Prediction of oxygen requirement in patients with COVID-19 using a pre-trained chest radiograph xAI model: efficient development of auditable risk prediction models via a fine-tuning approach.

Risk prediction requires comprehensive integration of clinical information and concurrent radiological findings. We present an upgraded chest radiograph (CXR) explainable artificial intelligence (xAI) model, which was trained on 241,723 well-annotated CXRs obtained prior to the onset of the COVID-19 pandemic. Mean area under the receiver operating characteristic curve (AUROC) for detection of 20 radiographic features was 0.955 (95% CI 0.938-0.955) on PA view and 0.909 (95% CI 0.890-0.925) on AP view. Coexistent and correlated radiographic findings are displayed in an interpretation table, and calibrated classifier confidence is displayed on an AI scoreboard. Retrieval of similar feature patches and comparable CXRs from a Model-Derived Atlas provides justification for model predictions. To demonstrate the feasibility of a fine-tuning approach for efficient and scalable development of xAI risk prediction models, we applied our CXR xAI model, in combination with clinical information, to predict oxygen requirement in COVID-19 patients. Prediction accuracy for high flow oxygen (HFO) and mechanical ventilation (MV) was 0.953 and 0.934 at 24 h and 0.932 and 0.836 at 72 h from the time of emergency department (ED) admission, respectively. Our CXR xAI model is auditable and captures key pathophysiological manifestations of cardiorespiratory diseases and cardiothoracic comorbidities. This model can be efficiently and broadly applied via a fine-tuning approach to provide fully automated risk and outcome predictions in various clinical scenarios in real-world practice.

Association of COVID-19 Quarantine Duration and Postquarantine Transmission Risk in 4 University Cohorts.

Importance: Optimal quarantine length for COVID-19 infection is unclear, in part owing to limited empirical data. Objective: To assess postquarantine transmission risk for various quarantine lengths and potential associations between quarantine strictness and transmission risk. Design, Setting, and Participants: Retrospective cohort study in 4 US universities from September 2020 to February 2021, including 3641 university students and staff who were identified as close contacts to individuals who tested positive for SARS-CoV-2 infection. Individuals were tested throughout the 10 to 14-day quarantine, and follow-up testing continued at least weekly throughout the 2020-2021 academic year. Exposures: Strict quarantine, including designated housing with a private room, private bathroom, and meal delivery, vs nonstrict, which potentially included interactions with household members. Main Outcomes and Measures: Dates of last known exposure, last negative test result, and first positive test result during quarantine. Results: This study included 301 quarantined university students and staff who tested SARS-CoV-2-positive (of 3641 quarantined total). These 301 individuals had a median (IQR) age of 22.0 (20.0-25.0) years; 131 (43.5%) identified as female; and 20 (6.6%) were staff. Of the 287 self-reporting race and ethnicity according to university-defined classifications, 21 (7.3%) were African American or Black, 60 (20.9%) Asian, 17 (5.9%) Hispanic or Latinx, 174 (60.6%) White, and 15 (5.2%) other (including multiracial and/or multiethnic). Of the 301 participants, 40 (13.3%; 95% CI, 9.9%-17.6%) had negative test results and were asymptomatic on day 7 compared with 15 (4.9%; 95% CI, 3.0%-8.1%) and 4 (1.4%; 95% CI, 0.4%-3.5%) on days 10 and 14, respectively. Individuals in strict quarantine tested positive less frequently than those in nonstrict quarantine (10% vs 12%; P = .04). Conclusions and Relevance: To maintain the 5% transmission risk used as the basis for US Centers for Disease Control and Prevention's 7-day test-based quarantine guidance, our data suggest that quarantine with quantitative polymerase chain reaction testing 1 day before intended release should be 10 days for nonstrict quarantine and 8 days for strict quarantine, as ongoing exposure during quarantine may be associated with the higher rate of positive test results following nonstrict quarantine.

Deep Convolutional Neural Network-based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs.

Background Multicenter studies are required to validate the added benefit of using deep convolutional neural network (DCNN) software for detecting malignant pulmonary nodules on chest radiographs. Purpose To compare the performance of radiologists in detecting malignant pulmonary nodules on chest radiographs when assisted by deep learning-based DCNN software with that of radiologists or DCNN software alone in a multicenter setting. Materials and Methods Investigators at four medical centers retrospectively identified 600 lung cancer-containing chest radiographs and 200 normal chest radiographs. Each radiograph with a lung cancer had at least one malignant nodule confirmed by CT and pathologic examination. Twelve radiologists from the four centers independently analyzed the chest radiographs and marked regions of interest. Commercially available deep learning-based computer-aided detection software separately trained, tested, and validated with 19 330 radiographs was used to find suspicious nodules. The radiologists then reviewed the images with the assistance of DCNN software. The sensitivity and number of false-positive findings per image of DCNN software, radiologists alone, and radiologists with the use of DCNN software were analyzed by using logistic regression and Poisson regression. Results The average sensitivity of radiologists improved (from 65.1% [1375 of 2112; 95% confidence interval {CI}: 62.0%, 68.1%] to 70.3% [1484 of 2112; 95% CI: 67.2%, 73.1%], P < .001) and the number of false-positive findings per radiograph declined (from 0.2 [488 of 2400; 95% CI: 0.18, 0.22] to 0.18 [422 of 2400; 95% CI: 0.16, 0.2], P < .001) when the radiologists re-reviewed radiographs with the DCNN software. For the 12 radiologists in this study, 104 of 2400 radiographs were positively changed (from false-negative to true-positive or from false-positive to true-negative) using the DCNN, while 56 of 2400 radiographs were changed negatively. Conclusion Radiologists had better performance with deep convolutional network software for the detection of malignant pulmonary nodules on chest radiographs than without. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Jacobson in this issue.

The Fever Coach Mobile App for Participatory Influenza Surveillance in Children: Usability Study.

BACKGROUND: Effective surveillance of influenza requires a broad network of health care providers actively reporting cases of influenza-like illnesses and positive laboratory results. Not only is this traditional surveillance system costly to establish and maintain but there is also a time lag between a change in influenza activity and its detection. A new surveillance system that is both reliable and timely will help public health officials to effectively control an epidemic and mitigate the burden of the disease. OBJECTIVE: This study aimed to evaluate the use of parent-reported data of febrile illnesses in children submitted through the Fever Coach app in real-time surveillance of influenza activities. METHODS: Fever Coach is a mobile app designed to help parents and caregivers manage fever in young children, currently mainly serviced in South Korea. The app analyzes data entered by a caregiver and provides tailored information for care of the child based on the child's age, sex, body weight, body temperature, and accompanying symptoms. Using the data submitted to the app during the 2016-2017 influenza season, we built a regression model that monitors influenza incidence for the 2017-2018 season and validated the model by comparing the predictions with the public influenza surveillance data from the Korea Centers for Disease Control and Prevention (KCDC). RESULTS: During the 2-year study period, 70,203 diagnosis data, including 7702 influenza reports, were submitted. There was a significant correlation between the influenza activity predicted by Fever Coach and that reported by KCDC (Spearman ρ=0.878; P<.001). Using this model, the influenza epidemic in the 2017-2018 season was detected 10 days before the epidemic alert announced by KCDC. CONCLUSIONS: The Fever Coach app successfully collected data from 7.73% (207,699/2,686,580) of the target population by providing care instruction for febrile children. These data were used to develop a model that accurately estimated influenza activity measured by the central government agency using reports from sentinel facilities in the national surveillance network.

Machine Friendly Machine Learning: Interpretation of Computed Tomography Without Image Reconstruction.

Recent advancements in deep learning for automated image processing and classification have accelerated many new applications for medical image analysis. However, most deep learning algorithms have been developed using reconstructed, human-interpretable medical images. While image reconstruction from raw sensor data is required for the creation of medical images, the reconstruction process only uses a partial representation of all the data acquired. Here, we report the development of a system to directly process raw computed tomography (CT) data in sinogram-space, bypassing the intermediary step of image reconstruction. Two classification tasks were evaluated for their feasibility of sinogram-space machine learning: body region identification and intracranial hemorrhage (ICH) detection. Our proposed SinoNet, a convolutional neural network optimized for interpreting sinograms, performed favorably compared to conventional reconstructed image-space-based systems for both tasks, regardless of scanning geometries in terms of projections or detectors. Further, SinoNet performed significantly better when using sparsely sampled sinograms than conventional networks operating in image-space. As a result, sinogram-space algorithms could be used in field settings for triage (presence of ICH), especially where low radiation dose is desired. These findings also demonstrate another strength of deep learning where it can analyze and interpret sinograms that are virtually impossible for human experts.

An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets.

Owing to improvements in image recognition via deep learning, machine-learning algorithms could eventually be applied to automated medical diagnoses that can guide clinical decision-making. However, these algorithms remain a 'black box' in terms of how they generate the predictions from the input data. Also, high-performance deep learning requires large, high-quality training datasets. Here, we report the development of an understandable deep-learning system that detects acute intracranial haemorrhage (ICH) and classifies five ICH subtypes from unenhanced head computed-tomography scans. By using a dataset of only 904 cases for algorithm training, the system achieved a performance similar to that of expert radiologists in two independent test datasets containing 200 cases (sensitivity of 98% and specificity of 95%) and 196 cases (sensitivity of 92% and specificity of 95%). The system includes an attention map and a prediction basis retrieved from training data to enhance explainability, and an iterative process that mimics the workflow of radiologists. Our approach to algorithm development can facilitate the development of deep-learning systems for a variety of clinical applications and accelerate their adoption into clinical practice.

Beyond Human Perception: Sexual Dimorphism in Hand and Wrist Radiographs Is Discernible by a Deep Learning Model.

Despite the well-established impact of sex and sex hormones on bone structure and density, there has been limited description of sexual dimorphism in the hand and wrist in the literature. We developed a deep convolutional neural network (CNN) model to predict sex based on hand radiographs of children and adults aged between 5 and 70 years. Of the 1531 radiographs tested, the algorithm predicted sex correctly in 95.9% (κ = 0.92) of the cases. Two human radiologists achieved 58% (κ = 0.15) and 46% (κ = - 0.07) accuracy. The class activation maps (CAM) showed that the model mostly focused on the 2nd and 3rd metacarpal base or thumb sesamoid in women, and distal radioulnar joint, distal radial physis and epiphysis, or 3rd metacarpophalangeal joint in men. The radiologists reviewed 70 cases (35 females and 35 males) labeled with sex along with heat maps generated by CAM, but they could not find any patterns that distinguish the two sexes. A small sample of patients (n = 44) with sexual developmental disorders or transgender identity was selected for a preliminary exploration of application of the model. The model prediction agreed with phenotypic sex in only 77.8% (κ = 0.54) of these cases. To the best of our knowledge, this is the first study that demonstrated a machine learning model to perform a task in which human experts could not fulfill.

Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability.

OBJECTIVE: Radiographic bone age assessment (BAA) is used in the evaluation of pediatric endocrine and metabolic disorders. We previously developed an automated artificial intelligence (AI) deep learning algorithm to perform BAA using convolutional neural networks. We compared the BAA performance of a cohort of pediatric radiologists with and without AI assistance. MATERIALS AND METHODS: Six board-certified, subspecialty trained pediatric radiologists interpreted 280 age- and gender-matched bone age radiographs ranging from 5 to 18 years. Three of those radiologists then performed BAA with AI assistance. Bone age accuracy and root mean squared error (RMSE) were used as measures of accuracy. Intraclass correlation coefficient evaluated inter-rater variation. RESULTS: AI BAA accuracy was 68.2% overall and 98.6% within 1 year, and the mean six-reader cohort accuracy was 63.6 and 97.4% within 1 year. AI RMSE was 0.601 years, while mean single-reader RMSE was 0.661 years. Pooled RMSE decreased from 0.661 to 0.508 years, all individually decreasing with AI assistance. ICC without AI was 0.9914 and with AI was 0.9951. CONCLUSIONS: AI improves radiologist's bone age assessment by increasing accuracy and decreasing variability and RMSE. The utilization of AI by radiologists improves performance compared to AI alone, a radiologist alone, or a pooled cohort of experts. This suggests that AI may optimally be utilized as an adjunct to radiologist interpretation of imaging studies to improve performance.

A Concise Tool for Measuring Care Coordination from the Provider's Perspective in the Hospital Setting.

BACKGROUND: To support hospital efforts to improve coordination of care, a tool is needed to evaluate care coordination from the perspective of inpatient healthcare professionals. OBJECTIVES: To develop a concise tool for assessing care coordination in hospital units from the perspective of healthcare professionals, and to assess the performance of the tool in measuring dimensions of care coordination in 2 hospitals after implementation of a care coordination initiative. METHODS: We developed a survey consisting of 12 specific items and 1 global item to measure provider perceptions of care coordination across a variety of domains, including teamwork and communication, handoffs, transitions, and patient engagement. The questionnaire was distributed online between October 2015 and January 2016 to nurses, physicians, social workers, case managers, and other professionals in 2 tertiary care hospitals. RESULTS: A total of 841 inpatient care professionals completed the survey (response rate = 56.6%). Among respondents, 590 (75%) were nurses and 37 (4.7%) were physicians. Exploratory factor analysis revealed 4 subscales: (1) Teamwork, (2) Patient Engagement, (3) Handoffs, and (4) Transitions (Cronbach's alpha 0.84-0.90). Scores were fairly consistent for 3 subscales but were lower for patient engagement. There were minor differences in scores by profession, department, and hospital. CONCLUSIONS: The new tool measures 4 important aspects of inpatient care coordination with evidence for internal consistency and construct validity, indicating that the tool can be used in monitoring, evaluating, and planning care coordination activities in hospital settings.

Clinical Characteristics and Treatment Response of Peripheral Neuropathy in the Presence of Eosinophilic Granulomatosis with Polyangiitis (Churg-Strauss Syndrome): Experience at a Single Tertiary Center.

BACKGROUND AND PURPOSE: Eosinophilic granulomatosis with polyangiitis (EGPA) is a rare systemic small-vessel vasculitis accompanied by asthma, eosinophilia, and eosinophilic inflammation of various tissues including the peripheral nerves. This study investigated the clinical course and long-term outcomes of peripheral neuropathy in patients with EGPA. METHODS: Seventy-one patients with physician-diagnosed EGPA were identified at Samsung Medical Center between January 1995 and April 2014. Sixty-one of these patients were followed-up for more than 1 year and received corticosteroid therapy with or without intravenous cyclophosphamide pulse therapy for 6 to 18 months. Medical records of the 61 patients including demographic data, clinical features, laboratory and pathological findings, treatments, and outcomes were reviewed. RESULTS: Peripheral neuropathy as a manifestation of EGPA was present in 46 (75%) of the 61 patients. The mean follow-up duration of the patients with neuropathy was 6.4 years (range 1.2-18.8 years). The scores on the neurological functional disability scale before and after the combination treatment with corticosteroid and cyclophosphamide were 2.43±0.86 and 0.54±0.95 (mean±SD; p<0.001), respectively. The peripheral neuropathy relapsed in one patient. CONCLUSIONS: The long-term clinical outcome of peripheral neuropathy in patients with EGPA receiving initial corticosteroid and cyclophosphamide combination therapy was favorable with a very low relapse rate.

Detecting cardiac involvement with magnetic resonance in patients with active eosinophilic granulomatosis with polyangiitis.

Cardiac involvement is the most important prognostic factor in eosinophilic granulomatosis with polyangiitis (EGPA, Churg-Strauss syndrome). The aims of this study were to describe findings of cardiac magnetic resonance (CMR) in patients with active EGPA and to find factors associated with cardiac involvement detected by CMR that could help identify patients who would benefit from the examination. Medical records and CMR images in 16 consecutive EGPA patients (8 women and 8 men, median age of 47 years ranging from 34 to 68 years) were reviewed. Clinical features and results of laboratory tests were compared according to the presence of myocardial late gadolinium enhancement (LGE) on CMR images. The patients were followed for the development of cardiac symptoms and signs (mean follow up duration, 40.5 ± 12.8 months). Among the total of 16 patients, 8 (50 %) had myocardial LGE according to CMR, located in the subendocardial layer in 7 of them (87.5 %). The extent of LGE had a significant negative correlation with left ventricular ejection fraction (LVEF, ρ = -0.723, p = 0.043). The presence of LGE was associated with larger end-systolic left ventricle internal dimension (34 vs. 28 mm, p = 0.027) and presence of diastolic dysfunction (75 vs. 0 %, p = 0.008) on echocardiography, elevated NT-proBNP (75 vs. 12.5 %, p = 0.012), and elevated CK-MB (62.5 vs. 0 %, p = 0.010) compared to the group without LGE. Only one patient (6.3 %) had cardiac symptoms before CMR and another patient (6.3 %) developed heart failure 4 years later during remission. The other 14 patients remained free from cardiac signs and symptoms during the follow-up period. In patients with active EGPA, CMR enables detection of cardiac involvement when cardiac symptoms are not present. Echocardiographic diastolic dysfunction and elevated NT-proBNP or CK-MB may help identify active EGPA patients who can benefit from CMR to detect cardiac involvement without cardiac symptoms.

The Hidden Culprit: A Case of Repeated Anaphylaxis to Cremophor.

Drug-induced anaphylaxis is a big pitfall in patients receiving antineoplastic chemotherapy. We report a case of lung cancer patient who experienced two near-fatal anaphylactic reactions that resulted from paclitaxel and multivitamin, seperately. Recurrent severe reactions to different agents led to further investigation to which material the patient was hypersensitive. The skin prick test revealed sensitization to cremophor, which is a commonly used emulsifying agent. This case emphasizes the importance of correctly identifying the culprit drug of anaphylaxis to avoid potentially fatal reaction.

The Prevalence of Toxocariasis and Diagnostic Value of Serologic Tests in Asymptomatic Korean Adults.

PURPOSE: Toxocariasis is the most common cause of peripheral blood eosinophilia in Korea and produces eosinophilic infiltration in various organs, including the lung. However, the prevalence of toxocariasis in the general population is rarely reported. METHODS: We investigated the seroprevalence of Toxocara larval antibody among asymptomatic people who attended Samsung Medical Center for a health checkup, including low-dose chest computed tomography (CT) between March 2012 and December 2013. A total of 633 people (400 men and 233 women) were prospectively recruited. RESULTS: The Toxocara-seropositive rate was 51.2% using the current cutoff value based on Toxocara enzyme-linked immunosorbent assay (ELISA) (67.0% for men and 24.0% for women). In the multivariate-adjusted model, age (odds ratio [OR], 1.08; 95% confidence intervals [CI], 1.04-1.11), male sex (OR, 3.47; 95% CI, 2.26-5.33), rural residence (OR, 1.55; 95% CI, 1.05-2.30), and history of raw liver intake (OR, 8.52; 95% CI, 3.61-20.11) were significantly associated with Toxocara seropositivity. When subjects were divided into 3 groups using cutoff values base on weak positive and strong positive control optical densities (ODs), the ORs for peripheral blood eosinophilia and serum hyperIgEaemia were 0.31 (95% CI, 0.02-2.89) in the weakpositive group and 36.64 (95% CI, 11.73-111.42) in the strong positive group compared to the seronegative group. Similarly, ORs for the solid nodule with surrounding halo were 2.54 (95% CI, 0.60-10.84) in the weak positive group and 15.08 (95 CI 4.09-55.56) in the strong positive group compared to the seronegative group. CONCLUSIONS: The study indicated that the Toxocara-seropositive rate obtained by using the current cutoff value based on ELISA was high in the asymptomatic population in Korea. The results of this study suggest that active toxocariasis may be more frequently seen in the Toxocara-strong positive group than in the Toxocara-weak positive group.

Fractional exhaled nitric oxide: comparison between portable devices and correlation with sputum eosinophils.

This study was performed to compare the 2 different portable devices measuring fractional exhaled nitric oxide (FeNO) and to see the correlation between FeNO and induced sputum eosinophil count (ISE). Forty consecutive subjects clinically suspected to have asthma underwent FeNO measurement by NIOX-MINO® and NObreath® concurrently. All also had induced sputum analysis, methacholine provocation test or bronchodilator response test, and spin prick test. Agreement between the 2 devices was evaluated. The correlation between FeNO and ISE was assessed, as well as the cut-off level of FeNO to identify ISE ≥3%. The intraclass correlation coefficient (ICC) between FeNO levels measured by NIOX-MINO® (FeNO(NIOX-MINO)) and NObreath® (FeNO(NObreath)) was 0.972 with 95% confidence interval of 0.948-0.985. The 95% limits of agreement were -28.9 to 19.9 ppb. The correlation coefficient between ISE and FeNO(NIOX-MINO) was 0.733 (P<0.001), and 0.751 between ISE and FeNO(NObreath) (P<0.001). The ROC curve found that the FeNO(NIOXMINO) of 37.5 ppb and the FeNO(NObreath) of 36.5 ppb identified ISE ≥3% with 90% sensitivity and 81% specificity. Age, sex, body mass index, smoking history, atopy, and the presence of asthma did not affect the FeNO level and its correlation with ISE. The NIOX-MINO ® and NObreath® agree with each other to a high degree. Both devices showed close correlation with ISE with similar cut-off value in identifying ISE ≥3%.

Hepatitis B virus reactivation during anti-cancer chemotherapy in patients with past hepatitis B virus infection.

BACKGROUND/AIMS: The necessity of preemptive antiviral therapy in patients with past HBV infection is uncertain. We evaluated the incidence and risk factors of HBV reactivation in cancer patients with past HBV infection who received anti-cancer chemotherapy. METHODOLOGY: Between Jan. 2009 and Dec. 2011, we reviewed 675 HBsAg negative and anti-HBc positive patients who had solid cancers or hematologic malignancies that were treated with intravenous cytotoxic chemotherapy. RESULTS: Among 675 patients, 321 (47.6%) patients had solid cancer and 354 (52.4%) had hematologic malignancy. HBV reactivation was observed in 13 patients (1.9%). In solid cancer patients, 1 (0.3%) patient had HBV reactivation, whereas 12 out of 365 (3.3%) patients with hematologic malignancy experienced HBV reactivation. Among the 12 HBV-reactivated patients with hematologic malignancy, 11 patients had lymphoma. Lymphoma carried a significantly higher risk for HBV reactivation than solid cancer in patients with past HBV infection (OR, 24.134; 95% CI, 3.027-192.406; P = 0.003). Among HBV-reactivated lymphoma patients, 2 patients experienced fulminant liver failure. The absence of anti- HBs was identified as a risk factor for HBV reactivation (OR, 22.446; 95% CI, 4.816-104.609; P < 0.001). CONCLUSIONS: Preemptive antiviral therapy should be considered in lymphoma patients with past HBV infection before starting anti-cancer chemotherapy

Unusual manifestation of intravascular large B-cell lymphoma: severe hypercalcemia with parathyroid hormone-related protein.

Intravascular large B-cell lymphoma (IVLBCL) is a rare subtype of non-Hodgkin lymphoma. It usually presents with nonspecific symptoms, such as fever, rather than with overt lymphadenopathy. Reports of hypercalcemia, as the initial presentation of IVLBCL, are limited in the literature, despite it being a well-known complication of various solid cancers. We present a 68-year-old male with severe hypercalcemia and increased levels of serum parathyroid hormone-related protein. He was diagnosed with IVLBCL, involving the bone marrow and spleen, and was successfully treated with rituximab-containing chemotherapy. A few previous case reports have shown hypercalcemia in patients with IVLBCL. Much like our case, previous cases with hypercalcemia had advanced diseases, including bone marrow invasion. Although it was an extremely rare manifestation of IVLBCL, we suggest that IVLBCL should be a part of the differential diagnosis in patients with unexplained hypercalcemia. Therefore, an active work-up might be recommended, including positron emission tomography/ computed tomography scan and bone marrow examination, which may be useful for early diagnosis.

Acute myocardial infarction after radiofrequency catheter ablation of typical atrial flutter.

A 53-yr-old man underwent radiofrequency ablation to treat persistent atrial flutter. After the procedure, the chest pain was getting worse, and the electrocardiogram showed ST-segment elevation in inferior leads with reciprocal changes. Immediate coronary angiography showed total occlusion with thrombi at the distal portion of the right coronary artery, which was very close to the ablation site. Intervention with thrombus aspiration and balloon dilatation was successful, and the patient recovered without any kind of sequelae. Although the exact mechanism is obscure, the most likely explanation is a thermal injury to the vascular wall that ruptured into the lumen and formed thrombus. Vasospasm and thromboembolism can also be other possibilities. This case raise the alarm to cardiologists who perform radiofrequency ablation to treat various kinds of cardiac arrhythmias, in that myocardial infarction has been rarely considered one of the complications.

Immunoglobulin g4 non-related sclerosing disease with intracardiac mass mimicking mitral stenosis: case report.

The cardiovascular system may be one of the target organs of both immunoglobulin G4 related and non-related systemic multifocal fibrosclerosis. We present a case of IgG4 non-related systemic multifocal fibrosclerosis mimicking mitral stenosis on echocardiography. For a more detailed differential diagnosis, we used multimodal imaging techniques. After surgical biopsy around the abdominal aortic area in the retroperitoneum, histological examination revealed IgG4 non-related systemic multifocal fibrosclerosis. We describe the multimodal imaging used to diagnose IgG4 non-related systemic multifocal fibrosclerosis and a positive response to steroid treatment. There have been no previous case reports of IgG4 non-related systemic multifocal fibrosclerosis with intracardiac involvement. Here, we report a case of IgG4 non-related systemic multifocal fibrosclerosis mimicking mitral stenosis.

[Predictive factors of response to medical therapy in Crohn's disease patients with intestinal obstruction].

BACKGROUND/AIMS: Crohn's disease is a chronic inflammatory bowel disease. Stricture is a very important indication for surgical intervention as strictures can lead to intestinal obstruction. Strictures can be divided into inflammatory and fibrous strictures. Intestinal obstruction due to inflammatory stricture is expected to be resolved with medical treatment. However, factors that can predict the response to medical treatments are unknown. In the present study, we aimed to identify the factors that can predict the response to medical treatments in Crohn's disease patients with intestinal obstruction. METHODS: Data were collected by retrospectively reviewing the medical records of patients with Crohn's disease who visited the emergency department at Samsung Medical Center in Seoul from January 1, 2000 to December 31, 2010 because of intestinal obstruction. Based on the response to medical treatments, we classified the patients as responders and non-responders and compared the clinical, biochemical, and radiological findings of the two groups. RESULTS: A total of 39 patients were enrolled. Twenty-nine patients responded to medical treatments whereas 10 patients did not. Significant differences were observed between the two groups in terms of vomiting and duration of disease before the development of obstruction. CONCLUSIONS: Patients who responded to the medical treatments exhibited a higher incidence of vomiting and longer duration of disease before the development of obstruction. However, further prospective studies are needed to identify the factors that can predict the response to medical treatments.