Vibrational characterization of cavitation in left ventricular assist device.

BACKGROUND: The left ventricular assist device (LVAD) is a mechanical circulatory support device for patients with severe heart failure. Microbubbles caused by cavitation in the LVAD can potentially lead to physiological and pump-related complications. The aim of this study is to characterize the vibrational patterns in the LVAD during cavitation. METHODS: The LVAD was integrated into an in vitro circuit and mounted with a high-frequency accelerometer. Accelerometry signals were acquired with different relative pump inlet pressures ranging from baseline (+20 mmHg) to -600 mmHg in order to induce cavitation. Microbubbles were monitored with dedicated sensors at the pump inlet and outlet to quantify the degree of cavitation. Acceleration signals were analyzed in the frequency domain to identify changes in the frequency patterns when cavitation occurred. RESULTS: Significant cavitation occurred at the low inlet pressure (-600 mmHg) and was detected in the frequency range between 1800 and 9000 Hz. Minor degrees of cavitation at higher inlet pressures (-300 to -500 mmHg) were detected in the frequency range between 500-700, 1600-1700 Hz, and around 12 000 Hz. The signal power of the dominating frequency ranges was statistically significantly different from baseline signals. CONCLUSION: Vibrational measurements in the LVAD can be used to detect cavitation. A significant degree of cavitation could be detected in a wide frequency range, while minor cavitation activity could only be detected in more narrow frequency ranges. Continuous vibrational LVAD monitoring can potentially be used to detect cavitation and minimize the damaging effect associated with cavitation.

Accuracy of a Clinical Applicable Method for Prediction of VO2max Using Seismocardiography.

Cardiorespiratory fitness measured as ˙VO2max is considered an important variable in the risk prediction of cardiovascular disease and all-cause mortality. Non-exercise ˙VO2max prediction models are applicable, but lack accuracy. Here a model for the prediction of ˙VO2max using seismocardiography (SCG) was investigated. 97 healthy participants (18-65 yrs., 51 females) underwent measurement of SCG at rest in the supine position combined with demographic data to predict ˙VO2max before performing a graded exercise test (GET) on a cycle ergometer for determination of ˙VO2max using pulmonary gas exchange measurements for comparison. Accuracy assessment revealed no significant difference between SCG and GET ˙VO2max (mean±95% CI; 38.3±1.6 and 39.3±1.6 ml·min-1·kg-1, respectively. P=0.075). Further, a Pearson correlation of r=0.73, a standard error of estimate (SEE) of 5.9 ml·min-1·kg-1, and a coefficient of variation (CV) of 8±1% were found. The SCG ˙VO2max showed higher accuracy, than the non-exercise model based on the FRIENDS study, when this was applied to the present population (bias=-3.7±1.3 ml·min-1·kg-1, p<0.0001. r=0.70. SEE=7.4 ml·min-1·kg-1, and CV=12±2%). The SCG ˙VO2max prediction model is an accurate method for the determination of ˙VO2max in a healthy adult population. However, further investigation on the validity and reliability of the SCG ˙VO2max prediction model in different populations is needed for consideration of clinical applicability.

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

AIMS: 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. METHODS AND RESULTS: 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 42 328 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. CONCLUSION: 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.

Prospective validation of an acoustic-based system for the detection of obstructive coronary artery disease in a high-prevalence population.

Recent guidelines recommend a risk-adjusted, non-invasive work-up in patients presenting with chest discomfort to exclude coronary artery disease (CAD). However, a risk-adjusted diagnostic approach remains challenging in clinical practice. An acoustic detection device for analyzing micro-bruits induced by stenosis-generated turbulence in the coronary circulation has shown potential for ruling out CAD in patients with low-to-intermediate likelihood. We examined the diagnostic value of this acoustic detection system in a high-prevalence cohort. In total, 226 patients scheduled for clinically indicated invasive coronary angiography (ICA) were prospectively enrolled at two centers and examined using a portable, acoustic detection system. The acoustic analysis was performed in double-blinded fashion prior to quantitative ICA and following percutaneous coronary intervention (PCI). An acoustic detection result (CAD score) was obtained in 94% of all patients. The mean baseline CAD score was 41.2 ± 11.9 in patients with obstructive CAD and 33.8 ± 13.4 in patients without obstructive CAD (p < 0.001). ROC analysis revealed an AUC of 0.661 (95% CI 0.584-0.737). Sensitivity was 97.6% (95% confidence interval (CI) 91.5-99.7%), specificity was 14.5% (CI 9.0-21.7%), negative predictive value was 90.5% (CI 69.6-98.8%), and positive predictive value was 41.7% (CI 34.6-49.0%). Following PCI, the mean CAD score decreased from 40.5 ± 11.2 to 38.3 ± 13.7 (p = 0.039). Using an acoustic detection device identified individuals with CAD in a high-prevalence cohort with high sensitivity but relatively low specificity. The negative predictive value was within the predicted range and may be of value for a fast rule-out of obstructive CAD even in a high-prevalence population.

A Clinical Method for Estimation of VO2max Using Seismocardiography.

The purpose of this study was to investigate the correlation between the seismocardiogram and cardiorespiratory fitness. Cardiorespiratory fitness can be estimated as VO2max using non-exercise algorithms, but the results can be inaccurate. Healthy subjects were recruited for this study. Seismocardiogram and electrocardiogram were recorded at rest. VO2max was measured during a maximal effort cycle ergometer test. Amplitudes and timing intervals were extracted from the seismocardiogram and used in combination with demographic data in a non-exercise prediction model for VO2max. 26 subjects were included, 17 females. Mean age: 38.3±9.1 years. The amplitude following the aortic valve closure derived from the seismocardiogram had a significant correlation of 0.80 (p<0.001) to VO2max. This feature combined with age, sex and BMI in the prediction model, yields a correlation to VO2max of 0.90 (p<0.001, 95% CI: 0.83-0.94) and a standard error of the estimate of 3.21 mL·kg-1·min-1 . The seismocardiogram carries information about the cardiorespiratory fitness. When comparing to other non-exercise models the proposed model performs better, even after cross validation. The model is limited when tracking changes in VO2max. The method could be used in the clinic for a more accurate estimation of VO2max compared to current non-exercise methods.

Danish study of Non-Invasive testing in Coronary Artery Disease 2 (Dan-NICAD 2): Study design for a controlled study of diagnostic accuracy.

BACKGROUND: Coronary computed tomography angiography (CTA) is the preferred primary diagnostic modality when examining patients with low to intermediate pre-test probability of coronary artery disease (CAD). Only 20-30% of these have potentially obstructive CAD. Because of the relatively poor positive predictive value of coronary CTA, unnecessary invasive coronary angiographies (ICAs) are conducted with the costs and risks associated with the procedure. Hence, an optimized diagnostic CAD algorithm may reduce the numbers of ICAs not followed by revascularization. The Dan-NICAD 2 study has 3 equivalent main aims: (1) To examine the diagnostic precision of a sound-based diagnostic algorithm, The CADScor®System (Acarix A/S, Denmark), in patients with a low to intermediate pre-test risk of CAD referred to a primary examination by coronary CTA. We hypothesize that the CADScor®System provides better stratification prior to coronary CTA than clinical risk stratification scores alone. (2) To compare the diagnostic accuracy of 3T cardiac magnetic resonance imaging (3T CMRI), 82rubidium positron emission tomography (82Rb-PET), and CT-derived fractional flow reserve (FFRCT) in patients where obstructive CAD cannot be ruled out by coronary CTA using ICA fractional flow reserve (FFR) as reference standard. (3) To compare the diagnostic performance of quantitative flow ratio (QFR) and ICA-FFR in patients with low to intermediate pre-test probability of CAD using 82Rb-PET as reference standard. METHODS: Dan-NICAD 2 is a prospective, multicenter, cross-sectional study including approximately 2,000 patients with low to intermediate pre-test probability of CAD and without previous history of CAD. Patients are referred to coronary CTA because of symptoms suggestive of CAD, as evaluated by a cardiologist. Patient interviews, sound recordings, and blood samples are obtained in connection with the coronary CTA. If coronary CTA does not rule out obstructive CAD, patients will be examined by 3T CMRI 82Rb-PET, FFRCT, ICA, and FFR. Reference standard is ICA-FFR. Obstructive CAD is defined as an FFR ≤0.80 or as high-grade stenosis (>90% diameter stenosis) by visual assessment. Diagnostic performance will be evaluated as sensitivity, specificity, predictive values, likelihood ratios, calibration, and discrimination. Enrolment started January 2018 and is expected to be completed by June 2020. Patients are followed for 10 years after inclusion. DISCUSSION: The results of the Dan-NICAD 2 study are expected to contribute to the improvement of diagnostic strategies for patients suspected of CAD in 3 different steps: risk stratification prior to coronary CTA, diagnostic strategy after coronary CTA, and invasive wireless QFR analysis as an alternative to ICA-FFR.

Engineering the biological conversion of methanol to specialty chemicals in Escherichia coli.

Methanol is an attractive substrate for biological production of chemicals and fuels. Engineering methylotrophic Escherichia coli as a platform organism for converting methanol to metabolites is desirable. Prior efforts to engineer methylotrophic E. coli were limited by methanol dehydrogenases (Mdhs) with unfavorable enzyme kinetics. We engineered E. coli to utilize methanol using a superior NAD-dependent Mdh from Bacillus stearothermophilus and ribulose monophosphate (RuMP) pathway enzymes from B. methanolicus. Using 13C-labeling, we demonstrate this E. coli strain converts methanol into biomass components. For example, the key TCA cycle intermediates, succinate and malate, exhibit labeling up to 39%, while the lower glycolytic intermediate, 3-phosphoglycerate, up to 53%. Multiple carbons are labeled for each compound, demonstrating a cycling RuMP pathway for methanol assimilation to support growth. By incorporating the pathway to synthesize the flavanone naringenin, we demonstrate the first example of in vivo conversion of methanol into a specialty chemical in E. coli.

Frapbot: An open-source application for FRAP data.

We introduce Frapbot, a free-of-charge open source software web application written in R, which provides manual and automated analyses of fluorescence recovery after photobleaching (FRAP) datasets. For automated operation, starting from data tables containing columns of time-dependent intensity values for various regions of interests within the images, a pattern recognition algorithm recognizes the relevant columns and identifies the presence or absence of prebleach values and the time point of photobleaching. Raw data, residuals, normalization, and boxplots indicating the distribution of half times of recovery (t1/2 ) of all uploaded files are visualized instantly in a batch-wise manner using a variety of user-definable fitting options. The fitted results are provided as .zip file, which contains .csv formatted output tables. Alternatively, the user can manually control any of the options described earlier. © 2017 International Society for Advancement of Cytometry.

Identification of Short Hydrophobic Cell-Penetrating Peptides for Cytosolic Peptide Delivery by Rational Design.

Cell-penetrating peptides (CPPs) enhance the cellular uptake of membrane-impermeable molecules. Most CPPs are highly cationic, potentially increasing the risk of toxic side effects and leading to accumulation in organs such as the liver. As a consequence, there is an unmet need for less cationic CPPs. However, design principles for effective CPPs are still missing. Here, we demonstrate a design principle based on a classification of peptides according to accumulated side-chain polarity and hydrophobicity. We show that in comparison to randomly selected peptides, CPPs cover a distinct parameter space. We designed peptides of only six to nine amino acids with a maximum of three positive charges covering this property space. All peptides were tested for cellular uptake and subcellular distribution. Following an initial round of screening we enriched the collection with short and hydrophobic peptides and introduced d-amino acid substitutions and lactam bridges which increased cell uptake, in particular for long-term incubation. Using a GFP complementation assay, for the most active peptides we demonstrate cytosolic delivery of a biologically active cargo peptide.

Minimal T-wave representation and its use in the assessment of drug arrhythmogenicity.

BACKGROUND: Recently, numerous models and techniques have been developed for analyzing and extracting features from the T wave which could be used as biomarkers for drug-induced abnormalities. The majority of these techniques and algorithms use features that determine readily apparent characteristics of the T wave, such as duration, area, amplitude, and slopes. METHODS: In the present work the T wave was down-sampled to a minimal rate, such that a good reconstruction was still possible. The entire T wave was then used as a feature vector to assess drug-induced repolarization effects. The ability of the samples or combinations of samples obtained from the minimal T-wave representation to correctly classify a group of subjects before and after receiving d,l-sotalol 160 mg and 320 mg was evaluated using a linear discriminant analysis (LDA). RESULTS: The results showed that a combination of eight samples from the minimal T-wave representation can be used to identify normal from abnormal repolarization significantly better compared to the heart rate-corrected QT interval (QTc). It was further indicated that the interval from the peak of the T wave to the end of the T wave (Tpe) becomes relatively shorter after IKr inhibition by d,l-sotalol and that the most pronounced repolarization changes were present in the ascending segment of the minimal T-wave representation. CONCLUSIONS: The minimal T-wave representation can potentially be used as a new tool to identify normal from abnormal repolarization in drug safety studies.

The stoichiometry of peptide-heparan sulfate binding as a determinant of uptake efficiency of cell-penetrating peptides.

Binding to negatively charged heparan sulfates (HS) at the cell surface is considered the first step in the internalization of cationic cell-penetrating peptides (CPPs). However, little is known about the relation of the characteristics of the HS-CPP interaction such as affinity, stoichiometry, and clustering with uptake. In this study, we investigated a collection of mutants of a cyclic CPP derived from human lactoferrin with respect to HS binding and uptake. The thermodynamic parameters of HS binding were determined by isothermal titration calorimetry, clustering of HS was investigated by dynamic light scattering, and cellular uptake by flow cytometry and confocal microscopy. Whereas mutations of non-arginine amino acids that are conserved across lactoferrins of different mammalia only had a minor effect on uptake efficiency, changes in the number of arginine residues influenced the uptake significantly. In general, introduction of arginine residues and cyclization improved the HS affinity and the ability to cluster HS. In particular, there was a strong negative correlation between stoichiometry and uptake, indicating that crosslinking of HS is the driving force for the uptake of arginine-rich CPPs. Using glycan microarrays presenting a collection of synthetic HS, we show that a minimal chain length of HS is required for peptide binding.

Detecting Cytosolic Peptide Delivery with the GFP Complementation Assay in the Low Micromolar Range.

Transfection of cells with a plasmid encoding for the first ten strands of the GFP protein (GFP1-10) provides the means to detect cytosolic peptide import at low micromolar concentrations. Cytosolic import of the eleventh strand of the GFP protein either by electroporation or by cell-penetrating peptide-mediated import leads to formation of the full-length GFP protein and fluorescence. An increase in sensitivity is achieved through structural modifications of the peptide and the expression of GFP1-10 as a fusion protein with mCherry.

Tooth autotransplantation: an overview and case study.

It is not uncommon for children or young adults to have congenitally missing teeth or early loss of teeth from trauma or caries. The restorative options are typically bridges, implants, and removable appliances. Often overlooked and misunderstood, another treatment option exists in autotransplantation, where a tooth is moved from one site to another in the same individual. Autotransplantation is well studied and has predictable results comparable to implants, with reported success rates often greater than 90%. This article will provide an overview of autotransplantation, its indications, advantages, complications, and treatment considerations, along with a case of a third molar autotransplant that will serve to highlight these points.

DARE-ISC model for prediction of 1-year ischaemic stroke risk in the general population and atrial fibrillation patients: a Danish nationwide cohort study.

OBJECTIVES: To develop a risk assessment model (DAnish REgister Ischaemic Stroke Classifier, DARE-ISC) for predicting 1-year primary ischaemic stroke/systemic embolism (SE) in the general population. Secondly, to validate the accuracy DARE-ISC in atrial fibrillation (AF) patients where well-established models and risk scores exist. DESIGN: Retrospective cohort study. DARE-ISC was developed using gradient boosting decision trees with information from 375 covariates including baseline information on relevant diagnoses, demographic characteristics, registered health-services, lifestyle-related covariates, hereditary stroke components, drug prescriptions and stress proxies. SETTING: Danish nationwide registries. PARTICIPANTS: All Danish individuals aged ≥18 from 2010 to 2017 (n=35 519 348 person-years). The model was trained on the 2010-2016 cohorts with validation in the 2017 cohort. PRIMARY AND SECONDARY OUTCOME MEASURES: Model optimisation and validation were performed through comparison of the area under the receiver operating characteristic curve (AUC) and average precision scores. Additionally, the relative importance of the model covariates was derived using SHAP values. RESULTS: DARE-ISC had an AUC (95% CI) of 0.874 (0.871 to 0.876) in the general population. In AF patients, DARE-ISC was superior to the GARFIELD-AF risk model and CHA2DS2-VASc score with AUC of 0.779 (95% CI 0.75 to 0.806), 0.704 (95% CI 0.674 to 0.732) and 0.681 (95% CI 0.652 to 0.709), respectively. Furthermore, in AF patients, DARE-ISC had an average threefold and fourfold higher ratio of correctly identified strokes compared with the GARFIELD-AF risk model and CHA2DS2-VASc score, as indicated by average precision scores of 0.119, 0.041 and 0.034, respectively. CONCLUSIONS: DARE-ISC had a very high stroke prediction accuracy in the general population and was superior to the GARFIELD-AF risk model and CHA2DS2-VASc score for predicting ischaemic stroke/SE in AF patients.

Exercise electrocardiography for pre-test assessment of the likelihood of coronary artery disease.

OBJECTIVES: To develop a tool including exercise electrocardiography (ExECG) for patient-specific clinical likelihood estimation of patients with suspected obstructive coronary artery disease (CAD). METHODS: An ExECG-weighted clinical likelihood (ExECG-CL) model was developed in a training cohort of patients with suspected obstructive CAD undergoing ExECG. Next, the ExECG-CL model was applied in a CAD validation cohort undergoing ExECG and clinically driven invasive coronary angiography and a prognosis validation cohort and compared with the risk factor-weighted clinical likelihood (RF-CL) model for obstructive CAD discrimination and prognostication, respectively.In the CAD validation cohort, obstructive CAD was defined as >50% diameter stenosis on invasive coronary angiography. For prognosis, the endpoint was non-fatal myocardial infarction and death. RESULTS: The training cohort consisted of 1214 patients (mean age 57 years, 57% males). In the CAD (N=408; mean age 55 years, 53% males) and prognosis validation (N=3283; mean age 57 years, 57% males) cohorts, 11.8% patients had obstructive CAD and 4.4% met the endpoint. In the CAD validation cohort, discrimination of obstructive CAD was similar between the ExECG-CL and RF-CL models: area under the receiver-operating characteristic curves 83.1% (95% CIs 77.5% to 88.7%) versus 80.7% (95% CI 74.6% to 86.8%), p=0.14. In the ExECG-CL model, more patients had very low (≤5%) clinical likelihood of obstructive CAD compared with the RF-CL (42.2% vs 36.0%, p<0.01) where obstructive CAD prevalence and event risk remained low. CONCLUSIONS: ExECG incorporated into a clinical likelihood model improves reclassification of patients to a very low clinical likelihood group with very low prevalence of obstructive CAD and favourable prognosis.

Seismocardiography and echocardiography: the correlation in the systolic complex.

Aim. This study aimed to investigate the correlation between seismocardiographic and echocardiographic systolic variables and whether a decrease in preload could be detected by the seismocardiography (SCG).Methods. This study included a total of 34 subjects. SCG and electrocardiography were recorded simultaneously followed by echocardiography (echo) in both supine and 30◦head-up tilted position. The SCG signals was segmented into individual heartbeats and systolic fiducial points were defined using a detection algorithm. Statistical analysis included correlation coefficient calculations and paired sample tests.Results. SCG was able to measure a decrease in preload by almost all of the examined systolic SCG variables. It was possible to correlate certain echo variables to SCG time intervals, amplitudes, and peak to peak intervals. Also, changes between supineand tilted position of some SCG variables were possible to correlate to changes in echo variables. LVET, IVCT, S', strain, SR, SV, and LVEF were significantly correlated to relevant SCG variables.Conclusion. This study showed a moderate correlation, between systolic echo and systolic SCG variables. Additionally, systolic SCG variables were able to detect a decrease in preload.

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

BACKGROUND: The 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. METHODS: Consecutive 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. RESULTS: HRPs 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. CONCLUSIONS: In patients with suspected CAD, incorporating genetic data with either clinical or proteomic data improves the prediction of high-risk plaque presence. TRIAL REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT02264717 (September 2014).

Recombinant manufacturing of multispecies biolubricants.

Lubricin, a lubricating glycoprotein abundant in synovial fluid, forms a low-friction brush polymer interface in tissues exposed to sliding motion including joints, tendon sheaths, and the surface of the eye. Despite its therapeutic potential in diseases such as osteoarthritis and dry eye disease, there are few sources available. Through rational design, we developed a series of recombinant lubricin analogs that utilize the species-specific tissue-binding domains at the N- and C-termini to increase biocompatibility while replacing the central mucin domain with an engineered variant that retains the lubricating properties of native lubricin. In this study, we demonstrate the tissue binding capacity of our engineered lubricin product and its retention in the joint space of rats. Next, we present a new bioprocess chain that utilizes a human-derived cell line to produce O-glycosylation consistent with that of native lubricin and a purification strategy that capitalizes on the positively charged, hydrophobic N- and C-terminal domains. The bioprocess chain is demonstrated at 10 L scale in industry-standard equipment utilizing commonly available ion exchange, hydrophobic interaction and size exclusion chromatography resins. Finally, we confirmed the purity and lubricating properties of the recombinant biolubricant. The biomolecular engineering and bioprocessing strategies presented here are an effective means of lubricin production and could have broad applications to the study of mucins in general.

Calcium Scoring Improves Clinical Management in Patients With Low Clinical Likelihood of Coronary Artery Disease.

BACKGROUND: Coronary artery calcium scoring (CACS) improves management of chest pain patients. However, it is unknown whether the benefit of CACS is dependent on the clinical likelihood (CL). OBJECTIVES: This study aims to investigate for which patients CACS has the greatest benefit when added to a CL model. METHODS: Based on data from a clinical database, the CL of obstructive coronary artery disease (CAD) was calculated for 39,837 patients referred for cardiac imaging due to symptoms suggestive of obstructive CAD. Patients were categorized according to the risk factor-weighted (RF-CL) model (very low, ≤5%; low, >5 to ≤15%; moderate >15 to ≤50%; high, >50%). CL was then recalculated incorporating the CACS result (CACS-CL). Reclassification rates and the number needed to test with CACS to reclassify patients were calculated and validated in 3 independent cohorts (n = 9,635). RESULTS: In total, 15,358 (39%) patients were down- or upclassified after including CACS. Reclassification rates were 8%, 75%, 53%, and 30% in the very low, low, moderate, and high RF-CL categories, respectively. Reclassification to very low CACS-CL occurred in 48% of reclassified patients. The number needed to test to reclassify 1 patient from low RF-CL to very low CACS-CL was 2.1 with consistency across age, sex, and cohorts. CACS-CL correlated better to obstructive CAD prevalence than RF-CL. CONCLUSIONS: Added to an RF-CL model for obstructive CAD, CACS identifies more patients unlikely to benefit from further testing. The number needed to test with CACS to reclassify patients depends on the pretest RF-CL and is lowest in patients with low (>5% to ≤15%) likelihood of CAD.