Evaluation of a new patient safety educational programme to reduce adverse events by encouraging staff to speak up: application of the trigger tool methodology.

BACKGROUND: Poor communication contributes to adverse events (AEs). In our hospital, following an experience of a fatal incident in 2014, we developed an educational programme aimed at improving communication for better teamwork that led to a reduction in AEs. METHODS: We developed and implemented an intervention bundle comprising external investigation committee reviews, the establishment of a working group (WG), standards and emergency response guidelines, as well as educational programmes and tools. To determine the effectiveness of the educational programmes, we measured communication abilities among doctors and nurses by administering psychological scales focused on their confidence in speaking up. Furthermore, we applied the trigger tool methodology in a retrospective study to determine if our interventions had reduced AEs. RESULTS: The nurses' scores for 'perceived barriers to speaking up' and 'negative attitude toward voicing opinions in the healthcare team' decreased significantly after the training from 3.20 to 3.00 and from 2.47 to 2.29 points, respectively. The junior doctors' scores for the same items also decreased significantly after the training from 3.34 to 2.51 and from 2.42 to 2.11 points, respectively. The number of AEs was 32.1 (median) before the WG, 39.9 (median) before the general training, 22.2 (median) after the general training and 18.4 (median) after implementing the leadership educational programmes. During the intervention period the hospital's incident reports per employee kept increasing. CONCLUSION: Our new educational programmes improved junior doctors and nurses' perceptions of speaking up. We speculated that our intervention may have improved staff communication, which in turn may have led to a reduction in AEs and a sustained increase in incident reports per employee.

Quantification of pulmonary perfusion using LSIM-CT correlates with pulmonary hemodynamics in patients with CTEPD.

Background: Lung subtraction iodine mapping (LSIM)-CT is a clinically useful technique that can visualize pulmonary mal-perfusion in patients with chronic thromboembolic pulmonary disease (CTEPD). However, little is known about the associations of LSIM images with hemodynamic parameters of patients with CTEPD. This study investigates a parameter of LSIM images associated with mean pulmonary arterial pressure (mPAP) and validates the association between pulmonary vascular resistance, right atrial pressure, cardiac index, and exercise capacity in patients with CTEPD. Methods: This single-center, prospective, observational study involved 30 patients diagnosed with CTEPD using lung perfusion scintigraphy. To examine the correlation of decreased pulmonary perfusion area (DPA) with mPAP, areas with 0-10, 0-15, 0-20, and 0-30 HU in lung subtraction images were adopted in statistical analysis. The DPA to total lung volume ratio (DPA ratio, %) was calculated as the ratio of each DPA volume to the total lung volume. To assess the correlation between DPA ratios of 0-10, 0-15, 0-20, and 0-30 HU and mPAP, Spearman's rank correlation coefficient was used. Results: The DPA ratio of 0-10 HU had the most preferable correlation with mPAP than DPA ratios of 0-15, 0-20, and 0-30 HU (ρ = 0.440, P = 0.015). The DPA ratio of 0-10 HU significantly correlates with pulmonary vascular resistance (ρ = 0.445, P = 0.015). The receiver operating characteristic curve analysis indicated that the best cutoff value of the DPA ratio of 0-10 HU for the prediction of an mPAP of ≥30 mmHg was 8.5% (AUC, 0.773; 95% CI, 0.572-0.974; sensitivity, 83.3%; specificity, 75.0%). Multivariate linear regression analysis, which was adjusted for the main pulmonary arterial to ascending aortic diameter ratio and right ventricular to left ventricular diameter ratio, indicated that the DPA ratio of 0-10 HU was independently and significantly associated with mPAP (B = 89.7; 95% CI, 46.3-133.1, P < 0.001). Conclusion: The DPA ratio calculated using LSIM-CT is possibly useful for estimating the hemodynamic status in patients with CTEPD.

Artificial intelligence-based model to classify cardiac functions from chest radiographs: a multi-institutional, retrospective model development and validation study.

BACKGROUND: Chest radiography is a common and widely available examination. Although cardiovascular structures-such as cardiac shadows and vessels-are visible on chest radiographs, the ability of these radiographs to estimate cardiac function and valvular disease is poorly understood. Using datasets from multiple institutions, we aimed to develop and validate a deep-learning model to simultaneously detect valvular disease and cardiac functions from chest radiographs. METHODS: In this model development and validation study, we trained, validated, and externally tested a deep learning-based model to classify left ventricular ejection fraction, tricuspid regurgitant velocity, mitral regurgitation, aortic stenosis, aortic regurgitation, mitral stenosis, tricuspid regurgitation, pulmonary regurgitation, and inferior vena cava dilation from chest radiographs. The chest radiographs and associated echocardiograms were collected from four institutions between April 1, 2013, and Dec 31, 2021: we used data from three sites (Osaka Metropolitan University Hospital, Osaka, Japan; Habikino Medical Center, Habikino, Japan; and Morimoto Hospital, Osaka, Japan) for training, validation, and internal testing, and data from one site (Kashiwara Municipal Hospital, Kashiwara, Japan) for external testing. We evaluated the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. FINDINGS: We included 22 551 radiographs associated with 22 551 echocardiograms obtained from 16 946 patients. The external test dataset featured 3311 radiographs from 2617 patients with a mean age of 72 years [SD 15], of whom 49·8% were male and 50·2% were female. The AUCs, accuracy, sensitivity, and specificity for this dataset were 0·92 (95% CI 0·90-0·95), 86% (85-87), 82% (75-87), and 86% (85-88) for classifying the left ventricular ejection fraction at a 40% cutoff, 0·85 (0·83-0·87), 75% (73-76), 83% (80-87), and 73% (71-75) for classifying the tricuspid regurgitant velocity at a 2·8 m/s cutoff, 0·89 (0·86-0·92), 85% (84-86), 82% (76-87), and 85% (84-86) for classifying mitral regurgitation at the none-mild versus moderate-severe cutoff, 0·83 (0·78-0·88), 73% (71-74), 79% (69-87), and 72% (71-74) for classifying aortic stenosis, 0·83 (0·79-0·87), 68% (67-70), 88% (81-92), and 67% (66-69) for classifying aortic regurgitation, 0·86 (0·67-1·00), 90% (89-91), 83% (36-100), and 90% (89-91) for classifying mitral stenosis, 0·92 (0·89-0·94), 83% (82-85), 87% (83-91), and 83% (82-84) for classifying tricuspid regurgitation, 0·86 (0·82-0·90), 69% (68-71), 91% (84-95), and 68% (67-70) for classifying pulmonary regurgitation, and 0·85 (0·81-0·89), 86% (85-88), 73% (65-81), and 87% (86-88) for classifying inferior vena cava dilation. INTERPRETATION: The deep learning-based model can accurately classify cardiac functions and valvular heart diseases using information from digital chest radiographs. This model can classify values typically obtained from echocardiography in a fraction of the time, with low system requirements and the potential to be continuously available in areas where echocardiography specialists are scarce or absent. FUNDING: None.

A case of hypertrophic cardiomyopathy with right ventricular outflow tract and left midventricular obstruction.

We describe a case of a 59-year-old woman with hypertrophic cardiomyopathy who remained with right ventricular outflow tract obstruction after the pressure gradient in the left midventricle was resolved by a drug with a negative inotropic effect. The patient was diagnosed with hypertrophic cardiomyopathy 30 years previously and was only on low-dose beta-blocker therapy. She presented at our hospital with suspected exacerbation of heart failure because of the development and exacerbation of dyspnea and chest tightness. Transthoracic echocardiography showed an accelerated blood flow of 3 m/s in the middle of the left ventricle; thus, she was started on cibenzoline, a drug with a negative inotropic effect. After admission, intracardiac pressure measurement showed no pressure gradient in the left chamber. However, there was a pressure gradient of 18 mmHg between the apex of the right ventricle and the right ventricular outflow tract, and right ventricular outflow tract obstruction was confirmed on cardiac magnetic resonance imaging. We decided to reinforce the negative inotropic effect by adding bisoprolol, and the subjective symptoms and auscultatory systolic murmur were eliminated 2 months later. Learning objective: Hypertrophy of the right ventricular myocardium can occur in patients with hypertrophic cardiomyopathy (HCM). However, right ventricular outflow tract obstruction remains a rare finding in patients with HCM, despite the presence of morphological abnormalities such as right ventricular hypertrophy. In patients with HCM, obstruction of the right ventricle should be considered if the symptoms and auscultatory findings do not match the left ventricular imaging findings.

Risk factors of pacing-induced cardiomyopathy-Insights from lead position.

There was no significant difference in the incidence of pacing-induced cardiomyopathy between right ventricular apex pacing group and OpenCurlyQuote;true CloseCurlyQuote; mid-right ventricular septum pacing group. The preoperative left ventricular end-systolic diameter and paced QRS duration were independent predictors of PICM.

Development and Validation of Artificial Intelligence-based Method for Diagnosis of Mitral Regurgitation from Chest Radiographs.

Purpose: To develop an artificial intelligence-based model to detect mitral regurgitation on chest radiographs. Materials and Methods: This retrospective study included echocardiographs and associated chest radiographs consecutively collected at a single institution between July 2016 and May 2019. Associated radiographs were those obtained within 30 days of echocardiography. These radiographs were labeled as positive or negative for mitral regurgitation on the basis of the echocardiographic reports and were divided into training, validation, and test datasets. An artificial intelligence model was developed by using the training dataset and was tuned by using the validation dataset. To evaluate the model, the area under the curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were assessed by using the test dataset. Results: This study included a total of 10 367 images from 5270 patients. The training dataset included 8240 images (4216 patients), the validation dataset included 1073 images (527 patients), and the test dataset included 1054 images (527 patients). The area under the curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value in the test dataset were 0.80 (95% CI: 0.77, 0.82), 71% (95% CI: 67, 75), 74% (95% CI: 70, 77), 73% (95% CI: 70, 75), 68% (95% CI: 64, 72), and 77% (95% CI: 73, 80), respectively. Conclusion: The developed deep learning-based artificial intelligence model may possibly differentiate patients with and without mitral regurgitation by using chest radiographs.Keywords: Computer-aided Diagnosis (CAD), Cardiac, Heart, Valves, Supervised Learning, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2022.

Artificial intelligence-based detection of atrial fibrillation from chest radiographs.

OBJECTIVE: The purpose of this study was to develop an artificial intelligence (AI)-based model to detect features of atrial fibrillation (AF) on chest radiographs. METHODS: This retrospective study included consecutively collected chest radiographs of patients who had echocardiography at our institution from July 2016 to May 2019. Eligible radiographs had been acquired within 30 days of the echocardiography. These radiographs were labeled as AF-positive or AF-negative based on the associated electronic medical records; then, each patient was randomly divided into training, validation, and test datasets in an 8:1:1 ratio. A deep learning-based model to classify radiographs as with or without AF was trained on the training dataset, tuned with the validation dataset, and evaluated with the test dataset. RESULTS: The training dataset included 11,105 images (5637 patients; 3145 male, mean age ± standard deviation, 68 ± 14 years), the validation dataset included 1388 images (704 patients, 397 male, 67 ± 14 years), and the test dataset included 1375 images (706 patients, 395 male, 68 ± 15 years). Applying the model to the validation and test datasets gave a respective area under the curve of 0.81 (95% confidence interval, 0.78-0.85) and 0.80 (0.76-0.84), sensitivity of 0.76 (0.70-0.81) and 0.70 (0.64-0.76), specificity of 0.75 (0.72-0.77) and 0.74 (0.72-0.77), and accuracy of 0.75 (0.72-0.77) and 0.74 (0.71-0.76). CONCLUSION: Our AI can identify AF on chest radiographs, which provides a new way for radiologists to infer AF. KEY POINTS: • A deep learning-based model was trained to detect atrial fibrillation in chest radiographs, showing that there are indicators of atrial fibrillation visible even on static images. • The validation and test datasets each gave a solid performance with area under the curve, sensitivity, and specificity of 0.81, 0.76, and 0.75, respectively, for the validation dataset, and 0.80, 0.70, and 0.74, respectively, for the test dataset. • The saliency maps highlighted anatomical areas consistent with those reported for atrial fibrillation on chest radiographs, such as the atria.

Epicardial adipose tissue volume is an independent predictor of left ventricular reverse remodeling in patients with non-ischemic cardiomyopathy.

BACKGROUND: In some patients with non-ischemic cardiomyopathy, left ventricular (LV) contraction is improved by optimal medical therapy, leading to LV reverse remodeling (RR). Patients with heart failure with improved ejection fraction and LVRR have a good prognosis, but the factors that predict RR are not fully understood. The relationship between body composition and cardiovascular disease has been reported. The present study aimed to assess the clinical predictors of LVRR in association with body composition. METHODS: We recruited patients who were diagnosed with non-ischemic cardiomyopathy between September 2017 and January 2020. Finally, 89 patients with a reduced LV ejection fraction were enrolled in this prospective study. Body composition, including ectopic fat, was measured in all patients using computed tomography. Echocardiography was performed 6 months after enrollment to evaluate LVRR. RESULTS: LVRR was observed in 39 patients (43.8%) after 6 months. In terms of the demographic findings, epicardial adipose tissue volume was greater in the LVRR group than in the non-LVRR group (135.2 cm3 [SD 128.4 cm3] vs. 88.9 cm3 [SD 54.6 cm3]; p = 0.040). The Kaplan-Meier analysis demonstrated that adverse cardiac events were significantly less frequent in the LVRR group than in the non-LVRR group (log-rank test, p = 0.013). The multivariate logistic regression analysis identified epicardial adipose tissue volume as an independent predictor of LVRR (odds ratio [OR]: 1.010, 95% confidence interval [CI]: 1.001-1.01; p = 0.036). CONCLUSION: Epicardial adipose tissue volume is an independent predictor of LVRR in patients with non-ischemic cardiomyopathy.

Thigh Intramuscular Fat on Prognosis of Patients With Nonischemic Cardiomyopathy.

Skeletal muscle atrophy is an independent prognostic predictor for patients with chronic heart failure, and the concept of sarcopenia is drawing attention. Furthermore, the importance of not only muscle mass but also ectopic fat has been pointed out. However, there is a lack of consensus on the implications of ectopic fat for the prognosis in patients with nonischemic cardiomyopathy. We investigated whether ectopic fat in the thigh affects the prognosis of nonischemic cardiomyopathy. This prospective study recruited 145 patients diagnosed with nonischemic cardiomyopathy between September 2017 and January 2020. Finally, 93 patients with a reduced ejection fraction were enrolled. The clinical end points were cardiovascular death or unexpected rehospitalization because of a cardiac event. Using computed tomography, the percentage of intramuscular fat (%IMF) in the thigh was measured in all patients. Patients were divided into 2 groups based on the median %IMF. The results of Spearman's correlation coefficient analysis revealed a correlation among %IMF and peak oxygen uptake (Spearman r = -0.221, p = 0.036). Kaplan-Meier analysis results showed significantly higher risk of adverse events in the high %IMF group (log-rank p = 0.013). Multivariate Cox regression analysis results revealed the %IMF as an independent factor for adverse events (hazard ratio 1.361; 95% confidence interval 1.043 to 1.745; p = 0.018). In conclusion, %IMF may have adverse consequences such as increased cardiac events in patients with nonischemic cardiomyopathy with a reduced ejection fraction.

Computed tomography to identify risk factors for left circumflex artery injury during mitral surgery.

OBJECTIVES: Cases in which the left circumflex coronary artery (LCX) runs close to the mitral annulus are considered high risk for LCX injury during mitral surgery. We investigated the anatomical relationship between the LCX and the mitral annulus using 3-dimensional (3D) computed tomography (CT). METHODS: We constructed 3D-CT images of the LCX and the mitral annulus before surgery in 122 patients with mitral regurgitation (MR). We classified coronary dominance by 3D-CT and MR aetiologies (degenerative, atrial functional MR, ventricular functional MR and Barlow's disease) using echocardiography. We detected the point on the mitral annulus closest to the LCX (X point) and measured the minimum distance from the LCX to the mitral annulus (mCAD). We judged whether atrioventricular disjunction existed using CT. We also investigated the factors affecting mCAD and examined how coronary dominance and MR aetiologies relate to the location of the X point. RESULTS: The median mCAD was 4.2 mm (range 0.9-11.4 mm). Considering coronary dominance and MR aetiologies, mCAD was shorter in patients with left coronary dominance and Barlow's disease. The X point mostly existed on the lateral side of the posterior annulus, but it sometimes existed on the medial side. Multiple regression revealed left dominance and mitral annular disjunction as significant factors affecting mCAD (P = 0.01). CONCLUSIONS: The anatomical relationship between the LCX and the mitral annulus can be recognized using superimposed 3D-CT images. This approach is useful to avoid LCX injury in mitral valve surgery since the X point varies between patients.

Angioscopic Ulcerated Plaques in the Femoropopliteal Artery Associated with Impaired Infrapopliteal Runoff.

PURPOSE: To determine the correlation between upstream atherosclerosis in the femoropopliteal arteries, assessed using angioscopy, and impaired infrapopliteal runoff. MATERIALS AND METHODS: Thirty-one patients with peripheral arterial disease who underwent endovascular therapy and angioscopy were prospectively included. Yellow plaque color scores were semiquantitatively determined as 0, 1, 2, or 3. Irregular plaques with rough surfaces, similar to gastric ulcers, were defined as ulcerated plaques (UPs). Angioscopic data were correlated with angiographic runoff scores (ARS). RESULTS: UPs were detected in 74.2% of enrolled diseased legs using angioscopy. Mural thrombi were more commonly observed in the femoropopliteal artery in patients with UPs than in those without UPs (91.3% vs 37.5%, respectively; P = .006) and were frequently found on the UPs (21/23 patients with UPs). Univariate and multivariate linear regression analyses revealed that the presence of UPs was positively and independently associated with a poor ARS and that oral anticoagulant use was independently associated with a preferable ARS (standardized ß = 0.462, P = .004 and standardized ß = -0.411, P = .009, respectively, in the multivariate analysis). CONCLUSIONS: UPs, associated with mural thrombi and diagnosed by angioscopic examination, were demonstrated to be one of the factors associated with poor infrapopliteal runoff.

Kihon checklist is useful for predicting outcomes in patients undergoing transcatheter aortic valve implantation.

BACKGROUND: Frailty is a major risk factor for death and disability following transcatheter aortic valve implantation (TAVI). The Kihon checklist (KCL) is a simple self-reporting yes/no survey consisting of 25 questions and is used as a screening tool to identify frailty in the primary care setting. No clinical studies have focused on frailty calculated by the KCL in the TAVI cohort. We investigated the 3-year prognostic impact of frailty evaluated by the KCL in patients who underwent TAVI. METHODS: This single-center prospective observational study included 280 consecutive patients with symptomatic severe aortic stenosis who underwent TAVI and evaluated pre-procedural physical performance focused on frailty at our institution. We assessed all patients' frailty by the KCL before TAVI, as described previously. We set the primary endpoint as the 3-year all-cause mortality after TAVI. RESULTS: The median patient age was 84 years (interquartile range, 81-87 years), and 31.1% were men. In the receiver operating characteristics curve, there were no significant differences between the KCL and Cardiovascular Health Study frailty index [area under the curve (AUC) 0.625 versus 0.628; p=0.93), KCL and Rockwood Clinical Frailty Scale (AUC 0.625 versus 0.542; p=0.15), and KCL and Short Physical Performance Battery (AUC 0.625 versus 0.612; p=0.91). The first and second tertiles of the total KCL score were 8 and 12, respectively. The multivariate Cox regression model indicated that the total KCL score [hazard ratio (HR), 1.104; 95% confidence interval (CI), 1.034-1.179; p=0.003], presence of diabetes mellitus (HR, 1.993; CI, 1.055-3.766; p=0.03), and presence of liver disease (HR, 3.007; CI, 1.067-8.477; p=0.04) were independently associated with 3-year all-cause mortality. CONCLUSIONS: The KCL is a simple and useful tool for evaluating frailty status and predicting 3-year all-cause mortality in patients undergoing TAVI.

Artificial intelligence-based detection of aortic stenosis from chest radiographs.

Aims: We aimed to develop models to detect aortic stenosis (AS) from chest radiographs-one of the most basic imaging tests-with artificial intelligence. Methods and results: We used 10 433 retrospectively collected digital chest radiographs from 5638 patients to train, validate, and test three deep learning models. Chest radiographs were collected from patients who had also undergone echocardiography at a single institution between July 2016 and May 2019. These were labelled from the corresponding echocardiography assessments as AS-positive or AS-negative. The radiographs were separated on a patient basis into training [8327 images from 4512 patients, mean age 65 ± (standard deviation) 15 years], validation (1041 images from 563 patients, mean age 65 ± 14 years), and test (1065 images from 563 patients, mean age 65 ± 14 years) datasets. The soft voting-based ensemble of the three developed models had the best overall performance for predicting AS with an area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of 0.83 (95% confidence interval 0.77-0.88), 0.78 (0.67-0.86), 0.71 (0.68-0.73), 0.71 (0.68-0.74), 0.18 (0.14-0.23), and 0.97 (0.96-0.98), respectively, in the validation dataset and 0.83 (0.78-0.88), 0.83 (0.74-0.90), 0.69 (0.66-0.72), 0.71 (0.68-0.73), 0.23 (0.19-0.28), and 0.97 (0.96-0.98), respectively, in the test dataset. Conclusion: Deep learning models using chest radiographs have the potential to differentiate between radiographs of patients with and without AS. Lay Summary: We created artificial intelligence (AI) models using deep learning to identify aortic stenosis (AS) from chest radiographs. Three AI models were developed and evaluated with 10 433 retrospectively collected radiographs and labelled from echocardiography reports. The ensemble AI model could detect AS in a test dataset with an area under the receiver operating characteristic curve of 0.83 (95% confidence interval 0.78-0.88). Since chest radiography is a cost-effective and widely available imaging test, our model can provide an additive resource for the detection of AS.

Role of Cardiac Magnetic Resonance (CMR) in Determining Implantable Cardioverter-Defibrillator (ICD) Indication in Acromegalic Cardiomyopathy: A Case Report.

Potentially fatal arrhythmia is one of the causes of premature death in acromegalic cardiomyopathy patients who are not properly treated. Implantable cardioverter-defibrillator (ICD) is one of the most effective and established treatments to prevent sudden cardiac death due to potentially fatal tachyarrhythmia. However, since the indication of ICD changes depending on whether it is ischemic cardiomyopathy or non-ischemic cardiomyopathy, proper diagnosis is important in patients with these diseases. Cardiac magnetic resonance (CMR) is a convenient and useful tool for diagnosing these. Both potentially fatal arrhythmias and coronary artery disease (CAD) are known to be important complications of acromegaly. Herein, we present a case of potentially fatal tachyarrhythmia due to acromegalic cardiomyopathy with the acute coronary syndrome. Furthermore, we mention the usefulness of CMR in the case which is difficult to determine the indication for an ICD.

Increased oxidative stress during exercise predicts poor prognosis in patients with acute decompensated heart failure.

AIMS: Oxidative stress plays an important role in the development and progression of heart failure (HF). Although exercise and oxidative stress are closely related, the effect of acute exercise on reactive oxygen species production and the fluctuation on prognosis are unclear. METHODS AND RESULTS: We enrolled 94 patients who were hospitalized for worsening HF (mean age 68.0 ± 14.5 years old, 63.8% male). The changes in diacron-reactive oxygen metabolites (d-ROM) values, a marker of oxidative stress, before and after a cardiopulmonary exercise test were considered as Δd-ROM. The mean follow-up period was 24 ± 13 months, during which 15 patients had all-cause death or left ventricular assist system implantation. Kaplan-Meier analysis demonstrated that all-cause death or left ventricular assist system implantation was significantly higher in the Δd-ROM-positive group than in the Δd-ROM-negative group (log-rank P = 0.047). Elevated Δd-ROM levels were associated with increased mortality risk. Multivariate analysis adjusted for body mass index and peak oxygen uptake revealed that Δd-ROM was an independent prognostic factor of adverse events (Tertile 3 vs. 1; hazard ratio: 4.57; 95% confidence interval: 1.21-29.77; P = 0.022). CONCLUSIONS: Patients with HF who underwent a cardiopulmonary exercise test and had an increased oxidative stress marker level had a poor prognosis. The appropriate exercise intensity could be determined by evaluating the changes in oxidative stress status in response to acute exercise in patients with HF.

Organizing Pneumonia as the First Presentation in a Patient with Takayasu Arteritis: A Report of Rare Complication.

A 48-year-old woman without any medical history visited an outpatient clinic with a chief complaint of cough persisting for more than 1 year and was diagnosed with organizing pneumonia. Computed tomography showed wall thickening with luminal stenosis of the main branch vessels of the aorta, and a detailed examination including fluorodeoxyglucose-positron emission tomography revealed Takayasu arteritis. There have been some reports of combined organizing pneumonia in similar vasculitis cases, but Takayasu arteritis and organizing pneumonia have not been reported to be associated. This case can be referred to when considering the association of lung lesions with Takayasu arteritis.

Relationship of high-intensity plaques on T1-weighted magnetic resonance imaging with coronary intraplaque hemorrhage: A directional coronary atherectomy study.

Background: Although intraplaque hemorrhage (IPH) has been identified as a key feature of rupture-prone plaques, noninvasive imaging-based features for its detection in coronary artery have not been clearly established. The aim of this study was to investigate the relationship of the ratio between the signal intensities of coronary plaque and cardiac muscle (PMR) on non-contrast T1-weighted imaging (T1WI) in magnetic resonance with IPH in the directional coronary atherectomy (DCA) specimens. Methods: Fifteen lesions from 15 patients, who underwent DCA and T1WI, were prospectively enrolled. The snap-frozen samples obtained by DCA were used for immunohistochemical staining against a protein specific to erythrocyte membranes (glycophorin A) and macrophages. The percentage of glycophorin A and macrophages was graded using a scale from 0 to 4, with higher scores indicating higher percentages. Results: PMR showed a strong positive correlation with glycophorin A scores (ρ = 0.772, p < 0.001), whreas, there was a weak correlation between the PMR and macrophage scores (ρ = 0.626, p < 0.05). The receiver-operating characteristic curve analysis showed that the optimal PMR cutoff value for predicting glycophorin A scores ≥grade 2 (glycophorin A-positive area ≥5% of the plaque) was 1.2 (area under the curve; 0.91, 95% confidence interval; 0.73-1.00), and this PMR value had a sensitivity of 8/9 (89%), specificity of 6/6 (100%), positive predictive value of 8/8 (100%), and negative predictive value of 6/7 (86%). Conclusions: In patients with ischemic heart disease, a high PMR on T1WI is a predictor of coronary IPH as assessed by DCA specimens.

Association between debulking area of rotational atherectomy and platform revolution speed-Frequency domain optical coherence tomography analysis.

OBJECTIVES: In this study, we sought to investigate the association between revolution speed of rotational atherectomy (RA) and debulking area assessed by frequency domain-optical coherence tomography (FD-OCT). BACKGROUND: The number of patients with severe calcified coronary artery disease requiring treatment with calcium ablation, such as RA, is increasing. However, there is little evidence available regarding the association between debulking area and revolution speed during RA. METHODS: We retrospectively investigated 30 consecutive severely calcified coronary lesions in 29 patients who underwent RA under FD-OCT guidance. The association between preset revolution speed of RA and burr size-corrected debulking area of the calcified lesion was evaluated using a multivariable regression model with nonlinear restricted-cubic-spline, which can help assess nonlinear associations between variables. RESULTS: The median age of study participants was 73 years (quartile 65-78); 82.8% were male. The median burr size was 1.5 mm (1.5-1.75); median total duration of ablation was 120 s (100-180). FD-OCT revealed that the post-procedural minimum lumen area increased significantly from 1.64 mm2 (1.40-2.09) to 2.45 mm2 (2.11-2.98) (p < .001). In addition, the burr size-corrected debulking area increased significantly as the preset revolution speed decreased (p = .018), especially when the revolution speed was less than 150,000 rpm. This result implies that additional lumen gain will be obtained by decreasing rpm when the burr speed is set at <150,000 rpm. CONCLUSIONS: FD-OCT demonstrated that RA with lower revolution speed, below 150,000 rpm, has the potential to achieve greater calcium debulking effect in patients with severe calcified coronary lesions.