Diagnostic yield of coronary angiography in patients with acute chest pain: role of noninvasive test

Objectives

This study investigated the diagnostic yield of invasive coronary angiography (CAG) and the impact of noninvasive test (NIV) in patients presented to emergency department (ED) with acute chest pain.

Methods

Patients 50 years or older who visited ED with acute chest pain and underwent CAG were identified retrospectively. Those with ischemic electrocardiogram, elevated cardiac enzyme, known coronary artery disease (CAD), history of cardiac surgery, renal failure, or allergy to radiocontrast were excluded. Diagnostic yields of CAG to detect significant CAD or differentiate the need for revascularization were analyzed according to whether NIV was performed and its result.

Results

Among the total 375 consecutive patients, significant CAD was observed in 244 (65.1%). Diagnostic yields of CAG were higher in patients who underwent NIV before CAG, but the discriminative effect was modest (59.7% vs 70.7% [P = .026] for detection of CAD; 45.0% vs 50.5% [P = .285] for revascularization). Positive results of NIV were significantly associated with the presence of CAD and the need for revascularization, when compared with patients without NIV or patients with negative results (P < .001, respectively).

Conclusion

The diagnostic yield of CAG was only 65% in low- to intermediate-risk ED patients with acute chest pain. Performing of NIV provided only modest improvement in diagnostic yield of CAG. The unexpectedly low diagnostic yield might be attributable to the underuse of NIV and misinterpretation of physicians. We suggest the use of NIV as a gatekeeper to discriminate patients who require CAG and/or revascularization, and for this, better risk stratification and appropriate application of NIV are required.     

Manassantin A and B from Saururus chinensis inhibiting cellular melanin production

Hyperpigmentation disorders such as freckles and senile lentigines in the skin are associated with abnormal accumulation of melanin pigments. In this study, two lignan constituents were isolated from Saururus chinensis Baill (Saururaceae) as inhibitors of cellular melanin production by bioassay‐guided fractionations. The active constituents were manassantin A and B that dose‐dependently inhibited melanin production in α‐melanocyte stimulating hormone (α‐MSH)‐activated melanoma B16 cells with IC₅₀ values of 13 nm and 8 nm , respectively. Arbutin as a positive control exhibited an IC₅₀ value of 96 µm on α‐MSH‐induced melanin production. Further, manassantin A inhibited forskolin‐ or 3‐isobutyl‐1‐methylxanthine (IBMX)‐induced melanin production with IC₅₀ values of 14 nm or 12 nm , respectively. Manassantin A decreased cellular amounts of IBMX‐inducible tyrosinase protein but could not affect the catalytic activity of cell‐free tyrosinase, a key enzyme in the biosynthetic pathway of melanin pigments. Finally, this study could provide a pharmacological potential of S. chinensis in hyperpigmentation disorders. Copyright © 2009 John Wiley & Sons, Ltd.     

Fly Ash-Added,Seawater-Mixed Pervious Concrete: Compressive Strength,Permeability, and Phosphorus Removal

A mix proportion of off-spec fly ash (FA)-added, seawater-mixed pervious concrete (SMPC) was optimized for compressive strength and permeability and then the optimized SMPC was tested for the rate and extent of aqueous phosphorus removal. An optimum mix proportion was obtained to attain the percentages (% wt.) of FA-to-binder at 15.0%, nano SiO₂ (NS)-to-FA at 3.0%, liquid-to-binder at 0.338, and water reducer-to-binder at 0.18% from which a 7-day compressive strength of 14.0 MPa and a permeability of 5.5 mm/s were predicted. A long-term maximum compressive strength was measured to be ~16 MPa for both the optimized SMPC and the control ordinary pervious concrete (Control PC). The phosphorus removal was favorable for both the optimized SMPC and the Control PC based on the dimensionless Freundlich parameter (1/n). Both the optimized SMPC and Control PC had a first-order phosphorus removal constant of ~0.03 h⁻¹. The optimized SMPC had a slightly lower capacity of phosphorus removal than the Control PC based on the Freundlich constant, K_f (mg^1−1/n kg⁻¹ L^1/n): 15.72 for the optimized SMPC vs. 16.63 for Control. This study demonstrates a cleaner production and application of off-spec FA-added, seawater-mixed pervious concrete to simultaneously attain water, waste, and concrete sustainability.     

Deep Learning-Assisted Diagnosis of Pediatric Skull Fractures on Plain Radiographs

ObjectiveTo develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children.Materials and MethodsThis retrospective multi-center study consisted of a development dataset acquired from two hospitals (n = 149 and 264) and an external test set (n = 95) from a third hospital. Datasets included children with head trauma who underwent both skull radiography and cranial computed tomography (CT). The development dataset was split into training, tuning, and internal test sets in a ratio of 7:1:2. The reference standard for skull fracture was cranial CT. Two radiology residents, a pediatric radiologist, and two emergency physicians participated in a two-session observer study on an external test set with and without AI assistance. We obtained the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity along with their 95% confidence intervals (CIs).ResultsThe AI model showed an AUROC of 0.922 (95% CI, 0.842–0.969) in the internal test set and 0.870 (95% CI, 0.785–0.930) in the external test set. The model had a sensitivity of 81.1% (95% CI, 64.8%–92.0%) and specificity of 91.3% (95% CI, 79.2%–97.6%) for the internal test set and 78.9% (95% CI, 54.4%–93.9%) and 88.2% (95% CI, 78.7%–94.4%), respectively, for the external test set. With the model’s assistance, significant AUROC improvement was observed in radiology residents (pooled results) and emergency physicians (pooled results) with the difference from reading without AI assistance of 0.094 (95% CI, 0.020–0.168; p = 0.012) and 0.069 (95% CI, 0.002–0.136; p = 0.043), respectively, but not in the pediatric radiologist with the difference of 0.008 (95% CI, -0.074–0.090; p = 0.850).ConclusionA deep learning-based AI model improved the performance of inexperienced radiologists and emergency physicians in diagnosing pediatric skull fractures on plain radiographs.     

Evaluation of an automated superimposition method based on multiple landmarks for growing patients

ObjectivesTo determine if an automated superimposition method using six landmarks (Sella, Nasion, Porion, Orbitale, Basion, and Pterygoid) would be more suitable than the traditional Sella-Nasion (SN) method to evaluate growth changes.Materials and MethodsSerial lateral cephalograms at an average interval of 2.7 years were taken on 268 growing children who had not undergone orthodontic treatment. The T1 and T2 lateral images were manually traced. Three different superimposition methods: Björk''s structural method, conventional SN, and the multiple landmark (ML) superimposition methods were applied. Bjork''s structural method was used as the gold standard. Comparisons among the superimposition methods were carried out by measuring the linear distances between Anterior Nasal Spine, point A, point B, and Pogonion using each superimposition method. Multiple linear regression analysis was performed to identify factors that could affect the accuracy of the superimpositions.ResultsThe ML superimposition method demonstrated smaller differences from Björk''s method than the conventional SN method did. Greater differences among the cephalometric landmarks tested resulted when: the designated point was farther from the cranial base, the T1 age was older, and the more time elapsed between T1 and T2.ConclusionsFrom the results of this study in growing patients, the ML superimposition method seems to be more similar to Björk''s structural method than the SN superimposition method. A major advantage of the ML method is likely to be that it can be applied automatically and may be just as reliable as manual superimposition methods.