostic yield without change in patient management. We evaluated the clinical consequences of the updated 2019 European Society of Cardiology (ESC) chronic coronary syndromes guidelines' PTP estimation and whether imaging could be safely deferred in patients with a low PTP. This retrospective cohort included all 1753 consecutive patients who underwent coronary computed tomography angiography for suspected coronary artery disease at Helsinki University Hospital between 2009 and 2017. PTP was calculated according to the 2013 and 2019 ESC guidelines. The overall mortality, cardiac deaths, myocardial infarctions, and hospitalizations for unstable angina were acquired from national registry data for 1 to 10 years of follow-up (median, 4 years). Updated 2019 ESC guidelines classified 72% of the patients as having low PTP, whose imaging could have been deferred. The revascularization rate (4.7%) and annual cardiac mortality (0.4%) were low in the 857 patients reclassified from the recommendation to test, according to the 2013 ESC guideline, to deferral of coronary computed tomography angiography, according to the new 2019 guideline. The updated 2019 ESC guideline PTP score aids clinicians in safely preventing the overuse of cardiac imaging in patients deemed at low PTP of coronary artery disease. Diagnostic yield, revascularization rate, and cardiac mortality are low in patients with low pretest risk.

Machine learning approaches that use clinical, laboratory, and electrocardiogram data enhance the prediction of obstructive coronary artery disease

Pretest probability (PTP) for assessing obstructive coronary artery disease (ObCAD) was updated to reduce overestimation. However, standard laboratory findings and electrocardiogram (ECG) raw data as first-line tests have not been evaluated for integration into the PTP estimation. Therefore, this study developed an ensemble model by adopting machine learning (ML) and deep learning (DL) algorithms with clinical, laboratory, and ECG data for the assessment of ObCAD. Data were extracted from the electronic medical records of patients with suspected ObCAD who underwent coronary angiography. With the ML algorithm, 27 clinical and laboratory data were included to identify ObCAD, whereas ECG waveform data were utilized with the DL algorithm. The ensemble method combined the clinical-laboratory and ECG models. We included 7907 patients between 2008 and 2020. The clinical and laboratory model showed an area under the curve (AUC) of 0.747; the ECG model had an AUC of 0.685. The ensemble model demonstrated the highest AUC of 0.767. The sensitivity, specificity, and F1 score of the ensemble model ObCAD were 0.761, 0.625, and 0.696, respectively. It demonstrated good performance and superior prediction over traditional PTP models. This may facilitate personalized decisions for ObCAD assessment and reduce PTP overestimation.

Comparison of risk assessment strategies incorporating coronary artery calcium score with estimation of pretest probability to defer cardiovascular testing in patients with stable chest pain

BackgroundThe risk assessment of patients with stable chest pain (SCP) to defer further cardiovascular testing is crucial, but the most appropriate risk assessment strategy remains unknown. We aimed to compare current strategies to identify low risk SCP patients.Methods5289 symptomatic patients who had undergone coronary artery calcium score (CACS) and coronary computed tomographic angiography scan were identified and followed. Pretest probability (PTP) of obstructive coronary artery disease (CAD) for every patient was estimated according to European Society of Cardiology (ESC)-PTP model and CACS-weighted clinical likelihood (CACS-CL) model, respectively. Based on the 2019 ESC guideline-determined risk assessment strategy (ESC strategy) and CACS-CL model-based risk assessment strategy (CACS-CL strategy), all patients were divided into low and high risk group, respectively. Area under receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI) and net reclassification improvement (NRI) was used.ResultsCACS-CL model provided more robust estimation of PTP than ESC-PTP model did, with a larger AUC (0.838 versus 0.735, p < 0.0001), positive IDI (9%, p < 0.0001) and less discrepancy between observed and predicted probabilities. As a result, compared to ESC strategy which only applied CACS-CL model to patients with borderline ESC-PTP, CACS-CL strategy incorporating CACS with estimation of PTP to entire SCP patients indicated a positive NRI (19%, p < 0.0001) and a stronger association to major adverse cardiovascular events, with hazard ratios: 3.97 (95% confidence intervals: 2.75–5.72) versus 5.11 (95% confidence intervals: 3.40–7.69).ConclusionThe additional use of CACS for all SCP patients in CACS-CL strategy improved the risk assessment of SCP patients to identify individuals at low risk.

An acoustic based risk score in addition to pre-test probability estimation for coronary artery disease improves risk classification in patients with suspected coronary artery disease

Abstract Background The 2021 American College of Cardiology (ACC)/American Heart Association (AHA) Guideline for Evaluation and Diagnosis of Chest Pain introduced a new pre-test probability (PTP)-table for risk stratification of patients with symptoms suggestive of coronary artery disease (CAD). The CAD-score, an acoustic-based risk score for obstructive CAD, has previously shown good rule out capability in patients with suggested CAD when added to PTP. Purpose To investigate the potential of the acoustic CAD-score to 1) reclassify patients from intermediate-high to low PTP, and 2) improve risk-stratification in patients with low PTP, when added to the 2021 AHA/ACC PTP. Methods This is a retrospective study of patients with successfully obtained CAD-score from The Danish study of Non-Invasive testing in Coronary artery Disease 1 (Dan-NICAD 1) of patients without known CAD referred for coronary computed tomography angiography with angina pectoris. Patients with suspected obstructive CAD underwent invasive angiography. CAD-score was obtained with a non-invasive acoustic device analysing heart sounds based on CAD-related turbulence. CAD-score≤20 indicated low probability of CAD. PTP was calculated from sex, age, and angina symptoms according to current AHA/ACC Guidelines. Low likelihood of CAD was defined as PTP≤15%, and intermediate-high likelihood as PTP&amp;gt;15%. Hemodynamic obstructive CAD was defined as visual luminal diameter stenosis &amp;gt;90% on angiography or FFR&amp;lt;0.80%. Results Among the 1475 included patients (52.3% women, median age 57 years IQR [50–64[), 36.4% were categorized as low likelihood of CAD (PTP≤15%), and 9.8% of patients had obstructive CAD. Compared to patients with PTP≤15%, patients with PTP&amp;gt;15%, were older, more often hypertensive (49.8% vs. 35.5%), had a higher median CAD-score (28 [22–38] vs. 16 [12–22]), and had more obstructive CAD (12.4% vs. 5.2%). CAD-score reclassified 17.6% of patients with intermediate-high likelihood to a low likelihood of CAD and decreased the post-test probability of obstructive CAD from 5.2% to 4.7%. Subgroup analyses showed reclassification was greater in men, patients with PTP ≤25%, age &amp;lt;70 years or no hypertension. In the 432 patients with PTP&amp;gt;15%, age&amp;lt;70 and no hypertension, 35.4% were reclassified to low likelihood, whereas only 2.4% of patients with PTP&amp;gt;15%, age≥70 years or hypertension were ruled out by CAD-score ≤20. In patients with low PTP≤15%, CAD-score≤20 risk-stratified 361 (67.2%) patients to very low and 176 (32.8%) to an intermediate-low likelihood of CAD, but the post-test probabilities of CAD in both subgroups were still below the cut-off of 15 (3.6% vs. 8.5%). Conclusion Adding an acoustic-based CAD-score to the AHA/ACC PTP could potentially reduce the need for further diagnostic testing by 17.6% in patients with intermediate-high likelihood of CAD and in subgroups a reduction of 35% can be achieved. Use of CAD-score in low-likelihood patients only moderately improves risk classification. Funding Acknowledgement Type of funding sources: None.

Role of biomarkers in lung nodule evaluation.

Worldwide, lung cancer is the leading cause of cancer mortality. Much of this mortality is thought to be secondary to detection in later stages, where treatment options and survivability are limited. The goals of lung nodule evaluation are to expedite the diagnosis and treatment of patients with malignant nodules and to minimize unnecessary diagnostic procedures in those with benign nodules. However, the differentiation between benign and malignant has been challenging and is further complicated by the benefits of early diagnosis competing with potential morbidity of invasive diagnostic procedures. Biomarkers have the potential to improve estimates of pretest probability of malignancy in pulmonary nodules, especially in the intermediate-risk subgroup. Four biomarkers have undergone extensive validation and are available for clinical use, and we will discuss each in this review. The application of biomarkers to lung cancer risk assessment has the potential to improve cancer probability assessments, which in turn can reduce unnecessary invasive testing and/or reduce delays in diagnosis and treatment.

The PROMISE Minimal Risk Score Improves Risk Classification of Symptomatic Patients With Suspected CAD

BackgroundGuidelines for evaluating patients with suspected coronary artery disease (CAD) recommend pretest probability (PTP) estimation but provide no clear recommendations regarding diagnostic testing in patients with >5% to 15% risk of obstructive CAD. The diagnostic and prognostic value of PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) minimal risk score (PMRS) calculation in this patient group is unknown. ObjectivesThis work aims to improve the evaluation of stable patients with suspected CAD by using the PMRS, which identifies patients at minimal risk of CAD and events in patients with >5% to 15% PTP of obstructive CAD. MethodsGreater than 5% to 15% PTP patients from 2 large clinical trials were used for subcohort derivation: PROMISE (N = 10,003) and Dan-NICAD (Danish study of Non-Invasive Testing in Coronary Artery Disease) (N = 3,252). First, the PMRS cutoff associated with a prevalence of obstructive CAD ≤5% was determined in the >5% to 15% PTP PROMISE core lab computed tomographic angiography patients (discovery cohort: n = 2,191). This cutoff was validated for obstructive CAD in >5% to 15% PTP Dan-NICAD patients (CAD validation cohort: n = 1,386) and for prognostic impact on death and myocardial infarction in >5% to 15% PTP PROMISE non–core lab computed tomographic angiography patients (prognosis validation cohort: n = 2,753). ResultsIn the discovery cohort, a CAD prevalence of ≤5% was found at a PMRS of ≥34%. In the CAD validation cohort, this cutoff down-classified 442 (31.9%) of >5% to 15% PTP patients into the low PTP group (CAD ≤5%); the prevalence of obstructive CAD in down-classified patients was 3.2% compared to 7.1% in non–down-classified patients. A PMRS ≥34% was nonsignificantly associated with a lower risk of myocardial infarction and death in the prognosis validation cohort (HR: 0.58 [95% CI: 0.29-1.18]; P = 0.13). ConclusionsFor evaluating patients with suspected CAD, a combined use of traditional PTP and the PMRS correctly down-classified one-third of >5% to 15% PTP patients into a group with very low prevalence of obstructive CAD and adverse events. The proposed strategy may improve risk stratification and help reduce unneeded diagnostic testing.

Physician judgement in predicting obstructive coronary artery disease and adverse events in chest pain patients

ObjectiveTo evaluate informal physician judgement versus pretest probability scores in estimating risk in patients with suspected coronary artery disease (CAD).MethodsWe included 4533 patients from the PROMISE (Prospective Multicenter Imaging Study...

The role of emotional sensitivity to probability in the decision to choose genetic testing.

While the availability of genetic testing is rapidly increasing, many opt out of testing. The decision to test or not is emotionally charged, and both clinical research and theoretical work in psychology show that in emotional decisions, people often struggle to interpret and utilize risk information. Clinical research on genetic testing uptake also shows that feeling overwhelmed by numeric information may be a deterrent to testing. However, recent psychological research indicates that some portion of the population has greater emotional sensitivity to probability, (i.e., the extent to which emotional reactions to risk depend on probabilities) than others. We hypothesize that participants high in emotional sensitivity to probability will be more interested in genetic testing as an opportunity to seek greater precision in risk estimates and that this relationship is moderated by the testing context itself. In an online survey of a nationally representative sample (not recruited as patients), participants were presented with a hypothetical scenario describing a suspected diagnosis with an option for genetic testing. The scenario experimentally varied the pre-test probability estimate of the diagnosis (low or high) and whether the test results would result in certainty (ruling in or ruling out the diagnosis), or reduced uncertainty (providing a more precise individual risk estimate). Results indicated that emotional sensitivity to probability was a strong predictor of intention to test, particularly when the test allowed participants to rule out a diagnosis, reducing an already low probability to zero. These results highlight the way patients' individual characteristics interact with the testing context to guide decision-making and provide important insight into the way patients integrate risk probability information into intensely emotional decisions.

Key concepts in clinical epidemiology: Estimating pre-test probability

Appropriate medical management largely hinges on correctly diagnosing the underlying disease. Often, clinicians are faced with a dizzying array of accurate, albeit expensive and/or invasive diagnostic tests. What tends to be overlooked is that the probability of a disease once the test results are in (post-test probability) is a function of both the probability of the disease before the test was done (pre-test probability) and the diagnostic accuracy of the test. Clinicians need to be cognizant of inherent limitations in estimating pre-test probability and be more adept at finding ways to overcome these limitations. An accurate estimate of pre-test probability is pivotal. It guides the decision whether or not to conduct further testing, the choice of diagnostic test to perform, the interpretation of the test result, and the subsequent management of the patient's disease.

Contemporary Management of Stable Coronary Artery Disease - Implications of the ISCHEMIA Trial.

Coronary artery disease (CAD) remains a leading cause of mortality and morbidity in developed countries. Although urgent revascularization is the cornerstone of management of acute coronary syndrome (ACS), for patients with stable CAD recent large-scale clinical trials indicate that a mechanical 'fix' of a narrowed artery is not obviously beneficial; ACS and stable CAD are increasingly recognized as different clinical entities. We review the perspectives on (1) modifying the diagnostic pathway of stable CAD with the incorporation of modern estimates of pretest probability, (2) non-imaging evaluations based on their availability, (3) the optimal timing of invasive coronary angiography and revascularization, and (4) the implementation of medical therapy during the work-up.

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

Abstract 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. Funding Acknowledgement Type of funding sources: None.

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